5488 lines
631 KiB
JSON
5488 lines
631 KiB
JSON
{
|
||
"info": {
|
||
"id": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"slug": "tidy-meadow",
|
||
"projectID": "45e61044497a123d750492db8a5bb735bc6664ea",
|
||
"directory": "/opt/bytetrack-counter",
|
||
"path": "",
|
||
"title": "Add version printing to compiled binary",
|
||
"agent": "build",
|
||
"model": {
|
||
"id": "deepseek-v4-pro",
|
||
"providerID": "deepseek"
|
||
},
|
||
"version": "1.17.11",
|
||
"summary": {
|
||
"additions": 0,
|
||
"deletions": 0,
|
||
"files": 0
|
||
},
|
||
"cost": 0.10242953700000004,
|
||
"tokens": {
|
||
"input": 186493,
|
||
"output": 8386,
|
||
"reasoning": 6093,
|
||
"cache": {
|
||
"read": 2402304,
|
||
"write": 0
|
||
}
|
||
},
|
||
"time": {
|
||
"created": 1783308022494,
|
||
"updated": 1785285181158
|
||
}
|
||
},
|
||
"messages": [
|
||
{
|
||
"info": {
|
||
"role": "user",
|
||
"time": {
|
||
"created": 1783308022565
|
||
},
|
||
"agent": "build",
|
||
"model": {
|
||
"providerID": "deepseek",
|
||
"modelID": "deepseek-v4-pro"
|
||
},
|
||
"summary": {
|
||
"diffs": []
|
||
},
|
||
"id": "msg_f3570f325001LLIc39xgDMbjmi",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"type": "text",
|
||
"text": "add feature to print version of compiled binary",
|
||
"id": "prt_f3570f328001cniFC5kj9CExRl",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_f3570f325001LLIc39xgDMbjmi"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"parentID": "msg_f3570f325001LLIc39xgDMbjmi",
|
||
"role": "assistant",
|
||
"mode": "build",
|
||
"agent": "build",
|
||
"path": {
|
||
"cwd": "/opt/bytetrack-counter",
|
||
"root": "/opt/bytetrack-counter"
|
||
},
|
||
"cost": 0,
|
||
"tokens": {
|
||
"input": 0,
|
||
"output": 0,
|
||
"reasoning": 0,
|
||
"cache": {
|
||
"read": 0,
|
||
"write": 0
|
||
}
|
||
},
|
||
"modelID": "deepseek-v4-pro",
|
||
"providerID": "deepseek",
|
||
"time": {
|
||
"created": 1783308022603,
|
||
"completed": 1783308053394
|
||
},
|
||
"error": {
|
||
"name": "MessageAbortedError",
|
||
"data": {
|
||
"message": "Aborted"
|
||
}
|
||
},
|
||
"id": "msg_f3570f34b001YbHLZypL941I6t",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"snapshot": "8dd1d5b751ba93c645ddb4a0e14c3d75f17a6197",
|
||
"type": "step-start",
|
||
"id": "prt_f3570f728001vJVIia8AVAav6d",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_f3570f34b001YbHLZypL941I6t"
|
||
},
|
||
{
|
||
"type": "reasoning",
|
||
"text": "The user wants to add a feature to print the version of a compiled binary. Let me explore the codebase to understand the project structure, find where the main entry point is, and understand how the binary is built.",
|
||
"time": {
|
||
"start": 1783308024338,
|
||
"end": 1783308025454
|
||
},
|
||
"id": "prt_f3570fa12001ob70CgaXuV5ByH",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_f3570f34b001YbHLZypL941I6t"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "task",
|
||
"callID": "call_00_uxwAWf2fzO77kQGQlsAz6862",
|
||
"state": {
|
||
"title": "Explore codebase structure",
|
||
"metadata": {
|
||
"parentSessionId": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"sessionId": "ses_0ca8ef7acffe5gGEvQ17HVlkkJ",
|
||
"model": {
|
||
"modelID": "deepseek-v4-pro",
|
||
"providerID": "deepseek"
|
||
},
|
||
"interrupted": true
|
||
},
|
||
"status": "error",
|
||
"input": {
|
||
"description": "Explore codebase structure",
|
||
"prompt": "Explore the codebase at /opt/bytetrack-counter to understand:\n1. What kind of project this is (language, build system, framework)\n2. Where the main entry point is\n3. How the binary is compiled/built\n4. Whether there's already any version or build info mechanism\n5. Look at build configuration files (Cargo.toml, CMakeLists.txt, Makefile, package.json, go.mod, etc.)\n\nReturn: project type, language, build system, main entry point file(s), build configuration content, and any existing version info mechanisms.",
|
||
"subagent_type": "explore"
|
||
},
|
||
"time": {
|
||
"start": 1783308027995,
|
||
"end": 1783308053390
|
||
},
|
||
"error": "Tool execution aborted"
|
||
},
|
||
"id": "prt_f3570fe77001DQAlRdM0zmtZUv",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_f3570f34b001YbHLZypL941I6t"
|
||
},
|
||
{
|
||
"type": "patch",
|
||
"hash": "8dd1d5b751ba93c645ddb4a0e14c3d75f17a6197",
|
||
"files": [
|
||
"/opt/bytetrack-counter/bytetrack_counter.db"
|
||
],
|
||
"id": "prt_f35716a860013zXLkb7kaQHKQs",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_f3570f34b001YbHLZypL941I6t"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"role": "user",
|
||
"time": {
|
||
"created": 1785284228706
|
||
},
|
||
"agent": "build",
|
||
"model": {
|
||
"providerID": "deepseek",
|
||
"modelID": "deepseek-v4-pro"
|
||
},
|
||
"summary": {
|
||
"diffs": [
|
||
{
|
||
"file": "AGENTS.md",
|
||
"patch": "Index: AGENTS.md\n===================================================================\n--- AGENTS.md\t\n+++ AGENTS.md\t\n@@ -0,0 +1,51 @@\n+# AGENTS.md — bytetrack-counter\n+\n+## Architecture\n+\n+- **Edge AI counter**: RTSP camera → YOLO RKNN (NPU) → tracking → line-crossing → SQLite + JSON state → Flask dashboard.\n+- **3 counter scripts**, only 1 deployed:\n+ - `counter_live.py` — Jetson TensorRT variant (CUDA, NOT used on RK3588).\n+ - `counter_live_rknn.py` — RK3588 with simple IoU tracker.\n+ - **`counter_live_rknn_bytetrack.py`** — RK3588 with ByteTrack. **This is what systemd runs.**\n+- `batch_store.py` — shared SQLite persistence + batch state machine.\n+- `counter_dashboard.py` — Flask dashboard on port 5000, same DB.\n+\n+## No build / test / lint\n+\n+There is no build system, no test framework, no linter config, no typechecker. \n+Do not try to run `pytest`, `ruff`, `mypy`, etc. — they don't exist here.\n+\n+## How to run\n+\n+```bash\n+# Copy env (required, .env is gitignored)\n+cp config.env.example .env\n+\n+# Venv (must use system-site-packages for RKNN toolkit)\n+python3 -m venv --system-site-packages venv\n+source venv/bin/pip install -r requirements.txt\n+\n+# Run counter (RK3588 only — needs rknn-toolkit-lite2 & RKNN model)\n+PYTHONNOUSERSITE=1 venv/bin/python counter_live_rknn_bytetrack.py\n+\n+# Run dashboard\n+PYTHONNOUSERSITE=1 venv/bin/python counter_dashboard.py\n+```\n+\n+## Key environment & install quirks\n+\n+- **`PYTHONNOUSERSITE=1`** is mandatory when running from the venv — without it, system/user packages leak in.\n+- **`.env` is gitignored** — always copy from `config.env.example` first.\n+- **`numpy<2`** is required for `rknn-toolkit-lite2` compatibility.\n+- **Install path in service files is `/opt/bytetrack-counter`** (not the `/opt/jetson-counter` mentioned in README/DEPLOY). The `.env.example` also reflects `/opt/bytetrack-counter`.\n+- Service user is **`root`**, not `jetson` (despite README saying otherwise).\n+- Two systemd units: `bytetrack-counter.service` + `bytetrack-counter-dashboard.service`.\n+- `counter_live.py` (TensorRT) is Jetson-only and won't work on RK3588.\n+\n+## Code conventions\n+\n+- All config lives in `.env` (dotenv), read via `os.getenv()` at module top-level in each script.\n+- The 3 counter scripts duplicate ~80% of each other (drawing helpers, batch loop, etc). Changes to logic may need replication across variants.\n+- `batch_store.py` has its own threading (cutoff watcher, batch timeout timer) — thread safety is via a single `state_lock`.\n+- The dashboard re-creates DB tables on startup (`_ensure_db()`) independently from `batch_store.py`.\n+- No formal version tracking exists anywhere in this codebase.\n",
|
||
"additions": 51,
|
||
"deletions": 0,
|
||
"status": "added"
|
||
}
|
||
]
|
||
},
|
||
"id": "msg_fab3b7662001asy7eTmLMm4mEq",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"type": "text",
|
||
"text": "Create or update `AGENTS.md` for this repository.\n\nThe goal is a compact instruction file that helps future OpenCode sessions avoid mistakes and ramp up quickly. Every line should answer: \"Would an agent likely miss this without help?\" If not, leave it out.\n\nUser-provided focus or constraints (honor these):\n\n\n## How to investigate\n\nRead the highest-value sources first:\n- `README*`, root manifests, workspace config, lockfiles\n- build, test, lint, formatter, typecheck, and codegen config\n- CI workflows and pre-commit / task runner config\n- existing instruction files (`AGENTS.md`, `CLAUDE.md`, `.cursor/rules/`, `.cursorrules`, `.github/copilot-instructions.md`)\n- repo-local OpenCode config such as `opencode.json`\n\nIf architecture is still unclear after reading config and docs, inspect a small number of representative code files to find the real entrypoints, package boundaries, and execution flow. Prefer reading the files that explain how the system is wired together over random leaf files.\n\nPrefer executable sources of truth over prose. If docs conflict with config or scripts, trust the executable source and only keep what you can verify.\n\n## What to extract\n\nLook for the highest-signal facts for an agent working in this repo:\n- exact developer commands, especially non-obvious ones\n- how to run a single test, a single package, or a focused verification step\n- required command order when it matters, such as `lint -> typecheck -> test`\n- monorepo or multi-package boundaries, ownership of major directories, and the real app/library entrypoints\n- framework or toolchain quirks: generated code, migrations, codegen, build artifacts, special env loading, dev servers, infra deploy flow\n- repo-specific style or workflow conventions that differ from defaults\n- testing quirks: fixtures, integration test prerequisites, snapshot workflows, required services, flaky or expensive suites\n- important constraints from existing instruction files worth preserving\n\nGood `AGENTS.md` content is usually hard-earned context that took reading multiple files to infer.\n\n## Questions\n\nOnly ask the user questions if the repo cannot answer something important. Use the `question` tool for one short batch at most.\n\nGood questions:\n- undocumented team conventions\n- branch / PR / release expectations\n- missing setup or test prerequisites that are known but not written down\n\nDo not ask about anything the repo already makes clear.\n\n## Writing rules\n\nInclude only high-signal, repo-specific guidance such as:\n- exact commands and shortcuts the agent would otherwise guess wrong\n- architecture notes that are not obvious from filenames\n- conventions that differ from language or framework defaults\n- setup requirements, environment quirks, and operational gotchas\n- references to existing instruction sources that matter\n\nExclude:\n- generic software advice\n- long tutorials or exhaustive file trees\n- obvious language conventions\n- speculative claims or anything you could not verify\n- content better stored in another file referenced via `opencode.json` `instructions`\n\nWhen in doubt, omit.\n\nPrefer short sections and bullets. If the repo is simple, keep the file simple. If the repo is large, summarize the few structural facts that actually change how an agent should work.\n\nIf `AGENTS.md` already exists at `/opt/bytetrack-counter`, improve it in place rather than rewriting blindly. Preserve verified useful guidance, delete fluff or stale claims, and reconcile it with the current codebase.",
|
||
"id": "prt_fab3b7667001OXhdyCDbKwqx4c",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3b7662001asy7eTmLMm4mEq"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"parentID": "msg_fab3b7662001asy7eTmLMm4mEq",
|
||
"role": "assistant",
|
||
"mode": "build",
|
||
"agent": "build",
|
||
"path": {
|
||
"cwd": "/opt/bytetrack-counter",
|
||
"root": "/opt/bytetrack-counter"
|
||
},
|
||
"cost": 0.00354438,
|
||
"tokens": {
|
||
"total": 9821,
|
||
"input": 7670,
|
||
"output": 212,
|
||
"reasoning": 19,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 1920
|
||
}
|
||
},
|
||
"modelID": "deepseek-v4-pro",
|
||
"providerID": "deepseek",
|
||
"time": {
|
||
"created": 1785284228748,
|
||
"completed": 1785284234332
|
||
},
|
||
"finish": "tool-calls",
|
||
"id": "msg_fab3b768b0016CKiVP10E6vAsf",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"snapshot": "e0e1414b68e2544fb2d71b977882e90620947d43",
|
||
"type": "step-start",
|
||
"id": "prt_fab3b8129001XaHaEJfKlPR5qP",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3b768b0016CKiVP10E6vAsf"
|
||
},
|
||
{
|
||
"type": "reasoning",
|
||
"text": "Let me explore the codebase to understand the project and then create/update AGENTS.md.",
|
||
"time": {
|
||
"start": 1785284232274,
|
||
"end": 1785284232833
|
||
},
|
||
"id": "prt_fab3b8452001stzG28Qh2k3CUZ",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3b768b0016CKiVP10E6vAsf"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "read",
|
||
"callID": "call_00_jK4A2K3Ongbsd2KIA1vj5882",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"filePath": "/opt/bytetrack-counter"
|
||
},
|
||
"output": "<path>/opt/bytetrack-counter</path>\n<type>directory</type>\n<entries>\n__pycache__/\n.env\n.git/\n.gitignore\nbatch_crossings.csv\nbatch_store.py\nbytetrack_counter.db\nbytetrack-counter-dashboard.service\nbytetrack-counter.service\nconfig.env.example\ncounter_dashboard.py\ncounter_live_rknn_bytetrack.py\ncounter_live_rknn.py\ncounter_live.py\nDEPLOY.md\nenv/\ninstall-services.sh\nREADME.md\nrequirements.txt\nsetup-venv.sh\ntemplates/\nuninstall-services.sh\nvenv/\n\n(23 entries)\n</entries>",
|
||
"metadata": {
|
||
"preview": "__pycache__/\n.env\n.git/\n.gitignore\nbatch_crossings.csv\nbatch_store.py\nbytetrack_counter.db\nbytetrack-counter-dashboard.service\nbytetrack-counter.service\nconfig.env.example\ncounter_dashboard.py\ncounter_live_rknn_bytetrack.py\ncounter_live_rknn.py\ncounter_live.py\nDEPLOY.md\nenv/\ninstall-services.sh\nREADME.md\nrequirements.txt\nsetup-venv.sh",
|
||
"truncated": false,
|
||
"loaded": [],
|
||
"display": {
|
||
"type": "directory",
|
||
"path": "/opt/bytetrack-counter",
|
||
"entries": [
|
||
"__pycache__/",
|
||
".env",
|
||
".git/",
|
||
".gitignore",
|
||
"batch_crossings.csv",
|
||
"batch_store.py",
|
||
"bytetrack_counter.db",
|
||
"bytetrack-counter-dashboard.service",
|
||
"bytetrack-counter.service",
|
||
"config.env.example",
|
||
"counter_dashboard.py",
|
||
"counter_live_rknn_bytetrack.py",
|
||
"counter_live_rknn.py",
|
||
"counter_live.py",
|
||
"DEPLOY.md",
|
||
"env/",
|
||
"install-services.sh",
|
||
"README.md",
|
||
"requirements.txt",
|
||
"setup-venv.sh",
|
||
"templates/",
|
||
"uninstall-services.sh",
|
||
"venv/"
|
||
],
|
||
"offset": 1,
|
||
"totalEntries": 23,
|
||
"truncated": false
|
||
}
|
||
},
|
||
"title": "",
|
||
"time": {
|
||
"start": 1785284232983,
|
||
"end": 1785284233008
|
||
}
|
||
},
|
||
"id": "prt_fab3b8689001qukGnyRb4iq1Wi",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3b768b0016CKiVP10E6vAsf"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "glob",
|
||
"callID": "call_01_KmjrUVbZ4X4d86sYSS0Z3134",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"pattern": "**/AGENTS.md",
|
||
"path": "/opt/bytetrack-counter"
|
||
},
|
||
"output": "No files found",
|
||
"metadata": {
|
||
"count": 0,
|
||
"truncated": false
|
||
},
|
||
"title": "",
|
||
"time": {
|
||
"start": 1785284233315,
|
||
"end": 1785284233359
|
||
}
|
||
},
|
||
"id": "prt_fab3b875f001giby1E7bbNhX8W",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3b768b0016CKiVP10E6vAsf"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "glob",
|
||
"callID": "call_02_RlzVEwcdJxWQh4j5pGt87999",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"pattern": "**/CLAUDE.md",
|
||
"path": "/opt/bytetrack-counter"
|
||
},
|
||
"output": "No files found",
|
||
"metadata": {
|
||
"count": 0,
|
||
"truncated": false
|
||
},
|
||
"title": "",
|
||
"time": {
|
||
"start": 1785284233696,
|
||
"end": 1785284233757
|
||
}
|
||
},
|
||
"id": "prt_fab3b88c0001bu28nlQR21fNPW",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3b768b0016CKiVP10E6vAsf"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "glob",
|
||
"callID": "call_03_iaTsWXV0yjAFZjEUANHm3432",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"pattern": "**/opencode.json",
|
||
"path": "/opt/bytetrack-counter"
|
||
},
|
||
"output": "No files found",
|
||
"metadata": {
|
||
"count": 0,
|
||
"truncated": false
|
||
},
|
||
"title": "",
|
||
"time": {
|
||
"start": 1785284234073,
|
||
"end": 1785284234118
|
||
}
|
||
},
|
||
"id": "prt_fab3b8a3f0018UdBO66GWB5QPd",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3b768b0016CKiVP10E6vAsf"
|
||
},
|
||
{
|
||
"reason": "tool-calls",
|
||
"snapshot": "e0e1414b68e2544fb2d71b977882e90620947d43",
|
||
"type": "step-finish",
|
||
"tokens": {
|
||
"total": 9821,
|
||
"input": 7670,
|
||
"output": 212,
|
||
"reasoning": 19,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 1920
|
||
}
|
||
},
|
||
"cost": 0.00354438,
|
||
"id": "prt_fab3b8c01001oRRWNmyUZU2kVd",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3b768b0016CKiVP10E6vAsf"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"parentID": "msg_fab3b7662001asy7eTmLMm4mEq",
|
||
"role": "assistant",
|
||
"mode": "build",
|
||
"agent": "build",
|
||
"path": {
|
||
"cwd": "/opt/bytetrack-counter",
|
||
"root": "/opt/bytetrack-counter"
|
||
},
|
||
"cost": 0.000391964,
|
||
"tokens": {
|
||
"total": 10274,
|
||
"input": 272,
|
||
"output": 248,
|
||
"reasoning": 26,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 9728
|
||
}
|
||
},
|
||
"modelID": "deepseek-v4-pro",
|
||
"providerID": "deepseek",
|
||
"time": {
|
||
"created": 1785284234339,
|
||
"completed": 1785284237633
|
||
},
|
||
"finish": "tool-calls",
|
||
"id": "msg_fab3b8c630019oax7kHIohIWBa",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"snapshot": "e0e1414b68e2544fb2d71b977882e90620947d43",
|
||
"type": "step-start",
|
||
"id": "prt_fab3b8e95001CCejmuIy60hMBE",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3b8c630019oax7kHIohIWBa"
|
||
},
|
||
{
|
||
"type": "reasoning",
|
||
"text": "No existing AGENTS.md, CLAUDE.md, or opencode.json. Let me read the key files to understand the project.",
|
||
"time": {
|
||
"start": 1785284235363,
|
||
"end": 1785284235828
|
||
},
|
||
"id": "prt_fab3b9063001lXlgeRKKMkQ345",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3b8c630019oax7kHIohIWBa"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "read",
|
||
"callID": "call_00_Q5AQBhUEOQySx1cZSzWo0273",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"filePath": "/opt/bytetrack-counter/README.md"
|
||
},
|
||
"output": "<path>/opt/bytetrack-counter/README.md</path>\n<type>file</type>\n<content>\n1: # Jetson Edge Counter (Production)\n2: \n3: RTSP + YOLO TensorRT line-crossing counter for edge Jetson. Replaces MQTT `frigate-counter` on site.\n4: \n5: ## Architecture\n6: \n7: - **Input:** Direct LAN camera RTSP (low latency)\n8: - **Inference:** YOLO11n `.engine` (TensorRT) on Jetson GPU\n9: - **Logic:** Line crossing (`ayam` count, `talenan` closes batch) via `batch_store.py`\n10: - **Output:** `jetson_counter.db` + `current_batch.json`\n11: - **Dashboard:** Flask on port **5000**\n12: \n13: Batch lifecycle: talenan closes batch → idle until next ayam line cross (count starts at 1).\n14: \n15: ## Deploy\n16: \n17: See **[DEPLOY.md](DEPLOY.md)**.\n18: \n19: | Item | Default |\n20: |------|---------|\n21: | Install path | `/opt/jetson-counter` |\n22: | Venv | `/opt/jetson-counter/venv` |\n23: | DB | `/opt/jetson-counter/jetson_counter.db` |\n24: | Dashboard | `http://<jetson-ip>:5000` |\n25: | Cutoff | `20:00` |\n26: \n27: ## Commands\n28: \n29: | Command | Purpose |\n30: |---------|---------|\n31: | `sudo systemctl status jetson-counter` | Counter running? |\n32: | `sudo journalctl -u jetson-counter -f` | Live logs |\n33: | `sudo systemctl restart jetson-counter` | After `.env` change |\n34: | `sudo ./uninstall-services.sh` | Remove services |\n35: \n36: ## Key env vars\n37: \n38: | Variable | Purpose |\n39: |----------|---------|\n40: | `SOURCE` | Direct camera RTSP URL |\n41: | `MODEL_PATH` | `.engine` file path |\n42: | `IMGSZ` / `HALF` | Must match engine export |\n43: | `CROSS_DIRECTION` | `rtl` (default), `ltr`, or `both` |\n44: | `LINE_X` / `LINE_X_FRAC` | Counting line position |\n45: \n46: ## Files\n47: \n48: | File | Purpose |\n49: |------|---------|\n50: | `counter_live.py` | RTSP + YOLO + line crossing |\n51: | `batch_store.py` | SQLite persistence |\n52: | `counter_dashboard.py` | Flask UI |\n53: | `config.env.example` | Env template |\n54: | `jetson-counter.service` | Counter systemd unit |\n55: | `install-services.sh` | Install + disable legacy MQTT counter |\n56: \n57: ## Dev stack\n58: \n59: Lab / comparison: [`jetson-counter-dev/`](../jetson-counter-dev/) (port 8081, separate DB).\n\n(End of file - total 59 lines)\n</content>",
|
||
"metadata": {
|
||
"preview": "# Jetson Edge Counter (Production)\n\nRTSP + YOLO TensorRT line-crossing counter for edge Jetson. Replaces MQTT `frigate-counter` on site.\n\n## Architecture\n\n- **Input:** Direct LAN camera RTSP (low latency)\n- **Inference:** YOLO11n `.engine` (TensorRT) on Jetson GPU\n- **Logic:** Line crossing (`ayam` count, `talenan` closes batch) via `batch_store.py`\n- **Output:** `jetson_counter.db` + `current_batch.json`\n- **Dashboard:** Flask on port **5000**\n\nBatch lifecycle: talenan closes batch → idle until next ayam line cross (count starts at 1).\n\n## Deploy\n\nSee **[DEPLOY.md](DEPLOY.md)**.\n\n| Item | Default |\n|------|---------|",
|
||
"truncated": false,
|
||
"loaded": [],
|
||
"display": {
|
||
"type": "file",
|
||
"path": "/opt/bytetrack-counter/README.md",
|
||
"text": "# Jetson Edge Counter (Production)\n\nRTSP + YOLO TensorRT line-crossing counter for edge Jetson. Replaces MQTT `frigate-counter` on site.\n\n## Architecture\n\n- **Input:** Direct LAN camera RTSP (low latency)\n- **Inference:** YOLO11n `.engine` (TensorRT) on Jetson GPU\n- **Logic:** Line crossing (`ayam` count, `talenan` closes batch) via `batch_store.py`\n- **Output:** `jetson_counter.db` + `current_batch.json`\n- **Dashboard:** Flask on port **5000**\n\nBatch lifecycle: talenan closes batch → idle until next ayam line cross (count starts at 1).\n\n## Deploy\n\nSee **[DEPLOY.md](DEPLOY.md)**.\n\n| Item | Default |\n|------|---------|\n| Install path | `/opt/jetson-counter` |\n| Venv | `/opt/jetson-counter/venv` |\n| DB | `/opt/jetson-counter/jetson_counter.db` |\n| Dashboard | `http://<jetson-ip>:5000` |\n| Cutoff | `20:00` |\n\n## Commands\n\n| Command | Purpose |\n|---------|---------|\n| `sudo systemctl status jetson-counter` | Counter running? |\n| `sudo journalctl -u jetson-counter -f` | Live logs |\n| `sudo systemctl restart jetson-counter` | After `.env` change |\n| `sudo ./uninstall-services.sh` | Remove services |\n\n## Key env vars\n\n| Variable | Purpose |\n|----------|---------|\n| `SOURCE` | Direct camera RTSP URL |\n| `MODEL_PATH` | `.engine` file path |\n| `IMGSZ` / `HALF` | Must match engine export |\n| `CROSS_DIRECTION` | `rtl` (default), `ltr`, or `both` |\n| `LINE_X` / `LINE_X_FRAC` | Counting line position |\n\n## Files\n\n| File | Purpose |\n|------|---------|\n| `counter_live.py` | RTSP + YOLO + line crossing |\n| `batch_store.py` | SQLite persistence |\n| `counter_dashboard.py` | Flask UI |\n| `config.env.example` | Env template |\n| `jetson-counter.service` | Counter systemd unit |\n| `install-services.sh` | Install + disable legacy MQTT counter |\n\n## Dev stack\n\nLab / comparison: [`jetson-counter-dev/`](../jetson-counter-dev/) (port 8081, separate DB).",
|
||
"lineStart": 1,
|
||
"lineEnd": 59,
|
||
"totalLines": 59,
|
||
"truncated": false
|
||
}
|
||
},
|
||
"title": "README.md",
|
||
"time": {
|
||
"start": 1785284235996,
|
||
"end": 1785284236128
|
||
}
|
||
},
|
||
"id": "prt_fab3b9238001gw2IVOq3OmFKCP",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3b8c630019oax7kHIohIWBa"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "read",
|
||
"callID": "call_01_FixcLZLgw20u0fGSN3BT6049",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"filePath": "/opt/bytetrack-counter/requirements.txt"
|
||
},
|
||
"output": "<path>/opt/bytetrack-counter/requirements.txt</path>\n<type>file</type>\n<content>\n1: numpy<2\n2: rknn-toolkit-lite2\n3: opencv-python\n4: flask\n5: python-dotenv\n6: openpyxl\n\n(End of file - total 6 lines)\n</content>",
|
||
"metadata": {
|
||
"preview": "numpy<2\nrknn-toolkit-lite2\nopencv-python\nflask\npython-dotenv\nopenpyxl",
|
||
"truncated": false,
|
||
"loaded": [],
|
||
"display": {
|
||
"type": "file",
|
||
"path": "/opt/bytetrack-counter/requirements.txt",
|
||
"text": "numpy<2\nrknn-toolkit-lite2\nopencv-python\nflask\npython-dotenv\nopenpyxl",
|
||
"lineStart": 1,
|
||
"lineEnd": 6,
|
||
"totalLines": 6,
|
||
"truncated": false
|
||
}
|
||
},
|
||
"title": "requirements.txt",
|
||
"time": {
|
||
"start": 1785284236303,
|
||
"end": 1785284236315
|
||
}
|
||
},
|
||
"id": "prt_fab3b9341001kG6xCTKFRZrEq0",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3b8c630019oax7kHIohIWBa"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "read",
|
||
"callID": "call_02_EA0ldEx8QS3NNn0tFRBT8644",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"filePath": "/opt/bytetrack-counter/DEPLOY.md"
|
||
},
|
||
"output": "<path>/opt/bytetrack-counter/DEPLOY.md</path>\n<type>file</type>\n<content>\n1: # Edge Jetson Deploy\n2: \n3: Production counter: **direct LAN RTSP** + **YOLO11n TensorRT** + SQLite batch store. \n4: Replaces MQTT `frigate-counter` on the edge Jetson.\n5: \n6: ## Quick install\n7: \n8: ```bash\n9: # 1. Copy this folder to Jetson\n10: sudo mkdir -p /opt/jetson-counter\n11: sudo cp -r jetson-counter/* /opt/jetson-counter/\n12: sudo chown -R jetson:jetson /opt/jetson-counter\n13: \n14: # 2. Configure\n15: cd /opt/jetson-counter\n16: cp config.env.example .env\n17: nano .env # SOURCE, MODEL_PATH, CAMERA_NAME, etc.\n18: sed -i 's/\\r$//' .env\n19: \n20: # 3. Venv + services\n21: chmod +x setup-venv.sh install-services.sh\n22: sudo ./setup-venv.sh\n23: sudo ./install-services.sh\n24: ```\n25: \n26: Dashboard: `http://<jetson-ip>:5000`\n27: \n28: ---\n29: \n30: ## YOLO11n TensorRT engine (one-time)\n31: \n32: On the Jetson (must match `IMGSZ` / `HALF` in `.env`):\n33: \n34: ```bash\n35: source /opt/jetson-counter/venv/bin/activate\n36: export PYTHONNOUSERSITE=1\n37: \n38: yolo export model=/media/jetson/DATA/yolo11n.pt format=engine half=True imgsz=416 device=0\n39: ```\n40: \n41: Verify classes:\n42: \n43: ```bash\n44: PYTHONNOUSERSITE=1 python -c \"\n45: from ultralytics import YOLO\n46: m = YOLO('/media/jetson/DATA/yolo11n.engine')\n47: print(m.names)\n48: \"\n49: ```\n50: \n51: Expect `ayam` and `talenan`.\n52: \n53: ---\n54: \n55: ## Direct camera RTSP\n56: \n57: Set in `.env`:\n58: \n59: ```env\n60: SOURCE=rtsp://user:pass@192.168.x.x:554/stream1\n61: ```\n62: \n63: Test before install:\n64: \n65: ```bash\n66: ffplay -rtsp_transport tcp -t 5 \"$SOURCE\"\n67: nc -zv <camera-ip> 554\n68: ```\n69: \n70: ---\n71: \n72: ## Cutover from MQTT frigate-counter\n73: \n74: `install-services.sh` automatically:\n75: \n76: 1. Disables `frigate-counter` and `frigate-counter-dashboard`\n77: 2. Enables `jetson-counter` + `jetson-counter-dashboard`\n78: \n79: Archive old DB (optional):\n80: \n81: ```bash\n82: sudo cp /opt/frigate-counter/frigate_counter.db ~/frigate_counter.db.backup\n83: ```\n84: \n85: ---\n86: \n87: ## Validation checklist\n88: \n89: ```bash\n90: sudo systemctl is-active jetson-counter jetson-counter-dashboard\n91: PYTHONNOUSERSITE=1 /opt/jetson-counter/venv/bin/python -c \"import torch; print('cuda', torch.cuda.is_available())\"\n92: sudo journalctl -u jetson-counter -n 20 --no-pager\n93: ```\n94: \n95: Good signs:\n96: \n97: - `Stream ready!`\n98: - `Loaded engine size: ... MiB`\n99: - `Frame 100 | Batch ...`\n100: \n101: ---\n102: \n103: ## Logs & restart\n104: \n105: ```bash\n106: sudo journalctl -u jetson-counter -f\n107: sudo systemctl restart jetson-counter # after .env change\n108: ```\n109: \n110: ---\n111: \n112: ## JetPack 6.0 torch wheel\n113: \n114: If `setup-venv.sh` fails on torch URL, list wheels:\n115: \n116: ```bash\n117: curl -s https://developer.download.nvidia.com/compute/redist/jp/v60/pytorch/ | grep cp310\n118: ```\n119: \n120: Set `TORCH_WHEEL_URL=...` when running `setup-venv.sh`.\n121: \n122: See also [jetson-counter-dev/GO_LIVE_TROUBLESHOOT.md](../jetson-counter-dev/GO_LIVE_TROUBLESHOOT.md) for torchvision and RTSP issues.\n\n(End of file - total 122 lines)\n</content>",
|
||
"metadata": {
|
||
"preview": "# Edge Jetson Deploy\n\nProduction counter: **direct LAN RTSP** + **YOLO11n TensorRT** + SQLite batch store. \nReplaces MQTT `frigate-counter` on the edge Jetson.\n\n## Quick install\n\n```bash\n# 1. Copy this folder to Jetson\nsudo mkdir -p /opt/jetson-counter\nsudo cp -r jetson-counter/* /opt/jetson-counter/\nsudo chown -R jetson:jetson /opt/jetson-counter\n\n# 2. Configure\ncd /opt/jetson-counter\ncp config.env.example .env\nnano .env # SOURCE, MODEL_PATH, CAMERA_NAME, etc.\nsed -i 's/\\r$//' .env\n\n# 3. Venv + services",
|
||
"truncated": false,
|
||
"loaded": [],
|
||
"display": {
|
||
"type": "file",
|
||
"path": "/opt/bytetrack-counter/DEPLOY.md",
|
||
"text": "# Edge Jetson Deploy\n\nProduction counter: **direct LAN RTSP** + **YOLO11n TensorRT** + SQLite batch store. \nReplaces MQTT `frigate-counter` on the edge Jetson.\n\n## Quick install\n\n```bash\n# 1. Copy this folder to Jetson\nsudo mkdir -p /opt/jetson-counter\nsudo cp -r jetson-counter/* /opt/jetson-counter/\nsudo chown -R jetson:jetson /opt/jetson-counter\n\n# 2. Configure\ncd /opt/jetson-counter\ncp config.env.example .env\nnano .env # SOURCE, MODEL_PATH, CAMERA_NAME, etc.\nsed -i 's/\\r$//' .env\n\n# 3. Venv + services\nchmod +x setup-venv.sh install-services.sh\nsudo ./setup-venv.sh\nsudo ./install-services.sh\n```\n\nDashboard: `http://<jetson-ip>:5000`\n\n---\n\n## YOLO11n TensorRT engine (one-time)\n\nOn the Jetson (must match `IMGSZ` / `HALF` in `.env`):\n\n```bash\nsource /opt/jetson-counter/venv/bin/activate\nexport PYTHONNOUSERSITE=1\n\nyolo export model=/media/jetson/DATA/yolo11n.pt format=engine half=True imgsz=416 device=0\n```\n\nVerify classes:\n\n```bash\nPYTHONNOUSERSITE=1 python -c \"\nfrom ultralytics import YOLO\nm = YOLO('/media/jetson/DATA/yolo11n.engine')\nprint(m.names)\n\"\n```\n\nExpect `ayam` and `talenan`.\n\n---\n\n## Direct camera RTSP\n\nSet in `.env`:\n\n```env\nSOURCE=rtsp://user:pass@192.168.x.x:554/stream1\n```\n\nTest before install:\n\n```bash\nffplay -rtsp_transport tcp -t 5 \"$SOURCE\"\nnc -zv <camera-ip> 554\n```\n\n---\n\n## Cutover from MQTT frigate-counter\n\n`install-services.sh` automatically:\n\n1. Disables `frigate-counter` and `frigate-counter-dashboard`\n2. Enables `jetson-counter` + `jetson-counter-dashboard`\n\nArchive old DB (optional):\n\n```bash\nsudo cp /opt/frigate-counter/frigate_counter.db ~/frigate_counter.db.backup\n```\n\n---\n\n## Validation checklist\n\n```bash\nsudo systemctl is-active jetson-counter jetson-counter-dashboard\nPYTHONNOUSERSITE=1 /opt/jetson-counter/venv/bin/python -c \"import torch; print('cuda', torch.cuda.is_available())\"\nsudo journalctl -u jetson-counter -n 20 --no-pager\n```\n\nGood signs:\n\n- `Stream ready!`\n- `Loaded engine size: ... MiB`\n- `Frame 100 | Batch ...`\n\n---\n\n## Logs & restart\n\n```bash\nsudo journalctl -u jetson-counter -f\nsudo systemctl restart jetson-counter # after .env change\n```\n\n---\n\n## JetPack 6.0 torch wheel\n\nIf `setup-venv.sh` fails on torch URL, list wheels:\n\n```bash\ncurl -s https://developer.download.nvidia.com/compute/redist/jp/v60/pytorch/ | grep cp310\n```\n\nSet `TORCH_WHEEL_URL=...` when running `setup-venv.sh`.\n\nSee also [jetson-counter-dev/GO_LIVE_TROUBLESHOOT.md](../jetson-counter-dev/GO_LIVE_TROUBLESHOOT.md) for torchvision and RTSP issues.",
|
||
"lineStart": 1,
|
||
"lineEnd": 122,
|
||
"totalLines": 122,
|
||
"truncated": false
|
||
}
|
||
},
|
||
"title": "DEPLOY.md",
|
||
"time": {
|
||
"start": 1785284236566,
|
||
"end": 1785284236618
|
||
}
|
||
},
|
||
"id": "prt_fab3b9421001hFUMAR8utSHwcF",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3b8c630019oax7kHIohIWBa"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "read",
|
||
"callID": "call_03_XcAXNMUkJ5Y7aLBm0Igk4940",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"filePath": "/opt/bytetrack-counter/.gitignore"
|
||
},
|
||
"output": "<path>/opt/bytetrack-counter/.gitignore</path>\n<type>file</type>\n<content>\n1: # Byte-compiled / optimized / DLL files\n2: __pycache__/\n3: *.py[codz]\n4: *$py.class\n5: \n6: # C extensions\n7: *.so\n8: \n9: # Distribution / packaging\n10: .Python\n11: build/\n12: develop-eggs/\n13: dist/\n14: downloads/\n15: eggs/\n16: .eggs/\n17: lib/\n18: lib64/\n19: parts/\n20: sdist/\n21: var/\n22: wheels/\n23: share/python-wheels/\n24: *.egg-info/\n25: .installed.cfg\n26: *.egg\n27: MANIFEST\n28: \n29: # PyInstaller\n30: # Usually these files are written by a python script from a template\n31: # before PyInstaller builds the exe, so as to inject date/other infos into it.\n32: *.manifest\n33: *.spec\n34: \n35: # Installer logs\n36: pip-log.txt\n37: pip-delete-this-directory.txt\n38: \n39: # Unit test / coverage reports\n40: htmlcov/\n41: .tox/\n42: .nox/\n43: .coverage\n44: .coverage.*\n45: .cache\n46: nosetests.xml\n47: coverage.xml\n48: *.cover\n49: *.py.cover\n50: *.lcov\n51: .hypothesis/\n52: .pytest_cache/\n53: cover/\n54: \n55: # Translations\n56: *.mo\n57: *.pot\n58: \n59: # Django stuff:\n60: *.log\n61: local_settings.py\n62: db.sqlite3\n63: db.sqlite3-journal\n64: \n65: # Flask stuff:\n66: instance/\n67: .webassets-cache\n68: \n69: # Scrapy stuff:\n70: .scrapy\n71: \n72: # Sphinx documentation\n73: docs/_build/\n74: \n75: # PyBuilder\n76: .pybuilder/\n77: target/\n78: \n79: # Jupyter Notebook\n80: .ipynb_checkpoints\n81: \n82: # IPython\n83: profile_default/\n84: ipython_config.py\n85: \n86: # pyenv\n87: # For a library or package, you might want to ignore these files since the code is\n88: # intended to run in multiple environments; otherwise, check them in:\n89: # .python-version\n90: \n91: # pipenv\n92: # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n93: # However, in case of collaboration, if having platform-specific dependencies or dependencies\n94: # having no cross-platform support, pipenv may install dependencies that don't work, or not\n95: # install all needed dependencies.\n96: # Pipfile.lock\n97: \n98: # UV\n99: # Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.\n100: # This is especially recommended for binary packages to ensure reproducibility, and is more\n101: # commonly ignored for libraries.\n102: # uv.lock\n103: \n104: # poetry\n105: # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.\n106: # This is especially recommended for binary packages to ensure reproducibility, and is more\n107: # commonly ignored for libraries.\n108: # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control\n109: # poetry.lock\n110: # poetry.toml\n111: \n112: # pdm\n113: # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.\n114: # pdm recommends including project-wide configuration in pdm.toml, but excluding .pdm-python.\n115: # https://pdm-project.org/en/latest/usage/project/#working-with-version-control\n116: # pdm.lock\n117: # pdm.toml\n118: .pdm-python\n119: .pdm-build/\n120: \n121: # pixi\n122: # Similar to Pipfile.lock, it is generally recommended to include pixi.lock in version control.\n123: # pixi.lock\n124: # Pixi creates a virtual environment in the .pixi directory, just like venv module creates one\n125: # in the .venv directory. It is recommended not to include this directory in version control.\n126: .pixi/*\n127: !.pixi/config.toml\n128: \n129: # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm\n130: __pypackages__/\n131: \n132: # Celery stuff\n133: celerybeat-schedule*\n134: celerybeat.pid\n135: \n136: # Redis\n137: *.rdb\n138: *.aof\n139: *.pid\n140: \n141: # RabbitMQ\n142: mnesia/\n143: rabbitmq/\n144: rabbitmq-data/\n145: \n146: # ActiveMQ\n147: activemq-data/\n148: \n149: # SageMath parsed files\n150: *.sage.py\n151: \n152: # Environments\n153: .env\n154: .envrc\n155: .venv\n156: env/\n157: venv/\n158: ENV/\n159: env.bak/\n160: venv.bak/\n161: \n162: # Spyder project settings\n163: .spyderproject\n164: .spyproject\n165: \n166: # Rope project settings\n167: .ropeproject\n168: \n169: # mkdocs documentation\n170: /site\n171: \n172: # mypy\n173: .mypy_cache/\n174: .dmypy.json\n175: dmypy.json\n176: \n177: # Pyre type checker\n178: .pyre/\n179: \n180: # pytype static type analyzer\n181: .pytype/\n182: \n183: # Cython debug symbols\n184: cython_debug/\n185: \n186: # PyCharm\n187: # JetBrains specific template is maintained in a separate JetBrains.gitignore that can\n188: # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore\n189: # and can be added to the global gitignore or merged into this file. For a more nuclear\n190: # option (not recommended) you can uncomment the following to ignore the entire idea folder.\n191: # .idea/\n192: \n193: # Abstra\n194: # Abstra is an AI-powered process automation framework.\n195: # Ignore directories containing user credentials, local state, and settings.\n196: # Learn more at https://abstra.io/docs\n197: .abstra/\n198: \n199: # Visual Studio Code\n200: # Visual Studio Code specific template is maintained in a separate VisualStudioCode.gitignore \n201: # that can be found at https://github.com/github/gitignore/blob/main/Global/VisualStudioCode.gitignore\n202: # and can be added to the global gitignore or merged into this file. However, if you prefer, \n203: # you could uncomment the following to ignore the entire vscode folder\n204: # .vscode/\n205: # Temporary file for partial code execution\n206: tempCodeRunnerFile.py\n207: \n208: # Ruff stuff:\n209: .ruff_cache/\n210: \n211: # PyPI configuration file\n212: .pypirc\n213: \n214: # Marimo\n215: marimo/_static/\n216: marimo/_lsp/\n217: __marimo__/\n218: \n219: # Streamlit\n220: .streamlit/secrets.toml\n\n(End of file - total 220 lines)\n</content>",
|
||
"metadata": {
|
||
"preview": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[codz]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/",
|
||
"truncated": false,
|
||
"loaded": [],
|
||
"display": {
|
||
"type": "file",
|
||
"path": "/opt/bytetrack-counter/.gitignore",
|
||
"text": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[codz]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/\nvar/\nwheels/\nshare/python-wheels/\n*.egg-info/\n.installed.cfg\n*.egg\nMANIFEST\n\n# PyInstaller\n# Usually these files are written by a python script from a template\n# before PyInstaller builds the exe, so as to inject date/other infos into it.\n*.manifest\n*.spec\n\n# Installer logs\npip-log.txt\npip-delete-this-directory.txt\n\n# Unit test / coverage reports\nhtmlcov/\n.tox/\n.nox/\n.coverage\n.coverage.*\n.cache\nnosetests.xml\ncoverage.xml\n*.cover\n*.py.cover\n*.lcov\n.hypothesis/\n.pytest_cache/\ncover/\n\n# Translations\n*.mo\n*.pot\n\n# Django stuff:\n*.log\nlocal_settings.py\ndb.sqlite3\ndb.sqlite3-journal\n\n# Flask stuff:\ninstance/\n.webassets-cache\n\n# Scrapy stuff:\n.scrapy\n\n# Sphinx documentation\ndocs/_build/\n\n# PyBuilder\n.pybuilder/\ntarget/\n\n# Jupyter Notebook\n.ipynb_checkpoints\n\n# IPython\nprofile_default/\nipython_config.py\n\n# pyenv\n# For a library or package, you might want to ignore these files since the code is\n# intended to run in multiple environments; otherwise, check them in:\n# .python-version\n\n# pipenv\n# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.\n# However, in case of collaboration, if having platform-specific dependencies or dependencies\n# having no cross-platform support, pipenv may install dependencies that don't work, or not\n# install all needed dependencies.\n# Pipfile.lock\n\n# UV\n# Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.\n# This is especially recommended for binary packages to ensure reproducibility, and is more\n# commonly ignored for libraries.\n# uv.lock\n\n# poetry\n# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.\n# This is especially recommended for binary packages to ensure reproducibility, and is more\n# commonly ignored for libraries.\n# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control\n# poetry.lock\n# poetry.toml\n\n# pdm\n# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.\n# pdm recommends including project-wide configuration in pdm.toml, but excluding .pdm-python.\n# https://pdm-project.org/en/latest/usage/project/#working-with-version-control\n# pdm.lock\n# pdm.toml\n.pdm-python\n.pdm-build/\n\n# pixi\n# Similar to Pipfile.lock, it is generally recommended to include pixi.lock in version control.\n# pixi.lock\n# Pixi creates a virtual environment in the .pixi directory, just like venv module creates one\n# in the .venv directory. It is recommended not to include this directory in version control.\n.pixi/*\n!.pixi/config.toml\n\n# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm\n__pypackages__/\n\n# Celery stuff\ncelerybeat-schedule*\ncelerybeat.pid\n\n# Redis\n*.rdb\n*.aof\n*.pid\n\n# RabbitMQ\nmnesia/\nrabbitmq/\nrabbitmq-data/\n\n# ActiveMQ\nactivemq-data/\n\n# SageMath parsed files\n*.sage.py\n\n# Environments\n.env\n.envrc\n.venv\nenv/\nvenv/\nENV/\nenv.bak/\nvenv.bak/\n\n# Spyder project settings\n.spyderproject\n.spyproject\n\n# Rope project settings\n.ropeproject\n\n# mkdocs documentation\n/site\n\n# mypy\n.mypy_cache/\n.dmypy.json\ndmypy.json\n\n# Pyre type checker\n.pyre/\n\n# pytype static type analyzer\n.pytype/\n\n# Cython debug symbols\ncython_debug/\n\n# PyCharm\n# JetBrains specific template is maintained in a separate JetBrains.gitignore that can\n# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore\n# and can be added to the global gitignore or merged into this file. For a more nuclear\n# option (not recommended) you can uncomment the following to ignore the entire idea folder.\n# .idea/\n\n# Abstra\n# Abstra is an AI-powered process automation framework.\n# Ignore directories containing user credentials, local state, and settings.\n# Learn more at https://abstra.io/docs\n.abstra/\n\n# Visual Studio Code\n# Visual Studio Code specific template is maintained in a separate VisualStudioCode.gitignore \n# that can be found at https://github.com/github/gitignore/blob/main/Global/VisualStudioCode.gitignore\n# and can be added to the global gitignore or merged into this file. However, if you prefer, \n# you could uncomment the following to ignore the entire vscode folder\n# .vscode/\n# Temporary file for partial code execution\ntempCodeRunnerFile.py\n\n# Ruff stuff:\n.ruff_cache/\n\n# PyPI configuration file\n.pypirc\n\n# Marimo\nmarimo/_static/\nmarimo/_lsp/\n__marimo__/\n\n# Streamlit\n.streamlit/secrets.toml",
|
||
"lineStart": 1,
|
||
"lineEnd": 220,
|
||
"totalLines": 220,
|
||
"truncated": false
|
||
}
|
||
},
|
||
"title": ".gitignore",
|
||
"time": {
|
||
"start": 1785284236844,
|
||
"end": 1785284236858
|
||
}
|
||
},
|
||
"id": "prt_fab3b956c001n37Ae0D8qA1FIm",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3b8c630019oax7kHIohIWBa"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "read",
|
||
"callID": "call_04_8kLRyFQlLLrl8gWMDuZu8872",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"filePath": "/opt/bytetrack-counter/setup-venv.sh"
|
||
},
|
||
"output": "<path>/opt/bytetrack-counter/setup-venv.sh</path>\n<type>file</type>\n<content>\n1: #!/usr/bin/env bash\n2: # One-time venv for edge Jetson counter — NVIDIA torch required (not PyPI).\n3: set -euo pipefail\n4: \n5: INSTALL_DIR=\"${INSTALL_DIR:-/opt/jetson-counter}\"\n6: VENV_DIR=\"${VENV_DIR:-/opt/jetson-counter/venv}\"\n7: SERVICE_USER=\"${SERVICE_USER:-jetson}\"\n8: TORCH_WHEEL_URL=\"${TORCH_WHEEL_URL:-https://developer.download.nvidia.com/compute/redist/jp/v60/pytorch/torch-2.4.0a0+3bcc3cddb5.nv24.07.16234504-cp310-cp310-linux_aarch64.whl}\"\n9: \n10: if [[ \"$(id -u)\" -ne 0 ]]; then\n11: echo \"Run as root: sudo ./setup-venv.sh\"\n12: exit 1\n13: fi\n14: \n15: apt-get install -y libopenblas-base libopenmpi-dev libomp-dev 2>/dev/null || true\n16: \n17: mkdir -p \"${INSTALL_DIR}\"\n18: if [[ ! -x \"${VENV_DIR}/bin/python\" ]]; then\n19: python3 -m venv --system-site-packages \"${VENV_DIR}\"\n20: fi\n21: chown -R \"${SERVICE_USER}:${SERVICE_USER}\" \"${INSTALL_DIR}\"\n22: \n23: sudo -u \"${SERVICE_USER}\" \"${VENV_DIR}/bin/pip\" install --upgrade pip\n24: sudo -u \"${SERVICE_USER}\" \"${VENV_DIR}/bin/pip\" install \"numpy<2\"\n25: \n26: if ! sudo -u \"${SERVICE_USER}\" PYTHONNOUSERSITE=1 \"${VENV_DIR}/bin/python\" -c \"import torch; assert torch.cuda.is_available()\" 2>/dev/null; then\n27: echo \"Installing NVIDIA Jetson torch wheel...\"\n28: sudo -u \"${SERVICE_USER}\" \"${VENV_DIR}/bin/pip\" install --no-cache-dir \"${TORCH_WHEEL_URL}\"\n29: fi\n30: \n31: sudo -u \"${SERVICE_USER}\" \"${VENV_DIR}/bin/pip\" install ultralytics flask opencv-python\n32: \n33: sudo -u \"${SERVICE_USER}\" PYTHONNOUSERSITE=1 \"${VENV_DIR}/bin/python\" -c \"\n34: import torch\n35: from ultralytics import YOLO\n36: import cv2\n37: import flask\n38: print('venv ok | torch', torch.__version__, '| cuda', torch.cuda.is_available())\n39: \"\n40: \n41: echo \"\"\n42: echo \"If torchvision import fails for ultralytics, copy/build torchvision into venv.\"\n43: echo \"Next: cp config.env.example .env && nano .env && sudo ./install-services.sh\"\n\n(End of file - total 43 lines)\n</content>",
|
||
"metadata": {
|
||
"preview": "#!/usr/bin/env bash\n# One-time venv for edge Jetson counter — NVIDIA torch required (not PyPI).\nset -euo pipefail\n\nINSTALL_DIR=\"${INSTALL_DIR:-/opt/jetson-counter}\"\nVENV_DIR=\"${VENV_DIR:-/opt/jetson-counter/venv}\"\nSERVICE_USER=\"${SERVICE_USER:-jetson}\"\nTORCH_WHEEL_URL=\"${TORCH_WHEEL_URL:-https://developer.download.nvidia.com/compute/redist/jp/v60/pytorch/torch-2.4.0a0+3bcc3cddb5.nv24.07.16234504-cp310-cp310-linux_aarch64.whl}\"\n\nif [[ \"$(id -u)\" -ne 0 ]]; then\n echo \"Run as root: sudo ./setup-venv.sh\"\n exit 1\nfi\n\napt-get install -y libopenblas-base libopenmpi-dev libomp-dev 2>/dev/null || true\n\nmkdir -p \"${INSTALL_DIR}\"\nif [[ ! -x \"${VENV_DIR}/bin/python\" ]]; then\n python3 -m venv --system-site-packages \"${VENV_DIR}\"\nfi",
|
||
"truncated": false,
|
||
"loaded": [],
|
||
"display": {
|
||
"type": "file",
|
||
"path": "/opt/bytetrack-counter/setup-venv.sh",
|
||
"text": "#!/usr/bin/env bash\n# One-time venv for edge Jetson counter — NVIDIA torch required (not PyPI).\nset -euo pipefail\n\nINSTALL_DIR=\"${INSTALL_DIR:-/opt/jetson-counter}\"\nVENV_DIR=\"${VENV_DIR:-/opt/jetson-counter/venv}\"\nSERVICE_USER=\"${SERVICE_USER:-jetson}\"\nTORCH_WHEEL_URL=\"${TORCH_WHEEL_URL:-https://developer.download.nvidia.com/compute/redist/jp/v60/pytorch/torch-2.4.0a0+3bcc3cddb5.nv24.07.16234504-cp310-cp310-linux_aarch64.whl}\"\n\nif [[ \"$(id -u)\" -ne 0 ]]; then\n echo \"Run as root: sudo ./setup-venv.sh\"\n exit 1\nfi\n\napt-get install -y libopenblas-base libopenmpi-dev libomp-dev 2>/dev/null || true\n\nmkdir -p \"${INSTALL_DIR}\"\nif [[ ! -x \"${VENV_DIR}/bin/python\" ]]; then\n python3 -m venv --system-site-packages \"${VENV_DIR}\"\nfi\nchown -R \"${SERVICE_USER}:${SERVICE_USER}\" \"${INSTALL_DIR}\"\n\nsudo -u \"${SERVICE_USER}\" \"${VENV_DIR}/bin/pip\" install --upgrade pip\nsudo -u \"${SERVICE_USER}\" \"${VENV_DIR}/bin/pip\" install \"numpy<2\"\n\nif ! sudo -u \"${SERVICE_USER}\" PYTHONNOUSERSITE=1 \"${VENV_DIR}/bin/python\" -c \"import torch; assert torch.cuda.is_available()\" 2>/dev/null; then\n echo \"Installing NVIDIA Jetson torch wheel...\"\n sudo -u \"${SERVICE_USER}\" \"${VENV_DIR}/bin/pip\" install --no-cache-dir \"${TORCH_WHEEL_URL}\"\nfi\n\nsudo -u \"${SERVICE_USER}\" \"${VENV_DIR}/bin/pip\" install ultralytics flask opencv-python\n\nsudo -u \"${SERVICE_USER}\" PYTHONNOUSERSITE=1 \"${VENV_DIR}/bin/python\" -c \"\nimport torch\nfrom ultralytics import YOLO\nimport cv2\nimport flask\nprint('venv ok | torch', torch.__version__, '| cuda', torch.cuda.is_available())\n\"\n\necho \"\"\necho \"If torchvision import fails for ultralytics, copy/build torchvision into venv.\"\necho \"Next: cp config.env.example .env && nano .env && sudo ./install-services.sh\"",
|
||
"lineStart": 1,
|
||
"lineEnd": 43,
|
||
"totalLines": 43,
|
||
"truncated": false
|
||
}
|
||
},
|
||
"title": "setup-venv.sh",
|
||
"time": {
|
||
"start": 1785284237140,
|
||
"end": 1785284237252
|
||
}
|
||
},
|
||
"id": "prt_fab3b965e001ivLihBr50XThW6",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3b8c630019oax7kHIohIWBa"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "read",
|
||
"callID": "call_05_tsusidwd3qAQiCqmM8XU8836",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"filePath": "/opt/bytetrack-counter/install-services.sh"
|
||
},
|
||
"output": "<path>/opt/bytetrack-counter/install-services.sh</path>\n<type>file</type>\n<content>\n1: #!/usr/bin/env bash\n2: # Install edge Jetson counter + dashboard; disable legacy MQTT frigate-counter.\n3: # Run on the Jetson: sudo ./install-services.sh\n4: set -euo pipefail\n5: \n6: INSTALL_DIR=\"${INSTALL_DIR:-/opt/jetson-counter}\"\n7: VENV_DIR=\"${VENV_DIR:-/opt/jetson-counter/venv}\"\n8: SERVICE_USER=\"${SERVICE_USER:-jetson}\"\n9: \n10: if [[ \"$(id -u)\" -ne 0 ]]; then\n11: echo \"Run as root: sudo ./install-services.sh\"\n12: exit 1\n13: fi\n14: \n15: if [[ ! -f \"${INSTALL_DIR}/.env\" ]]; then\n16: echo \"Missing ${INSTALL_DIR}/.env\"\n17: echo \" cp ${INSTALL_DIR}/config.env.example ${INSTALL_DIR}/.env && nano ${INSTALL_DIR}/.env\"\n18: exit 1\n19: fi\n20: \n21: if [[ ! -x \"${VENV_DIR}/bin/python\" ]]; then\n22: echo \"Missing venv: ${VENV_DIR}/bin/python\"\n23: echo \" sudo ./setup-venv.sh\"\n24: exit 1\n25: fi\n26: \n27: sed -i 's/\\r$//' \"${INSTALL_DIR}/.env\" 2>/dev/null || true\n28: \n29: mkdir -p \"${INSTALL_DIR}/.ultralytics\" \"${INSTALL_DIR}/.torch\"\n30: chown -R \"${SERVICE_USER}:${SERVICE_USER}\" \"${INSTALL_DIR}\"\n31: \n32: # Disable legacy MQTT counter (replace mode)\n33: for legacy in frigate-counter frigate-counter-dashboard; do\n34: if systemctl is-enabled \"${legacy}\" &>/dev/null; then\n35: systemctl disable --now \"${legacy}\" || true\n36: echo \"Disabled legacy ${legacy}\"\n37: fi\n38: done\n39: \n40: for unit in jetson-counter jetson-counter-dashboard; do\n41: sed -e \"s|/opt/jetson-counter|${INSTALL_DIR}|g\" \\\n42: -e \"s|User=jetson|User=${SERVICE_USER}|g\" \\\n43: -e \"s|Group=jetson|Group=${SERVICE_USER}|g\" \\\n44: \"${INSTALL_DIR}/${unit}.service\" > \"/etc/systemd/system/${unit}.service\"\n45: echo \"Installed /etc/systemd/system/${unit}.service\"\n46: done\n47: \n48: chown -R \"${SERVICE_USER}:${SERVICE_USER}\" \"${INSTALL_DIR}\"\n49: \n50: PYTHONNOUSERSITE=1 \"${VENV_DIR}/bin/python\" -c \"\n51: from ultralytics import YOLO\n52: import torch\n53: print('import ok | cuda', torch.cuda.is_available())\n54: \" || {\n55: echo \"Import check failed — fix venv before starting services.\"\n56: exit 1\n57: }\n58: \n59: systemctl daemon-reload\n60: systemctl reset-failed jetson-counter jetson-counter-dashboard 2>/dev/null || true\n61: systemctl enable jetson-counter jetson-counter-dashboard\n62: systemctl restart jetson-counter jetson-counter-dashboard\n63: \n64: echo \"\"\n65: systemctl --no-pager status jetson-counter jetson-counter-dashboard || true\n66: echo \"\"\n67: echo \"Logs: sudo journalctl -u jetson-counter -f\"\n68: echo \"Dashboard: http://$(hostname -I | awk '{print $1}'):5000\"\n\n(End of file - total 68 lines)\n</content>",
|
||
"metadata": {
|
||
"preview": "#!/usr/bin/env bash\n# Install edge Jetson counter + dashboard; disable legacy MQTT frigate-counter.\n# Run on the Jetson: sudo ./install-services.sh\nset -euo pipefail\n\nINSTALL_DIR=\"${INSTALL_DIR:-/opt/jetson-counter}\"\nVENV_DIR=\"${VENV_DIR:-/opt/jetson-counter/venv}\"\nSERVICE_USER=\"${SERVICE_USER:-jetson}\"\n\nif [[ \"$(id -u)\" -ne 0 ]]; then\n echo \"Run as root: sudo ./install-services.sh\"\n exit 1\nfi\n\nif [[ ! -f \"${INSTALL_DIR}/.env\" ]]; then\n echo \"Missing ${INSTALL_DIR}/.env\"\n echo \" cp ${INSTALL_DIR}/config.env.example ${INSTALL_DIR}/.env && nano ${INSTALL_DIR}/.env\"\n exit 1\nfi\n",
|
||
"truncated": false,
|
||
"loaded": [],
|
||
"display": {
|
||
"type": "file",
|
||
"path": "/opt/bytetrack-counter/install-services.sh",
|
||
"text": "#!/usr/bin/env bash\n# Install edge Jetson counter + dashboard; disable legacy MQTT frigate-counter.\n# Run on the Jetson: sudo ./install-services.sh\nset -euo pipefail\n\nINSTALL_DIR=\"${INSTALL_DIR:-/opt/jetson-counter}\"\nVENV_DIR=\"${VENV_DIR:-/opt/jetson-counter/venv}\"\nSERVICE_USER=\"${SERVICE_USER:-jetson}\"\n\nif [[ \"$(id -u)\" -ne 0 ]]; then\n echo \"Run as root: sudo ./install-services.sh\"\n exit 1\nfi\n\nif [[ ! -f \"${INSTALL_DIR}/.env\" ]]; then\n echo \"Missing ${INSTALL_DIR}/.env\"\n echo \" cp ${INSTALL_DIR}/config.env.example ${INSTALL_DIR}/.env && nano ${INSTALL_DIR}/.env\"\n exit 1\nfi\n\nif [[ ! -x \"${VENV_DIR}/bin/python\" ]]; then\n echo \"Missing venv: ${VENV_DIR}/bin/python\"\n echo \" sudo ./setup-venv.sh\"\n exit 1\nfi\n\nsed -i 's/\\r$//' \"${INSTALL_DIR}/.env\" 2>/dev/null || true\n\nmkdir -p \"${INSTALL_DIR}/.ultralytics\" \"${INSTALL_DIR}/.torch\"\nchown -R \"${SERVICE_USER}:${SERVICE_USER}\" \"${INSTALL_DIR}\"\n\n# Disable legacy MQTT counter (replace mode)\nfor legacy in frigate-counter frigate-counter-dashboard; do\n if systemctl is-enabled \"${legacy}\" &>/dev/null; then\n systemctl disable --now \"${legacy}\" || true\n echo \"Disabled legacy ${legacy}\"\n fi\ndone\n\nfor unit in jetson-counter jetson-counter-dashboard; do\n sed -e \"s|/opt/jetson-counter|${INSTALL_DIR}|g\" \\\n -e \"s|User=jetson|User=${SERVICE_USER}|g\" \\\n -e \"s|Group=jetson|Group=${SERVICE_USER}|g\" \\\n \"${INSTALL_DIR}/${unit}.service\" > \"/etc/systemd/system/${unit}.service\"\n echo \"Installed /etc/systemd/system/${unit}.service\"\ndone\n\nchown -R \"${SERVICE_USER}:${SERVICE_USER}\" \"${INSTALL_DIR}\"\n\nPYTHONNOUSERSITE=1 \"${VENV_DIR}/bin/python\" -c \"\nfrom ultralytics import YOLO\nimport torch\nprint('import ok | cuda', torch.cuda.is_available())\n\" || {\n echo \"Import check failed — fix venv before starting services.\"\n exit 1\n}\n\nsystemctl daemon-reload\nsystemctl reset-failed jetson-counter jetson-counter-dashboard 2>/dev/null || true\nsystemctl enable jetson-counter jetson-counter-dashboard\nsystemctl restart jetson-counter jetson-counter-dashboard\n\necho \"\"\nsystemctl --no-pager status jetson-counter jetson-counter-dashboard || true\necho \"\"\necho \"Logs: sudo journalctl -u jetson-counter -f\"\necho \"Dashboard: http://$(hostname -I | awk '{print $1}'):5000\"",
|
||
"lineStart": 1,
|
||
"lineEnd": 68,
|
||
"totalLines": 68,
|
||
"truncated": false
|
||
}
|
||
},
|
||
"title": "install-services.sh",
|
||
"time": {
|
||
"start": 1785284237384,
|
||
"end": 1785284237428
|
||
}
|
||
},
|
||
"id": "prt_fab3b9781001ZEY72RPwnM7F0w",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3b8c630019oax7kHIohIWBa"
|
||
},
|
||
{
|
||
"reason": "tool-calls",
|
||
"snapshot": "e0e1414b68e2544fb2d71b977882e90620947d43",
|
||
"type": "step-finish",
|
||
"tokens": {
|
||
"total": 10274,
|
||
"input": 272,
|
||
"output": 248,
|
||
"reasoning": 26,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 9728
|
||
}
|
||
},
|
||
"cost": 0.000391964,
|
||
"id": "prt_fab3b98f4001tSAip2xPIP13qH",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3b8c630019oax7kHIohIWBa"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"parentID": "msg_fab3b7662001asy7eTmLMm4mEq",
|
||
"role": "assistant",
|
||
"mode": "build",
|
||
"agent": "build",
|
||
"path": {
|
||
"cwd": "/opt/bytetrack-counter",
|
||
"root": "/opt/bytetrack-counter"
|
||
},
|
||
"cost": 0.00258274,
|
||
"tokens": {
|
||
"total": 15862,
|
||
"input": 5392,
|
||
"output": 214,
|
||
"reasoning": 16,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 10240
|
||
}
|
||
},
|
||
"modelID": "deepseek-v4-pro",
|
||
"providerID": "deepseek",
|
||
"time": {
|
||
"created": 1785284237640,
|
||
"completed": 1785284240946
|
||
},
|
||
"finish": "tool-calls",
|
||
"id": "msg_fab3b9948001NqWLx9PXClyyv8",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"snapshot": "e0e1414b68e2544fb2d71b977882e90620947d43",
|
||
"type": "step-start",
|
||
"id": "prt_fab3b9be5001YEAxOxjPaxN0rA",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3b9948001NqWLx9PXClyyv8"
|
||
},
|
||
{
|
||
"type": "reasoning",
|
||
"text": "Now let me look at the main source files to understand the codebase better.",
|
||
"time": {
|
||
"start": 1785284238684,
|
||
"end": 1785284239121
|
||
},
|
||
"id": "prt_fab3b9d5c0015PbtmRrSWhyK6Q",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3b9948001NqWLx9PXClyyv8"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "read",
|
||
"callID": "call_00_z5R05gvWfSAWc2BWe9nR5485",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"filePath": "/opt/bytetrack-counter/counter_live.py"
|
||
},
|
||
"output": "<path>/opt/bytetrack-counter/counter_live.py</path>\n<type>file</type>\n<content>\n1: \"\"\"\n2: Edge production live counter — RTSP + YOLO TensorRT + line crossing.\n3: Replaces MQTT frigate-counter on Jetson with local LAN camera inference.\n4: \"\"\"\n5: from ultralytics import YOLO\n6: import cv2\n7: import csv\n8: import numpy as np\n9: import os\n10: import signal\n11: import time\n12: from datetime import datetime\n13: from pathlib import Path\n14: \n15: from dotenv import load_dotenv\n16: load_dotenv()\n17: \n18: from batch_store import BatchStore\n19: \n20: # --- config (override via env / .env) ---\n21: OUTPUT_DIR = os.getenv('OUTPUT_DIR', '/opt/jetson-counter')\n22: DB_PATH = os.getenv('DB_PATH', f'{OUTPUT_DIR}/jetson_counter.db')\n23: STATE_FILE = os.getenv('STATE_FILE', f'{OUTPUT_DIR}/current_batch.json')\n24: SOURCE = os.getenv('SOURCE', 'rtsp://user:pass@192.168.0.100:554/stream1')\n25: MODEL_PATH = os.getenv('MODEL_PATH', '/media/jetson/DATA/yolo11n.engine')\n26: CAMERA_NAME = os.getenv('CAMERA_NAME', 'CC1')\n27: OBJECT_LABEL = os.getenv('OBJECT_LABEL', 'ayam-potong')\n28: CLASS_AYAM = os.getenv('CLASS_AYAM', 'ayam')\n29: CLASS_TALENAN = os.getenv('CLASS_TALENAN', 'talenan')\n30: \n31: LINE_X = int(os.getenv('LINE_X')) if os.getenv('LINE_X') else None\n32: LINE_X_FRAC = float(os.getenv('LINE_X_FRAC', '0.5'))\n33: CROSS_DIRECTION = os.getenv('CROSS_DIRECTION', 'rtl').lower()\n34: \n35: IMGSZ = int(os.getenv('IMGSZ', '416'))\n36: HALF = os.getenv('HALF', 'true').lower() == 'true'\n37: CONF = float(os.getenv('CONF', '0.3'))\n38: DEVICE = int(os.getenv('DEVICE', '0'))\n39: TRACKER = os.getenv('TRACKER', 'bytetrack.yaml')\n40: \n41: DAILY_CUTOFF_TIME = os.getenv('DAILY_CUTOFF_TIME', '20:00')\n42: BATCH_TIMEOUT_SECONDS = float(os.getenv('BATCH_TIMEOUT_SECONDS', '300'))\n43: IGNORE_BATCH_LABEL_TIMEOUT = float(os.getenv('IGNORE_BATCH_LABEL_TIMEOUT_SECONDS', '30'))\n44: MIN_OBJECT_PER_BATCH = int(os.getenv('MIN_OBJECT_PER_BATCH', '60'))\n45: MIN_DURATION_PER_BATCH = int(os.getenv('MIN_DURATION_PER_BATCH', '60'))\n46: \n47: EXPORT_CSV = os.getenv('EXPORT_CSV', 'true').lower() == 'true'\n48: CROSS_CSV = os.getenv('CROSS_CSV', f'{OUTPUT_DIR}/batch_crossings.csv')\n49: \n50: WARMUP_FRAMES = int(os.getenv('WARMUP_FRAMES', '30'))\n51: RECONNECT_DELAY_SEC = int(os.getenv('RECONNECT_DELAY_SEC', '3'))\n52: MAX_RECONNECT_ATTEMPTS = int(os.getenv('MAX_RECONNECT_ATTEMPTS', '0'))\n53: FLUSH_EVERY_N_FRAMES = int(os.getenv('FLUSH_EVERY_N_FRAMES', '100'))\n54: TRACKED_PRUNE_SEC = int(os.getenv('TRACKED_PRUNE_SEC', '300'))\n55: RECORD_VIDEO = os.getenv('RECORD_VIDEO', 'false').lower() == 'true'\n56: VIDEO_SEGMENT_SEC = int(os.getenv('VIDEO_SEGMENT_SEC', '3600'))\n57: OUTPUT_FPS = int(os.getenv('OUTPUT_FPS', '15'))\n58: \n59: LIVE_STREAM_ENABLED = os.getenv('LIVE_STREAM_ENABLED', 'false').lower() == 'true'\n60: LIVE_STREAM_FRAME_PATH = os.getenv('LIVE_STREAM_FRAME_PATH', '/dev/shm/jetson-counter/live_frame.jpg')\n61: LIVE_STREAM_QUALITY = int(os.getenv('LIVE_STREAM_QUALITY', '75'))\n62: LIVE_STREAM_EVERY_N = int(os.getenv('LIVE_STREAM_EVERY_N', '2'))\n63: \n64: RTSP_FFMPEG_OPTIONS = os.getenv(\n65: 'OPENCV_FFMPEG_CAPTURE_OPTIONS',\n66: 'rtsp_transport;tcp|fflags;nobuffer|flags;low_delay',\n67: )\n68: \n69: IS_LIVE = SOURCE.lower().startswith(('rtsp://', 'http://'))\n70: \n71: CROSS_FLASH_FRAMES = 12\n72: POPUP_LIFETIME = 20\n73: LINE_PULSE_FRAMES = 12\n74: COUNT_PULSE_FRAMES = 15\n75: BATCH_PULSE_FRAMES = 20\n76: \n77: SKELETON = [(0, 1), (4, 3), (1, 2), (3, 2), (2, 6), (2, 5), (2, 7), (7, 8)]\n78: SK_COLORS = [\n79: (0, 255, 255), (0, 255, 255), (255, 0, 255), (255, 0, 255),\n80: (0, 255, 0), (255, 255, 0), (0, 0, 255), (200, 200, 0),\n81: ]\n82: \n83: C_PANEL = (28, 24, 18)\n84: C_BORDER = (90, 85, 75)\n85: C_ACCENT = (255, 200, 60)\n86: C_GREEN = (80, 220, 100)\n87: C_TEXT = (235, 235, 235)\n88: C_MUTED = (150, 150, 150)\n89: C_AYAM_BOX = (0, 165, 255)\n90: C_TALENAN_BOX = (220, 120, 60)\n91: C_LINE_CORE = (180, 220, 255)\n92: C_LINE_GLOW = (100, 160, 220)\n93: \n94: shutdown_requested = False\n95: \n96: \n97: def request_shutdown(signum, frame):\n98: global shutdown_requested\n99: shutdown_requested = True\n100: print('\\nShutdown requested — finishing current frame...')\n101: \n102: \n103: signal.signal(signal.SIGINT, request_shutdown)\n104: signal.signal(signal.SIGTERM, request_shutdown)\n105: \n106: \n107: def resolve_class_ids(names):\n108: name_to_id = {v: k for k, v in names.items()}\n109: missing = [n for n in (CLASS_AYAM, CLASS_TALENAN) if n not in name_to_id]\n110: if missing:\n111: raise ValueError(f'Model missing classes {missing}. Available: {list(names.values())}')\n112: return name_to_id[CLASS_AYAM], name_to_id[CLASS_TALENAN]\n113: \n114: \n115: def box_cx(box):\n116: return (int(box[0]) + int(box[2])) // 2\n117: \n118: \n119: def resolve_line_x(frame_width):\n120: if LINE_X is not None:\n121: return LINE_X\n122: if LINE_X_FRAC != 0.5:\n123: return int(frame_width * LINE_X_FRAC)\n124: return frame_width // 2\n125: \n126: \n127: def crossed_line(prev_cx, cx, line_x, direction=CROSS_DIRECTION):\n128: if direction == 'ltr':\n129: return prev_cx < line_x <= cx\n130: if direction == 'both':\n131: return (prev_cx > line_x >= cx) or (prev_cx < line_x <= cx)\n132: return prev_cx > line_x >= cx\n133: \n134: \n135: def now_str():\n136: return datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n137: \n138: \n139: def open_capture(source):\n140: if source.lower().startswith(('rtsp://', 'http://')):\n141: os.environ['OPENCV_FFMPEG_CAPTURE_OPTIONS'] = RTSP_FFMPEG_OPTIONS\n142: cap = cv2.VideoCapture(source, cv2.CAP_FFMPEG)\n143: cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)\n144: return cap\n145: \n146: \n147: def warmup_stream(cap, n=WARMUP_FRAMES):\n148: print('Warming up stream...')\n149: for _ in range(n):\n150: cap.read()\n151: print('Stream ready!')\n152: \n153: \n154: def open_video_writer(path, w, h, fps):\n155: return cv2.VideoWriter(path, cv2.VideoWriter_fourcc(*'avc1'), fps, (w, h))\n156: \n157: \n158: class CsvLogger:\n159: def __init__(self, path, header):\n160: Path(path).parent.mkdir(parents=True, exist_ok=True)\n161: new_file = not Path(path).exists() or Path(path).stat().st_size == 0\n162: self.file = open(path, 'a', newline='', buffering=1)\n163: self.writer = csv.writer(self.file)\n164: if new_file:\n165: self.writer.writerow(header)\n166: self.file.flush()\n167: \n168: def write_row(self, row):\n169: self.writer.writerow(row)\n170: self.file.flush()\n171: \n172: def close(self):\n173: self.file.close()\n174: \n175: \n176: class VideoSegmentWriter:\n177: def __init__(self, output_dir, w, h, fps, segment_sec):\n178: self.output_dir = Path(output_dir)\n179: self.output_dir.mkdir(parents=True, exist_ok=True)\n180: self.w, self.h, self.fps = w, h, fps\n181: self.segment_sec = segment_sec\n182: self.segment_start = time.monotonic()\n183: self.writer = None\n184: self._open_next()\n185: \n186: def _segment_path(self):\n187: ts = datetime.now().strftime('%Y%m%d_%H%M%S')\n188: return str(self.output_dir / f'live_{ts}.mp4')\n189: \n190: def _open_next(self):\n191: if self.writer is not None:\n192: self.writer.release()\n193: path = self._segment_path()\n194: self.writer = open_video_writer(path, self.w, self.h, self.fps)\n195: self.segment_start = time.monotonic()\n196: print(f'Recording segment: {path}')\n197: \n198: def write(self, frame):\n199: if time.monotonic() - self.segment_start >= self.segment_sec:\n200: self._open_next()\n201: self.writer.write(frame)\n202: \n203: def release(self):\n204: if self.writer is not None:\n205: self.writer.release()\n206: \n207: \n208: def prune_stale_tracks(tracked, now_mono):\n209: stale = [tid for tid, (_, ts) in tracked.items() if now_mono - ts > TRACKED_PRUNE_SEC]\n210: for tid in stale:\n211: del tracked[tid]\n212: \n213: \n214: def overlay_rect(img, x1, y1, x2, y2, color, alpha=0.65):\n215: x1, y1 = max(0, x1), max(0, y1)\n216: x2, y2 = min(img.shape[1], x2), min(img.shape[0], y2)\n217: if x2 <= x1 or y2 <= y1:\n218: return\n219: roi = img[y1:y2, x1:x2]\n220: patch = np.full_like(roi, color, dtype=np.uint8)\n221: cv2.addWeighted(patch, alpha, roi, 1 - alpha, 0, roi)\n222: \n223: \n224: def draw_pill(img, text, x, y, bg, fg=C_TEXT, font_scale=0.45, pad_x=6, pad_y=4):\n225: font = cv2.FONT_HERSHEY_SIMPLEX\n226: (tw, th), baseline = cv2.getTextSize(text, font, font_scale, 1)\n227: x1, y1 = x, y - th - pad_y\n228: x2, y2 = x + tw + pad_x * 2, y + baseline + pad_y\n229: cv2.rectangle(img, (x1, y1), (x2, y2), bg, -1)\n230: cv2.rectangle(img, (x1, y1), (x2, y2), C_BORDER, 1)\n231: cv2.putText(img, text, (x + pad_x, y), font, font_scale, fg, 1, cv2.LINE_AA)\n232: \n233: \n234: def draw_elegant_counting_line(img, line_x, h, pulse_remaining=0):\n235: strength = pulse_remaining / max(LINE_PULSE_FRAMES, 1)\n236: glow_alpha = 0.12 + 0.18 * strength\n237: for offset in (14, 9, 5):\n238: color = tuple(int(c * glow_alpha) for c in C_LINE_GLOW)\n239: cv2.line(img, (line_x - offset, 0), (line_x - offset, h), color, 1, cv2.LINE_AA)\n240: cv2.line(img, (line_x + offset, 0), (line_x + offset, h), color, 1, cv2.LINE_AA)\n241: dash_len, gap = 18, 12\n242: y = 0\n243: while y < h:\n244: y_end = min(y + dash_len, h)\n245: cv2.line(img, (line_x, y), (line_x, y_end), C_LINE_CORE, 2, cv2.LINE_AA)\n246: y += dash_len + gap\n247: cv2.putText(img, 'COUNT LINE', (line_x - 46, 24), cv2.FONT_HERSHEY_SIMPLEX, 0.42, C_LINE_CORE, 1, cv2.LINE_AA)\n248: \n249: \n250: def draw_hero_count(img, line_x, h, count, pulse_remaining=0):\n251: text = str(count)\n252: font = cv2.FONT_HERSHEY_SIMPLEX\n253: boost = 0.35 * (pulse_remaining / max(COUNT_PULSE_FRAMES, 1))\n254: font_scale, thickness = 1.6 + boost, 3\n255: (tw, th), _ = cv2.getTextSize(text, font, font_scale, thickness)\n256: pad = 14\n257: tx, ty = line_x - tw // 2, h // 2 + th // 2\n258: overlay_rect(img, tx - pad, ty - th - pad, tx + tw + pad, ty + pad // 2, C_PANEL, alpha=0.78)\n259: cv2.rectangle(img, (tx - pad, ty - th - pad), (tx + tw + pad, ty + pad // 2), C_LINE_CORE, 2)\n260: cv2.putText(img, text, (tx, ty), font, font_scale, C_GREEN, thickness, cv2.LINE_AA)\n261: \n262: \n263: def draw_hud(img, w, batch_num, batch_count, total_ayam, elapsed_sec, rate, camera_id, clock):\n264: bar_h = 52\n265: overlay_rect(img, 0, 0, w, bar_h, C_PANEL, alpha=0.72)\n266: cv2.line(img, (0, bar_h), (w, bar_h), C_BORDER, 1)\n267: cv2.putText(img, 'BATCH', (16, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.45, C_MUTED, 1, cv2.LINE_AA)\n268: batch_label = str(batch_num) if batch_num else '—'\n269: cv2.putText(img, batch_label, (16, 44), cv2.FONT_HERSHEY_SIMPLEX, 0.9, C_ACCENT, 2, cv2.LINE_AA)\n270: cv2.putText(img, 'COUNT', (100, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.45, C_MUTED, 1, cv2.LINE_AA)\n271: cv2.putText(img, str(batch_count), (100, 44), cv2.FONT_HERSHEY_SIMPLEX, 0.9, C_GREEN, 2, cv2.LINE_AA)\n272: cv2.putText(img, 'TOTAL', (190, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.45, C_MUTED, 1, cv2.LINE_AA)\n273: cv2.putText(img, str(total_ayam), (190, 44), cv2.FONT_HERSHEY_SIMPLEX, 0.7, C_TEXT, 1, cv2.LINE_AA)\n274: cv2.putText(img, 'UPTIME', (280, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.45, C_MUTED, 1, cv2.LINE_AA)\n275: cv2.putText(img, f'{elapsed_sec / 3600:.1f}h', (280, 44), cv2.FONT_HERSHEY_SIMPLEX, 0.7, C_TEXT, 1, cv2.LINE_AA)\n276: cv2.putText(img, 'RATE', (380, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.45, C_MUTED, 1, cv2.LINE_AA)\n277: cv2.putText(img, f'{rate:.1f}/min', (380, 44), cv2.FONT_HERSHEY_SIMPLEX, 0.7, C_ACCENT, 1, cv2.LINE_AA)\n278: cv2.putText(img, clock, (w - 180, 36), cv2.FONT_HERSHEY_SIMPLEX, 0.55, C_TEXT, 1, cv2.LINE_AA)\n279: cv2.putText(img, f'CAM {camera_id}', (w - 180, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.45, C_MUTED, 1, cv2.LINE_AA)\n280: \n281: \n282: def draw_footer(img, w, h, frame_idx, live_tag):\n283: bar_h = 28\n284: overlay_rect(img, 0, h - bar_h, w, h, C_PANEL, alpha=0.55)\n285: cv2.putText(img, f'{live_tag} | Frame {frame_idx}', (12, h - 9), cv2.FONT_HERSHEY_SIMPLEX, 0.45, C_MUTED, 1, cv2.LINE_AA)\n286: \n287: \n288: def draw_skeleton_bold(img, kpts):\n289: for (a, b), color in zip(SKELETON, SK_COLORS):\n290: if a < len(kpts) and b < len(kpts):\n291: xa, ya = int(kpts[a][0]), int(kpts[a][1])\n292: xb, yb = int(kpts[b][0]), int(kpts[b][1])\n293: if xa > 0 and ya > 0 and xb > 0 and yb > 0:\n294: cv2.line(img, (xa, ya), (xb, yb), color, 3, cv2.LINE_AA)\n295: for kp in kpts:\n296: x, y = int(kp[0]), int(kp[1])\n297: if x > 0 and y > 0:\n298: cv2.circle(img, (x, y), 6, (255, 255, 255), -1, cv2.LINE_AA)\n299: cv2.circle(img, (x, y), 6, (40, 40, 40), 2, cv2.LINE_AA)\n300: \n301: \n302: def draw_popups(img, popups, frame_idx):\n303: alive = []\n304: for pop in popups:\n305: age = frame_idx - pop['born']\n306: if age > POPUP_LIFETIME:\n307: continue\n308: alive.append(pop)\n309: fade = 1.0 - age / POPUP_LIFETIME\n310: y = pop['y'] - int(age * 1.8)\n311: color = (int(C_GREEN[0] * fade), int(C_GREEN[1] * fade), int(C_GREEN[2] * fade))\n312: cv2.putText(img, pop['text'], (pop['x'], y), cv2.FONT_HERSHEY_SIMPLEX, 0.7, color, 2, cv2.LINE_AA)\n313: return alive\n314: \n315: \n316: def draw_batch_banner(img, w, batch_num, pulse_remaining):\n317: if pulse_remaining <= 0:\n318: return\n319: text = f'NEW BATCH {batch_num}'\n320: font = cv2.FONT_HERSHEY_SIMPLEX\n321: (tw, th), _ = cv2.getTextSize(text, font, 0.8, 2)\n322: x1, y1 = w // 2 - tw // 2 - 16, 62\n323: x2, y2 = w // 2 + tw // 2 + 16, 62 + th + 20\n324: overlay_rect(img, x1, y1, x2, y2, C_PANEL, alpha=0.7)\n325: cv2.rectangle(img, (x1, y1), (x2, y2), C_ACCENT, 2)\n326: cv2.putText(img, text, (w // 2 - tw // 2, 62 + th + 4), font, 0.8, C_ACCENT, 2, cv2.LINE_AA)\n327: \n328: \n329: def connect_stream(source, warmup=WARMUP_FRAMES):\n330: attempts = 0\n331: while not shutdown_requested:\n332: cap = open_capture(source)\n333: if not cap.isOpened():\n334: attempts += 1\n335: if MAX_RECONNECT_ATTEMPTS and attempts >= MAX_RECONNECT_ATTEMPTS:\n336: raise RuntimeError(f'Cannot open source after {attempts} attempts: {source}')\n337: print(f'Cannot open source, retry in {RECONNECT_DELAY_SEC}s...')\n338: time.sleep(RECONNECT_DELAY_SEC)\n339: continue\n340: if warmup > 0 and source.lower().startswith(('rtsp://', 'http://')):\n341: warmup_stream(cap, warmup)\n342: w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n343: h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n344: fps = cap.get(cv2.CAP_PROP_FPS)\n345: if not fps or fps <= 1:\n346: fps = OUTPUT_FPS\n347: return cap, w, h, fps\n348: return None, 0, 0, OUTPUT_FPS\n349: \n350: \n351: def run():\n352: global shutdown_requested\n353: \n354: store = BatchStore(\n355: db_path=DB_PATH,\n356: state_file=STATE_FILE,\n357: camera_name=CAMERA_NAME,\n358: object_label=OBJECT_LABEL,\n359: cutoff_time=DAILY_CUTOFF_TIME,\n360: batch_timeout=BATCH_TIMEOUT_SECONDS,\n361: ignore_batch_label_timeout=IGNORE_BATCH_LABEL_TIMEOUT,\n362: min_object_per_batch=MIN_OBJECT_PER_BATCH,\n363: min_duration_per_batch=MIN_DURATION_PER_BATCH,\n364: logger=lambda msg: print(f'[{now_str()}] {msg}'),\n365: )\n366: store.start_cutoff_watcher()\n367: \n368: cross_logger = None\n369: if EXPORT_CSV:\n370: cross_logger = CsvLogger(CROSS_CSV, ['batch', 'frame', 'timestamp', 'chicken_id'])\n371: \n372: model = YOLO(MODEL_PATH)\n373: ayam_cls, talenan_cls = resolve_class_ids(model.names)\n374: \n375: ayam_tracked = {}\n376: talenan_tracked = {}\n377: ayam_line_crossed = set()\n378: talenan_line_crossed = set()\n379: \n380: ayam_cross_flash = {}\n381: talenan_cross_flash = {}\n382: line_pulse = count_pulse = batch_pulse = 0\n383: popups = []\n384: \n385: session_start = time.time()\n386: frame_idx = 0\n387: video_writer = None\n388: \n389: cap, w, h, fps = connect_stream(SOURCE)\n390: if cap is None:\n391: store.shutdown()\n392: return\n393: \n394: line_x = resolve_line_x(w)\n395: print(f'Jetson counter | {w}x{h} @ {fps}fps | line x={line_x} | cross={CROSS_DIRECTION}')\n396: print(f'Model: {MODEL_PATH} | imgsz={IMGSZ} half={HALF}')\n397: print(f'DB: {DB_PATH}')\n398: print(f'State: {STATE_FILE}')\n399: \n400: if RECORD_VIDEO:\n401: video_writer = VideoSegmentWriter(OUTPUT_DIR, w, h, fps, VIDEO_SEGMENT_SEC)\n402: \n403: reconnect_count = 0\n404: \n405: while not shutdown_requested:\n406: ret, frame = cap.read()\n407: if not ret:\n408: if not IS_LIVE:\n409: break\n410: reconnect_count += 1\n411: print(f'Stream dropped (attempt {reconnect_count}), reconnecting in {RECONNECT_DELAY_SEC}s...')\n412: cap.release()\n413: time.sleep(RECONNECT_DELAY_SEC)\n414: cap, w, h, fps = connect_stream(SOURCE)\n415: if cap is None:\n416: break\n417: line_x = resolve_line_x(w)\n418: continue\n419: \n420: now = time.time()\n421: elapsed = now - session_start\n422: mono = time.monotonic()\n423: ayam_crossed_frame = batch_closed_frame = batch_started_frame = False\n424: \n425: results = model.track(\n426: frame,\n427: device=DEVICE,\n428: persist=True,\n429: conf=CONF,\n430: imgsz=IMGSZ,\n431: half=HALF,\n432: tracker=TRACKER,\n433: verbose=False,\n434: )\n435: r = results[0]\n436: \n437: if r.boxes.id is not None:\n438: ids = r.boxes.id.int().tolist()\n439: boxes = r.boxes.xyxy.tolist()\n440: clss = r.boxes.cls.int().tolist()\n441: kpts_all = r.keypoints.xy.tolist() if r.keypoints else []\n442: \n443: talenan_items, ayam_items = [], []\n444: for i, (track_id, box, cls_id) in enumerate(zip(ids, boxes, clss)):\n445: cx = box_cx(box)\n446: x1, y1, x2, y2 = [int(v) for v in box]\n447: kpts = kpts_all[i] if i < len(kpts_all) else None\n448: item = (track_id, cx, x1, y1, x2, y2, kpts)\n449: if cls_id == talenan_cls:\n450: talenan_items.append(item)\n451: elif cls_id == ayam_cls:\n452: ayam_items.append(item)\n453: \n454: for track_id, cx, x1, y1, x2, y2, _ in talenan_items:\n455: if track_id in talenan_tracked:\n456: prev_cx, _ = talenan_tracked[track_id]\n457: if crossed_line(prev_cx, cx, line_x) and track_id not in talenan_line_crossed:\n458: talenan_line_crossed.add(track_id)\n459: if store.record_talenan_crossing(track_id):\n460: batch_closed_frame = True\n461: talenan_cross_flash[track_id] = CROSS_FLASH_FRAMES\n462: popups.append({'x': cx - 20, 'y': (y1 + y2) // 2, 'born': frame_idx, 'text': 'BATCH CLOSED'})\n463: talenan_tracked[track_id] = (cx, mono)\n464: \n465: for track_id, cx, x1, y1, x2, y2, kpts in ayam_items:\n466: if track_id in ayam_tracked:\n467: prev_cx, _ = ayam_tracked[track_id]\n468: if crossed_line(prev_cx, cx, line_x) and track_id not in ayam_line_crossed:\n469: ayam_line_crossed.add(track_id)\n470: _, started_new = store.record_ayam_crossing(track_id)\n471: if cross_logger:\n472: cross_logger.write_row([\n473: store.current_batch_number, frame_idx,\n474: datetime.now().isoformat(), track_id,\n475: ])\n476: ayam_crossed_frame = True\n477: if started_new:\n478: batch_started_frame = True\n479: ayam_cross_flash[track_id] = CROSS_FLASH_FRAMES\n480: popups.append({'x': cx - 12, 'y': (y1 + y2) // 2, 'born': frame_idx, 'text': '+1'})\n481: ayam_tracked[track_id] = (cx, mono)\n482: \n483: for track_id, cx, x1, y1, x2, y2, _ in talenan_items:\n484: flash = talenan_cross_flash.get(track_id, 0)\n485: color = C_GREEN if flash > 0 else C_TALENAN_BOX\n486: cv2.rectangle(frame, (x1, y1), (x2, y2), color, 3 if flash > 0 else 2)\n487: draw_pill(frame, f'TALENAN {track_id}', x1, y1 - 4, color)\n488: \n489: for track_id, cx, x1, y1, x2, y2, kpts in ayam_items:\n490: flash = ayam_cross_flash.get(track_id, 0)\n491: color = C_GREEN if flash > 0 else C_AYAM_BOX\n492: cv2.rectangle(frame, (x1, y1), (x2, y2), color, 3 if flash > 0 else 2)\n493: draw_pill(frame, f'ID {track_id}', x1, y1 - 4, color)\n494: if kpts is not None:\n495: draw_skeleton_bold(frame, kpts)\n496: \n497: if ayam_crossed_frame:\n498: line_pulse = LINE_PULSE_FRAMES\n499: count_pulse = COUNT_PULSE_FRAMES\n500: if batch_closed_frame:\n501: line_pulse = LINE_PULSE_FRAMES\n502: if batch_started_frame:\n503: batch_pulse = BATCH_PULSE_FRAMES\n504: \n505: batch_num = store.current_batch_number or 0\n506: batch_count = store.current_batch_count\n507: display_total = store.display_total()\n508: rate = (display_total / elapsed * 60) if elapsed > 0 else 0.0\n509: \n510: draw_elegant_counting_line(frame, line_x, h, line_pulse)\n511: draw_hero_count(frame, line_x, h, batch_count, count_pulse)\n512: draw_hud(frame, w, batch_num, batch_count, display_total, elapsed, rate, CAMERA_NAME, now_str())\n513: draw_batch_banner(frame, w, batch_num, batch_pulse)\n514: draw_footer(frame, w, h, frame_idx, 'LIVE' if IS_LIVE else 'FILE')\n515: popups = draw_popups(frame, popups, frame_idx)\n516: \n517: for flash_store in (ayam_cross_flash, talenan_cross_flash):\n518: for tid in list(flash_store):\n519: flash_store[tid] -= 1\n520: if flash_store[tid] <= 0:\n521: del flash_store[tid]\n522: line_pulse = max(0, line_pulse - 1)\n523: count_pulse = max(0, count_pulse - 1)\n524: batch_pulse = max(0, batch_pulse - 1)\n525: \n526: if video_writer is not None:\n527: video_writer.write(frame)\n528: \n529: if LIVE_STREAM_ENABLED and frame_idx % LIVE_STREAM_EVERY_N == 0:\n530: try:\n531: Path(LIVE_STREAM_FRAME_PATH).parent.mkdir(parents=True, exist_ok=True)\n532: _, jpeg = cv2.imencode('.jpg', frame, [cv2.IMWRITE_JPEG_QUALITY, LIVE_STREAM_QUALITY])\n533: with open(LIVE_STREAM_FRAME_PATH, 'wb') as f:\n534: f.write(jpeg.tobytes())\n535: except Exception:\n536: pass\n537: \n538: frame_idx += 1\n539: if frame_idx % FLUSH_EVERY_N_FRAMES == 0:\n540: print(\n541: f'[{now_str()}] Frame {frame_idx} | Batch {batch_num}: {batch_count} '\n542: f'| Total: {display_total} | Uptime {elapsed / 3600:.2f}h'\n543: )\n544: prune_stale_tracks(ayam_tracked, mono)\n545: prune_stale_tracks(talenan_tracked, mono)\n546: \n547: cap.release()\n548: if video_writer is not None:\n549: video_writer.release()\n550: if cross_logger:\n551: cross_logger.close()\n552: store.shutdown()\n553: \n554: print('\\n=== Batch Summary (SQLite) ===')\n555: print(f'Database: {DB_PATH}')\n556: \n557: \n558: if __name__ == '__main__':\n559: run()\n\n(End of file - total 559 lines)\n</content>",
|
||
"metadata": {
|
||
"preview": "\"\"\"\nEdge production live counter — RTSP + YOLO TensorRT + line crossing.\nReplaces MQTT frigate-counter on Jetson with local LAN camera inference.\n\"\"\"\nfrom ultralytics import YOLO\nimport cv2\nimport csv\nimport numpy as np\nimport os\nimport signal\nimport time\nfrom datetime import datetime\nfrom pathlib import Path\n\nfrom dotenv import load_dotenv\nload_dotenv()\n\nfrom batch_store import BatchStore\n\n# --- config (override via env / .env) ---",
|
||
"truncated": false,
|
||
"loaded": [],
|
||
"display": {
|
||
"type": "file",
|
||
"path": "/opt/bytetrack-counter/counter_live.py",
|
||
"text": "\"\"\"\nEdge production live counter — RTSP + YOLO TensorRT + line crossing.\nReplaces MQTT frigate-counter on Jetson with local LAN camera inference.\n\"\"\"\nfrom ultralytics import YOLO\nimport cv2\nimport csv\nimport numpy as np\nimport os\nimport signal\nimport time\nfrom datetime import datetime\nfrom pathlib import Path\n\nfrom dotenv import load_dotenv\nload_dotenv()\n\nfrom batch_store import BatchStore\n\n# --- config (override via env / .env) ---\nOUTPUT_DIR = os.getenv('OUTPUT_DIR', '/opt/jetson-counter')\nDB_PATH = os.getenv('DB_PATH', f'{OUTPUT_DIR}/jetson_counter.db')\nSTATE_FILE = os.getenv('STATE_FILE', f'{OUTPUT_DIR}/current_batch.json')\nSOURCE = os.getenv('SOURCE', 'rtsp://user:pass@192.168.0.100:554/stream1')\nMODEL_PATH = os.getenv('MODEL_PATH', '/media/jetson/DATA/yolo11n.engine')\nCAMERA_NAME = os.getenv('CAMERA_NAME', 'CC1')\nOBJECT_LABEL = os.getenv('OBJECT_LABEL', 'ayam-potong')\nCLASS_AYAM = os.getenv('CLASS_AYAM', 'ayam')\nCLASS_TALENAN = os.getenv('CLASS_TALENAN', 'talenan')\n\nLINE_X = int(os.getenv('LINE_X')) if os.getenv('LINE_X') else None\nLINE_X_FRAC = float(os.getenv('LINE_X_FRAC', '0.5'))\nCROSS_DIRECTION = os.getenv('CROSS_DIRECTION', 'rtl').lower()\n\nIMGSZ = int(os.getenv('IMGSZ', '416'))\nHALF = os.getenv('HALF', 'true').lower() == 'true'\nCONF = float(os.getenv('CONF', '0.3'))\nDEVICE = int(os.getenv('DEVICE', '0'))\nTRACKER = os.getenv('TRACKER', 'bytetrack.yaml')\n\nDAILY_CUTOFF_TIME = os.getenv('DAILY_CUTOFF_TIME', '20:00')\nBATCH_TIMEOUT_SECONDS = float(os.getenv('BATCH_TIMEOUT_SECONDS', '300'))\nIGNORE_BATCH_LABEL_TIMEOUT = float(os.getenv('IGNORE_BATCH_LABEL_TIMEOUT_SECONDS', '30'))\nMIN_OBJECT_PER_BATCH = int(os.getenv('MIN_OBJECT_PER_BATCH', '60'))\nMIN_DURATION_PER_BATCH = int(os.getenv('MIN_DURATION_PER_BATCH', '60'))\n\nEXPORT_CSV = os.getenv('EXPORT_CSV', 'true').lower() == 'true'\nCROSS_CSV = os.getenv('CROSS_CSV', f'{OUTPUT_DIR}/batch_crossings.csv')\n\nWARMUP_FRAMES = int(os.getenv('WARMUP_FRAMES', '30'))\nRECONNECT_DELAY_SEC = int(os.getenv('RECONNECT_DELAY_SEC', '3'))\nMAX_RECONNECT_ATTEMPTS = int(os.getenv('MAX_RECONNECT_ATTEMPTS', '0'))\nFLUSH_EVERY_N_FRAMES = int(os.getenv('FLUSH_EVERY_N_FRAMES', '100'))\nTRACKED_PRUNE_SEC = int(os.getenv('TRACKED_PRUNE_SEC', '300'))\nRECORD_VIDEO = os.getenv('RECORD_VIDEO', 'false').lower() == 'true'\nVIDEO_SEGMENT_SEC = int(os.getenv('VIDEO_SEGMENT_SEC', '3600'))\nOUTPUT_FPS = int(os.getenv('OUTPUT_FPS', '15'))\n\nLIVE_STREAM_ENABLED = os.getenv('LIVE_STREAM_ENABLED', 'false').lower() == 'true'\nLIVE_STREAM_FRAME_PATH = os.getenv('LIVE_STREAM_FRAME_PATH', '/dev/shm/jetson-counter/live_frame.jpg')\nLIVE_STREAM_QUALITY = int(os.getenv('LIVE_STREAM_QUALITY', '75'))\nLIVE_STREAM_EVERY_N = int(os.getenv('LIVE_STREAM_EVERY_N', '2'))\n\nRTSP_FFMPEG_OPTIONS = os.getenv(\n 'OPENCV_FFMPEG_CAPTURE_OPTIONS',\n 'rtsp_transport;tcp|fflags;nobuffer|flags;low_delay',\n)\n\nIS_LIVE = SOURCE.lower().startswith(('rtsp://', 'http://'))\n\nCROSS_FLASH_FRAMES = 12\nPOPUP_LIFETIME = 20\nLINE_PULSE_FRAMES = 12\nCOUNT_PULSE_FRAMES = 15\nBATCH_PULSE_FRAMES = 20\n\nSKELETON = [(0, 1), (4, 3), (1, 2), (3, 2), (2, 6), (2, 5), (2, 7), (7, 8)]\nSK_COLORS = [\n (0, 255, 255), (0, 255, 255), (255, 0, 255), (255, 0, 255),\n (0, 255, 0), (255, 255, 0), (0, 0, 255), (200, 200, 0),\n]\n\nC_PANEL = (28, 24, 18)\nC_BORDER = (90, 85, 75)\nC_ACCENT = (255, 200, 60)\nC_GREEN = (80, 220, 100)\nC_TEXT = (235, 235, 235)\nC_MUTED = (150, 150, 150)\nC_AYAM_BOX = (0, 165, 255)\nC_TALENAN_BOX = (220, 120, 60)\nC_LINE_CORE = (180, 220, 255)\nC_LINE_GLOW = (100, 160, 220)\n\nshutdown_requested = False\n\n\ndef request_shutdown(signum, frame):\n global shutdown_requested\n shutdown_requested = True\n print('\\nShutdown requested — finishing current frame...')\n\n\nsignal.signal(signal.SIGINT, request_shutdown)\nsignal.signal(signal.SIGTERM, request_shutdown)\n\n\ndef resolve_class_ids(names):\n name_to_id = {v: k for k, v in names.items()}\n missing = [n for n in (CLASS_AYAM, CLASS_TALENAN) if n not in name_to_id]\n if missing:\n raise ValueError(f'Model missing classes {missing}. Available: {list(names.values())}')\n return name_to_id[CLASS_AYAM], name_to_id[CLASS_TALENAN]\n\n\ndef box_cx(box):\n return (int(box[0]) + int(box[2])) // 2\n\n\ndef resolve_line_x(frame_width):\n if LINE_X is not None:\n return LINE_X\n if LINE_X_FRAC != 0.5:\n return int(frame_width * LINE_X_FRAC)\n return frame_width // 2\n\n\ndef crossed_line(prev_cx, cx, line_x, direction=CROSS_DIRECTION):\n if direction == 'ltr':\n return prev_cx < line_x <= cx\n if direction == 'both':\n return (prev_cx > line_x >= cx) or (prev_cx < line_x <= cx)\n return prev_cx > line_x >= cx\n\n\ndef now_str():\n return datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n\n\ndef open_capture(source):\n if source.lower().startswith(('rtsp://', 'http://')):\n os.environ['OPENCV_FFMPEG_CAPTURE_OPTIONS'] = RTSP_FFMPEG_OPTIONS\n cap = cv2.VideoCapture(source, cv2.CAP_FFMPEG)\n cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)\n return cap\n\n\ndef warmup_stream(cap, n=WARMUP_FRAMES):\n print('Warming up stream...')\n for _ in range(n):\n cap.read()\n print('Stream ready!')\n\n\ndef open_video_writer(path, w, h, fps):\n return cv2.VideoWriter(path, cv2.VideoWriter_fourcc(*'avc1'), fps, (w, h))\n\n\nclass CsvLogger:\n def __init__(self, path, header):\n Path(path).parent.mkdir(parents=True, exist_ok=True)\n new_file = not Path(path).exists() or Path(path).stat().st_size == 0\n self.file = open(path, 'a', newline='', buffering=1)\n self.writer = csv.writer(self.file)\n if new_file:\n self.writer.writerow(header)\n self.file.flush()\n\n def write_row(self, row):\n self.writer.writerow(row)\n self.file.flush()\n\n def close(self):\n self.file.close()\n\n\nclass VideoSegmentWriter:\n def __init__(self, output_dir, w, h, fps, segment_sec):\n self.output_dir = Path(output_dir)\n self.output_dir.mkdir(parents=True, exist_ok=True)\n self.w, self.h, self.fps = w, h, fps\n self.segment_sec = segment_sec\n self.segment_start = time.monotonic()\n self.writer = None\n self._open_next()\n\n def _segment_path(self):\n ts = datetime.now().strftime('%Y%m%d_%H%M%S')\n return str(self.output_dir / f'live_{ts}.mp4')\n\n def _open_next(self):\n if self.writer is not None:\n self.writer.release()\n path = self._segment_path()\n self.writer = open_video_writer(path, self.w, self.h, self.fps)\n self.segment_start = time.monotonic()\n print(f'Recording segment: {path}')\n\n def write(self, frame):\n if time.monotonic() - self.segment_start >= self.segment_sec:\n self._open_next()\n self.writer.write(frame)\n\n def release(self):\n if self.writer is not None:\n self.writer.release()\n\n\ndef prune_stale_tracks(tracked, now_mono):\n stale = [tid for tid, (_, ts) in tracked.items() if now_mono - ts > TRACKED_PRUNE_SEC]\n for tid in stale:\n del tracked[tid]\n\n\ndef overlay_rect(img, x1, y1, x2, y2, color, alpha=0.65):\n x1, y1 = max(0, x1), max(0, y1)\n x2, y2 = min(img.shape[1], x2), min(img.shape[0], y2)\n if x2 <= x1 or y2 <= y1:\n return\n roi = img[y1:y2, x1:x2]\n patch = np.full_like(roi, color, dtype=np.uint8)\n cv2.addWeighted(patch, alpha, roi, 1 - alpha, 0, roi)\n\n\ndef draw_pill(img, text, x, y, bg, fg=C_TEXT, font_scale=0.45, pad_x=6, pad_y=4):\n font = cv2.FONT_HERSHEY_SIMPLEX\n (tw, th), baseline = cv2.getTextSize(text, font, font_scale, 1)\n x1, y1 = x, y - th - pad_y\n x2, y2 = x + tw + pad_x * 2, y + baseline + pad_y\n cv2.rectangle(img, (x1, y1), (x2, y2), bg, -1)\n cv2.rectangle(img, (x1, y1), (x2, y2), C_BORDER, 1)\n cv2.putText(img, text, (x + pad_x, y), font, font_scale, fg, 1, cv2.LINE_AA)\n\n\ndef draw_elegant_counting_line(img, line_x, h, pulse_remaining=0):\n strength = pulse_remaining / max(LINE_PULSE_FRAMES, 1)\n glow_alpha = 0.12 + 0.18 * strength\n for offset in (14, 9, 5):\n color = tuple(int(c * glow_alpha) for c in C_LINE_GLOW)\n cv2.line(img, (line_x - offset, 0), (line_x - offset, h), color, 1, cv2.LINE_AA)\n cv2.line(img, (line_x + offset, 0), (line_x + offset, h), color, 1, cv2.LINE_AA)\n dash_len, gap = 18, 12\n y = 0\n while y < h:\n y_end = min(y + dash_len, h)\n cv2.line(img, (line_x, y), (line_x, y_end), C_LINE_CORE, 2, cv2.LINE_AA)\n y += dash_len + gap\n cv2.putText(img, 'COUNT LINE', (line_x - 46, 24), cv2.FONT_HERSHEY_SIMPLEX, 0.42, C_LINE_CORE, 1, cv2.LINE_AA)\n\n\ndef draw_hero_count(img, line_x, h, count, pulse_remaining=0):\n text = str(count)\n font = cv2.FONT_HERSHEY_SIMPLEX\n boost = 0.35 * (pulse_remaining / max(COUNT_PULSE_FRAMES, 1))\n font_scale, thickness = 1.6 + boost, 3\n (tw, th), _ = cv2.getTextSize(text, font, font_scale, thickness)\n pad = 14\n tx, ty = line_x - tw // 2, h // 2 + th // 2\n overlay_rect(img, tx - pad, ty - th - pad, tx + tw + pad, ty + pad // 2, C_PANEL, alpha=0.78)\n cv2.rectangle(img, (tx - pad, ty - th - pad), (tx + tw + pad, ty + pad // 2), C_LINE_CORE, 2)\n cv2.putText(img, text, (tx, ty), font, font_scale, C_GREEN, thickness, cv2.LINE_AA)\n\n\ndef draw_hud(img, w, batch_num, batch_count, total_ayam, elapsed_sec, rate, camera_id, clock):\n bar_h = 52\n overlay_rect(img, 0, 0, w, bar_h, C_PANEL, alpha=0.72)\n cv2.line(img, (0, bar_h), (w, bar_h), C_BORDER, 1)\n cv2.putText(img, 'BATCH', (16, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.45, C_MUTED, 1, cv2.LINE_AA)\n batch_label = str(batch_num) if batch_num else '—'\n cv2.putText(img, batch_label, (16, 44), cv2.FONT_HERSHEY_SIMPLEX, 0.9, C_ACCENT, 2, cv2.LINE_AA)\n cv2.putText(img, 'COUNT', (100, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.45, C_MUTED, 1, cv2.LINE_AA)\n cv2.putText(img, str(batch_count), (100, 44), cv2.FONT_HERSHEY_SIMPLEX, 0.9, C_GREEN, 2, cv2.LINE_AA)\n cv2.putText(img, 'TOTAL', (190, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.45, C_MUTED, 1, cv2.LINE_AA)\n cv2.putText(img, str(total_ayam), (190, 44), cv2.FONT_HERSHEY_SIMPLEX, 0.7, C_TEXT, 1, cv2.LINE_AA)\n cv2.putText(img, 'UPTIME', (280, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.45, C_MUTED, 1, cv2.LINE_AA)\n cv2.putText(img, f'{elapsed_sec / 3600:.1f}h', (280, 44), cv2.FONT_HERSHEY_SIMPLEX, 0.7, C_TEXT, 1, cv2.LINE_AA)\n cv2.putText(img, 'RATE', (380, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.45, C_MUTED, 1, cv2.LINE_AA)\n cv2.putText(img, f'{rate:.1f}/min', (380, 44), cv2.FONT_HERSHEY_SIMPLEX, 0.7, C_ACCENT, 1, cv2.LINE_AA)\n cv2.putText(img, clock, (w - 180, 36), cv2.FONT_HERSHEY_SIMPLEX, 0.55, C_TEXT, 1, cv2.LINE_AA)\n cv2.putText(img, f'CAM {camera_id}', (w - 180, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.45, C_MUTED, 1, cv2.LINE_AA)\n\n\ndef draw_footer(img, w, h, frame_idx, live_tag):\n bar_h = 28\n overlay_rect(img, 0, h - bar_h, w, h, C_PANEL, alpha=0.55)\n cv2.putText(img, f'{live_tag} | Frame {frame_idx}', (12, h - 9), cv2.FONT_HERSHEY_SIMPLEX, 0.45, C_MUTED, 1, cv2.LINE_AA)\n\n\ndef draw_skeleton_bold(img, kpts):\n for (a, b), color in zip(SKELETON, SK_COLORS):\n if a < len(kpts) and b < len(kpts):\n xa, ya = int(kpts[a][0]), int(kpts[a][1])\n xb, yb = int(kpts[b][0]), int(kpts[b][1])\n if xa > 0 and ya > 0 and xb > 0 and yb > 0:\n cv2.line(img, (xa, ya), (xb, yb), color, 3, cv2.LINE_AA)\n for kp in kpts:\n x, y = int(kp[0]), int(kp[1])\n if x > 0 and y > 0:\n cv2.circle(img, (x, y), 6, (255, 255, 255), -1, cv2.LINE_AA)\n cv2.circle(img, (x, y), 6, (40, 40, 40), 2, cv2.LINE_AA)\n\n\ndef draw_popups(img, popups, frame_idx):\n alive = []\n for pop in popups:\n age = frame_idx - pop['born']\n if age > POPUP_LIFETIME:\n continue\n alive.append(pop)\n fade = 1.0 - age / POPUP_LIFETIME\n y = pop['y'] - int(age * 1.8)\n color = (int(C_GREEN[0] * fade), int(C_GREEN[1] * fade), int(C_GREEN[2] * fade))\n cv2.putText(img, pop['text'], (pop['x'], y), cv2.FONT_HERSHEY_SIMPLEX, 0.7, color, 2, cv2.LINE_AA)\n return alive\n\n\ndef draw_batch_banner(img, w, batch_num, pulse_remaining):\n if pulse_remaining <= 0:\n return\n text = f'NEW BATCH {batch_num}'\n font = cv2.FONT_HERSHEY_SIMPLEX\n (tw, th), _ = cv2.getTextSize(text, font, 0.8, 2)\n x1, y1 = w // 2 - tw // 2 - 16, 62\n x2, y2 = w // 2 + tw // 2 + 16, 62 + th + 20\n overlay_rect(img, x1, y1, x2, y2, C_PANEL, alpha=0.7)\n cv2.rectangle(img, (x1, y1), (x2, y2), C_ACCENT, 2)\n cv2.putText(img, text, (w // 2 - tw // 2, 62 + th + 4), font, 0.8, C_ACCENT, 2, cv2.LINE_AA)\n\n\ndef connect_stream(source, warmup=WARMUP_FRAMES):\n attempts = 0\n while not shutdown_requested:\n cap = open_capture(source)\n if not cap.isOpened():\n attempts += 1\n if MAX_RECONNECT_ATTEMPTS and attempts >= MAX_RECONNECT_ATTEMPTS:\n raise RuntimeError(f'Cannot open source after {attempts} attempts: {source}')\n print(f'Cannot open source, retry in {RECONNECT_DELAY_SEC}s...')\n time.sleep(RECONNECT_DELAY_SEC)\n continue\n if warmup > 0 and source.lower().startswith(('rtsp://', 'http://')):\n warmup_stream(cap, warmup)\n w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n fps = cap.get(cv2.CAP_PROP_FPS)\n if not fps or fps <= 1:\n fps = OUTPUT_FPS\n return cap, w, h, fps\n return None, 0, 0, OUTPUT_FPS\n\n\ndef run():\n global shutdown_requested\n\n store = BatchStore(\n db_path=DB_PATH,\n state_file=STATE_FILE,\n camera_name=CAMERA_NAME,\n object_label=OBJECT_LABEL,\n cutoff_time=DAILY_CUTOFF_TIME,\n batch_timeout=BATCH_TIMEOUT_SECONDS,\n ignore_batch_label_timeout=IGNORE_BATCH_LABEL_TIMEOUT,\n min_object_per_batch=MIN_OBJECT_PER_BATCH,\n min_duration_per_batch=MIN_DURATION_PER_BATCH,\n logger=lambda msg: print(f'[{now_str()}] {msg}'),\n )\n store.start_cutoff_watcher()\n\n cross_logger = None\n if EXPORT_CSV:\n cross_logger = CsvLogger(CROSS_CSV, ['batch', 'frame', 'timestamp', 'chicken_id'])\n\n model = YOLO(MODEL_PATH)\n ayam_cls, talenan_cls = resolve_class_ids(model.names)\n\n ayam_tracked = {}\n talenan_tracked = {}\n ayam_line_crossed = set()\n talenan_line_crossed = set()\n\n ayam_cross_flash = {}\n talenan_cross_flash = {}\n line_pulse = count_pulse = batch_pulse = 0\n popups = []\n\n session_start = time.time()\n frame_idx = 0\n video_writer = None\n\n cap, w, h, fps = connect_stream(SOURCE)\n if cap is None:\n store.shutdown()\n return\n\n line_x = resolve_line_x(w)\n print(f'Jetson counter | {w}x{h} @ {fps}fps | line x={line_x} | cross={CROSS_DIRECTION}')\n print(f'Model: {MODEL_PATH} | imgsz={IMGSZ} half={HALF}')\n print(f'DB: {DB_PATH}')\n print(f'State: {STATE_FILE}')\n\n if RECORD_VIDEO:\n video_writer = VideoSegmentWriter(OUTPUT_DIR, w, h, fps, VIDEO_SEGMENT_SEC)\n\n reconnect_count = 0\n\n while not shutdown_requested:\n ret, frame = cap.read()\n if not ret:\n if not IS_LIVE:\n break\n reconnect_count += 1\n print(f'Stream dropped (attempt {reconnect_count}), reconnecting in {RECONNECT_DELAY_SEC}s...')\n cap.release()\n time.sleep(RECONNECT_DELAY_SEC)\n cap, w, h, fps = connect_stream(SOURCE)\n if cap is None:\n break\n line_x = resolve_line_x(w)\n continue\n\n now = time.time()\n elapsed = now - session_start\n mono = time.monotonic()\n ayam_crossed_frame = batch_closed_frame = batch_started_frame = False\n\n results = model.track(\n frame,\n device=DEVICE,\n persist=True,\n conf=CONF,\n imgsz=IMGSZ,\n half=HALF,\n tracker=TRACKER,\n verbose=False,\n )\n r = results[0]\n\n if r.boxes.id is not None:\n ids = r.boxes.id.int().tolist()\n boxes = r.boxes.xyxy.tolist()\n clss = r.boxes.cls.int().tolist()\n kpts_all = r.keypoints.xy.tolist() if r.keypoints else []\n\n talenan_items, ayam_items = [], []\n for i, (track_id, box, cls_id) in enumerate(zip(ids, boxes, clss)):\n cx = box_cx(box)\n x1, y1, x2, y2 = [int(v) for v in box]\n kpts = kpts_all[i] if i < len(kpts_all) else None\n item = (track_id, cx, x1, y1, x2, y2, kpts)\n if cls_id == talenan_cls:\n talenan_items.append(item)\n elif cls_id == ayam_cls:\n ayam_items.append(item)\n\n for track_id, cx, x1, y1, x2, y2, _ in talenan_items:\n if track_id in talenan_tracked:\n prev_cx, _ = talenan_tracked[track_id]\n if crossed_line(prev_cx, cx, line_x) and track_id not in talenan_line_crossed:\n talenan_line_crossed.add(track_id)\n if store.record_talenan_crossing(track_id):\n batch_closed_frame = True\n talenan_cross_flash[track_id] = CROSS_FLASH_FRAMES\n popups.append({'x': cx - 20, 'y': (y1 + y2) // 2, 'born': frame_idx, 'text': 'BATCH CLOSED'})\n talenan_tracked[track_id] = (cx, mono)\n\n for track_id, cx, x1, y1, x2, y2, kpts in ayam_items:\n if track_id in ayam_tracked:\n prev_cx, _ = ayam_tracked[track_id]\n if crossed_line(prev_cx, cx, line_x) and track_id not in ayam_line_crossed:\n ayam_line_crossed.add(track_id)\n _, started_new = store.record_ayam_crossing(track_id)\n if cross_logger:\n cross_logger.write_row([\n store.current_batch_number, frame_idx,\n datetime.now().isoformat(), track_id,\n ])\n ayam_crossed_frame = True\n if started_new:\n batch_started_frame = True\n ayam_cross_flash[track_id] = CROSS_FLASH_FRAMES\n popups.append({'x': cx - 12, 'y': (y1 + y2) // 2, 'born': frame_idx, 'text': '+1'})\n ayam_tracked[track_id] = (cx, mono)\n\n for track_id, cx, x1, y1, x2, y2, _ in talenan_items:\n flash = talenan_cross_flash.get(track_id, 0)\n color = C_GREEN if flash > 0 else C_TALENAN_BOX\n cv2.rectangle(frame, (x1, y1), (x2, y2), color, 3 if flash > 0 else 2)\n draw_pill(frame, f'TALENAN {track_id}', x1, y1 - 4, color)\n\n for track_id, cx, x1, y1, x2, y2, kpts in ayam_items:\n flash = ayam_cross_flash.get(track_id, 0)\n color = C_GREEN if flash > 0 else C_AYAM_BOX\n cv2.rectangle(frame, (x1, y1), (x2, y2), color, 3 if flash > 0 else 2)\n draw_pill(frame, f'ID {track_id}', x1, y1 - 4, color)\n if kpts is not None:\n draw_skeleton_bold(frame, kpts)\n\n if ayam_crossed_frame:\n line_pulse = LINE_PULSE_FRAMES\n count_pulse = COUNT_PULSE_FRAMES\n if batch_closed_frame:\n line_pulse = LINE_PULSE_FRAMES\n if batch_started_frame:\n batch_pulse = BATCH_PULSE_FRAMES\n\n batch_num = store.current_batch_number or 0\n batch_count = store.current_batch_count\n display_total = store.display_total()\n rate = (display_total / elapsed * 60) if elapsed > 0 else 0.0\n\n draw_elegant_counting_line(frame, line_x, h, line_pulse)\n draw_hero_count(frame, line_x, h, batch_count, count_pulse)\n draw_hud(frame, w, batch_num, batch_count, display_total, elapsed, rate, CAMERA_NAME, now_str())\n draw_batch_banner(frame, w, batch_num, batch_pulse)\n draw_footer(frame, w, h, frame_idx, 'LIVE' if IS_LIVE else 'FILE')\n popups = draw_popups(frame, popups, frame_idx)\n\n for flash_store in (ayam_cross_flash, talenan_cross_flash):\n for tid in list(flash_store):\n flash_store[tid] -= 1\n if flash_store[tid] <= 0:\n del flash_store[tid]\n line_pulse = max(0, line_pulse - 1)\n count_pulse = max(0, count_pulse - 1)\n batch_pulse = max(0, batch_pulse - 1)\n\n if video_writer is not None:\n video_writer.write(frame)\n\n if LIVE_STREAM_ENABLED and frame_idx % LIVE_STREAM_EVERY_N == 0:\n try:\n Path(LIVE_STREAM_FRAME_PATH).parent.mkdir(parents=True, exist_ok=True)\n _, jpeg = cv2.imencode('.jpg', frame, [cv2.IMWRITE_JPEG_QUALITY, LIVE_STREAM_QUALITY])\n with open(LIVE_STREAM_FRAME_PATH, 'wb') as f:\n f.write(jpeg.tobytes())\n except Exception:\n pass\n\n frame_idx += 1\n if frame_idx % FLUSH_EVERY_N_FRAMES == 0:\n print(\n f'[{now_str()}] Frame {frame_idx} | Batch {batch_num}: {batch_count} '\n f'| Total: {display_total} | Uptime {elapsed / 3600:.2f}h'\n )\n prune_stale_tracks(ayam_tracked, mono)\n prune_stale_tracks(talenan_tracked, mono)\n\n cap.release()\n if video_writer is not None:\n video_writer.release()\n if cross_logger:\n cross_logger.close()\n store.shutdown()\n\n print('\\n=== Batch Summary (SQLite) ===')\n print(f'Database: {DB_PATH}')\n\n\nif __name__ == '__main__':\n run()",
|
||
"lineStart": 1,
|
||
"lineEnd": 559,
|
||
"totalLines": 559,
|
||
"truncated": false
|
||
}
|
||
},
|
||
"title": "counter_live.py",
|
||
"time": {
|
||
"start": 1785284239384,
|
||
"end": 1785284239564
|
||
}
|
||
},
|
||
"id": "prt_fab3b9f17001mQ2o24A7ATDGTQ",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3b9948001NqWLx9PXClyyv8"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "read",
|
||
"callID": "call_01_EH0xKXBAcnACHOs2YfUx7328",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"filePath": "/opt/bytetrack-counter/batch_store.py"
|
||
},
|
||
"output": "<path>/opt/bytetrack-counter/batch_store.py</path>\n<type>file</type>\n<content>\n1: \"\"\"\n2: Production batch persistence for edge Jetson counter.\n3: Mirrors frigate-counter SQLite schema + current_batch.json contract.\n4: \"\"\"\n5: import json\n6: import sqlite3\n7: import threading\n8: import time\n9: from datetime import datetime, timedelta\n10: from pathlib import Path\n11: \n12: \n13: class BatchStore:\n14: def __init__(\n15: self,\n16: db_path,\n17: state_file,\n18: camera_name,\n19: object_label='ayam-potong',\n20: cutoff_time='20:00',\n21: batch_timeout=300.0,\n22: ignore_batch_label_timeout=30.0,\n23: min_object_per_batch=60,\n24: min_duration_per_batch=60,\n25: carry_ids=50,\n26: logger=print,\n27: ):\n28: self.db_path = db_path\n29: self.state_file = Path(state_file)\n30: self.camera_name = camera_name\n31: self.object_label = object_label\n32: self.cutoff_time_str = cutoff_time\n33: datetime.strptime(cutoff_time, '%H:%M')\n34: \n35: self.batch_timeout = float(batch_timeout)\n36: self.ignore_batch_label_timeout = float(ignore_batch_label_timeout)\n37: self.min_object_per_batch = int(min_object_per_batch)\n38: self.min_duration_per_batch = int(min_duration_per_batch)\n39: self.carry_ids = int(carry_ids)\n40: self.log = logger\n41: \n42: self.state_lock = threading.Lock()\n43: self.batch_timer = None\n44: self.ignore_batch_label = False\n45: self.ignore_batch_label_timer = None\n46: self.previous_state = None\n47: self.shutdown_event = threading.Event()\n48: \n49: Path(db_path).parent.mkdir(parents=True, exist_ok=True)\n50: self.state_file.parent.mkdir(parents=True, exist_ok=True)\n51: \n52: self.db = sqlite3.connect(db_path, check_same_thread=False)\n53: self._init_db()\n54: self.current_state = self._load_state()\n55: self.previous_state = self.current_state\n56: \n57: def _init_db(self):\n58: cur = self.db.cursor()\n59: cur.execute(\n60: \"\"\"\n61: CREATE TABLE IF NOT EXISTS batches (\n62: id INTEGER PRIMARY KEY AUTOINCREMENT,\n63: counting_date TEXT NOT NULL,\n64: batch_number INTEGER NOT NULL,\n65: camera_name TEXT NOT NULL,\n66: object_label TEXT NOT NULL,\n67: count INTEGER NOT NULL,\n68: start_time TEXT NOT NULL,\n69: end_time TEXT NOT NULL,\n70: created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,\n71: UNIQUE(counting_date, batch_number, camera_name, object_label)\n72: )\n73: \"\"\"\n74: )\n75: cur.execute(\n76: \"\"\"\n77: CREATE TABLE IF NOT EXISTS daily_summaries (\n78: id INTEGER PRIMARY KEY AUTOINCREMENT,\n79: counting_date TEXT NOT NULL,\n80: camera_name TEXT NOT NULL,\n81: object_label TEXT NOT NULL,\n82: total_count INTEGER NOT NULL DEFAULT 0,\n83: total_batches INTEGER NOT NULL DEFAULT 0,\n84: updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,\n85: UNIQUE(counting_date, camera_name, object_label)\n86: )\n87: \"\"\"\n88: )\n89: self.db.commit()\n90: \n91: def get_counting_date(self, dt=None):\n92: if dt is None:\n93: dt = datetime.now()\n94: cutoff = datetime.strptime(self.cutoff_time_str, '%H:%M').time()\n95: if dt.time() < cutoff:\n96: return dt.date().isoformat()\n97: return (dt.date() + timedelta(days=1)).isoformat()\n98: \n99: def _load_state(self):\n100: if not self.state_file.exists():\n101: return None\n102: try:\n103: with open(self.state_file, 'r', encoding='utf-8') as f:\n104: state = json.load(f)\n105: current_date = self.get_counting_date()\n106: if state.get('counting_date') != current_date:\n107: self.log(\n108: f\"State file belongs to previous counting day ({state.get('counting_date')}). \"\n109: 'Finalizing before fresh start.'\n110: )\n111: self._insert_batch(\n112: state['counting_date'],\n113: state['batch_number'],\n114: state['count'],\n115: state['start_time'],\n116: datetime.now().isoformat(),\n117: )\n118: self.state_file.unlink(missing_ok=True)\n119: return None\n120: self.log(\n121: f\"Resumed batch #{state['batch_number']} from {state['start_time']} \"\n122: f\"with count={state['count']}\"\n123: )\n124: self._reset_batch_timer()\n125: return state\n126: except Exception as exc:\n127: self.log(f'Failed to load state file: {exc}')\n128: return None\n129: \n130: def save_state(self):\n131: if self.current_state is None:\n132: self.state_file.unlink(missing_ok=True)\n133: return\n134: with open(self.state_file, 'w', encoding='utf-8') as f:\n135: json.dump(self.current_state, f, indent=2, ensure_ascii=False)\n136: \n137: def get_next_batch_number(self, counting_date):\n138: cur = self.db.cursor()\n139: cur.execute(\n140: \"\"\"\n141: SELECT COALESCE(MAX(batch_number), 0)\n142: FROM batches\n143: WHERE counting_date = ? AND camera_name = ? AND object_label = ?\n144: \"\"\",\n145: (counting_date, self.camera_name, self.object_label),\n146: )\n147: return cur.fetchone()[0] + 1\n148: \n149: def start_new_batch(self, counting_date):\n150: batch_number = self.get_next_batch_number(counting_date)\n151: now = datetime.now().isoformat()\n152: counted_ids = []\n153: if self.previous_state is not None:\n154: try:\n155: counted_ids = self.previous_state['counted_event_ids'][-self.carry_ids:]\n156: except (KeyError, TypeError):\n157: counted_ids = []\n158: self.current_state = {\n159: 'counting_date': counting_date,\n160: 'batch_number': batch_number,\n161: 'count': 0,\n162: 'start_time': now,\n163: 'last_detection_time': now,\n164: 'counted_event_ids': counted_ids,\n165: }\n166: self.save_state()\n167: self.log(f'Started batch #{batch_number} for {counting_date} ({self.object_label})')\n168: \n169: def _reset_batch_timer(self):\n170: if self.batch_timer:\n171: self.batch_timer.cancel()\n172: self.batch_timer = threading.Timer(self.batch_timeout, self._on_batch_timeout)\n173: self.batch_timer.daemon = True\n174: self.batch_timer.start()\n175: \n176: def _on_batch_timeout(self):\n177: self.log(f'Batch inactivity timeout ({self.batch_timeout}s) reached')\n178: self.end_batch(closed_by='timeout')\n179: \n180: def _ignore_batch_label(self):\n181: if not self.ignore_batch_label_timer:\n182: self.ignore_batch_label = True\n183: self.ignore_batch_label_timer = threading.Timer(\n184: self.ignore_batch_label_timeout, self._on_ignore_batch_label_timeout\n185: )\n186: self.ignore_batch_label_timer.daemon = True\n187: self.ignore_batch_label_timer.start()\n188: self.log(\n189: f'Ignore batch label for {self.ignore_batch_label_timeout}s'\n190: )\n191: \n192: def _on_ignore_batch_label_timeout(self):\n193: self.ignore_batch_label_timer = None\n194: self.ignore_batch_label = False\n195: self.log('Ignore batch label cooldown finished')\n196: \n197: def record_ayam_crossing(self, track_id):\n198: \"\"\"Line-cross equivalent of production ayam-potong MQTT event.\"\"\"\n199: with self.state_lock:\n200: counting_date = self.get_counting_date()\n201: started_new = False\n202: if self.current_state is None:\n203: self.start_new_batch(counting_date)\n204: started_new = True\n205: elif self.current_state['counting_date'] != counting_date:\n206: self._end_batch_locked(closed_by='cutoff')\n207: self.start_new_batch(counting_date)\n208: started_new = True\n209: \n210: event_key = str(track_id)\n211: if event_key not in self.current_state['counted_event_ids']:\n212: self.current_state['count'] += 1\n213: self.current_state['counted_event_ids'].append(event_key)\n214: self.log(\n215: f'Counted ayam (track {track_id}) | batch #{self.current_state[\"batch_number\"]} '\n216: f'total: {self.current_state[\"count\"]}'\n217: )\n218: \n219: self.current_state['last_detection_time'] = datetime.now().isoformat()\n220: self.save_state()\n221: self._reset_batch_timer()\n222: return self.current_state['count'], started_new\n223: \n224: def record_talenan_crossing(self, track_id):\n225: \"\"\"Line-cross equivalent of production telenan MQTT batch close.\"\"\"\n226: if self.ignore_batch_label:\n227: return False\n228: with self.state_lock:\n229: self._ignore_batch_label()\n230: self._end_batch_locked(closed_by='talenan')\n231: self.log(f'Batch closed by talenan (track {track_id})')\n232: if self.batch_timer:\n233: self.batch_timer.cancel()\n234: self.batch_timer = None\n235: return True\n236: \n237: def end_batch(self, closed_by='manual'):\n238: with self.state_lock:\n239: self._end_batch_locked(closed_by=closed_by)\n240: \n241: def _end_batch_locked(self, closed_by='manual'):\n242: if self.current_state is None:\n243: return False\n244: \n245: self.previous_state = self.current_state\n246: state = self.current_state\n247: \n248: start_time_obj = datetime.fromisoformat(state['start_time'])\n249: end_time_obj = datetime.now()\n250: duration_seconds = (end_time_obj - start_time_obj).total_seconds()\n251: \n252: if (state['count'] < self.min_object_per_batch\n253: or duration_seconds < self.min_duration_per_batch):\n254: self.current_state = None\n255: self.save_state()\n256: if self.batch_timer:\n257: self.batch_timer.cancel()\n258: self.batch_timer = None\n259: self.log(\n260: f'Batch #{state[\"batch_number\"]} discarded '\n261: f'(count={state[\"count\"]}, duration={duration_seconds:.0f}s)'\n262: )\n263: return False\n264: \n265: end_time = end_time_obj.isoformat()\n266: try:\n267: self._insert_batch(\n268: state['counting_date'],\n269: state['batch_number'],\n270: state['count'],\n271: state['start_time'],\n272: end_time,\n273: )\n274: cps = state['count'] / duration_seconds if duration_seconds > 0 else 0\n275: self.log(\n276: f'Batch #{state[\"batch_number\"]} ended | count={state[\"count\"]} | '\n277: f'duration={duration_seconds:.0f}s | cps={cps:.3f} | closed_by={closed_by}'\n278: )\n279: except Exception as exc:\n280: self.log(f'Failed to persist batch: {exc}')\n281: return False\n282: \n283: self.current_state = None\n284: self.save_state()\n285: if self.batch_timer:\n286: self.batch_timer.cancel()\n287: self.batch_timer = None\n288: return True\n289: \n290: def _insert_batch(self, counting_date, batch_number, count, start_time, end_time):\n291: cur = self.db.cursor()\n292: cur.execute(\n293: \"\"\"\n294: INSERT INTO batches\n295: (counting_date, batch_number, camera_name, object_label, count, start_time, end_time)\n296: VALUES (?, ?, ?, ?, ?, ?, ?)\n297: \"\"\",\n298: (counting_date, batch_number, self.camera_name, self.object_label, count, start_time, end_time),\n299: )\n300: cur.execute(\n301: \"\"\"\n302: INSERT INTO daily_summaries\n303: (counting_date, camera_name, object_label, total_count, total_batches)\n304: VALUES (?, ?, ?, ?, 1)\n305: ON CONFLICT(counting_date, camera_name, object_label)\n306: DO UPDATE SET\n307: total_count = total_count + excluded.total_count,\n308: total_batches = total_batches + excluded.total_batches,\n309: updated_at = CURRENT_TIMESTAMP\n310: \"\"\",\n311: (counting_date, self.camera_name, self.object_label, count),\n312: )\n313: self.db.commit()\n314: \n315: cur.execute(\n316: \"\"\"\n317: SELECT total_count, total_batches\n318: FROM daily_summaries\n319: WHERE counting_date = ? AND camera_name = ? AND object_label = ?\n320: \"\"\",\n321: (counting_date, self.camera_name, self.object_label),\n322: )\n323: row = cur.fetchone()\n324: if row:\n325: self.log(\n326: f'Daily totals for {counting_date}: {row[0]} objects across {row[1]} batch(es)'\n327: )\n328: \n329: def cutoff_watcher_loop(self):\n330: while not self.shutdown_event.is_set():\n331: time.sleep(60)\n332: with self.state_lock:\n333: if self.current_state is None:\n334: continue\n335: if self.current_state['counting_date'] != self.get_counting_date():\n336: self.log('Daily cutoff reached – finalizing batch')\n337: self._end_batch_locked(closed_by='cutoff')\n338: \n339: def start_cutoff_watcher(self):\n340: t = threading.Thread(target=self.cutoff_watcher_loop, daemon=True)\n341: t.start()\n342: return t\n343: \n344: @property\n345: def current_batch_number(self):\n346: if self.current_state is None:\n347: return 0\n348: return self.current_state['batch_number']\n349: \n350: @property\n351: def current_batch_count(self):\n352: if self.current_state is None:\n353: return 0\n354: return self.current_state['count']\n355: \n356: def get_closed_total_for_day(self, counting_date=None):\n357: if counting_date is None:\n358: counting_date = self.get_counting_date()\n359: cur = self.db.cursor()\n360: cur.execute(\n361: \"\"\"\n362: SELECT COALESCE(total_count, 0)\n363: FROM daily_summaries\n364: WHERE counting_date = ? AND camera_name = ? AND object_label = ?\n365: \"\"\",\n366: (counting_date, self.camera_name, self.object_label),\n367: )\n368: row = cur.fetchone()\n369: return row[0] if row else 0\n370: \n371: def display_total(self):\n372: return self.get_closed_total_for_day() + self.current_batch_count\n373: \n374: def shutdown(self):\n375: self.shutdown_event.set()\n376: self.end_batch(closed_by='shutdown')\n377: if self.batch_timer:\n378: self.batch_timer.cancel()\n379: self.db.close()\n\n(End of file - total 379 lines)\n</content>",
|
||
"metadata": {
|
||
"preview": "\"\"\"\nProduction batch persistence for edge Jetson counter.\nMirrors frigate-counter SQLite schema + current_batch.json contract.\n\"\"\"\nimport json\nimport sqlite3\nimport threading\nimport time\nfrom datetime import datetime, timedelta\nfrom pathlib import Path\n\n\nclass BatchStore:\n def __init__(\n self,\n db_path,\n state_file,\n camera_name,\n object_label='ayam-potong',\n cutoff_time='20:00',",
|
||
"truncated": false,
|
||
"loaded": [],
|
||
"display": {
|
||
"type": "file",
|
||
"path": "/opt/bytetrack-counter/batch_store.py",
|
||
"text": "\"\"\"\nProduction batch persistence for edge Jetson counter.\nMirrors frigate-counter SQLite schema + current_batch.json contract.\n\"\"\"\nimport json\nimport sqlite3\nimport threading\nimport time\nfrom datetime import datetime, timedelta\nfrom pathlib import Path\n\n\nclass BatchStore:\n def __init__(\n self,\n db_path,\n state_file,\n camera_name,\n object_label='ayam-potong',\n cutoff_time='20:00',\n batch_timeout=300.0,\n ignore_batch_label_timeout=30.0,\n min_object_per_batch=60,\n min_duration_per_batch=60,\n carry_ids=50,\n logger=print,\n ):\n self.db_path = db_path\n self.state_file = Path(state_file)\n self.camera_name = camera_name\n self.object_label = object_label\n self.cutoff_time_str = cutoff_time\n datetime.strptime(cutoff_time, '%H:%M')\n\n self.batch_timeout = float(batch_timeout)\n self.ignore_batch_label_timeout = float(ignore_batch_label_timeout)\n self.min_object_per_batch = int(min_object_per_batch)\n self.min_duration_per_batch = int(min_duration_per_batch)\n self.carry_ids = int(carry_ids)\n self.log = logger\n\n self.state_lock = threading.Lock()\n self.batch_timer = None\n self.ignore_batch_label = False\n self.ignore_batch_label_timer = None\n self.previous_state = None\n self.shutdown_event = threading.Event()\n\n Path(db_path).parent.mkdir(parents=True, exist_ok=True)\n self.state_file.parent.mkdir(parents=True, exist_ok=True)\n\n self.db = sqlite3.connect(db_path, check_same_thread=False)\n self._init_db()\n self.current_state = self._load_state()\n self.previous_state = self.current_state\n\n def _init_db(self):\n cur = self.db.cursor()\n cur.execute(\n \"\"\"\n CREATE TABLE IF NOT EXISTS batches (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n counting_date TEXT NOT NULL,\n batch_number INTEGER NOT NULL,\n camera_name TEXT NOT NULL,\n object_label TEXT NOT NULL,\n count INTEGER NOT NULL,\n start_time TEXT NOT NULL,\n end_time TEXT NOT NULL,\n created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,\n UNIQUE(counting_date, batch_number, camera_name, object_label)\n )\n \"\"\"\n )\n cur.execute(\n \"\"\"\n CREATE TABLE IF NOT EXISTS daily_summaries (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n counting_date TEXT NOT NULL,\n camera_name TEXT NOT NULL,\n object_label TEXT NOT NULL,\n total_count INTEGER NOT NULL DEFAULT 0,\n total_batches INTEGER NOT NULL DEFAULT 0,\n updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,\n UNIQUE(counting_date, camera_name, object_label)\n )\n \"\"\"\n )\n self.db.commit()\n\n def get_counting_date(self, dt=None):\n if dt is None:\n dt = datetime.now()\n cutoff = datetime.strptime(self.cutoff_time_str, '%H:%M').time()\n if dt.time() < cutoff:\n return dt.date().isoformat()\n return (dt.date() + timedelta(days=1)).isoformat()\n\n def _load_state(self):\n if not self.state_file.exists():\n return None\n try:\n with open(self.state_file, 'r', encoding='utf-8') as f:\n state = json.load(f)\n current_date = self.get_counting_date()\n if state.get('counting_date') != current_date:\n self.log(\n f\"State file belongs to previous counting day ({state.get('counting_date')}). \"\n 'Finalizing before fresh start.'\n )\n self._insert_batch(\n state['counting_date'],\n state['batch_number'],\n state['count'],\n state['start_time'],\n datetime.now().isoformat(),\n )\n self.state_file.unlink(missing_ok=True)\n return None\n self.log(\n f\"Resumed batch #{state['batch_number']} from {state['start_time']} \"\n f\"with count={state['count']}\"\n )\n self._reset_batch_timer()\n return state\n except Exception as exc:\n self.log(f'Failed to load state file: {exc}')\n return None\n\n def save_state(self):\n if self.current_state is None:\n self.state_file.unlink(missing_ok=True)\n return\n with open(self.state_file, 'w', encoding='utf-8') as f:\n json.dump(self.current_state, f, indent=2, ensure_ascii=False)\n\n def get_next_batch_number(self, counting_date):\n cur = self.db.cursor()\n cur.execute(\n \"\"\"\n SELECT COALESCE(MAX(batch_number), 0)\n FROM batches\n WHERE counting_date = ? AND camera_name = ? AND object_label = ?\n \"\"\",\n (counting_date, self.camera_name, self.object_label),\n )\n return cur.fetchone()[0] + 1\n\n def start_new_batch(self, counting_date):\n batch_number = self.get_next_batch_number(counting_date)\n now = datetime.now().isoformat()\n counted_ids = []\n if self.previous_state is not None:\n try:\n counted_ids = self.previous_state['counted_event_ids'][-self.carry_ids:]\n except (KeyError, TypeError):\n counted_ids = []\n self.current_state = {\n 'counting_date': counting_date,\n 'batch_number': batch_number,\n 'count': 0,\n 'start_time': now,\n 'last_detection_time': now,\n 'counted_event_ids': counted_ids,\n }\n self.save_state()\n self.log(f'Started batch #{batch_number} for {counting_date} ({self.object_label})')\n\n def _reset_batch_timer(self):\n if self.batch_timer:\n self.batch_timer.cancel()\n self.batch_timer = threading.Timer(self.batch_timeout, self._on_batch_timeout)\n self.batch_timer.daemon = True\n self.batch_timer.start()\n\n def _on_batch_timeout(self):\n self.log(f'Batch inactivity timeout ({self.batch_timeout}s) reached')\n self.end_batch(closed_by='timeout')\n\n def _ignore_batch_label(self):\n if not self.ignore_batch_label_timer:\n self.ignore_batch_label = True\n self.ignore_batch_label_timer = threading.Timer(\n self.ignore_batch_label_timeout, self._on_ignore_batch_label_timeout\n )\n self.ignore_batch_label_timer.daemon = True\n self.ignore_batch_label_timer.start()\n self.log(\n f'Ignore batch label for {self.ignore_batch_label_timeout}s'\n )\n\n def _on_ignore_batch_label_timeout(self):\n self.ignore_batch_label_timer = None\n self.ignore_batch_label = False\n self.log('Ignore batch label cooldown finished')\n\n def record_ayam_crossing(self, track_id):\n \"\"\"Line-cross equivalent of production ayam-potong MQTT event.\"\"\"\n with self.state_lock:\n counting_date = self.get_counting_date()\n started_new = False\n if self.current_state is None:\n self.start_new_batch(counting_date)\n started_new = True\n elif self.current_state['counting_date'] != counting_date:\n self._end_batch_locked(closed_by='cutoff')\n self.start_new_batch(counting_date)\n started_new = True\n\n event_key = str(track_id)\n if event_key not in self.current_state['counted_event_ids']:\n self.current_state['count'] += 1\n self.current_state['counted_event_ids'].append(event_key)\n self.log(\n f'Counted ayam (track {track_id}) | batch #{self.current_state[\"batch_number\"]} '\n f'total: {self.current_state[\"count\"]}'\n )\n\n self.current_state['last_detection_time'] = datetime.now().isoformat()\n self.save_state()\n self._reset_batch_timer()\n return self.current_state['count'], started_new\n\n def record_talenan_crossing(self, track_id):\n \"\"\"Line-cross equivalent of production telenan MQTT batch close.\"\"\"\n if self.ignore_batch_label:\n return False\n with self.state_lock:\n self._ignore_batch_label()\n self._end_batch_locked(closed_by='talenan')\n self.log(f'Batch closed by talenan (track {track_id})')\n if self.batch_timer:\n self.batch_timer.cancel()\n self.batch_timer = None\n return True\n\n def end_batch(self, closed_by='manual'):\n with self.state_lock:\n self._end_batch_locked(closed_by=closed_by)\n\n def _end_batch_locked(self, closed_by='manual'):\n if self.current_state is None:\n return False\n\n self.previous_state = self.current_state\n state = self.current_state\n\n start_time_obj = datetime.fromisoformat(state['start_time'])\n end_time_obj = datetime.now()\n duration_seconds = (end_time_obj - start_time_obj).total_seconds()\n\n if (state['count'] < self.min_object_per_batch\n or duration_seconds < self.min_duration_per_batch):\n self.current_state = None\n self.save_state()\n if self.batch_timer:\n self.batch_timer.cancel()\n self.batch_timer = None\n self.log(\n f'Batch #{state[\"batch_number\"]} discarded '\n f'(count={state[\"count\"]}, duration={duration_seconds:.0f}s)'\n )\n return False\n\n end_time = end_time_obj.isoformat()\n try:\n self._insert_batch(\n state['counting_date'],\n state['batch_number'],\n state['count'],\n state['start_time'],\n end_time,\n )\n cps = state['count'] / duration_seconds if duration_seconds > 0 else 0\n self.log(\n f'Batch #{state[\"batch_number\"]} ended | count={state[\"count\"]} | '\n f'duration={duration_seconds:.0f}s | cps={cps:.3f} | closed_by={closed_by}'\n )\n except Exception as exc:\n self.log(f'Failed to persist batch: {exc}')\n return False\n\n self.current_state = None\n self.save_state()\n if self.batch_timer:\n self.batch_timer.cancel()\n self.batch_timer = None\n return True\n\n def _insert_batch(self, counting_date, batch_number, count, start_time, end_time):\n cur = self.db.cursor()\n cur.execute(\n \"\"\"\n INSERT INTO batches\n (counting_date, batch_number, camera_name, object_label, count, start_time, end_time)\n VALUES (?, ?, ?, ?, ?, ?, ?)\n \"\"\",\n (counting_date, batch_number, self.camera_name, self.object_label, count, start_time, end_time),\n )\n cur.execute(\n \"\"\"\n INSERT INTO daily_summaries\n (counting_date, camera_name, object_label, total_count, total_batches)\n VALUES (?, ?, ?, ?, 1)\n ON CONFLICT(counting_date, camera_name, object_label)\n DO UPDATE SET\n total_count = total_count + excluded.total_count,\n total_batches = total_batches + excluded.total_batches,\n updated_at = CURRENT_TIMESTAMP\n \"\"\",\n (counting_date, self.camera_name, self.object_label, count),\n )\n self.db.commit()\n\n cur.execute(\n \"\"\"\n SELECT total_count, total_batches\n FROM daily_summaries\n WHERE counting_date = ? AND camera_name = ? AND object_label = ?\n \"\"\",\n (counting_date, self.camera_name, self.object_label),\n )\n row = cur.fetchone()\n if row:\n self.log(\n f'Daily totals for {counting_date}: {row[0]} objects across {row[1]} batch(es)'\n )\n\n def cutoff_watcher_loop(self):\n while not self.shutdown_event.is_set():\n time.sleep(60)\n with self.state_lock:\n if self.current_state is None:\n continue\n if self.current_state['counting_date'] != self.get_counting_date():\n self.log('Daily cutoff reached – finalizing batch')\n self._end_batch_locked(closed_by='cutoff')\n\n def start_cutoff_watcher(self):\n t = threading.Thread(target=self.cutoff_watcher_loop, daemon=True)\n t.start()\n return t\n\n @property\n def current_batch_number(self):\n if self.current_state is None:\n return 0\n return self.current_state['batch_number']\n\n @property\n def current_batch_count(self):\n if self.current_state is None:\n return 0\n return self.current_state['count']\n\n def get_closed_total_for_day(self, counting_date=None):\n if counting_date is None:\n counting_date = self.get_counting_date()\n cur = self.db.cursor()\n cur.execute(\n \"\"\"\n SELECT COALESCE(total_count, 0)\n FROM daily_summaries\n WHERE counting_date = ? AND camera_name = ? AND object_label = ?\n \"\"\",\n (counting_date, self.camera_name, self.object_label),\n )\n row = cur.fetchone()\n return row[0] if row else 0\n\n def display_total(self):\n return self.get_closed_total_for_day() + self.current_batch_count\n\n def shutdown(self):\n self.shutdown_event.set()\n self.end_batch(closed_by='shutdown')\n if self.batch_timer:\n self.batch_timer.cancel()\n self.db.close()",
|
||
"lineStart": 1,
|
||
"lineEnd": 379,
|
||
"totalLines": 379,
|
||
"truncated": false
|
||
}
|
||
},
|
||
"title": "batch_store.py",
|
||
"time": {
|
||
"start": 1785284239603,
|
||
"end": 1785284239654
|
||
}
|
||
},
|
||
"id": "prt_fab3ba023001b11UUWdv72InOW",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3b9948001NqWLx9PXClyyv8"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "read",
|
||
"callID": "call_02_GngfrJznz8i7yllztxzQ2349",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"filePath": "/opt/bytetrack-counter/counter_dashboard.py"
|
||
},
|
||
"output": "<path>/opt/bytetrack-counter/counter_dashboard.py</path>\n<type>file</type>\n<content>\n1: #!/usr/bin/env python3\n2: \"\"\"\n3: Edge Jetson production counter dashboard.\n4: Reads jetson_counter.db + current_batch.json from jetson-counter stack.\n5: Default port 5000 (replaces frigate-counter dashboard role).\n6: \"\"\"\n7: \n8: import json\n9: import os\n10: import sqlite3\n11: import time\n12: from io import BytesIO\n13: from datetime import datetime, timedelta\n14: \n15: from openpyxl import Workbook\n16: from openpyxl.styles import Font, Alignment, PatternFill, Border, Side\n17: \n18: from flask import Flask, render_template, jsonify, request, Response\n19: from werkzeug.serving import WSGIRequestHandler\n20: \n21: from dotenv import load_dotenv\n22: load_dotenv()\n23: \n24: \n25: app = Flask(__name__, template_folder=\"templates\")\n26: app.config[\"SECRET_KEY\"] = os.getenv(\"SECRET_KEY\", \"change-me-in-production\")\n27: \n28: _DEFAULT_DIR = \"/opt/jetson-counter\"\n29: DB_PATH = os.getenv(\"DB_PATH\", f\"{_DEFAULT_DIR}/jetson_counter.db\")\n30: CURRENT_BATCH_PATH = os.getenv(\"STATE_FILE\", os.getenv(\"CURRENT_BATCH_PATH\", f\"{_DEFAULT_DIR}/current_batch.json\"))\n31: CUTOFF_TIME = os.getenv(\"CUTOFF_TIME\", os.getenv(\"DAILY_CUTOFF_TIME\", \"20:00\"))\n32: \n33: LIVE_STREAM_FRAME_PATH = os.getenv(\"LIVE_STREAM_FRAME_PATH\", \"/dev/shm/jetson-counter/live_frame.jpg\")\n34: \n35: SITE_NAME = os.getenv(\"SITE_NAME\", \"LIVE\")\n36: \n37: DASHBOARD_PORT = int(os.getenv(\"DASHBOARD_PORT\", \"5000\"))\n38: DASHBOARD_HOST = os.getenv(\"DASHBOARD_HOST\", \"0.0.0.0\")\n39: FLASK_DEBUG = os.getenv(\"FLASK_DEBUG\", \"false\").lower() == \"true\"\n40: \n41: @app.route(\"/api/live-video\")\n42: def api_live_video():\n43: if not os.path.isfile(LIVE_STREAM_FRAME_PATH):\n44: return jsonify({\"success\": False, \"error\": \"Live stream frame not available yet\"}), 503\n45: \n46: def generate():\n47: consecutive_fails = 0\n48: MAX_FAILS = 30\n49: while True:\n50: try:\n51: with open(LIVE_STREAM_FRAME_PATH, \"rb\") as f:\n52: jpeg = f.read()\n53: consecutive_fails = 0\n54: yield (b\"--frame\\r\\n\"\n55: b\"Content-Type: image/jpeg\\r\\n\\r\\n\" + jpeg + b\"\\r\\n\")\n56: except FileNotFoundError:\n57: consecutive_fails += 1\n58: if consecutive_fails >= MAX_FAILS:\n59: return\n60: time.sleep(1.0)\n61: continue\n62: except Exception:\n63: consecutive_fails += 1\n64: if consecutive_fails >= MAX_FAILS:\n65: return\n66: time.sleep(0.5)\n67: continue\n68: time.sleep(0.05)\n69: return Response(generate(), mimetype=\"multipart/x-mixed-replace; boundary=frame\")\n70: \n71: \n72: def _ensure_db():\n73: conn = sqlite3.connect(DB_PATH)\n74: cur = conn.cursor()\n75: cur.execute(\n76: \"\"\"\n77: CREATE TABLE IF NOT EXISTS batches (\n78: id INTEGER PRIMARY KEY AUTOINCREMENT,\n79: counting_date TEXT NOT NULL,\n80: batch_number INTEGER NOT NULL,\n81: camera_name TEXT NOT NULL,\n82: object_label TEXT NOT NULL,\n83: count INTEGER NOT NULL,\n84: start_time TEXT NOT NULL,\n85: end_time TEXT NOT NULL,\n86: created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,\n87: UNIQUE(counting_date, batch_number, camera_name, object_label)\n88: )\n89: \"\"\"\n90: )\n91: cur.execute(\n92: \"\"\"\n93: CREATE TABLE IF NOT EXISTS daily_summaries (\n94: id INTEGER PRIMARY KEY AUTOINCREMENT,\n95: counting_date TEXT NOT NULL,\n96: camera_name TEXT NOT NULL,\n97: object_label TEXT NOT NULL,\n98: total_count INTEGER NOT NULL DEFAULT 0,\n99: total_batches INTEGER NOT NULL DEFAULT 0,\n100: updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,\n101: UNIQUE(counting_date, camera_name, object_label)\n102: )\n103: \"\"\"\n104: )\n105: conn.commit()\n106: conn.close()\n107: \n108: _ensure_db()\n109: \n110: \n111: def get_db():\n112: conn = sqlite3.connect(DB_PATH)\n113: conn.row_factory = sqlite3.Row\n114: return conn\n115: \n116: \n117: def get_counting_date(dt=None, cutoff_str=CUTOFF_TIME):\n118: if dt is None:\n119: dt = datetime.now()\n120: cutoff = datetime.strptime(cutoff_str, \"%H:%M\").time()\n121: if dt.time() < cutoff:\n122: return dt.date().isoformat()\n123: return (dt.date() + timedelta(days=1)).isoformat()\n124: \n125: \n126: @app.route(\"/\")\n127: def index():\n128: return render_template(\"dashboard.html\", site_name=SITE_NAME)\n129: \n130: \n131: @app.route(\"/api/current-batch\")\n132: def api_current_batch():\n133: try:\n134: with open(CURRENT_BATCH_PATH, \"r\") as f:\n135: data = json.load(f)\n136: return jsonify(\n137: {\n138: \"success\": True,\n139: \"counting_date\": data.get(\"counting_date\"),\n140: \"batch_number\": data.get(\"batch_number\"),\n141: \"count\": data.get(\"count\", 0),\n142: \"start_time\": data.get(\"start_time\"),\n143: \"last_detection_time\": data.get(\"last_detection_time\"),\n144: }\n145: )\n146: except FileNotFoundError:\n147: return jsonify(\n148: {\n149: \"success\": False,\n150: \"error\": \"No active batch\",\n151: \"count\": 0,\n152: \"batch_number\": None,\n153: \"counting_date\": None,\n154: }\n155: ), 200\n156: except Exception as e:\n157: return jsonify(\n158: {\n159: \"success\": False,\n160: \"error\": str(e),\n161: \"count\": 0,\n162: \"batch_number\": None,\n163: \"counting_date\": None,\n164: }\n165: ), 500\n166: \n167: \n168: @app.route(\"/api/previous-batch\")\n169: def api_previous_batch():\n170: try:\n171: conn = get_db()\n172: cur = conn.cursor()\n173: cur.execute(\n174: \"\"\"\n175: SELECT counting_date, batch_number, count, start_time, end_time,\n176: ROUND((julianday(end_time) - julianday(start_time)) * 24 * 60, 1) as duration_minutes\n177: FROM batches\n178: ORDER BY end_time DESC\n179: LIMIT 1\n180: \"\"\"\n181: )\n182: row = cur.fetchone()\n183: conn.close()\n184: if row:\n185: return jsonify(\n186: {\n187: \"success\": True,\n188: \"date\": row[\"counting_date\"],\n189: \"batch_number\": row[\"batch_number\"],\n190: \"count\": row[\"count\"],\n191: \"start_time\": row[\"start_time\"],\n192: \"end_time\": row[\"end_time\"],\n193: \"duration_minutes\": row[\"duration_minutes\"],\n194: }\n195: )\n196: return jsonify({\"success\": False, \"error\": \"No previous batch\"}), 200\n197: except sqlite3.OperationalError as e:\n198: return jsonify({\"success\": False, \"error\": f\"Database unavailable: {e}\"}), 200\n199: except Exception as e:\n200: return jsonify({\"success\": False, \"error\": str(e)}), 500\n201: \n202: \n203: @app.route(\"/api/summary\")\n204: def api_summary():\n205: try:\n206: conn = get_db()\n207: cur = conn.cursor()\n208: today = get_counting_date()\n209: cur.execute(\n210: \"\"\"\n211: SELECT COALESCE(total_count, 0) as total_count,\n212: COALESCE(total_batches, 0) as total_batches\n213: FROM daily_summaries\n214: WHERE counting_date = ?\n215: \"\"\",\n216: (today,),\n217: )\n218: today_row = cur.fetchone()\n219: yesterday = (datetime.strptime(today, \"%Y-%m-%d\").date() - timedelta(days=1)).isoformat()\n220: cur.execute(\n221: \"\"\"\n222: SELECT COALESCE(total_count, 0) as total_count,\n223: COALESCE(total_batches, 0) as total_batches\n224: FROM daily_summaries\n225: WHERE counting_date = ?\n226: \"\"\",\n227: (yesterday,),\n228: )\n229: yesterday_row = cur.fetchone()\n230: cur.execute(\n231: \"\"\"\n232: SELECT COALESCE(SUM(total_count), 0) as grand_total,\n233: COALESCE(SUM(total_batches), 0) as grand_batches,\n234: COUNT(DISTINCT counting_date) as total_days\n235: FROM daily_summaries\n236: \"\"\"\n237: )\n238: all_time = cur.fetchone()\n239: cur.execute(\"SELECT ROUND(AVG(total_count), 1) as avg_per_day FROM daily_summaries\")\n240: avg = cur.fetchone()\n241: cur.execute(\n242: \"\"\"\n243: SELECT counting_date, total_count\n244: FROM daily_summaries\n245: ORDER BY total_count DESC\n246: LIMIT 1\n247: \"\"\"\n248: )\n249: best = cur.fetchone()\n250: conn.close()\n251: return jsonify(\n252: {\n253: \"today\": {\n254: \"date\": today,\n255: \"total_count\": today_row[\"total_count\"] if today_row else 0,\n256: \"total_batches\": today_row[\"total_batches\"] if today_row else 0,\n257: },\n258: \"yesterday\": {\n259: \"date\": yesterday,\n260: \"total_count\": yesterday_row[\"total_count\"] if yesterday_row else 0,\n261: \"total_batches\": yesterday_row[\"total_batches\"] if yesterday_row else 0,\n262: },\n263: \"all_time\": {\n264: \"grand_total\": all_time[\"grand_total\"],\n265: \"grand_batches\": all_time[\"grand_batches\"],\n266: \"total_days\": all_time[\"total_days\"],\n267: },\n268: \"average_per_day\": avg[\"avg_per_day\"] or 0,\n269: \"best_day\": {\n270: \"date\": best[\"counting_date\"] if best else None,\n271: \"count\": best[\"total_count\"] if best else 0,\n272: },\n273: }\n274: )\n275: except sqlite3.OperationalError as e:\n276: return jsonify({\"success\": False, \"error\": f\"Database unavailable: {e}\", \"today\": {\"date\": datetime.now().date().isoformat(), \"total_count\": 0, \"total_batches\": 0}, \"yesterday\": {\"date\": \"\", \"total_count\": 0, \"total_batches\": 0}, \"all_time\": {\"grand_total\": 0, \"grand_batches\": 0, \"total_days\": 0}, \"average_per_day\": 0, \"best_day\": {\"date\": None, \"count\": 0}}), 200\n277: except Exception as e:\n278: return jsonify({\"success\": False, \"error\": str(e)}), 500\n279: \n280: \n281: @app.route(\"/api/daily-data\")\n282: def api_daily_data():\n283: try:\n284: days = request.args.get(\"days\", 30, type=int)\n285: date_from = (datetime.now() - timedelta(days=days)).date().isoformat()\n286: conn = get_db()\n287: cur = conn.cursor()\n288: cur.execute(\n289: \"\"\"\n290: SELECT counting_date, total_count, total_batches,\n291: ROUND(CAST(total_count AS FLOAT) / total_batches, 1) as avg_per_batch\n292: FROM daily_summaries\n293: WHERE counting_date >= ?\n294: ORDER BY counting_date ASC\n295: \"\"\",\n296: (date_from,),\n297: )\n298: daily_data = [\n299: {\n300: \"date\": row[\"counting_date\"],\n301: \"total_count\": row[\"total_count\"],\n302: \"total_batches\": row[\"total_batches\"],\n303: \"avg_per_batch\": row[\"avg_per_batch\"] or 0,\n304: }\n305: for row in cur.fetchall()\n306: ]\n307: conn.close()\n308: return jsonify(daily_data)\n309: except sqlite3.OperationalError as e:\n310: return jsonify([]), 200\n311: except Exception as e:\n312: return jsonify({\"success\": False, \"error\": str(e)}), 500\n313: \n314: \n315: @app.route(\"/api/day-detail/<date>\")\n316: def api_day_detail(date):\n317: try:\n318: conn = get_db()\n319: cur = conn.cursor()\n320: cur.execute(\n321: \"\"\"\n322: SELECT batch_number, count, start_time, end_time,\n323: ROUND((julianday(end_time) - julianday(start_time)) * 24 * 60, 1) as duration_minutes\n324: FROM batches\n325: WHERE counting_date = ?\n326: ORDER BY batch_number ASC\n327: \"\"\",\n328: (date,),\n329: )\n330: batches = []\n331: total_duration = 0\n332: for row in cur.fetchall():\n333: duration = row[\"duration_minutes\"] or 0\n334: total_duration += duration\n335: batches.append(\n336: {\n337: \"batch_number\": row[\"batch_number\"],\n338: \"count\": row[\"count\"],\n339: \"start_time\": row[\"start_time\"],\n340: \"end_time\": row[\"end_time\"],\n341: \"duration_minutes\": duration,\n342: }\n343: )\n344: cur.execute(\n345: \"\"\"\n346: SELECT total_count, total_batches\n347: FROM daily_summaries\n348: WHERE counting_date = ?\n349: \"\"\",\n350: (date,),\n351: )\n352: summary = cur.fetchone()\n353: conn.close()\n354: return jsonify(\n355: {\n356: \"date\": date,\n357: \"total_count\": summary[\"total_count\"] if summary else 0,\n358: \"total_batches\": summary[\"total_batches\"] if summary else 0,\n359: \"total_duration_minutes\": round(total_duration, 1),\n360: \"avg_duration_minutes\": round(total_duration / len(batches), 1) if batches else 0,\n361: \"batches\": batches,\n362: }\n363: )\n364: except sqlite3.OperationalError as e:\n365: return jsonify({\"date\": date, \"total_count\": 0, \"total_batches\": 0, \"total_duration_minutes\": 0, \"avg_duration_minutes\": 0, \"batches\": [], \"error\": f\"Database unavailable: {e}\"}), 200\n366: except Exception as e:\n367: return jsonify({\"success\": False, \"error\": str(e)}), 500\n368: \n369: \n370: @app.route(\"/api/recent-batches\")\n371: def api_recent_batches():\n372: try:\n373: limit = request.args.get(\"limit\", 10, type=int)\n374: conn = get_db()\n375: cur = conn.cursor()\n376: cur.execute(\n377: \"\"\"\n378: SELECT counting_date, batch_number, count, start_time, end_time,\n379: ROUND((julianday(end_time) - julianday(start_time)) * 24 * 60, 1) as duration_minutes\n380: FROM batches\n381: ORDER BY end_time DESC\n382: LIMIT ?\n383: \"\"\",\n384: (limit,),\n385: )\n386: batches = [\n387: {\n388: \"date\": row[\"counting_date\"],\n389: \"batch_number\": row[\"batch_number\"],\n390: \"count\": row[\"count\"],\n391: \"start_time\": row[\"start_time\"],\n392: \"end_time\": row[\"end_time\"],\n393: \"duration_minutes\": row[\"duration_minutes\"] or 0,\n394: }\n395: for row in cur.fetchall()\n396: ]\n397: conn.close()\n398: return jsonify(batches)\n399: except sqlite3.OperationalError as e:\n400: return jsonify([]), 200\n401: except Exception as e:\n402: return jsonify({\"success\": False, \"error\": str(e)}), 500\n403: \n404: \n405: @app.route(\"/api/available-dates\")\n406: def api_available_dates():\n407: try:\n408: conn = get_db()\n409: cur = conn.cursor()\n410: cur.execute(\n411: \"\"\"\n412: SELECT counting_date, total_count, total_batches\n413: FROM daily_summaries\n414: ORDER BY counting_date DESC\n415: \"\"\"\n416: )\n417: dates = [\n418: {\n419: \"date\": row[\"counting_date\"],\n420: \"total_count\": row[\"total_count\"],\n421: \"total_batches\": row[\"total_batches\"],\n422: }\n423: for row in cur.fetchall()\n424: ]\n425: conn.close()\n426: return jsonify(dates)\n427: except sqlite3.OperationalError as e:\n428: return jsonify([]), 200\n429: except Exception as e:\n430: return jsonify({\"success\": False, \"error\": str(e)}), 500\n431: \n432: \n433: def _excel_response(wb, filename):\n434: output = BytesIO()\n435: wb.save(output)\n436: output.seek(0)\n437: return Response(\n438: output.getvalue(),\n439: mimetype=\"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet\",\n440: headers={\"Content-Disposition\": f\"attachment; filename={filename}\"},\n441: )\n442: \n443: \n444: def _style_header(ws, cols):\n445: header_font = Font(bold=True, color=\"FFFFFF\", size=11)\n446: header_fill = PatternFill(start_color=\"2F5496\", end_color=\"2F5496\", fill_type=\"solid\")\n447: thin_border = Border(\n448: left=Side(style=\"thin\"), right=Side(style=\"thin\"),\n449: top=Side(style=\"thin\"), bottom=Side(style=\"thin\"),\n450: )\n451: for col_idx, (col_letter, text) in enumerate(cols, 1):\n452: cell = ws.cell(row=1, column=col_idx, value=text)\n453: cell.font = header_font\n454: cell.fill = header_fill\n455: cell.alignment = Alignment(horizontal=\"center\")\n456: cell.border = thin_border\n457: ws.freeze_panes = \"A2\"\n458: \n459: \n460: def _auto_width(ws):\n461: for col in ws.columns:\n462: max_len = 0\n463: col_letter = col[0].column_letter\n464: for cell in col:\n465: if cell.value is not None:\n466: max_len = max(max_len, len(str(cell.value)))\n467: ws.column_dimensions[col_letter].width = max_len + 4\n468: \n469: \n470: @app.route(\"/api/export-daily-csv\")\n471: def export_daily_xlsx():\n472: try:\n473: days = request.args.get(\"days\", 30, type=int)\n474: date_from = (datetime.now() - timedelta(days=days)).date().isoformat()\n475: conn = get_db()\n476: cur = conn.cursor()\n477: cur.execute(\n478: \"\"\"\n479: SELECT counting_date, batch_number, count, start_time, end_time,\n480: ROUND((julianday(end_time) - julianday(start_time)) * 24 * 60, 1) as duration_minutes\n481: FROM batches\n482: WHERE counting_date >= ?\n483: ORDER BY counting_date ASC, batch_number ASC\n484: \"\"\",\n485: (date_from,),\n486: )\n487: rows = cur.fetchall()\n488: conn.close()\n489: except sqlite3.OperationalError as e:\n490: return jsonify({\"success\": False, \"error\": f\"Database unavailable: {e}\"}), 503\n491: except Exception as e:\n492: return jsonify({\"success\": False, \"error\": str(e)}), 500\n493: \n494: wb = Workbook()\n495: ws = wb.active\n496: ws.title = \"Batch Details\"\n497: _style_header(ws, [(\"A\", \"Date\"), (\"B\", \"Batch #\"), (\"C\", \"Count\"), (\"D\", \"Start Time\"), (\"E\", \"End Time\"), (\"F\", \"Duration (min)\")])\n498: \n499: for r_idx, row in enumerate(rows, 2):\n500: ws.cell(row=r_idx, column=1, value=row[\"counting_date\"])\n501: ws.cell(row=r_idx, column=2, value=row[\"batch_number\"])\n502: ws.cell(row=r_idx, column=3, value=row[\"count\"])\n503: ws.cell(row=r_idx, column=4, value=row[\"start_time\"])\n504: ws.cell(row=r_idx, column=5, value=row[\"end_time\"])\n505: ws.cell(row=r_idx, column=6, value=row[\"duration_minutes\"] or 0)\n506: \n507: _auto_width(ws)\n508: filename = f\"{SITE_NAME}_daily_records_{datetime.now().strftime('%Y%m%d_%H%M%S')}.xlsx\"\n509: return _excel_response(wb, filename)\n510: \n511: \n512: @app.route(\"/api/export-day-csv/<date>\")\n513: def export_day_xlsx(date):\n514: try:\n515: conn = get_db()\n516: cur = conn.cursor()\n517: cur.execute(\n518: \"\"\"\n519: SELECT batch_number, count, start_time, end_time,\n520: ROUND((julianday(end_time) - julianday(start_time)) * 24 * 60, 1) as duration_minutes\n521: FROM batches\n522: WHERE counting_date = ?\n523: ORDER BY batch_number ASC\n524: \"\"\",\n525: (date,),\n526: )\n527: rows = cur.fetchall()\n528: conn.close()\n529: except sqlite3.OperationalError as e:\n530: return jsonify({\"success\": False, \"error\": f\"Database unavailable: {e}\"}), 503\n531: except Exception as e:\n532: return jsonify({\"success\": False, \"error\": str(e)}), 500\n533: \n534: wb = Workbook()\n535: ws = wb.active\n536: ws.title = f\"Day {date}\"\n537: _style_header(ws, [(\"A\", \"Batch Number\"), (\"B\", \"Count\"), (\"C\", \"Start Time\"), (\"D\", \"End Time\"), (\"E\", \"Duration (min)\")])\n538: \n539: for r_idx, row in enumerate(rows, 2):\n540: ws.cell(row=r_idx, column=1, value=row[\"batch_number\"])\n541: ws.cell(row=r_idx, column=2, value=row[\"count\"])\n542: ws.cell(row=r_idx, column=3, value=row[\"start_time\"])\n543: ws.cell(row=r_idx, column=4, value=row[\"end_time\"])\n544: ws.cell(row=r_idx, column=5, value=row[\"duration_minutes\"] or 0)\n545: \n546: _auto_width(ws)\n547: filename = f\"{SITE_NAME}_day_detail_{date}.xlsx\"\n548: return _excel_response(wb, filename)\n549: \n550: \n551: if __name__ == \"__main__\":\n552: WSGIRequestHandler.protocol_version = \"HTTP/1.1\"\n553: print(f\"Jetson counter dashboard at http://{DASHBOARD_HOST}:{DASHBOARD_PORT}\")\n554: print(f\"DB: {DB_PATH}\")\n555: print(f\"State: {CURRENT_BATCH_PATH}\")\n556: app.run(host=DASHBOARD_HOST, port=DASHBOARD_PORT, debug=FLASK_DEBUG)\n\n(End of file - total 556 lines)\n</content>",
|
||
"metadata": {
|
||
"preview": "#!/usr/bin/env python3\n\"\"\"\nEdge Jetson production counter dashboard.\nReads jetson_counter.db + current_batch.json from jetson-counter stack.\nDefault port 5000 (replaces frigate-counter dashboard role).\n\"\"\"\n\nimport json\nimport os\nimport sqlite3\nimport time\nfrom io import BytesIO\nfrom datetime import datetime, timedelta\n\nfrom openpyxl import Workbook\nfrom openpyxl.styles import Font, Alignment, PatternFill, Border, Side\n\nfrom flask import Flask, render_template, jsonify, request, Response\nfrom werkzeug.serving import WSGIRequestHandler\n",
|
||
"truncated": false,
|
||
"loaded": [],
|
||
"display": {
|
||
"type": "file",
|
||
"path": "/opt/bytetrack-counter/counter_dashboard.py",
|
||
"text": "#!/usr/bin/env python3\n\"\"\"\nEdge Jetson production counter dashboard.\nReads jetson_counter.db + current_batch.json from jetson-counter stack.\nDefault port 5000 (replaces frigate-counter dashboard role).\n\"\"\"\n\nimport json\nimport os\nimport sqlite3\nimport time\nfrom io import BytesIO\nfrom datetime import datetime, timedelta\n\nfrom openpyxl import Workbook\nfrom openpyxl.styles import Font, Alignment, PatternFill, Border, Side\n\nfrom flask import Flask, render_template, jsonify, request, Response\nfrom werkzeug.serving import WSGIRequestHandler\n\nfrom dotenv import load_dotenv\nload_dotenv()\n\n\napp = Flask(__name__, template_folder=\"templates\")\napp.config[\"SECRET_KEY\"] = os.getenv(\"SECRET_KEY\", \"change-me-in-production\")\n\n_DEFAULT_DIR = \"/opt/jetson-counter\"\nDB_PATH = os.getenv(\"DB_PATH\", f\"{_DEFAULT_DIR}/jetson_counter.db\")\nCURRENT_BATCH_PATH = os.getenv(\"STATE_FILE\", os.getenv(\"CURRENT_BATCH_PATH\", f\"{_DEFAULT_DIR}/current_batch.json\"))\nCUTOFF_TIME = os.getenv(\"CUTOFF_TIME\", os.getenv(\"DAILY_CUTOFF_TIME\", \"20:00\"))\n\nLIVE_STREAM_FRAME_PATH = os.getenv(\"LIVE_STREAM_FRAME_PATH\", \"/dev/shm/jetson-counter/live_frame.jpg\")\n\nSITE_NAME = os.getenv(\"SITE_NAME\", \"LIVE\")\n\nDASHBOARD_PORT = int(os.getenv(\"DASHBOARD_PORT\", \"5000\"))\nDASHBOARD_HOST = os.getenv(\"DASHBOARD_HOST\", \"0.0.0.0\")\nFLASK_DEBUG = os.getenv(\"FLASK_DEBUG\", \"false\").lower() == \"true\"\n\n@app.route(\"/api/live-video\")\ndef api_live_video():\n if not os.path.isfile(LIVE_STREAM_FRAME_PATH):\n return jsonify({\"success\": False, \"error\": \"Live stream frame not available yet\"}), 503\n\n def generate():\n consecutive_fails = 0\n MAX_FAILS = 30\n while True:\n try:\n with open(LIVE_STREAM_FRAME_PATH, \"rb\") as f:\n jpeg = f.read()\n consecutive_fails = 0\n yield (b\"--frame\\r\\n\"\n b\"Content-Type: image/jpeg\\r\\n\\r\\n\" + jpeg + b\"\\r\\n\")\n except FileNotFoundError:\n consecutive_fails += 1\n if consecutive_fails >= MAX_FAILS:\n return\n time.sleep(1.0)\n continue\n except Exception:\n consecutive_fails += 1\n if consecutive_fails >= MAX_FAILS:\n return\n time.sleep(0.5)\n continue\n time.sleep(0.05)\n return Response(generate(), mimetype=\"multipart/x-mixed-replace; boundary=frame\")\n\n\ndef _ensure_db():\n conn = sqlite3.connect(DB_PATH)\n cur = conn.cursor()\n cur.execute(\n \"\"\"\n CREATE TABLE IF NOT EXISTS batches (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n counting_date TEXT NOT NULL,\n batch_number INTEGER NOT NULL,\n camera_name TEXT NOT NULL,\n object_label TEXT NOT NULL,\n count INTEGER NOT NULL,\n start_time TEXT NOT NULL,\n end_time TEXT NOT NULL,\n created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,\n UNIQUE(counting_date, batch_number, camera_name, object_label)\n )\n \"\"\"\n )\n cur.execute(\n \"\"\"\n CREATE TABLE IF NOT EXISTS daily_summaries (\n id INTEGER PRIMARY KEY AUTOINCREMENT,\n counting_date TEXT NOT NULL,\n camera_name TEXT NOT NULL,\n object_label TEXT NOT NULL,\n total_count INTEGER NOT NULL DEFAULT 0,\n total_batches INTEGER NOT NULL DEFAULT 0,\n updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,\n UNIQUE(counting_date, camera_name, object_label)\n )\n \"\"\"\n )\n conn.commit()\n conn.close()\n\n_ensure_db()\n\n\ndef get_db():\n conn = sqlite3.connect(DB_PATH)\n conn.row_factory = sqlite3.Row\n return conn\n\n\ndef get_counting_date(dt=None, cutoff_str=CUTOFF_TIME):\n if dt is None:\n dt = datetime.now()\n cutoff = datetime.strptime(cutoff_str, \"%H:%M\").time()\n if dt.time() < cutoff:\n return dt.date().isoformat()\n return (dt.date() + timedelta(days=1)).isoformat()\n\n\n@app.route(\"/\")\ndef index():\n return render_template(\"dashboard.html\", site_name=SITE_NAME)\n\n\n@app.route(\"/api/current-batch\")\ndef api_current_batch():\n try:\n with open(CURRENT_BATCH_PATH, \"r\") as f:\n data = json.load(f)\n return jsonify(\n {\n \"success\": True,\n \"counting_date\": data.get(\"counting_date\"),\n \"batch_number\": data.get(\"batch_number\"),\n \"count\": data.get(\"count\", 0),\n \"start_time\": data.get(\"start_time\"),\n \"last_detection_time\": data.get(\"last_detection_time\"),\n }\n )\n except FileNotFoundError:\n return jsonify(\n {\n \"success\": False,\n \"error\": \"No active batch\",\n \"count\": 0,\n \"batch_number\": None,\n \"counting_date\": None,\n }\n ), 200\n except Exception as e:\n return jsonify(\n {\n \"success\": False,\n \"error\": str(e),\n \"count\": 0,\n \"batch_number\": None,\n \"counting_date\": None,\n }\n ), 500\n\n\n@app.route(\"/api/previous-batch\")\ndef api_previous_batch():\n try:\n conn = get_db()\n cur = conn.cursor()\n cur.execute(\n \"\"\"\n SELECT counting_date, batch_number, count, start_time, end_time,\n ROUND((julianday(end_time) - julianday(start_time)) * 24 * 60, 1) as duration_minutes\n FROM batches\n ORDER BY end_time DESC\n LIMIT 1\n \"\"\"\n )\n row = cur.fetchone()\n conn.close()\n if row:\n return jsonify(\n {\n \"success\": True,\n \"date\": row[\"counting_date\"],\n \"batch_number\": row[\"batch_number\"],\n \"count\": row[\"count\"],\n \"start_time\": row[\"start_time\"],\n \"end_time\": row[\"end_time\"],\n \"duration_minutes\": row[\"duration_minutes\"],\n }\n )\n return jsonify({\"success\": False, \"error\": \"No previous batch\"}), 200\n except sqlite3.OperationalError as e:\n return jsonify({\"success\": False, \"error\": f\"Database unavailable: {e}\"}), 200\n except Exception as e:\n return jsonify({\"success\": False, \"error\": str(e)}), 500\n\n\n@app.route(\"/api/summary\")\ndef api_summary():\n try:\n conn = get_db()\n cur = conn.cursor()\n today = get_counting_date()\n cur.execute(\n \"\"\"\n SELECT COALESCE(total_count, 0) as total_count,\n COALESCE(total_batches, 0) as total_batches\n FROM daily_summaries\n WHERE counting_date = ?\n \"\"\",\n (today,),\n )\n today_row = cur.fetchone()\n yesterday = (datetime.strptime(today, \"%Y-%m-%d\").date() - timedelta(days=1)).isoformat()\n cur.execute(\n \"\"\"\n SELECT COALESCE(total_count, 0) as total_count,\n COALESCE(total_batches, 0) as total_batches\n FROM daily_summaries\n WHERE counting_date = ?\n \"\"\",\n (yesterday,),\n )\n yesterday_row = cur.fetchone()\n cur.execute(\n \"\"\"\n SELECT COALESCE(SUM(total_count), 0) as grand_total,\n COALESCE(SUM(total_batches), 0) as grand_batches,\n COUNT(DISTINCT counting_date) as total_days\n FROM daily_summaries\n \"\"\"\n )\n all_time = cur.fetchone()\n cur.execute(\"SELECT ROUND(AVG(total_count), 1) as avg_per_day FROM daily_summaries\")\n avg = cur.fetchone()\n cur.execute(\n \"\"\"\n SELECT counting_date, total_count\n FROM daily_summaries\n ORDER BY total_count DESC\n LIMIT 1\n \"\"\"\n )\n best = cur.fetchone()\n conn.close()\n return jsonify(\n {\n \"today\": {\n \"date\": today,\n \"total_count\": today_row[\"total_count\"] if today_row else 0,\n \"total_batches\": today_row[\"total_batches\"] if today_row else 0,\n },\n \"yesterday\": {\n \"date\": yesterday,\n \"total_count\": yesterday_row[\"total_count\"] if yesterday_row else 0,\n \"total_batches\": yesterday_row[\"total_batches\"] if yesterday_row else 0,\n },\n \"all_time\": {\n \"grand_total\": all_time[\"grand_total\"],\n \"grand_batches\": all_time[\"grand_batches\"],\n \"total_days\": all_time[\"total_days\"],\n },\n \"average_per_day\": avg[\"avg_per_day\"] or 0,\n \"best_day\": {\n \"date\": best[\"counting_date\"] if best else None,\n \"count\": best[\"total_count\"] if best else 0,\n },\n }\n )\n except sqlite3.OperationalError as e:\n return jsonify({\"success\": False, \"error\": f\"Database unavailable: {e}\", \"today\": {\"date\": datetime.now().date().isoformat(), \"total_count\": 0, \"total_batches\": 0}, \"yesterday\": {\"date\": \"\", \"total_count\": 0, \"total_batches\": 0}, \"all_time\": {\"grand_total\": 0, \"grand_batches\": 0, \"total_days\": 0}, \"average_per_day\": 0, \"best_day\": {\"date\": None, \"count\": 0}}), 200\n except Exception as e:\n return jsonify({\"success\": False, \"error\": str(e)}), 500\n\n\n@app.route(\"/api/daily-data\")\ndef api_daily_data():\n try:\n days = request.args.get(\"days\", 30, type=int)\n date_from = (datetime.now() - timedelta(days=days)).date().isoformat()\n conn = get_db()\n cur = conn.cursor()\n cur.execute(\n \"\"\"\n SELECT counting_date, total_count, total_batches,\n ROUND(CAST(total_count AS FLOAT) / total_batches, 1) as avg_per_batch\n FROM daily_summaries\n WHERE counting_date >= ?\n ORDER BY counting_date ASC\n \"\"\",\n (date_from,),\n )\n daily_data = [\n {\n \"date\": row[\"counting_date\"],\n \"total_count\": row[\"total_count\"],\n \"total_batches\": row[\"total_batches\"],\n \"avg_per_batch\": row[\"avg_per_batch\"] or 0,\n }\n for row in cur.fetchall()\n ]\n conn.close()\n return jsonify(daily_data)\n except sqlite3.OperationalError as e:\n return jsonify([]), 200\n except Exception as e:\n return jsonify({\"success\": False, \"error\": str(e)}), 500\n\n\n@app.route(\"/api/day-detail/<date>\")\ndef api_day_detail(date):\n try:\n conn = get_db()\n cur = conn.cursor()\n cur.execute(\n \"\"\"\n SELECT batch_number, count, start_time, end_time,\n ROUND((julianday(end_time) - julianday(start_time)) * 24 * 60, 1) as duration_minutes\n FROM batches\n WHERE counting_date = ?\n ORDER BY batch_number ASC\n \"\"\",\n (date,),\n )\n batches = []\n total_duration = 0\n for row in cur.fetchall():\n duration = row[\"duration_minutes\"] or 0\n total_duration += duration\n batches.append(\n {\n \"batch_number\": row[\"batch_number\"],\n \"count\": row[\"count\"],\n \"start_time\": row[\"start_time\"],\n \"end_time\": row[\"end_time\"],\n \"duration_minutes\": duration,\n }\n )\n cur.execute(\n \"\"\"\n SELECT total_count, total_batches\n FROM daily_summaries\n WHERE counting_date = ?\n \"\"\",\n (date,),\n )\n summary = cur.fetchone()\n conn.close()\n return jsonify(\n {\n \"date\": date,\n \"total_count\": summary[\"total_count\"] if summary else 0,\n \"total_batches\": summary[\"total_batches\"] if summary else 0,\n \"total_duration_minutes\": round(total_duration, 1),\n \"avg_duration_minutes\": round(total_duration / len(batches), 1) if batches else 0,\n \"batches\": batches,\n }\n )\n except sqlite3.OperationalError as e:\n return jsonify({\"date\": date, \"total_count\": 0, \"total_batches\": 0, \"total_duration_minutes\": 0, \"avg_duration_minutes\": 0, \"batches\": [], \"error\": f\"Database unavailable: {e}\"}), 200\n except Exception as e:\n return jsonify({\"success\": False, \"error\": str(e)}), 500\n\n\n@app.route(\"/api/recent-batches\")\ndef api_recent_batches():\n try:\n limit = request.args.get(\"limit\", 10, type=int)\n conn = get_db()\n cur = conn.cursor()\n cur.execute(\n \"\"\"\n SELECT counting_date, batch_number, count, start_time, end_time,\n ROUND((julianday(end_time) - julianday(start_time)) * 24 * 60, 1) as duration_minutes\n FROM batches\n ORDER BY end_time DESC\n LIMIT ?\n \"\"\",\n (limit,),\n )\n batches = [\n {\n \"date\": row[\"counting_date\"],\n \"batch_number\": row[\"batch_number\"],\n \"count\": row[\"count\"],\n \"start_time\": row[\"start_time\"],\n \"end_time\": row[\"end_time\"],\n \"duration_minutes\": row[\"duration_minutes\"] or 0,\n }\n for row in cur.fetchall()\n ]\n conn.close()\n return jsonify(batches)\n except sqlite3.OperationalError as e:\n return jsonify([]), 200\n except Exception as e:\n return jsonify({\"success\": False, \"error\": str(e)}), 500\n\n\n@app.route(\"/api/available-dates\")\ndef api_available_dates():\n try:\n conn = get_db()\n cur = conn.cursor()\n cur.execute(\n \"\"\"\n SELECT counting_date, total_count, total_batches\n FROM daily_summaries\n ORDER BY counting_date DESC\n \"\"\"\n )\n dates = [\n {\n \"date\": row[\"counting_date\"],\n \"total_count\": row[\"total_count\"],\n \"total_batches\": row[\"total_batches\"],\n }\n for row in cur.fetchall()\n ]\n conn.close()\n return jsonify(dates)\n except sqlite3.OperationalError as e:\n return jsonify([]), 200\n except Exception as e:\n return jsonify({\"success\": False, \"error\": str(e)}), 500\n\n\ndef _excel_response(wb, filename):\n output = BytesIO()\n wb.save(output)\n output.seek(0)\n return Response(\n output.getvalue(),\n mimetype=\"application/vnd.openxmlformats-officedocument.spreadsheetml.sheet\",\n headers={\"Content-Disposition\": f\"attachment; filename={filename}\"},\n )\n\n\ndef _style_header(ws, cols):\n header_font = Font(bold=True, color=\"FFFFFF\", size=11)\n header_fill = PatternFill(start_color=\"2F5496\", end_color=\"2F5496\", fill_type=\"solid\")\n thin_border = Border(\n left=Side(style=\"thin\"), right=Side(style=\"thin\"),\n top=Side(style=\"thin\"), bottom=Side(style=\"thin\"),\n )\n for col_idx, (col_letter, text) in enumerate(cols, 1):\n cell = ws.cell(row=1, column=col_idx, value=text)\n cell.font = header_font\n cell.fill = header_fill\n cell.alignment = Alignment(horizontal=\"center\")\n cell.border = thin_border\n ws.freeze_panes = \"A2\"\n\n\ndef _auto_width(ws):\n for col in ws.columns:\n max_len = 0\n col_letter = col[0].column_letter\n for cell in col:\n if cell.value is not None:\n max_len = max(max_len, len(str(cell.value)))\n ws.column_dimensions[col_letter].width = max_len + 4\n\n\n@app.route(\"/api/export-daily-csv\")\ndef export_daily_xlsx():\n try:\n days = request.args.get(\"days\", 30, type=int)\n date_from = (datetime.now() - timedelta(days=days)).date().isoformat()\n conn = get_db()\n cur = conn.cursor()\n cur.execute(\n \"\"\"\n SELECT counting_date, batch_number, count, start_time, end_time,\n ROUND((julianday(end_time) - julianday(start_time)) * 24 * 60, 1) as duration_minutes\n FROM batches\n WHERE counting_date >= ?\n ORDER BY counting_date ASC, batch_number ASC\n \"\"\",\n (date_from,),\n )\n rows = cur.fetchall()\n conn.close()\n except sqlite3.OperationalError as e:\n return jsonify({\"success\": False, \"error\": f\"Database unavailable: {e}\"}), 503\n except Exception as e:\n return jsonify({\"success\": False, \"error\": str(e)}), 500\n\n wb = Workbook()\n ws = wb.active\n ws.title = \"Batch Details\"\n _style_header(ws, [(\"A\", \"Date\"), (\"B\", \"Batch #\"), (\"C\", \"Count\"), (\"D\", \"Start Time\"), (\"E\", \"End Time\"), (\"F\", \"Duration (min)\")])\n\n for r_idx, row in enumerate(rows, 2):\n ws.cell(row=r_idx, column=1, value=row[\"counting_date\"])\n ws.cell(row=r_idx, column=2, value=row[\"batch_number\"])\n ws.cell(row=r_idx, column=3, value=row[\"count\"])\n ws.cell(row=r_idx, column=4, value=row[\"start_time\"])\n ws.cell(row=r_idx, column=5, value=row[\"end_time\"])\n ws.cell(row=r_idx, column=6, value=row[\"duration_minutes\"] or 0)\n\n _auto_width(ws)\n filename = f\"{SITE_NAME}_daily_records_{datetime.now().strftime('%Y%m%d_%H%M%S')}.xlsx\"\n return _excel_response(wb, filename)\n\n\n@app.route(\"/api/export-day-csv/<date>\")\ndef export_day_xlsx(date):\n try:\n conn = get_db()\n cur = conn.cursor()\n cur.execute(\n \"\"\"\n SELECT batch_number, count, start_time, end_time,\n ROUND((julianday(end_time) - julianday(start_time)) * 24 * 60, 1) as duration_minutes\n FROM batches\n WHERE counting_date = ?\n ORDER BY batch_number ASC\n \"\"\",\n (date,),\n )\n rows = cur.fetchall()\n conn.close()\n except sqlite3.OperationalError as e:\n return jsonify({\"success\": False, \"error\": f\"Database unavailable: {e}\"}), 503\n except Exception as e:\n return jsonify({\"success\": False, \"error\": str(e)}), 500\n\n wb = Workbook()\n ws = wb.active\n ws.title = f\"Day {date}\"\n _style_header(ws, [(\"A\", \"Batch Number\"), (\"B\", \"Count\"), (\"C\", \"Start Time\"), (\"D\", \"End Time\"), (\"E\", \"Duration (min)\")])\n\n for r_idx, row in enumerate(rows, 2):\n ws.cell(row=r_idx, column=1, value=row[\"batch_number\"])\n ws.cell(row=r_idx, column=2, value=row[\"count\"])\n ws.cell(row=r_idx, column=3, value=row[\"start_time\"])\n ws.cell(row=r_idx, column=4, value=row[\"end_time\"])\n ws.cell(row=r_idx, column=5, value=row[\"duration_minutes\"] or 0)\n\n _auto_width(ws)\n filename = f\"{SITE_NAME}_day_detail_{date}.xlsx\"\n return _excel_response(wb, filename)\n\n\nif __name__ == \"__main__\":\n WSGIRequestHandler.protocol_version = \"HTTP/1.1\"\n print(f\"Jetson counter dashboard at http://{DASHBOARD_HOST}:{DASHBOARD_PORT}\")\n print(f\"DB: {DB_PATH}\")\n print(f\"State: {CURRENT_BATCH_PATH}\")\n app.run(host=DASHBOARD_HOST, port=DASHBOARD_PORT, debug=FLASK_DEBUG)",
|
||
"lineStart": 1,
|
||
"lineEnd": 556,
|
||
"totalLines": 556,
|
||
"truncated": false
|
||
}
|
||
},
|
||
"title": "counter_dashboard.py",
|
||
"time": {
|
||
"start": 1785284239884,
|
||
"end": 1785284239971
|
||
}
|
||
},
|
||
"id": "prt_fab3ba13f001b784GzzBuK3pIL",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3b9948001NqWLx9PXClyyv8"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "read",
|
||
"callID": "call_03_FLmzGWGb68M2KNx7MD5C8536",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"filePath": "/opt/bytetrack-counter/config.env.example"
|
||
},
|
||
"output": "<path>/opt/bytetrack-counter/config.env.example</path>\n<type>file</type>\n<content>\n1: # =============================================================================\n2: # Edge RK3588 production counter + dashboard\n3: # Shared config for: counter_live_rknn_bytetrack.py + counter_dashboard.py\n4: # Copy to .env on device: cp config.env.example .env && nano .env\n5: # =============================================================================\n6: \n7: # --- Core paths ---\n8: # Root output directory (logs, DB, video, CSV)\n9: OUTPUT_DIR=/opt/bytetrack-counter\n10: # SQLite database path for batch entries & crossing logs\n11: DB_PATH=/opt/bytetrack-counter/bytetrack_counter.db\n12: # JSON file persisting the current active batch state\n13: STATE_FILE=/tmp/bytetrack_current_batch.json\n14: \n15: # --- Input source ---\n16: # RTSP / HTTP live stream, or a local video file path\n17: SOURCE=rtsp://user:pass@192.168.0.100:554/stream1\n18: # FFmpeg capture options passed to cv2.VideoCapture (RTSP low-latency flags)\n19: OPENCV_FFMPEG_CAPTURE_OPTIONS=rtsp_transport;tcp|fflags;nobuffer|flags;low_delay\n20: \n21: # --- RKNN model ---\n22: # Path to exported .rknn model (YOLO format, e.g. yolo11n.rknn)\n23: MODEL_PATH=/opt/models/yolo9t.rknn\n24: # Input image size for the model (square, e.g. 320 → 320×320)\n25: IMGSZ=320\n26: # Use FP16 inference on NPU (true/false); currently unused in ByteTrack variant\n27: HALF=false\n28: # NPU core mask: 1=core0, 2=core1, 3=core0+core1, 7=all three\n29: CORE_MASK=7\n30: # Compute device index (reserved; not used at runtime)\n31: DEVICE=0\n32: \n33: # --- YOLO decoder ---\n34: # Number of object classes the model outputs (e.g. 2 = ayam + talenan)\n35: NUM_CLASSES=2\n36: # Apply sigmoid to raw class scores (true/false); set true if model head uses BCE logits\n37: SCORE_SIGMOID=false\n38: \n39: # --- Detection ---\n40: # Confidence threshold – detections below this are discarded before NMS\n41: CONF=0.3\n42: \n43: # --- ByteTrack tracking ---\n44: # General for ayam, index 0\n45: # Detections with score ≥ this get priority matching in the first association stage\n46: TRACK_HIGH_THRESH_0=0.5\n47: # Detections with score between this and TRACK_HIGH_THRESH are matched in the second stage\n48: TRACK_LOW_THRESH_0=0.1\n49: # IoU threshold for the first-stage association (0–1). Higher = stricter overlap required\n50: TRACK_MATCH_THRESH_0=0.8\n51: # Frames a track survives without a match before being permanently removed\n52: TRACK_BUFFER_0=30\n53: # Minimum consecutive (or total) hits needed before a track is considered confirmed\n54: TRACK_MIN_HITS_0=3\n55: \n56: # For talenan, index 1\n57: # Detections with score ≥ this get priority matching in the first association stage\n58: TRACK_HIGH_THRESH_1=0.5\n59: # Detections with score between this and TRACK_HIGH_THRESH are matched in the second stage\n60: TRACK_LOW_THRESH_1=0.1\n61: # IoU threshold for the first-stage association (0–1). Higher = stricter overlap required\n62: TRACK_MATCH_THRESH_1=0.6\n63: # Frames a track survives without a match before being permanently removed\n64: TRACK_BUFFER_1=30\n65: # Minimum consecutive (or total) hits needed before a track is considered confirmed\n66: TRACK_MIN_HITS_1=3\n67: \n68: # --- Display ---\n69: # Site name shown on the dashboard header (top-right)\n70: SITE_NAME=ZenAi\n71: \n72: # --- Object class names ---\n73: # Camera / location identifier shown in HUD and stored in DB\n74: CAMERA_NAME=ZenAi\n75: # Label used for batch grouping in the database\n76: OBJECT_LABEL=ayam-potong\n77: # Class name for the counted object (must match NUM_CLASSES order, index 0)\n78: CLASS_AYAM=ayam\n79: # Class name for the batch-closing trigger object (must match NUM_CLASSES order, index 1)\n80: CLASS_TALENAN=talenan\n81: \n82: # --- Line crossing ---\n83: # Direction for counting: rtl (right-to-left, default) | ltr (left-to-right) | both\n84: CROSS_DIRECTION=rtl\n85: # Fixed x-coordinate for the counting line (overrides LINE_X_FRAC if set)\n86: LINE_X=\n87: # Fraction of frame width where the counting line is drawn (default 0.5 = centre)\n88: LINE_X_FRAC=0.5\n89: \n90: # --- Batch management ---\n91: # Daily cutoff time (HH:MM) – a new day's batch numbering starts after this time.\n92: # CUTOFF_TIME is an alias used by the dashboard; DAILY_CUTOFF_TIME takes priority in counter_live_rknn_bytetrack.py.\n93: DAILY_CUTOFF_TIME=20:00\n94: CUTOFF_TIME=20:00\n95: # Reset frame counter and ByteTrack track-ID counter back to 0 when the daily cutoff is reached (true/false, default: true)\n96: RESET_COUNTERS_AT_CUTOFF=true\n97: # Seconds of inactivity after which the current batch is auto-closed\n98: BATCH_TIMEOUT_SECONDS=300\n99: # Seconds a newly-opened batch ignores the talenan label before accepting a close trigger\n100: IGNORE_BATCH_LABEL_TIMEOUT_SECONDS=30\n101: # Minimum number of objects required for a batch to be saved as valid\n102: MIN_OBJECT_PER_BATCH=60\n103: # Minimum duration in seconds a batch must be open to be saved as valid\n104: MIN_DURATION_PER_BATCH=60\n105: \n106: # --- CSV export ---\n107: # Write per-crossing events to a CSV file (true/false)\n108: EXPORT_CSV=true\n109: # Path where the crossing CSV is written\n110: CROSS_CSV=/opt/batch-counter/batch_crossings.csv\n111: \n112: # --- Rate / performance ---\n113: # Enable motion detection pre-filter: skip inference on frames with no movement\n114: # (true/false, default: false). When enabled, frames below MOTION_THRESHOLD are\n115: # skipped, saving NPU/CPU load.\n116: MOTION_DETECTION_ENABLED=false\n117: # Mean absolute pixel difference threshold (0–255) to consider a frame as having\n118: # motion. Lower = more sensitive. Default 5.0.\n119: MOTION_THRESHOLD=5.0\n120: # Sliding window in seconds for computing the crossing rate (objects/minute)\n121: RATE_WINDOW_SEC=60\n122: # Number of frames to discard at startup to let the stream buffer stabilise\n123: WARMUP_FRAMES=30\n124: # Delay in seconds between stream reconnection attempts\n125: RECONNECT_DELAY_SEC=3\n126: # Maximum reconnection attempts (0 = infinite)\n127: MAX_RECONNECT_ATTEMPTS=0\n128: # Print status log every N processed frames\n129: FLUSH_EVERY_N_FRAMES=100\n130: # Seconds after which a tracked but unseen object is pruned from the active set\n131: TRACKED_PRUNE_SEC=300\n132: \n133: # --- Video recording ---\n134: # Save annotated frames to segmented MP4 files (true/false)\n135: RECORD_VIDEO=false\n136: # Duration in seconds of each video segment file\n137: VIDEO_SEGMENT_SEC=3600\n138: # Output video FPS (fallback if source FPS is unknown or ≤ 1)\n139: OUTPUT_FPS=15\n140: \n141: # --- Live stream snapshot ---\n142: # Periodically write the latest annotated frame as JPEG for an external web server\n143: LIVE_STREAM_ENABLED=false\n144: # Path to the shared-memory snapshot file (served by nginx / lighttpd)\n145: LIVE_STREAM_FRAME_PATH=/dev/shm/byetrack-counter/live_frame.jpg\n146: # JPEG quality (1–100)\n147: LIVE_STREAM_QUALITY=75\n148: # Write the snapshot every N frames (lower = more frequent updates)\n149: LIVE_STREAM_EVERY_N=2\n150: \n151: # --- Dashboard (counter_dashboard.py) ---\n152: # Flask secret key for session/cookie signing — change in production!\n153: SECRET_KEY=change-me-in-production\n154: # Bind address for the Flask web server\n155: DASHBOARD_HOST=0.0.0.0\n156: # Listen port for the dashboard web UI\n157: DASHBOARD_PORT=5000\n158: # Enable Flask debug mode (true/false) — auto-reloads on code changes; disable in production\n159: FLASK_DEBUG=false\n160: # Fallback name for the active-batch JSON state file used by the dashboard\n161: CURRENT_BATCH_PATH=/tmp/bytetrack_current_batch.json\n\n(End of file - total 161 lines)\n</content>",
|
||
"metadata": {
|
||
"preview": "# =============================================================================\n# Edge RK3588 production counter + dashboard\n# Shared config for: counter_live_rknn_bytetrack.py + counter_dashboard.py\n# Copy to .env on device: cp config.env.example .env && nano .env\n# =============================================================================\n\n# --- Core paths ---\n# Root output directory (logs, DB, video, CSV)\nOUTPUT_DIR=/opt/bytetrack-counter\n# SQLite database path for batch entries & crossing logs\nDB_PATH=/opt/bytetrack-counter/bytetrack_counter.db\n# JSON file persisting the current active batch state\nSTATE_FILE=/tmp/bytetrack_current_batch.json\n\n# --- Input source ---\n# RTSP / HTTP live stream, or a local video file path\nSOURCE=rtsp://user:pass@192.168.0.100:554/stream1\n# FFmpeg capture options passed to cv2.VideoCapture (RTSP low-latency flags)\nOPENCV_FFMPEG_CAPTURE_OPTIONS=rtsp_transport;tcp|fflags;nobuffer|flags;low_delay\n",
|
||
"truncated": false,
|
||
"loaded": [],
|
||
"display": {
|
||
"type": "file",
|
||
"path": "/opt/bytetrack-counter/config.env.example",
|
||
"text": "# =============================================================================\n# Edge RK3588 production counter + dashboard\n# Shared config for: counter_live_rknn_bytetrack.py + counter_dashboard.py\n# Copy to .env on device: cp config.env.example .env && nano .env\n# =============================================================================\n\n# --- Core paths ---\n# Root output directory (logs, DB, video, CSV)\nOUTPUT_DIR=/opt/bytetrack-counter\n# SQLite database path for batch entries & crossing logs\nDB_PATH=/opt/bytetrack-counter/bytetrack_counter.db\n# JSON file persisting the current active batch state\nSTATE_FILE=/tmp/bytetrack_current_batch.json\n\n# --- Input source ---\n# RTSP / HTTP live stream, or a local video file path\nSOURCE=rtsp://user:pass@192.168.0.100:554/stream1\n# FFmpeg capture options passed to cv2.VideoCapture (RTSP low-latency flags)\nOPENCV_FFMPEG_CAPTURE_OPTIONS=rtsp_transport;tcp|fflags;nobuffer|flags;low_delay\n\n# --- RKNN model ---\n# Path to exported .rknn model (YOLO format, e.g. yolo11n.rknn)\nMODEL_PATH=/opt/models/yolo9t.rknn\n# Input image size for the model (square, e.g. 320 → 320×320)\nIMGSZ=320\n# Use FP16 inference on NPU (true/false); currently unused in ByteTrack variant\nHALF=false\n# NPU core mask: 1=core0, 2=core1, 3=core0+core1, 7=all three\nCORE_MASK=7\n# Compute device index (reserved; not used at runtime)\nDEVICE=0\n\n# --- YOLO decoder ---\n# Number of object classes the model outputs (e.g. 2 = ayam + talenan)\nNUM_CLASSES=2\n# Apply sigmoid to raw class scores (true/false); set true if model head uses BCE logits\nSCORE_SIGMOID=false\n\n# --- Detection ---\n# Confidence threshold – detections below this are discarded before NMS\nCONF=0.3\n\n# --- ByteTrack tracking ---\n# General for ayam, index 0\n# Detections with score ≥ this get priority matching in the first association stage\nTRACK_HIGH_THRESH_0=0.5\n# Detections with score between this and TRACK_HIGH_THRESH are matched in the second stage\nTRACK_LOW_THRESH_0=0.1\n# IoU threshold for the first-stage association (0–1). Higher = stricter overlap required\nTRACK_MATCH_THRESH_0=0.8\n# Frames a track survives without a match before being permanently removed\nTRACK_BUFFER_0=30\n# Minimum consecutive (or total) hits needed before a track is considered confirmed\nTRACK_MIN_HITS_0=3\n\n# For talenan, index 1\n# Detections with score ≥ this get priority matching in the first association stage\nTRACK_HIGH_THRESH_1=0.5\n# Detections with score between this and TRACK_HIGH_THRESH are matched in the second stage\nTRACK_LOW_THRESH_1=0.1\n# IoU threshold for the first-stage association (0–1). Higher = stricter overlap required\nTRACK_MATCH_THRESH_1=0.6\n# Frames a track survives without a match before being permanently removed\nTRACK_BUFFER_1=30\n# Minimum consecutive (or total) hits needed before a track is considered confirmed\nTRACK_MIN_HITS_1=3\n\n# --- Display ---\n# Site name shown on the dashboard header (top-right)\nSITE_NAME=ZenAi\n\n# --- Object class names ---\n# Camera / location identifier shown in HUD and stored in DB\nCAMERA_NAME=ZenAi\n# Label used for batch grouping in the database\nOBJECT_LABEL=ayam-potong\n# Class name for the counted object (must match NUM_CLASSES order, index 0)\nCLASS_AYAM=ayam\n# Class name for the batch-closing trigger object (must match NUM_CLASSES order, index 1)\nCLASS_TALENAN=talenan\n\n# --- Line crossing ---\n# Direction for counting: rtl (right-to-left, default) | ltr (left-to-right) | both\nCROSS_DIRECTION=rtl\n# Fixed x-coordinate for the counting line (overrides LINE_X_FRAC if set)\nLINE_X=\n# Fraction of frame width where the counting line is drawn (default 0.5 = centre)\nLINE_X_FRAC=0.5\n\n# --- Batch management ---\n# Daily cutoff time (HH:MM) – a new day's batch numbering starts after this time.\n# CUTOFF_TIME is an alias used by the dashboard; DAILY_CUTOFF_TIME takes priority in counter_live_rknn_bytetrack.py.\nDAILY_CUTOFF_TIME=20:00\nCUTOFF_TIME=20:00\n# Reset frame counter and ByteTrack track-ID counter back to 0 when the daily cutoff is reached (true/false, default: true)\nRESET_COUNTERS_AT_CUTOFF=true\n# Seconds of inactivity after which the current batch is auto-closed\nBATCH_TIMEOUT_SECONDS=300\n# Seconds a newly-opened batch ignores the talenan label before accepting a close trigger\nIGNORE_BATCH_LABEL_TIMEOUT_SECONDS=30\n# Minimum number of objects required for a batch to be saved as valid\nMIN_OBJECT_PER_BATCH=60\n# Minimum duration in seconds a batch must be open to be saved as valid\nMIN_DURATION_PER_BATCH=60\n\n# --- CSV export ---\n# Write per-crossing events to a CSV file (true/false)\nEXPORT_CSV=true\n# Path where the crossing CSV is written\nCROSS_CSV=/opt/batch-counter/batch_crossings.csv\n\n# --- Rate / performance ---\n# Enable motion detection pre-filter: skip inference on frames with no movement\n# (true/false, default: false). When enabled, frames below MOTION_THRESHOLD are\n# skipped, saving NPU/CPU load.\nMOTION_DETECTION_ENABLED=false\n# Mean absolute pixel difference threshold (0–255) to consider a frame as having\n# motion. Lower = more sensitive. Default 5.0.\nMOTION_THRESHOLD=5.0\n# Sliding window in seconds for computing the crossing rate (objects/minute)\nRATE_WINDOW_SEC=60\n# Number of frames to discard at startup to let the stream buffer stabilise\nWARMUP_FRAMES=30\n# Delay in seconds between stream reconnection attempts\nRECONNECT_DELAY_SEC=3\n# Maximum reconnection attempts (0 = infinite)\nMAX_RECONNECT_ATTEMPTS=0\n# Print status log every N processed frames\nFLUSH_EVERY_N_FRAMES=100\n# Seconds after which a tracked but unseen object is pruned from the active set\nTRACKED_PRUNE_SEC=300\n\n# --- Video recording ---\n# Save annotated frames to segmented MP4 files (true/false)\nRECORD_VIDEO=false\n# Duration in seconds of each video segment file\nVIDEO_SEGMENT_SEC=3600\n# Output video FPS (fallback if source FPS is unknown or ≤ 1)\nOUTPUT_FPS=15\n\n# --- Live stream snapshot ---\n# Periodically write the latest annotated frame as JPEG for an external web server\nLIVE_STREAM_ENABLED=false\n# Path to the shared-memory snapshot file (served by nginx / lighttpd)\nLIVE_STREAM_FRAME_PATH=/dev/shm/byetrack-counter/live_frame.jpg\n# JPEG quality (1–100)\nLIVE_STREAM_QUALITY=75\n# Write the snapshot every N frames (lower = more frequent updates)\nLIVE_STREAM_EVERY_N=2\n\n# --- Dashboard (counter_dashboard.py) ---\n# Flask secret key for session/cookie signing — change in production!\nSECRET_KEY=change-me-in-production\n# Bind address for the Flask web server\nDASHBOARD_HOST=0.0.0.0\n# Listen port for the dashboard web UI\nDASHBOARD_PORT=5000\n# Enable Flask debug mode (true/false) — auto-reloads on code changes; disable in production\nFLASK_DEBUG=false\n# Fallback name for the active-batch JSON state file used by the dashboard\nCURRENT_BATCH_PATH=/tmp/bytetrack_current_batch.json",
|
||
"lineStart": 1,
|
||
"lineEnd": 161,
|
||
"totalLines": 161,
|
||
"truncated": false
|
||
}
|
||
},
|
||
"title": "config.env.example",
|
||
"time": {
|
||
"start": 1785284240169,
|
||
"end": 1785284240183
|
||
}
|
||
},
|
||
"id": "prt_fab3ba25c001Bo0KQtIHlu6El0",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3b9948001NqWLx9PXClyyv8"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "read",
|
||
"callID": "call_04_y5rZw0rEq6o3tZU2iuyD7373",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"filePath": "/opt/bytetrack-counter/counter_live_rknn.py"
|
||
},
|
||
"output": "<path>/opt/bytetrack-counter/counter_live_rknn.py</path>\n<type>file</type>\n<content>\n1: \"\"\"\n2: Edge production live counter — RTSP + YOLO RKNN + line crossing.\n3: Runs on RK3588 hardware with RKNN model (320×320 input).\n4: Replaces the Jetson/TensorRT variant.\n5: \"\"\"\n6: \n7: import numpy as np\n8: import cv2\n9: import csv\n10: import os\n11: import signal\n12: import time\n13: from datetime import datetime\n14: from pathlib import Path\n15: \n16: from dotenv import load_dotenv\n17: \n18: load_dotenv()\n19: \n20: from rknnlite.api import RKNNLite\n21: from batch_store import BatchStore\n22: \n23: # --- config (override via env / .env) ---\n24: OUTPUT_DIR = os.getenv(\"OUTPUT_DIR\", \"/opt/jetson-counter\")\n25: DB_PATH = os.getenv(\"DB_PATH\", f\"{OUTPUT_DIR}/jetson_counter.db\")\n26: STATE_FILE = os.getenv(\"STATE_FILE\", f\"{OUTPUT_DIR}/current_batch.json\")\n27: SOURCE = os.getenv(\"SOURCE\", \"rtsp://user:pass@192.168.0.100:554/stream1\")\n28: MODEL_PATH = os.getenv(\"MODEL_PATH\", \"/opt/jetson-counter/yolo11n.rknn\")\n29: CAMERA_NAME = os.getenv(\"CAMERA_NAME\", \"CC1\")\n30: OBJECT_LABEL = os.getenv(\"OBJECT_LABEL\", \"ayam-potong\")\n31: CLASS_AYAM = os.getenv(\"CLASS_AYAM\", \"ayam\")\n32: CLASS_TALENAN = os.getenv(\"CLASS_TALENAN\", \"talenan\")\n33: \n34: LINE_X = int(os.getenv(\"LINE_X\")) if os.getenv(\"LINE_X\") else None\n35: LINE_X_FRAC = float(os.getenv(\"LINE_X_FRAC\", \"0.5\"))\n36: CROSS_DIRECTION = os.getenv(\"CROSS_DIRECTION\", \"rtl\").lower()\n37: \n38: IMGSZ = int(os.getenv(\"IMGSZ\", \"320\"))\n39: HALF = os.getenv(\"HALF\", \"false\").lower() == \"true\"\n40: CONF = float(os.getenv(\"CONF\", \"0.3\"))\n41: DEVICE = int(os.getenv(\"DEVICE\", \"0\"))\n42: \n43: # RKNN-specific — core mask for NPU\n44: # 1 = core0, 2 = core1, 3 = core0+core1 (dual), 7 = all three\n45: CORE_MASK = int(os.getenv(\"CORE_MASK\", \"1\"))\n46: \n47: # YOLO decoder config\n48: NUM_CLASSES = int(os.getenv(\"NUM_CLASSES\", \"2\"))\n49: SCORE_SIGMOID = os.getenv(\"SCORE_SIGMOID\", \"false\").lower() == \"true\"\n50: \n51: DAILY_CUTOFF_TIME = os.getenv(\"DAILY_CUTOFF_TIME\", \"20:00\")\n52: BATCH_TIMEOUT_SECONDS = float(os.getenv(\"BATCH_TIMEOUT_SECONDS\", \"300\"))\n53: IGNORE_BATCH_LABEL_TIMEOUT = float(\n54: os.getenv(\"IGNORE_BATCH_LABEL_TIMEOUT_SECONDS\", \"30\")\n55: )\n56: MIN_OBJECT_PER_BATCH = int(os.getenv(\"MIN_OBJECT_PER_BATCH\", \"60\"))\n57: MIN_DURATION_PER_BATCH = int(os.getenv(\"MIN_DURATION_PER_BATCH\", \"60\"))\n58: \n59: EXPORT_CSV = os.getenv(\"EXPORT_CSV\", \"true\").lower() == \"true\"\n60: CROSS_CSV = os.getenv(\"CROSS_CSV\", f\"{OUTPUT_DIR}/batch_crossings.csv\")\n61: \n62: WARMUP_FRAMES = int(os.getenv(\"WARMUP_FRAMES\", \"30\"))\n63: RECONNECT_DELAY_SEC = int(os.getenv(\"RECONNECT_DELAY_SEC\", \"3\"))\n64: MAX_RECONNECT_ATTEMPTS = int(os.getenv(\"MAX_RECONNECT_ATTEMPTS\", \"0\"))\n65: FLUSH_EVERY_N_FRAMES = int(os.getenv(\"FLUSH_EVERY_N_FRAMES\", \"100\"))\n66: TRACKED_PRUNE_SEC = int(os.getenv(\"TRACKED_PRUNE_SEC\", \"300\"))\n67: RECORD_VIDEO = os.getenv(\"RECORD_VIDEO\", \"false\").lower() == \"true\"\n68: VIDEO_SEGMENT_SEC = int(os.getenv(\"VIDEO_SEGMENT_SEC\", \"3600\"))\n69: OUTPUT_FPS = int(os.getenv(\"OUTPUT_FPS\", \"15\"))\n70: \n71: LIVE_STREAM_ENABLED = os.getenv(\"LIVE_STREAM_ENABLED\", \"false\").lower() == \"true\"\n72: LIVE_STREAM_FRAME_PATH = os.getenv(\n73: \"LIVE_STREAM_FRAME_PATH\", \"/dev/shm/jetson-counter/live_frame.jpg\"\n74: )\n75: LIVE_STREAM_QUALITY = int(os.getenv(\"LIVE_STREAM_QUALITY\", \"75\"))\n76: LIVE_STREAM_EVERY_N = int(os.getenv(\"LIVE_STREAM_EVERY_N\", \"2\"))\n77: \n78: RTSP_FFMPEG_OPTIONS = os.getenv(\n79: \"OPENCV_FFMPEG_CAPTURE_OPTIONS\",\n80: \"rtsp_transport;tcp|fflags;nobuffer|flags;low_delay\",\n81: )\n82: \n83: IS_LIVE = SOURCE.lower().startswith((\"rtsp://\", \"http://\"))\n84: \n85: CROSS_FLASH_FRAMES = 12\n86: POPUP_LIFETIME = 20\n87: LINE_PULSE_FRAMES = 12\n88: COUNT_PULSE_FRAMES = 15\n89: BATCH_PULSE_FRAMES = 20\n90: \n91: SKELETON = [(0, 1), (4, 3), (1, 2), (3, 2), (2, 6), (2, 5), (2, 7), (7, 8)]\n92: SK_COLORS = [\n93: (0, 255, 255),\n94: (0, 255, 255),\n95: (255, 0, 255),\n96: (255, 0, 255),\n97: (0, 255, 0),\n98: (255, 255, 0),\n99: (0, 0, 255),\n100: (200, 200, 0),\n101: ]\n102: \n103: C_PANEL = (28, 24, 18)\n104: C_BORDER = (90, 85, 75)\n105: C_ACCENT = (255, 200, 60)\n106: C_GREEN = (80, 220, 100)\n107: C_TEXT = (235, 235, 235)\n108: C_MUTED = (150, 150, 150)\n109: C_AYAM_BOX = (0, 165, 255)\n110: C_TALENAN_BOX = (220, 120, 60)\n111: C_LINE_CORE = (180, 220, 255)\n112: C_LINE_GLOW = (100, 160, 220)\n113: \n114: shutdown_requested = False\n115: \n116: \n117: def request_shutdown(signum, frame):\n118: global shutdown_requested\n119: shutdown_requested = True\n120: print(\"\\nShutdown requested — finishing current frame...\")\n121: \n122: \n123: signal.signal(signal.SIGINT, request_shutdown)\n124: signal.signal(signal.SIGTERM, request_shutdown)\n125: \n126: \n127: # =============================================================================\n128: # YOLO output decoder (NMS only — boxes are pre-decoded by the model)\n129: # =============================================================================\n130: \n131: \n132: def _nms(boxes, scores, iou_thr=0.45):\n133: order = np.argsort(scores)[::-1]\n134: keep = []\n135: while len(order) > 0:\n136: idx = order[0]\n137: keep.append(idx)\n138: if len(order) == 1:\n139: break\n140: xx1 = np.maximum(boxes[idx, 0], boxes[order[1:], 0])\n141: yy1 = np.maximum(boxes[idx, 1], boxes[order[1:], 1])\n142: xx2 = np.minimum(boxes[idx, 2], boxes[order[1:], 2])\n143: yy2 = np.minimum(boxes[idx, 3], boxes[order[1:], 3])\n144: w = np.maximum(0.0, xx2 - xx1)\n145: h = np.maximum(0.0, yy2 - yy1)\n146: inter = w * h\n147: area_i = (boxes[idx, 2] - boxes[idx, 0]) * (boxes[idx, 3] - boxes[idx, 1])\n148: area_o = (boxes[order[1:], 2] - boxes[order[1:], 0]) * (\n149: boxes[order[1:], 3] - boxes[order[1:], 1]\n150: )\n151: iou = inter / (area_i + area_o - inter + 1e-16)\n152: order = order[1:][iou < iou_thr]\n153: return np.array(keep)\n154: \n155: \n156: def _compute_iou(box1, boxes2):\n157: \"\"\"IoU of one box (cxcywh) against a set (2D) or single box (1D).\"\"\"\n158: if boxes2.ndim == 1:\n159: boxes2 = boxes2.reshape(1, -1)\n160: cx, cy, w, h = box1\n161: x1, y1 = cx - w / 2, cy - h / 2\n162: x2, y2 = cx + w / 2, cy + h / 2\n163: area1 = w * h\n164: \n165: cxs, cys, ws, hs = boxes2[:, 0], boxes2[:, 1], boxes2[:, 2], boxes2[:, 3]\n166: x1s, y1s = cxs - ws / 2, cys - hs / 2\n167: x2s, y2s = cxs + ws / 2, cys + hs / 2\n168: areas2 = ws * hs\n169: \n170: xx1 = np.maximum(x1, x1s)\n171: yy1 = np.maximum(y1, y1s)\n172: xx2 = np.minimum(x2, x2s)\n173: yy2 = np.minimum(y2, y2s)\n174: inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1)\n175: return inter / (area1 + areas2 - inter + 1e-16)\n176: \n177: \n178: # =============================================================================\n179: # Simple IoU tracker (replaces bytetrack — same persist behaviour)\n180: # =============================================================================\n181: \n182: \n183: class SimpleTracker:\n184: def __init__(self, max_age=30, min_hits=1, iou_threshold=0.3):\n185: self.max_age = max_age\n186: self.min_hits = min_hits\n187: self.iou_threshold = iou_threshold\n188: self.tracks = {} # track_id -> {box, cx, age, hits, time_since_update}\n189: self.next_id = 1\n190: \n191: def update(self, detections):\n192: \"\"\"detections: list of (cx, box_cxcywh). Returns (track_map, det_to_track).\"\"\"\n193: now = time.monotonic()\n194: \n195: for tid in self.tracks:\n196: self.tracks[tid][\"time_since_update\"] += 1\n197: \n198: matched_det = set()\n199: matched_track = set()\n200: assignments = [] # (track_id, det_idx)\n201: det_to_track = {} # det_idx → track_id\n202: \n203: if detections and self.tracks:\n204: track_ids = list(self.tracks.keys())\n205: track_boxes = np.stack([self.tracks[t][\"box\"] for t in track_ids], axis=0)\n206: \n207: for di, det in enumerate(detections):\n208: _, det_box = det\n209: ious = np.array(\n210: [\n211: _compute_iou(det_box, track_boxes[t : t + 1])\n212: for t in range(len(track_ids))\n213: ]\n214: )\n215: best_j = int(np.argmax(ious))\n216: if (\n217: ious[best_j] >= self.iou_threshold\n218: and track_ids[best_j] not in matched_track\n219: ):\n220: assignments.append((track_ids[best_j], di))\n221: matched_track.add(track_ids[best_j])\n222: matched_det.add(di)\n223: \n224: for tid, di in assignments:\n225: cx, box = detections[di]\n226: self.tracks[tid][\"cx\"] = cx\n227: self.tracks[tid][\"box\"] = box\n228: self.tracks[tid][\"hits\"] += 1\n229: self.tracks[tid][\"time_since_update\"] = 0\n230: self.tracks[tid][\"last_update\"] = now\n231: det_to_track[di] = tid\n232: \n233: for di, det in enumerate(detections):\n234: if di not in matched_det:\n235: cx, box = det\n236: new_id = self.next_id\n237: self.next_id += 1\n238: self.tracks[new_id] = {\n239: \"cx\": cx,\n240: \"box\": box,\n241: \"hits\": 1,\n242: \"time_since_update\": 0,\n243: \"last_update\": now,\n244: }\n245: det_to_track[di] = new_id\n246: \n247: stale = [\n248: tid\n249: for tid, t in self.tracks.items()\n250: if t[\"time_since_update\"] > self.max_age\n251: ]\n252: for tid in stale:\n253: del self.tracks[tid]\n254: \n255: track_map = {\n256: tid: self.tracks[tid][\"cx\"]\n257: for tid in self.tracks\n258: if self.tracks[tid][\"hits\"] >= self.min_hits\n259: }\n260: return track_map, det_to_track\n261: \n262: \n263: # =============================================================================\n264: # RKNN YOLO wrapper (detect output format: (1, 4+num_classes, N))\n265: # =============================================================================\n266: \n267: \n268: class RKNNYOLO:\n269: def __init__(\n270: self,\n271: model_path,\n272: core_mask=1,\n273: imgsz=320,\n274: conf=0.3,\n275: iou=0.45,\n276: num_classes=2,\n277: num_keypoints=0,\n278: score_sigmoid=False,\n279: ):\n280: self.imgsz = imgsz\n281: self.conf = conf\n282: self.iou = iou\n283: self.num_classes = num_classes\n284: self.num_keypoints = num_keypoints\n285: self.score_sigmoid = score_sigmoid\n286: \n287: self.rknn = RKNNLite(verbose=False)\n288: ret = self.rknn.load_rknn(model_path)\n289: if ret != 0:\n290: raise RuntimeError(f\"Failed to load RKNN model: {model_path}\")\n291: ret = self.rknn.init_runtime(core_mask=core_mask)\n292: if ret != 0:\n293: raise RuntimeError(f\"Failed to init RKNN runtime (core_mask={core_mask})\")\n294: \n295: try:\n296: from rknnlite.api import RKNNLite as _RK\n297: \n298: sdk_ver = self.rknn.get_sdk_version()\n299: print(f\"RKNN SDK version: {sdk_ver}\")\n300: except Exception:\n301: pass\n302: \n303: print(f\"RKNN model loaded: {model_path} imgsz={imgsz} core_mask={core_mask}\")\n304: \n305: def _preprocess(self, frame):\n306: \"\"\"Letterbox-resize to imgsz×imgsz, maintain aspect ratio, BGR→RGB, normalize.\"\"\"\n307: h0, w0 = frame.shape[:2]\n308: scale = min(self.imgsz / h0, self.imgsz / w0)\n309: nh, nw = int(h0 * scale), int(w0 * scale)\n310: resized = cv2.resize(frame, (nw, nh), interpolation=cv2.INTER_LINEAR)\n311: \n312: letterbox = np.full((self.imgsz, self.imgsz, 3), 114, dtype=np.uint8)\n313: dy = (self.imgsz - nh) // 2\n314: dx = (self.imgsz - nw) // 2\n315: letterbox[dy : dy + nh, dx : dx + nw] = resized\n316: \n317: rgb = cv2.cvtColor(letterbox, cv2.COLOR_BGR2RGB)\n318: gains = np.array([scale, scale, dy, dx], dtype=np.float32)\n319: return rgb, gains\n320: \n321: def __call__(self, frame):\n322: \"\"\"Run inference on BGR frame. Returns list of detection dicts.\"\"\"\n323: h0, w0 = frame.shape[:2]\n324: rgb, gains = self._preprocess(frame)\n325: scale, _, pad_y, pad_x = gains\n326: \n327: inp = np.expand_dims(rgb, axis=0)\n328: inp = np.ascontiguousarray(inp.astype(np.uint8))\n329: \n330: outputs = self.rknn.inference(inputs=[inp])\n331: \n332: if len(outputs) == 0:\n333: return []\n334: \n335: out = outputs[0] # (1, 4+num_classes, N) or (1, N, 4+num_classes)\n336: out = np.squeeze(out, axis=0) # (C, N) or (N, C)\n337: \n338: if out.shape[0] == self.num_classes + 4:\n339: out = out.T # (C, N) → (N, C)\n340: \n341: boxes_cxcywh = out[:, :4].copy() # cx, cy, w, h at model resolution\n342: cls_raw = out[:, 4:].copy()\n343: \n344: if self.score_sigmoid:\n345: cls_scores = 1.0 / (1.0 + np.exp(-np.clip(cls_raw, -10, 10)))\n346: else:\n347: cls_scores = cls_raw\n348: \n349: boxes_xyxy = np.stack(\n350: [\n351: boxes_cxcywh[:, 0] - boxes_cxcywh[:, 2] / 2,\n352: boxes_cxcywh[:, 1] - boxes_cxcywh[:, 3] / 2,\n353: boxes_cxcywh[:, 0] + boxes_cxcywh[:, 2] / 2,\n354: boxes_cxcywh[:, 1] + boxes_cxcywh[:, 3] / 2,\n355: ],\n356: axis=1,\n357: )\n358: \n359: max_scores = cls_scores.max(axis=1)\n360: class_ids = cls_scores.argmax(axis=1)\n361: mask = max_scores > self.conf\n362: \n363: if mask.sum() == 0:\n364: return []\n365: \n366: bboxes = boxes_xyxy[mask].astype(np.float32)\n367: scores = max_scores[mask].astype(np.float32)\n368: clses = class_ids[mask]\n369: \n370: bboxes[:, 0] = (bboxes[:, 0] - pad_x) / scale\n371: bboxes[:, 1] = (bboxes[:, 1] - pad_y) / scale\n372: bboxes[:, 2] = (bboxes[:, 2] - pad_x) / scale\n373: bboxes[:, 3] = (bboxes[:, 3] - pad_y) / scale\n374: \n375: bboxes[:, 0] = np.clip(bboxes[:, 0], 0, w0)\n376: bboxes[:, 1] = np.clip(bboxes[:, 1], 0, h0)\n377: bboxes[:, 2] = np.clip(bboxes[:, 2], 0, w0)\n378: bboxes[:, 3] = np.clip(bboxes[:, 3], 0, h0)\n379: \n380: detections = []\n381: for cls_id in range(self.num_classes):\n382: idx = np.where(clses == cls_id)[0]\n383: if len(idx) == 0:\n384: continue\n385: keep = _nms(bboxes[idx], scores[idx], iou_thr=self.iou)\n386: for k in keep:\n387: j = idx[k]\n388: detections.append(\n389: {\n390: \"bbox\": bboxes[j].tolist(),\n391: \"score\": float(scores[j]),\n392: \"cls\": int(clses[j]),\n393: \"keypoints\": None,\n394: }\n395: )\n396: return detections\n397: \n398: def release(self):\n399: self.rknn.release()\n400: \n401: \n402: # =============================================================================\n403: # Drawing helpers (unchanged from original)\n404: # =============================================================================\n405: \n406: \n407: def resolve_line_x(frame_width):\n408: if LINE_X is not None:\n409: return LINE_X\n410: if LINE_X_FRAC != 0.5:\n411: return int(frame_width * LINE_X_FRAC)\n412: return frame_width // 2\n413: \n414: \n415: def crossed_line(prev_cx, cx, line_x, direction=CROSS_DIRECTION):\n416: if direction == \"ltr\":\n417: return prev_cx < line_x <= cx\n418: if direction == \"both\":\n419: return (prev_cx > line_x >= cx) or (prev_cx < line_x <= cx)\n420: return prev_cx > line_x >= cx\n421: \n422: \n423: def now_str():\n424: return datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n425: \n426: \n427: def open_capture(source):\n428: if source.lower().startswith((\"rtsp://\", \"http://\")):\n429: os.environ[\"OPENCV_FFMPEG_CAPTURE_OPTIONS\"] = RTSP_FFMPEG_OPTIONS\n430: cap = cv2.VideoCapture(source, cv2.CAP_FFMPEG)\n431: cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)\n432: return cap\n433: \n434: \n435: def warmup_stream(cap, n=WARMUP_FRAMES):\n436: print(\"Warming up stream...\")\n437: for _ in range(n):\n438: cap.read()\n439: print(\"Stream ready!\")\n440: \n441: \n442: def open_video_writer(path, w, h, fps):\n443: return cv2.VideoWriter(path, cv2.VideoWriter_fourcc(*\"avc1\"), fps, (w, h))\n444: \n445: \n446: class CsvLogger:\n447: def __init__(self, path, header):\n448: Path(path).parent.mkdir(parents=True, exist_ok=True)\n449: new_file = not Path(path).exists() or Path(path).stat().st_size == 0\n450: self.file = open(path, \"a\", newline=\"\", buffering=1)\n451: self.writer = csv.writer(self.file)\n452: if new_file:\n453: self.writer.writerow(header)\n454: self.file.flush()\n455: \n456: def write_row(self, row):\n457: self.writer.writerow(row)\n458: self.file.flush()\n459: \n460: def close(self):\n461: self.file.close()\n462: \n463: \n464: class VideoSegmentWriter:\n465: def __init__(self, output_dir, w, h, fps, segment_sec):\n466: self.output_dir = Path(output_dir)\n467: self.output_dir.mkdir(parents=True, exist_ok=True)\n468: self.w, self.h, self.fps = w, h, fps\n469: self.segment_sec = segment_sec\n470: self.segment_start = time.monotonic()\n471: self.writer = None\n472: self._open_next()\n473: \n474: def _segment_path(self):\n475: ts = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n476: return str(self.output_dir / f\"live_{ts}.mp4\")\n477: \n478: def _open_next(self):\n479: if self.writer is not None:\n480: self.writer.release()\n481: path = self._segment_path()\n482: self.writer = open_video_writer(path, self.w, self.h, self.fps)\n483: self.segment_start = time.monotonic()\n484: print(f\"Recording segment: {path}\")\n485: \n486: def write(self, frame):\n487: if time.monotonic() - self.segment_start >= self.segment_sec:\n488: self._open_next()\n489: self.writer.write(frame)\n490: \n491: def release(self):\n492: if self.writer is not None:\n493: self.writer.release()\n494: \n495: \n496: def prune_stale_tracks(tracked, now_mono):\n497: stale = [\n498: tid for tid, (_, ts) in tracked.items() if now_mono - ts > TRACKED_PRUNE_SEC\n499: ]\n500: for tid in stale:\n501: del tracked[tid]\n502: \n503: \n504: def overlay_rect(img, x1, y1, x2, y2, color, alpha=0.65):\n505: x1, y1 = max(0, x1), max(0, y1)\n506: x2, y2 = min(img.shape[1], x2), min(img.shape[0], y2)\n507: if x2 <= x1 or y2 <= y1:\n508: return\n509: roi = img[y1:y2, x1:x2]\n510: patch = np.full_like(roi, color, dtype=np.uint8)\n511: cv2.addWeighted(patch, alpha, roi, 1 - alpha, 0, roi)\n512: \n513: \n514: def draw_pill(img, text, x, y, bg, fg=C_TEXT, font_scale=0.45, pad_x=6, pad_y=4):\n515: font = cv2.FONT_HERSHEY_SIMPLEX\n516: (tw, th), baseline = cv2.getTextSize(text, font, font_scale, 1)\n517: x1, y1 = x, y - th - pad_y\n518: x2, y2 = x + tw + pad_x * 2, y + baseline + pad_y\n519: cv2.rectangle(img, (x1, y1), (x2, y2), bg, -1)\n520: cv2.rectangle(img, (x1, y1), (x2, y2), C_BORDER, 1)\n521: cv2.putText(img, text, (x + pad_x, y), font, font_scale, fg, 1, cv2.LINE_AA)\n522: \n523: \n524: def draw_elegant_counting_line(img, line_x, h, pulse_remaining=0):\n525: strength = pulse_remaining / max(LINE_PULSE_FRAMES, 1)\n526: glow_alpha = 0.12 + 0.18 * strength\n527: for offset in (14, 9, 5):\n528: color = tuple(int(c * glow_alpha) for c in C_LINE_GLOW)\n529: cv2.line(img, (line_x - offset, 0), (line_x - offset, h), color, 1, cv2.LINE_AA)\n530: cv2.line(img, (line_x + offset, 0), (line_x + offset, h), color, 1, cv2.LINE_AA)\n531: dash_len, gap = 18, 12\n532: y = 0\n533: while y < h:\n534: y_end = min(y + dash_len, h)\n535: cv2.line(img, (line_x, y), (line_x, y_end), C_LINE_CORE, 2, cv2.LINE_AA)\n536: y += dash_len + gap\n537: cv2.putText(\n538: img,\n539: \"COUNT LINE\",\n540: (line_x - 46, 24),\n541: cv2.FONT_HERSHEY_SIMPLEX,\n542: 0.42,\n543: C_LINE_CORE,\n544: 1,\n545: cv2.LINE_AA,\n546: )\n547: \n548: \n549: def draw_hero_count(img, line_x, h, count, pulse_remaining=0):\n550: text = str(count)\n551: font = cv2.FONT_HERSHEY_SIMPLEX\n552: boost = 0.35 * (pulse_remaining / max(COUNT_PULSE_FRAMES, 1))\n553: font_scale, thickness = 1.6 + boost, 3\n554: (tw, th), _ = cv2.getTextSize(text, font, font_scale, thickness)\n555: pad = 14\n556: tx, ty = line_x - tw // 2, h // 2 + th // 2\n557: overlay_rect(\n558: img, tx - pad, ty - th - pad, tx + tw + pad, ty + pad // 2, C_PANEL, alpha=0.78\n559: )\n560: cv2.rectangle(\n561: img, (tx - pad, ty - th - pad), (tx + tw + pad, ty + pad // 2), C_LINE_CORE, 2\n562: )\n563: cv2.putText(img, text, (tx, ty), font, font_scale, C_GREEN, thickness, cv2.LINE_AA)\n564: \n565: \n566: def draw_hud(\n567: img, w, batch_num, batch_count, total_ayam, elapsed_sec, rate, camera_id, clock\n568: ):\n569: bar_h = 52\n570: overlay_rect(img, 0, 0, w, bar_h, C_PANEL, alpha=0.72)\n571: cv2.line(img, (0, bar_h), (w, bar_h), C_BORDER, 1)\n572: cv2.putText(\n573: img, \"BATCH\", (16, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.45, C_MUTED, 1, cv2.LINE_AA\n574: )\n575: batch_label = str(batch_num) if batch_num else \"—\"\n576: cv2.putText(\n577: img,\n578: batch_label,\n579: (16, 44),\n580: cv2.FONT_HERSHEY_SIMPLEX,\n581: 0.9,\n582: C_ACCENT,\n583: 2,\n584: cv2.LINE_AA,\n585: )\n586: cv2.putText(\n587: img, \"COUNT\", (100, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.45, C_MUTED, 1, cv2.LINE_AA\n588: )\n589: cv2.putText(\n590: img,\n591: str(batch_count),\n592: (100, 44),\n593: cv2.FONT_HERSHEY_SIMPLEX,\n594: 0.9,\n595: C_GREEN,\n596: 2,\n597: cv2.LINE_AA,\n598: )\n599: cv2.putText(\n600: img, \"TOTAL\", (190, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.45, C_MUTED, 1, cv2.LINE_AA\n601: )\n602: cv2.putText(\n603: img,\n604: str(total_ayam),\n605: (190, 44),\n606: cv2.FONT_HERSHEY_SIMPLEX,\n607: 0.7,\n608: C_TEXT,\n609: 1,\n610: cv2.LINE_AA,\n611: )\n612: cv2.putText(\n613: img,\n614: \"UPTIME\",\n615: (280, 20),\n616: cv2.FONT_HERSHEY_SIMPLEX,\n617: 0.45,\n618: C_MUTED,\n619: 1,\n620: cv2.LINE_AA,\n621: )\n622: cv2.putText(\n623: img,\n624: f\"{elapsed_sec / 3600:.1f}h\",\n625: (280, 44),\n626: cv2.FONT_HERSHEY_SIMPLEX,\n627: 0.7,\n628: C_TEXT,\n629: 1,\n630: cv2.LINE_AA,\n631: )\n632: cv2.putText(\n633: img, \"RATE\", (380, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.45, C_MUTED, 1, cv2.LINE_AA\n634: )\n635: cv2.putText(\n636: img,\n637: f\"{rate:.1f}/min\",\n638: (380, 44),\n639: cv2.FONT_HERSHEY_SIMPLEX,\n640: 0.7,\n641: C_ACCENT,\n642: 1,\n643: cv2.LINE_AA,\n644: )\n645: # cv2.putText(img, clock, (w - 180, 36), cv2.FONT_HERSHEY_SIMPLEX, 0.55, C_TEXT, 1, cv2.LINE_AA)\n646: cv2.putText(\n647: img,\n648: f\"CAM {camera_id}\",\n649: (w - 180, 20),\n650: cv2.FONT_HERSHEY_SIMPLEX,\n651: 0.45,\n652: C_MUTED,\n653: 1,\n654: cv2.LINE_AA,\n655: )\n656: \n657: \n658: def draw_footer(img, w, h, frame_idx, live_tag):\n659: bar_h = 28\n660: overlay_rect(img, 0, h - bar_h, w, h, C_PANEL, alpha=0.55)\n661: cv2.putText(\n662: img,\n663: f\"{live_tag} | Frame {frame_idx}\",\n664: (12, h - 9),\n665: cv2.FONT_HERSHEY_SIMPLEX,\n666: 0.45,\n667: C_MUTED,\n668: 1,\n669: cv2.LINE_AA,\n670: )\n671: \n672: \n673: def draw_skeleton_bold(img, kpts):\n674: for (a, b), color in zip(SKELETON, SK_COLORS):\n675: if a < len(kpts) and b < len(kpts):\n676: xa, ya = int(kpts[a][0]), int(kpts[a][1])\n677: xb, yb = int(kpts[b][0]), int(kpts[b][1])\n678: if xa > 0 and ya > 0 and xb > 0 and yb > 0:\n679: cv2.line(img, (xa, ya), (xb, yb), color, 3, cv2.LINE_AA)\n680: for kp in kpts:\n681: x, y = int(kp[0]), int(kp[1])\n682: if x > 0 and y > 0:\n683: cv2.circle(img, (x, y), 6, (255, 255, 255), -1, cv2.LINE_AA)\n684: cv2.circle(img, (x, y), 6, (40, 40, 40), 2, cv2.LINE_AA)\n685: \n686: \n687: def draw_popups(img, popups, frame_idx):\n688: alive = []\n689: for pop in popups:\n690: age = frame_idx - pop[\"born\"]\n691: if age > POPUP_LIFETIME:\n692: continue\n693: alive.append(pop)\n694: fade = 1.0 - age / POPUP_LIFETIME\n695: y = pop[\"y\"] - int(age * 1.8)\n696: color = (int(C_GREEN[0] * fade), int(C_GREEN[1] * fade), int(C_GREEN[2] * fade))\n697: cv2.putText(\n698: img,\n699: pop[\"text\"],\n700: (pop[\"x\"], y),\n701: cv2.FONT_HERSHEY_SIMPLEX,\n702: 0.7,\n703: color,\n704: 2,\n705: cv2.LINE_AA,\n706: )\n707: return alive\n708: \n709: \n710: def draw_batch_banner(img, w, batch_num, pulse_remaining):\n711: if pulse_remaining <= 0:\n712: return\n713: text = f\"NEW BATCH {batch_num}\"\n714: font = cv2.FONT_HERSHEY_SIMPLEX\n715: (tw, th), _ = cv2.getTextSize(text, font, 0.8, 2)\n716: x1, y1 = w // 2 - tw // 2 - 16, 62\n717: x2, y2 = w // 2 + tw // 2 + 16, 62 + th + 20\n718: overlay_rect(img, x1, y1, x2, y2, C_PANEL, alpha=0.7)\n719: cv2.rectangle(img, (x1, y1), (x2, y2), C_ACCENT, 2)\n720: cv2.putText(\n721: img, text, (w // 2 - tw // 2, 62 + th + 4), font, 0.8, C_ACCENT, 2, cv2.LINE_AA\n722: )\n723: \n724: \n725: def connect_stream(source, warmup=WARMUP_FRAMES):\n726: attempts = 0\n727: while not shutdown_requested:\n728: cap = open_capture(source)\n729: if not cap.isOpened():\n730: attempts += 1\n731: if MAX_RECONNECT_ATTEMPTS and attempts >= MAX_RECONNECT_ATTEMPTS:\n732: raise RuntimeError(\n733: f\"Cannot open source after {attempts} attempts: {source}\"\n734: )\n735: print(f\"Cannot open source, retry in {RECONNECT_DELAY_SEC}s...\")\n736: time.sleep(RECONNECT_DELAY_SEC)\n737: continue\n738: if warmup > 0 and source.lower().startswith((\"rtsp://\", \"http://\")):\n739: warmup_stream(cap, warmup)\n740: w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n741: h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n742: fps = cap.get(cv2.CAP_PROP_FPS)\n743: if not fps or fps <= 1:\n744: fps = OUTPUT_FPS\n745: return cap, w, h, fps\n746: return None, 0, 0, OUTPUT_FPS\n747: \n748: \n749: # =============================================================================\n750: # Main loop\n751: # =============================================================================\n752: \n753: \n754: def run():\n755: global shutdown_requested\n756: \n757: store = BatchStore(\n758: db_path=DB_PATH,\n759: state_file=STATE_FILE,\n760: camera_name=CAMERA_NAME,\n761: object_label=OBJECT_LABEL,\n762: cutoff_time=DAILY_CUTOFF_TIME,\n763: batch_timeout=BATCH_TIMEOUT_SECONDS,\n764: ignore_batch_label_timeout=IGNORE_BATCH_LABEL_TIMEOUT,\n765: min_object_per_batch=MIN_OBJECT_PER_BATCH,\n766: min_duration_per_batch=MIN_DURATION_PER_BATCH,\n767: logger=lambda msg: print(f\"[{now_str()}] {msg}\"),\n768: )\n769: store.start_cutoff_watcher()\n770: \n771: cross_logger = None\n772: if EXPORT_CSV:\n773: cross_logger = CsvLogger(\n774: CROSS_CSV, [\"batch\", \"frame\", \"timestamp\", \"chicken_id\"]\n775: )\n776: \n777: # Load RKNN model\n778: model = RKNNYOLO(\n779: model_path=MODEL_PATH,\n780: core_mask=CORE_MASK,\n781: imgsz=IMGSZ,\n782: conf=CONF,\n783: num_classes=NUM_CLASSES,\n784: score_sigmoid=SCORE_SIGMOID,\n785: )\n786: \n787: # Class IDs — order comes from RKNN model output (class index)\n788: # class index 0 → CLASS_AYAM, index 1 → CLASS_TALENAN (or env-specified)\n789: # Use class names in env order: first CLASS_AYAM → id 0, then CLASS_TALENAN → id 1\n790: CLASS_IDS = {\n791: os.getenv(\"CLASS_AYAM\", \"ayam\"): 0,\n792: os.getenv(\"CLASS_TALENAN\", \"talenan\"): 1,\n793: }\n794: ayam_cls = CLASS_IDS[CLASS_AYAM]\n795: talenan_cls = CLASS_IDS[CLASS_TALENAN]\n796: \n797: ayam_tracker = SimpleTracker(max_age=60)\n798: talenan_tracker = SimpleTracker(max_age=60)\n799: \n800: ayam_line_crossed = set()\n801: talenan_line_crossed = set()\n802: \n803: ayam_cross_flash = {}\n804: talenan_cross_flash = {}\n805: line_pulse = count_pulse = batch_pulse = 0\n806: popups = []\n807: \n808: session_start = time.time()\n809: frame_idx = 0\n810: video_writer = None\n811: \n812: cap, w, h, fps = connect_stream(SOURCE)\n813: if cap is None:\n814: store.shutdown()\n815: model.release()\n816: return\n817: \n818: line_x = resolve_line_x(w)\n819: print(\n820: f\"RKNN counter | {w}x{h} @ {fps}fps | line x={line_x} | cross={CROSS_DIRECTION}\"\n821: )\n822: print(f\"Model: {MODEL_PATH} | imgsz={IMGSZ} | core_mask={CORE_MASK}\")\n823: print(f\"DB: {DB_PATH}\")\n824: print(f\"State: {STATE_FILE}\")\n825: \n826: if RECORD_VIDEO:\n827: video_writer = VideoSegmentWriter(OUTPUT_DIR, w, h, fps, VIDEO_SEGMENT_SEC)\n828: \n829: reconnect_count = 0\n830: \n831: while not shutdown_requested:\n832: ret, frame = cap.read()\n833: if not ret:\n834: if not IS_LIVE:\n835: break\n836: reconnect_count += 1\n837: print(\n838: f\"Stream dropped (attempt {reconnect_count}), reconnecting in {RECONNECT_DELAY_SEC}s...\"\n839: )\n840: cap.release()\n841: time.sleep(RECONNECT_DELAY_SEC)\n842: cap, w, h, fps = connect_stream(SOURCE)\n843: if cap is None:\n844: break\n845: line_x = resolve_line_x(w)\n846: continue\n847: \n848: now = time.time()\n849: elapsed = now - session_start\n850: mono = time.monotonic()\n851: ayam_crossed_frame = batch_closed_frame = batch_started_frame = False\n852: \n853: # RKNN inference\n854: detections = model(frame)\n855: \n856: if detections:\n857: ayam_dets = [] # list of (cx, xywh_box)\n858: talenan_dets = []\n859: ayam_kpts_map = {} # det_idx → keypoints\n860: talenan_kpts_map = {}\n861: \n862: for di, det in enumerate(detections):\n863: bbox = det[\"bbox\"]\n864: cls_id = det[\"cls\"]\n865: cx = (bbox[0] + bbox[2]) / 2.0\n866: x1, y1, x2, y2 = bbox\n867: wb, hb = x2 - x1, y2 - y1\n868: box_cxcywh = np.array([cx, (y1 + y2) / 2, wb, hb], dtype=np.float32)\n869: \n870: if cls_id == talenan_cls:\n871: talenan_dets.append((cx, box_cxcywh))\n872: if det[\"keypoints\"] is not None:\n873: talenan_kpts_map[len(talenan_dets) - 1] = det[\"keypoints\"]\n874: elif cls_id == ayam_cls:\n875: ayam_dets.append((cx, box_cxcywh))\n876: if det[\"keypoints\"] is not None:\n877: ayam_kpts_map[len(ayam_dets) - 1] = det[\"keypoints\"]\n878: \n879: # Track ayam — returns (track_id → cx, detection_idx → track_id)\n880: ayam_cx_map, ayam_det_to_track = ayam_tracker.update(ayam_dets)\n881: \n882: # Track talenan\n883: talenan_cx_map, talenan_det_to_track = talenan_tracker.update(talenan_dets)\n884: \n885: # Process talenan crossings\n886: for di, (cx, box) in enumerate(talenan_dets):\n887: tid = talenan_det_to_track.get(di)\n888: if tid is None:\n889: continue\n890: if tid in talenan_tracker.tracks:\n891: if tid in talenan_tracked:\n892: prev_cx = talenan_tracked[tid][0]\n893: if (\n894: crossed_line(prev_cx, cx, line_x)\n895: and tid not in talenan_line_crossed\n896: ):\n897: talenan_line_crossed.add(tid)\n898: if store.record_talenan_crossing(tid):\n899: batch_closed_frame = True\n900: talenan_cross_flash[tid] = CROSS_FLASH_FRAMES\n901: popups.append(\n902: {\n903: \"x\": int(cx) - 20,\n904: \"y\": int(box[1]),\n905: \"born\": frame_idx,\n906: \"text\": \"BATCH CLOSED\",\n907: }\n908: )\n909: talenan_tracked[tid] = (cx, mono)\n910: \n911: # Process ayam crossings\n912: for di, (cx, box) in enumerate(ayam_dets):\n913: tid = ayam_det_to_track.get(di)\n914: if tid is None:\n915: continue\n916: if tid in ayam_tracker.tracks:\n917: if tid in ayam_tracked:\n918: prev_cx = ayam_tracked[tid][0]\n919: if (\n920: crossed_line(prev_cx, cx, line_x)\n921: and tid not in ayam_line_crossed\n922: ):\n923: ayam_line_crossed.add(tid)\n924: _, started_new = store.record_ayam_crossing(tid)\n925: if cross_logger:\n926: cross_logger.write_row(\n927: [\n928: store.current_batch_number,\n929: frame_idx,\n930: datetime.now().isoformat(),\n931: tid,\n932: ]\n933: )\n934: ayam_crossed_frame = True\n935: if started_new:\n936: batch_started_frame = True\n937: ayam_cross_flash[tid] = CROSS_FLASH_FRAMES\n938: popups.append(\n939: {\n940: \"x\": int(cx) - 12,\n941: \"y\": int(box[1]),\n942: \"born\": frame_idx,\n943: \"text\": \"+1\",\n944: }\n945: )\n946: ayam_tracked[tid] = (cx, mono)\n947: \n948: # Draw talenan\n949: for di, (cx, box) in enumerate(talenan_dets):\n950: tid = talenan_det_to_track.get(di)\n951: if tid is None:\n952: continue\n953: x1 = int(box[0] - box[2] / 2)\n954: y1 = int(box[1] - box[3] / 2)\n955: x2 = int(box[0] + box[2] / 2)\n956: y2 = int(box[1] + box[3] / 2)\n957: flash = talenan_cross_flash.get(tid, 0)\n958: color = C_GREEN if flash > 0 else C_TALENAN_BOX\n959: cv2.rectangle(frame, (x1, y1), (x2, y2), color, 3 if flash > 0 else 2)\n960: draw_pill(frame, f\"TALENAN {tid}\", x1, y1 - 4, color)\n961: \n962: # Draw ayam\n963: for di, (cx, box) in enumerate(ayam_dets):\n964: tid = ayam_det_to_track.get(di)\n965: if tid is None:\n966: continue\n967: x1 = int(box[0] - box[2] / 2)\n968: y1 = int(box[1] - box[3] / 2)\n969: x2 = int(box[0] + box[2] / 2)\n970: y2 = int(box[1] + box[3] / 2)\n971: flash = ayam_cross_flash.get(tid, 0)\n972: color = C_GREEN if flash > 0 else C_AYAM_BOX\n973: cv2.rectangle(frame, (x1, y1), (x2, y2), color, 3 if flash > 0 else 2)\n974: draw_pill(frame, f\"ID {tid}\", x1, y1 - 4, color)\n975: kpts = ayam_kpts_map.get(di)\n976: if kpts is not None:\n977: draw_skeleton_bold(frame, kpts)\n978: \n979: if ayam_crossed_frame:\n980: line_pulse = LINE_PULSE_FRAMES\n981: count_pulse = COUNT_PULSE_FRAMES\n982: if batch_closed_frame:\n983: line_pulse = LINE_PULSE_FRAMES\n984: if batch_started_frame:\n985: batch_pulse = BATCH_PULSE_FRAMES\n986: \n987: batch_num = store.current_batch_number or 0\n988: batch_count = store.current_batch_count\n989: display_total = store.display_total()\n990: rate = (display_total / elapsed * 60) if elapsed > 0 else 0.0\n991: \n992: draw_elegant_counting_line(frame, line_x, h, line_pulse)\n993: draw_hero_count(frame, line_x, h, batch_count, count_pulse)\n994: draw_hud(\n995: frame,\n996: w,\n997: batch_num,\n998: batch_count,\n999: display_total,\n1000: elapsed,\n1001: rate,\n1002: CAMERA_NAME,\n1003: now_str(),\n1004: )\n1005: draw_batch_banner(frame, w, batch_num, batch_pulse)\n1006: draw_footer(frame, w, h, frame_idx, \"LIVE-RKNN\" if IS_LIVE else \"FILE-RKNN\")\n1007: popups = draw_popups(frame, popups, frame_idx)\n1008: \n1009: for flash_store in (ayam_cross_flash, talenan_cross_flash):\n1010: for tid in list(flash_store):\n1011: flash_store[tid] -= 1\n1012: if flash_store[tid] <= 0:\n1013: del flash_store[tid]\n1014: line_pulse = max(0, line_pulse - 1)\n1015: count_pulse = max(0, count_pulse - 1)\n1016: batch_pulse = max(0, batch_pulse - 1)\n1017: \n1018: if video_writer is not None:\n1019: video_writer.write(frame)\n1020: \n1021: if LIVE_STREAM_ENABLED and frame_idx % LIVE_STREAM_EVERY_N == 0:\n1022: try:\n1023: Path(LIVE_STREAM_FRAME_PATH).parent.mkdir(parents=True, exist_ok=True)\n1024: _, jpeg = cv2.imencode(\n1025: \".jpg\", frame, [cv2.IMWRITE_JPEG_QUALITY, LIVE_STREAM_QUALITY]\n1026: )\n1027: with open(LIVE_STREAM_FRAME_PATH, \"wb\") as f:\n1028: f.write(jpeg.tobytes())\n1029: except Exception:\n1030: pass\n1031: \n1032: frame_idx += 1\n1033: if frame_idx % FLUSH_EVERY_N_FRAMES == 0:\n1034: print(\n1035: f\"[{now_str()}] Frame {frame_idx} | Batch {batch_num}: {batch_count} \"\n1036: f\"| Total: {display_total} | Uptime {elapsed / 3600:.2f}h\"\n1037: )\n1038: prune_stale_tracks(ayam_tracked, mono)\n1039: prune_stale_tracks(talenan_tracked, mono)\n1040: \n1041: cap.release()\n1042: if video_writer is not None:\n1043: video_writer.release()\n1044: if cross_logger:\n1045: cross_logger.close()\n1046: model.release()\n1047: store.shutdown()\n1048: \n1049: print(\"\\n=== Batch Summary (SQLite) ===\")\n1050: print(f\"Database: {DB_PATH}\")\n1051: \n1052: \n1053: # Tracked state dicts: track_id → (cx, monotonic_time)\n1054: ayam_tracked = {}\n1055: talenan_tracked = {}\n1056: \n1057: \n1058: if __name__ == \"__main__\":\n1059: run()\n\n(End of file - total 1059 lines)\n</content>",
|
||
"metadata": {
|
||
"preview": "\"\"\"\nEdge production live counter — RTSP + YOLO RKNN + line crossing.\nRuns on RK3588 hardware with RKNN model (320×320 input).\nReplaces the Jetson/TensorRT variant.\n\"\"\"\n\nimport numpy as np\nimport cv2\nimport csv\nimport os\nimport signal\nimport time\nfrom datetime import datetime\nfrom pathlib import Path\n\nfrom dotenv import load_dotenv\n\nload_dotenv()\n\nfrom rknnlite.api import RKNNLite",
|
||
"truncated": false,
|
||
"loaded": [],
|
||
"display": {
|
||
"type": "file",
|
||
"path": "/opt/bytetrack-counter/counter_live_rknn.py",
|
||
"text": "\"\"\"\nEdge production live counter — RTSP + YOLO RKNN + line crossing.\nRuns on RK3588 hardware with RKNN model (320×320 input).\nReplaces the Jetson/TensorRT variant.\n\"\"\"\n\nimport numpy as np\nimport cv2\nimport csv\nimport os\nimport signal\nimport time\nfrom datetime import datetime\nfrom pathlib import Path\n\nfrom dotenv import load_dotenv\n\nload_dotenv()\n\nfrom rknnlite.api import RKNNLite\nfrom batch_store import BatchStore\n\n# --- config (override via env / .env) ---\nOUTPUT_DIR = os.getenv(\"OUTPUT_DIR\", \"/opt/jetson-counter\")\nDB_PATH = os.getenv(\"DB_PATH\", f\"{OUTPUT_DIR}/jetson_counter.db\")\nSTATE_FILE = os.getenv(\"STATE_FILE\", f\"{OUTPUT_DIR}/current_batch.json\")\nSOURCE = os.getenv(\"SOURCE\", \"rtsp://user:pass@192.168.0.100:554/stream1\")\nMODEL_PATH = os.getenv(\"MODEL_PATH\", \"/opt/jetson-counter/yolo11n.rknn\")\nCAMERA_NAME = os.getenv(\"CAMERA_NAME\", \"CC1\")\nOBJECT_LABEL = os.getenv(\"OBJECT_LABEL\", \"ayam-potong\")\nCLASS_AYAM = os.getenv(\"CLASS_AYAM\", \"ayam\")\nCLASS_TALENAN = os.getenv(\"CLASS_TALENAN\", \"talenan\")\n\nLINE_X = int(os.getenv(\"LINE_X\")) if os.getenv(\"LINE_X\") else None\nLINE_X_FRAC = float(os.getenv(\"LINE_X_FRAC\", \"0.5\"))\nCROSS_DIRECTION = os.getenv(\"CROSS_DIRECTION\", \"rtl\").lower()\n\nIMGSZ = int(os.getenv(\"IMGSZ\", \"320\"))\nHALF = os.getenv(\"HALF\", \"false\").lower() == \"true\"\nCONF = float(os.getenv(\"CONF\", \"0.3\"))\nDEVICE = int(os.getenv(\"DEVICE\", \"0\"))\n\n# RKNN-specific — core mask for NPU\n# 1 = core0, 2 = core1, 3 = core0+core1 (dual), 7 = all three\nCORE_MASK = int(os.getenv(\"CORE_MASK\", \"1\"))\n\n# YOLO decoder config\nNUM_CLASSES = int(os.getenv(\"NUM_CLASSES\", \"2\"))\nSCORE_SIGMOID = os.getenv(\"SCORE_SIGMOID\", \"false\").lower() == \"true\"\n\nDAILY_CUTOFF_TIME = os.getenv(\"DAILY_CUTOFF_TIME\", \"20:00\")\nBATCH_TIMEOUT_SECONDS = float(os.getenv(\"BATCH_TIMEOUT_SECONDS\", \"300\"))\nIGNORE_BATCH_LABEL_TIMEOUT = float(\n os.getenv(\"IGNORE_BATCH_LABEL_TIMEOUT_SECONDS\", \"30\")\n)\nMIN_OBJECT_PER_BATCH = int(os.getenv(\"MIN_OBJECT_PER_BATCH\", \"60\"))\nMIN_DURATION_PER_BATCH = int(os.getenv(\"MIN_DURATION_PER_BATCH\", \"60\"))\n\nEXPORT_CSV = os.getenv(\"EXPORT_CSV\", \"true\").lower() == \"true\"\nCROSS_CSV = os.getenv(\"CROSS_CSV\", f\"{OUTPUT_DIR}/batch_crossings.csv\")\n\nWARMUP_FRAMES = int(os.getenv(\"WARMUP_FRAMES\", \"30\"))\nRECONNECT_DELAY_SEC = int(os.getenv(\"RECONNECT_DELAY_SEC\", \"3\"))\nMAX_RECONNECT_ATTEMPTS = int(os.getenv(\"MAX_RECONNECT_ATTEMPTS\", \"0\"))\nFLUSH_EVERY_N_FRAMES = int(os.getenv(\"FLUSH_EVERY_N_FRAMES\", \"100\"))\nTRACKED_PRUNE_SEC = int(os.getenv(\"TRACKED_PRUNE_SEC\", \"300\"))\nRECORD_VIDEO = os.getenv(\"RECORD_VIDEO\", \"false\").lower() == \"true\"\nVIDEO_SEGMENT_SEC = int(os.getenv(\"VIDEO_SEGMENT_SEC\", \"3600\"))\nOUTPUT_FPS = int(os.getenv(\"OUTPUT_FPS\", \"15\"))\n\nLIVE_STREAM_ENABLED = os.getenv(\"LIVE_STREAM_ENABLED\", \"false\").lower() == \"true\"\nLIVE_STREAM_FRAME_PATH = os.getenv(\n \"LIVE_STREAM_FRAME_PATH\", \"/dev/shm/jetson-counter/live_frame.jpg\"\n)\nLIVE_STREAM_QUALITY = int(os.getenv(\"LIVE_STREAM_QUALITY\", \"75\"))\nLIVE_STREAM_EVERY_N = int(os.getenv(\"LIVE_STREAM_EVERY_N\", \"2\"))\n\nRTSP_FFMPEG_OPTIONS = os.getenv(\n \"OPENCV_FFMPEG_CAPTURE_OPTIONS\",\n \"rtsp_transport;tcp|fflags;nobuffer|flags;low_delay\",\n)\n\nIS_LIVE = SOURCE.lower().startswith((\"rtsp://\", \"http://\"))\n\nCROSS_FLASH_FRAMES = 12\nPOPUP_LIFETIME = 20\nLINE_PULSE_FRAMES = 12\nCOUNT_PULSE_FRAMES = 15\nBATCH_PULSE_FRAMES = 20\n\nSKELETON = [(0, 1), (4, 3), (1, 2), (3, 2), (2, 6), (2, 5), (2, 7), (7, 8)]\nSK_COLORS = [\n (0, 255, 255),\n (0, 255, 255),\n (255, 0, 255),\n (255, 0, 255),\n (0, 255, 0),\n (255, 255, 0),\n (0, 0, 255),\n (200, 200, 0),\n]\n\nC_PANEL = (28, 24, 18)\nC_BORDER = (90, 85, 75)\nC_ACCENT = (255, 200, 60)\nC_GREEN = (80, 220, 100)\nC_TEXT = (235, 235, 235)\nC_MUTED = (150, 150, 150)\nC_AYAM_BOX = (0, 165, 255)\nC_TALENAN_BOX = (220, 120, 60)\nC_LINE_CORE = (180, 220, 255)\nC_LINE_GLOW = (100, 160, 220)\n\nshutdown_requested = False\n\n\ndef request_shutdown(signum, frame):\n global shutdown_requested\n shutdown_requested = True\n print(\"\\nShutdown requested — finishing current frame...\")\n\n\nsignal.signal(signal.SIGINT, request_shutdown)\nsignal.signal(signal.SIGTERM, request_shutdown)\n\n\n# =============================================================================\n# YOLO output decoder (NMS only — boxes are pre-decoded by the model)\n# =============================================================================\n\n\ndef _nms(boxes, scores, iou_thr=0.45):\n order = np.argsort(scores)[::-1]\n keep = []\n while len(order) > 0:\n idx = order[0]\n keep.append(idx)\n if len(order) == 1:\n break\n xx1 = np.maximum(boxes[idx, 0], boxes[order[1:], 0])\n yy1 = np.maximum(boxes[idx, 1], boxes[order[1:], 1])\n xx2 = np.minimum(boxes[idx, 2], boxes[order[1:], 2])\n yy2 = np.minimum(boxes[idx, 3], boxes[order[1:], 3])\n w = np.maximum(0.0, xx2 - xx1)\n h = np.maximum(0.0, yy2 - yy1)\n inter = w * h\n area_i = (boxes[idx, 2] - boxes[idx, 0]) * (boxes[idx, 3] - boxes[idx, 1])\n area_o = (boxes[order[1:], 2] - boxes[order[1:], 0]) * (\n boxes[order[1:], 3] - boxes[order[1:], 1]\n )\n iou = inter / (area_i + area_o - inter + 1e-16)\n order = order[1:][iou < iou_thr]\n return np.array(keep)\n\n\ndef _compute_iou(box1, boxes2):\n \"\"\"IoU of one box (cxcywh) against a set (2D) or single box (1D).\"\"\"\n if boxes2.ndim == 1:\n boxes2 = boxes2.reshape(1, -1)\n cx, cy, w, h = box1\n x1, y1 = cx - w / 2, cy - h / 2\n x2, y2 = cx + w / 2, cy + h / 2\n area1 = w * h\n\n cxs, cys, ws, hs = boxes2[:, 0], boxes2[:, 1], boxes2[:, 2], boxes2[:, 3]\n x1s, y1s = cxs - ws / 2, cys - hs / 2\n x2s, y2s = cxs + ws / 2, cys + hs / 2\n areas2 = ws * hs\n\n xx1 = np.maximum(x1, x1s)\n yy1 = np.maximum(y1, y1s)\n xx2 = np.minimum(x2, x2s)\n yy2 = np.minimum(y2, y2s)\n inter = np.maximum(0.0, xx2 - xx1) * np.maximum(0.0, yy2 - yy1)\n return inter / (area1 + areas2 - inter + 1e-16)\n\n\n# =============================================================================\n# Simple IoU tracker (replaces bytetrack — same persist behaviour)\n# =============================================================================\n\n\nclass SimpleTracker:\n def __init__(self, max_age=30, min_hits=1, iou_threshold=0.3):\n self.max_age = max_age\n self.min_hits = min_hits\n self.iou_threshold = iou_threshold\n self.tracks = {} # track_id -> {box, cx, age, hits, time_since_update}\n self.next_id = 1\n\n def update(self, detections):\n \"\"\"detections: list of (cx, box_cxcywh). Returns (track_map, det_to_track).\"\"\"\n now = time.monotonic()\n\n for tid in self.tracks:\n self.tracks[tid][\"time_since_update\"] += 1\n\n matched_det = set()\n matched_track = set()\n assignments = [] # (track_id, det_idx)\n det_to_track = {} # det_idx → track_id\n\n if detections and self.tracks:\n track_ids = list(self.tracks.keys())\n track_boxes = np.stack([self.tracks[t][\"box\"] for t in track_ids], axis=0)\n\n for di, det in enumerate(detections):\n _, det_box = det\n ious = np.array(\n [\n _compute_iou(det_box, track_boxes[t : t + 1])\n for t in range(len(track_ids))\n ]\n )\n best_j = int(np.argmax(ious))\n if (\n ious[best_j] >= self.iou_threshold\n and track_ids[best_j] not in matched_track\n ):\n assignments.append((track_ids[best_j], di))\n matched_track.add(track_ids[best_j])\n matched_det.add(di)\n\n for tid, di in assignments:\n cx, box = detections[di]\n self.tracks[tid][\"cx\"] = cx\n self.tracks[tid][\"box\"] = box\n self.tracks[tid][\"hits\"] += 1\n self.tracks[tid][\"time_since_update\"] = 0\n self.tracks[tid][\"last_update\"] = now\n det_to_track[di] = tid\n\n for di, det in enumerate(detections):\n if di not in matched_det:\n cx, box = det\n new_id = self.next_id\n self.next_id += 1\n self.tracks[new_id] = {\n \"cx\": cx,\n \"box\": box,\n \"hits\": 1,\n \"time_since_update\": 0,\n \"last_update\": now,\n }\n det_to_track[di] = new_id\n\n stale = [\n tid\n for tid, t in self.tracks.items()\n if t[\"time_since_update\"] > self.max_age\n ]\n for tid in stale:\n del self.tracks[tid]\n\n track_map = {\n tid: self.tracks[tid][\"cx\"]\n for tid in self.tracks\n if self.tracks[tid][\"hits\"] >= self.min_hits\n }\n return track_map, det_to_track\n\n\n# =============================================================================\n# RKNN YOLO wrapper (detect output format: (1, 4+num_classes, N))\n# =============================================================================\n\n\nclass RKNNYOLO:\n def __init__(\n self,\n model_path,\n core_mask=1,\n imgsz=320,\n conf=0.3,\n iou=0.45,\n num_classes=2,\n num_keypoints=0,\n score_sigmoid=False,\n ):\n self.imgsz = imgsz\n self.conf = conf\n self.iou = iou\n self.num_classes = num_classes\n self.num_keypoints = num_keypoints\n self.score_sigmoid = score_sigmoid\n\n self.rknn = RKNNLite(verbose=False)\n ret = self.rknn.load_rknn(model_path)\n if ret != 0:\n raise RuntimeError(f\"Failed to load RKNN model: {model_path}\")\n ret = self.rknn.init_runtime(core_mask=core_mask)\n if ret != 0:\n raise RuntimeError(f\"Failed to init RKNN runtime (core_mask={core_mask})\")\n\n try:\n from rknnlite.api import RKNNLite as _RK\n\n sdk_ver = self.rknn.get_sdk_version()\n print(f\"RKNN SDK version: {sdk_ver}\")\n except Exception:\n pass\n\n print(f\"RKNN model loaded: {model_path} imgsz={imgsz} core_mask={core_mask}\")\n\n def _preprocess(self, frame):\n \"\"\"Letterbox-resize to imgsz×imgsz, maintain aspect ratio, BGR→RGB, normalize.\"\"\"\n h0, w0 = frame.shape[:2]\n scale = min(self.imgsz / h0, self.imgsz / w0)\n nh, nw = int(h0 * scale), int(w0 * scale)\n resized = cv2.resize(frame, (nw, nh), interpolation=cv2.INTER_LINEAR)\n\n letterbox = np.full((self.imgsz, self.imgsz, 3), 114, dtype=np.uint8)\n dy = (self.imgsz - nh) // 2\n dx = (self.imgsz - nw) // 2\n letterbox[dy : dy + nh, dx : dx + nw] = resized\n\n rgb = cv2.cvtColor(letterbox, cv2.COLOR_BGR2RGB)\n gains = np.array([scale, scale, dy, dx], dtype=np.float32)\n return rgb, gains\n\n def __call__(self, frame):\n \"\"\"Run inference on BGR frame. Returns list of detection dicts.\"\"\"\n h0, w0 = frame.shape[:2]\n rgb, gains = self._preprocess(frame)\n scale, _, pad_y, pad_x = gains\n\n inp = np.expand_dims(rgb, axis=0)\n inp = np.ascontiguousarray(inp.astype(np.uint8))\n\n outputs = self.rknn.inference(inputs=[inp])\n\n if len(outputs) == 0:\n return []\n\n out = outputs[0] # (1, 4+num_classes, N) or (1, N, 4+num_classes)\n out = np.squeeze(out, axis=0) # (C, N) or (N, C)\n\n if out.shape[0] == self.num_classes + 4:\n out = out.T # (C, N) → (N, C)\n\n boxes_cxcywh = out[:, :4].copy() # cx, cy, w, h at model resolution\n cls_raw = out[:, 4:].copy()\n\n if self.score_sigmoid:\n cls_scores = 1.0 / (1.0 + np.exp(-np.clip(cls_raw, -10, 10)))\n else:\n cls_scores = cls_raw\n\n boxes_xyxy = np.stack(\n [\n boxes_cxcywh[:, 0] - boxes_cxcywh[:, 2] / 2,\n boxes_cxcywh[:, 1] - boxes_cxcywh[:, 3] / 2,\n boxes_cxcywh[:, 0] + boxes_cxcywh[:, 2] / 2,\n boxes_cxcywh[:, 1] + boxes_cxcywh[:, 3] / 2,\n ],\n axis=1,\n )\n\n max_scores = cls_scores.max(axis=1)\n class_ids = cls_scores.argmax(axis=1)\n mask = max_scores > self.conf\n\n if mask.sum() == 0:\n return []\n\n bboxes = boxes_xyxy[mask].astype(np.float32)\n scores = max_scores[mask].astype(np.float32)\n clses = class_ids[mask]\n\n bboxes[:, 0] = (bboxes[:, 0] - pad_x) / scale\n bboxes[:, 1] = (bboxes[:, 1] - pad_y) / scale\n bboxes[:, 2] = (bboxes[:, 2] - pad_x) / scale\n bboxes[:, 3] = (bboxes[:, 3] - pad_y) / scale\n\n bboxes[:, 0] = np.clip(bboxes[:, 0], 0, w0)\n bboxes[:, 1] = np.clip(bboxes[:, 1], 0, h0)\n bboxes[:, 2] = np.clip(bboxes[:, 2], 0, w0)\n bboxes[:, 3] = np.clip(bboxes[:, 3], 0, h0)\n\n detections = []\n for cls_id in range(self.num_classes):\n idx = np.where(clses == cls_id)[0]\n if len(idx) == 0:\n continue\n keep = _nms(bboxes[idx], scores[idx], iou_thr=self.iou)\n for k in keep:\n j = idx[k]\n detections.append(\n {\n \"bbox\": bboxes[j].tolist(),\n \"score\": float(scores[j]),\n \"cls\": int(clses[j]),\n \"keypoints\": None,\n }\n )\n return detections\n\n def release(self):\n self.rknn.release()\n\n\n# =============================================================================\n# Drawing helpers (unchanged from original)\n# =============================================================================\n\n\ndef resolve_line_x(frame_width):\n if LINE_X is not None:\n return LINE_X\n if LINE_X_FRAC != 0.5:\n return int(frame_width * LINE_X_FRAC)\n return frame_width // 2\n\n\ndef crossed_line(prev_cx, cx, line_x, direction=CROSS_DIRECTION):\n if direction == \"ltr\":\n return prev_cx < line_x <= cx\n if direction == \"both\":\n return (prev_cx > line_x >= cx) or (prev_cx < line_x <= cx)\n return prev_cx > line_x >= cx\n\n\ndef now_str():\n return datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n\n\ndef open_capture(source):\n if source.lower().startswith((\"rtsp://\", \"http://\")):\n os.environ[\"OPENCV_FFMPEG_CAPTURE_OPTIONS\"] = RTSP_FFMPEG_OPTIONS\n cap = cv2.VideoCapture(source, cv2.CAP_FFMPEG)\n cap.set(cv2.CAP_PROP_BUFFERSIZE, 1)\n return cap\n\n\ndef warmup_stream(cap, n=WARMUP_FRAMES):\n print(\"Warming up stream...\")\n for _ in range(n):\n cap.read()\n print(\"Stream ready!\")\n\n\ndef open_video_writer(path, w, h, fps):\n return cv2.VideoWriter(path, cv2.VideoWriter_fourcc(*\"avc1\"), fps, (w, h))\n\n\nclass CsvLogger:\n def __init__(self, path, header):\n Path(path).parent.mkdir(parents=True, exist_ok=True)\n new_file = not Path(path).exists() or Path(path).stat().st_size == 0\n self.file = open(path, \"a\", newline=\"\", buffering=1)\n self.writer = csv.writer(self.file)\n if new_file:\n self.writer.writerow(header)\n self.file.flush()\n\n def write_row(self, row):\n self.writer.writerow(row)\n self.file.flush()\n\n def close(self):\n self.file.close()\n\n\nclass VideoSegmentWriter:\n def __init__(self, output_dir, w, h, fps, segment_sec):\n self.output_dir = Path(output_dir)\n self.output_dir.mkdir(parents=True, exist_ok=True)\n self.w, self.h, self.fps = w, h, fps\n self.segment_sec = segment_sec\n self.segment_start = time.monotonic()\n self.writer = None\n self._open_next()\n\n def _segment_path(self):\n ts = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n return str(self.output_dir / f\"live_{ts}.mp4\")\n\n def _open_next(self):\n if self.writer is not None:\n self.writer.release()\n path = self._segment_path()\n self.writer = open_video_writer(path, self.w, self.h, self.fps)\n self.segment_start = time.monotonic()\n print(f\"Recording segment: {path}\")\n\n def write(self, frame):\n if time.monotonic() - self.segment_start >= self.segment_sec:\n self._open_next()\n self.writer.write(frame)\n\n def release(self):\n if self.writer is not None:\n self.writer.release()\n\n\ndef prune_stale_tracks(tracked, now_mono):\n stale = [\n tid for tid, (_, ts) in tracked.items() if now_mono - ts > TRACKED_PRUNE_SEC\n ]\n for tid in stale:\n del tracked[tid]\n\n\ndef overlay_rect(img, x1, y1, x2, y2, color, alpha=0.65):\n x1, y1 = max(0, x1), max(0, y1)\n x2, y2 = min(img.shape[1], x2), min(img.shape[0], y2)\n if x2 <= x1 or y2 <= y1:\n return\n roi = img[y1:y2, x1:x2]\n patch = np.full_like(roi, color, dtype=np.uint8)\n cv2.addWeighted(patch, alpha, roi, 1 - alpha, 0, roi)\n\n\ndef draw_pill(img, text, x, y, bg, fg=C_TEXT, font_scale=0.45, pad_x=6, pad_y=4):\n font = cv2.FONT_HERSHEY_SIMPLEX\n (tw, th), baseline = cv2.getTextSize(text, font, font_scale, 1)\n x1, y1 = x, y - th - pad_y\n x2, y2 = x + tw + pad_x * 2, y + baseline + pad_y\n cv2.rectangle(img, (x1, y1), (x2, y2), bg, -1)\n cv2.rectangle(img, (x1, y1), (x2, y2), C_BORDER, 1)\n cv2.putText(img, text, (x + pad_x, y), font, font_scale, fg, 1, cv2.LINE_AA)\n\n\ndef draw_elegant_counting_line(img, line_x, h, pulse_remaining=0):\n strength = pulse_remaining / max(LINE_PULSE_FRAMES, 1)\n glow_alpha = 0.12 + 0.18 * strength\n for offset in (14, 9, 5):\n color = tuple(int(c * glow_alpha) for c in C_LINE_GLOW)\n cv2.line(img, (line_x - offset, 0), (line_x - offset, h), color, 1, cv2.LINE_AA)\n cv2.line(img, (line_x + offset, 0), (line_x + offset, h), color, 1, cv2.LINE_AA)\n dash_len, gap = 18, 12\n y = 0\n while y < h:\n y_end = min(y + dash_len, h)\n cv2.line(img, (line_x, y), (line_x, y_end), C_LINE_CORE, 2, cv2.LINE_AA)\n y += dash_len + gap\n cv2.putText(\n img,\n \"COUNT LINE\",\n (line_x - 46, 24),\n cv2.FONT_HERSHEY_SIMPLEX,\n 0.42,\n C_LINE_CORE,\n 1,\n cv2.LINE_AA,\n )\n\n\ndef draw_hero_count(img, line_x, h, count, pulse_remaining=0):\n text = str(count)\n font = cv2.FONT_HERSHEY_SIMPLEX\n boost = 0.35 * (pulse_remaining / max(COUNT_PULSE_FRAMES, 1))\n font_scale, thickness = 1.6 + boost, 3\n (tw, th), _ = cv2.getTextSize(text, font, font_scale, thickness)\n pad = 14\n tx, ty = line_x - tw // 2, h // 2 + th // 2\n overlay_rect(\n img, tx - pad, ty - th - pad, tx + tw + pad, ty + pad // 2, C_PANEL, alpha=0.78\n )\n cv2.rectangle(\n img, (tx - pad, ty - th - pad), (tx + tw + pad, ty + pad // 2), C_LINE_CORE, 2\n )\n cv2.putText(img, text, (tx, ty), font, font_scale, C_GREEN, thickness, cv2.LINE_AA)\n\n\ndef draw_hud(\n img, w, batch_num, batch_count, total_ayam, elapsed_sec, rate, camera_id, clock\n):\n bar_h = 52\n overlay_rect(img, 0, 0, w, bar_h, C_PANEL, alpha=0.72)\n cv2.line(img, (0, bar_h), (w, bar_h), C_BORDER, 1)\n cv2.putText(\n img, \"BATCH\", (16, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.45, C_MUTED, 1, cv2.LINE_AA\n )\n batch_label = str(batch_num) if batch_num else \"—\"\n cv2.putText(\n img,\n batch_label,\n (16, 44),\n cv2.FONT_HERSHEY_SIMPLEX,\n 0.9,\n C_ACCENT,\n 2,\n cv2.LINE_AA,\n )\n cv2.putText(\n img, \"COUNT\", (100, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.45, C_MUTED, 1, cv2.LINE_AA\n )\n cv2.putText(\n img,\n str(batch_count),\n (100, 44),\n cv2.FONT_HERSHEY_SIMPLEX,\n 0.9,\n C_GREEN,\n 2,\n cv2.LINE_AA,\n )\n cv2.putText(\n img, \"TOTAL\", (190, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.45, C_MUTED, 1, cv2.LINE_AA\n )\n cv2.putText(\n img,\n str(total_ayam),\n (190, 44),\n cv2.FONT_HERSHEY_SIMPLEX,\n 0.7,\n C_TEXT,\n 1,\n cv2.LINE_AA,\n )\n cv2.putText(\n img,\n \"UPTIME\",\n (280, 20),\n cv2.FONT_HERSHEY_SIMPLEX,\n 0.45,\n C_MUTED,\n 1,\n cv2.LINE_AA,\n )\n cv2.putText(\n img,\n f\"{elapsed_sec / 3600:.1f}h\",\n (280, 44),\n cv2.FONT_HERSHEY_SIMPLEX,\n 0.7,\n C_TEXT,\n 1,\n cv2.LINE_AA,\n )\n cv2.putText(\n img, \"RATE\", (380, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.45, C_MUTED, 1, cv2.LINE_AA\n )\n cv2.putText(\n img,\n f\"{rate:.1f}/min\",\n (380, 44),\n cv2.FONT_HERSHEY_SIMPLEX,\n 0.7,\n C_ACCENT,\n 1,\n cv2.LINE_AA,\n )\n # cv2.putText(img, clock, (w - 180, 36), cv2.FONT_HERSHEY_SIMPLEX, 0.55, C_TEXT, 1, cv2.LINE_AA)\n cv2.putText(\n img,\n f\"CAM {camera_id}\",\n (w - 180, 20),\n cv2.FONT_HERSHEY_SIMPLEX,\n 0.45,\n C_MUTED,\n 1,\n cv2.LINE_AA,\n )\n\n\ndef draw_footer(img, w, h, frame_idx, live_tag):\n bar_h = 28\n overlay_rect(img, 0, h - bar_h, w, h, C_PANEL, alpha=0.55)\n cv2.putText(\n img,\n f\"{live_tag} | Frame {frame_idx}\",\n (12, h - 9),\n cv2.FONT_HERSHEY_SIMPLEX,\n 0.45,\n C_MUTED,\n 1,\n cv2.LINE_AA,\n )\n\n\ndef draw_skeleton_bold(img, kpts):\n for (a, b), color in zip(SKELETON, SK_COLORS):\n if a < len(kpts) and b < len(kpts):\n xa, ya = int(kpts[a][0]), int(kpts[a][1])\n xb, yb = int(kpts[b][0]), int(kpts[b][1])\n if xa > 0 and ya > 0 and xb > 0 and yb > 0:\n cv2.line(img, (xa, ya), (xb, yb), color, 3, cv2.LINE_AA)\n for kp in kpts:\n x, y = int(kp[0]), int(kp[1])\n if x > 0 and y > 0:\n cv2.circle(img, (x, y), 6, (255, 255, 255), -1, cv2.LINE_AA)\n cv2.circle(img, (x, y), 6, (40, 40, 40), 2, cv2.LINE_AA)\n\n\ndef draw_popups(img, popups, frame_idx):\n alive = []\n for pop in popups:\n age = frame_idx - pop[\"born\"]\n if age > POPUP_LIFETIME:\n continue\n alive.append(pop)\n fade = 1.0 - age / POPUP_LIFETIME\n y = pop[\"y\"] - int(age * 1.8)\n color = (int(C_GREEN[0] * fade), int(C_GREEN[1] * fade), int(C_GREEN[2] * fade))\n cv2.putText(\n img,\n pop[\"text\"],\n (pop[\"x\"], y),\n cv2.FONT_HERSHEY_SIMPLEX,\n 0.7,\n color,\n 2,\n cv2.LINE_AA,\n )\n return alive\n\n\ndef draw_batch_banner(img, w, batch_num, pulse_remaining):\n if pulse_remaining <= 0:\n return\n text = f\"NEW BATCH {batch_num}\"\n font = cv2.FONT_HERSHEY_SIMPLEX\n (tw, th), _ = cv2.getTextSize(text, font, 0.8, 2)\n x1, y1 = w // 2 - tw // 2 - 16, 62\n x2, y2 = w // 2 + tw // 2 + 16, 62 + th + 20\n overlay_rect(img, x1, y1, x2, y2, C_PANEL, alpha=0.7)\n cv2.rectangle(img, (x1, y1), (x2, y2), C_ACCENT, 2)\n cv2.putText(\n img, text, (w // 2 - tw // 2, 62 + th + 4), font, 0.8, C_ACCENT, 2, cv2.LINE_AA\n )\n\n\ndef connect_stream(source, warmup=WARMUP_FRAMES):\n attempts = 0\n while not shutdown_requested:\n cap = open_capture(source)\n if not cap.isOpened():\n attempts += 1\n if MAX_RECONNECT_ATTEMPTS and attempts >= MAX_RECONNECT_ATTEMPTS:\n raise RuntimeError(\n f\"Cannot open source after {attempts} attempts: {source}\"\n )\n print(f\"Cannot open source, retry in {RECONNECT_DELAY_SEC}s...\")\n time.sleep(RECONNECT_DELAY_SEC)\n continue\n if warmup > 0 and source.lower().startswith((\"rtsp://\", \"http://\")):\n warmup_stream(cap, warmup)\n w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))\n h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))\n fps = cap.get(cv2.CAP_PROP_FPS)\n if not fps or fps <= 1:\n fps = OUTPUT_FPS\n return cap, w, h, fps\n return None, 0, 0, OUTPUT_FPS\n\n\n# =============================================================================\n# Main loop\n# =============================================================================\n\n\ndef run():\n global shutdown_requested\n\n store = BatchStore(\n db_path=DB_PATH,\n state_file=STATE_FILE,\n camera_name=CAMERA_NAME,\n object_label=OBJECT_LABEL,\n cutoff_time=DAILY_CUTOFF_TIME,\n batch_timeout=BATCH_TIMEOUT_SECONDS,\n ignore_batch_label_timeout=IGNORE_BATCH_LABEL_TIMEOUT,\n min_object_per_batch=MIN_OBJECT_PER_BATCH,\n min_duration_per_batch=MIN_DURATION_PER_BATCH,\n logger=lambda msg: print(f\"[{now_str()}] {msg}\"),\n )\n store.start_cutoff_watcher()\n\n cross_logger = None\n if EXPORT_CSV:\n cross_logger = CsvLogger(\n CROSS_CSV, [\"batch\", \"frame\", \"timestamp\", \"chicken_id\"]\n )\n\n # Load RKNN model\n model = RKNNYOLO(\n model_path=MODEL_PATH,\n core_mask=CORE_MASK,\n imgsz=IMGSZ,\n conf=CONF,\n num_classes=NUM_CLASSES,\n score_sigmoid=SCORE_SIGMOID,\n )\n\n # Class IDs — order comes from RKNN model output (class index)\n # class index 0 → CLASS_AYAM, index 1 → CLASS_TALENAN (or env-specified)\n # Use class names in env order: first CLASS_AYAM → id 0, then CLASS_TALENAN → id 1\n CLASS_IDS = {\n os.getenv(\"CLASS_AYAM\", \"ayam\"): 0,\n os.getenv(\"CLASS_TALENAN\", \"talenan\"): 1,\n }\n ayam_cls = CLASS_IDS[CLASS_AYAM]\n talenan_cls = CLASS_IDS[CLASS_TALENAN]\n\n ayam_tracker = SimpleTracker(max_age=60)\n talenan_tracker = SimpleTracker(max_age=60)\n\n ayam_line_crossed = set()\n talenan_line_crossed = set()\n\n ayam_cross_flash = {}\n talenan_cross_flash = {}\n line_pulse = count_pulse = batch_pulse = 0\n popups = []\n\n session_start = time.time()\n frame_idx = 0\n video_writer = None\n\n cap, w, h, fps = connect_stream(SOURCE)\n if cap is None:\n store.shutdown()\n model.release()\n return\n\n line_x = resolve_line_x(w)\n print(\n f\"RKNN counter | {w}x{h} @ {fps}fps | line x={line_x} | cross={CROSS_DIRECTION}\"\n )\n print(f\"Model: {MODEL_PATH} | imgsz={IMGSZ} | core_mask={CORE_MASK}\")\n print(f\"DB: {DB_PATH}\")\n print(f\"State: {STATE_FILE}\")\n\n if RECORD_VIDEO:\n video_writer = VideoSegmentWriter(OUTPUT_DIR, w, h, fps, VIDEO_SEGMENT_SEC)\n\n reconnect_count = 0\n\n while not shutdown_requested:\n ret, frame = cap.read()\n if not ret:\n if not IS_LIVE:\n break\n reconnect_count += 1\n print(\n f\"Stream dropped (attempt {reconnect_count}), reconnecting in {RECONNECT_DELAY_SEC}s...\"\n )\n cap.release()\n time.sleep(RECONNECT_DELAY_SEC)\n cap, w, h, fps = connect_stream(SOURCE)\n if cap is None:\n break\n line_x = resolve_line_x(w)\n continue\n\n now = time.time()\n elapsed = now - session_start\n mono = time.monotonic()\n ayam_crossed_frame = batch_closed_frame = batch_started_frame = False\n\n # RKNN inference\n detections = model(frame)\n\n if detections:\n ayam_dets = [] # list of (cx, xywh_box)\n talenan_dets = []\n ayam_kpts_map = {} # det_idx → keypoints\n talenan_kpts_map = {}\n\n for di, det in enumerate(detections):\n bbox = det[\"bbox\"]\n cls_id = det[\"cls\"]\n cx = (bbox[0] + bbox[2]) / 2.0\n x1, y1, x2, y2 = bbox\n wb, hb = x2 - x1, y2 - y1\n box_cxcywh = np.array([cx, (y1 + y2) / 2, wb, hb], dtype=np.float32)\n\n if cls_id == talenan_cls:\n talenan_dets.append((cx, box_cxcywh))\n if det[\"keypoints\"] is not None:\n talenan_kpts_map[len(talenan_dets) - 1] = det[\"keypoints\"]\n elif cls_id == ayam_cls:\n ayam_dets.append((cx, box_cxcywh))\n if det[\"keypoints\"] is not None:\n ayam_kpts_map[len(ayam_dets) - 1] = det[\"keypoints\"]\n\n # Track ayam — returns (track_id → cx, detection_idx → track_id)\n ayam_cx_map, ayam_det_to_track = ayam_tracker.update(ayam_dets)\n\n # Track talenan\n talenan_cx_map, talenan_det_to_track = talenan_tracker.update(talenan_dets)\n\n # Process talenan crossings\n for di, (cx, box) in enumerate(talenan_dets):\n tid = talenan_det_to_track.get(di)\n if tid is None:\n continue\n if tid in talenan_tracker.tracks:\n if tid in talenan_tracked:\n prev_cx = talenan_tracked[tid][0]\n if (\n crossed_line(prev_cx, cx, line_x)\n and tid not in talenan_line_crossed\n ):\n talenan_line_crossed.add(tid)\n if store.record_talenan_crossing(tid):\n batch_closed_frame = True\n talenan_cross_flash[tid] = CROSS_FLASH_FRAMES\n popups.append(\n {\n \"x\": int(cx) - 20,\n \"y\": int(box[1]),\n \"born\": frame_idx,\n \"text\": \"BATCH CLOSED\",\n }\n )\n talenan_tracked[tid] = (cx, mono)\n\n # Process ayam crossings\n for di, (cx, box) in enumerate(ayam_dets):\n tid = ayam_det_to_track.get(di)\n if tid is None:\n continue\n if tid in ayam_tracker.tracks:\n if tid in ayam_tracked:\n prev_cx = ayam_tracked[tid][0]\n if (\n crossed_line(prev_cx, cx, line_x)\n and tid not in ayam_line_crossed\n ):\n ayam_line_crossed.add(tid)\n _, started_new = store.record_ayam_crossing(tid)\n if cross_logger:\n cross_logger.write_row(\n [\n store.current_batch_number,\n frame_idx,\n datetime.now().isoformat(),\n tid,\n ]\n )\n ayam_crossed_frame = True\n if started_new:\n batch_started_frame = True\n ayam_cross_flash[tid] = CROSS_FLASH_FRAMES\n popups.append(\n {\n \"x\": int(cx) - 12,\n \"y\": int(box[1]),\n \"born\": frame_idx,\n \"text\": \"+1\",\n }\n )\n ayam_tracked[tid] = (cx, mono)\n\n # Draw talenan\n for di, (cx, box) in enumerate(talenan_dets):\n tid = talenan_det_to_track.get(di)\n if tid is None:\n continue\n x1 = int(box[0] - box[2] / 2)\n y1 = int(box[1] - box[3] / 2)\n x2 = int(box[0] + box[2] / 2)\n y2 = int(box[1] + box[3] / 2)\n flash = talenan_cross_flash.get(tid, 0)\n color = C_GREEN if flash > 0 else C_TALENAN_BOX\n cv2.rectangle(frame, (x1, y1), (x2, y2), color, 3 if flash > 0 else 2)\n draw_pill(frame, f\"TALENAN {tid}\", x1, y1 - 4, color)\n\n # Draw ayam\n for di, (cx, box) in enumerate(ayam_dets):\n tid = ayam_det_to_track.get(di)\n if tid is None:\n continue\n x1 = int(box[0] - box[2] / 2)\n y1 = int(box[1] - box[3] / 2)\n x2 = int(box[0] + box[2] / 2)\n y2 = int(box[1] + box[3] / 2)\n flash = ayam_cross_flash.get(tid, 0)\n color = C_GREEN if flash > 0 else C_AYAM_BOX\n cv2.rectangle(frame, (x1, y1), (x2, y2), color, 3 if flash > 0 else 2)\n draw_pill(frame, f\"ID {tid}\", x1, y1 - 4, color)\n kpts = ayam_kpts_map.get(di)\n if kpts is not None:\n draw_skeleton_bold(frame, kpts)\n\n if ayam_crossed_frame:\n line_pulse = LINE_PULSE_FRAMES\n count_pulse = COUNT_PULSE_FRAMES\n if batch_closed_frame:\n line_pulse = LINE_PULSE_FRAMES\n if batch_started_frame:\n batch_pulse = BATCH_PULSE_FRAMES\n\n batch_num = store.current_batch_number or 0\n batch_count = store.current_batch_count\n display_total = store.display_total()\n rate = (display_total / elapsed * 60) if elapsed > 0 else 0.0\n\n draw_elegant_counting_line(frame, line_x, h, line_pulse)\n draw_hero_count(frame, line_x, h, batch_count, count_pulse)\n draw_hud(\n frame,\n w,\n batch_num,\n batch_count,\n display_total,\n elapsed,\n rate,\n CAMERA_NAME,\n now_str(),\n )\n draw_batch_banner(frame, w, batch_num, batch_pulse)\n draw_footer(frame, w, h, frame_idx, \"LIVE-RKNN\" if IS_LIVE else \"FILE-RKNN\")\n popups = draw_popups(frame, popups, frame_idx)\n\n for flash_store in (ayam_cross_flash, talenan_cross_flash):\n for tid in list(flash_store):\n flash_store[tid] -= 1\n if flash_store[tid] <= 0:\n del flash_store[tid]\n line_pulse = max(0, line_pulse - 1)\n count_pulse = max(0, count_pulse - 1)\n batch_pulse = max(0, batch_pulse - 1)\n\n if video_writer is not None:\n video_writer.write(frame)\n\n if LIVE_STREAM_ENABLED and frame_idx % LIVE_STREAM_EVERY_N == 0:\n try:\n Path(LIVE_STREAM_FRAME_PATH).parent.mkdir(parents=True, exist_ok=True)\n _, jpeg = cv2.imencode(\n \".jpg\", frame, [cv2.IMWRITE_JPEG_QUALITY, LIVE_STREAM_QUALITY]\n )\n with open(LIVE_STREAM_FRAME_PATH, \"wb\") as f:\n f.write(jpeg.tobytes())\n except Exception:\n pass\n\n frame_idx += 1\n if frame_idx % FLUSH_EVERY_N_FRAMES == 0:\n print(\n f\"[{now_str()}] Frame {frame_idx} | Batch {batch_num}: {batch_count} \"\n f\"| Total: {display_total} | Uptime {elapsed / 3600:.2f}h\"\n )\n prune_stale_tracks(ayam_tracked, mono)\n prune_stale_tracks(talenan_tracked, mono)\n\n cap.release()\n if video_writer is not None:\n video_writer.release()\n if cross_logger:\n cross_logger.close()\n model.release()\n store.shutdown()\n\n print(\"\\n=== Batch Summary (SQLite) ===\")\n print(f\"Database: {DB_PATH}\")\n\n\n# Tracked state dicts: track_id → (cx, monotonic_time)\nayam_tracked = {}\ntalenan_tracked = {}\n\n\nif __name__ == \"__main__\":\n run()",
|
||
"lineStart": 1,
|
||
"lineEnd": 1059,
|
||
"totalLines": 1059,
|
||
"truncated": false
|
||
}
|
||
},
|
||
"title": "counter_live_rknn.py",
|
||
"time": {
|
||
"start": 1785284240529,
|
||
"end": 1785284240709
|
||
}
|
||
},
|
||
"id": "prt_fab3ba35b001zKNIeRyf42jQNF",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3b9948001NqWLx9PXClyyv8"
|
||
},
|
||
{
|
||
"reason": "tool-calls",
|
||
"snapshot": "e0e1414b68e2544fb2d71b977882e90620947d43",
|
||
"type": "step-finish",
|
||
"tokens": {
|
||
"total": 15862,
|
||
"input": 5392,
|
||
"output": 214,
|
||
"reasoning": 16,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 10240
|
||
}
|
||
},
|
||
"cost": 0.00258274,
|
||
"id": "prt_fab3ba5d8001gigTvJdY0dDaxc",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3b9948001NqWLx9PXClyyv8"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"parentID": "msg_fab3b7662001asy7eTmLMm4mEq",
|
||
"role": "assistant",
|
||
"mode": "build",
|
||
"agent": "build",
|
||
"path": {
|
||
"cwd": "/opt/bytetrack-counter",
|
||
"root": "/opt/bytetrack-counter"
|
||
},
|
||
"cost": 0.015093282,
|
||
"tokens": {
|
||
"total": 50054,
|
||
"input": 34054,
|
||
"output": 234,
|
||
"reasoning": 22,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 15744
|
||
}
|
||
},
|
||
"modelID": "deepseek-v4-pro",
|
||
"providerID": "deepseek",
|
||
"time": {
|
||
"created": 1785284240955,
|
||
"completed": 1785284247206
|
||
},
|
||
"finish": "tool-calls",
|
||
"id": "msg_fab3ba63b001ubMqr9d9Qa8oQb",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"snapshot": "e0e1414b68e2544fb2d71b977882e90620947d43",
|
||
"type": "step-start",
|
||
"id": "prt_fab3ba9cf001LT6TNHAWViUNp6",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3ba63b001ubMqr9d9Qa8oQb"
|
||
},
|
||
{
|
||
"type": "reasoning",
|
||
"text": "Now let me read the remaining key files: the service files, templates, and the bytetrack variant.",
|
||
"time": {
|
||
"start": 1785284244953,
|
||
"end": 1785284245454
|
||
},
|
||
"id": "prt_fab3bb5d9001yd9j4YkBy3oqxi",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3ba63b001ubMqr9d9Qa8oQb"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "read",
|
||
"callID": "call_00_f8jmNYi8WXfxjMO9KPiv3266",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"filePath": "/opt/bytetrack-counter/bytetrack-counter.service"
|
||
},
|
||
"output": "<path>/opt/bytetrack-counter/bytetrack-counter.service</path>\n<type>file</type>\n<content>\n1: [Unit]\n2: Description=NanoPi Edge YOLO Batch Counter (RTSP + RKNN)\n3: Documentation=file:///opt/bytetrack-counter/DEPLOY.md\n4: After=network-online.target\n5: Wants=network-online.target\n6: \n7: [Service]\n8: Type=simple\n9: User=root\n10: Group=root\n11: \n12: WorkingDirectory=/opt/bytetrack-counter\n13: \n14: EnvironmentFile=/opt/bytetrack-counter/.env\n15: Environment=PYTHONNOUSERSITE=1\n16: Environment=YOLO_CONFIG_DIR=/opt/bytetrack-counter/.ultralytics\n17: Environment=TORCH_HOME=/opt/bytetrack-counter/.torch\n18: Environment=PATH=/opt/bytetrack-counter/venv/bin:/usr/local/bin:/usr/bin:/bin\n19: \n20: ExecStart=/opt/bytetrack-counter/venv/bin/python counter_live_rknn_bytetrack.py\n21: \n22: TimeoutStopSec=30\n23: KillSignal=SIGTERM\n24: \n25: Restart=on-failure\n26: RestartSec=10\n27: StartLimitInterval=120s\n28: StartLimitBurst=5\n29: \n30: NoNewPrivileges=true\n31: ProtectHome=true\n32: PrivateTmp=false\n33: \n34: [Install]\n35: WantedBy=multi-user.target\n\n(End of file - total 35 lines)\n</content>",
|
||
"metadata": {
|
||
"preview": "[Unit]\nDescription=NanoPi Edge YOLO Batch Counter (RTSP + RKNN)\nDocumentation=file:///opt/bytetrack-counter/DEPLOY.md\nAfter=network-online.target\nWants=network-online.target\n\n[Service]\nType=simple\nUser=root\nGroup=root\n\nWorkingDirectory=/opt/bytetrack-counter\n\nEnvironmentFile=/opt/bytetrack-counter/.env\nEnvironment=PYTHONNOUSERSITE=1\nEnvironment=YOLO_CONFIG_DIR=/opt/bytetrack-counter/.ultralytics\nEnvironment=TORCH_HOME=/opt/bytetrack-counter/.torch\nEnvironment=PATH=/opt/bytetrack-counter/venv/bin:/usr/local/bin:/usr/bin:/bin\n\nExecStart=/opt/bytetrack-counter/venv/bin/python counter_live_rknn_bytetrack.py",
|
||
"truncated": false,
|
||
"loaded": [],
|
||
"display": {
|
||
"type": "file",
|
||
"path": "/opt/bytetrack-counter/bytetrack-counter.service",
|
||
"text": "[Unit]\nDescription=NanoPi Edge YOLO Batch Counter (RTSP + RKNN)\nDocumentation=file:///opt/bytetrack-counter/DEPLOY.md\nAfter=network-online.target\nWants=network-online.target\n\n[Service]\nType=simple\nUser=root\nGroup=root\n\nWorkingDirectory=/opt/bytetrack-counter\n\nEnvironmentFile=/opt/bytetrack-counter/.env\nEnvironment=PYTHONNOUSERSITE=1\nEnvironment=YOLO_CONFIG_DIR=/opt/bytetrack-counter/.ultralytics\nEnvironment=TORCH_HOME=/opt/bytetrack-counter/.torch\nEnvironment=PATH=/opt/bytetrack-counter/venv/bin:/usr/local/bin:/usr/bin:/bin\n\nExecStart=/opt/bytetrack-counter/venv/bin/python counter_live_rknn_bytetrack.py\n\nTimeoutStopSec=30\nKillSignal=SIGTERM\n\nRestart=on-failure\nRestartSec=10\nStartLimitInterval=120s\nStartLimitBurst=5\n\nNoNewPrivileges=true\nProtectHome=true\nPrivateTmp=false\n\n[Install]\nWantedBy=multi-user.target",
|
||
"lineStart": 1,
|
||
"lineEnd": 35,
|
||
"totalLines": 35,
|
||
"truncated": false
|
||
}
|
||
},
|
||
"title": "bytetrack-counter.service",
|
||
"time": {
|
||
"start": 1785284245737,
|
||
"end": 1785284245772
|
||
}
|
||
},
|
||
"id": "prt_fab3bb7d3001mgQKznucCGwdEP",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3ba63b001ubMqr9d9Qa8oQb"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "read",
|
||
"callID": "call_01_aFvuJpL4hnlAmNDM3Iox5868",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"filePath": "/opt/bytetrack-counter/bytetrack-counter-dashboard.service"
|
||
},
|
||
"output": "<path>/opt/bytetrack-counter/bytetrack-counter-dashboard.service</path>\n<type>file</type>\n<content>\n1: [Unit]\n2: Description=Nano Edge Counter Dashboard (Flask, port 5000)\n3: Documentation=file:///opt/bytetrack-counter/DEPLOY.md\n4: After=network-online.target bytetrack-counter.service\n5: Wants=network-online.target\n6: \n7: [Service]\n8: Type=simple\n9: User=root\n10: Group=root\n11: \n12: WorkingDirectory=/opt/bytetrack-counter\n13: \n14: EnvironmentFile=/opt/bytetrack-counter/.env\n15: Environment=PATH=/opt/bytetrack-counter/venv/bin:/usr/local/bin:/usr/bin:/bin\n16: Environment=FLASK_DEBUG=false\n17: \n18: ExecStart=/opt/bytetrack-counter/venv/bin/python counter_dashboard.py\n19: \n20: TimeoutStopSec=15\n21: KillSignal=SIGTERM\n22: \n23: Restart=on-failure\n24: RestartSec=5\n25: StartLimitInterval=60s\n26: StartLimitBurst=3\n27: \n28: NoNewPrivileges=true\n29: ProtectHome=true\n30: PrivateTmp=false\n31: \n32: [Install]\n33: WantedBy=multi-user.target\n\n(End of file - total 33 lines)\n</content>",
|
||
"metadata": {
|
||
"preview": "[Unit]\nDescription=Nano Edge Counter Dashboard (Flask, port 5000)\nDocumentation=file:///opt/bytetrack-counter/DEPLOY.md\nAfter=network-online.target bytetrack-counter.service\nWants=network-online.target\n\n[Service]\nType=simple\nUser=root\nGroup=root\n\nWorkingDirectory=/opt/bytetrack-counter\n\nEnvironmentFile=/opt/bytetrack-counter/.env\nEnvironment=PATH=/opt/bytetrack-counter/venv/bin:/usr/local/bin:/usr/bin:/bin\nEnvironment=FLASK_DEBUG=false\n\nExecStart=/opt/bytetrack-counter/venv/bin/python counter_dashboard.py\n\nTimeoutStopSec=15",
|
||
"truncated": false,
|
||
"loaded": [],
|
||
"display": {
|
||
"type": "file",
|
||
"path": "/opt/bytetrack-counter/bytetrack-counter-dashboard.service",
|
||
"text": "[Unit]\nDescription=Nano Edge Counter Dashboard (Flask, port 5000)\nDocumentation=file:///opt/bytetrack-counter/DEPLOY.md\nAfter=network-online.target bytetrack-counter.service\nWants=network-online.target\n\n[Service]\nType=simple\nUser=root\nGroup=root\n\nWorkingDirectory=/opt/bytetrack-counter\n\nEnvironmentFile=/opt/bytetrack-counter/.env\nEnvironment=PATH=/opt/bytetrack-counter/venv/bin:/usr/local/bin:/usr/bin:/bin\nEnvironment=FLASK_DEBUG=false\n\nExecStart=/opt/bytetrack-counter/venv/bin/python counter_dashboard.py\n\nTimeoutStopSec=15\nKillSignal=SIGTERM\n\nRestart=on-failure\nRestartSec=5\nStartLimitInterval=60s\nStartLimitBurst=3\n\nNoNewPrivileges=true\nProtectHome=true\nPrivateTmp=false\n\n[Install]\nWantedBy=multi-user.target",
|
||
"lineStart": 1,
|
||
"lineEnd": 33,
|
||
"totalLines": 33,
|
||
"truncated": false
|
||
}
|
||
},
|
||
"title": "bytetrack-counter-dashboard.service",
|
||
"time": {
|
||
"start": 1785284245977,
|
||
"end": 1785284246024
|
||
}
|
||
},
|
||
"id": "prt_fab3bb8f7001299RcdAR4Pn9So",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3ba63b001ubMqr9d9Qa8oQb"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "read",
|
||
"callID": "call_02_P90DorXhzpjuuJ4Hn5zo1862",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"filePath": "/opt/bytetrack-counter/counter_live_rknn_bytetrack.py",
|
||
"limit": 60
|
||
},
|
||
"output": "<path>/opt/bytetrack-counter/counter_live_rknn_bytetrack.py</path>\n<type>file</type>\n<content>\n1: \"\"\"\n2: Edge production live counter — RTSP + YOLO RKNN + ByteTrack + line crossing.\n3: Runs on RK3588 hardware with RKNN model (320×320 input).\n4: Uses ByteTrack (Kalman filter + two-stage IoU association) for tracking.\n5: \"\"\"\n6: \n7: import numpy as np\n8: import cv2\n9: import csv\n10: import os\n11: import signal\n12: import time\n13: from collections import deque\n14: from datetime import datetime\n15: from pathlib import Path\n16: \n17: from dotenv import load_dotenv\n18: \n19: load_dotenv()\n20: \n21: from rknnlite.api import RKNNLite\n22: from batch_store import BatchStore\n23: \n24: # --- config (override via env / .env) ---\n25: OUTPUT_DIR = os.getenv(\"OUTPUT_DIR\", \"/opt/jetson-counter\")\n26: DB_PATH = os.getenv(\"DB_PATH\", f\"{OUTPUT_DIR}/jetson_counter.db\")\n27: STATE_FILE = os.getenv(\"STATE_FILE\", f\"{OUTPUT_DIR}/current_batch.json\")\n28: SOURCE = os.getenv(\"SOURCE\", \"rtsp://user:pass@192.168.0.100:554/stream1\")\n29: MODEL_PATH = os.getenv(\"MODEL_PATH\", \"/opt/jetson-counter/yolo11n.rknn\")\n30: CAMERA_NAME = os.getenv(\"CAMERA_NAME\", \"CC1\")\n31: OBJECT_LABEL = os.getenv(\"OBJECT_LABEL\", \"ayam-potong\")\n32: CLASS_AYAM = os.getenv(\"CLASS_AYAM\", \"ayam\")\n33: CLASS_TALENAN = os.getenv(\"CLASS_TALENAN\", \"talenan\")\n34: \n35: LINE_X = int(os.getenv(\"LINE_X\")) if os.getenv(\"LINE_X\") else None\n36: LINE_X_FRAC = float(os.getenv(\"LINE_X_FRAC\", \"0.5\"))\n37: CROSS_DIRECTION = os.getenv(\"CROSS_DIRECTION\", \"rtl\").lower()\n38: \n39: IMGSZ = int(os.getenv(\"IMGSZ\", \"320\"))\n40: HALF = os.getenv(\"HALF\", \"false\").lower() == \"true\"\n41: CONF = float(os.getenv(\"CONF\", \"0.3\"))\n42: DEVICE = int(os.getenv(\"DEVICE\", \"0\"))\n43: \n44: # RKNN NPU core mask\n45: CORE_MASK = int(os.getenv(\"CORE_MASK\", \"1\"))\n46: \n47: # YOLO decoder config\n48: NUM_CLASSES = int(os.getenv(\"NUM_CLASSES\", \"2\"))\n49: SCORE_SIGMOID = os.getenv(\"SCORE_SIGMOID\", \"false\").lower() == \"true\"\n50: \n51: # ByteTrack settings\n52: # Ayam index 0\n53: TRACK_HIGH_THRESH_0 = float(os.getenv(\"TRACK_HIGH_THRESH_0\", \"0.5\"))\n54: TRACK_LOW_THRESH_0 = float(os.getenv(\"TRACK_LOW_THRESH_0\", \"0.1\"))\n55: TRACK_MATCH_THRESH_0 = float(os.getenv(\"TRACK_MATCH_THRESH_0\", \"0.8\"))\n56: TRACK_BUFFER_0 = int(os.getenv(\"TRACK_BUFFER_0\", \"30\"))\n57: TRACK_MIN_HITS_0 = int(os.getenv(\"TRACK_MIN_HITS_0\", \"3\"))\n58: # Talenan index 1\n59: TRACK_HIGH_THRESH_1 = float(os.getenv(\"TRACK_HIGH_THRESH_1\", \"0.5\"))\n60: TRACK_LOW_THRESH_1 = float(os.getenv(\"TRACK_LOW_THRESH_1\", \"0.1\"))\n\n(Showing lines 1-60 of 1373. Use offset=61 to continue.)\n</content>",
|
||
"metadata": {
|
||
"preview": "\"\"\"\nEdge production live counter — RTSP + YOLO RKNN + ByteTrack + line crossing.\nRuns on RK3588 hardware with RKNN model (320×320 input).\nUses ByteTrack (Kalman filter + two-stage IoU association) for tracking.\n\"\"\"\n\nimport numpy as np\nimport cv2\nimport csv\nimport os\nimport signal\nimport time\nfrom collections import deque\nfrom datetime import datetime\nfrom pathlib import Path\n\nfrom dotenv import load_dotenv\n\nload_dotenv()\n",
|
||
"truncated": true,
|
||
"loaded": [],
|
||
"display": {
|
||
"type": "file",
|
||
"path": "/opt/bytetrack-counter/counter_live_rknn_bytetrack.py",
|
||
"text": "\"\"\"\nEdge production live counter — RTSP + YOLO RKNN + ByteTrack + line crossing.\nRuns on RK3588 hardware with RKNN model (320×320 input).\nUses ByteTrack (Kalman filter + two-stage IoU association) for tracking.\n\"\"\"\n\nimport numpy as np\nimport cv2\nimport csv\nimport os\nimport signal\nimport time\nfrom collections import deque\nfrom datetime import datetime\nfrom pathlib import Path\n\nfrom dotenv import load_dotenv\n\nload_dotenv()\n\nfrom rknnlite.api import RKNNLite\nfrom batch_store import BatchStore\n\n# --- config (override via env / .env) ---\nOUTPUT_DIR = os.getenv(\"OUTPUT_DIR\", \"/opt/jetson-counter\")\nDB_PATH = os.getenv(\"DB_PATH\", f\"{OUTPUT_DIR}/jetson_counter.db\")\nSTATE_FILE = os.getenv(\"STATE_FILE\", f\"{OUTPUT_DIR}/current_batch.json\")\nSOURCE = os.getenv(\"SOURCE\", \"rtsp://user:pass@192.168.0.100:554/stream1\")\nMODEL_PATH = os.getenv(\"MODEL_PATH\", \"/opt/jetson-counter/yolo11n.rknn\")\nCAMERA_NAME = os.getenv(\"CAMERA_NAME\", \"CC1\")\nOBJECT_LABEL = os.getenv(\"OBJECT_LABEL\", \"ayam-potong\")\nCLASS_AYAM = os.getenv(\"CLASS_AYAM\", \"ayam\")\nCLASS_TALENAN = os.getenv(\"CLASS_TALENAN\", \"talenan\")\n\nLINE_X = int(os.getenv(\"LINE_X\")) if os.getenv(\"LINE_X\") else None\nLINE_X_FRAC = float(os.getenv(\"LINE_X_FRAC\", \"0.5\"))\nCROSS_DIRECTION = os.getenv(\"CROSS_DIRECTION\", \"rtl\").lower()\n\nIMGSZ = int(os.getenv(\"IMGSZ\", \"320\"))\nHALF = os.getenv(\"HALF\", \"false\").lower() == \"true\"\nCONF = float(os.getenv(\"CONF\", \"0.3\"))\nDEVICE = int(os.getenv(\"DEVICE\", \"0\"))\n\n# RKNN NPU core mask\nCORE_MASK = int(os.getenv(\"CORE_MASK\", \"1\"))\n\n# YOLO decoder config\nNUM_CLASSES = int(os.getenv(\"NUM_CLASSES\", \"2\"))\nSCORE_SIGMOID = os.getenv(\"SCORE_SIGMOID\", \"false\").lower() == \"true\"\n\n# ByteTrack settings\n# Ayam index 0\nTRACK_HIGH_THRESH_0 = float(os.getenv(\"TRACK_HIGH_THRESH_0\", \"0.5\"))\nTRACK_LOW_THRESH_0 = float(os.getenv(\"TRACK_LOW_THRESH_0\", \"0.1\"))\nTRACK_MATCH_THRESH_0 = float(os.getenv(\"TRACK_MATCH_THRESH_0\", \"0.8\"))\nTRACK_BUFFER_0 = int(os.getenv(\"TRACK_BUFFER_0\", \"30\"))\nTRACK_MIN_HITS_0 = int(os.getenv(\"TRACK_MIN_HITS_0\", \"3\"))\n# Talenan index 1\nTRACK_HIGH_THRESH_1 = float(os.getenv(\"TRACK_HIGH_THRESH_1\", \"0.5\"))\nTRACK_LOW_THRESH_1 = float(os.getenv(\"TRACK_LOW_THRESH_1\", \"0.1\"))",
|
||
"lineStart": 1,
|
||
"lineEnd": 60,
|
||
"totalLines": 1373,
|
||
"truncated": true
|
||
}
|
||
},
|
||
"title": "counter_live_rknn_bytetrack.py",
|
||
"time": {
|
||
"start": 1785284246442,
|
||
"end": 1785284246461
|
||
}
|
||
},
|
||
"id": "prt_fab3bba44001f87bMH3lTuKZmh",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3ba63b001ubMqr9d9Qa8oQb"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "read",
|
||
"callID": "call_03_5IekWxo8eZuCKMxXI4mn0963",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"filePath": "/opt/bytetrack-counter/uninstall-services.sh"
|
||
},
|
||
"output": "<path>/opt/bytetrack-counter/uninstall-services.sh</path>\n<type>file</type>\n<content>\n1: #!/usr/bin/env bash\n2: # Remove Jetson edge counter systemd services.\n3: # Run on the Jetson: sudo ./uninstall-services.sh\n4: set -euo pipefail\n5: \n6: if [[ \"$(id -u)\" -ne 0 ]]; then\n7: echo \"Run as root: sudo ./uninstall-services.sh\"\n8: exit 1\n9: fi\n10: \n11: for unit in jetson-counter jetson-counter-dashboard; do\n12: systemctl stop \"${unit}\" 2>/dev/null || true\n13: systemctl disable \"${unit}\" 2>/dev/null || true\n14: rm -f \"/etc/systemd/system/${unit}.service\"\n15: done\n16: \n17: systemctl daemon-reload\n18: echo \"Removed jetson-counter and jetson-counter-dashboard services.\"\n\n(End of file - total 18 lines)\n</content>",
|
||
"metadata": {
|
||
"preview": "#!/usr/bin/env bash\n# Remove Jetson edge counter systemd services.\n# Run on the Jetson: sudo ./uninstall-services.sh\nset -euo pipefail\n\nif [[ \"$(id -u)\" -ne 0 ]]; then\n echo \"Run as root: sudo ./uninstall-services.sh\"\n exit 1\nfi\n\nfor unit in jetson-counter jetson-counter-dashboard; do\n systemctl stop \"${unit}\" 2>/dev/null || true\n systemctl disable \"${unit}\" 2>/dev/null || true\n rm -f \"/etc/systemd/system/${unit}.service\"\ndone\n\nsystemctl daemon-reload\necho \"Removed jetson-counter and jetson-counter-dashboard services.\"",
|
||
"truncated": false,
|
||
"loaded": [],
|
||
"display": {
|
||
"type": "file",
|
||
"path": "/opt/bytetrack-counter/uninstall-services.sh",
|
||
"text": "#!/usr/bin/env bash\n# Remove Jetson edge counter systemd services.\n# Run on the Jetson: sudo ./uninstall-services.sh\nset -euo pipefail\n\nif [[ \"$(id -u)\" -ne 0 ]]; then\n echo \"Run as root: sudo ./uninstall-services.sh\"\n exit 1\nfi\n\nfor unit in jetson-counter jetson-counter-dashboard; do\n systemctl stop \"${unit}\" 2>/dev/null || true\n systemctl disable \"${unit}\" 2>/dev/null || true\n rm -f \"/etc/systemd/system/${unit}.service\"\ndone\n\nsystemctl daemon-reload\necho \"Removed jetson-counter and jetson-counter-dashboard services.\"",
|
||
"lineStart": 1,
|
||
"lineEnd": 18,
|
||
"totalLines": 18,
|
||
"truncated": false
|
||
}
|
||
},
|
||
"title": "uninstall-services.sh",
|
||
"time": {
|
||
"start": 1785284246730,
|
||
"end": 1785284246773
|
||
}
|
||
},
|
||
"id": "prt_fab3bbc03001Lnq6bR3dHSKBV8",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3ba63b001ubMqr9d9Qa8oQb"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "read",
|
||
"callID": "call_04_XaTxhp7G94JyiAbveNio1027",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"filePath": "/opt/bytetrack-counter/templates"
|
||
},
|
||
"output": "<path>/opt/bytetrack-counter/templates</path>\n<type>directory</type>\n<entries>\ndashboard.html\n\n(1 entries)\n</entries>",
|
||
"metadata": {
|
||
"preview": "dashboard.html",
|
||
"truncated": false,
|
||
"loaded": [],
|
||
"display": {
|
||
"type": "directory",
|
||
"path": "/opt/bytetrack-counter/templates",
|
||
"entries": [
|
||
"dashboard.html"
|
||
],
|
||
"offset": 1,
|
||
"totalEntries": 1,
|
||
"truncated": false
|
||
}
|
||
},
|
||
"title": "templates",
|
||
"time": {
|
||
"start": 1785284246986,
|
||
"end": 1785284246993
|
||
}
|
||
},
|
||
"id": "prt_fab3bbd400016ioCyqgklUEtfx",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3ba63b001ubMqr9d9Qa8oQb"
|
||
},
|
||
{
|
||
"reason": "tool-calls",
|
||
"snapshot": "e0e1414b68e2544fb2d71b977882e90620947d43",
|
||
"type": "step-finish",
|
||
"tokens": {
|
||
"total": 50054,
|
||
"input": 34054,
|
||
"output": 234,
|
||
"reasoning": 22,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 15744
|
||
}
|
||
},
|
||
"cost": 0.015093282,
|
||
"id": "prt_fab3bbe4c0019nHaPhmiqAhdOR",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3ba63b001ubMqr9d9Qa8oQb"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"parentID": "msg_fab3b7662001asy7eTmLMm4mEq",
|
||
"role": "assistant",
|
||
"mode": "build",
|
||
"agent": "build",
|
||
"path": {
|
||
"cwd": "/opt/bytetrack-counter",
|
||
"root": "/opt/bytetrack-counter"
|
||
},
|
||
"cost": 0.001526009,
|
||
"tokens": {
|
||
"total": 52556,
|
||
"input": 1925,
|
||
"output": 148,
|
||
"reasoning": 435,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 50048
|
||
}
|
||
},
|
||
"modelID": "deepseek-v4-pro",
|
||
"providerID": "deepseek",
|
||
"time": {
|
||
"created": 1785284247247,
|
||
"completed": 1785284255745
|
||
},
|
||
"finish": "tool-calls",
|
||
"id": "msg_fab3bbece001uI2KIIvs954jfg",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"snapshot": "e0e1414b68e2544fb2d71b977882e90620947d43",
|
||
"type": "step-start",
|
||
"id": "prt_fab3bc1c2001kavY1kjaybuBjC",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3bbece001uI2KIIvs954jfg"
|
||
},
|
||
{
|
||
"type": "reasoning",
|
||
"text": "Now I have a thorough understanding of the codebase. Let me write the AGENTS.md file. Key observations:\n\n1. **Project type**: Python, on edge Jetson/RK3588 hardware\n2. **No build system** – it's a collection of Python scripts run directly\n3. **Multiple entry points**: \n - `counter_live.py` – original Jetson TensorRT variant\n - `counter_live_rknn.py` – RK3588 variant with simple IoU tracker\n - `counter_live_rknn_bytetrack.py` – RK3588 variant with ByteTrack (this is the one the systemd service runs)\n - `counter_dashboard.py` – Flask dashboard on port 5000\n4. **No tests** – no test framework, no test files\n5. **No lint/typecheck config** – no ruff, mypy, etc.\n6. **Systemd services** – the \"build\" is just systemd service installation\n7. **Key directories**: Actual install path is `/opt/bytetrack-counter` (based on service files), but README says `/opt/jetson-counter`\n8. **Environment**: Requires `.env` file, venv with system-site-packages, RKNN Toolkit Lite2, NVIDIA torch on Jetson\n9. **Important gotchas**:\n - `PYTHONNOUSERSITE=1` is critical for venv isolation\n - Two separate service units: counter and dashboard\n - The service runs `counter_live_rknn_bytetrack.py` by default\n - The `.env` file is not in git (gitignored), must be copied from `config.env.example`\n - The install path in service files is `/opt/bytetrack-counter` but README/DEPLOY mention `/opt/jetson-counter`\n - `numpy<2` is required\n - The project name on disk is `bytetrack-counter` but references `jetson-counter` in docs\n\nLet me write a concise, high-signal AGENTS.md.",
|
||
"time": {
|
||
"start": 1785284248461,
|
||
"end": 1785284254540
|
||
},
|
||
"id": "prt_fab3bc38d001LFx3nSpgSbSOnu",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3bbece001uI2KIIvs954jfg"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "read",
|
||
"callID": "call_00_9HbQKv8N5nsoY1OWjyrB5032",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"filePath": "/opt/bytetrack-counter/templates/dashboard.html",
|
||
"limit": 20
|
||
},
|
||
"output": "<path>/opt/bytetrack-counter/templates/dashboard.html</path>\n<type>file</type>\n<content>\n1: <!DOCTYPE html>\n2: <html lang=\"en\">\n3: <head>\n4: <meta charset=\"UTF-8\">\n5: <meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\">\n6: <title>ZenAI APC Dashboard</title>\n7: <script src=\"https://cdn.jsdelivr.net/npm/chart.js\"></script>\n8: <link rel=\"stylesheet\" href=\"https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.0/css/all.min.css\">\n9: <style>\n10: @import url('https://fonts.googleapis.com/css2?family=JetBrains+Mono:wght@300;400;500;700&family=Orbitron:wght@400;500;700;900&display=swap');\n11: \n12: * { margin: 0; padding: 0; box-sizing: border-box; }\n13: \n14: :root {\n15: --bg-deep: #06080e;\n16: --bg-surface: rgba(12, 16, 28, 0.9);\n17: --border-glow: rgba(0, 240, 255, 0.12);\n18: --accent: #00f0ff;\n19: --accent2: #7b2fff;\n20: --accent3: #ff2d78;\n\n(Showing lines 1-20 of 1217. Use offset=21 to continue.)\n</content>",
|
||
"metadata": {
|
||
"preview": "<!DOCTYPE html>\n<html lang=\"en\">\n<head>\n<meta charset=\"UTF-8\">\n<meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\">\n<title>ZenAI APC Dashboard</title>\n<script src=\"https://cdn.jsdelivr.net/npm/chart.js\"></script>\n<link rel=\"stylesheet\" href=\"https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.0/css/all.min.css\">\n<style>\n @import url('https://fonts.googleapis.com/css2?family=JetBrains+Mono:wght@300;400;500;700&family=Orbitron:wght@400;500;700;900&display=swap');\n\n * { margin: 0; padding: 0; box-sizing: border-box; }\n\n :root {\n --bg-deep: #06080e;\n --bg-surface: rgba(12, 16, 28, 0.9);\n --border-glow: rgba(0, 240, 255, 0.12);\n --accent: #00f0ff;\n --accent2: #7b2fff;\n --accent3: #ff2d78;",
|
||
"truncated": true,
|
||
"loaded": [],
|
||
"display": {
|
||
"type": "file",
|
||
"path": "/opt/bytetrack-counter/templates/dashboard.html",
|
||
"text": "<!DOCTYPE html>\n<html lang=\"en\">\n<head>\n<meta charset=\"UTF-8\">\n<meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\">\n<title>ZenAI APC Dashboard</title>\n<script src=\"https://cdn.jsdelivr.net/npm/chart.js\"></script>\n<link rel=\"stylesheet\" href=\"https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.0/css/all.min.css\">\n<style>\n @import url('https://fonts.googleapis.com/css2?family=JetBrains+Mono:wght@300;400;500;700&family=Orbitron:wght@400;500;700;900&display=swap');\n\n * { margin: 0; padding: 0; box-sizing: border-box; }\n\n :root {\n --bg-deep: #06080e;\n --bg-surface: rgba(12, 16, 28, 0.9);\n --border-glow: rgba(0, 240, 255, 0.12);\n --accent: #00f0ff;\n --accent2: #7b2fff;\n --accent3: #ff2d78;",
|
||
"lineStart": 1,
|
||
"lineEnd": 20,
|
||
"totalLines": 1217,
|
||
"truncated": true
|
||
}
|
||
},
|
||
"title": "templates/dashboard.html",
|
||
"time": {
|
||
"start": 1785284254889,
|
||
"end": 1785284254952
|
||
}
|
||
},
|
||
"id": "prt_fab3bdb52001khdR3eu45nLoX9",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3bbece001uI2KIIvs954jfg"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "read",
|
||
"callID": "call_01_360kqT9rIwatJfS6mE3G6175",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"filePath": "/opt/bytetrack-counter/counter_live_rknn_bytetrack.py",
|
||
"offset": 60,
|
||
"limit": 60
|
||
},
|
||
"output": "<path>/opt/bytetrack-counter/counter_live_rknn_bytetrack.py</path>\n<type>file</type>\n<content>\n60: TRACK_LOW_THRESH_1 = float(os.getenv(\"TRACK_LOW_THRESH_1\", \"0.1\"))\n61: TRACK_MATCH_THRESH_1 = float(os.getenv(\"TRACK_MATCH_THRESH_1\", \"0.6\"))\n62: TRACK_BUFFER_1 = int(os.getenv(\"TRACK_BUFFER_1\", \"30\"))\n63: TRACK_MIN_HITS_1 = int(os.getenv(\"TRACK_MIN_HITS_1\", \"3\"))\n64: \n65: DAILY_CUTOFF_TIME = os.getenv(\"DAILY_CUTOFF_TIME\", \"20:00\")\n66: BATCH_TIMEOUT_SECONDS = float(os.getenv(\"BATCH_TIMEOUT_SECONDS\", \"300\"))\n67: IGNORE_BATCH_LABEL_TIMEOUT = float(\n68: os.getenv(\"IGNORE_BATCH_LABEL_TIMEOUT_SECONDS\", \"30\")\n69: )\n70: MIN_OBJECT_PER_BATCH = int(os.getenv(\"MIN_OBJECT_PER_BATCH\", \"60\"))\n71: MIN_DURATION_PER_BATCH = int(os.getenv(\"MIN_DURATION_PER_BATCH\", \"60\"))\n72: \n73: EXPORT_CSV = os.getenv(\"EXPORT_CSV\", \"true\").lower() == \"true\"\n74: CROSS_CSV = os.getenv(\"CROSS_CSV\", f\"{OUTPUT_DIR}/batch_crossings.csv\")\n75: \n76: RATE_WINDOW_SEC = int(os.getenv(\"RATE_WINDOW_SEC\", \"60\"))\n77: WARMUP_FRAMES = int(os.getenv(\"WARMUP_FRAMES\", \"30\"))\n78: RECONNECT_DELAY_SEC = int(os.getenv(\"RECONNECT_DELAY_SEC\", \"3\"))\n79: MAX_RECONNECT_ATTEMPTS = int(os.getenv(\"MAX_RECONNECT_ATTEMPTS\", \"0\"))\n80: FLUSH_EVERY_N_FRAMES = int(os.getenv(\"FLUSH_EVERY_N_FRAMES\", \"100\"))\n81: TRACKED_PRUNE_SEC = int(os.getenv(\"TRACKED_PRUNE_SEC\", \"300\"))\n82: RECORD_VIDEO = os.getenv(\"RECORD_VIDEO\", \"false\").lower() == \"true\"\n83: VIDEO_SEGMENT_SEC = int(os.getenv(\"VIDEO_SEGMENT_SEC\", \"3600\"))\n84: OUTPUT_FPS = int(os.getenv(\"OUTPUT_FPS\", \"15\"))\n85: \n86: LIVE_STREAM_ENABLED = os.getenv(\"LIVE_STREAM_ENABLED\", \"false\").lower() == \"true\"\n87: LIVE_STREAM_FRAME_PATH = os.getenv(\n88: \"LIVE_STREAM_FRAME_PATH\", \"/dev/shm/jetson-counter/live_frame.jpg\"\n89: )\n90: LIVE_STREAM_QUALITY = int(os.getenv(\"LIVE_STREAM_QUALITY\", \"75\"))\n91: LIVE_STREAM_EVERY_N = int(os.getenv(\"LIVE_STREAM_EVERY_N\", \"2\"))\n92: \n93: RTSP_FFMPEG_OPTIONS = os.getenv(\n94: \"OPENCV_FFMPEG_CAPTURE_OPTIONS\",\n95: \"rtsp_transport;tcp|fflags;nobuffer|flags;low_delay\",\n96: )\n97: \n98: IS_LIVE = SOURCE.lower().startswith((\"rtsp://\", \"http://\"))\n99: \n100: MOTION_DETECTION_ENABLED = os.getenv(\"MOTION_DETECTION_ENABLED\", \"false\").lower() == \"true\"\n101: MOTION_THRESHOLD = float(os.getenv(\"MOTION_THRESHOLD\", \"5.0\"))\n102: \n103: CROSS_FLASH_FRAMES = 12\n104: POPUP_LIFETIME = 20\n105: LINE_PULSE_FRAMES = 12\n106: COUNT_PULSE_FRAMES = 15\n107: BATCH_PULSE_FRAMES = 20\n108: \n109: SKELETON = [(0, 1), (4, 3), (1, 2), (3, 2), (2, 6), (2, 5), (2, 7), (7, 8)]\n110: SK_COLORS = [\n111: (0, 255, 255),\n112: (0, 255, 255),\n113: (255, 0, 255),\n114: (255, 0, 255),\n115: (0, 255, 0),\n116: (255, 255, 0),\n117: (0, 0, 255),\n118: (200, 200, 0),\n119: ]\n\n(Showing lines 60-119 of 1373. Use offset=120 to continue.)\n</content>",
|
||
"metadata": {
|
||
"preview": "TRACK_LOW_THRESH_1 = float(os.getenv(\"TRACK_LOW_THRESH_1\", \"0.1\"))\nTRACK_MATCH_THRESH_1 = float(os.getenv(\"TRACK_MATCH_THRESH_1\", \"0.6\"))\nTRACK_BUFFER_1 = int(os.getenv(\"TRACK_BUFFER_1\", \"30\"))\nTRACK_MIN_HITS_1 = int(os.getenv(\"TRACK_MIN_HITS_1\", \"3\"))\n\nDAILY_CUTOFF_TIME = os.getenv(\"DAILY_CUTOFF_TIME\", \"20:00\")\nBATCH_TIMEOUT_SECONDS = float(os.getenv(\"BATCH_TIMEOUT_SECONDS\", \"300\"))\nIGNORE_BATCH_LABEL_TIMEOUT = float(\n os.getenv(\"IGNORE_BATCH_LABEL_TIMEOUT_SECONDS\", \"30\")\n)\nMIN_OBJECT_PER_BATCH = int(os.getenv(\"MIN_OBJECT_PER_BATCH\", \"60\"))\nMIN_DURATION_PER_BATCH = int(os.getenv(\"MIN_DURATION_PER_BATCH\", \"60\"))\n\nEXPORT_CSV = os.getenv(\"EXPORT_CSV\", \"true\").lower() == \"true\"\nCROSS_CSV = os.getenv(\"CROSS_CSV\", f\"{OUTPUT_DIR}/batch_crossings.csv\")\n\nRATE_WINDOW_SEC = int(os.getenv(\"RATE_WINDOW_SEC\", \"60\"))\nWARMUP_FRAMES = int(os.getenv(\"WARMUP_FRAMES\", \"30\"))\nRECONNECT_DELAY_SEC = int(os.getenv(\"RECONNECT_DELAY_SEC\", \"3\"))\nMAX_RECONNECT_ATTEMPTS = int(os.getenv(\"MAX_RECONNECT_ATTEMPTS\", \"0\"))",
|
||
"truncated": true,
|
||
"loaded": [],
|
||
"display": {
|
||
"type": "file",
|
||
"path": "/opt/bytetrack-counter/counter_live_rknn_bytetrack.py",
|
||
"text": "TRACK_LOW_THRESH_1 = float(os.getenv(\"TRACK_LOW_THRESH_1\", \"0.1\"))\nTRACK_MATCH_THRESH_1 = float(os.getenv(\"TRACK_MATCH_THRESH_1\", \"0.6\"))\nTRACK_BUFFER_1 = int(os.getenv(\"TRACK_BUFFER_1\", \"30\"))\nTRACK_MIN_HITS_1 = int(os.getenv(\"TRACK_MIN_HITS_1\", \"3\"))\n\nDAILY_CUTOFF_TIME = os.getenv(\"DAILY_CUTOFF_TIME\", \"20:00\")\nBATCH_TIMEOUT_SECONDS = float(os.getenv(\"BATCH_TIMEOUT_SECONDS\", \"300\"))\nIGNORE_BATCH_LABEL_TIMEOUT = float(\n os.getenv(\"IGNORE_BATCH_LABEL_TIMEOUT_SECONDS\", \"30\")\n)\nMIN_OBJECT_PER_BATCH = int(os.getenv(\"MIN_OBJECT_PER_BATCH\", \"60\"))\nMIN_DURATION_PER_BATCH = int(os.getenv(\"MIN_DURATION_PER_BATCH\", \"60\"))\n\nEXPORT_CSV = os.getenv(\"EXPORT_CSV\", \"true\").lower() == \"true\"\nCROSS_CSV = os.getenv(\"CROSS_CSV\", f\"{OUTPUT_DIR}/batch_crossings.csv\")\n\nRATE_WINDOW_SEC = int(os.getenv(\"RATE_WINDOW_SEC\", \"60\"))\nWARMUP_FRAMES = int(os.getenv(\"WARMUP_FRAMES\", \"30\"))\nRECONNECT_DELAY_SEC = int(os.getenv(\"RECONNECT_DELAY_SEC\", \"3\"))\nMAX_RECONNECT_ATTEMPTS = int(os.getenv(\"MAX_RECONNECT_ATTEMPTS\", \"0\"))\nFLUSH_EVERY_N_FRAMES = int(os.getenv(\"FLUSH_EVERY_N_FRAMES\", \"100\"))\nTRACKED_PRUNE_SEC = int(os.getenv(\"TRACKED_PRUNE_SEC\", \"300\"))\nRECORD_VIDEO = os.getenv(\"RECORD_VIDEO\", \"false\").lower() == \"true\"\nVIDEO_SEGMENT_SEC = int(os.getenv(\"VIDEO_SEGMENT_SEC\", \"3600\"))\nOUTPUT_FPS = int(os.getenv(\"OUTPUT_FPS\", \"15\"))\n\nLIVE_STREAM_ENABLED = os.getenv(\"LIVE_STREAM_ENABLED\", \"false\").lower() == \"true\"\nLIVE_STREAM_FRAME_PATH = os.getenv(\n \"LIVE_STREAM_FRAME_PATH\", \"/dev/shm/jetson-counter/live_frame.jpg\"\n)\nLIVE_STREAM_QUALITY = int(os.getenv(\"LIVE_STREAM_QUALITY\", \"75\"))\nLIVE_STREAM_EVERY_N = int(os.getenv(\"LIVE_STREAM_EVERY_N\", \"2\"))\n\nRTSP_FFMPEG_OPTIONS = os.getenv(\n \"OPENCV_FFMPEG_CAPTURE_OPTIONS\",\n \"rtsp_transport;tcp|fflags;nobuffer|flags;low_delay\",\n)\n\nIS_LIVE = SOURCE.lower().startswith((\"rtsp://\", \"http://\"))\n\nMOTION_DETECTION_ENABLED = os.getenv(\"MOTION_DETECTION_ENABLED\", \"false\").lower() == \"true\"\nMOTION_THRESHOLD = float(os.getenv(\"MOTION_THRESHOLD\", \"5.0\"))\n\nCROSS_FLASH_FRAMES = 12\nPOPUP_LIFETIME = 20\nLINE_PULSE_FRAMES = 12\nCOUNT_PULSE_FRAMES = 15\nBATCH_PULSE_FRAMES = 20\n\nSKELETON = [(0, 1), (4, 3), (1, 2), (3, 2), (2, 6), (2, 5), (2, 7), (7, 8)]\nSK_COLORS = [\n (0, 255, 255),\n (0, 255, 255),\n (255, 0, 255),\n (255, 0, 255),\n (0, 255, 0),\n (255, 255, 0),\n (0, 0, 255),\n (200, 200, 0),\n]",
|
||
"lineStart": 60,
|
||
"lineEnd": 119,
|
||
"totalLines": 1373,
|
||
"truncated": true
|
||
}
|
||
},
|
||
"title": "counter_live_rknn_bytetrack.py",
|
||
"time": {
|
||
"start": 1785284255459,
|
||
"end": 1785284255534
|
||
}
|
||
},
|
||
"id": "prt_fab3bdced001KJxtl4QludpC2S",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3bbece001uI2KIIvs954jfg"
|
||
},
|
||
{
|
||
"reason": "tool-calls",
|
||
"snapshot": "e0e1414b68e2544fb2d71b977882e90620947d43",
|
||
"type": "step-finish",
|
||
"tokens": {
|
||
"total": 52556,
|
||
"input": 1925,
|
||
"output": 148,
|
||
"reasoning": 435,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 50048
|
||
}
|
||
},
|
||
"cost": 0.001526009,
|
||
"id": "prt_fab3bdf9d001OvxqWwqcHYSrjC",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3bbece001uI2KIIvs954jfg"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"parentID": "msg_fab3b7662001asy7eTmLMm4mEq",
|
||
"role": "assistant",
|
||
"mode": "build",
|
||
"agent": "build",
|
||
"path": {
|
||
"cwd": "/opt/bytetrack-counter",
|
||
"root": "/opt/bytetrack-counter"
|
||
},
|
||
"cost": 0.00192154,
|
||
"tokens": {
|
||
"total": 55255,
|
||
"input": 1570,
|
||
"output": 767,
|
||
"reasoning": 438,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 52480
|
||
}
|
||
},
|
||
"modelID": "deepseek-v4-pro",
|
||
"providerID": "deepseek",
|
||
"time": {
|
||
"created": 1785284255754,
|
||
"completed": 1785284274732
|
||
},
|
||
"finish": "tool-calls",
|
||
"id": "msg_fab3be00a001fp1QodZ1oaBB64",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"snapshot": "e0e1414b68e2544fb2d71b977882e90620947d43",
|
||
"type": "step-start",
|
||
"id": "prt_fab3be31a0013nZ3yHEcVstzJb",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3be00a001fp1QodZ1oaBB64"
|
||
},
|
||
{
|
||
"type": "reasoning",
|
||
"text": "Now I have a comprehensive understanding. Let me write AGENTS.md.\n\nKey high-signal facts:\n\n1. **No build/test/lint**: Python scripts run directly. No test framework, no lint/typecheck config. Don't try to run tests or lint.\n2. **The primary entry point** is `counter_live_rknn_bytetrack.py` — that's what systemd runs. There are 3 counter variants, but only the bytetrack one is the deployed one.\n3. **Install path inconsistency**: Service files hardcode `/opt/bytetrack-counter`, but README/DEPLOY/docs reference `/opt/jetson-counter`. The config.env.example also uses `/opt/bytetrack-counter`. The actual install directory on the target is `/opt/bytetrack-counter`.\n4. **`.env` is gitignored** and required. Copy from `config.env.example`.\n5. **PYTHONNOUSERSITE=1** is critical for the venv to work correctly.\n6. **numpy<2** is required.\n7. **Two systemd units**: `bytetrack-counter.service` (counter) and `bytetrack-counter-dashboard.service` (Flask dashboard on :5000).\n8. **Hardware-specific**: RKNN Toolkit Lite2 (RK3588 NPU). `counter_live.py` is the Jetson CUDA variant, the RKNN variants are the ones used on NanoPi/RK3588.\n9. **Dependencies**: `rknn-toolkit-lite2`, `opencv-python`, `flask`, `python-dotenv`, `openpyxl` from pip. torch wheel from NVIDIA for Jetson variant only.\n10. **The service user** is `root` (not `jetson`).\n11. **No version info mechanism** exists anywhere.\n\nLet me also note the `env/` and `venv/` directories — both are in gitignore. The project has both an `env/` (possibly a legacy system venv) and `venv/` (from setup-venv.sh).",
|
||
"time": {
|
||
"start": 1785284257010,
|
||
"end": 1785284263460
|
||
},
|
||
"id": "prt_fab3be4f2001GOycyGwoFRzddT",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3be00a001fp1QodZ1oaBB64"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "write",
|
||
"callID": "call_00_J2NHcbYV4OXzqhpDDIun0277",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"filePath": "/opt/bytetrack-counter/AGENTS.md",
|
||
"content": "# AGENTS.md — bytetrack-counter\n\n## Architecture\n\n- **Edge AI counter**: RTSP camera → YOLO RKNN (NPU) → tracking → line-crossing → SQLite + JSON state → Flask dashboard.\n- **3 counter scripts**, only 1 deployed:\n - `counter_live.py` — Jetson TensorRT variant (CUDA, NOT used on RK3588).\n - `counter_live_rknn.py` — RK3588 with simple IoU tracker.\n - **`counter_live_rknn_bytetrack.py`** — RK3588 with ByteTrack. **This is what systemd runs.**\n- `batch_store.py` — shared SQLite persistence + batch state machine.\n- `counter_dashboard.py` — Flask dashboard on port 5000, same DB.\n\n## No build / test / lint\n\nThere is no build system, no test framework, no linter config, no typechecker. \nDo not try to run `pytest`, `ruff`, `mypy`, etc. — they don't exist here.\n\n## How to run\n\n```bash\n# Copy env (required, .env is gitignored)\ncp config.env.example .env\n\n# Venv (must use system-site-packages for RKNN toolkit)\npython3 -m venv --system-site-packages venv\nsource venv/bin/pip install -r requirements.txt\n\n# Run counter (RK3588 only — needs rknn-toolkit-lite2 & RKNN model)\nPYTHONNOUSERSITE=1 venv/bin/python counter_live_rknn_bytetrack.py\n\n# Run dashboard\nPYTHONNOUSERSITE=1 venv/bin/python counter_dashboard.py\n```\n\n## Key environment & install quirks\n\n- **`PYTHONNOUSERSITE=1`** is mandatory when running from the venv — without it, system/user packages leak in.\n- **`.env` is gitignored** — always copy from `config.env.example` first.\n- **`numpy<2`** is required for `rknn-toolkit-lite2` compatibility.\n- **Install path in service files is `/opt/bytetrack-counter`** (not the `/opt/jetson-counter` mentioned in README/DEPLOY). The `.env.example` also reflects `/opt/bytetrack-counter`.\n- Service user is **`root`**, not `jetson` (despite README saying otherwise).\n- Two systemd units: `bytetrack-counter.service` + `bytetrack-counter-dashboard.service`.\n- `counter_live.py` (TensorRT) is Jetson-only and won't work on RK3588.\n\n## Code conventions\n\n- All config lives in `.env` (dotenv), read via `os.getenv()` at module top-level in each script.\n- The 3 counter scripts duplicate ~80% of each other (drawing helpers, batch loop, etc). Changes to logic may need replication across variants.\n- `batch_store.py` has its own threading (cutoff watcher, batch timeout timer) — thread safety is via a single `state_lock`.\n- The dashboard re-creates DB tables on startup (`_ensure_db()`) independently from `batch_store.py`.\n- No formal version tracking exists anywhere in this codebase.\n"
|
||
},
|
||
"output": "Wrote file successfully.",
|
||
"metadata": {
|
||
"diagnostics": {},
|
||
"filepath": "/opt/bytetrack-counter/AGENTS.md",
|
||
"exists": false,
|
||
"truncated": false
|
||
},
|
||
"title": "AGENTS.md",
|
||
"time": {
|
||
"start": 1785284274274,
|
||
"end": 1785284274306
|
||
}
|
||
},
|
||
"id": "prt_fab3bfe29001x7rVc75F3tfsDe",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3be00a001fp1QodZ1oaBB64"
|
||
},
|
||
{
|
||
"reason": "tool-calls",
|
||
"snapshot": "19818784677404bc50d6414ea41a4d12a2d11390",
|
||
"type": "step-finish",
|
||
"tokens": {
|
||
"total": 55255,
|
||
"input": 1570,
|
||
"output": 767,
|
||
"reasoning": 438,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 52480
|
||
}
|
||
},
|
||
"cost": 0.00192154,
|
||
"id": "prt_fab3c297c0014oWCHweUmJW309",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3be00a001fp1QodZ1oaBB64"
|
||
},
|
||
{
|
||
"type": "patch",
|
||
"hash": "e0e1414b68e2544fb2d71b977882e90620947d43",
|
||
"files": [
|
||
"/opt/bytetrack-counter/AGENTS.md"
|
||
],
|
||
"id": "prt_fab3c2a26001RZ7MoL3GbRzEF1",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3be00a001fp1QodZ1oaBB64"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"parentID": "msg_fab3b7662001asy7eTmLMm4mEq",
|
||
"role": "assistant",
|
||
"mode": "build",
|
||
"agent": "build",
|
||
"path": {
|
||
"cwd": "/opt/bytetrack-counter",
|
||
"root": "/opt/bytetrack-counter"
|
||
},
|
||
"cost": 0.02359266,
|
||
"tokens": {
|
||
"total": 56062,
|
||
"input": 54064,
|
||
"output": 53,
|
||
"reasoning": 25,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 1920
|
||
}
|
||
},
|
||
"modelID": "deepseek-v4-pro",
|
||
"providerID": "deepseek",
|
||
"time": {
|
||
"created": 1785284274743,
|
||
"completed": 1785284282317
|
||
},
|
||
"finish": "tool-calls",
|
||
"id": "msg_fab3c2a37001vNQbjyhvytGUr2",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"snapshot": "19818784677404bc50d6414ea41a4d12a2d11390",
|
||
"type": "step-start",
|
||
"id": "prt_fab3c2e18001dS15xXjxbZfgVz",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3c2a37001vNQbjyhvytGUr2"
|
||
},
|
||
{
|
||
"type": "reasoning",
|
||
"text": "I've written the AGENTS.md. Let me verify it's correct by re-reading it to make sure it looks good.",
|
||
"time": {
|
||
"start": 1785284281104,
|
||
"end": 1785284281689
|
||
},
|
||
"id": "prt_fab3c430f001KdRRYtuc6VDK9F",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3c2a37001vNQbjyhvytGUr2"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "read",
|
||
"callID": "call_00_WmAE5vca6LMYxVTFM7SQ8563",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"filePath": "/opt/bytetrack-counter/AGENTS.md"
|
||
},
|
||
"output": "<path>/opt/bytetrack-counter/AGENTS.md</path>\n<type>file</type>\n<content>\n1: # AGENTS.md — bytetrack-counter\n2: \n3: ## Architecture\n4: \n5: - **Edge AI counter**: RTSP camera → YOLO RKNN (NPU) → tracking → line-crossing → SQLite + JSON state → Flask dashboard.\n6: - **3 counter scripts**, only 1 deployed:\n7: - `counter_live.py` — Jetson TensorRT variant (CUDA, NOT used on RK3588).\n8: - `counter_live_rknn.py` — RK3588 with simple IoU tracker.\n9: - **`counter_live_rknn_bytetrack.py`** — RK3588 with ByteTrack. **This is what systemd runs.**\n10: - `batch_store.py` — shared SQLite persistence + batch state machine.\n11: - `counter_dashboard.py` — Flask dashboard on port 5000, same DB.\n12: \n13: ## No build / test / lint\n14: \n15: There is no build system, no test framework, no linter config, no typechecker. \n16: Do not try to run `pytest`, `ruff`, `mypy`, etc. — they don't exist here.\n17: \n18: ## How to run\n19: \n20: ```bash\n21: # Copy env (required, .env is gitignored)\n22: cp config.env.example .env\n23: \n24: # Venv (must use system-site-packages for RKNN toolkit)\n25: python3 -m venv --system-site-packages venv\n26: source venv/bin/pip install -r requirements.txt\n27: \n28: # Run counter (RK3588 only — needs rknn-toolkit-lite2 & RKNN model)\n29: PYTHONNOUSERSITE=1 venv/bin/python counter_live_rknn_bytetrack.py\n30: \n31: # Run dashboard\n32: PYTHONNOUSERSITE=1 venv/bin/python counter_dashboard.py\n33: ```\n34: \n35: ## Key environment & install quirks\n36: \n37: - **`PYTHONNOUSERSITE=1`** is mandatory when running from the venv — without it, system/user packages leak in.\n38: - **`.env` is gitignored** — always copy from `config.env.example` first.\n39: - **`numpy<2`** is required for `rknn-toolkit-lite2` compatibility.\n40: - **Install path in service files is `/opt/bytetrack-counter`** (not the `/opt/jetson-counter` mentioned in README/DEPLOY). The `.env.example` also reflects `/opt/bytetrack-counter`.\n41: - Service user is **`root`**, not `jetson` (despite README saying otherwise).\n42: - Two systemd units: `bytetrack-counter.service` + `bytetrack-counter-dashboard.service`.\n43: - `counter_live.py` (TensorRT) is Jetson-only and won't work on RK3588.\n44: \n45: ## Code conventions\n46: \n47: - All config lives in `.env` (dotenv), read via `os.getenv()` at module top-level in each script.\n48: - The 3 counter scripts duplicate ~80% of each other (drawing helpers, batch loop, etc). Changes to logic may need replication across variants.\n49: - `batch_store.py` has its own threading (cutoff watcher, batch timeout timer) — thread safety is via a single `state_lock`.\n50: - The dashboard re-creates DB tables on startup (`_ensure_db()`) independently from `batch_store.py`.\n51: - No formal version tracking exists anywhere in this codebase.\n\n(End of file - total 51 lines)\n</content>",
|
||
"metadata": {
|
||
"preview": "# AGENTS.md — bytetrack-counter\n\n## Architecture\n\n- **Edge AI counter**: RTSP camera → YOLO RKNN (NPU) → tracking → line-crossing → SQLite + JSON state → Flask dashboard.\n- **3 counter scripts**, only 1 deployed:\n - `counter_live.py` — Jetson TensorRT variant (CUDA, NOT used on RK3588).\n - `counter_live_rknn.py` — RK3588 with simple IoU tracker.\n - **`counter_live_rknn_bytetrack.py`** — RK3588 with ByteTrack. **This is what systemd runs.**\n- `batch_store.py` — shared SQLite persistence + batch state machine.\n- `counter_dashboard.py` — Flask dashboard on port 5000, same DB.\n\n## No build / test / lint\n\nThere is no build system, no test framework, no linter config, no typechecker. \nDo not try to run `pytest`, `ruff`, `mypy`, etc. — they don't exist here.\n\n## How to run\n\n```bash",
|
||
"truncated": false,
|
||
"loaded": [],
|
||
"display": {
|
||
"type": "file",
|
||
"path": "/opt/bytetrack-counter/AGENTS.md",
|
||
"text": "# AGENTS.md — bytetrack-counter\n\n## Architecture\n\n- **Edge AI counter**: RTSP camera → YOLO RKNN (NPU) → tracking → line-crossing → SQLite + JSON state → Flask dashboard.\n- **3 counter scripts**, only 1 deployed:\n - `counter_live.py` — Jetson TensorRT variant (CUDA, NOT used on RK3588).\n - `counter_live_rknn.py` — RK3588 with simple IoU tracker.\n - **`counter_live_rknn_bytetrack.py`** — RK3588 with ByteTrack. **This is what systemd runs.**\n- `batch_store.py` — shared SQLite persistence + batch state machine.\n- `counter_dashboard.py` — Flask dashboard on port 5000, same DB.\n\n## No build / test / lint\n\nThere is no build system, no test framework, no linter config, no typechecker. \nDo not try to run `pytest`, `ruff`, `mypy`, etc. — they don't exist here.\n\n## How to run\n\n```bash\n# Copy env (required, .env is gitignored)\ncp config.env.example .env\n\n# Venv (must use system-site-packages for RKNN toolkit)\npython3 -m venv --system-site-packages venv\nsource venv/bin/pip install -r requirements.txt\n\n# Run counter (RK3588 only — needs rknn-toolkit-lite2 & RKNN model)\nPYTHONNOUSERSITE=1 venv/bin/python counter_live_rknn_bytetrack.py\n\n# Run dashboard\nPYTHONNOUSERSITE=1 venv/bin/python counter_dashboard.py\n```\n\n## Key environment & install quirks\n\n- **`PYTHONNOUSERSITE=1`** is mandatory when running from the venv — without it, system/user packages leak in.\n- **`.env` is gitignored** — always copy from `config.env.example` first.\n- **`numpy<2`** is required for `rknn-toolkit-lite2` compatibility.\n- **Install path in service files is `/opt/bytetrack-counter`** (not the `/opt/jetson-counter` mentioned in README/DEPLOY). The `.env.example` also reflects `/opt/bytetrack-counter`.\n- Service user is **`root`**, not `jetson` (despite README saying otherwise).\n- Two systemd units: `bytetrack-counter.service` + `bytetrack-counter-dashboard.service`.\n- `counter_live.py` (TensorRT) is Jetson-only and won't work on RK3588.\n\n## Code conventions\n\n- All config lives in `.env` (dotenv), read via `os.getenv()` at module top-level in each script.\n- The 3 counter scripts duplicate ~80% of each other (drawing helpers, batch loop, etc). Changes to logic may need replication across variants.\n- `batch_store.py` has its own threading (cutoff watcher, batch timeout timer) — thread safety is via a single `state_lock`.\n- The dashboard re-creates DB tables on startup (`_ensure_db()`) independently from `batch_store.py`.\n- No formal version tracking exists anywhere in this codebase.",
|
||
"lineStart": 1,
|
||
"lineEnd": 51,
|
||
"totalLines": 51,
|
||
"truncated": false
|
||
}
|
||
},
|
||
"title": "AGENTS.md",
|
||
"time": {
|
||
"start": 1785284281909,
|
||
"end": 1785284282039
|
||
}
|
||
},
|
||
"id": "prt_fab3c455f001X4X3Kem4IJjgLm",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3c2a37001vNQbjyhvytGUr2"
|
||
},
|
||
{
|
||
"reason": "tool-calls",
|
||
"snapshot": "19818784677404bc50d6414ea41a4d12a2d11390",
|
||
"type": "step-finish",
|
||
"tokens": {
|
||
"total": 56062,
|
||
"input": 54064,
|
||
"output": 53,
|
||
"reasoning": 25,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 1920
|
||
}
|
||
},
|
||
"cost": 0.02359266,
|
||
"id": "prt_fab3c473d001BD0lMpdK0W4oYj",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3c2a37001vNQbjyhvytGUr2"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"parentID": "msg_fab3b7662001asy7eTmLMm4mEq",
|
||
"role": "assistant",
|
||
"mode": "build",
|
||
"agent": "build",
|
||
"path": {
|
||
"cwd": "/opt/bytetrack-counter",
|
||
"root": "/opt/bytetrack-counter"
|
||
},
|
||
"cost": 0.000997078,
|
||
"tokens": {
|
||
"total": 57343,
|
||
"input": 988,
|
||
"output": 133,
|
||
"reasoning": 286,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 55936
|
||
}
|
||
},
|
||
"modelID": "deepseek-v4-pro",
|
||
"providerID": "deepseek",
|
||
"time": {
|
||
"created": 1785284282329,
|
||
"completed": 1785284290270
|
||
},
|
||
"finish": "stop",
|
||
"id": "msg_fab3c47d9001MoRXhjqaN4TmBw",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"snapshot": "19818784677404bc50d6414ea41a4d12a2d11390",
|
||
"type": "step-start",
|
||
"id": "prt_fab3c4b56001OPztSDoZqkeEcM",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3c47d9001MoRXhjqaN4TmBw"
|
||
},
|
||
{
|
||
"type": "reasoning",
|
||
"text": "The AGENTS.md looks good. It's concise (~50 lines), covers high-signal facts an agent would miss, and avoids generic advice. Let me verify a few things:\n\n1. ✅ Architecture — which scripts exist, which one is deployed, the pipeline flow\n2. ✅ No build/test/lint — critical to avoid agent trying tools that don't exist\n3. ✅ How to run — exact commands including PYTHONNOUSERSITE=1\n4. ✅ Key quirks — PYTHONNOUSERSITE=1, gitignored .env, numpy<2, install path discrepancy, service user, systemd units, Jetson vs RK3588\n5. ✅ Code conventions — dotenv config, code duplication, threading model, independent DB init, no version tracking\n\nEverything is executable and verified against actual source files, not speculative. Let me also verify: the README says install path is `/opt/jetson-counter` but the service files say `/opt/bytetrack-counter/counter_live_rknn_bytetrack.py` and this repo is at `/opt/bytetrack-counter`. Yes - I confirmed that the service files use `/opt/bytetrack-counter` everywhere and the README/DEPLOY still say `/opt/jetson-counter`. This is a significant gotcha I correctly documented.\n\nThe file is ready. I should present this to the user.",
|
||
"time": {
|
||
"start": 1785284283646,
|
||
"end": 1785284288029
|
||
},
|
||
"id": "prt_fab3c4cfe0018CeOrlhqF02EMY",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3c47d9001MoRXhjqaN4TmBw"
|
||
},
|
||
{
|
||
"type": "text",
|
||
"text": "AGENTS.md created at `/opt/bytetrack-counter/AGENTS.md` — 51 lines of verified, repo-specific guidance covering:\n\n- Which script is actually deployed (`counter_live_rknn_bytetrack.py`)\n- No build/test/lint exists\n- Exact run commands with `PYTHONNOUSERSITE=1`\n- Key gotchas: gitignored `.env`, `numpy<2`, install path discrepancy (`bytetrack-counter` vs `jetson-counter`), service user is `root`\n- Code conventions (dotenv config, 80% duplication across counter variants, threading model, independent DB init)",
|
||
"time": {
|
||
"start": 1785284288035,
|
||
"end": 1785284290081
|
||
},
|
||
"id": "prt_fab3c5e220015wujcInvNWrFPZ",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3c47d9001MoRXhjqaN4TmBw"
|
||
},
|
||
{
|
||
"reason": "stop",
|
||
"snapshot": "19818784677404bc50d6414ea41a4d12a2d11390",
|
||
"type": "step-finish",
|
||
"tokens": {
|
||
"total": 57343,
|
||
"input": 988,
|
||
"output": 133,
|
||
"reasoning": 286,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 55936
|
||
}
|
||
},
|
||
"cost": 0.000997078,
|
||
"id": "prt_fab3c6691001g87MCviVyt6rdW",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3c47d9001MoRXhjqaN4TmBw"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"role": "user",
|
||
"time": {
|
||
"created": 1785284299032
|
||
},
|
||
"agent": "build",
|
||
"model": {
|
||
"providerID": "deepseek",
|
||
"modelID": "deepseek-v4-pro"
|
||
},
|
||
"summary": {
|
||
"diffs": []
|
||
},
|
||
"id": "msg_fab3c8917001WYuSkLjsItpiIS",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"type": "text",
|
||
"text": "# spec — spec mutator\n\nRead `FORMAT.md` at repo root if not already loaded. Caveman skill applies to all writes here.\n\n## DISPATCH\n\nInspect user request and project state:\n\n1. No `SPEC.md` at repo root AND args describe idea → **NEW**\n2. No `SPEC.md` AND `from-code` in args → **DISTILL**\n3. `SPEC.md` exists AND args start `bug:` → **BACKPROP**\n4. `SPEC.md` exists AND args start `amend` → **AMEND**\n5. `SPEC.md` exists, no args → ask user which mode\n\n## INPUTS — spec is the sole mutator\n\nThe other verbs produce material; spec writes it. Ingest their handoff blocks\ninto the right section, show a diff, write on OK:\n\n- **grill** → sharpened §G + §C\n- **research** → §R rows (add the §R section if absent)\n- **review** → drafted §V lines + the risk verdict\n- **deepen** → §I/§V/§T amendments\n\n⊥ rewrite a section the handoff did not name. Sectioned ownership (see FORMAT.md).\n\n## NEW — idea → spec\n\nInput: user idea. If it arrived fuzzy, prefer running **grill** first.\n\nSteps:\n1. Extract goal (1 line, caveman). → §G.\n2. List constraints user stated or implied. → §C.\n3. List external surfaces user named. → §I.\n4. §R only if **research** ran — else omit the section (right-size).\n5. Propose initial invariants. → §V (numbered V1…).\n6. Break goal into ordered tasks. → §T pipe table, all status `.`, ids T1…\n7. §B section with header row only (`id|date|cause|fix`).\n\nWrite to `SPEC.md`. Show user full file. Ask: \"spec OK? `/review` if high-blast-radius, else `/build`.\"\n\n## DISTILL — code → spec\n\nWalk repo. Produce §G (infer from README/package.json/main entry), §C (infer from stack), §I (enumerate public APIs/CLIs/configs), §V (derive from tests and assertions), §T (one task per known TODO or missing test), §B (empty).\n\nCaveman everywhere. Flag uncertain items with `?` in text so user can confirm.\n\n## BACKPROP — bug → §B + §V\n\nInput: `bug: <description>`.\n\nSteps:\n1. Parse bug description.\n2. Find root cause (read relevant code).\n3. Decide: would a new invariant catch recurrence? If yes → draft `V<next>`.\n4. Append §B row: `B<next>|<date>|<cause>|V<N>`.\n5. Append new invariant to §V.\n6. If fix also changes behavior → add/update §T rows.\n7. Show diff. Apply only on user OK.\n\nRule: every bug gets a §B entry. Invariant optional but preferred.\n\n## AMEND — targeted edit\n\nInput: `amend §V.3` or `amend §T` etc.\n\nRead that section. Show current. Ask user what changes. Write. Show diff.\n\nNever silently rewrite sections user did not name.\n\n## OUTPUT RULES\n\n- Caveman format per `FORMAT.md`.\n- Preserve identifiers, paths, code verbatim.\n- Numbering monotonic — never reuse §V.N or §B.N.\n- §T row `cites` column ! list §V/§I deps: `T5|.|impl auth mw|V2,I.api`.\n\n## NON-GOALS\n\n- No sub-agents. Main thread writes.\n- No dashboards, no logs, no state files beyond SPEC.md itself.\n- No auto-build after spec. User invokes build explicitly.\n\n\nBase directory for this skill: /root/.agents/skills/spec\nRelative paths in this skill (e.g., scripts/, references/) are relative to this base directory.",
|
||
"id": "prt_fab3c891b001gc2J88JxHf3CfB",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3c8917001WYuSkLjsItpiIS"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"parentID": "msg_fab3c8917001WYuSkLjsItpiIS",
|
||
"role": "assistant",
|
||
"mode": "build",
|
||
"agent": "build",
|
||
"path": {
|
||
"cwd": "/opt/bytetrack-counter",
|
||
"root": "/opt/bytetrack-counter"
|
||
},
|
||
"cost": 0.000771168,
|
||
"tokens": {
|
||
"total": 58362,
|
||
"input": 996,
|
||
"output": 117,
|
||
"reasoning": 33,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 57216
|
||
}
|
||
},
|
||
"modelID": "deepseek-v4-pro",
|
||
"providerID": "deepseek",
|
||
"time": {
|
||
"created": 1785284299093,
|
||
"completed": 1785284302212
|
||
},
|
||
"finish": "tool-calls",
|
||
"id": "msg_fab3c8955001UfkRgspoDB4fxl",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"snapshot": "19818784677404bc50d6414ea41a4d12a2d11390",
|
||
"type": "step-start",
|
||
"id": "prt_fab3c8c8a001BFr6FsvHEpvWgu",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3c8955001UfkRgspoDB4fxl"
|
||
},
|
||
{
|
||
"type": "reasoning",
|
||
"text": "The user is invoking the `spec` skill. Let me first check if SPEC.md and FORMAT.md exist at the repo root, then follow the dispatch rules.",
|
||
"time": {
|
||
"start": 1785284300363,
|
||
"end": 1785284301245
|
||
},
|
||
"id": "prt_fab3c8e4b0012qyneG5naZ1CCf",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3c8955001UfkRgspoDB4fxl"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "glob",
|
||
"callID": "call_00_8wNcjzibVRzTuBuEXX0y0674",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"path": "/opt/bytetrack-counter",
|
||
"pattern": "SPEC.md"
|
||
},
|
||
"output": "No files found",
|
||
"metadata": {
|
||
"count": 0,
|
||
"truncated": false
|
||
},
|
||
"title": "",
|
||
"time": {
|
||
"start": 1785284301592,
|
||
"end": 1785284301615
|
||
}
|
||
},
|
||
"id": "prt_fab3c91c80015OX1f6J0PkF3c7",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3c8955001UfkRgspoDB4fxl"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "glob",
|
||
"callID": "call_01_VdpeoICDf2IFqkd2hJ1N0969",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"path": "/opt/bytetrack-counter",
|
||
"pattern": "FORMAT.md"
|
||
},
|
||
"output": "No files found",
|
||
"metadata": {
|
||
"count": 0,
|
||
"truncated": false
|
||
},
|
||
"title": "",
|
||
"time": {
|
||
"start": 1785284301893,
|
||
"end": 1785284301930
|
||
}
|
||
},
|
||
"id": "prt_fab3c9346001sETMrovjXaGRqK",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3c8955001UfkRgspoDB4fxl"
|
||
},
|
||
{
|
||
"reason": "tool-calls",
|
||
"snapshot": "19818784677404bc50d6414ea41a4d12a2d11390",
|
||
"type": "step-finish",
|
||
"tokens": {
|
||
"total": 58362,
|
||
"input": 996,
|
||
"output": 117,
|
||
"reasoning": 33,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 57216
|
||
}
|
||
},
|
||
"cost": 0.000771168,
|
||
"id": "prt_fab3c94eb0013KwvDCMvckRp4p",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3c8955001UfkRgspoDB4fxl"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"parentID": "msg_fab3c8917001WYuSkLjsItpiIS",
|
||
"role": "assistant",
|
||
"mode": "build",
|
||
"agent": "build",
|
||
"path": {
|
||
"cwd": "/opt/bytetrack-counter",
|
||
"root": "/opt/bytetrack-counter"
|
||
},
|
||
"cost": 0.00075052,
|
||
"tokens": {
|
||
"total": 58934,
|
||
"input": 148,
|
||
"output": 68,
|
||
"reasoning": 478,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 58240
|
||
}
|
||
},
|
||
"modelID": "deepseek-v4-pro",
|
||
"providerID": "deepseek",
|
||
"time": {
|
||
"created": 1785284302224,
|
||
"completed": 1785284311467
|
||
},
|
||
"finish": "stop",
|
||
"id": "msg_fab3c9590001hWmCpw9In5LIZx",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"snapshot": "19818784677404bc50d6414ea41a4d12a2d11390",
|
||
"type": "step-start",
|
||
"id": "prt_fab3c9a08001cIOAxzgRBff35P",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3c9590001hWmCpw9In5LIZx"
|
||
},
|
||
{
|
||
"type": "reasoning",
|
||
"text": "No SPEC.md, no FORMAT.md, and no args were provided beyond the spec invocation. According to the dispatch rules:\n\n\"5. `SPEC.md` exists, no args → ask user which mode\"\n\nBut wait, SPEC.md does NOT exist here. And there are no args in the user message. Hmm, but looking at the previous conversation, the user said \"add feature to print version of compiled binary\" — that's an idea. But then the user said \"Create or update AGENTS.md\" which I did.\n\nNow the user is saying \"spec — spec mutator\" which looks like they're loading the spec skill. Looking more carefully at the user message, it starts with `# spec — spec mutator` — this is the skill being loaded. The instructions under \"## DISPATCH\" say:\n\n1. No `SPEC.md` at repo root AND args describe idea → **NEW**\n2. No `SPEC.md` AND `from-code` in args → **DISTILL**\n3. `SPEC.md` exists AND args start `bug:` → **BACKPROP**\n4. `SPEC.md` exists AND args start `amend` → **AMEND**\n5. `SPEC.md` exists, no args → ask user which mode\n\nSo: No SPEC.md, and... there don't seem to be explicit args in the user message. The user's message is just the skill instruction text. \n\nWait, let me re-read. The user message is just \"# spec — spec mutator\" — that's the skill being loaded. The content after is the skill instructions. There are no user-provided args in this message.\n\nActually, looking at the conversation history, the original request was \"add feature to print version of compiled binary\" — but that seems to be from a prior session. In this message it seems like the user is just invoking the spec skill without additional arguments.\n\nFollowing the dispatch rules: No SPEC.md, no args → this falls through all rules. The closest match would be to ask the user which mode. But this isn't exactly match #5 (which is specifically for when SPEC.md exists).\n\nLet me ask the user what they want to do. They need to tell me whether they want a NEW spec (describe an idea), DISTILL from code, or give me a bug/amend instruction.",
|
||
"time": {
|
||
"start": 1785284303824,
|
||
"end": 1785284310209
|
||
},
|
||
"id": "prt_fab3c9bd00013h2U58mvc3hXfn",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3c9590001hWmCpw9In5LIZx"
|
||
},
|
||
{
|
||
"type": "text",
|
||
"text": "No `SPEC.md` exists yet. What mode?\n\n- **new**: describe an idea and I'll write a fresh spec\n- **distill**: I'll reverse-engineer a spec from the existing codebase\n- **bug**: you have a `bug: description` for me to backprop\n\nWhich should I run?",
|
||
"time": {
|
||
"start": 1785284310215,
|
||
"end": 1785284311331
|
||
},
|
||
"id": "prt_fab3cb4c7001Z4X2YiquFRqyFi",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3c9590001hWmCpw9In5LIZx"
|
||
},
|
||
{
|
||
"reason": "stop",
|
||
"snapshot": "19818784677404bc50d6414ea41a4d12a2d11390",
|
||
"type": "step-finish",
|
||
"tokens": {
|
||
"total": 58934,
|
||
"input": 148,
|
||
"output": 68,
|
||
"reasoning": 478,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 58240
|
||
}
|
||
},
|
||
"cost": 0.00075052,
|
||
"id": "prt_fab3cb96f001XUTa0eLcnUQyb5",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3c9590001hWmCpw9In5LIZx"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"role": "user",
|
||
"time": {
|
||
"created": 1785284318641
|
||
},
|
||
"agent": "build",
|
||
"model": {
|
||
"providerID": "deepseek",
|
||
"modelID": "deepseek-v4-pro"
|
||
},
|
||
"summary": {
|
||
"diffs": [
|
||
{
|
||
"file": "SPEC.md",
|
||
"patch": "Index: SPEC.md\n===================================================================\n--- SPEC.md\t\n+++ SPEC.md\t\n@@ -0,0 +1,126 @@\n+# SPEC — bytetrack-counter\n+\n+## §G — Goal\n+\n+RTSP camera → YOLO RKNN (NPU) → ByteTrack → line-crossing counter → SQLite + Flask dashboard.\n+Count `ayam` (chicken) crossing counting line. Close batch on `talenan` (cutting-board) crossing.\n+Daily cutoff @ HH:MM resets batch numbering. RK3588 hardware.\n+\n+## §C — Constraints\n+\n+- Python 3.10, `rknn-toolkit-lite2` (NPU), `opencv-python` (RTSP/FFmpeg)\n+- `numpy<2` (rknn-toolkit-lite2 incompatible with numpy≥2)\n+- `PYTHONNOUSERSITE=1` ! set or venv breaks\n+- SQLite for persistence, JSON file for active-batch state\n+- Flask on port 5000, systemd supervision\n+- `counter_live.py` ⊥ run on RK3588 (Jetson TensorRT only)\n+- Single deployment: `counter_live_rknn_bytetrack.py` + `counter_dashboard.py`\n+\n+## §I — Interfaces\n+\n+### Systemd\n+```\n+unit: bytetrack-counter.service → `venv/bin/python counter_live_rknn_bytetrack.py`\n+unit: bytetrack-counter-dashboard.service → `venv/bin/python counter_dashboard.py`\n+env: PYTHONNOUSERSITE=1 ! set in both units\n+env: EnvironmentFile=/opt/bytetrack-counter/.env\n+user: root (not jetson)\n+path: /opt/bytetrack-counter (fixed in service file)\n+```\n+\n+### `.env` config (40+ vars, gitignored)\n+```\n+env: SOURCE ! set → RTSP URL | file path\n+env: MODEL_PATH ! set → .rknn file\n+env: IMGSZ ! set → model input size (e.g. 320)\n+env: CORE_MASK → NPU core mask (1=core0, 2=core1, 3=dual, 7=all)\n+env: NUM_CLASSES ! match model output count\n+env: CONF → detection confidence threshold (default 0.3)\n+env: DAILY_CUTOFF_TIME → HH:MM, default \"20:00\"\n+env: CROSS_DIRECTION → rtl | ltr | both (default rtl)\n+env: LINE_X | LINE_X_FRAC → counting line position\n+env: CLASS_AYAM → class name for counted object (index 0)\n+env: CLASS_TALENAN → class name for batch-close trigger (index 1)\n+env: RESET_COUNTERS_AT_CUTOFF → defined in example, ! consumed by code ? zombie\n+env: MOTION_DETECTION_ENABLED | MOTION_THRESHOLD → skip inference on still frames ?\n+env: TRACK_HIGH_THRESH_{0,1} | TRACK_LOW_THRESH_{0,1} | TRACK_MATCH_THRESH_{0,1} → ByteTrack params per class\n+env: BATCH_TIMEOUT_SECONDS → auto-close after inactivity (default 300)\n+env: IGNORE_BATCH_LABEL_TIMEOUT_SECONDS → suppress talenan close after batch start (default 30)\n+env: MIN_OBJECT_PER_BATCH → min count to persist batch (default 60)\n+env: MIN_DURATION_PER_BATCH → min seconds to persist batch (default 60)\n+env: LIVE_STREAM_ENABLED → write annotated JPEG snapshot each N frames\n+env: EXPORT_CSV → write per-crossing CSV (default true)\n+```\n+\n+### SQLite\n+```\n+table: batches (date, batch#, camera, label, count, start, end, created_at)\n+ UNIQUE(counting_date, batch_number, camera_name, object_label)\n+table: daily_summaries (date, camera, label, total_count, total_batches, updated_at)\n+ UNIQUE(counting_date, camera_name, object_label)\n+```\n+\n+### JSON state file\n+```\n+path: /tmp/bytetrack_current_batch.json (default)\n+schema: {counting_date, batch_number, count, start_time, last_detection_time, counted_event_ids[]}\n+```\n+\n+### Flask API\n+```\n+api: GET / → dashboard HTML\n+api: GET /api/current-batch → {count, batch_number, counting_date, start_time, last_detection_time}\n+api: GET /api/previous-batch → {date, batch#, count, start/end, duration_minutes}\n+api: GET /api/summary → {today, yesterday, all_time, average_per_day, best_day}\n+api: GET /api/daily-data?days=N → [ {date, total_count, total_batches, avg_per_batch} ]\n+api: GET /api/day-detail/<date> → {date, total_count, total_batches, total_duration, avg_duration, batches[]}\n+api: GET /api/recent-batches?limit=N → [ {date, batch#, count, start/end, duration} ]\n+api: GET /api/available-dates → [ {date, total_count, total_batches} ]\n+api: GET /api/export-daily-csv?days=N → .xlsx download (named csv, emits xlsx)\n+api: GET /api/export-day-csv/<date> → .xlsx download (named csv, emits xlsx)\n+api: GET /api/live-video → MJPEG stream from shared-memory JPEG\n+```\n+\n+## §V — Invariants\n+\n+```\n+V1: NUM_CLASSES must match model output → class 0=ayam, class 1=talenan\n+V2: line crossing → prev_cx > line_x ≥ cx (rtl) | prev_cx < line_x ≤ cx (ltr)\n+V3: ∀ track_id → counted at most once per batch (counted_event_ids set)\n+V4: batch persisted → count ≥ MIN_OBJECT_PER_BATCH & duration ≥ MIN_DURATION_PER_BATCH\n+V5: dt.time() < DAILY_CUTOFF_TIME → counting_date = today, else tomorrow\n+V6: talenan crossing → close batch, but ignored ∀ IGNORE_BATCH_LABEL_TIMEOUT_SEC after batch start\n+V7: batch inactivity ≥ BATCH_TIMEOUT_SECONDS → auto-close\n+V8: PYTHONNOUSERSITE=1 ! set for venv isolation\n+V9: .env ! exist before counter or dashboard starts\n+V10: DB tables ! exist on startup (created if absent, both store & dashboard)\n+V11: previous batch → last CARRY_IDS (default 50) track IDs carried forward to next batch\n+V12: ∃ ! batch per (date, batch#, camera, label) — UNIQUE constraint in DB\n+V13: counter & dashboard share DB path → no process-level coordination\n+V14: stream disconnect → reconnect with delay (RECONNECT_DELAY_SEC), ! block main loop\n+V15: model inference → letterbox-resize to IMGSZ×IMGSZ, BGR→RGB, run NPU\n+V16: NMS postprocessing → iou_thr=0.45, class-aware grouping, score > CONF\n+V17: tracks pruned after TRACKED_PRUNE_SEC (default 300s) without update\n+V18: .env missing → scripts fail at import (os.getenv falls back to defaults, may mismatch)\n+```\n+\n+## §T — Tasks\n+\n+```\n+id|status|task|cites\n+T1|.|unify install paths — README/DEPLOY/setup-venv.sh/uninstall say `/opt/jetson-counter`, service files & .env.example say `/opt/bytetrack-counter`|\n+T2|.|remove zombie var RESET_COUNTERS_AT_CUTOFF or wire it — defined in .env.example, unused in code|\n+T3|.|add version flag — no `--version` or git-derived version exists|\n+T4|.|dedup counter scripts — 3 variants share ~80% code; extract shared drawing/batch loop|\n+T5|.|rename export routes — routes named `export-daily-csv` but emit `.xlsx`|I.flask\n+T6|.|fix live-stream snapshot path — some scripts default `/dev/shm/jetson-counter/`, example says `bytetrack-counter`|\n+T7|.|add dashboard health-check endpoint (no `/health` or `/api/status` exists)|\n+T8|.|add model checks on startup — model class names ! validated against CLASS_AYAM/CLASS_TALENAN (TensorRT variant validates; RKNN variants do not)|\n+```\n+\n+## §B — Bugs\n+\n+```\n+id|date|cause|fix\n+```\n+\n",
|
||
"additions": 126,
|
||
"deletions": 0,
|
||
"status": "added"
|
||
}
|
||
]
|
||
},
|
||
"id": "msg_fab3cd5b1001fzYWspbB8xhyKY",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"type": "text",
|
||
"text": "dsitill",
|
||
"id": "prt_fab3cd5b7001pvjmtMWWcHCWsQ",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3cd5b1001fzYWspbB8xhyKY"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"parentID": "msg_fab3cd5b1001fzYWspbB8xhyKY",
|
||
"role": "assistant",
|
||
"mode": "build",
|
||
"agent": "build",
|
||
"path": {
|
||
"cwd": "/opt/bytetrack-counter",
|
||
"root": "/opt/bytetrack-counter"
|
||
},
|
||
"cost": 0.000551435,
|
||
"tokens": {
|
||
"total": 59299,
|
||
"input": 61,
|
||
"output": 68,
|
||
"reasoning": 290,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 58880
|
||
}
|
||
},
|
||
"modelID": "deepseek-v4-pro",
|
||
"providerID": "deepseek",
|
||
"time": {
|
||
"created": 1785284318680,
|
||
"completed": 1785284324234
|
||
},
|
||
"finish": "tool-calls",
|
||
"id": "msg_fab3cd5d8001mg34wcFkGM99sh",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"snapshot": "19818784677404bc50d6414ea41a4d12a2d11390",
|
||
"type": "step-start",
|
||
"id": "prt_fab3cd989001n0ROr1o43GWZJV",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3cd5d8001mg34wcFkGM99sh"
|
||
},
|
||
{
|
||
"type": "reasoning",
|
||
"text": "The user wants to DISTILL — reverse-engineer a spec from the existing codebase. Let me follow the DISTILL workflow:\n\n1. Walk the repo\n2. Produce §G (infer from README/package.json/main entry)\n3. §C (infer from stack)\n4. §I (enumerate public APIs/CLIs/configs)\n5. §V (derive from tests and assertions)\n6. §T (one task per known TODO or missing test)\n7. §B (empty)\n\nI also need to read FORMAT.md first since I haven't loaded it. But it doesn't exist at repo root. Let me check if there's a FORMAT.md in the spec skill directory.\n\nActually, the skill instructions say \"Read `FORMAT.md` at repo root if not already loaded.\" Since it doesn't exist at repo root, I should check the skill directory. Let me also load the caveman skill since \"Caveman skill applies to all writes here.\"\n\nLet me first read FORMAT.md from the skill directory, then load caveman, then distill the spec.\n\nActually, I already have a thorough understanding of the codebase from creating AGENTS.md. Let me proceed efficiently:\n1. Read FORMAT.md from the skill directory\n2. Load caveman skill\n3. Write SPEC.md based on my existing knowledge\n\nLet me check for FORMAT.md in the skill directory.",
|
||
"time": {
|
||
"start": 1785284320241,
|
||
"end": 1785284323732
|
||
},
|
||
"id": "prt_fab3cdbf1001mDFXejiM65leT9",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3cd5d8001mg34wcFkGM99sh"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "glob",
|
||
"callID": "call_00_gwJY7Io0XpFhRWRpRFXW3090",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"path": "/root/.agents/skills/spec",
|
||
"pattern": "**/*"
|
||
},
|
||
"output": "/root/.agents/skills/spec/SKILL.md",
|
||
"metadata": {
|
||
"count": 1,
|
||
"truncated": false
|
||
},
|
||
"title": "../../root/.agents/skills/spec",
|
||
"time": {
|
||
"start": 1785284324031,
|
||
"end": 1785284324055
|
||
}
|
||
},
|
||
"id": "prt_fab3ce99d001XTUO6KNlibQKUB",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3cd5d8001mg34wcFkGM99sh"
|
||
},
|
||
{
|
||
"reason": "tool-calls",
|
||
"snapshot": "19818784677404bc50d6414ea41a4d12a2d11390",
|
||
"type": "step-finish",
|
||
"tokens": {
|
||
"total": 59299,
|
||
"input": 61,
|
||
"output": 68,
|
||
"reasoning": 290,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 58880
|
||
}
|
||
},
|
||
"cost": 0.000551435,
|
||
"id": "prt_fab3ceb3e001i70wEVamIwJmMF",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3cd5d8001mg34wcFkGM99sh"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"parentID": "msg_fab3cd5b1001fzYWspbB8xhyKY",
|
||
"role": "assistant",
|
||
"mode": "build",
|
||
"agent": "build",
|
||
"path": {
|
||
"cwd": "/opt/bytetrack-counter",
|
||
"root": "/opt/bytetrack-counter"
|
||
},
|
||
"cost": 0.000345767,
|
||
"tokens": {
|
||
"total": 59444,
|
||
"input": 59,
|
||
"output": 84,
|
||
"reasoning": 37,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 59264
|
||
}
|
||
},
|
||
"modelID": "deepseek-v4-pro",
|
||
"providerID": "deepseek",
|
||
"time": {
|
||
"created": 1785284324242,
|
||
"completed": 1785284326911
|
||
},
|
||
"finish": "tool-calls",
|
||
"id": "msg_fab3ceb92001R30cLKg80SzijZ",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"snapshot": "19818784677404bc50d6414ea41a4d12a2d11390",
|
||
"type": "step-start",
|
||
"id": "prt_fab3ceee4001fKpyLsQ3FWVzfE",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3ceb92001R30cLKg80SzijZ"
|
||
},
|
||
{
|
||
"type": "reasoning",
|
||
"text": "No FORMAT.md in the skill directory either. Let me check the caveman skill for formatting guidance, since the spec skill says \"Caveman skill applies to all writes here.\"",
|
||
"time": {
|
||
"start": 1785284325545,
|
||
"end": 1785284326308
|
||
},
|
||
"id": "prt_fab3cf0a90018e05rq4P7aZthe",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3ceb92001R30cLKg80SzijZ"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "skill",
|
||
"callID": "call_00_i1GiR9PkFkz0KAw7FD8s3474",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"name": "caveman"
|
||
},
|
||
"output": "<skill_content name=\"caveman\">\n# Skill: caveman\n\n# caveman — spec encoding\n\nApplies to SPEC.md writes, spec-referencing prose, backprop entries.\nDoes NOT apply to code, error strings, commit messages, PR descriptions.\n\n## GRAMMAR\n\n- Drop articles (a, an, the).\n- Drop filler (just, really, basically, simply, actually).\n- Drop aux verbs where fragment works (is, are, was, were, being).\n- Drop pleasantries.\n- No hedging (skip \"might\", \"perhaps\", \"could be worth\").\n- Fragments fine.\n- Short synonyms: fix > implement, big > extensive, run > execute.\n\n## SYMBOLS\n\nPrefer over words:\n\n```\n→ leads to / becomes / on <x>\n∴ therefore / fix\n∀ for all / every\n∃ exists / some\n! must / required\n? may / optional / unknown\n⊥ never / forbidden / nil\n≠ not equal\n∈ in\n∉ not in\n≤ at most\n≥ at least\n& and\n| or\n§ section reference\n```\n\n## PRESERVE VERBATIM\n\nNever compress:\n\n- Code blocks, snippets, one-liners with backticks.\n- Paths: `src/auth/mw.go`.\n- URLs.\n- Identifiers: function names, variable names, env vars.\n- Numbers and versions.\n- Error message strings.\n- SQL, regex, JSON, YAML.\n- Quoted strings.\n\n## SHAPES\n\n**Invariant**:\n```\nV<n>: <subject> <relation> <condition>\nV1: ∀ req → auth check before handler\nV2: token expiry ≤ current_time → reject\n```\n\n**Bug row** (pipe table under §B):\n```\nid|date|cause|fix\nB1|2026-04-20|token `<` not `≤`|V2\n```\n\n**Task row** (pipe table under §T):\n```\nid|status|task|cites\nT3|x|add auth mw|V1,I.api\n```\nStatus: `x` done, `~` wip, `.` todo. Escape literal `|` as `\\|`.\n\n**Interface**:\n```\n<kind>: <name> → <shape>\napi: POST /x → 200 {id:string}\ncmd: `foo bar <arg>` → stdout JSON\nenv: FOO_KEY ! set\n```\n\n## EXAMPLES\n\n**Bad**:\n> The system should ensure that every incoming request is properly authenticated before being forwarded to its corresponding handler function.\n\n**Good**:\n> V1: ∀ req → auth check before handler\n\n**Bad**:\n> We discovered that the token expiration check in the middleware was using a strict less-than comparison operator, which meant tokens were being rejected at the exact moment of their expiry.\n\n**Good**:\n> B1: token `<` not `≤` → reject @ expiry boundary.\n\n**Bad**:\n> The POST endpoint at /x accepts a JSON body and returns a 200 response with an object containing the created id.\n\n**Good**:\n> api: POST /x → 200 {id}\n\n## BOUNDARIES\n\n- User asks for prose explanation → switch to normal English.\n- Spec documents for external review (RFC, pitch) → normal English.\n- Commit message → normal English (git readers expect it).\n- Diff comment in code → normal English.\n\n## WHEN UNSURE\n\nIf cutting a word loses a fact, keep it. Caveman is compression, not amputation.\n\nBase directory for this skill: /root/.agents/skills/caveman\nRelative paths in this skill (e.g., scripts/, reference/) are relative to this base directory.\nNote: file list is sampled.\n\n<skill_files>\n\n</skill_files>\n</skill_content>",
|
||
"metadata": {
|
||
"name": "caveman",
|
||
"dir": "/root/.agents/skills/caveman",
|
||
"truncated": false
|
||
},
|
||
"title": "Loaded skill: caveman",
|
||
"time": {
|
||
"start": 1785284326443,
|
||
"end": 1785284326462
|
||
}
|
||
},
|
||
"id": "prt_fab3cf3a7001TLnTIz0caY5GVl",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3ceb92001R30cLKg80SzijZ"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "read",
|
||
"callID": "call_01_TQeh5csWKf8ptb0QQYfh6020",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"filePath": "/root/.agents/skills/caveman"
|
||
},
|
||
"output": "<path>/root/.agents/skills/caveman</path>\n<type>directory</type>\n<entries>\nSKILL.md\n\n(1 entries)\n</entries>",
|
||
"metadata": {
|
||
"preview": "SKILL.md",
|
||
"truncated": false,
|
||
"loaded": [],
|
||
"display": {
|
||
"type": "directory",
|
||
"path": "/root/.agents/skills/caveman",
|
||
"entries": [
|
||
"SKILL.md"
|
||
],
|
||
"offset": 1,
|
||
"totalEntries": 1,
|
||
"truncated": false
|
||
}
|
||
},
|
||
"title": "../../root/.agents/skills/caveman",
|
||
"time": {
|
||
"start": 1785284326731,
|
||
"end": 1785284326737
|
||
}
|
||
},
|
||
"id": "prt_fab3cf492001ud2qr5EOIE55Fb",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3ceb92001R30cLKg80SzijZ"
|
||
},
|
||
{
|
||
"reason": "tool-calls",
|
||
"snapshot": "19818784677404bc50d6414ea41a4d12a2d11390",
|
||
"type": "step-finish",
|
||
"tokens": {
|
||
"total": 59444,
|
||
"input": 59,
|
||
"output": 84,
|
||
"reasoning": 37,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 59264
|
||
}
|
||
},
|
||
"cost": 0.000345767,
|
||
"id": "prt_fab3cf5cf00107DQs9VOVzcbmz",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3ceb92001R30cLKg80SzijZ"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"parentID": "msg_fab3cd5b1001fzYWspbB8xhyKY",
|
||
"role": "assistant",
|
||
"mode": "build",
|
||
"agent": "build",
|
||
"path": {
|
||
"cwd": "/opt/bytetrack-counter",
|
||
"root": "/opt/bytetrack-counter"
|
||
},
|
||
"cost": 0.001686901,
|
||
"tokens": {
|
||
"total": 61539,
|
||
"input": 911,
|
||
"output": 120,
|
||
"reasoning": 1116,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 59392
|
||
}
|
||
},
|
||
"modelID": "deepseek-v4-pro",
|
||
"providerID": "deepseek",
|
||
"time": {
|
||
"created": 1785284326919,
|
||
"completed": 1785284345490
|
||
},
|
||
"finish": "tool-calls",
|
||
"id": "msg_fab3cf607001IUOJuI5nNXeyLv",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"snapshot": "19818784677404bc50d6414ea41a4d12a2d11390",
|
||
"type": "step-start",
|
||
"id": "prt_fab3cf964001FuFcaKUdu27MOE",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3cf607001IUOJuI5nNXeyLv"
|
||
},
|
||
{
|
||
"type": "reasoning",
|
||
"text": "Now I have the caveman encoding rules. Let me distill the spec from the codebase. I already have thorough knowledge from creating AGENTS.md. Let me produce:\n\n§G: Goal — infer from README and code\n§C: Constraints — from stack/env\n§I: Interfaces — public APIs, CLI, config, systemd\n§V: Invariants — from assertions, logic patterns, validation\n§T: Tasks — gaps/TODOs discovered in code\n§B: Empty\n\nLet me go through each section systematically.\n\n**§G — Goal**: \nEdge AI counter: RTSP camera feeds → YOLO object detection → tracking → line-crossing counting → SQLite persistence + Flask dashboard. Counts ayam objects crossing a line; closes batches on talenan crossing. Runs on RK3588 NPU.\n\n**§C — Constraints**:\n- Python 3.10 on RK3588\n- rknn-toolkit-lite2 (NPU inference)\n- numpy <2\n- CV2 VideoCapture for RTSP\n- SQLite for persistence\n- Flask on port 5000\n- systemd supervision\n- NO CUDA (TensorRT variant incompatible)\n\n**§I — Interfaces**:\n- `.env` file with 40+ config vars\n- systemd units: `bytetrack-counter.service`, `bytetrack-counter-dashboard.service`\n- Flask API: `/api/current-batch`, `/api/previous-batch`, `/api/summary`, `/api/daily-data`, `/api/day-detail/<date>`, `/api/recent-batches`, `/api/available-dates`, `/api/export-daily-csv`, `/api/export-day-csv/<date>`, `/api/live-video`\n- SQLite DB: `bytetrack_counter.db` with `batches` and `daily_summaries` tables\n- JSON state file: `/tmp/bytetrack_current_batch.json`\n- CSV export: `batch_crossings.csv`\n\n**§V — Invariants** (from code assertions, logic patterns, validation):\nLooking at the code:\n- V1: `NUM_CLASSES` must match model output (class mapping hardcoded: index 0→ayam, index 1→talenan)\n- V2: Line crossing direction: rtl (right→left) by default, prev_cx > line_x ≥ cx\n- V3: Track ID deduplication: each track_id counted at most once per batch (tracked in counted_event_ids set)\n- V4: Batch discarded if count < MIN_OBJECT_PER_BATCH or duration < MIN_DURATION_PER_BATCH\n- V5: Daily cutoff: batches after DAILY_CUTOFF_TIME assigned next day's date\n- V6: Talenan crossing closes batch, but ignored for IGNORE_BATCH_LABEL_TIMEOUT_SEC after batch start\n- V7: Batch auto-closes after BATCH_TIMEOUT_SECONDS of inactivity\n- V8: `PYTHONNOUSERSITE=1` required for venv isolation\n- V9: `.env` must exist before counter or dashboard starts\n- V10: DB tables created on startup if absent (both batch_store and dashboard)\n- V11: Previous batch's last N track IDs carried forward as counted_event_ids (CARRY_IDS, default 50)\n- V12: No duplicate batches: UNIQUE(counting_date, batch_number, camera_name, object_label) enforced at DB level\n- V13: Counter and dashboard share DB path but no process-level coordination\n\nLet me also note uncertainty (?) items:\n- RESET_COUNTERS_AT_CUTOFF: mentioned in .env.example but code behavior? Let me check...\n\nActually, from my reading of counter_live_rknn_bytetrack.py, I only read lines 1-119. Let me check for RESET_COUNTERS_AT_CUTOFF usage.\n\nActually, I should be careful about flagging uncertain items with `?`. Let me note what I'm confident about vs uncertain.\n\nI'm confident about the architecture, configs, APIs, and key invariants from reading the full codebase. The main things I didn't fully trace: \n- ByteTrack internals in counter_live_rknn_bytetrack.py (I read partial)\n- Motion detection implementation details\n- Rate window sliding window details\n\nBut for a distill spec, the goal is to capture the high-level architecture, not every implementation detail. The `?` flag is for genuinely uncertain claims.\n\nLet me also look for TODOs or known gaps...\n\nLooking at the code, I notice:\n1. counter_live.py has TensorRT specific code marked as Jetson-only — not portable\n2. 3 counter scripts duplicate ~80% code — maintenance debt\n3. No version mechanism exists (noted in AGENTS.md)\n4. Live stream frame path hardcoded as `/dev/shm/jetson-counter/` in some scripts but `/dev/shm/bytetrack-counter/` in .env.example — inconsistency\n5. counter_dashboard.py hardcodes default dir as `/opt/jetson-counter` — mismatch with actual `/opt/bytetrack-counter`\n\nLet me list these as §T tasks.\n\nNow let me write the SPEC.md using caveman encoding.",
|
||
"time": {
|
||
"start": 1785284328378,
|
||
"end": 1785284344328
|
||
},
|
||
"id": "prt_fab3cfbba001uFQ73gh4ChR5hr",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3cf607001IUOJuI5nNXeyLv"
|
||
},
|
||
{
|
||
"type": "text",
|
||
"text": "Now I'll distill SPEC.md from the codebase. I already have thorough knowledge from the AGENTS.md exploration.",
|
||
"time": {
|
||
"start": 1785284344337,
|
||
"end": 1785284345364
|
||
},
|
||
"id": "prt_fab3d3a11001GuuBXnAQY9by2f",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3cf607001IUOJuI5nNXeyLv"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "grep",
|
||
"callID": "call_00_EQfDxmyHIWCCqPLvcvvY4696",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"path": "/opt/bytetrack-counter",
|
||
"pattern": "RESET_COUNTERS|TODO|FIXME|HACK|XXX",
|
||
"include": "*.py"
|
||
},
|
||
"output": "No files found",
|
||
"metadata": {
|
||
"matches": 0,
|
||
"truncated": false
|
||
},
|
||
"title": "RESET_COUNTERS|TODO|FIXME|HACK|XXX",
|
||
"time": {
|
||
"start": 1785284345321,
|
||
"end": 1785284345395
|
||
}
|
||
},
|
||
"id": "prt_fab3d3c03001lBcSzyInyAXL08",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3cf607001IUOJuI5nNXeyLv"
|
||
},
|
||
{
|
||
"reason": "tool-calls",
|
||
"snapshot": "19818784677404bc50d6414ea41a4d12a2d11390",
|
||
"type": "step-finish",
|
||
"tokens": {
|
||
"total": 61539,
|
||
"input": 911,
|
||
"output": 120,
|
||
"reasoning": 1116,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 59392
|
||
}
|
||
},
|
||
"cost": 0.001686901,
|
||
"id": "prt_fab3d3e65001O7qTTJl6p115QV",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3cf607001IUOJuI5nNXeyLv"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"parentID": "msg_fab3cd5b1001fzYWspbB8xhyKY",
|
||
"role": "assistant",
|
||
"mode": "build",
|
||
"agent": "build",
|
||
"path": {
|
||
"cwd": "/opt/bytetrack-counter",
|
||
"root": "/opt/bytetrack-counter"
|
||
},
|
||
"cost": 0.000435,
|
||
"tokens": {
|
||
"total": 61741,
|
||
"input": 114,
|
||
"output": 149,
|
||
"reasoning": 38,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 61440
|
||
}
|
||
},
|
||
"modelID": "deepseek-v4-pro",
|
||
"providerID": "deepseek",
|
||
"time": {
|
||
"created": 1785284345500,
|
||
"completed": 1785284349161
|
||
},
|
||
"finish": "tool-calls",
|
||
"id": "msg_fab3d3e9c001xdakyPYo0WtZIi",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"snapshot": "19818784677404bc50d6414ea41a4d12a2d11390",
|
||
"type": "step-start",
|
||
"id": "prt_fab3d4265001fa0pmviLFu3Tsu",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3d3e9c001xdakyPYo0WtZIi"
|
||
},
|
||
{
|
||
"type": "reasoning",
|
||
"text": "No TODOs or FIXMEs. Let me check for RESET_COUNTERS_AT_CUTOFF in the bytetrack script since I only read part of it.",
|
||
"time": {
|
||
"start": 1785284347002,
|
||
"end": 1785284347662
|
||
},
|
||
"id": "prt_fab3d447a001DyyVfeNw1be1bO",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3d3e9c001xdakyPYo0WtZIi"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "grep",
|
||
"callID": "call_00_T43LdKonYgKYxKg8z7aR9189",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"path": "/opt/bytetrack-counter",
|
||
"pattern": "RESET_COUNTERS|counter_reset|reset.*counter|track_id.*reset|frame_idx.*=.*0"
|
||
},
|
||
"output": "Found 12 matches\n/opt/bytetrack-counter/config.env.example:\n Line 96: RESET_COUNTERS_AT_CUTOFF=true\n\n\n/opt/bytetrack-counter/install-services.sh:\n Line 60: systemctl reset-failed jetson-counter jetson-counter-dashboard 2>/dev/null || true\n\n\n/opt/bytetrack-counter/counter_live_rknn.py:\n Line 809: frame_idx = 0\n\n Line 1021: if LIVE_STREAM_ENABLED and frame_idx % LIVE_STREAM_EVERY_N == 0:\n\n Line 1033: if frame_idx % FLUSH_EVERY_N_FRAMES == 0:\n\n\n/opt/bytetrack-counter/counter_live.py:\n Line 386: frame_idx = 0\n\n Line 529: if LIVE_STREAM_ENABLED and frame_idx % LIVE_STREAM_EVERY_N == 0:\n\n Line 539: if frame_idx % FLUSH_EVERY_N_FRAMES == 0:\n\n\n/opt/bytetrack-counter/counter_live_rknn_bytetrack.py:\n Line 863: def draw_footer(img, w, h, frame_idx, live_tag, inf_ms=0.0, model_name=\"\"):\n\n Line 1018: frame_idx = 0\n\n Line 1336: if LIVE_STREAM_ENABLED and frame_idx % LIVE_STREAM_EVERY_N == 0:\n\n Line 1348: if frame_idx % FLUSH_EVERY_N_FRAMES == 0:\n",
|
||
"metadata": {
|
||
"matches": 12,
|
||
"truncated": false
|
||
},
|
||
"title": "RESET_COUNTERS|counter_reset|reset.*counter|track_id.*reset|frame_idx.*=.*0",
|
||
"time": {
|
||
"start": 1785284348319,
|
||
"end": 1785284348393
|
||
}
|
||
},
|
||
"id": "prt_fab3d47130014YPpRLyzfPzjJW",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3d3e9c001xdakyPYo0WtZIi"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "grep",
|
||
"callID": "call_01_LRiYpduuRmWNJfl0BL8b8071",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"path": "/opt/bytetrack-counter",
|
||
"pattern": "CARRY_IDS|carry_ids"
|
||
},
|
||
"output": "Found 3 matches\n/opt/bytetrack-counter/batch_store.py:\n Line 25: carry_ids=50,\n\n Line 39: self.carry_ids = int(carry_ids)\n\n Line 155: counted_ids = self.previous_state['counted_event_ids'][-self.carry_ids:]\n",
|
||
"metadata": {
|
||
"matches": 3,
|
||
"truncated": false
|
||
},
|
||
"title": "CARRY_IDS|carry_ids",
|
||
"time": {
|
||
"start": 1785284348850,
|
||
"end": 1785284348914
|
||
}
|
||
},
|
||
"id": "prt_fab3d4a1a001mgCYIr2QQ0GkAU",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3d3e9c001xdakyPYo0WtZIi"
|
||
},
|
||
{
|
||
"reason": "tool-calls",
|
||
"snapshot": "19818784677404bc50d6414ea41a4d12a2d11390",
|
||
"type": "step-finish",
|
||
"tokens": {
|
||
"total": 61741,
|
||
"input": 114,
|
||
"output": 149,
|
||
"reasoning": 38,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 61440
|
||
}
|
||
},
|
||
"cost": 0.000435,
|
||
"id": "prt_fab3d4c70001Hk1v4J8Rf5jY66",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3d3e9c001xdakyPYo0WtZIi"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"parentID": "msg_fab3cd5b1001fzYWspbB8xhyKY",
|
||
"role": "assistant",
|
||
"mode": "build",
|
||
"agent": "build",
|
||
"path": {
|
||
"cwd": "/opt/bytetrack-counter",
|
||
"root": "/opt/bytetrack-counter"
|
||
},
|
||
"cost": 0.000594703,
|
||
"tokens": {
|
||
"total": 62360,
|
||
"input": 475,
|
||
"output": 157,
|
||
"reasoning": 32,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 61696
|
||
}
|
||
},
|
||
"modelID": "deepseek-v4-pro",
|
||
"providerID": "deepseek",
|
||
"time": {
|
||
"created": 1785284349173,
|
||
"completed": 1785284352812
|
||
},
|
||
"finish": "tool-calls",
|
||
"id": "msg_fab3d4cf50011xl28aGKR7Nfxa",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"snapshot": "19818784677404bc50d6414ea41a4d12a2d11390",
|
||
"type": "step-start",
|
||
"id": "prt_fab3d5152001N1drDWRUdCj1gj",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3d4cf50011xl28aGKR7Nfxa"
|
||
},
|
||
{
|
||
"type": "reasoning",
|
||
"text": "Let me also check if RESET_COUNTERS_AT_CUTOFF is actually used in any Python script (not just defined in .env.example).",
|
||
"time": {
|
||
"start": 1785284350684,
|
||
"end": 1785284351309
|
||
},
|
||
"id": "prt_fab3d52dc0014H8AsQTN36t0XZ",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3d4cf50011xl28aGKR7Nfxa"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "grep",
|
||
"callID": "call_00_3c1fowA1G7wA7MkXoqDP8858",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"pattern": "RESET_COUNTERS_AT_CUTOFF",
|
||
"path": "/opt/bytetrack-counter"
|
||
},
|
||
"output": "Found 1 matches\n/opt/bytetrack-counter/config.env.example:\n Line 96: RESET_COUNTERS_AT_CUTOFF=true\n",
|
||
"metadata": {
|
||
"matches": 1,
|
||
"truncated": false
|
||
},
|
||
"title": "RESET_COUNTERS_AT_CUTOFF",
|
||
"time": {
|
||
"start": 1785284351757,
|
||
"end": 1785284351801
|
||
}
|
||
},
|
||
"id": "prt_fab3d5557001bH7FA5J9GzHLf1",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3d4cf50011xl28aGKR7Nfxa"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "grep",
|
||
"callID": "call_01_r1JXoAwAFCvplWO0LkQm6598",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"path": "/opt/bytetrack-counter",
|
||
"pattern": "juniorday.*end_time|juniorday.*start_time",
|
||
"include": "*.py"
|
||
},
|
||
"output": "No files found",
|
||
"metadata": {
|
||
"matches": 0,
|
||
"truncated": false
|
||
},
|
||
"title": "juniorday.*end_time|juniorday.*start_time",
|
||
"time": {
|
||
"start": 1785284352547,
|
||
"end": 1785284352610
|
||
}
|
||
},
|
||
"id": "prt_fab3d575b001GpU6pYh1lFXyvA",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3d4cf50011xl28aGKR7Nfxa"
|
||
},
|
||
{
|
||
"reason": "tool-calls",
|
||
"snapshot": "19818784677404bc50d6414ea41a4d12a2d11390",
|
||
"type": "step-finish",
|
||
"tokens": {
|
||
"total": 62360,
|
||
"input": 475,
|
||
"output": 157,
|
||
"reasoning": 32,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 61696
|
||
}
|
||
},
|
||
"cost": 0.000594703,
|
||
"id": "prt_fab3d5afd001B6H1zc7xCgKrqS",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3d4cf50011xl28aGKR7Nfxa"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"parentID": "msg_fab3cd5b1001fzYWspbB8xhyKY",
|
||
"role": "assistant",
|
||
"mode": "build",
|
||
"agent": "build",
|
||
"path": {
|
||
"cwd": "/opt/bytetrack-counter",
|
||
"root": "/opt/bytetrack-counter"
|
||
},
|
||
"cost": 0.002051228,
|
||
"tokens": {
|
||
"total": 64474,
|
||
"input": 80,
|
||
"output": 1943,
|
||
"reasoning": 115,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 62336
|
||
}
|
||
},
|
||
"modelID": "deepseek-v4-pro",
|
||
"providerID": "deepseek",
|
||
"time": {
|
||
"created": 1785284352822,
|
||
"completed": 1785284381102
|
||
},
|
||
"finish": "tool-calls",
|
||
"id": "msg_fab3d5b36001lZ5djLIu4ibMxV",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"snapshot": "19818784677404bc50d6414ea41a4d12a2d11390",
|
||
"type": "step-start",
|
||
"id": "prt_fab3d5ec30011WIKqWj9BUmgAq",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3d5b36001lZ5djLIu4ibMxV"
|
||
},
|
||
{
|
||
"type": "reasoning",
|
||
"text": "`RESET_COUNTERS_AT_CUTOFF` is defined in `.env.example` but NOT used in any Python code. That's an interesting finding — it's a dead config var.\n\nNow let me also note the mismatch: the dashboard SQL uses `julianday()` which is SQLite specific. And I notice the dashboard exports `.xlsx` but the routes are named `export-daily-csv` and `export-day-csv`.\n\nLet me now write the SPEC.md. I need to use caveman encoding per the caveman skill.",
|
||
"time": {
|
||
"start": 1785284354172,
|
||
"end": 1785284355919
|
||
},
|
||
"id": "prt_fab3d607c001mzdGnZ70jO6tYT",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3d5b36001lZ5djLIu4ibMxV"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "write",
|
||
"callID": "call_00_MwMKBkNRpr02H22OdwKX2242",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"filePath": "/opt/bytetrack-counter/SPEC.md",
|
||
"content": "# SPEC — bytetrack-counter\n\n## §G — Goal\n\nRTSP camera → YOLO RKNN (NPU) → ByteTrack → line-crossing counter → SQLite + Flask dashboard.\nCount `ayam` (chicken) crossing counting line. Close batch on `talenan` (cutting-board) crossing.\nDaily cutoff @ HH:MM resets batch numbering. RK3588 hardware.\n\n## §C — Constraints\n\n- Python 3.10, `rknn-toolkit-lite2` (NPU), `opencv-python` (RTSP/FFmpeg)\n- `numpy<2` (rknn-toolkit-lite2 incompatible with numpy≥2)\n- `PYTHONNOUSERSITE=1` ! set or venv breaks\n- SQLite for persistence, JSON file for active-batch state\n- Flask on port 5000, systemd supervision\n- `counter_live.py` ⊥ run on RK3588 (Jetson TensorRT only)\n- Single deployment: `counter_live_rknn_bytetrack.py` + `counter_dashboard.py`\n\n## §I — Interfaces\n\n### Systemd\n```\nunit: bytetrack-counter.service → `venv/bin/python counter_live_rknn_bytetrack.py`\nunit: bytetrack-counter-dashboard.service → `venv/bin/python counter_dashboard.py`\nenv: PYTHONNOUSERSITE=1 ! set in both units\nenv: EnvironmentFile=/opt/bytetrack-counter/.env\nuser: root (not jetson)\npath: /opt/bytetrack-counter (fixed in service file)\n```\n\n### `.env` config (40+ vars, gitignored)\n```\nenv: SOURCE ! set → RTSP URL | file path\nenv: MODEL_PATH ! set → .rknn file\nenv: IMGSZ ! set → model input size (e.g. 320)\nenv: CORE_MASK → NPU core mask (1=core0, 2=core1, 3=dual, 7=all)\nenv: NUM_CLASSES ! match model output count\nenv: CONF → detection confidence threshold (default 0.3)\nenv: DAILY_CUTOFF_TIME → HH:MM, default \"20:00\"\nenv: CROSS_DIRECTION → rtl | ltr | both (default rtl)\nenv: LINE_X | LINE_X_FRAC → counting line position\nenv: CLASS_AYAM → class name for counted object (index 0)\nenv: CLASS_TALENAN → class name for batch-close trigger (index 1)\nenv: RESET_COUNTERS_AT_CUTOFF → defined in example, ! consumed by code ? zombie\nenv: MOTION_DETECTION_ENABLED | MOTION_THRESHOLD → skip inference on still frames ?\nenv: TRACK_HIGH_THRESH_{0,1} | TRACK_LOW_THRESH_{0,1} | TRACK_MATCH_THRESH_{0,1} → ByteTrack params per class\nenv: BATCH_TIMEOUT_SECONDS → auto-close after inactivity (default 300)\nenv: IGNORE_BATCH_LABEL_TIMEOUT_SECONDS → suppress talenan close after batch start (default 30)\nenv: MIN_OBJECT_PER_BATCH → min count to persist batch (default 60)\nenv: MIN_DURATION_PER_BATCH → min seconds to persist batch (default 60)\nenv: LIVE_STREAM_ENABLED → write annotated JPEG snapshot each N frames\nenv: EXPORT_CSV → write per-crossing CSV (default true)\n```\n\n### SQLite\n```\ntable: batches (date, batch#, camera, label, count, start, end, created_at)\n UNIQUE(counting_date, batch_number, camera_name, object_label)\ntable: daily_summaries (date, camera, label, total_count, total_batches, updated_at)\n UNIQUE(counting_date, camera_name, object_label)\n```\n\n### JSON state file\n```\npath: /tmp/bytetrack_current_batch.json (default)\nschema: {counting_date, batch_number, count, start_time, last_detection_time, counted_event_ids[]}\n```\n\n### Flask API\n```\napi: GET / → dashboard HTML\napi: GET /api/current-batch → {count, batch_number, counting_date, start_time, last_detection_time}\napi: GET /api/previous-batch → {date, batch#, count, start/end, duration_minutes}\napi: GET /api/summary → {today, yesterday, all_time, average_per_day, best_day}\napi: GET /api/daily-data?days=N → [ {date, total_count, total_batches, avg_per_batch} ]\napi: GET /api/day-detail/<date> → {date, total_count, total_batches, total_duration, avg_duration, batches[]}\napi: GET /api/recent-batches?limit=N → [ {date, batch#, count, start/end, duration} ]\napi: GET /api/available-dates → [ {date, total_count, total_batches} ]\napi: GET /api/export-daily-csv?days=N → .xlsx download (named csv, emits xlsx)\napi: GET /api/export-day-csv/<date> → .xlsx download (named csv, emits xlsx)\napi: GET /api/live-video → MJPEG stream from shared-memory JPEG\n```\n\n## §V — Invariants\n\n```\nV1: NUM_CLASSES must match model output → class 0=ayam, class 1=talenan\nV2: line crossing → prev_cx > line_x ≥ cx (rtl) | prev_cx < line_x ≤ cx (ltr)\nV3: ∀ track_id → counted at most once per batch (counted_event_ids set)\nV4: batch persisted → count ≥ MIN_OBJECT_PER_BATCH & duration ≥ MIN_DURATION_PER_BATCH\nV5: dt.time() < DAILY_CUTOFF_TIME → counting_date = today, else tomorrow\nV6: talenan crossing → close batch, but ignored ∀ IGNORE_BATCH_LABEL_TIMEOUT_SEC after batch start\nV7: batch inactivity ≥ BATCH_TIMEOUT_SECONDS → auto-close\nV8: PYTHONNOUSERSITE=1 ! set for venv isolation\nV9: .env ! exist before counter or dashboard starts\nV10: DB tables ! exist on startup (created if absent, both store & dashboard)\nV11: previous batch → last CARRY_IDS (default 50) track IDs carried forward to next batch\nV12: ∃ ! batch per (date, batch#, camera, label) — UNIQUE constraint in DB\nV13: counter & dashboard share DB path → no process-level coordination\nV14: stream disconnect → reconnect with delay (RECONNECT_DELAY_SEC), ! block main loop\nV15: model inference → letterbox-resize to IMGSZ×IMGSZ, BGR→RGB, run NPU\nV16: NMS postprocessing → iou_thr=0.45, class-aware grouping, score > CONF\nV17: tracks pruned after TRACKED_PRUNE_SEC (default 300s) without update\nV18: .env missing → scripts fail at import (os.getenv falls back to defaults, may mismatch)\n```\n\n## §T — Tasks\n\n```\nid|status|task|cites\nT1|.|unify install paths — README/DEPLOY/setup-venv.sh/uninstall say `/opt/jetson-counter`, service files & .env.example say `/opt/bytetrack-counter`|\nT2|.|remove zombie var RESET_COUNTERS_AT_CUTOFF or wire it — defined in .env.example, unused in code|\nT3|.|add version flag — no `--version` or git-derived version exists|\nT4|.|dedup counter scripts — 3 variants share ~80% code; extract shared drawing/batch loop|\nT5|.|rename export routes — routes named `export-daily-csv` but emit `.xlsx`|I.flask\nT6|.|fix live-stream snapshot path — some scripts default `/dev/shm/jetson-counter/`, example says `bytetrack-counter`|\nT7|.|add dashboard health-check endpoint (no `/health` or `/api/status` exists)|\nT8|.|add model checks on startup — model class names ! validated against CLASS_AYAM/CLASS_TALENAN (TensorRT variant validates; RKNN variants do not)|\n```\n\n## §B — Bugs\n\n```\nid|date|cause|fix\n```\n\n"
|
||
},
|
||
"output": "Wrote file successfully.",
|
||
"metadata": {
|
||
"diagnostics": {},
|
||
"filepath": "/opt/bytetrack-counter/SPEC.md",
|
||
"exists": false,
|
||
"truncated": false
|
||
},
|
||
"title": "SPEC.md",
|
||
"time": {
|
||
"start": 1785284380832,
|
||
"end": 1785284380848
|
||
}
|
||
},
|
||
"id": "prt_fab3d675300147jCdkZAkasuT1",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3d5b36001lZ5djLIu4ibMxV"
|
||
},
|
||
{
|
||
"reason": "tool-calls",
|
||
"snapshot": "8cee875229dc8fed3b74a6f8e65f0bb1d9875b4d",
|
||
"type": "step-finish",
|
||
"tokens": {
|
||
"total": 64474,
|
||
"input": 80,
|
||
"output": 1943,
|
||
"reasoning": 115,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 62336
|
||
}
|
||
},
|
||
"cost": 0.002051228,
|
||
"id": "prt_fab3dc930001DD2wqMjkQRvEU4",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3d5b36001lZ5djLIu4ibMxV"
|
||
},
|
||
{
|
||
"type": "patch",
|
||
"hash": "19818784677404bc50d6414ea41a4d12a2d11390",
|
||
"files": [
|
||
"/opt/bytetrack-counter/SPEC.md"
|
||
],
|
||
"id": "prt_fab3dc9a9001kYLriq1QKPRKs1",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3d5b36001lZ5djLIu4ibMxV"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"parentID": "msg_fab3cd5b1001fzYWspbB8xhyKY",
|
||
"role": "assistant",
|
||
"mode": "build",
|
||
"agent": "build",
|
||
"path": {
|
||
"cwd": "/opt/bytetrack-counter",
|
||
"root": "/opt/bytetrack-counter"
|
||
},
|
||
"cost": 0.000637072,
|
||
"tokens": {
|
||
"total": 64901,
|
||
"input": 106,
|
||
"output": 179,
|
||
"reasoning": 232,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 64384
|
||
}
|
||
},
|
||
"modelID": "deepseek-v4-pro",
|
||
"providerID": "deepseek",
|
||
"time": {
|
||
"created": 1785284381111,
|
||
"completed": 1785284388707
|
||
},
|
||
"finish": "stop",
|
||
"id": "msg_fab3dc9b7001zlj5iltsgCJepl",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"snapshot": "8cee875229dc8fed3b74a6f8e65f0bb1d9875b4d",
|
||
"type": "step-start",
|
||
"id": "prt_fab3dcd1b001JfXwCNqfeZZ7by",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3dc9b7001zlj5iltsgCJepl"
|
||
},
|
||
{
|
||
"type": "reasoning",
|
||
"text": "The SPEC.md is written. Let me present it to the user per the skill's output rules: \"Show user full file. Ask: 'spec OK? `/review` if high-blast-radius, else `/build`.'\"\n\nLet me verify I captured everything correctly and flag the uncertain items with `?` per the caveman rules.\n\nLooking at what I wrote:\n- §G: Clean, captures the goal\n- §C: Clean, captures the platform constraints\n- §I: Comprehensive listing of all interfaces\n- §V: 18 invariants derived from code - good coverage\n- §T: 8 tasks — reasonable for a distill\n- §B: Empty header row only (correct for distill)\n\nI flagged:\n- RESET_COUNTERS_AT_CUTOFF as `? zombie` (uncertain why defined but unused)\n- MOTION_DETECTION_ENABLED | MOTION_THRESHOLD with `?` (I only read part of the bytetrack script, not sure if it's wired)\n- The install path mismatch is correctly captured\n\nThis looks good. Let me present it.",
|
||
"time": {
|
||
"start": 1785284382651,
|
||
"end": 1785284386063
|
||
},
|
||
"id": "prt_fab3dcfbb001yGLhS1i9CX1zV2",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3dc9b7001zlj5iltsgCJepl"
|
||
},
|
||
{
|
||
"type": "text",
|
||
"text": "SPEC.md written. Here's the distill:\n\n**§G** — Edge AI counter pipeline: RTSP → YOLO RKNN → ByteTrack → line-crossing → SQLite + Flask dashboard.\n\n**§V** — 18 invariants extracted from code (line-crossing logic, batch lifecycle, dedup, cutoff, track pruning, reconnection).\n\n**§T** — 8 tasks for known gaps: install path inconsistency, zombie env var, no version flag, ~80% code duplication, route name vs file mismatch, path drift, no health-check endpoint, incomplete model validation.\n\nKey uncertain item flagged with `?`: `RESET_COUNTERS_AT_CUTOFF` is defined in `.env.example` but no code consumes it — zombie or unimplemented feature.\n\nspec OK? `/review` if high-blast-radius, else `/build`.",
|
||
"time": {
|
||
"start": 1785284386069,
|
||
"end": 1785284388568
|
||
},
|
||
"id": "prt_fab3ddd14001ij10oyOviXXolG",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3dc9b7001zlj5iltsgCJepl"
|
||
},
|
||
{
|
||
"reason": "stop",
|
||
"snapshot": "8cee875229dc8fed3b74a6f8e65f0bb1d9875b4d",
|
||
"type": "step-finish",
|
||
"tokens": {
|
||
"total": 64901,
|
||
"input": 106,
|
||
"output": 179,
|
||
"reasoning": 232,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 64384
|
||
}
|
||
},
|
||
"cost": 0.000637072,
|
||
"id": "prt_fab3de735001S0KfkC3h1GiPGb",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3dc9b7001zlj5iltsgCJepl"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"role": "user",
|
||
"time": {
|
||
"created": 1785284458953
|
||
},
|
||
"agent": "build",
|
||
"model": {
|
||
"providerID": "deepseek",
|
||
"modelID": "deepseek-v4-pro"
|
||
},
|
||
"id": "msg_fab3ef9c8001mJLyqEEUHYjfYd",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"type": "subtask",
|
||
"agent": "build",
|
||
"description": "review changes [commit|branch|pr], defaults to uncommitted",
|
||
"command": "review",
|
||
"model": {
|
||
"providerID": "deepseek",
|
||
"modelID": "deepseek-v4-pro"
|
||
},
|
||
"prompt": "You are a code reviewer. Your job is to review code changes and provide actionable feedback.\n\n---\n\nInput: \n\n---\n\n## Determining What to Review\n\nBased on the input provided, determine which type of review to perform:\n\n1. **No arguments (default)**: Review all uncommitted changes\n - Run: `git diff` for unstaged changes\n - Run: `git diff --cached` for staged changes\n - Run: `git status --short` to identify untracked (net new) files\n\n2. **Commit hash** (40-char SHA or short hash): Review that specific commit\n - Run: `git show `\n\n3. **Branch name**: Compare current branch to the specified branch\n - Run: `git diff ...HEAD`\n\n4. **PR URL or number** (contains \"github.com\" or \"pull\" or looks like a PR number): Review the pull request\n - Run: `gh pr view ` to get PR context\n - Run: `gh pr diff ` to get the diff\n\nUse best judgement when processing input.\n\n---\n\n## Gathering Context\n\n**Diffs alone are not enough.** After getting the diff, read the entire file(s) being modified to understand the full context. Code that looks wrong in isolation may be correct given surrounding logic—and vice versa.\n\n- Use the diff to identify which files changed\n- Use `git status --short` to identify untracked files, then read their full contents\n- Read the full file to understand existing patterns, control flow, and error handling\n- Check for existing style guide or conventions files (CONVENTIONS.md, AGENTS.md, .editorconfig, etc.)\n\n---\n\n## What to Look For\n\n**Bugs** - Your primary focus.\n- Logic errors, off-by-one mistakes, incorrect conditionals\n- If-else guards: missing guards, incorrect branching, unreachable code paths\n- Edge cases: null/empty/undefined inputs, error conditions, race conditions\n- Security issues: injection, auth bypass, data exposure\n- Broken error handling that swallows failures, throws unexpectedly or returns error types that are not caught.\n\n**Structure** - Does the code fit the codebase?\n- Does it follow existing patterns and conventions?\n- Are there established abstractions it should use but doesn't?\n- Excessive nesting that could be flattened with early returns or extraction\n\n**Performance** - Only flag if obviously problematic.\n- O(n²) on unbounded data, N+1 queries, blocking I/O on hot paths\n\n**Behavior Changes** - If a behavioral change is introduced, raise it (especially if it's possibly unintentional).\n\n---\n\n## Before You Flag Something\n\n**Be certain.** If you're going to call something a bug, you need to be confident it actually is one.\n\n- Only review the changes - do not review pre-existing code that wasn't modified\n- Don't flag something as a bug if you're unsure - investigate first\n- Don't invent hypothetical problems - if an edge case matters, explain the realistic scenario where it breaks\n- If you need more context to be sure, use the tools below to get it\n\n**Don't be a zealot about style.** When checking code against conventions:\n\n- Verify the code is *actually* in violation. Don't complain about else statements if early returns are already being used correctly.\n- Some \"violations\" are acceptable when they're the simplest option. A `let` statement is fine if the alternative is convoluted.\n- Excessive nesting is a legitimate concern regardless of other style choices.\n- Don't flag style preferences as issues unless they clearly violate established project conventions.\n\n---\n\n## Tools\n\nUse these to inform your review:\n\n- **Explore agent** - Find how existing code handles similar problems. Check patterns, conventions, and prior art before claiming something doesn't fit.\n- **Exa Code Context** - Verify correct usage of libraries/APIs before flagging something as wrong.\n- **Web Search** - Research best practices if you're unsure about a pattern.\n\nIf you're uncertain about something and can't verify it with these tools, say \"I'm not sure about X\" rather than flagging it as a definite issue.\n\n---\n\n## Output\n\n1. If there is a bug, be direct and clear about why it is a bug.\n2. Clearly communicate severity of issues. Do not overstate severity.\n3. Critiques should clearly and explicitly communicate the scenarios, environments, or inputs that are necessary for the bug to arise. The comment should immediately indicate that the issue's severity depends on these factors.\n4. Your tone should be matter-of-fact and not accusatory or overly positive. It should read as a helpful AI assistant suggestion without sounding too much like a human reviewer.\n5. Write so the reader can quickly understand the issue without reading too closely.\n6. AVOID flattery, do not give any comments that are not helpful to the reader. Avoid phrasing like \"Great job ...\", \"Thanks for ...\".",
|
||
"id": "prt_fab3ef9cb001P2xx9BxV94Fte1",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3ef9c8001mJLyqEEUHYjfYd"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"role": "assistant",
|
||
"parentID": "msg_fab3ef9c8001mJLyqEEUHYjfYd",
|
||
"mode": "build",
|
||
"agent": "build",
|
||
"path": {
|
||
"cwd": "/opt/bytetrack-counter",
|
||
"root": "/opt/bytetrack-counter"
|
||
},
|
||
"cost": 0,
|
||
"tokens": {
|
||
"input": 0,
|
||
"output": 0,
|
||
"reasoning": 0,
|
||
"cache": {
|
||
"read": 0,
|
||
"write": 0
|
||
}
|
||
},
|
||
"modelID": "deepseek-v4-pro",
|
||
"providerID": "deepseek",
|
||
"time": {
|
||
"created": 1785284458999,
|
||
"completed": 1785284536735
|
||
},
|
||
"finish": "tool-calls",
|
||
"id": "msg_fab3ef9f7001PbCKfJ7ZmCfSCJ",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"type": "tool",
|
||
"callID": "01KYNKXYFYGMCMHP4JZCAC2MJ1",
|
||
"tool": "task",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"prompt": "You are a code reviewer. Your job is to review code changes and provide actionable feedback.\n\n---\n\nInput: \n\n---\n\n## Determining What to Review\n\nBased on the input provided, determine which type of review to perform:\n\n1. **No arguments (default)**: Review all uncommitted changes\n - Run: `git diff` for unstaged changes\n - Run: `git diff --cached` for staged changes\n - Run: `git status --short` to identify untracked (net new) files\n\n2. **Commit hash** (40-char SHA or short hash): Review that specific commit\n - Run: `git show `\n\n3. **Branch name**: Compare current branch to the specified branch\n - Run: `git diff ...HEAD`\n\n4. **PR URL or number** (contains \"github.com\" or \"pull\" or looks like a PR number): Review the pull request\n - Run: `gh pr view ` to get PR context\n - Run: `gh pr diff ` to get the diff\n\nUse best judgement when processing input.\n\n---\n\n## Gathering Context\n\n**Diffs alone are not enough.** After getting the diff, read the entire file(s) being modified to understand the full context. Code that looks wrong in isolation may be correct given surrounding logic—and vice versa.\n\n- Use the diff to identify which files changed\n- Use `git status --short` to identify untracked files, then read their full contents\n- Read the full file to understand existing patterns, control flow, and error handling\n- Check for existing style guide or conventions files (CONVENTIONS.md, AGENTS.md, .editorconfig, etc.)\n\n---\n\n## What to Look For\n\n**Bugs** - Your primary focus.\n- Logic errors, off-by-one mistakes, incorrect conditionals\n- If-else guards: missing guards, incorrect branching, unreachable code paths\n- Edge cases: null/empty/undefined inputs, error conditions, race conditions\n- Security issues: injection, auth bypass, data exposure\n- Broken error handling that swallows failures, throws unexpectedly or returns error types that are not caught.\n\n**Structure** - Does the code fit the codebase?\n- Does it follow existing patterns and conventions?\n- Are there established abstractions it should use but doesn't?\n- Excessive nesting that could be flattened with early returns or extraction\n\n**Performance** - Only flag if obviously problematic.\n- O(n²) on unbounded data, N+1 queries, blocking I/O on hot paths\n\n**Behavior Changes** - If a behavioral change is introduced, raise it (especially if it's possibly unintentional).\n\n---\n\n## Before You Flag Something\n\n**Be certain.** If you're going to call something a bug, you need to be confident it actually is one.\n\n- Only review the changes - do not review pre-existing code that wasn't modified\n- Don't flag something as a bug if you're unsure - investigate first\n- Don't invent hypothetical problems - if an edge case matters, explain the realistic scenario where it breaks\n- If you need more context to be sure, use the tools below to get it\n\n**Don't be a zealot about style.** When checking code against conventions:\n\n- Verify the code is *actually* in violation. Don't complain about else statements if early returns are already being used correctly.\n- Some \"violations\" are acceptable when they're the simplest option. A `let` statement is fine if the alternative is convoluted.\n- Excessive nesting is a legitimate concern regardless of other style choices.\n- Don't flag style preferences as issues unless they clearly violate established project conventions.\n\n---\n\n## Tools\n\nUse these to inform your review:\n\n- **Explore agent** - Find how existing code handles similar problems. Check patterns, conventions, and prior art before claiming something doesn't fit.\n- **Exa Code Context** - Verify correct usage of libraries/APIs before flagging something as wrong.\n- **Web Search** - Research best practices if you're unsure about a pattern.\n\nIf you're uncertain about something and can't verify it with these tools, say \"I'm not sure about X\" rather than flagging it as a definite issue.\n\n---\n\n## Output\n\n1. If there is a bug, be direct and clear about why it is a bug.\n2. Clearly communicate severity of issues. Do not overstate severity.\n3. Critiques should clearly and explicitly communicate the scenarios, environments, or inputs that are necessary for the bug to arise. The comment should immediately indicate that the issue's severity depends on these factors.\n4. Your tone should be matter-of-fact and not accusatory or overly positive. It should read as a helpful AI assistant suggestion without sounding too much like a human reviewer.\n5. Write so the reader can quickly understand the issue without reading too closely.\n6. AVOID flattery, do not give any comments that are not helpful to the reader. Avoid phrasing like \"Great job ...\", \"Thanks for ...\".",
|
||
"description": "review changes [commit|branch|pr], defaults to uncommitted",
|
||
"subagent_type": "build",
|
||
"command": "review"
|
||
},
|
||
"title": "review changes [commit|branch|pr], defaults to uncommitted",
|
||
"metadata": {
|
||
"parentSessionId": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"sessionId": "ses_054c105efffeXG17gfMvT4Khmk",
|
||
"model": {
|
||
"modelID": "deepseek-v4-pro",
|
||
"providerID": "deepseek"
|
||
},
|
||
"truncated": false
|
||
},
|
||
"output": "<task id=\"ses_054c105efffeXG17gfMvT4Khmk\" state=\"completed\">\n<task_result>\nHere's my review of the changes:\n\n---\n\n### 1. BUG — `prev_gray` not reset on stream reconnection\n\n**File**: `counter_live_rknn_bytetrack.py`, lines 1053–1066 (reconnection) and 1074–1080 (motion detection)\n\nWhen the stream drops and reconnects, `prev_gray` still holds a reference to the grayscale image from the old (dead) connection. On the next frame of the new connection:\n\n- If the new connection has **different dimensions** (different camera or resolution change), `cv2.absdiff(gray, prev_gray)` will raise a `cv2.error` — crashing the loop.\n- If dimensions match, it won't crash but the diff is comparing a frame from the new stream to the last frame of the old stream — a temporally meaningless comparison that will almost certainly trigger a false-positive motion detection.\n\n**Fix**: Set `prev_gray = None` after the `continue` at line 1066 (or inside the reconnection block before `continue`).\n\n---\n\n### 2. INCOMPLETE FEATURE — `RESET_COUNTERS_AT_CUTOFF` is documented but not implemented\n\n**File**: `config.env.example` line 96 documents `RESET_COUNTERS_AT_CUTOFF=true` with a description about resetting frame counters and ByteTrack track-ID counters at daily cutoff. However, no Python file reads this env var or implements the described behavior. This appears to be a feature that was written into the config but never wired into the code. Either the code implementation is missing, or the config entry was added prematurely.\n\n---\n\n### 3. OBSERVATION — Stale `inf_ms` display when inference is skipped\n\n**File**: `counter_live_rknn_bytetrack.py`, lines 1086 (inf_ms update inside skip guard) and 1319 (display)\n\nWhen motion detection skips inference, `inf_ms` is not updated. Since it's an EMA (0.9 decay), it stays constant at the last value rather than decaying toward 0. The HUD will display a stale inference time, which could mislead an operator into thinking the model is still processing every frame when it isn't. Not a correctness bug, but worth considering if observability matters.\n\n---\n\n### 4. NOTE — No issues with the motion detection logic itself\n\nThe `prev_gray = None` initialization at line 1020, the single-frame skip on the first iteration (when `prev_gray is None`), and the mean-diff threshold comparison are all correct. The `skip_inference = False` default means motion detection is a clean no-op when disabled. The grayscale conversion only runs when enabled. No other issues found with this feature.\n</task_result>\n</task>",
|
||
"time": {
|
||
"start": 1785284459007,
|
||
"end": 1785284536738
|
||
}
|
||
},
|
||
"id": "prt_fab3ef9fe001htzeYL0vlz2H1A",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab3ef9f7001PbCKfJ7ZmCfSCJ"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"role": "user",
|
||
"time": {
|
||
"created": 1785284536743
|
||
},
|
||
"agent": "build",
|
||
"model": {
|
||
"providerID": "deepseek",
|
||
"modelID": "deepseek-v4-pro"
|
||
},
|
||
"summary": {
|
||
"diffs": [
|
||
{
|
||
"file": "SPEC.md",
|
||
"patch": "Index: SPEC.md\n===================================================================\n--- SPEC.md\t\n+++ SPEC.md\t\n@@ -1,126 +1,130 @@\n # SPEC — bytetrack-counter\n \n ## §G — Goal\n \n RTSP camera → YOLO RKNN (NPU) → ByteTrack → line-crossing counter → SQLite + Flask dashboard.\n Count `ayam` (chicken) crossing counting line. Close batch on `talenan` (cutting-board) crossing.\n Daily cutoff @ HH:MM resets batch numbering. RK3588 hardware.\n \n ## §C — Constraints\n \n - Python 3.10, `rknn-toolkit-lite2` (NPU), `opencv-python` (RTSP/FFmpeg)\n - `numpy<2` (rknn-toolkit-lite2 incompatible with numpy≥2)\n - `PYTHONNOUSERSITE=1` ! set or venv breaks\n - SQLite for persistence, JSON file for active-batch state\n - Flask on port 5000, systemd supervision\n - `counter_live.py` ⊥ run on RK3588 (Jetson TensorRT only)\n - Single deployment: `counter_live_rknn_bytetrack.py` + `counter_dashboard.py`\n \n ## §I — Interfaces\n \n ### Systemd\n ```\n unit: bytetrack-counter.service → `venv/bin/python counter_live_rknn_bytetrack.py`\n unit: bytetrack-counter-dashboard.service → `venv/bin/python counter_dashboard.py`\n env: PYTHONNOUSERSITE=1 ! set in both units\n env: EnvironmentFile=/opt/bytetrack-counter/.env\n user: root (not jetson)\n path: /opt/bytetrack-counter (fixed in service file)\n ```\n \n ### `.env` config (40+ vars, gitignored)\n ```\n env: SOURCE ! set → RTSP URL | file path\n env: MODEL_PATH ! set → .rknn file\n env: IMGSZ ! set → model input size (e.g. 320)\n env: CORE_MASK → NPU core mask (1=core0, 2=core1, 3=dual, 7=all)\n env: NUM_CLASSES ! match model output count\n env: CONF → detection confidence threshold (default 0.3)\n env: DAILY_CUTOFF_TIME → HH:MM, default \"20:00\"\n env: CROSS_DIRECTION → rtl | ltr | both (default rtl)\n env: LINE_X | LINE_X_FRAC → counting line position\n env: CLASS_AYAM → class name for counted object (index 0)\n env: CLASS_TALENAN → class name for batch-close trigger (index 1)\n env: RESET_COUNTERS_AT_CUTOFF → defined in example, ! consumed by code ? zombie\n env: MOTION_DETECTION_ENABLED | MOTION_THRESHOLD → skip inference on still frames ?\n env: TRACK_HIGH_THRESH_{0,1} | TRACK_LOW_THRESH_{0,1} | TRACK_MATCH_THRESH_{0,1} → ByteTrack params per class\n env: BATCH_TIMEOUT_SECONDS → auto-close after inactivity (default 300)\n env: IGNORE_BATCH_LABEL_TIMEOUT_SECONDS → suppress talenan close after batch start (default 30)\n env: MIN_OBJECT_PER_BATCH → min count to persist batch (default 60)\n env: MIN_DURATION_PER_BATCH → min seconds to persist batch (default 60)\n env: LIVE_STREAM_ENABLED → write annotated JPEG snapshot each N frames\n env: EXPORT_CSV → write per-crossing CSV (default true)\n ```\n \n ### SQLite\n ```\n table: batches (date, batch#, camera, label, count, start, end, created_at)\n UNIQUE(counting_date, batch_number, camera_name, object_label)\n table: daily_summaries (date, camera, label, total_count, total_batches, updated_at)\n UNIQUE(counting_date, camera_name, object_label)\n ```\n \n ### JSON state file\n ```\n path: /tmp/bytetrack_current_batch.json (default)\n schema: {counting_date, batch_number, count, start_time, last_detection_time, counted_event_ids[]}\n ```\n \n ### Flask API\n ```\n api: GET / → dashboard HTML\n api: GET /api/current-batch → {count, batch_number, counting_date, start_time, last_detection_time}\n api: GET /api/previous-batch → {date, batch#, count, start/end, duration_minutes}\n api: GET /api/summary → {today, yesterday, all_time, average_per_day, best_day}\n api: GET /api/daily-data?days=N → [ {date, total_count, total_batches, avg_per_batch} ]\n api: GET /api/day-detail/<date> → {date, total_count, total_batches, total_duration, avg_duration, batches[]}\n api: GET /api/recent-batches?limit=N → [ {date, batch#, count, start/end, duration} ]\n api: GET /api/available-dates → [ {date, total_count, total_batches} ]\n api: GET /api/export-daily-csv?days=N → .xlsx download (named csv, emits xlsx)\n api: GET /api/export-day-csv/<date> → .xlsx download (named csv, emits xlsx)\n api: GET /api/live-video → MJPEG stream from shared-memory JPEG\n ```\n \n ## §V — Invariants\n \n ```\n V1: NUM_CLASSES must match model output → class 0=ayam, class 1=talenan\n V2: line crossing → prev_cx > line_x ≥ cx (rtl) | prev_cx < line_x ≤ cx (ltr)\n V3: ∀ track_id → counted at most once per batch (counted_event_ids set)\n V4: batch persisted → count ≥ MIN_OBJECT_PER_BATCH & duration ≥ MIN_DURATION_PER_BATCH\n V5: dt.time() < DAILY_CUTOFF_TIME → counting_date = today, else tomorrow\n V6: talenan crossing → close batch, but ignored ∀ IGNORE_BATCH_LABEL_TIMEOUT_SEC after batch start\n V7: batch inactivity ≥ BATCH_TIMEOUT_SECONDS → auto-close\n V8: PYTHONNOUSERSITE=1 ! set for venv isolation\n V9: .env ! exist before counter or dashboard starts\n V10: DB tables ! exist on startup (created if absent, both store & dashboard)\n V11: previous batch → last CARRY_IDS (default 50) track IDs carried forward to next batch\n V12: ∃ ! batch per (date, batch#, camera, label) — UNIQUE constraint in DB\n V13: counter & dashboard share DB path → no process-level coordination\n V14: stream disconnect → reconnect with delay (RECONNECT_DELAY_SEC), ! block main loop\n V15: model inference → letterbox-resize to IMGSZ×IMGSZ, BGR→RGB, run NPU\n V16: NMS postprocessing → iou_thr=0.45, class-aware grouping, score > CONF\n V17: tracks pruned after TRACKED_PRUNE_SEC (default 300s) without update\n V18: .env missing → scripts fail at import (os.getenv falls back to defaults, may mismatch)\n+V19: stream reconnect → reset motion detection state (prev_gray = None)\n ```\n \n ## §T — Tasks\n \n ```\n id|status|task|cites\n T1|.|unify install paths — README/DEPLOY/setup-venv.sh/uninstall say `/opt/jetson-counter`, service files & .env.example say `/opt/bytetrack-counter`|\n-T2|.|remove zombie var RESET_COUNTERS_AT_CUTOFF or wire it — defined in .env.example, unused in code|\n+T2|.|zombie var RESET_COUNTERS_AT_CUTOFF — defined in .env.example, unused in code (confirmed by review)|\n T3|.|add version flag — no `--version` or git-derived version exists|\n T4|.|dedup counter scripts — 3 variants share ~80% code; extract shared drawing/batch loop|\n T5|.|rename export routes — routes named `export-daily-csv` but emit `.xlsx`|I.flask\n T6|.|fix live-stream snapshot path — some scripts default `/dev/shm/jetson-counter/`, example says `bytetrack-counter`|\n T7|.|add dashboard health-check endpoint (no `/health` or `/api/status` exists)|\n T8|.|add model checks on startup — model class names ! validated against CLASS_AYAM/CLASS_TALENAN (TensorRT variant validates; RKNN variants do not)|\n+T9|.|reset prev_gray on stream reconnect — stale gray ref causes crash or false motion|V19,B1\n+T10|.|decay inf_ms toward 0 when inference skipped — stale display misleads operator|\n ```\n \n ## §B — Bugs\n \n ```\n id|date|cause|fix\n+B1|2026-07-29|prev_gray not reset on stream reconnect → cv2.absdiff crash or false motion|V19\n ```\n \n",
|
||
"additions": 5,
|
||
"deletions": 1,
|
||
"status": "modified"
|
||
}
|
||
]
|
||
},
|
||
"id": "msg_fab4029a60014acGT5OxMYD1IR",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"type": "text",
|
||
"text": "Summarize the task tool output above and continue with your task.",
|
||
"synthetic": true,
|
||
"id": "prt_fab4029aa001oJQG7Fa9I5Vy8F",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab4029a60014acGT5OxMYD1IR"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"parentID": "msg_fab4029a60014acGT5OxMYD1IR",
|
||
"role": "assistant",
|
||
"mode": "build",
|
||
"agent": "build",
|
||
"path": {
|
||
"cwd": "/opt/bytetrack-counter",
|
||
"root": "/opt/bytetrack-counter"
|
||
},
|
||
"cost": 0.001384083,
|
||
"tokens": {
|
||
"total": 67114,
|
||
"input": 1795,
|
||
"output": 128,
|
||
"reasoning": 295,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 64896
|
||
}
|
||
},
|
||
"modelID": "deepseek-v4-pro",
|
||
"providerID": "deepseek",
|
||
"time": {
|
||
"created": 1785284536757,
|
||
"completed": 1785284545389
|
||
},
|
||
"finish": "tool-calls",
|
||
"id": "msg_fab4029b5001bd2Cn1dqrCCyHE",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"snapshot": "8cee875229dc8fed3b74a6f8e65f0bb1d9875b4d",
|
||
"type": "step-start",
|
||
"id": "prt_fab402ecf001J7UE0iZpy94u9E",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab4029b5001bd2Cn1dqrCCyHE"
|
||
},
|
||
{
|
||
"type": "reasoning",
|
||
"text": "The review output contains findings from someone else's review of the codebase. Let me summarize the key points:\n\n1. **BUG**: `prev_gray` not reset on stream reconnection — can crash or produce false positives after reconnect\n2. **INCOMPLETE FEATURE**: `RESET_COUNTERS_AT_CUTOFF` documented but never implemented (zombie var)\n3. **OBSERVATION**: Stale `inf_ms` display when inference is skipped by motion detection\n4. **NOTE**: Motion detection logic itself is correct\n\nThe last two items (3 and 4) are about the `counter_live_rknn_bytetrack.py` file specifically, related to motion detection feature which I didn't fully read. Items 1, 3, and 4 are new findings not captured in my SPEC.md. Item 2 confirms my `? zombie` flag on RESET_COUNTERS_AT_CUTOFF.\n\nI should update SPEC.md to incorporate these review findings:\n- §B: Add bug B1 for prev_gray not reset on reconnect → new invariant\n- §V: Add invariant about prev_gray reset on reconnect\n- §T: T2 (RESET_COUNTERS_AT_CUTOFF zombie) confirmed, add new task for prev_gray fix\n- §T: Add task for inf_ms display when skip inference\n\nLet me update the spec.",
|
||
"time": {
|
||
"start": 1785284539463,
|
||
"end": 1785284543655
|
||
},
|
||
"id": "prt_fab403447001zaLiykQESH5BpW",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab4029b5001bd2Cn1dqrCCyHE"
|
||
},
|
||
{
|
||
"type": "text",
|
||
"text": "Review found:\n\n1. **Bug**: `prev_gray` not reset on stream reconnect → crash or false motion positives\n2. **Confirmed zombie**: `RESET_COUNTERS_AT_CUTOFF` in config, zero Python consumers\n3. **Observation**: stale `inf_ms` display when motion detection skips inference\n\nBackprop the bug into the spec:",
|
||
"time": {
|
||
"start": 1785284543659,
|
||
"end": 1785284545107
|
||
},
|
||
"id": "prt_fab4044ab001rheFTA8Sf3esLd",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab4029b5001bd2Cn1dqrCCyHE"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "read",
|
||
"callID": "call_00_OYvaWYX39mIKVfIWtcyC3279",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"filePath": "/opt/bytetrack-counter/SPEC.md"
|
||
},
|
||
"output": "<path>/opt/bytetrack-counter/SPEC.md</path>\n<type>file</type>\n<content>\n1: # SPEC — bytetrack-counter\n2: \n3: ## §G — Goal\n4: \n5: RTSP camera → YOLO RKNN (NPU) → ByteTrack → line-crossing counter → SQLite + Flask dashboard.\n6: Count `ayam` (chicken) crossing counting line. Close batch on `talenan` (cutting-board) crossing.\n7: Daily cutoff @ HH:MM resets batch numbering. RK3588 hardware.\n8: \n9: ## §C — Constraints\n10: \n11: - Python 3.10, `rknn-toolkit-lite2` (NPU), `opencv-python` (RTSP/FFmpeg)\n12: - `numpy<2` (rknn-toolkit-lite2 incompatible with numpy≥2)\n13: - `PYTHONNOUSERSITE=1` ! set or venv breaks\n14: - SQLite for persistence, JSON file for active-batch state\n15: - Flask on port 5000, systemd supervision\n16: - `counter_live.py` ⊥ run on RK3588 (Jetson TensorRT only)\n17: - Single deployment: `counter_live_rknn_bytetrack.py` + `counter_dashboard.py`\n18: \n19: ## §I — Interfaces\n20: \n21: ### Systemd\n22: ```\n23: unit: bytetrack-counter.service → `venv/bin/python counter_live_rknn_bytetrack.py`\n24: unit: bytetrack-counter-dashboard.service → `venv/bin/python counter_dashboard.py`\n25: env: PYTHONNOUSERSITE=1 ! set in both units\n26: env: EnvironmentFile=/opt/bytetrack-counter/.env\n27: user: root (not jetson)\n28: path: /opt/bytetrack-counter (fixed in service file)\n29: ```\n30: \n31: ### `.env` config (40+ vars, gitignored)\n32: ```\n33: env: SOURCE ! set → RTSP URL | file path\n34: env: MODEL_PATH ! set → .rknn file\n35: env: IMGSZ ! set → model input size (e.g. 320)\n36: env: CORE_MASK → NPU core mask (1=core0, 2=core1, 3=dual, 7=all)\n37: env: NUM_CLASSES ! match model output count\n38: env: CONF → detection confidence threshold (default 0.3)\n39: env: DAILY_CUTOFF_TIME → HH:MM, default \"20:00\"\n40: env: CROSS_DIRECTION → rtl | ltr | both (default rtl)\n41: env: LINE_X | LINE_X_FRAC → counting line position\n42: env: CLASS_AYAM → class name for counted object (index 0)\n43: env: CLASS_TALENAN → class name for batch-close trigger (index 1)\n44: env: RESET_COUNTERS_AT_CUTOFF → defined in example, ! consumed by code ? zombie\n45: env: MOTION_DETECTION_ENABLED | MOTION_THRESHOLD → skip inference on still frames ?\n46: env: TRACK_HIGH_THRESH_{0,1} | TRACK_LOW_THRESH_{0,1} | TRACK_MATCH_THRESH_{0,1} → ByteTrack params per class\n47: env: BATCH_TIMEOUT_SECONDS → auto-close after inactivity (default 300)\n48: env: IGNORE_BATCH_LABEL_TIMEOUT_SECONDS → suppress talenan close after batch start (default 30)\n49: env: MIN_OBJECT_PER_BATCH → min count to persist batch (default 60)\n50: env: MIN_DURATION_PER_BATCH → min seconds to persist batch (default 60)\n51: env: LIVE_STREAM_ENABLED → write annotated JPEG snapshot each N frames\n52: env: EXPORT_CSV → write per-crossing CSV (default true)\n53: ```\n54: \n55: ### SQLite\n56: ```\n57: table: batches (date, batch#, camera, label, count, start, end, created_at)\n58: UNIQUE(counting_date, batch_number, camera_name, object_label)\n59: table: daily_summaries (date, camera, label, total_count, total_batches, updated_at)\n60: UNIQUE(counting_date, camera_name, object_label)\n61: ```\n62: \n63: ### JSON state file\n64: ```\n65: path: /tmp/bytetrack_current_batch.json (default)\n66: schema: {counting_date, batch_number, count, start_time, last_detection_time, counted_event_ids[]}\n67: ```\n68: \n69: ### Flask API\n70: ```\n71: api: GET / → dashboard HTML\n72: api: GET /api/current-batch → {count, batch_number, counting_date, start_time, last_detection_time}\n73: api: GET /api/previous-batch → {date, batch#, count, start/end, duration_minutes}\n74: api: GET /api/summary → {today, yesterday, all_time, average_per_day, best_day}\n75: api: GET /api/daily-data?days=N → [ {date, total_count, total_batches, avg_per_batch} ]\n76: api: GET /api/day-detail/<date> → {date, total_count, total_batches, total_duration, avg_duration, batches[]}\n77: api: GET /api/recent-batches?limit=N → [ {date, batch#, count, start/end, duration} ]\n78: api: GET /api/available-dates → [ {date, total_count, total_batches} ]\n79: api: GET /api/export-daily-csv?days=N → .xlsx download (named csv, emits xlsx)\n80: api: GET /api/export-day-csv/<date> → .xlsx download (named csv, emits xlsx)\n81: api: GET /api/live-video → MJPEG stream from shared-memory JPEG\n82: ```\n83: \n84: ## §V — Invariants\n85: \n86: ```\n87: V1: NUM_CLASSES must match model output → class 0=ayam, class 1=talenan\n88: V2: line crossing → prev_cx > line_x ≥ cx (rtl) | prev_cx < line_x ≤ cx (ltr)\n89: V3: ∀ track_id → counted at most once per batch (counted_event_ids set)\n90: V4: batch persisted → count ≥ MIN_OBJECT_PER_BATCH & duration ≥ MIN_DURATION_PER_BATCH\n91: V5: dt.time() < DAILY_CUTOFF_TIME → counting_date = today, else tomorrow\n92: V6: talenan crossing → close batch, but ignored ∀ IGNORE_BATCH_LABEL_TIMEOUT_SEC after batch start\n93: V7: batch inactivity ≥ BATCH_TIMEOUT_SECONDS → auto-close\n94: V8: PYTHONNOUSERSITE=1 ! set for venv isolation\n95: V9: .env ! exist before counter or dashboard starts\n96: V10: DB tables ! exist on startup (created if absent, both store & dashboard)\n97: V11: previous batch → last CARRY_IDS (default 50) track IDs carried forward to next batch\n98: V12: ∃ ! batch per (date, batch#, camera, label) — UNIQUE constraint in DB\n99: V13: counter & dashboard share DB path → no process-level coordination\n100: V14: stream disconnect → reconnect with delay (RECONNECT_DELAY_SEC), ! block main loop\n101: V15: model inference → letterbox-resize to IMGSZ×IMGSZ, BGR→RGB, run NPU\n102: V16: NMS postprocessing → iou_thr=0.45, class-aware grouping, score > CONF\n103: V17: tracks pruned after TRACKED_PRUNE_SEC (default 300s) without update\n104: V18: .env missing → scripts fail at import (os.getenv falls back to defaults, may mismatch)\n105: ```\n106: \n107: ## §T — Tasks\n108: \n109: ```\n110: id|status|task|cites\n111: T1|.|unify install paths — README/DEPLOY/setup-venv.sh/uninstall say `/opt/jetson-counter`, service files & .env.example say `/opt/bytetrack-counter`|\n112: T2|.|remove zombie var RESET_COUNTERS_AT_CUTOFF or wire it — defined in .env.example, unused in code|\n113: T3|.|add version flag — no `--version` or git-derived version exists|\n114: T4|.|dedup counter scripts — 3 variants share ~80% code; extract shared drawing/batch loop|\n115: T5|.|rename export routes — routes named `export-daily-csv` but emit `.xlsx`|I.flask\n116: T6|.|fix live-stream snapshot path — some scripts default `/dev/shm/jetson-counter/`, example says `bytetrack-counter`|\n117: T7|.|add dashboard health-check endpoint (no `/health` or `/api/status` exists)|\n118: T8|.|add model checks on startup — model class names ! validated against CLASS_AYAM/CLASS_TALENAN (TensorRT variant validates; RKNN variants do not)|\n119: ```\n120: \n121: ## §B — Bugs\n122: \n123: ```\n124: id|date|cause|fix\n125: ```\n126: \n\n(End of file - total 126 lines)\n</content>",
|
||
"metadata": {
|
||
"preview": "# SPEC — bytetrack-counter\n\n## §G — Goal\n\nRTSP camera → YOLO RKNN (NPU) → ByteTrack → line-crossing counter → SQLite + Flask dashboard.\nCount `ayam` (chicken) crossing counting line. Close batch on `talenan` (cutting-board) crossing.\nDaily cutoff @ HH:MM resets batch numbering. RK3588 hardware.\n\n## §C — Constraints\n\n- Python 3.10, `rknn-toolkit-lite2` (NPU), `opencv-python` (RTSP/FFmpeg)\n- `numpy<2` (rknn-toolkit-lite2 incompatible with numpy≥2)\n- `PYTHONNOUSERSITE=1` ! set or venv breaks\n- SQLite for persistence, JSON file for active-batch state\n- Flask on port 5000, systemd supervision\n- `counter_live.py` ⊥ run on RK3588 (Jetson TensorRT only)\n- Single deployment: `counter_live_rknn_bytetrack.py` + `counter_dashboard.py`\n\n## §I — Interfaces\n",
|
||
"truncated": false,
|
||
"loaded": [],
|
||
"display": {
|
||
"type": "file",
|
||
"path": "/opt/bytetrack-counter/SPEC.md",
|
||
"text": "# SPEC — bytetrack-counter\n\n## §G — Goal\n\nRTSP camera → YOLO RKNN (NPU) → ByteTrack → line-crossing counter → SQLite + Flask dashboard.\nCount `ayam` (chicken) crossing counting line. Close batch on `talenan` (cutting-board) crossing.\nDaily cutoff @ HH:MM resets batch numbering. RK3588 hardware.\n\n## §C — Constraints\n\n- Python 3.10, `rknn-toolkit-lite2` (NPU), `opencv-python` (RTSP/FFmpeg)\n- `numpy<2` (rknn-toolkit-lite2 incompatible with numpy≥2)\n- `PYTHONNOUSERSITE=1` ! set or venv breaks\n- SQLite for persistence, JSON file for active-batch state\n- Flask on port 5000, systemd supervision\n- `counter_live.py` ⊥ run on RK3588 (Jetson TensorRT only)\n- Single deployment: `counter_live_rknn_bytetrack.py` + `counter_dashboard.py`\n\n## §I — Interfaces\n\n### Systemd\n```\nunit: bytetrack-counter.service → `venv/bin/python counter_live_rknn_bytetrack.py`\nunit: bytetrack-counter-dashboard.service → `venv/bin/python counter_dashboard.py`\nenv: PYTHONNOUSERSITE=1 ! set in both units\nenv: EnvironmentFile=/opt/bytetrack-counter/.env\nuser: root (not jetson)\npath: /opt/bytetrack-counter (fixed in service file)\n```\n\n### `.env` config (40+ vars, gitignored)\n```\nenv: SOURCE ! set → RTSP URL | file path\nenv: MODEL_PATH ! set → .rknn file\nenv: IMGSZ ! set → model input size (e.g. 320)\nenv: CORE_MASK → NPU core mask (1=core0, 2=core1, 3=dual, 7=all)\nenv: NUM_CLASSES ! match model output count\nenv: CONF → detection confidence threshold (default 0.3)\nenv: DAILY_CUTOFF_TIME → HH:MM, default \"20:00\"\nenv: CROSS_DIRECTION → rtl | ltr | both (default rtl)\nenv: LINE_X | LINE_X_FRAC → counting line position\nenv: CLASS_AYAM → class name for counted object (index 0)\nenv: CLASS_TALENAN → class name for batch-close trigger (index 1)\nenv: RESET_COUNTERS_AT_CUTOFF → defined in example, ! consumed by code ? zombie\nenv: MOTION_DETECTION_ENABLED | MOTION_THRESHOLD → skip inference on still frames ?\nenv: TRACK_HIGH_THRESH_{0,1} | TRACK_LOW_THRESH_{0,1} | TRACK_MATCH_THRESH_{0,1} → ByteTrack params per class\nenv: BATCH_TIMEOUT_SECONDS → auto-close after inactivity (default 300)\nenv: IGNORE_BATCH_LABEL_TIMEOUT_SECONDS → suppress talenan close after batch start (default 30)\nenv: MIN_OBJECT_PER_BATCH → min count to persist batch (default 60)\nenv: MIN_DURATION_PER_BATCH → min seconds to persist batch (default 60)\nenv: LIVE_STREAM_ENABLED → write annotated JPEG snapshot each N frames\nenv: EXPORT_CSV → write per-crossing CSV (default true)\n```\n\n### SQLite\n```\ntable: batches (date, batch#, camera, label, count, start, end, created_at)\n UNIQUE(counting_date, batch_number, camera_name, object_label)\ntable: daily_summaries (date, camera, label, total_count, total_batches, updated_at)\n UNIQUE(counting_date, camera_name, object_label)\n```\n\n### JSON state file\n```\npath: /tmp/bytetrack_current_batch.json (default)\nschema: {counting_date, batch_number, count, start_time, last_detection_time, counted_event_ids[]}\n```\n\n### Flask API\n```\napi: GET / → dashboard HTML\napi: GET /api/current-batch → {count, batch_number, counting_date, start_time, last_detection_time}\napi: GET /api/previous-batch → {date, batch#, count, start/end, duration_minutes}\napi: GET /api/summary → {today, yesterday, all_time, average_per_day, best_day}\napi: GET /api/daily-data?days=N → [ {date, total_count, total_batches, avg_per_batch} ]\napi: GET /api/day-detail/<date> → {date, total_count, total_batches, total_duration, avg_duration, batches[]}\napi: GET /api/recent-batches?limit=N → [ {date, batch#, count, start/end, duration} ]\napi: GET /api/available-dates → [ {date, total_count, total_batches} ]\napi: GET /api/export-daily-csv?days=N → .xlsx download (named csv, emits xlsx)\napi: GET /api/export-day-csv/<date> → .xlsx download (named csv, emits xlsx)\napi: GET /api/live-video → MJPEG stream from shared-memory JPEG\n```\n\n## §V — Invariants\n\n```\nV1: NUM_CLASSES must match model output → class 0=ayam, class 1=talenan\nV2: line crossing → prev_cx > line_x ≥ cx (rtl) | prev_cx < line_x ≤ cx (ltr)\nV3: ∀ track_id → counted at most once per batch (counted_event_ids set)\nV4: batch persisted → count ≥ MIN_OBJECT_PER_BATCH & duration ≥ MIN_DURATION_PER_BATCH\nV5: dt.time() < DAILY_CUTOFF_TIME → counting_date = today, else tomorrow\nV6: talenan crossing → close batch, but ignored ∀ IGNORE_BATCH_LABEL_TIMEOUT_SEC after batch start\nV7: batch inactivity ≥ BATCH_TIMEOUT_SECONDS → auto-close\nV8: PYTHONNOUSERSITE=1 ! set for venv isolation\nV9: .env ! exist before counter or dashboard starts\nV10: DB tables ! exist on startup (created if absent, both store & dashboard)\nV11: previous batch → last CARRY_IDS (default 50) track IDs carried forward to next batch\nV12: ∃ ! batch per (date, batch#, camera, label) — UNIQUE constraint in DB\nV13: counter & dashboard share DB path → no process-level coordination\nV14: stream disconnect → reconnect with delay (RECONNECT_DELAY_SEC), ! block main loop\nV15: model inference → letterbox-resize to IMGSZ×IMGSZ, BGR→RGB, run NPU\nV16: NMS postprocessing → iou_thr=0.45, class-aware grouping, score > CONF\nV17: tracks pruned after TRACKED_PRUNE_SEC (default 300s) without update\nV18: .env missing → scripts fail at import (os.getenv falls back to defaults, may mismatch)\n```\n\n## §T — Tasks\n\n```\nid|status|task|cites\nT1|.|unify install paths — README/DEPLOY/setup-venv.sh/uninstall say `/opt/jetson-counter`, service files & .env.example say `/opt/bytetrack-counter`|\nT2|.|remove zombie var RESET_COUNTERS_AT_CUTOFF or wire it — defined in .env.example, unused in code|\nT3|.|add version flag — no `--version` or git-derived version exists|\nT4|.|dedup counter scripts — 3 variants share ~80% code; extract shared drawing/batch loop|\nT5|.|rename export routes — routes named `export-daily-csv` but emit `.xlsx`|I.flask\nT6|.|fix live-stream snapshot path — some scripts default `/dev/shm/jetson-counter/`, example says `bytetrack-counter`|\nT7|.|add dashboard health-check endpoint (no `/health` or `/api/status` exists)|\nT8|.|add model checks on startup — model class names ! validated against CLASS_AYAM/CLASS_TALENAN (TensorRT variant validates; RKNN variants do not)|\n```\n\n## §B — Bugs\n\n```\nid|date|cause|fix\n```\n",
|
||
"lineStart": 1,
|
||
"lineEnd": 126,
|
||
"totalLines": 126,
|
||
"truncated": false
|
||
}
|
||
},
|
||
"title": "SPEC.md",
|
||
"time": {
|
||
"start": 1785284545009,
|
||
"end": 1785284545129
|
||
}
|
||
},
|
||
"id": "prt_fab40494e00177NcoCKvCZ06lN",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab4029b5001bd2Cn1dqrCCyHE"
|
||
},
|
||
{
|
||
"reason": "tool-calls",
|
||
"snapshot": "8cee875229dc8fed3b74a6f8e65f0bb1d9875b4d",
|
||
"type": "step-finish",
|
||
"tokens": {
|
||
"total": 67114,
|
||
"input": 1795,
|
||
"output": 128,
|
||
"reasoning": 295,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 64896
|
||
}
|
||
},
|
||
"cost": 0.001384083,
|
||
"id": "prt_fab404aeb001sJN3WTfl68E7iM",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab4029b5001bd2Cn1dqrCCyHE"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"parentID": "msg_fab4029a60014acGT5OxMYD1IR",
|
||
"role": "assistant",
|
||
"mode": "build",
|
||
"agent": "build",
|
||
"path": {
|
||
"cwd": "/opt/bytetrack-counter",
|
||
"root": "/opt/bytetrack-counter"
|
||
},
|
||
"cost": 0.001542046,
|
||
"tokens": {
|
||
"total": 69682,
|
||
"input": 2234,
|
||
"output": 158,
|
||
"reasoning": 218,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 67072
|
||
}
|
||
},
|
||
"modelID": "deepseek-v4-pro",
|
||
"providerID": "deepseek",
|
||
"time": {
|
||
"created": 1785284545398,
|
||
"completed": 1785284551832
|
||
},
|
||
"finish": "tool-calls",
|
||
"id": "msg_fab404b76001sG0G95Zi3f1aEr",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"snapshot": "8cee875229dc8fed3b74a6f8e65f0bb1d9875b4d",
|
||
"type": "step-start",
|
||
"id": "prt_fab404f1c001XcmhokZB5VJdiC",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab404b76001sG0G95Zi3f1aEr"
|
||
},
|
||
{
|
||
"type": "reasoning",
|
||
"text": "Now I need to backprop the review findings. According to the BACKPROP rules:\n1. Root cause: `prev_gray` is not reset to `None` after stream reconnection, so the next motion detection frame compares against an image from the dead stream.\n2. Would a new invariant catch recurrence? Yes — `V19: stream reconnect → reset motion detection state (prev_gray = None)`.\n3. Append §B row.\n4. Append new invariant to §V.\n5. If fix also changes behavior → add/update §T rows.\n\nLet me also update T2 status since the review confirmed it's a zombie var, and add a T9 for the prev_gray fix.\n\nActually, looking at the review more carefully - item 2 confirms T2 is accurate (zombie var, not wired), and item 1 is a genuine bug. Let me:\n- Add B1 to §B\n- Add V19 to §V\n- Add T9 for prev_gray fix\n- Add T10 for inf_ms stale display",
|
||
"time": {
|
||
"start": 1785284546782,
|
||
"end": 1785284550177
|
||
},
|
||
"id": "prt_fab4050de001eZxtc8Q7zofDp1",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab404b76001sG0G95Zi3f1aEr"
|
||
},
|
||
{
|
||
"type": "text",
|
||
"text": "Now backprop the bug and update confirmed findings:",
|
||
"time": {
|
||
"start": 1785284550181,
|
||
"end": 1785284551616
|
||
},
|
||
"id": "prt_fab405e25001sYIa7SX4cFvxDP",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab404b76001sG0G95Zi3f1aEr"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "edit",
|
||
"callID": "call_00_58BS1fL782HkhfiJVact9582",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"filePath": "/opt/bytetrack-counter/SPEC.md",
|
||
"oldString": "V18: .env missing → scripts fail at import (os.getenv falls back to defaults, may mismatch)\n```",
|
||
"newString": "V18: .env missing → scripts fail at import (os.getenv falls back to defaults, may mismatch)\nV19: stream reconnect → reset motion detection state (prev_gray = None)\n```"
|
||
},
|
||
"output": "Edit applied successfully.",
|
||
"metadata": {
|
||
"diagnostics": {},
|
||
"diff": "Index: /opt/bytetrack-counter/SPEC.md\n===================================================================\n--- /opt/bytetrack-counter/SPEC.md\n+++ /opt/bytetrack-counter/SPEC.md\n@@ -101,8 +101,9 @@\n V15: model inference → letterbox-resize to IMGSZ×IMGSZ, BGR→RGB, run NPU\n V16: NMS postprocessing → iou_thr=0.45, class-aware grouping, score > CONF\n V17: tracks pruned after TRACKED_PRUNE_SEC (default 300s) without update\n V18: .env missing → scripts fail at import (os.getenv falls back to defaults, may mismatch)\n+V19: stream reconnect → reset motion detection state (prev_gray = None)\n ```\n \n ## §T — Tasks\n \n",
|
||
"filediff": {
|
||
"file": "/opt/bytetrack-counter/SPEC.md",
|
||
"patch": "Index: /opt/bytetrack-counter/SPEC.md\n===================================================================\n--- /opt/bytetrack-counter/SPEC.md\n+++ /opt/bytetrack-counter/SPEC.md\n@@ -101,8 +101,9 @@\n V15: model inference → letterbox-resize to IMGSZ×IMGSZ, BGR→RGB, run NPU\n V16: NMS postprocessing → iou_thr=0.45, class-aware grouping, score > CONF\n V17: tracks pruned after TRACKED_PRUNE_SEC (default 300s) without update\n V18: .env missing → scripts fail at import (os.getenv falls back to defaults, may mismatch)\n+V19: stream reconnect → reset motion detection state (prev_gray = None)\n ```\n \n ## §T — Tasks\n \n",
|
||
"additions": 1,
|
||
"deletions": 0
|
||
},
|
||
"truncated": false
|
||
},
|
||
"title": "SPEC.md",
|
||
"time": {
|
||
"start": 1785284551604,
|
||
"end": 1785284551611
|
||
}
|
||
},
|
||
"id": "prt_fab405f77001ldXVMLzhNufi1n",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab404b76001sG0G95Zi3f1aEr"
|
||
},
|
||
{
|
||
"reason": "tool-calls",
|
||
"snapshot": "76a3faeacb75b717a84e439e6a94e69c70837358",
|
||
"type": "step-finish",
|
||
"tokens": {
|
||
"total": 69682,
|
||
"input": 2234,
|
||
"output": 158,
|
||
"reasoning": 218,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 67072
|
||
}
|
||
},
|
||
"cost": 0.001542046,
|
||
"id": "prt_fab40645e001wlhpVMweJKzQgw",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab404b76001sG0G95Zi3f1aEr"
|
||
},
|
||
{
|
||
"type": "patch",
|
||
"hash": "8cee875229dc8fed3b74a6f8e65f0bb1d9875b4d",
|
||
"files": [
|
||
"/opt/bytetrack-counter/SPEC.md"
|
||
],
|
||
"id": "prt_fab406494001CLpD6Ga1g1N5jb",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab404b76001sG0G95Zi3f1aEr"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"parentID": "msg_fab4029a60014acGT5OxMYD1IR",
|
||
"role": "assistant",
|
||
"mode": "build",
|
||
"agent": "build",
|
||
"path": {
|
||
"cwd": "/opt/bytetrack-counter",
|
||
"root": "/opt/bytetrack-counter"
|
||
},
|
||
"cost": 0.000766151,
|
||
"tokens": {
|
||
"total": 70255,
|
||
"input": 65,
|
||
"output": 530,
|
||
"reasoning": 28,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 69632
|
||
}
|
||
},
|
||
"modelID": "deepseek-v4-pro",
|
||
"providerID": "deepseek",
|
||
"time": {
|
||
"created": 1785284551841,
|
||
"completed": 1785284559357
|
||
},
|
||
"finish": "tool-calls",
|
||
"id": "msg_fab4064a1001ft6J1hsVLN2YAW",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"snapshot": "76a3faeacb75b717a84e439e6a94e69c70837358",
|
||
"type": "step-start",
|
||
"id": "prt_fab4068980017qNc1HVXP4wrSS",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab4064a1001ft6J1hsVLN2YAW"
|
||
},
|
||
{
|
||
"type": "reasoning",
|
||
"text": "Now add T9 and T10 to §T, and add B1 to §B, and update T2 note about confirmed zombie.",
|
||
"time": {
|
||
"start": 1785284553275,
|
||
"end": 1785284553973
|
||
},
|
||
"id": "prt_fab406a3b001P8s58GDTRsTNC2",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab4064a1001ft6J1hsVLN2YAW"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "edit",
|
||
"callID": "call_00_PXgGrJG817ezijVntvtz1915",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"filePath": "/opt/bytetrack-counter/SPEC.md",
|
||
"newString": "T2|.|zombie var RESET_COUNTERS_AT_CUTOFF — defined in .env.example, unused in code (confirmed by review)|\nT3|.|add version flag — no `--version` or git-derived version exists|\nT4|.|dedup counter scripts — 3 variants share ~80% code; extract shared drawing/batch loop|\nT5|.|rename export routes — routes named `export-daily-csv` but emit `.xlsx`|I.flask\nT6|.|fix live-stream snapshot path — some scripts default `/dev/shm/jetson-counter/`, example says `bytetrack-counter`|\nT7|.|add dashboard health-check endpoint (no `/health` or `/api/status` exists)|\nT8|.|add model checks on startup — model class names ! validated against CLASS_AYAM/CLASS_TALENAN (TensorRT variant validates; RKNN variants do not)|\nT9|.|reset prev_gray on stream reconnect — stale gray ref causes crash or false motion|V19,B1\nT10|.|decay inf_ms toward 0 when inference skipped — stale display misleads operator|",
|
||
"oldString": "T2|.|remove zombie var RESET_COUNTERS_AT_CUTOFF or wire it — defined in .env.example, unused in code|\nT3|.|add version flag — no `--version` or git-derived version exists|\nT4|.|dedup counter scripts — 3 variants share ~80% code; extract shared drawing/batch loop|\nT5|.|rename export routes — routes named `export-daily-csv` but emit `.xlsx`|I.flask\nT6|.|fix live-stream snapshot path — some scripts default `/dev/shm/jetson-counter/`, example says `bytetrack-counter`|\nT7|.|add dashboard health-check endpoint (no `/health` or `/api/status` exists)|\nT8|.|add model checks on startup — model class names ! validated against CLASS_AYAM/CLASS_TALENAN (TensorRT variant validates; RKNN variants do not)|"
|
||
},
|
||
"output": "Edit applied successfully.",
|
||
"metadata": {
|
||
"diagnostics": {},
|
||
"diff": "Index: /opt/bytetrack-counter/SPEC.md\n===================================================================\n--- /opt/bytetrack-counter/SPEC.md\n+++ /opt/bytetrack-counter/SPEC.md\n@@ -109,15 +109,17 @@\n \n ```\n id|status|task|cites\n T1|.|unify install paths — README/DEPLOY/setup-venv.sh/uninstall say `/opt/jetson-counter`, service files & .env.example say `/opt/bytetrack-counter`|\n-T2|.|remove zombie var RESET_COUNTERS_AT_CUTOFF or wire it — defined in .env.example, unused in code|\n+T2|.|zombie var RESET_COUNTERS_AT_CUTOFF — defined in .env.example, unused in code (confirmed by review)|\n T3|.|add version flag — no `--version` or git-derived version exists|\n T4|.|dedup counter scripts — 3 variants share ~80% code; extract shared drawing/batch loop|\n T5|.|rename export routes — routes named `export-daily-csv` but emit `.xlsx`|I.flask\n T6|.|fix live-stream snapshot path — some scripts default `/dev/shm/jetson-counter/`, example says `bytetrack-counter`|\n T7|.|add dashboard health-check endpoint (no `/health` or `/api/status` exists)|\n T8|.|add model checks on startup — model class names ! validated against CLASS_AYAM/CLASS_TALENAN (TensorRT variant validates; RKNN variants do not)|\n+T9|.|reset prev_gray on stream reconnect — stale gray ref causes crash or false motion|V19,B1\n+T10|.|decay inf_ms toward 0 when inference skipped — stale display misleads operator|\n ```\n \n ## §B — Bugs\n \n",
|
||
"filediff": {
|
||
"file": "/opt/bytetrack-counter/SPEC.md",
|
||
"patch": "Index: /opt/bytetrack-counter/SPEC.md\n===================================================================\n--- /opt/bytetrack-counter/SPEC.md\n+++ /opt/bytetrack-counter/SPEC.md\n@@ -109,15 +109,17 @@\n \n ```\n id|status|task|cites\n T1|.|unify install paths — README/DEPLOY/setup-venv.sh/uninstall say `/opt/jetson-counter`, service files & .env.example say `/opt/bytetrack-counter`|\n-T2|.|remove zombie var RESET_COUNTERS_AT_CUTOFF or wire it — defined in .env.example, unused in code|\n+T2|.|zombie var RESET_COUNTERS_AT_CUTOFF — defined in .env.example, unused in code (confirmed by review)|\n T3|.|add version flag — no `--version` or git-derived version exists|\n T4|.|dedup counter scripts — 3 variants share ~80% code; extract shared drawing/batch loop|\n T5|.|rename export routes — routes named `export-daily-csv` but emit `.xlsx`|I.flask\n T6|.|fix live-stream snapshot path — some scripts default `/dev/shm/jetson-counter/`, example says `bytetrack-counter`|\n T7|.|add dashboard health-check endpoint (no `/health` or `/api/status` exists)|\n T8|.|add model checks on startup — model class names ! validated against CLASS_AYAM/CLASS_TALENAN (TensorRT variant validates; RKNN variants do not)|\n+T9|.|reset prev_gray on stream reconnect — stale gray ref causes crash or false motion|V19,B1\n+T10|.|decay inf_ms toward 0 when inference skipped — stale display misleads operator|\n ```\n \n ## §B — Bugs\n \n",
|
||
"additions": 3,
|
||
"deletions": 1
|
||
},
|
||
"truncated": false
|
||
},
|
||
"title": "SPEC.md",
|
||
"time": {
|
||
"start": 1785284558903,
|
||
"end": 1785284558910
|
||
}
|
||
},
|
||
"id": "prt_fab406cfa001Vj47FTYzMtC2Qe",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab4064a1001ft6J1hsVLN2YAW"
|
||
},
|
||
{
|
||
"reason": "tool-calls",
|
||
"snapshot": "1f7ad23bf152168edd26ba683bfee48197e1ba43",
|
||
"type": "step-finish",
|
||
"tokens": {
|
||
"total": 70255,
|
||
"input": 65,
|
||
"output": 530,
|
||
"reasoning": 28,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 69632
|
||
}
|
||
},
|
||
"cost": 0.000766151,
|
||
"id": "prt_fab40816c001LH195J8fuBoGsa",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab4064a1001ft6J1hsVLN2YAW"
|
||
},
|
||
{
|
||
"type": "patch",
|
||
"hash": "76a3faeacb75b717a84e439e6a94e69c70837358",
|
||
"files": [
|
||
"/opt/bytetrack-counter/SPEC.md"
|
||
],
|
||
"id": "prt_fab4081f800183mlv6ZvJ46d7E",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab4064a1001ft6J1hsVLN2YAW"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"parentID": "msg_fab4029a60014acGT5OxMYD1IR",
|
||
"role": "assistant",
|
||
"mode": "build",
|
||
"agent": "build",
|
||
"path": {
|
||
"cwd": "/opt/bytetrack-counter",
|
||
"root": "/opt/bytetrack-counter"
|
||
},
|
||
"cost": 0.000426532,
|
||
"tokens": {
|
||
"total": 70405,
|
||
"input": 126,
|
||
"output": 135,
|
||
"reasoning": 0,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 70144
|
||
}
|
||
},
|
||
"modelID": "deepseek-v4-pro",
|
||
"providerID": "deepseek",
|
||
"time": {
|
||
"created": 1785284559367,
|
||
"completed": 1785284562324
|
||
},
|
||
"finish": "tool-calls",
|
||
"id": "msg_fab4082070014zM5Ucn0MkHo4G",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"snapshot": "1f7ad23bf152168edd26ba683bfee48197e1ba43",
|
||
"type": "step-start",
|
||
"id": "prt_fab40858f001IZtwg79aGMFw4p",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab4082070014zM5Ucn0MkHo4G"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "edit",
|
||
"callID": "call_00_ET_EDHBmI7lpBhkZfnW20fi8104",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"filePath": "/opt/bytetrack-counter/SPEC.md",
|
||
"newString": "```\nid|date|cause|fix\nB1|2026-07-29|prev_gray not reset on stream reconnect → cv2.absdiff crash or false motion|V19\n```",
|
||
"oldString": "```\nid|date|cause|fix\n```"
|
||
},
|
||
"output": "Edit applied successfully.",
|
||
"metadata": {
|
||
"diagnostics": {},
|
||
"diff": "Index: /opt/bytetrack-counter/SPEC.md\n===================================================================\n--- /opt/bytetrack-counter/SPEC.md\n+++ /opt/bytetrack-counter/SPEC.md\n@@ -124,6 +124,7 @@\n ## §B — Bugs\n \n ```\n id|date|cause|fix\n+B1|2026-07-29|prev_gray not reset on stream reconnect → cv2.absdiff crash or false motion|V19\n ```\n \n",
|
||
"filediff": {
|
||
"file": "/opt/bytetrack-counter/SPEC.md",
|
||
"patch": "Index: /opt/bytetrack-counter/SPEC.md\n===================================================================\n--- /opt/bytetrack-counter/SPEC.md\n+++ /opt/bytetrack-counter/SPEC.md\n@@ -124,6 +124,7 @@\n ## §B — Bugs\n \n ```\n id|date|cause|fix\n+B1|2026-07-29|prev_gray not reset on stream reconnect → cv2.absdiff crash or false motion|V19\n ```\n \n",
|
||
"additions": 1,
|
||
"deletions": 0
|
||
},
|
||
"truncated": false
|
||
},
|
||
"title": "SPEC.md",
|
||
"time": {
|
||
"start": 1785284562057,
|
||
"end": 1785284562065
|
||
}
|
||
},
|
||
"id": "prt_fab4088cc0012OSkexgJ2Px4OV",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab4082070014zM5Ucn0MkHo4G"
|
||
},
|
||
{
|
||
"reason": "tool-calls",
|
||
"snapshot": "c87a468b6d81043f8b38bef8538b2fcf91a378f2",
|
||
"type": "step-finish",
|
||
"tokens": {
|
||
"total": 70405,
|
||
"input": 126,
|
||
"output": 135,
|
||
"reasoning": 0,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 70144
|
||
}
|
||
},
|
||
"cost": 0.000426532,
|
||
"id": "prt_fab408d57001RlNIQ5PEL3PdoS",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab4082070014zM5Ucn0MkHo4G"
|
||
},
|
||
{
|
||
"type": "patch",
|
||
"hash": "1f7ad23bf152168edd26ba683bfee48197e1ba43",
|
||
"files": [
|
||
"/opt/bytetrack-counter/SPEC.md"
|
||
],
|
||
"id": "prt_fab408d8f00108NLn01rSOcfAo",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab4082070014zM5Ucn0MkHo4G"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"parentID": "msg_fab4029a60014acGT5OxMYD1IR",
|
||
"role": "assistant",
|
||
"mode": "build",
|
||
"agent": "build",
|
||
"path": {
|
||
"cwd": "/opt/bytetrack-counter",
|
||
"root": "/opt/bytetrack-counter"
|
||
},
|
||
"cost": 0.00040136,
|
||
"tokens": {
|
||
"total": 70578,
|
||
"input": 20,
|
||
"output": 151,
|
||
"reasoning": 7,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 70400
|
||
}
|
||
},
|
||
"modelID": "deepseek-v4-pro",
|
||
"providerID": "deepseek",
|
||
"time": {
|
||
"created": 1785284562335,
|
||
"completed": 1785284566096
|
||
},
|
||
"finish": "stop",
|
||
"id": "msg_fab408d9f001WKSvsK70BQO0Dq",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"snapshot": "c87a468b6d81043f8b38bef8538b2fcf91a378f2",
|
||
"type": "step-start",
|
||
"id": "prt_fab4091ec001s0EANlsghvw4Vi",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab408d9f001WKSvsK70BQO0Dq"
|
||
},
|
||
{
|
||
"type": "reasoning",
|
||
"text": "Let me verify the final spec.",
|
||
"time": {
|
||
"start": 1785284564003,
|
||
"end": 1785284564226
|
||
},
|
||
"id": "prt_fab409423001EWw998jqtoSYV8",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab408d9f001WKSvsK70BQO0Dq"
|
||
},
|
||
{
|
||
"type": "text",
|
||
"text": "Backprop done. Changes:\n\n- **§B** — added `B1|2026-07-29|prev_gray not reset on stream reconnect → cv2.absdiff crash or false motion|V19`\n- **§V** — added `V19: stream reconnect → reset motion detection state (prev_gray = None)`\n- **§T** — added `T9` (fix prev_gray on reconnect, cites V19+B1) and `T10` (decay inf_ms to 0 when skip inference)\n- **§T** — updated `T2` note: confirmed zombie by review\n\nSpec now has 10 tasks and 1 bug on record. `/build` when ready.",
|
||
"time": {
|
||
"start": 1785284564231,
|
||
"end": 1785284565972
|
||
},
|
||
"id": "prt_fab409507001aogJEQmykLtehV",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab408d9f001WKSvsK70BQO0Dq"
|
||
},
|
||
{
|
||
"reason": "stop",
|
||
"snapshot": "c87a468b6d81043f8b38bef8538b2fcf91a378f2",
|
||
"type": "step-finish",
|
||
"tokens": {
|
||
"total": 70578,
|
||
"input": 20,
|
||
"output": 151,
|
||
"reasoning": 7,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 70400
|
||
}
|
||
},
|
||
"cost": 0.00040136,
|
||
"id": "prt_fab409c0e001hrKVb57Uck7H7c",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab408d9f001WKSvsK70BQO0Dq"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"role": "user",
|
||
"time": {
|
||
"created": 1785284847679
|
||
},
|
||
"agent": "build",
|
||
"model": {
|
||
"providerID": "deepseek",
|
||
"modelID": "deepseek-v4-pro"
|
||
},
|
||
"summary": {
|
||
"diffs": [
|
||
{
|
||
"file": "SPEC.md",
|
||
"patch": "Index: SPEC.md\n===================================================================\n--- SPEC.md\t\n+++ SPEC.md\t\n@@ -1,130 +1,133 @@\n # SPEC — bytetrack-counter\n \n ## §G — Goal\n \n RTSP camera → YOLO RKNN (NPU) → ByteTrack → line-crossing counter → SQLite + Flask dashboard.\n Count `ayam` (chicken) crossing counting line. Close batch on `talenan` (cutting-board) crossing.\n Daily cutoff @ HH:MM resets batch numbering. RK3588 hardware.\n+**Reference implementation for C++ port in separate repo.**\n \n ## §C — Constraints\n \n - Python 3.10, `rknn-toolkit-lite2` (NPU), `opencv-python` (RTSP/FFmpeg)\n - `numpy<2` (rknn-toolkit-lite2 incompatible with numpy≥2)\n - `PYTHONNOUSERSITE=1` ! set or venv breaks\n - SQLite for persistence, JSON file for active-batch state\n - Flask on port 5000, systemd supervision\n-- `counter_live.py` ⊥ run on RK3588 (Jetson TensorRT only)\n+- `counter_live.py` ⊥ run on RK3588 — Jetson TensorRT artifact, ! port target\n+- `counter_live_rknn_bytetrack.py` — reference for C++ port, this is what systemd runs\n+- `counter_live_rknn.py` — simpler IoU tracker variant, less used\n - Single deployment: `counter_live_rknn_bytetrack.py` + `counter_dashboard.py`\n \n ## §I — Interfaces\n \n ### Systemd\n ```\n unit: bytetrack-counter.service → `venv/bin/python counter_live_rknn_bytetrack.py`\n unit: bytetrack-counter-dashboard.service → `venv/bin/python counter_dashboard.py`\n env: PYTHONNOUSERSITE=1 ! set in both units\n env: EnvironmentFile=/opt/bytetrack-counter/.env\n user: root (not jetson)\n path: /opt/bytetrack-counter (fixed in service file)\n ```\n \n ### `.env` config (40+ vars, gitignored)\n ```\n env: SOURCE ! set → RTSP URL | file path\n env: MODEL_PATH ! set → .rknn file\n env: IMGSZ ! set → model input size (e.g. 320)\n env: CORE_MASK → NPU core mask (1=core0, 2=core1, 3=dual, 7=all)\n env: NUM_CLASSES ! match model output count\n env: CONF → detection confidence threshold (default 0.3)\n env: DAILY_CUTOFF_TIME → HH:MM, default \"20:00\"\n env: CROSS_DIRECTION → rtl | ltr | both (default rtl)\n env: LINE_X | LINE_X_FRAC → counting line position\n env: CLASS_AYAM → class name for counted object (index 0)\n env: CLASS_TALENAN → class name for batch-close trigger (index 1)\n env: RESET_COUNTERS_AT_CUTOFF → defined in example, ! consumed by code ? zombie\n env: MOTION_DETECTION_ENABLED | MOTION_THRESHOLD → skip inference on still frames ?\n env: TRACK_HIGH_THRESH_{0,1} | TRACK_LOW_THRESH_{0,1} | TRACK_MATCH_THRESH_{0,1} → ByteTrack params per class\n env: BATCH_TIMEOUT_SECONDS → auto-close after inactivity (default 300)\n env: IGNORE_BATCH_LABEL_TIMEOUT_SECONDS → suppress talenan close after batch start (default 30)\n env: MIN_OBJECT_PER_BATCH → min count to persist batch (default 60)\n env: MIN_DURATION_PER_BATCH → min seconds to persist batch (default 60)\n env: LIVE_STREAM_ENABLED → write annotated JPEG snapshot each N frames\n env: EXPORT_CSV → write per-crossing CSV (default true)\n ```\n \n ### SQLite\n ```\n table: batches (date, batch#, camera, label, count, start, end, created_at)\n UNIQUE(counting_date, batch_number, camera_name, object_label)\n table: daily_summaries (date, camera, label, total_count, total_batches, updated_at)\n UNIQUE(counting_date, camera_name, object_label)\n ```\n \n ### JSON state file\n ```\n path: /tmp/bytetrack_current_batch.json (default)\n schema: {counting_date, batch_number, count, start_time, last_detection_time, counted_event_ids[]}\n ```\n \n ### Flask API\n ```\n api: GET / → dashboard HTML\n api: GET /api/current-batch → {count, batch_number, counting_date, start_time, last_detection_time}\n api: GET /api/previous-batch → {date, batch#, count, start/end, duration_minutes}\n api: GET /api/summary → {today, yesterday, all_time, average_per_day, best_day}\n api: GET /api/daily-data?days=N → [ {date, total_count, total_batches, avg_per_batch} ]\n api: GET /api/day-detail/<date> → {date, total_count, total_batches, total_duration, avg_duration, batches[]}\n api: GET /api/recent-batches?limit=N → [ {date, batch#, count, start/end, duration} ]\n api: GET /api/available-dates → [ {date, total_count, total_batches} ]\n api: GET /api/export-daily-csv?days=N → .xlsx download (named csv, emits xlsx)\n api: GET /api/export-day-csv/<date> → .xlsx download (named csv, emits xlsx)\n api: GET /api/live-video → MJPEG stream from shared-memory JPEG\n ```\n \n ## §V — Invariants\n \n ```\n V1: NUM_CLASSES must match model output → class 0=ayam, class 1=talenan\n V2: line crossing → prev_cx > line_x ≥ cx (rtl) | prev_cx < line_x ≤ cx (ltr)\n V3: ∀ track_id → counted at most once per batch (counted_event_ids set)\n V4: batch persisted → count ≥ MIN_OBJECT_PER_BATCH & duration ≥ MIN_DURATION_PER_BATCH\n V5: dt.time() < DAILY_CUTOFF_TIME → counting_date = today, else tomorrow\n V6: talenan crossing → close batch, but ignored ∀ IGNORE_BATCH_LABEL_TIMEOUT_SEC after batch start\n V7: batch inactivity ≥ BATCH_TIMEOUT_SECONDS → auto-close\n V8: PYTHONNOUSERSITE=1 ! set for venv isolation\n V9: .env ! exist before counter or dashboard starts\n V10: DB tables ! exist on startup (created if absent, both store & dashboard)\n V11: previous batch → last CARRY_IDS (default 50) track IDs carried forward to next batch\n V12: ∃ ! batch per (date, batch#, camera, label) — UNIQUE constraint in DB\n V13: counter & dashboard share DB path → no process-level coordination\n V14: stream disconnect → reconnect with delay (RECONNECT_DELAY_SEC), ! block main loop\n V15: model inference → letterbox-resize to IMGSZ×IMGSZ, BGR→RGB, run NPU\n V16: NMS postprocessing → iou_thr=0.45, class-aware grouping, score > CONF\n V17: tracks pruned after TRACKED_PRUNE_SEC (default 300s) without update\n V18: .env missing → scripts fail at import (os.getenv falls back to defaults, may mismatch)\n V19: stream reconnect → reset motion detection state (prev_gray = None)\n ```\n \n ## §T — Tasks\n \n ```\n id|status|task|cites\n T1|.|unify install paths — README/DEPLOY/setup-venv.sh/uninstall say `/opt/jetson-counter`, service files & .env.example say `/opt/bytetrack-counter`|\n T2|.|zombie var RESET_COUNTERS_AT_CUTOFF — defined in .env.example, unused in code (confirmed by review)|\n T3|.|add version flag — no `--version` or git-derived version exists|\n-T4|.|dedup counter scripts — 3 variants share ~80% code; extract shared drawing/batch loop|\n+T4|.|keep reference Python clean — counter_live.py & counter_live_rknn.py are artifacts/secondary; focus edits on counter_live_rknn_bytetrack.py as port source|\n T5|.|rename export routes — routes named `export-daily-csv` but emit `.xlsx`|I.flask\n T6|.|fix live-stream snapshot path — some scripts default `/dev/shm/jetson-counter/`, example says `bytetrack-counter`|\n T7|.|add dashboard health-check endpoint (no `/health` or `/api/status` exists)|\n T8|.|add model checks on startup — model class names ! validated against CLASS_AYAM/CLASS_TALENAN (TensorRT variant validates; RKNN variants do not)|\n T9|.|reset prev_gray on stream reconnect — stale gray ref causes crash or false motion|V19,B1\n T10|.|decay inf_ms toward 0 when inference skipped — stale display misleads operator|\n ```\n \n ## §B — Bugs\n \n ```\n id|date|cause|fix\n B1|2026-07-29|prev_gray not reset on stream reconnect → cv2.absdiff crash or false motion|V19\n ```\n \n",
|
||
"additions": 5,
|
||
"deletions": 2,
|
||
"status": "modified"
|
||
}
|
||
]
|
||
},
|
||
"id": "msg_fab44e83e0015b7qCfG5HqRNRx",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"type": "text",
|
||
"text": "counter_live.py is artifact for Jetson, while counter_live_rknn_bytetrack.py is RKNN version which is used as reference to be ported to C++ in other repo",
|
||
"id": "prt_fab44e854001Liumb4qYneZZN5",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab44e83e0015b7qCfG5HqRNRx"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"parentID": "msg_fab44e83e0015b7qCfG5HqRNRx",
|
||
"role": "assistant",
|
||
"mode": "build",
|
||
"agent": "build",
|
||
"path": {
|
||
"cwd": "/opt/bytetrack-counter",
|
||
"root": "/opt/bytetrack-counter"
|
||
},
|
||
"cost": 0.000755914,
|
||
"tokens": {
|
||
"total": 71150,
|
||
"input": 94,
|
||
"output": 252,
|
||
"reasoning": 276,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 70528
|
||
}
|
||
},
|
||
"modelID": "deepseek-v4-pro",
|
||
"providerID": "deepseek",
|
||
"time": {
|
||
"created": 1785284847798,
|
||
"completed": 1785284857993
|
||
},
|
||
"finish": "tool-calls",
|
||
"id": "msg_fab44e8b6001BJHjugrf6E5EWC",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"snapshot": "d9a9485fd3217b8c4f4aaada9d48fc94301b4e94",
|
||
"type": "step-start",
|
||
"id": "prt_fab44f44c001jf4fOh5U1PCwAv",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab44e8b6001BJHjugrf6E5EWC"
|
||
},
|
||
{
|
||
"type": "reasoning",
|
||
"text": "The user is providing context about the codebase:\n- `counter_live.py` is a Jetson artifact (TensorRT), not relevant for active development\n- `counter_live_rknn_bytetrack.py` is the RKNN version that serves as reference for a C++ port in another repo\n- `counter_live_rknn.py` was not mentioned — it's likely an intermediate/deprecated variant\n\nThis means I should update the spec to reflect this accurately. Key implications:\n1. `counter_live.py` (TensorRT) is legacy/artifact — not a primary concern\n2. `counter_live_rknn.py` (simple IoU tracker) may be deprecated or less important\n3. The main focus is `counter_live_rknn_bytetrack.py` as the reference implementation for a C++ port\n4. `batch_store.py` and `counter_dashboard.py` are shared infrastructure\n\nLet me update the spec to reflect this context:\n- §G should note it's a reference for C++ port\n- §C should note the C++ port relationship\n- Maybe add a note in §T about port parity (e.g. ensure C++ port matches Python reference behavior)\n\nLet me also clean up how I describe the 3 scripts — only mention them briefly but make clear which matters.",
|
||
"time": {
|
||
"start": 1785284851333,
|
||
"end": 1785284855490
|
||
},
|
||
"id": "prt_fab44f685001HACw3tizrxCdU1",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab44e8b6001BJHjugrf6E5EWC"
|
||
},
|
||
{
|
||
"type": "text",
|
||
"text": "Got it — that reframes the architecture. Let me update the spec:",
|
||
"time": {
|
||
"start": 1785284855498,
|
||
"end": 1785284857645
|
||
},
|
||
"id": "prt_fab4506ca001z4EddKQ7a7xK95",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab44e8b6001BJHjugrf6E5EWC"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "edit",
|
||
"callID": "call_00_eMvvmjPo7a8AYEgR6KWH8327",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"filePath": "/opt/bytetrack-counter/SPEC.md",
|
||
"newString": "RTSP camera → YOLO RKNN (NPU) → ByteTrack → line-crossing counter → SQLite + Flask dashboard.\nCount `ayam` (chicken) crossing counting line. Close batch on `talenan` (cutting-board) crossing.\nDaily cutoff @ HH:MM resets batch numbering. RK3588 hardware.\n**Reference implementation for C++ port in separate repo.**",
|
||
"oldString": "RTSP camera → YOLO RKNN (NPU) → ByteTrack → line-crossing counter → SQLite + Flask dashboard.\nCount `ayam` (chicken) crossing counting line. Close batch on `talenan` (cutting-board) crossing.\nDaily cutoff @ HH:MM resets batch numbering. RK3588 hardware."
|
||
},
|
||
"output": "Edit applied successfully.",
|
||
"metadata": {
|
||
"diagnostics": {},
|
||
"diff": "Index: /opt/bytetrack-counter/SPEC.md\n===================================================================\n--- /opt/bytetrack-counter/SPEC.md\n+++ /opt/bytetrack-counter/SPEC.md\n@@ -4,8 +4,9 @@\n \n RTSP camera → YOLO RKNN (NPU) → ByteTrack → line-crossing counter → SQLite + Flask dashboard.\n Count `ayam` (chicken) crossing counting line. Close batch on `talenan` (cutting-board) crossing.\n Daily cutoff @ HH:MM resets batch numbering. RK3588 hardware.\n+**Reference implementation for C++ port in separate repo.**\n \n ## §C — Constraints\n \n - Python 3.10, `rknn-toolkit-lite2` (NPU), `opencv-python` (RTSP/FFmpeg)\n",
|
||
"filediff": {
|
||
"file": "/opt/bytetrack-counter/SPEC.md",
|
||
"patch": "Index: /opt/bytetrack-counter/SPEC.md\n===================================================================\n--- /opt/bytetrack-counter/SPEC.md\n+++ /opt/bytetrack-counter/SPEC.md\n@@ -4,8 +4,9 @@\n \n RTSP camera → YOLO RKNN (NPU) → ByteTrack → line-crossing counter → SQLite + Flask dashboard.\n Count `ayam` (chicken) crossing counting line. Close batch on `talenan` (cutting-board) crossing.\n Daily cutoff @ HH:MM resets batch numbering. RK3588 hardware.\n+**Reference implementation for C++ port in separate repo.**\n \n ## §C — Constraints\n \n - Python 3.10, `rknn-toolkit-lite2` (NPU), `opencv-python` (RTSP/FFmpeg)\n",
|
||
"additions": 1,
|
||
"deletions": 0
|
||
},
|
||
"truncated": false
|
||
},
|
||
"title": "SPEC.md",
|
||
"time": {
|
||
"start": 1785284857626,
|
||
"end": 1785284857637
|
||
}
|
||
},
|
||
"id": "prt_fab4507e5001Da1f2cDTLfsjP2",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab44e8b6001BJHjugrf6E5EWC"
|
||
},
|
||
{
|
||
"reason": "tool-calls",
|
||
"snapshot": "9a6f271ea18cc640e4d757ccc8fd0c2a2f1eca1f",
|
||
"type": "step-finish",
|
||
"tokens": {
|
||
"total": 71150,
|
||
"input": 94,
|
||
"output": 252,
|
||
"reasoning": 276,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 70528
|
||
}
|
||
},
|
||
"cost": 0.000755914,
|
||
"id": "prt_fab451012001J6GSygUanYG4o1",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab44e8b6001BJHjugrf6E5EWC"
|
||
},
|
||
{
|
||
"type": "patch",
|
||
"hash": "d9a9485fd3217b8c4f4aaada9d48fc94301b4e94",
|
||
"files": [
|
||
"/opt/bytetrack-counter/SPEC.md"
|
||
],
|
||
"id": "prt_fab451082001Y13We0QIrTxNWN",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab44e8b6001BJHjugrf6E5EWC"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"parentID": "msg_fab44e83e0015b7qCfG5HqRNRx",
|
||
"role": "assistant",
|
||
"mode": "build",
|
||
"agent": "build",
|
||
"path": {
|
||
"cwd": "/opt/bytetrack-counter",
|
||
"root": "/opt/bytetrack-counter"
|
||
},
|
||
"cost": 0.000506775,
|
||
"tokens": {
|
||
"total": 71389,
|
||
"input": 125,
|
||
"output": 224,
|
||
"reasoning": 0,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 71040
|
||
}
|
||
},
|
||
"modelID": "deepseek-v4-pro",
|
||
"providerID": "deepseek",
|
||
"time": {
|
||
"created": 1785284858004,
|
||
"completed": 1785284862215
|
||
},
|
||
"finish": "tool-calls",
|
||
"id": "msg_fab451094001EOVKzIU5LNBgFC",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"snapshot": "9a6f271ea18cc640e4d757ccc8fd0c2a2f1eca1f",
|
||
"type": "step-start",
|
||
"id": "prt_fab45144200161DDMermK2sU4o",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab451094001EOVKzIU5LNBgFC"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "edit",
|
||
"callID": "call_00_ET_BZREWV7yZZSiEMpDXYN02188",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"filePath": "/opt/bytetrack-counter/SPEC.md",
|
||
"newString": "- `counter_live.py` ⊥ run on RK3588 — Jetson TensorRT artifact, ! port target\n- `counter_live_rknn_bytetrack.py` — reference for C++ port, this is what systemd runs\n- `counter_live_rknn.py` — simpler IoU tracker variant, less used\n- Single deployment: `counter_live_rknn_bytetrack.py` + `counter_dashboard.py`",
|
||
"oldString": "- `counter_live.py` ⊥ run on RK3588 (Jetson TensorRT only)\n- Single deployment: `counter_live_rknn_bytetrack.py` + `counter_dashboard.py`"
|
||
},
|
||
"output": "Edit applied successfully.",
|
||
"metadata": {
|
||
"diagnostics": {},
|
||
"diff": "Index: /opt/bytetrack-counter/SPEC.md\n===================================================================\n--- /opt/bytetrack-counter/SPEC.md\n+++ /opt/bytetrack-counter/SPEC.md\n@@ -13,9 +13,11 @@\n - `numpy<2` (rknn-toolkit-lite2 incompatible with numpy≥2)\n - `PYTHONNOUSERSITE=1` ! set or venv breaks\n - SQLite for persistence, JSON file for active-batch state\n - Flask on port 5000, systemd supervision\n-- `counter_live.py` ⊥ run on RK3588 (Jetson TensorRT only)\n+- `counter_live.py` ⊥ run on RK3588 — Jetson TensorRT artifact, ! port target\n+- `counter_live_rknn_bytetrack.py` — reference for C++ port, this is what systemd runs\n+- `counter_live_rknn.py` — simpler IoU tracker variant, less used\n - Single deployment: `counter_live_rknn_bytetrack.py` + `counter_dashboard.py`\n \n ## §I — Interfaces\n \n",
|
||
"filediff": {
|
||
"file": "/opt/bytetrack-counter/SPEC.md",
|
||
"patch": "Index: /opt/bytetrack-counter/SPEC.md\n===================================================================\n--- /opt/bytetrack-counter/SPEC.md\n+++ /opt/bytetrack-counter/SPEC.md\n@@ -13,9 +13,11 @@\n - `numpy<2` (rknn-toolkit-lite2 incompatible with numpy≥2)\n - `PYTHONNOUSERSITE=1` ! set or venv breaks\n - SQLite for persistence, JSON file for active-batch state\n - Flask on port 5000, systemd supervision\n-- `counter_live.py` ⊥ run on RK3588 (Jetson TensorRT only)\n+- `counter_live.py` ⊥ run on RK3588 — Jetson TensorRT artifact, ! port target\n+- `counter_live_rknn_bytetrack.py` — reference for C++ port, this is what systemd runs\n+- `counter_live_rknn.py` — simpler IoU tracker variant, less used\n - Single deployment: `counter_live_rknn_bytetrack.py` + `counter_dashboard.py`\n \n ## §I — Interfaces\n \n",
|
||
"additions": 3,
|
||
"deletions": 1
|
||
},
|
||
"truncated": false
|
||
},
|
||
"title": "SPEC.md",
|
||
"time": {
|
||
"start": 1785284861705,
|
||
"end": 1785284861714
|
||
}
|
||
},
|
||
"id": "prt_fab4517b5001HRFM0aTOHkMF0u",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab451094001EOVKzIU5LNBgFC"
|
||
},
|
||
{
|
||
"reason": "tool-calls",
|
||
"snapshot": "843d71a5531aa9dff7c93e3cd726d99b35316d72",
|
||
"type": "step-finish",
|
||
"tokens": {
|
||
"total": 71389,
|
||
"input": 125,
|
||
"output": 224,
|
||
"reasoning": 0,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 71040
|
||
}
|
||
},
|
||
"cost": 0.000506775,
|
||
"id": "prt_fab452032001PyJrO7sKFRhCAU",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab451094001EOVKzIU5LNBgFC"
|
||
},
|
||
{
|
||
"type": "patch",
|
||
"hash": "9a6f271ea18cc640e4d757ccc8fd0c2a2f1eca1f",
|
||
"files": [
|
||
"/opt/bytetrack-counter/SPEC.md"
|
||
],
|
||
"id": "prt_fab452100001LvNAKVbcMchceQ",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab451094001EOVKzIU5LNBgFC"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"parentID": "msg_fab44e83e0015b7qCfG5HqRNRx",
|
||
"role": "assistant",
|
||
"mode": "build",
|
||
"agent": "build",
|
||
"path": {
|
||
"cwd": "/opt/bytetrack-counter",
|
||
"root": "/opt/bytetrack-counter"
|
||
},
|
||
"cost": 0.000535978,
|
||
"tokens": {
|
||
"total": 71669,
|
||
"input": 108,
|
||
"output": 152,
|
||
"reasoning": 113,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 71296
|
||
}
|
||
},
|
||
"modelID": "deepseek-v4-pro",
|
||
"providerID": "deepseek",
|
||
"time": {
|
||
"created": 1785284862227,
|
||
"completed": 1785284867878
|
||
},
|
||
"finish": "tool-calls",
|
||
"id": "msg_fab4521130010ClPXSwl4sSEZV",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"snapshot": "843d71a5531aa9dff7c93e3cd726d99b35316d72",
|
||
"type": "step-start",
|
||
"id": "prt_fab4524d6001X4rHYUJobx6mqy",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab4521130010ClPXSwl4sSEZV"
|
||
},
|
||
{
|
||
"type": "reasoning",
|
||
"text": "Now let me also add a note in §T about the C++ port — maybe convert the dedup task since it's not about merging Python scripts but about keeping the Python reference clean for porting. Actually, T4 (dedup counter scripts) is less relevant now since the Jetson variant is an artifact and the IoU variant is less used. The RKNN bytetrack variant IS the reference. The code duplication concern is between the Python reference and the C++ port, not between the Python variants.\n\nLet me update T4 to reflect this.",
|
||
"time": {
|
||
"start": 1785284863761,
|
||
"end": 1785284866002
|
||
},
|
||
"id": "prt_fab452711001KERlMNyNX8eTxW",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab4521130010ClPXSwl4sSEZV"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "edit",
|
||
"callID": "call_00_RoceDKmdvVaoARdDIklX0550",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"filePath": "/opt/bytetrack-counter/SPEC.md",
|
||
"newString": "T4|.|keep reference Python clean — counter_live.py & counter_live_rknn.py are artifacts/secondary; focus edits on counter_live_rknn_bytetrack.py as port source|",
|
||
"oldString": "T4|.|dedup counter scripts — 3 variants share ~80% code; extract shared drawing/batch loop|"
|
||
},
|
||
"output": "Edit applied successfully.",
|
||
"metadata": {
|
||
"diagnostics": {},
|
||
"diff": "Index: /opt/bytetrack-counter/SPEC.md\n===================================================================\n--- /opt/bytetrack-counter/SPEC.md\n+++ /opt/bytetrack-counter/SPEC.md\n@@ -114,9 +114,9 @@\n id|status|task|cites\n T1|.|unify install paths — README/DEPLOY/setup-venv.sh/uninstall say `/opt/jetson-counter`, service files & .env.example say `/opt/bytetrack-counter`|\n T2|.|zombie var RESET_COUNTERS_AT_CUTOFF — defined in .env.example, unused in code (confirmed by review)|\n T3|.|add version flag — no `--version` or git-derived version exists|\n-T4|.|dedup counter scripts — 3 variants share ~80% code; extract shared drawing/batch loop|\n+T4|.|keep reference Python clean — counter_live.py & counter_live_rknn.py are artifacts/secondary; focus edits on counter_live_rknn_bytetrack.py as port source|\n T5|.|rename export routes — routes named `export-daily-csv` but emit `.xlsx`|I.flask\n T6|.|fix live-stream snapshot path — some scripts default `/dev/shm/jetson-counter/`, example says `bytetrack-counter`|\n T7|.|add dashboard health-check endpoint (no `/health` or `/api/status` exists)|\n T8|.|add model checks on startup — model class names ! validated against CLASS_AYAM/CLASS_TALENAN (TensorRT variant validates; RKNN variants do not)|\n",
|
||
"filediff": {
|
||
"file": "/opt/bytetrack-counter/SPEC.md",
|
||
"patch": "Index: /opt/bytetrack-counter/SPEC.md\n===================================================================\n--- /opt/bytetrack-counter/SPEC.md\n+++ /opt/bytetrack-counter/SPEC.md\n@@ -114,9 +114,9 @@\n id|status|task|cites\n T1|.|unify install paths — README/DEPLOY/setup-venv.sh/uninstall say `/opt/jetson-counter`, service files & .env.example say `/opt/bytetrack-counter`|\n T2|.|zombie var RESET_COUNTERS_AT_CUTOFF — defined in .env.example, unused in code (confirmed by review)|\n T3|.|add version flag — no `--version` or git-derived version exists|\n-T4|.|dedup counter scripts — 3 variants share ~80% code; extract shared drawing/batch loop|\n+T4|.|keep reference Python clean — counter_live.py & counter_live_rknn.py are artifacts/secondary; focus edits on counter_live_rknn_bytetrack.py as port source|\n T5|.|rename export routes — routes named `export-daily-csv` but emit `.xlsx`|I.flask\n T6|.|fix live-stream snapshot path — some scripts default `/dev/shm/jetson-counter/`, example says `bytetrack-counter`|\n T7|.|add dashboard health-check endpoint (no `/health` or `/api/status` exists)|\n T8|.|add model checks on startup — model class names ! validated against CLASS_AYAM/CLASS_TALENAN (TensorRT variant validates; RKNN variants do not)|\n",
|
||
"additions": 1,
|
||
"deletions": 1
|
||
},
|
||
"truncated": false
|
||
},
|
||
"title": "SPEC.md",
|
||
"time": {
|
||
"start": 1785284867307,
|
||
"end": 1785284867315
|
||
}
|
||
},
|
||
"id": "prt_fab452fd9001lCzhfl6tefWe4V",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab4521130010ClPXSwl4sSEZV"
|
||
},
|
||
{
|
||
"reason": "tool-calls",
|
||
"snapshot": "06fb16ed052d492c38e500441ba6a23c8feabeb2",
|
||
"type": "step-finish",
|
||
"tokens": {
|
||
"total": 71669,
|
||
"input": 108,
|
||
"output": 152,
|
||
"reasoning": 113,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 71296
|
||
}
|
||
},
|
||
"cost": 0.000535978,
|
||
"id": "prt_fab453629001X19ufHf6n8CxFS",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab4521130010ClPXSwl4sSEZV"
|
||
},
|
||
{
|
||
"type": "patch",
|
||
"hash": "843d71a5531aa9dff7c93e3cd726d99b35316d72",
|
||
"files": [
|
||
"/opt/bytetrack-counter/SPEC.md"
|
||
],
|
||
"id": "prt_fab453720001HsqWaGAJp8rms8",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab4521130010ClPXSwl4sSEZV"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"parentID": "msg_fab44e83e0015b7qCfG5HqRNRx",
|
||
"role": "assistant",
|
||
"mode": "build",
|
||
"agent": "build",
|
||
"path": {
|
||
"cwd": "/opt/bytetrack-counter",
|
||
"root": "/opt/bytetrack-counter"
|
||
},
|
||
"cost": 0.000404666,
|
||
"tokens": {
|
||
"total": 71785,
|
||
"input": 132,
|
||
"output": 101,
|
||
"reasoning": 0,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 71552
|
||
}
|
||
},
|
||
"modelID": "deepseek-v4-pro",
|
||
"providerID": "deepseek",
|
||
"time": {
|
||
"created": 1785284867890,
|
||
"completed": 1785284870980
|
||
},
|
||
"finish": "stop",
|
||
"id": "msg_fab453732001KewrYqODjnLwYp",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"snapshot": "06fb16ed052d492c38e500441ba6a23c8feabeb2",
|
||
"type": "step-start",
|
||
"id": "prt_fab453b060010pbHfIdTAps63f",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab453732001KewrYqODjnLwYp"
|
||
},
|
||
{
|
||
"type": "text",
|
||
"text": "Updated §G and §C to clarify:\n- `counter_live_rknn_bytetrack.py` is the **reference implementation for a C++ port** in another repo\n- `counter_live.py` is a Jetson artifact, not a port target\n- `counter_live_rknn.py` is a secondary IoU variant\n- T4 reframed to \"keep reference Python clean for port\" instead of dedup concern\n\n`/build` when ready.",
|
||
"time": {
|
||
"start": 1785284869496,
|
||
"end": 1785284870750
|
||
},
|
||
"id": "prt_fab453d780018d0f6pfbsPeZtH",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab453732001KewrYqODjnLwYp"
|
||
},
|
||
{
|
||
"reason": "stop",
|
||
"snapshot": "06fb16ed052d492c38e500441ba6a23c8feabeb2",
|
||
"type": "step-finish",
|
||
"tokens": {
|
||
"total": 71785,
|
||
"input": 132,
|
||
"output": 101,
|
||
"reasoning": 0,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 71552
|
||
}
|
||
},
|
||
"cost": 0.000404666,
|
||
"id": "prt_fab454316001klvyMuL7bFYawM",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab453732001KewrYqODjnLwYp"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"role": "user",
|
||
"time": {
|
||
"created": 1785284900089
|
||
},
|
||
"agent": "build",
|
||
"model": {
|
||
"providerID": "deepseek",
|
||
"modelID": "deepseek-v4-pro"
|
||
},
|
||
"summary": {
|
||
"diffs": [
|
||
{
|
||
"file": "AGENTS.md",
|
||
"patch": "Index: AGENTS.md\n===================================================================\n--- AGENTS.md\t\n+++ AGENTS.md\t\n@@ -1,51 +1,50 @@\n # AGENTS.md — bytetrack-counter\n \n ## Architecture\n \n - **Edge AI counter**: RTSP camera → YOLO RKNN (NPU) → tracking → line-crossing → SQLite + JSON state → Flask dashboard.\n-- **3 counter scripts**, only 1 deployed:\n+- **2 counter scripts**, only 1 deployed:\n - `counter_live.py` — Jetson TensorRT variant (CUDA, NOT used on RK3588).\n- - `counter_live_rknn.py` — RK3588 with simple IoU tracker.\n- - **`counter_live_rknn_bytetrack.py`** — RK3588 with ByteTrack. **This is what systemd runs.**\n+ - **`counter_live_rknn_bytetrack.py`** — RK3588 with ByteTrack. **This is what systemd runs.** Reference for C++ port.\n - `batch_store.py` — shared SQLite persistence + batch state machine.\n - `counter_dashboard.py` — Flask dashboard on port 5000, same DB.\n \n ## No build / test / lint\n \n There is no build system, no test framework, no linter config, no typechecker. \n Do not try to run `pytest`, `ruff`, `mypy`, etc. — they don't exist here.\n \n ## How to run\n \n ```bash\n # Copy env (required, .env is gitignored)\n cp config.env.example .env\n \n # Venv (must use system-site-packages for RKNN toolkit)\n python3 -m venv --system-site-packages venv\n source venv/bin/pip install -r requirements.txt\n \n # Run counter (RK3588 only — needs rknn-toolkit-lite2 & RKNN model)\n PYTHONNOUSERSITE=1 venv/bin/python counter_live_rknn_bytetrack.py\n \n # Run dashboard\n PYTHONNOUSERSITE=1 venv/bin/python counter_dashboard.py\n ```\n \n ## Key environment & install quirks\n \n - **`PYTHONNOUSERSITE=1`** is mandatory when running from the venv — without it, system/user packages leak in.\n - **`.env` is gitignored** — always copy from `config.env.example` first.\n - **`numpy<2`** is required for `rknn-toolkit-lite2` compatibility.\n - **Install path in service files is `/opt/bytetrack-counter`** (not the `/opt/jetson-counter` mentioned in README/DEPLOY). The `.env.example` also reflects `/opt/bytetrack-counter`.\n - Service user is **`root`**, not `jetson` (despite README saying otherwise).\n - Two systemd units: `bytetrack-counter.service` + `bytetrack-counter-dashboard.service`.\n - `counter_live.py` (TensorRT) is Jetson-only and won't work on RK3588.\n \n ## Code conventions\n \n - All config lives in `.env` (dotenv), read via `os.getenv()` at module top-level in each script.\n - The 3 counter scripts duplicate ~80% of each other (drawing helpers, batch loop, etc). Changes to logic may need replication across variants.\n - `batch_store.py` has its own threading (cutoff watcher, batch timeout timer) — thread safety is via a single `state_lock`.\n - The dashboard re-creates DB tables on startup (`_ensure_db()`) independently from `batch_store.py`.\n - No formal version tracking exists anywhere in this codebase.\n",
|
||
"additions": 2,
|
||
"deletions": 3,
|
||
"status": "modified"
|
||
},
|
||
{
|
||
"file": "SPEC.md",
|
||
"patch": "Index: SPEC.md\n===================================================================\n--- SPEC.md\t\n+++ SPEC.md\t\n@@ -1,133 +1,132 @@\n # SPEC — bytetrack-counter\n \n ## §G — Goal\n \n RTSP camera → YOLO RKNN (NPU) → ByteTrack → line-crossing counter → SQLite + Flask dashboard.\n Count `ayam` (chicken) crossing counting line. Close batch on `talenan` (cutting-board) crossing.\n Daily cutoff @ HH:MM resets batch numbering. RK3588 hardware.\n **Reference implementation for C++ port in separate repo.**\n \n ## §C — Constraints\n \n - Python 3.10, `rknn-toolkit-lite2` (NPU), `opencv-python` (RTSP/FFmpeg)\n - `numpy<2` (rknn-toolkit-lite2 incompatible with numpy≥2)\n - `PYTHONNOUSERSITE=1` ! set or venv breaks\n - SQLite for persistence, JSON file for active-batch state\n - Flask on port 5000, systemd supervision\n - `counter_live.py` ⊥ run on RK3588 — Jetson TensorRT artifact, ! port target\n - `counter_live_rknn_bytetrack.py` — reference for C++ port, this is what systemd runs\n-- `counter_live_rknn.py` — simpler IoU tracker variant, less used\n - Single deployment: `counter_live_rknn_bytetrack.py` + `counter_dashboard.py`\n \n ## §I — Interfaces\n \n ### Systemd\n ```\n unit: bytetrack-counter.service → `venv/bin/python counter_live_rknn_bytetrack.py`\n unit: bytetrack-counter-dashboard.service → `venv/bin/python counter_dashboard.py`\n env: PYTHONNOUSERSITE=1 ! set in both units\n env: EnvironmentFile=/opt/bytetrack-counter/.env\n user: root (not jetson)\n path: /opt/bytetrack-counter (fixed in service file)\n ```\n \n ### `.env` config (40+ vars, gitignored)\n ```\n env: SOURCE ! set → RTSP URL | file path\n env: MODEL_PATH ! set → .rknn file\n env: IMGSZ ! set → model input size (e.g. 320)\n env: CORE_MASK → NPU core mask (1=core0, 2=core1, 3=dual, 7=all)\n env: NUM_CLASSES ! match model output count\n env: CONF → detection confidence threshold (default 0.3)\n env: DAILY_CUTOFF_TIME → HH:MM, default \"20:00\"\n env: CROSS_DIRECTION → rtl | ltr | both (default rtl)\n env: LINE_X | LINE_X_FRAC → counting line position\n env: CLASS_AYAM → class name for counted object (index 0)\n env: CLASS_TALENAN → class name for batch-close trigger (index 1)\n env: RESET_COUNTERS_AT_CUTOFF → defined in example, ! consumed by code ? zombie\n env: MOTION_DETECTION_ENABLED | MOTION_THRESHOLD → skip inference on still frames ?\n env: TRACK_HIGH_THRESH_{0,1} | TRACK_LOW_THRESH_{0,1} | TRACK_MATCH_THRESH_{0,1} → ByteTrack params per class\n env: BATCH_TIMEOUT_SECONDS → auto-close after inactivity (default 300)\n env: IGNORE_BATCH_LABEL_TIMEOUT_SECONDS → suppress talenan close after batch start (default 30)\n env: MIN_OBJECT_PER_BATCH → min count to persist batch (default 60)\n env: MIN_DURATION_PER_BATCH → min seconds to persist batch (default 60)\n env: LIVE_STREAM_ENABLED → write annotated JPEG snapshot each N frames\n env: EXPORT_CSV → write per-crossing CSV (default true)\n ```\n \n ### SQLite\n ```\n table: batches (date, batch#, camera, label, count, start, end, created_at)\n UNIQUE(counting_date, batch_number, camera_name, object_label)\n table: daily_summaries (date, camera, label, total_count, total_batches, updated_at)\n UNIQUE(counting_date, camera_name, object_label)\n ```\n \n ### JSON state file\n ```\n path: /tmp/bytetrack_current_batch.json (default)\n schema: {counting_date, batch_number, count, start_time, last_detection_time, counted_event_ids[]}\n ```\n \n ### Flask API\n ```\n api: GET / → dashboard HTML\n api: GET /api/current-batch → {count, batch_number, counting_date, start_time, last_detection_time}\n api: GET /api/previous-batch → {date, batch#, count, start/end, duration_minutes}\n api: GET /api/summary → {today, yesterday, all_time, average_per_day, best_day}\n api: GET /api/daily-data?days=N → [ {date, total_count, total_batches, avg_per_batch} ]\n api: GET /api/day-detail/<date> → {date, total_count, total_batches, total_duration, avg_duration, batches[]}\n api: GET /api/recent-batches?limit=N → [ {date, batch#, count, start/end, duration} ]\n api: GET /api/available-dates → [ {date, total_count, total_batches} ]\n api: GET /api/export-daily-csv?days=N → .xlsx download (named csv, emits xlsx)\n api: GET /api/export-day-csv/<date> → .xlsx download (named csv, emits xlsx)\n api: GET /api/live-video → MJPEG stream from shared-memory JPEG\n ```\n \n ## §V — Invariants\n \n ```\n V1: NUM_CLASSES must match model output → class 0=ayam, class 1=talenan\n V2: line crossing → prev_cx > line_x ≥ cx (rtl) | prev_cx < line_x ≤ cx (ltr)\n V3: ∀ track_id → counted at most once per batch (counted_event_ids set)\n V4: batch persisted → count ≥ MIN_OBJECT_PER_BATCH & duration ≥ MIN_DURATION_PER_BATCH\n V5: dt.time() < DAILY_CUTOFF_TIME → counting_date = today, else tomorrow\n V6: talenan crossing → close batch, but ignored ∀ IGNORE_BATCH_LABEL_TIMEOUT_SEC after batch start\n V7: batch inactivity ≥ BATCH_TIMEOUT_SECONDS → auto-close\n V8: PYTHONNOUSERSITE=1 ! set for venv isolation\n V9: .env ! exist before counter or dashboard starts\n V10: DB tables ! exist on startup (created if absent, both store & dashboard)\n V11: previous batch → last CARRY_IDS (default 50) track IDs carried forward to next batch\n V12: ∃ ! batch per (date, batch#, camera, label) — UNIQUE constraint in DB\n V13: counter & dashboard share DB path → no process-level coordination\n V14: stream disconnect → reconnect with delay (RECONNECT_DELAY_SEC), ! block main loop\n V15: model inference → letterbox-resize to IMGSZ×IMGSZ, BGR→RGB, run NPU\n V16: NMS postprocessing → iou_thr=0.45, class-aware grouping, score > CONF\n V17: tracks pruned after TRACKED_PRUNE_SEC (default 300s) without update\n V18: .env missing → scripts fail at import (os.getenv falls back to defaults, may mismatch)\n V19: stream reconnect → reset motion detection state (prev_gray = None)\n ```\n \n ## §T — Tasks\n \n ```\n id|status|task|cites\n T1|.|unify install paths — README/DEPLOY/setup-venv.sh/uninstall say `/opt/jetson-counter`, service files & .env.example say `/opt/bytetrack-counter`|\n T2|.|zombie var RESET_COUNTERS_AT_CUTOFF — defined in .env.example, unused in code (confirmed by review)|\n T3|.|add version flag — no `--version` or git-derived version exists|\n-T4|.|keep reference Python clean — counter_live.py & counter_live_rknn.py are artifacts/secondary; focus edits on counter_live_rknn_bytetrack.py as port source|\n+T4|.|keep reference Python clean — counter_live.py is artifact; focus edits on counter_live_rknn_bytetrack.py as port source|\n T5|.|rename export routes — routes named `export-daily-csv` but emit `.xlsx`|I.flask\n T6|.|fix live-stream snapshot path — some scripts default `/dev/shm/jetson-counter/`, example says `bytetrack-counter`|\n T7|.|add dashboard health-check endpoint (no `/health` or `/api/status` exists)|\n T8|.|add model checks on startup — model class names ! validated against CLASS_AYAM/CLASS_TALENAN (TensorRT variant validates; RKNN variants do not)|\n T9|.|reset prev_gray on stream reconnect — stale gray ref causes crash or false motion|V19,B1\n T10|.|decay inf_ms toward 0 when inference skipped — stale display misleads operator|\n ```\n \n ## §B — Bugs\n \n ```\n id|date|cause|fix\n B1|2026-07-29|prev_gray not reset on stream reconnect → cv2.absdiff crash or false motion|V19\n ```\n \n",
|
||
"additions": 1,
|
||
"deletions": 2,
|
||
"status": "modified"
|
||
}
|
||
]
|
||
},
|
||
"id": "msg_fab45b4f9001LDyE3IGzMO2RsQ",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"type": "text",
|
||
"text": "remove counter_live_rknn.py",
|
||
"id": "prt_fab45b4fc001NQGmeQFw2JPJLv",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab45b4f9001LDyE3IGzMO2RsQ"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"parentID": "msg_fab45b4f9001LDyE3IGzMO2RsQ",
|
||
"role": "assistant",
|
||
"mode": "build",
|
||
"agent": "build",
|
||
"path": {
|
||
"cwd": "/opt/bytetrack-counter",
|
||
"root": "/opt/bytetrack-counter"
|
||
},
|
||
"cost": 0.000572605,
|
||
"tokens": {
|
||
"total": 72098,
|
||
"input": 117,
|
||
"output": 254,
|
||
"reasoning": 47,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 71680
|
||
}
|
||
},
|
||
"modelID": "deepseek-v4-pro",
|
||
"providerID": "deepseek",
|
||
"time": {
|
||
"created": 1785284900137,
|
||
"completed": 1785284904689
|
||
},
|
||
"finish": "tool-calls",
|
||
"id": "msg_fab45b529001tAN1KjLKiQaK1P",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"snapshot": "06fb16ed052d492c38e500441ba6a23c8feabeb2",
|
||
"type": "step-start",
|
||
"id": "prt_fab45b8e2001l4QbmwXNMrJJGv",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab45b529001tAN1KjLKiQaK1P"
|
||
},
|
||
{
|
||
"type": "reasoning",
|
||
"text": "The user wants to delete `counter_live_rknn.py` - the simpler IoU tracker variant that is less used and not the reference for the C++ port. Let me update the spec first, then delete the file.",
|
||
"time": {
|
||
"start": 1785284901580,
|
||
"end": 1785284902503
|
||
},
|
||
"id": "prt_fab45bacc001l219JADg3cOxoM",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab45b529001tAN1KjLKiQaK1P"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "edit",
|
||
"callID": "call_00_N3cqeoU5DcTQs0JyF1q02728",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"filePath": "/opt/bytetrack-counter/SPEC.md",
|
||
"oldString": "- `counter_live.py` ⊥ run on RK3588 — Jetson TensorRT artifact, ! port target\n- `counter_live_rknn_bytetrack.py` — reference for C++ port, this is what systemd runs\n- `counter_live_rknn.py` — simpler IoU tracker variant, less used\n- Single deployment: `counter_live_rknn_bytetrack.py` + `counter_dashboard.py`",
|
||
"newString": "- `counter_live.py` ⊥ run on RK3588 — Jetson TensorRT artifact, ! port target\n- `counter_live_rknn_bytetrack.py` — reference for C++ port, this is what systemd runs\n- Single deployment: `counter_live_rknn_bytetrack.py` + `counter_dashboard.py`"
|
||
},
|
||
"output": "Edit applied successfully.",
|
||
"metadata": {
|
||
"diagnostics": {},
|
||
"diff": "Index: /opt/bytetrack-counter/SPEC.md\n===================================================================\n--- /opt/bytetrack-counter/SPEC.md\n+++ /opt/bytetrack-counter/SPEC.md\n@@ -15,9 +15,8 @@\n - SQLite for persistence, JSON file for active-batch state\n - Flask on port 5000, systemd supervision\n - `counter_live.py` ⊥ run on RK3588 — Jetson TensorRT artifact, ! port target\n - `counter_live_rknn_bytetrack.py` — reference for C++ port, this is what systemd runs\n-- `counter_live_rknn.py` — simpler IoU tracker variant, less used\n - Single deployment: `counter_live_rknn_bytetrack.py` + `counter_dashboard.py`\n \n ## §I — Interfaces\n \n",
|
||
"filediff": {
|
||
"file": "/opt/bytetrack-counter/SPEC.md",
|
||
"patch": "Index: /opt/bytetrack-counter/SPEC.md\n===================================================================\n--- /opt/bytetrack-counter/SPEC.md\n+++ /opt/bytetrack-counter/SPEC.md\n@@ -15,9 +15,8 @@\n - SQLite for persistence, JSON file for active-batch state\n - Flask on port 5000, systemd supervision\n - `counter_live.py` ⊥ run on RK3588 — Jetson TensorRT artifact, ! port target\n - `counter_live_rknn_bytetrack.py` — reference for C++ port, this is what systemd runs\n-- `counter_live_rknn.py` — simpler IoU tracker variant, less used\n - Single deployment: `counter_live_rknn_bytetrack.py` + `counter_dashboard.py`\n \n ## §I — Interfaces\n \n",
|
||
"additions": 0,
|
||
"deletions": 1
|
||
},
|
||
"truncated": false
|
||
},
|
||
"title": "SPEC.md",
|
||
"time": {
|
||
"start": 1785284904298,
|
||
"end": 1785284904305
|
||
}
|
||
},
|
||
"id": "prt_fab45be6b001maGMSpnjlUgCCX",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab45b529001tAN1KjLKiQaK1P"
|
||
},
|
||
{
|
||
"reason": "tool-calls",
|
||
"snapshot": "c1e76524f6fd3536c3e6b174f99dd3e5bf25cd6a",
|
||
"type": "step-finish",
|
||
"tokens": {
|
||
"total": 72098,
|
||
"input": 117,
|
||
"output": 254,
|
||
"reasoning": 47,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 71680
|
||
}
|
||
},
|
||
"cost": 0.000572605,
|
||
"id": "prt_fab45c639001W4XG9zVqLKF2Wk",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab45b529001tAN1KjLKiQaK1P"
|
||
},
|
||
{
|
||
"type": "patch",
|
||
"hash": "06fb16ed052d492c38e500441ba6a23c8feabeb2",
|
||
"files": [
|
||
"/opt/bytetrack-counter/SPEC.md"
|
||
],
|
||
"id": "prt_fab45c6ec001yvIfz3vwTvZUXs",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab45b529001tAN1KjLKiQaK1P"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"parentID": "msg_fab45b4f9001LDyE3IGzMO2RsQ",
|
||
"role": "assistant",
|
||
"mode": "build",
|
||
"agent": "build",
|
||
"path": {
|
||
"cwd": "/opt/bytetrack-counter",
|
||
"root": "/opt/bytetrack-counter"
|
||
},
|
||
"cost": 0.000421747,
|
||
"tokens": {
|
||
"total": 72273,
|
||
"input": 49,
|
||
"output": 160,
|
||
"reasoning": 0,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 72064
|
||
}
|
||
},
|
||
"modelID": "deepseek-v4-pro",
|
||
"providerID": "deepseek",
|
||
"time": {
|
||
"created": 1785284904700,
|
||
"completed": 1785284907739
|
||
},
|
||
"finish": "tool-calls",
|
||
"id": "msg_fab45c6fc001MUUwn4vLLEbTEG",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"snapshot": "c1e76524f6fd3536c3e6b174f99dd3e5bf25cd6a",
|
||
"type": "step-start",
|
||
"id": "prt_fab45ca68001MXVWjSDPfJ7QAP",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab45c6fc001MUUwn4vLLEbTEG"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "edit",
|
||
"callID": "call_00_ET_0PFCci3trdKCD0d2kKwR3171",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"filePath": "/opt/bytetrack-counter/SPEC.md",
|
||
"newString": "T4|.|keep reference Python clean — counter_live.py is artifact; focus edits on counter_live_rknn_bytetrack.py as port source|",
|
||
"oldString": "T4|.|keep reference Python clean — counter_live.py & counter_live_rknn.py are artifacts/secondary; focus edits on counter_live_rknn_bytetrack.py as port source|"
|
||
},
|
||
"output": "Edit applied successfully.",
|
||
"metadata": {
|
||
"diagnostics": {},
|
||
"diff": "Index: /opt/bytetrack-counter/SPEC.md\n===================================================================\n--- /opt/bytetrack-counter/SPEC.md\n+++ /opt/bytetrack-counter/SPEC.md\n@@ -113,9 +113,9 @@\n id|status|task|cites\n T1|.|unify install paths — README/DEPLOY/setup-venv.sh/uninstall say `/opt/jetson-counter`, service files & .env.example say `/opt/bytetrack-counter`|\n T2|.|zombie var RESET_COUNTERS_AT_CUTOFF — defined in .env.example, unused in code (confirmed by review)|\n T3|.|add version flag — no `--version` or git-derived version exists|\n-T4|.|keep reference Python clean — counter_live.py & counter_live_rknn.py are artifacts/secondary; focus edits on counter_live_rknn_bytetrack.py as port source|\n+T4|.|keep reference Python clean — counter_live.py is artifact; focus edits on counter_live_rknn_bytetrack.py as port source|\n T5|.|rename export routes — routes named `export-daily-csv` but emit `.xlsx`|I.flask\n T6|.|fix live-stream snapshot path — some scripts default `/dev/shm/jetson-counter/`, example says `bytetrack-counter`|\n T7|.|add dashboard health-check endpoint (no `/health` or `/api/status` exists)|\n T8|.|add model checks on startup — model class names ! validated against CLASS_AYAM/CLASS_TALENAN (TensorRT variant validates; RKNN variants do not)|\n",
|
||
"filediff": {
|
||
"file": "/opt/bytetrack-counter/SPEC.md",
|
||
"patch": "Index: /opt/bytetrack-counter/SPEC.md\n===================================================================\n--- /opt/bytetrack-counter/SPEC.md\n+++ /opt/bytetrack-counter/SPEC.md\n@@ -113,9 +113,9 @@\n id|status|task|cites\n T1|.|unify install paths — README/DEPLOY/setup-venv.sh/uninstall say `/opt/jetson-counter`, service files & .env.example say `/opt/bytetrack-counter`|\n T2|.|zombie var RESET_COUNTERS_AT_CUTOFF — defined in .env.example, unused in code (confirmed by review)|\n T3|.|add version flag — no `--version` or git-derived version exists|\n-T4|.|keep reference Python clean — counter_live.py & counter_live_rknn.py are artifacts/secondary; focus edits on counter_live_rknn_bytetrack.py as port source|\n+T4|.|keep reference Python clean — counter_live.py is artifact; focus edits on counter_live_rknn_bytetrack.py as port source|\n T5|.|rename export routes — routes named `export-daily-csv` but emit `.xlsx`|I.flask\n T6|.|fix live-stream snapshot path — some scripts default `/dev/shm/jetson-counter/`, example says `bytetrack-counter`|\n T7|.|add dashboard health-check endpoint (no `/health` or `/api/status` exists)|\n T8|.|add model checks on startup — model class names ! validated against CLASS_AYAM/CLASS_TALENAN (TensorRT variant validates; RKNN variants do not)|\n",
|
||
"additions": 1,
|
||
"deletions": 1
|
||
},
|
||
"truncated": false
|
||
},
|
||
"title": "SPEC.md",
|
||
"time": {
|
||
"start": 1785284907294,
|
||
"end": 1785284907303
|
||
}
|
||
},
|
||
"id": "prt_fab45cce9001X5HHvEaaNGgi6u",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab45c6fc001MUUwn4vLLEbTEG"
|
||
},
|
||
{
|
||
"reason": "tool-calls",
|
||
"snapshot": "a091aacaf5cfed6e4bdef31d84e4e40fa3e0cf2e",
|
||
"type": "step-finish",
|
||
"tokens": {
|
||
"total": 72273,
|
||
"input": 49,
|
||
"output": 160,
|
||
"reasoning": 0,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 72064
|
||
}
|
||
},
|
||
"cost": 0.000421747,
|
||
"id": "prt_fab45d257001IPkNN4Cs1M3dU3",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab45c6fc001MUUwn4vLLEbTEG"
|
||
},
|
||
{
|
||
"type": "patch",
|
||
"hash": "c1e76524f6fd3536c3e6b174f99dd3e5bf25cd6a",
|
||
"files": [
|
||
"/opt/bytetrack-counter/SPEC.md"
|
||
],
|
||
"id": "prt_fab45d2d5001GifSyDT9CGnmxQ",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab45c6fc001MUUwn4vLLEbTEG"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"parentID": "msg_fab45b4f9001LDyE3IGzMO2RsQ",
|
||
"role": "assistant",
|
||
"mode": "build",
|
||
"agent": "build",
|
||
"path": {
|
||
"cwd": "/opt/bytetrack-counter",
|
||
"root": "/opt/bytetrack-counter"
|
||
},
|
||
"cost": 0.000540096,
|
||
"tokens": {
|
||
"total": 72560,
|
||
"input": 96,
|
||
"output": 247,
|
||
"reasoning": 25,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 72192
|
||
}
|
||
},
|
||
"modelID": "deepseek-v4-pro",
|
||
"providerID": "deepseek",
|
||
"time": {
|
||
"created": 1785284907772,
|
||
"completed": 1785284912139
|
||
},
|
||
"finish": "tool-calls",
|
||
"id": "msg_fab45d2fc001GAhhf0rkEovBtG",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"snapshot": "a091aacaf5cfed6e4bdef31d84e4e40fa3e0cf2e",
|
||
"type": "step-start",
|
||
"id": "prt_fab45d739001j17YOvr7DkjZp3",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab45d2fc001GAhhf0rkEovBtG"
|
||
},
|
||
{
|
||
"type": "reasoning",
|
||
"text": "Now update AGENTS.md because I don't need a task tool for that - just editing directly. And delete the file.",
|
||
"time": {
|
||
"start": 1785284909242,
|
||
"end": 1785284909948
|
||
},
|
||
"id": "prt_fab45d8ba001ZWD0NSaAZSH6vA",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab45d2fc001GAhhf0rkEovBtG"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "edit",
|
||
"callID": "call_00_FWnuX1VDxPPMutxGMg7b1858",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"filePath": "/opt/bytetrack-counter/AGENTS.md",
|
||
"newString": "- **2 counter scripts**, only 1 deployed:\n - `counter_live.py` — Jetson TensorRT variant (CUDA, NOT used on RK3588).\n - **`counter_live_rknn_bytetrack.py`** — RK3588 with ByteTrack. **This is what systemd runs.** Reference for C++ port.",
|
||
"oldString": "- **3 counter scripts**, only 1 deployed:\n - `counter_live.py` — Jetson TensorRT variant (CUDA, NOT used on RK3588).\n - `counter_live_rknn.py` — RK3588 with simple IoU tracker.\n - **`counter_live_rknn_bytetrack.py`** — RK3588 with ByteTrack. **This is what systemd runs.**"
|
||
},
|
||
"output": "Edit applied successfully.",
|
||
"metadata": {
|
||
"diagnostics": {},
|
||
"diff": "Index: /opt/bytetrack-counter/AGENTS.md\n===================================================================\n--- /opt/bytetrack-counter/AGENTS.md\n+++ /opt/bytetrack-counter/AGENTS.md\n@@ -2,12 +2,11 @@\n \n ## Architecture\n \n - **Edge AI counter**: RTSP camera → YOLO RKNN (NPU) → tracking → line-crossing → SQLite + JSON state → Flask dashboard.\n-- **3 counter scripts**, only 1 deployed:\n+- **2 counter scripts**, only 1 deployed:\n - `counter_live.py` — Jetson TensorRT variant (CUDA, NOT used on RK3588).\n- - `counter_live_rknn.py` — RK3588 with simple IoU tracker.\n- - **`counter_live_rknn_bytetrack.py`** — RK3588 with ByteTrack. **This is what systemd runs.**\n+ - **`counter_live_rknn_bytetrack.py`** — RK3588 with ByteTrack. **This is what systemd runs.** Reference for C++ port.\n - `batch_store.py` — shared SQLite persistence + batch state machine.\n - `counter_dashboard.py` — Flask dashboard on port 5000, same DB.\n \n ## No build / test / lint\n",
|
||
"filediff": {
|
||
"file": "/opt/bytetrack-counter/AGENTS.md",
|
||
"patch": "Index: /opt/bytetrack-counter/AGENTS.md\n===================================================================\n--- /opt/bytetrack-counter/AGENTS.md\n+++ /opt/bytetrack-counter/AGENTS.md\n@@ -2,12 +2,11 @@\n \n ## Architecture\n \n - **Edge AI counter**: RTSP camera → YOLO RKNN (NPU) → tracking → line-crossing → SQLite + JSON state → Flask dashboard.\n-- **3 counter scripts**, only 1 deployed:\n+- **2 counter scripts**, only 1 deployed:\n - `counter_live.py` — Jetson TensorRT variant (CUDA, NOT used on RK3588).\n- - `counter_live_rknn.py` — RK3588 with simple IoU tracker.\n- - **`counter_live_rknn_bytetrack.py`** — RK3588 with ByteTrack. **This is what systemd runs.**\n+ - **`counter_live_rknn_bytetrack.py`** — RK3588 with ByteTrack. **This is what systemd runs.** Reference for C++ port.\n - `batch_store.py` — shared SQLite persistence + batch state machine.\n - `counter_dashboard.py` — Flask dashboard on port 5000, same DB.\n \n ## No build / test / lint\n",
|
||
"additions": 2,
|
||
"deletions": 3
|
||
},
|
||
"truncated": false
|
||
},
|
||
"title": "AGENTS.md",
|
||
"time": {
|
||
"start": 1785284911643,
|
||
"end": 1785284911651
|
||
}
|
||
},
|
||
"id": "prt_fab45db82001TlOHQl7ebGub6l",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab45d2fc001GAhhf0rkEovBtG"
|
||
},
|
||
{
|
||
"reason": "tool-calls",
|
||
"snapshot": "903513119f5f892d9f5642024f6839426e6caf93",
|
||
"type": "step-finish",
|
||
"tokens": {
|
||
"total": 72560,
|
||
"input": 96,
|
||
"output": 247,
|
||
"reasoning": 25,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 72192
|
||
}
|
||
},
|
||
"cost": 0.000540096,
|
||
"id": "prt_fab45e3b40010wRCcrRbwEwzhi",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab45d2fc001GAhhf0rkEovBtG"
|
||
},
|
||
{
|
||
"type": "patch",
|
||
"hash": "a091aacaf5cfed6e4bdef31d84e4e40fa3e0cf2e",
|
||
"files": [
|
||
"/opt/bytetrack-counter/AGENTS.md"
|
||
],
|
||
"id": "prt_fab45e406001W8lXtOBcRx27Si",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab45d2fc001GAhhf0rkEovBtG"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"parentID": "msg_fab45b4f9001LDyE3IGzMO2RsQ",
|
||
"role": "assistant",
|
||
"mode": "build",
|
||
"agent": "build",
|
||
"path": {
|
||
"cwd": "/opt/bytetrack-counter",
|
||
"root": "/opt/bytetrack-counter"
|
||
},
|
||
"cost": 0.030809745,
|
||
"tokens": {
|
||
"total": 72645,
|
||
"input": 70639,
|
||
"output": 86,
|
||
"reasoning": 0,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 1920
|
||
}
|
||
},
|
||
"modelID": "deepseek-v4-pro",
|
||
"providerID": "deepseek",
|
||
"time": {
|
||
"created": 1785284912151,
|
||
"completed": 1785284920676
|
||
},
|
||
"finish": "tool-calls",
|
||
"id": "msg_fab45e417001jBgHUecC9yGlRh",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"snapshot": "903513119f5f892d9f5642024f6839426e6caf93",
|
||
"type": "step-start",
|
||
"id": "prt_fab45e8bb0016i1ghDgQ1v916v",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab45e417001jBgHUecC9yGlRh"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "bash",
|
||
"callID": "call_00_ET_eaaqUy8EANnf3O8ijtwi2341",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"command": "rm /opt/bytetrack-counter/counter_live_rknn.py && echo \"removed\"",
|
||
"description": "Delete counter_live_rknn.py"
|
||
},
|
||
"output": "rm: cannot remove '/opt/bytetrack-counter/counter_live_rknn.py': No such file or directory\n",
|
||
"metadata": {
|
||
"output": "rm: cannot remove '/opt/bytetrack-counter/counter_live_rknn.py': No such file or directory\n",
|
||
"exit": 1,
|
||
"truncated": false
|
||
},
|
||
"title": "rm /opt/bytetrack-counter/counter_live_rknn.py && echo \"removed\"",
|
||
"time": {
|
||
"start": 1785284920419,
|
||
"end": 1785284920438
|
||
}
|
||
},
|
||
"id": "prt_fab4601d8001iJx0MX86RDZkVs",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab45e417001jBgHUecC9yGlRh"
|
||
},
|
||
{
|
||
"reason": "tool-calls",
|
||
"snapshot": "903513119f5f892d9f5642024f6839426e6caf93",
|
||
"type": "step-finish",
|
||
"tokens": {
|
||
"total": 72645,
|
||
"input": 70639,
|
||
"output": 86,
|
||
"reasoning": 0,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 1920
|
||
}
|
||
},
|
||
"cost": 0.030809745,
|
||
"id": "prt_fab4604c8001RwikwMZBFTWfNc",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab45e417001jBgHUecC9yGlRh"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"parentID": "msg_fab45b4f9001LDyE3IGzMO2RsQ",
|
||
"role": "assistant",
|
||
"mode": "build",
|
||
"agent": "build",
|
||
"path": {
|
||
"cwd": "/opt/bytetrack-counter",
|
||
"root": "/opt/bytetrack-counter"
|
||
},
|
||
"cost": 0.000385758,
|
||
"tokens": {
|
||
"total": 72770,
|
||
"input": 106,
|
||
"output": 70,
|
||
"reasoning": 18,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 72576
|
||
}
|
||
},
|
||
"modelID": "deepseek-v4-pro",
|
||
"providerID": "deepseek",
|
||
"time": {
|
||
"created": 1785284920690,
|
||
"completed": 1785284923601
|
||
},
|
||
"finish": "tool-calls",
|
||
"id": "msg_fab460572001INQaO2ATC5cXhe",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"snapshot": "903513119f5f892d9f5642024f6839426e6caf93",
|
||
"type": "step-start",
|
||
"id": "prt_fab46093e001j0DpjMouZhglQx",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab460572001INQaO2ATC5cXhe"
|
||
},
|
||
{
|
||
"type": "reasoning",
|
||
"text": "The file doesn't exist at the expected path. Let me check where it actually is.",
|
||
"time": {
|
||
"start": 1785284922277,
|
||
"end": 1785284922852
|
||
},
|
||
"id": "prt_fab460ba4001nyzdu5BRYNkiIA",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab460572001INQaO2ATC5cXhe"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "glob",
|
||
"callID": "call_00_eTlChAN4qLDcXvcdjEil1020",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"path": "/opt/bytetrack-counter",
|
||
"pattern": "counter_live_rknn.py"
|
||
},
|
||
"output": "No files found",
|
||
"metadata": {
|
||
"count": 0,
|
||
"truncated": false
|
||
},
|
||
"title": "",
|
||
"time": {
|
||
"start": 1785284923178,
|
||
"end": 1785284923305
|
||
}
|
||
},
|
||
"id": "prt_fab460dea0011NKrVRFwQ1p2vn",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab460572001INQaO2ATC5cXhe"
|
||
},
|
||
{
|
||
"reason": "tool-calls",
|
||
"snapshot": "903513119f5f892d9f5642024f6839426e6caf93",
|
||
"type": "step-finish",
|
||
"tokens": {
|
||
"total": 72770,
|
||
"input": 106,
|
||
"output": 70,
|
||
"reasoning": 18,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 72576
|
||
}
|
||
},
|
||
"cost": 0.000385758,
|
||
"id": "prt_fab461054001eqVUgI7oAEgn60",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab460572001INQaO2ATC5cXhe"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"parentID": "msg_fab45b4f9001LDyE3IGzMO2RsQ",
|
||
"role": "assistant",
|
||
"mode": "build",
|
||
"agent": "build",
|
||
"path": {
|
||
"cwd": "/opt/bytetrack-counter",
|
||
"root": "/opt/bytetrack-counter"
|
||
},
|
||
"cost": 0.000577187,
|
||
"tokens": {
|
||
"total": 73105,
|
||
"input": 81,
|
||
"output": 49,
|
||
"reasoning": 271,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 72704
|
||
}
|
||
},
|
||
"modelID": "deepseek-v4-pro",
|
||
"providerID": "deepseek",
|
||
"time": {
|
||
"created": 1785284923612,
|
||
"completed": 1785284929041
|
||
},
|
||
"finish": "tool-calls",
|
||
"id": "msg_fab4610dc001meIdDOSmJHS7fe",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"snapshot": "903513119f5f892d9f5642024f6839426e6caf93",
|
||
"type": "step-start",
|
||
"id": "prt_fab461445001C02PuyqHIwc2jP",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab4610dc001meIdDOSmJHS7fe"
|
||
},
|
||
{
|
||
"type": "reasoning",
|
||
"text": "Hmm, the glob says no results, but I read the file earlier with a successful read at `/opt/bytetrack-counter/counter_live_rknn.py`. Let me check the directory listing again to see what's actually there now.\n\nActually wait - maybe the file was renamed or never existed in the first place and my read function read it from another location? Let me re-check. Earlier I did read `/opt/bytetrack-counter/counter_live_rknn.py` and got 1059 lines of Python code. But when I listed the directory initially (read /opt/bytetrack-counter), it showed:\n\n```\n__pycache__/\n.env\n.git/\n.gitignore\nbatch_crossings.csv\nbatch_store.py\nbytetrack_counter.db\nbytetrack-counter-dashboard.service\nbytetrack-counter.service\nconfig.env.example\ncounter_dashboard.py\ncounter_live_rknn_bytetrack.py\ncounter_live_rknn.py\ncounter_live.py\nDEPLOY.md\nenv/\ninstall-services.sh\nREADME.md\nrequirements.txt\nsetup-venv.sh\ntemplates/\nuninstall-services.sh\nvenv/\n```\n\nSo `counter_live_rknn.py` was there. But now the file doesn't exist? Maybe something else happened... Let me check the directory again.",
|
||
"time": {
|
||
"start": 1785284925086,
|
||
"end": 1785284928588
|
||
},
|
||
"id": "prt_fab46169e001d2BwdTumUYzUHz",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab4610dc001meIdDOSmJHS7fe"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "read",
|
||
"callID": "call_00_nHbwdPklBmc2KJXfITMK3494",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"filePath": "/opt/bytetrack-counter"
|
||
},
|
||
"output": "<path>/opt/bytetrack-counter</path>\n<type>directory</type>\n<entries>\n__pycache__/\n.env\n.git/\n.gitignore\nAGENTS.md\nbatch_crossings.csv\nbatch_store.py\nbytetrack_counter.db\nbytetrack-counter-dashboard.service\nbytetrack-counter.service\nconfig.env.example\ncounter_dashboard.py\ncounter_live_rknn_bytetrack.py\ncounter_live.py\nDEPLOY.md\nenv/\ninstall-services.sh\nREADME.md\nrequirements.txt\nsetup-venv.sh\nSPEC.md\ntemplates/\nuninstall-services.sh\nvenv/\n\n(24 entries)\n</entries>",
|
||
"metadata": {
|
||
"preview": "__pycache__/\n.env\n.git/\n.gitignore\nAGENTS.md\nbatch_crossings.csv\nbatch_store.py\nbytetrack_counter.db\nbytetrack-counter-dashboard.service\nbytetrack-counter.service\nconfig.env.example\ncounter_dashboard.py\ncounter_live_rknn_bytetrack.py\ncounter_live.py\nDEPLOY.md\nenv/\ninstall-services.sh\nREADME.md\nrequirements.txt\nsetup-venv.sh",
|
||
"truncated": false,
|
||
"loaded": [],
|
||
"display": {
|
||
"type": "directory",
|
||
"path": "/opt/bytetrack-counter",
|
||
"entries": [
|
||
"__pycache__/",
|
||
".env",
|
||
".git/",
|
||
".gitignore",
|
||
"AGENTS.md",
|
||
"batch_crossings.csv",
|
||
"batch_store.py",
|
||
"bytetrack_counter.db",
|
||
"bytetrack-counter-dashboard.service",
|
||
"bytetrack-counter.service",
|
||
"config.env.example",
|
||
"counter_dashboard.py",
|
||
"counter_live_rknn_bytetrack.py",
|
||
"counter_live.py",
|
||
"DEPLOY.md",
|
||
"env/",
|
||
"install-services.sh",
|
||
"README.md",
|
||
"requirements.txt",
|
||
"setup-venv.sh",
|
||
"SPEC.md",
|
||
"templates/",
|
||
"uninstall-services.sh",
|
||
"venv/"
|
||
],
|
||
"offset": 1,
|
||
"totalEntries": 24,
|
||
"truncated": false
|
||
}
|
||
},
|
||
"title": "",
|
||
"time": {
|
||
"start": 1785284928871,
|
||
"end": 1785284928879
|
||
}
|
||
},
|
||
"id": "prt_fab4624500011K3eN3CuOtmAgv",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab4610dc001meIdDOSmJHS7fe"
|
||
},
|
||
{
|
||
"reason": "tool-calls",
|
||
"snapshot": "903513119f5f892d9f5642024f6839426e6caf93",
|
||
"type": "step-finish",
|
||
"tokens": {
|
||
"total": 73105,
|
||
"input": 81,
|
||
"output": 49,
|
||
"reasoning": 271,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 72704
|
||
}
|
||
},
|
||
"cost": 0.000577187,
|
||
"id": "prt_fab462599001krxVc6GtqmHjXc",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab4610dc001meIdDOSmJHS7fe"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"parentID": "msg_fab45b4f9001LDyE3IGzMO2RsQ",
|
||
"role": "assistant",
|
||
"mode": "build",
|
||
"agent": "build",
|
||
"path": {
|
||
"cwd": "/opt/bytetrack-counter",
|
||
"root": "/opt/bytetrack-counter"
|
||
},
|
||
"cost": 0.000466784,
|
||
"tokens": {
|
||
"total": 73401,
|
||
"input": 162,
|
||
"output": 29,
|
||
"reasoning": 122,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 73088
|
||
}
|
||
},
|
||
"modelID": "deepseek-v4-pro",
|
||
"providerID": "deepseek",
|
||
"time": {
|
||
"created": 1785284929052,
|
||
"completed": 1785284933023
|
||
},
|
||
"finish": "stop",
|
||
"id": "msg_fab46261c001a5K6Ee5NrgOVtC",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"snapshot": "903513119f5f892d9f5642024f6839426e6caf93",
|
||
"type": "step-start",
|
||
"id": "prt_fab462a15001idbK1Pl0VNjNGE",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab46261c001a5K6Ee5NrgOVtC"
|
||
},
|
||
{
|
||
"type": "reasoning",
|
||
"text": "`counter_live_rknn.py` is no longer in the directory listing. It was there in the initial listing but is now gone. The file must have been deleted between my initial read and now. It's already been removed. The `rm` command failed because the file was already gone.\n\nMy read of the file earlier succeeded and returned 1059 lines - the file definitely existed at that time. Now it's gone. Perhaps the user or another process deleted it.\n\nIn any case, it's already removed. My spec and AGENTS.md updates already reflect this. Nothing more to do.",
|
||
"time": {
|
||
"start": 1785284930604,
|
||
"end": 1785284932449
|
||
},
|
||
"id": "prt_fab462c2b001KiFZRLZRLKs6iY",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab46261c001a5K6Ee5NrgOVtC"
|
||
},
|
||
{
|
||
"type": "text",
|
||
"text": "Already gone — it's not in the current directory listing. Spec and AGENTS.md already updated to reflect only the two remaining counter scripts.",
|
||
"time": {
|
||
"start": 1785284932453,
|
||
"end": 1785284932879
|
||
},
|
||
"id": "prt_fab463365001hKWesdxj41YMxg",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab46261c001a5K6Ee5NrgOVtC"
|
||
},
|
||
{
|
||
"reason": "stop",
|
||
"snapshot": "903513119f5f892d9f5642024f6839426e6caf93",
|
||
"type": "step-finish",
|
||
"tokens": {
|
||
"total": 73401,
|
||
"input": 162,
|
||
"output": 29,
|
||
"reasoning": 122,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 73088
|
||
}
|
||
},
|
||
"cost": 0.000466784,
|
||
"id": "prt_fab463547001jT4Bu3u1G5tMnc",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab46261c001a5K6Ee5NrgOVtC"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"role": "user",
|
||
"time": {
|
||
"created": 1785284986099
|
||
},
|
||
"agent": "build",
|
||
"model": {
|
||
"providerID": "deepseek",
|
||
"modelID": "deepseek-v4-pro"
|
||
},
|
||
"summary": {
|
||
"diffs": []
|
||
},
|
||
"id": "msg_fab4704f3001n7gPEt13obxXB5",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"type": "text",
|
||
"text": "now counter_dashboard.py is web dashboard to provide progress, live action and summary of counter_live_rknn_bytetrack.py",
|
||
"id": "prt_fab4704f5001kbGMje7IjRthKa",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab4704f3001n7gPEt13obxXB5"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"parentID": "msg_fab4704f3001n7gPEt13obxXB5",
|
||
"role": "assistant",
|
||
"mode": "build",
|
||
"agent": "build",
|
||
"path": {
|
||
"cwd": "/opt/bytetrack-counter",
|
||
"root": "/opt/bytetrack-counter"
|
||
},
|
||
"cost": 0.000536442,
|
||
"tokens": {
|
||
"total": 73699,
|
||
"input": 88,
|
||
"output": 75,
|
||
"reasoning": 192,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 73344
|
||
}
|
||
},
|
||
"modelID": "deepseek-v4-pro",
|
||
"providerID": "deepseek",
|
||
"time": {
|
||
"created": 1785284986133,
|
||
"completed": 1785284991711
|
||
},
|
||
"finish": "stop",
|
||
"id": "msg_fab470515001JJvTF3QJ4xSk6r",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"snapshot": "903513119f5f892d9f5642024f6839426e6caf93",
|
||
"type": "step-start",
|
||
"id": "prt_fab4708aa001eQ7aXe4Fwt1nB7",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab470515001JJvTF3QJ4xSk6r"
|
||
},
|
||
{
|
||
"type": "reasoning",
|
||
"text": "The user is clarifying that `counter_dashboard.py` is a web dashboard that provides progress, live action, and summary views for `counter_live_rknn_bytetrack.py`. This is already captured in the spec, but let me make sure it's clear and accurate. Let me check what I have currently.\n\nLooking at my spec:\n- §G says: \"→ SQLite + Flask dashboard\" — that's a bit vague\n- §I lists all the Flask API endpoints\n- §C mentions Flask on port 5000\n\nI think the spec already captures this. But maybe the user wants me to update the wording to be more explicit about the relationship? The key point is: the dashboard reads from the same DB/SQLite and JSON state file that the counter writes to. It's a read-only consumer.\n\nActually, I think the user is just providing context, not asking me to change anything. Let me just acknowledge.",
|
||
"time": {
|
||
"start": 1785284987550,
|
||
"end": 1785284990475
|
||
},
|
||
"id": "prt_fab470a9e001aaxuPVhuA42Ijm",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab470515001JJvTF3QJ4xSk6r"
|
||
},
|
||
{
|
||
"type": "text",
|
||
"text": "Acknowledged. The spec already captures this — dashboard reads the same SQLite DB and JSON state file that the counter writes, providing real-time progress (`/api/current-batch`), live MJPEG feed (`/api/live-video`), and historical summaries (`/api/summary`, `/api/daily-data`). No changes needed.",
|
||
"time": {
|
||
"start": 1785284990482,
|
||
"end": 1785284991441
|
||
},
|
||
"id": "prt_fab471612001T9xuTQOjrThy0x",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab470515001JJvTF3QJ4xSk6r"
|
||
},
|
||
{
|
||
"reason": "stop",
|
||
"snapshot": "903513119f5f892d9f5642024f6839426e6caf93",
|
||
"type": "step-finish",
|
||
"tokens": {
|
||
"total": 73699,
|
||
"input": 88,
|
||
"output": 75,
|
||
"reasoning": 192,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 73344
|
||
}
|
||
},
|
||
"cost": 0.000536442,
|
||
"id": "prt_fab471a58001A0KZUWZR461yuk",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab470515001JJvTF3QJ4xSk6r"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"role": "user",
|
||
"time": {
|
||
"created": 1785285047249
|
||
},
|
||
"agent": "build",
|
||
"model": {
|
||
"providerID": "deepseek",
|
||
"modelID": "deepseek-v4-pro"
|
||
},
|
||
"summary": {
|
||
"diffs": []
|
||
},
|
||
"id": "msg_fab47f3d10015FboS45534SaOi",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"type": "text",
|
||
"text": "now how you make counter_dashboard.py in Live counter part, the counter show real time and not choppy.",
|
||
"id": "prt_fab47f3d2001F3i1bRyR83Lfx8",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab47f3d10015FboS45534SaOi"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"parentID": "msg_fab47f3d10015FboS45534SaOi",
|
||
"role": "assistant",
|
||
"mode": "build",
|
||
"agent": "build",
|
||
"path": {
|
||
"cwd": "/opt/bytetrack-counter",
|
||
"root": "/opt/bytetrack-counter"
|
||
},
|
||
"cost": 0,
|
||
"tokens": {
|
||
"input": 0,
|
||
"output": 0,
|
||
"reasoning": 0,
|
||
"cache": {
|
||
"read": 0,
|
||
"write": 0
|
||
}
|
||
},
|
||
"modelID": "deepseek-v4-pro",
|
||
"providerID": "deepseek",
|
||
"time": {
|
||
"created": 1785285047284,
|
||
"completed": 1785285074535
|
||
},
|
||
"error": {
|
||
"name": "MessageAbortedError",
|
||
"data": {
|
||
"message": "Aborted"
|
||
}
|
||
},
|
||
"id": "msg_fab47f3f4001ux5pWXCYgAik8R",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"snapshot": "903513119f5f892d9f5642024f6839426e6caf93",
|
||
"type": "step-start",
|
||
"id": "prt_fab47f796001GuQDL93oVfUmCE",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab47f3f4001ux5pWXCYgAik8R"
|
||
},
|
||
{
|
||
"type": "reasoning",
|
||
"text": "The user is asking about making the live video stream in the dashboard show real-time, smooth video rather than choppy. Let me look at how the live stream currently works in the codebase.\n\nThe dashboard's `/api/live-video` endpoint reads a JPEG file from disk and streams it as MJPEG. The counter writes this JPEG file every N frames. Let me look at the current implementation to understand the bottleneck.",
|
||
"time": {
|
||
"start": 1785285048836,
|
||
"end": 1785285050567
|
||
},
|
||
"id": "prt_fab47fa04001fqtMzQ6QoLV7lv",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab47f3f4001ux5pWXCYgAik8R"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "task",
|
||
"callID": "call_00_IY7BWrDKOZ2sbuAHZhMW3911",
|
||
"state": {
|
||
"status": "error",
|
||
"input": {
|
||
"description": "Analyze live stream pipeline",
|
||
"prompt": "In the codebase at /opt/bytetrack-counter, analyze the live video streaming pipeline end-to-end:\n\n1. In `counter_live_rknn_bytetrack.py`: how frames are written to disk (LIVE_STREAM_ENABLED, LIVE_STREAM_FRAME_PATH, LIVE_STREAM_QUALITY, LIVE_STREAM_EVERY_N). Find the exact code block that writes the JPEG snapshot.\n\n2. In `counter_dashboard.py`: the `/api/live-video` route — how it reads and serves the JPEG, the sleep intervals, error handling.\n\n3. In `templates/dashboard.html`: how the frontend displays the live feed (is it an <img> tag pointing to `/api/live-video`? Any refresh logic?)\n\nReturn the exact code sections (with line numbers) for each of these three pieces, plus any sleep/delay values and their effects on perceived smoothness.",
|
||
"subagent_type": "explore"
|
||
},
|
||
"error": "Task cancelled",
|
||
"metadata": {
|
||
"parentSessionId": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"sessionId": "ses_054b7f2afffeGlRO4CksKc3VMR",
|
||
"model": {
|
||
"modelID": "deepseek-v4-pro",
|
||
"providerID": "deepseek"
|
||
}
|
||
},
|
||
"time": {
|
||
"start": 1785285053785,
|
||
"end": 1785285074287
|
||
}
|
||
},
|
||
"id": "prt_fab4800d4001cooNrO78EgfwhK",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab47f3f4001ux5pWXCYgAik8R"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"role": "user",
|
||
"time": {
|
||
"created": 1785285098916
|
||
},
|
||
"agent": "build",
|
||
"model": {
|
||
"providerID": "deepseek",
|
||
"modelID": "deepseek-v4-pro"
|
||
},
|
||
"summary": {
|
||
"diffs": []
|
||
},
|
||
"id": "msg_fab48bda40015lqVHM4ZNeUgia",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"type": "text",
|
||
"text": "Not LIVE STREAM per se. But only the Live Counter part",
|
||
"id": "prt_fab48bda80017zhxvkfAfuZGOF",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab48bda40015lqVHM4ZNeUgia"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"parentID": "msg_fab48bda40015lqVHM4ZNeUgia",
|
||
"role": "assistant",
|
||
"mode": "build",
|
||
"agent": "build",
|
||
"path": {
|
||
"cwd": "/opt/bytetrack-counter",
|
||
"root": "/opt/bytetrack-counter"
|
||
},
|
||
"cost": 0.000526118,
|
||
"tokens": {
|
||
"total": 74186,
|
||
"input": 66,
|
||
"output": 100,
|
||
"reasoning": 164,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 73856
|
||
}
|
||
},
|
||
"modelID": "deepseek-v4-pro",
|
||
"providerID": "deepseek",
|
||
"time": {
|
||
"created": 1785285098975,
|
||
"completed": 1785285104320
|
||
},
|
||
"finish": "tool-calls",
|
||
"id": "msg_fab48bddf00166Y40cREaTO9L5",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"snapshot": "903513119f5f892d9f5642024f6839426e6caf93",
|
||
"type": "step-start",
|
||
"id": "prt_fab48c302001aQp1D55LY9yUkK",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab48bddf00166Y40cREaTO9L5"
|
||
},
|
||
{
|
||
"type": "reasoning",
|
||
"text": "The user is not asking about the live video stream (MJPEG). They're asking about the \"Live Counter\" part of the dashboard — the real-time display showing current batch count, progress, etc. They want it to show real-time updates and not be choppy.\n\nLet me look at how the dashboard currently fetches live counter data. The counter writes the current batch state to a JSON file (`/tmp/bytetrack_current_batch.json`), and the dashboard reads it via `/api/current-batch`. The frontend must be polling this endpoint. The choppiness could be from:\n1. Polling interval being too long\n2. The counter not writing state frequently enough\n3. JavaScript rendering issues\n\nLet me check the dashboard HTML to see how it polls `current-batch`.",
|
||
"time": {
|
||
"start": 1785285100834,
|
||
"end": 1785285103398
|
||
},
|
||
"id": "prt_fab48c522001cZJwM894nn01fP",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab48bddf00166Y40cREaTO9L5"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "grep",
|
||
"callID": "call_00_uDQ1WCM85d8Wwnl27ZbX0823",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"include": "*.html",
|
||
"path": "/opt/bytetrack-counter/templates",
|
||
"pattern": "current-batch|setInterval|setTimeout|fetch|poll|refresh|update"
|
||
},
|
||
"output": "Found 15 matches\n/opt/bytetrack-counter/templates/dashboard.html:\n Line 784: updateToggleIcon();\r\n\n Line 794: updateToggleIcon();\r\n\n Line 797: function updateToggleIcon() {\r\n\n Line 814: setInterval(() => {\r\n\n Line 819: setInterval(() => {\r\n\n Line 826: const res = await fetch('/api/current-batch');\r\n\n Line 849: const res = await fetch('/api/summary');\r\n\n Line 885: const res = await fetch(`/api/daily-data?days=${days}`);\r\n\n Line 979: const res = await fetch('/api/recent-batches?limit=5');\r\n\n Line 1009: const res = await fetch('/api/available-dates');\r\n\n Line 1069: const res = await fetch(`/api/day-detail/${date}`);\r\n\n Line 1109: setTimeout(() => modal.classList.add('hidden'), 200);\r\n\n Line 1116: fetch('/api/available-dates')\r\n\n Line 1176: videoErrorTimer = setTimeout(() => {\r\n\n Line 1196: const res = await fetch(url);\r\n",
|
||
"metadata": {
|
||
"matches": 15,
|
||
"truncated": false
|
||
},
|
||
"title": "current-batch|setInterval|setTimeout|fetch|poll|refresh|update",
|
||
"time": {
|
||
"start": 1785285104086,
|
||
"end": 1785285104143
|
||
}
|
||
},
|
||
"id": "prt_fab48cf3e001RnEOCjwCAXi3be",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab48bddf00166Y40cREaTO9L5"
|
||
},
|
||
{
|
||
"reason": "tool-calls",
|
||
"snapshot": "903513119f5f892d9f5642024f6839426e6caf93",
|
||
"type": "step-finish",
|
||
"tokens": {
|
||
"total": 74186,
|
||
"input": 66,
|
||
"output": 100,
|
||
"reasoning": 164,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 73856
|
||
}
|
||
},
|
||
"cost": 0.000526118,
|
||
"id": "prt_fab48d28c001Qeqd9HUO6bZRuP",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab48bddf00166Y40cREaTO9L5"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"parentID": "msg_fab48bda40015lqVHM4ZNeUgia",
|
||
"role": "assistant",
|
||
"mode": "build",
|
||
"agent": "build",
|
||
"path": {
|
||
"cwd": "/opt/bytetrack-counter",
|
||
"root": "/opt/bytetrack-counter"
|
||
},
|
||
"cost": 0.000504426,
|
||
"tokens": {
|
||
"total": 74568,
|
||
"input": 370,
|
||
"output": 86,
|
||
"reasoning": 0,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 74112
|
||
}
|
||
},
|
||
"modelID": "deepseek-v4-pro",
|
||
"providerID": "deepseek",
|
||
"time": {
|
||
"created": 1785285104334,
|
||
"completed": 1785285107137
|
||
},
|
||
"finish": "tool-calls",
|
||
"id": "msg_fab48d2ce001dIMhYyJDhWi6PZ",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"snapshot": "903513119f5f892d9f5642024f6839426e6caf93",
|
||
"type": "step-start",
|
||
"id": "prt_fab48d7390014Hqb2EhXx4hs6M",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab48d2ce001dIMhYyJDhWi6PZ"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "read",
|
||
"callID": "call_00_ET_OwCNxsOhWQt21X6hTJ2f5971",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"filePath": "/opt/bytetrack-counter/templates/dashboard.html",
|
||
"offset": 810,
|
||
"limit": 50
|
||
},
|
||
"output": "<path>/opt/bytetrack-counter/templates/dashboard.html</path>\n<type>file</type>\n<content>\n810: loadChartData(7);\n811: loadRecentBatches();\n812: resetFilter();\n813: \n814: setInterval(() => {\n815: loadSummary();\n816: loadRecentBatches();\n817: }, 30000);\n818: \n819: setInterval(() => {\n820: loadCurrentBatch();\n821: }, 2000);\n822: });\n823: \n824: async function loadCurrentBatch() {\n825: try {\n826: const res = await fetch('/api/current-batch');\n827: if (!res.ok) throw new Error('No active batch');\n828: const data = await res.json();\n829: \n830: document.getElementById('live-count').textContent = data.count.toLocaleString();\n831: document.getElementById('live-batch-number').textContent = data.batch_number || '--';\n832: \n833: if (data.last_detection_time) {\n834: const lastDet = new Date(data.last_detection_time);\n835: document.getElementById('live-last-detection').textContent =\n836: lastDet.toLocaleTimeString('en-US', { hour12: false, hour: '2-digit', minute: '2-digit', second: '2-digit' });\n837: } else {\n838: document.getElementById('live-last-detection').textContent = 'waiting...';\n839: }\n840: } catch (err) {\n841: document.getElementById('live-count').textContent = '0';\n842: document.getElementById('live-batch-number').textContent = '--';\n843: document.getElementById('live-last-detection').textContent = 'no active batch';\n844: }\n845: }\n846: \n847: async function loadSummary() {\n848: try {\n849: const res = await fetch('/api/summary');\n850: const data = await res.json();\n851: \n852: document.getElementById('current-date').textContent = data.today.date;\n853: document.getElementById('today-count').textContent = data.today.total_count.toLocaleString();\n854: document.getElementById('today-batches').textContent = `${data.today.total_batches} batches`;\n855: \n856: document.getElementById('yesterday-count').textContent = data.yesterday.total_count.toLocaleString();\n857: document.getElementById('yesterday-batches').textContent = `${data.yesterday.total_batches} batches`;\n858: \n859: document.getElementById('avg-count').textContent = Number(data.average_per_day).toLocaleString();\n\n(Showing lines 810-859 of 1217. Use offset=860 to continue.)\n</content>",
|
||
"metadata": {
|
||
"preview": " loadChartData(7);\n loadRecentBatches();\n resetFilter();\n\n setInterval(() => {\n loadSummary();\n loadRecentBatches();\n }, 30000);\n\n setInterval(() => {\n loadCurrentBatch();\n }, 2000);\n});\n\nasync function loadCurrentBatch() {\n try {\n const res = await fetch('/api/current-batch');\n if (!res.ok) throw new Error('No active batch');\n const data = await res.json();\n",
|
||
"truncated": true,
|
||
"loaded": [],
|
||
"display": {
|
||
"type": "file",
|
||
"path": "/opt/bytetrack-counter/templates/dashboard.html",
|
||
"text": " loadChartData(7);\n loadRecentBatches();\n resetFilter();\n\n setInterval(() => {\n loadSummary();\n loadRecentBatches();\n }, 30000);\n\n setInterval(() => {\n loadCurrentBatch();\n }, 2000);\n});\n\nasync function loadCurrentBatch() {\n try {\n const res = await fetch('/api/current-batch');\n if (!res.ok) throw new Error('No active batch');\n const data = await res.json();\n\n document.getElementById('live-count').textContent = data.count.toLocaleString();\n document.getElementById('live-batch-number').textContent = data.batch_number || '--';\n\n if (data.last_detection_time) {\n const lastDet = new Date(data.last_detection_time);\n document.getElementById('live-last-detection').textContent =\n lastDet.toLocaleTimeString('en-US', { hour12: false, hour: '2-digit', minute: '2-digit', second: '2-digit' });\n } else {\n document.getElementById('live-last-detection').textContent = 'waiting...';\n }\n } catch (err) {\n document.getElementById('live-count').textContent = '0';\n document.getElementById('live-batch-number').textContent = '--';\n document.getElementById('live-last-detection').textContent = 'no active batch';\n }\n}\n\nasync function loadSummary() {\n try {\n const res = await fetch('/api/summary');\n const data = await res.json();\n\n document.getElementById('current-date').textContent = data.today.date;\n document.getElementById('today-count').textContent = data.today.total_count.toLocaleString();\n document.getElementById('today-batches').textContent = `${data.today.total_batches} batches`;\n\n document.getElementById('yesterday-count').textContent = data.yesterday.total_count.toLocaleString();\n document.getElementById('yesterday-batches').textContent = `${data.yesterday.total_batches} batches`;\n\n document.getElementById('avg-count').textContent = Number(data.average_per_day).toLocaleString();",
|
||
"lineStart": 810,
|
||
"lineEnd": 859,
|
||
"totalLines": 1217,
|
||
"truncated": true
|
||
}
|
||
},
|
||
"title": "templates/dashboard.html",
|
||
"time": {
|
||
"start": 1785285106876,
|
||
"end": 1785285106898
|
||
}
|
||
},
|
||
"id": "prt_fab48daf3001StudXmdaS4NQxx",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab48d2ce001dIMhYyJDhWi6PZ"
|
||
},
|
||
{
|
||
"reason": "tool-calls",
|
||
"snapshot": "903513119f5f892d9f5642024f6839426e6caf93",
|
||
"type": "step-finish",
|
||
"tokens": {
|
||
"total": 74568,
|
||
"input": 370,
|
||
"output": 86,
|
||
"reasoning": 0,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 74112
|
||
}
|
||
},
|
||
"cost": 0.000504426,
|
||
"id": "prt_fab48dd84001TcpEOo98OIoSUI",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab48d2ce001dIMhYyJDhWi6PZ"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"parentID": "msg_fab48bda40015lqVHM4ZNeUgia",
|
||
"role": "assistant",
|
||
"mode": "build",
|
||
"agent": "build",
|
||
"path": {
|
||
"cwd": "/opt/bytetrack-counter",
|
||
"root": "/opt/bytetrack-counter"
|
||
},
|
||
"cost": 0.000949518,
|
||
"tokens": {
|
||
"total": 75616,
|
||
"input": 678,
|
||
"output": 160,
|
||
"reasoning": 282,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 74496
|
||
}
|
||
},
|
||
"modelID": "deepseek-v4-pro",
|
||
"providerID": "deepseek",
|
||
"time": {
|
||
"created": 1785285107155,
|
||
"completed": 1785285116295
|
||
},
|
||
"finish": "stop",
|
||
"id": "msg_fab48ddd3001SQQGezK130MHgO",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"snapshot": "903513119f5f892d9f5642024f6839426e6caf93",
|
||
"type": "step-start",
|
||
"id": "prt_fab48e23d0010g5JoyolwYaKEN",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab48ddd3001SQQGezK130MHgO"
|
||
},
|
||
{
|
||
"type": "reasoning",
|
||
"text": "Now I see the issue. The dashboard polls `/api/current-batch` every **2000ms** (setInterval at line 819-821). That's 2 seconds — which makes the counter display feel choppy/laggy.\n\nThe counter writes the state file on every crossing event (in `batch_store.py`, `record_ayam_crossing` calls `save_state()` after each increment). So the data is up-to-date on disk, but the dashboard only checks every 2 seconds.\n\nTo make it feel real-time, the polling interval should be much shorter — like 200-500ms. But we also don't want to hammer the server. At 200ms that's 5 requests/second, totally fine for a local Flask server.\n\nAdditionally, the counter writes state to `/api/current-batch` which reads from the JSON state file. The counter only writes state on crossing events — but the dashboard's polling is what makes it feel choppy.\n\nLet me also check if there's any animation/transition missing that makes it feel jumpy vs smooth. The `textContent` update is instant — no CSS transition.\n\nThe fix is:\n1. Reduce polling interval from 2000ms to something like 200-500ms\n2. Optionally add a CSS transition or count animation for visual smoothness\n\nLet me present this to the user.",
|
||
"time": {
|
||
"start": 1785285108860,
|
||
"end": 1785285113529
|
||
},
|
||
"id": "prt_fab48e47c001pHq4ydwxVIiNKf",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab48ddd3001SQQGezK130MHgO"
|
||
},
|
||
{
|
||
"type": "text",
|
||
"text": "The \"Live Counter\" panel polls `/api/current-batch` every **2000ms** (line 819). That's a full 2-second gap between updates — the counter writes state instantly on each crossing, but the dashboard is slow to pick it up.\n\n**Fix**: tighten the polling interval. At `templates/dashboard.html:819`:\n\n```js\nsetInterval(() => {\n loadCurrentBatch();\n}, 2000); // ← too slow, makes it choppy\n```\n\nChange `2000` to `200`–`500`:\n\n```js\n}, 200);\n```\n\nThat's 5 req/s to a local Flask server — negligible load, and the count will update near-instantly after each crossing. Want me to apply this?",
|
||
"time": {
|
||
"start": 1785285113534,
|
||
"end": 1785285116134
|
||
},
|
||
"id": "prt_fab48f6be001uD74YQ4xKT1HzH",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab48ddd3001SQQGezK130MHgO"
|
||
},
|
||
{
|
||
"reason": "stop",
|
||
"snapshot": "903513119f5f892d9f5642024f6839426e6caf93",
|
||
"type": "step-finish",
|
||
"tokens": {
|
||
"total": 75616,
|
||
"input": 678,
|
||
"output": 160,
|
||
"reasoning": 282,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 74496
|
||
}
|
||
},
|
||
"cost": 0.000949518,
|
||
"id": "prt_fab49012a001IOTPgG8TwqRJTb",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab48ddd3001SQQGezK130MHgO"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"role": "user",
|
||
"time": {
|
||
"created": 1785285147145
|
||
},
|
||
"agent": "build",
|
||
"model": {
|
||
"providerID": "deepseek",
|
||
"modelID": "deepseek-v4-pro"
|
||
},
|
||
"summary": {
|
||
"diffs": []
|
||
},
|
||
"id": "msg_fab497a09001VBZCl0Q7xQuTuW",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"type": "text",
|
||
"text": "will it affect the CPU load?",
|
||
"id": "prt_fab497a0b001V3q4Qc8yX78nHJ",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab497a09001VBZCl0Q7xQuTuW"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"parentID": "msg_fab497a09001VBZCl0Q7xQuTuW",
|
||
"role": "assistant",
|
||
"mode": "build",
|
||
"agent": "build",
|
||
"path": {
|
||
"cwd": "/opt/bytetrack-counter",
|
||
"root": "/opt/bytetrack-counter"
|
||
},
|
||
"cost": 0.000563905,
|
||
"tokens": {
|
||
"total": 75907,
|
||
"input": 107,
|
||
"output": 53,
|
||
"reasoning": 227,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 75520
|
||
}
|
||
},
|
||
"modelID": "deepseek-v4-pro",
|
||
"providerID": "deepseek",
|
||
"time": {
|
||
"created": 1785285147183,
|
||
"completed": 1785285154006
|
||
},
|
||
"finish": "stop",
|
||
"id": "msg_fab497a2f001ztFxGlAEaVpvyp",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"snapshot": "903513119f5f892d9f5642024f6839426e6caf93",
|
||
"type": "step-start",
|
||
"id": "prt_fab497e2b0017FXAxaK2h3lbP4",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab497a2f001ztFxGlAEaVpvyp"
|
||
},
|
||
{
|
||
"type": "reasoning",
|
||
"text": "The user is asking if reducing the polling interval from 2000ms to 200ms will affect CPU load. Let me think about this:\n\n1. The `/api/current-batch` endpoint reads a small JSON file from disk (`/tmp/bytetrack_current_batch.json`) — it's a stat + read of a tiny file. No DB query involved.\n2. Even at 5 req/s, the server is handling trivial work — JSON file read, parse, jsonify.\n3. The Flask server is already running on the same RK3588 device that's doing NPU inference, tracking, and video decoding.\n4. However, the RK3588 is a power-constrained edge device.\n\nBut honestly, 200ms polling = 5 file reads per second of a tiny JSON file. The NPU inference, OpenCV video decoding, and ByteTrack tracking are orders of magnitude more CPU-intensive. The polling cost is negligible compared to the main inference loop.\n\nI should just answer directly — no, it won't meaningfully affect CPU load. The file read is trivial compared to the running inference pipeline.",
|
||
"time": {
|
||
"start": 1785285149063,
|
||
"end": 1785285152968
|
||
},
|
||
"id": "prt_fab498187001C4pZnvnxXFrlKp",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab497a2f001ztFxGlAEaVpvyp"
|
||
},
|
||
{
|
||
"type": "text",
|
||
"text": "No. `/api/current-batch` just reads a tiny JSON file — no DB query, no computation. 5 `stat` + `read` calls per second is noise compared to the NPU inference and video decoding already running on every frame.",
|
||
"time": {
|
||
"start": 1785285152979,
|
||
"end": 1785285153861
|
||
},
|
||
"id": "prt_fab4990d3001NHQ7xKlETlCQrv",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab497a2f001ztFxGlAEaVpvyp"
|
||
},
|
||
{
|
||
"reason": "stop",
|
||
"snapshot": "903513119f5f892d9f5642024f6839426e6caf93",
|
||
"type": "step-finish",
|
||
"tokens": {
|
||
"total": 75907,
|
||
"input": 107,
|
||
"output": 53,
|
||
"reasoning": 227,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 75520
|
||
}
|
||
},
|
||
"cost": 0.000563905,
|
||
"id": "prt_fab49949a001cLXHFSHey50DNX",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab497a2f001ztFxGlAEaVpvyp"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"role": "user",
|
||
"time": {
|
||
"created": 1785285169703
|
||
},
|
||
"agent": "build",
|
||
"model": {
|
||
"providerID": "deepseek",
|
||
"modelID": "deepseek-v4-pro"
|
||
},
|
||
"summary": {
|
||
"diffs": [
|
||
{
|
||
"file": "SPEC.md",
|
||
"patch": "Index: SPEC.md\n===================================================================\n--- SPEC.md\t\n+++ SPEC.md\t\n@@ -1,132 +1,133 @@\n # SPEC — bytetrack-counter\n \n ## §G — Goal\n \n RTSP camera → YOLO RKNN (NPU) → ByteTrack → line-crossing counter → SQLite + Flask dashboard.\n Count `ayam` (chicken) crossing counting line. Close batch on `talenan` (cutting-board) crossing.\n Daily cutoff @ HH:MM resets batch numbering. RK3588 hardware.\n **Reference implementation for C++ port in separate repo.**\n \n ## §C — Constraints\n \n - Python 3.10, `rknn-toolkit-lite2` (NPU), `opencv-python` (RTSP/FFmpeg)\n - `numpy<2` (rknn-toolkit-lite2 incompatible with numpy≥2)\n - `PYTHONNOUSERSITE=1` ! set or venv breaks\n - SQLite for persistence, JSON file for active-batch state\n - Flask on port 5000, systemd supervision\n - `counter_live.py` ⊥ run on RK3588 — Jetson TensorRT artifact, ! port target\n - `counter_live_rknn_bytetrack.py` — reference for C++ port, this is what systemd runs\n - Single deployment: `counter_live_rknn_bytetrack.py` + `counter_dashboard.py`\n \n ## §I — Interfaces\n \n ### Systemd\n ```\n unit: bytetrack-counter.service → `venv/bin/python counter_live_rknn_bytetrack.py`\n unit: bytetrack-counter-dashboard.service → `venv/bin/python counter_dashboard.py`\n env: PYTHONNOUSERSITE=1 ! set in both units\n env: EnvironmentFile=/opt/bytetrack-counter/.env\n user: root (not jetson)\n path: /opt/bytetrack-counter (fixed in service file)\n ```\n \n ### `.env` config (40+ vars, gitignored)\n ```\n env: SOURCE ! set → RTSP URL | file path\n env: MODEL_PATH ! set → .rknn file\n env: IMGSZ ! set → model input size (e.g. 320)\n env: CORE_MASK → NPU core mask (1=core0, 2=core1, 3=dual, 7=all)\n env: NUM_CLASSES ! match model output count\n env: CONF → detection confidence threshold (default 0.3)\n env: DAILY_CUTOFF_TIME → HH:MM, default \"20:00\"\n env: CROSS_DIRECTION → rtl | ltr | both (default rtl)\n env: LINE_X | LINE_X_FRAC → counting line position\n env: CLASS_AYAM → class name for counted object (index 0)\n env: CLASS_TALENAN → class name for batch-close trigger (index 1)\n env: RESET_COUNTERS_AT_CUTOFF → defined in example, ! consumed by code ? zombie\n env: MOTION_DETECTION_ENABLED | MOTION_THRESHOLD → skip inference on still frames ?\n env: TRACK_HIGH_THRESH_{0,1} | TRACK_LOW_THRESH_{0,1} | TRACK_MATCH_THRESH_{0,1} → ByteTrack params per class\n env: BATCH_TIMEOUT_SECONDS → auto-close after inactivity (default 300)\n env: IGNORE_BATCH_LABEL_TIMEOUT_SECONDS → suppress talenan close after batch start (default 30)\n env: MIN_OBJECT_PER_BATCH → min count to persist batch (default 60)\n env: MIN_DURATION_PER_BATCH → min seconds to persist batch (default 60)\n env: LIVE_STREAM_ENABLED → write annotated JPEG snapshot each N frames\n env: EXPORT_CSV → write per-crossing CSV (default true)\n ```\n \n ### SQLite\n ```\n table: batches (date, batch#, camera, label, count, start, end, created_at)\n UNIQUE(counting_date, batch_number, camera_name, object_label)\n table: daily_summaries (date, camera, label, total_count, total_batches, updated_at)\n UNIQUE(counting_date, camera_name, object_label)\n ```\n \n ### JSON state file\n ```\n path: /tmp/bytetrack_current_batch.json (default)\n schema: {counting_date, batch_number, count, start_time, last_detection_time, counted_event_ids[]}\n ```\n \n ### Flask API\n ```\n api: GET / → dashboard HTML\n api: GET /api/current-batch → {count, batch_number, counting_date, start_time, last_detection_time}\n api: GET /api/previous-batch → {date, batch#, count, start/end, duration_minutes}\n api: GET /api/summary → {today, yesterday, all_time, average_per_day, best_day}\n api: GET /api/daily-data?days=N → [ {date, total_count, total_batches, avg_per_batch} ]\n api: GET /api/day-detail/<date> → {date, total_count, total_batches, total_duration, avg_duration, batches[]}\n api: GET /api/recent-batches?limit=N → [ {date, batch#, count, start/end, duration} ]\n api: GET /api/available-dates → [ {date, total_count, total_batches} ]\n api: GET /api/export-daily-csv?days=N → .xlsx download (named csv, emits xlsx)\n api: GET /api/export-day-csv/<date> → .xlsx download (named csv, emits xlsx)\n api: GET /api/live-video → MJPEG stream from shared-memory JPEG\n ```\n \n ## §V — Invariants\n \n ```\n V1: NUM_CLASSES must match model output → class 0=ayam, class 1=talenan\n V2: line crossing → prev_cx > line_x ≥ cx (rtl) | prev_cx < line_x ≤ cx (ltr)\n V3: ∀ track_id → counted at most once per batch (counted_event_ids set)\n V4: batch persisted → count ≥ MIN_OBJECT_PER_BATCH & duration ≥ MIN_DURATION_PER_BATCH\n V5: dt.time() < DAILY_CUTOFF_TIME → counting_date = today, else tomorrow\n V6: talenan crossing → close batch, but ignored ∀ IGNORE_BATCH_LABEL_TIMEOUT_SEC after batch start\n V7: batch inactivity ≥ BATCH_TIMEOUT_SECONDS → auto-close\n V8: PYTHONNOUSERSITE=1 ! set for venv isolation\n V9: .env ! exist before counter or dashboard starts\n V10: DB tables ! exist on startup (created if absent, both store & dashboard)\n V11: previous batch → last CARRY_IDS (default 50) track IDs carried forward to next batch\n V12: ∃ ! batch per (date, batch#, camera, label) — UNIQUE constraint in DB\n V13: counter & dashboard share DB path → no process-level coordination\n V14: stream disconnect → reconnect with delay (RECONNECT_DELAY_SEC), ! block main loop\n V15: model inference → letterbox-resize to IMGSZ×IMGSZ, BGR→RGB, run NPU\n V16: NMS postprocessing → iou_thr=0.45, class-aware grouping, score > CONF\n V17: tracks pruned after TRACKED_PRUNE_SEC (default 300s) without update\n V18: .env missing → scripts fail at import (os.getenv falls back to defaults, may mismatch)\n V19: stream reconnect → reset motion detection state (prev_gray = None)\n ```\n \n ## §T — Tasks\n \n ```\n id|status|task|cites\n T1|.|unify install paths — README/DEPLOY/setup-venv.sh/uninstall say `/opt/jetson-counter`, service files & .env.example say `/opt/bytetrack-counter`|\n T2|.|zombie var RESET_COUNTERS_AT_CUTOFF — defined in .env.example, unused in code (confirmed by review)|\n T3|.|add version flag — no `--version` or git-derived version exists|\n T4|.|keep reference Python clean — counter_live.py is artifact; focus edits on counter_live_rknn_bytetrack.py as port source|\n T5|.|rename export routes — routes named `export-daily-csv` but emit `.xlsx`|I.flask\n T6|.|fix live-stream snapshot path — some scripts default `/dev/shm/jetson-counter/`, example says `bytetrack-counter`|\n T7|.|add dashboard health-check endpoint (no `/health` or `/api/status` exists)|\n T8|.|add model checks on startup — model class names ! validated against CLASS_AYAM/CLASS_TALENAN (TensorRT variant validates; RKNN variants do not)|\n T9|.|reset prev_gray on stream reconnect — stale gray ref causes crash or false motion|V19,B1\n T10|.|decay inf_ms toward 0 when inference skipped — stale display misleads operator|\n+T11|x|reduce live-counter poll interval 2000→200ms for real-time feel|\n ```\n \n ## §B — Bugs\n \n ```\n id|date|cause|fix\n B1|2026-07-29|prev_gray not reset on stream reconnect → cv2.absdiff crash or false motion|V19\n ```\n \n",
|
||
"additions": 1,
|
||
"deletions": 0,
|
||
"status": "modified"
|
||
},
|
||
{
|
||
"file": "templates/dashboard.html",
|
||
"patch": "Index: templates/dashboard.html\n===================================================================\n--- templates/dashboard.html\t\n+++ templates/dashboard.html\t\n@@ -1,1217 +1,1217 @@\n <!DOCTYPE html>\r\n <html lang=\"en\">\r\n <head>\r\n <meta charset=\"UTF-8\">\r\n <meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\">\r\n <title>ZenAI APC Dashboard</title>\r\n <script src=\"https://cdn.jsdelivr.net/npm/chart.js\"></script>\r\n <link rel=\"stylesheet\" href=\"https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.4.0/css/all.min.css\">\r\n <style>\r\n @import url('https://fonts.googleapis.com/css2?family=JetBrains+Mono:wght@300;400;500;700&family=Orbitron:wght@400;500;700;900&display=swap');\r\n \r\n * { margin: 0; padding: 0; box-sizing: border-box; }\r\n \r\n :root {\r\n --bg-deep: #06080e;\r\n --bg-surface: rgba(12, 16, 28, 0.9);\r\n --border-glow: rgba(0, 240, 255, 0.12);\r\n --accent: #00f0ff;\r\n --accent2: #7b2fff;\r\n --accent3: #ff2d78;\r\n --accent4: #00ff88;\r\n --text-primary: #dce2f0;\r\n --text-secondary: #6b7394;\r\n --glass: rgba(12, 16, 32, 0.7);\r\n --cell-bg: rgba(255, 255, 255, 0.02);\r\n --cell-border: rgba(255, 255, 255, 0.05);\r\n --card-bg: rgba(255, 255, 255, 0.025);\r\n --ring-bg: rgba(255, 255, 255, 0.04);\r\n --divider: rgba(255, 255, 255, 0.05);\r\n --input-bg: rgba(0, 0, 0, 0.4);\r\n --radius: 18px;\r\n }\r\n \r\n [data-theme=\"light\"] {\r\n --bg-deep: #eef1f5;\r\n --bg-surface: rgba(255, 255, 255, 0.9);\r\n --border-glow: rgba(0, 160, 200, 0.15);\r\n --accent: #0090c0;\r\n --accent2: #6a20e0;\r\n --accent3: #d02060;\r\n --accent4: #00a060;\r\n --text-primary: #1a1d30;\r\n --text-secondary: #5a6088;\r\n --glass: rgba(255, 255, 255, 0.8);\r\n --cell-bg: rgba(0, 0, 0, 0.03);\r\n --cell-border: rgba(0, 0, 0, 0.06);\r\n --card-bg: rgba(0, 0, 0, 0.03);\r\n --ring-bg: rgba(0, 0, 0, 0.06);\r\n --divider: rgba(0, 0, 0, 0.06);\r\n --input-bg: rgba(0, 0, 0, 0.05);\r\n }\r\n \r\n body {\r\n font-family: 'JetBrains Mono', monospace;\r\n background: var(--bg-deep);\r\n color: var(--text-primary);\r\n min-height: 100vh;\r\n overflow-x: hidden;\r\n }\r\n \r\n /* Animated grid background */\r\n .bg-grid {\r\n position: fixed; inset: 0; z-index: 0;\r\n background-image:\r\n linear-gradient(rgba(0, 240, 255, 0.025) 1px, transparent 1px),\r\n linear-gradient(90deg, rgba(0, 240, 255, 0.025) 1px, transparent 1px);\r\n background-size: 64px 64px;\r\n animation: gridScroll 20s linear infinite;\r\n pointer-events: none;\r\n }\r\n @keyframes gridScroll {\r\n 0% { background-position: 0 0; }\r\n 100% { background-position: 64px 64px; }\r\n }\r\n \r\n /* Floating orbs */\r\n .orb {\r\n position: fixed; border-radius: 50%; filter: blur(140px); pointer-events: none;\r\n animation: orbFloat 18s ease-in-out infinite;\r\n }\r\n .orb-1 { width: 600px; height: 600px; background: rgba(0, 240, 255, 0.04); top: -250px; left: -150px; }\r\n .orb-2 { width: 500px; height: 500px; background: rgba(123, 47, 255, 0.04); bottom: -200px; right: -100px; animation-delay: -6s; }\r\n .orb-3 { width: 400px; height: 400px; background: rgba(255, 45, 120, 0.03); top: 40%; left: 40%; animation-delay: -12s; }\r\n @keyframes orbFloat {\r\n 0%, 100% { transform: translate(0, 0) scale(1); }\r\n 33% { transform: translate(50px, -40px) scale(1.06); }\r\n 66% { transform: translate(-30px, 30px) scale(0.94); }\r\n }\r\n \r\n .container {\r\n position: relative; z-index: 1;\r\n max-width: 1600px; margin: 0 auto; padding: 24px 20px;\r\n }\r\n \r\n /* Header */\r\n .header {\r\n display: flex; align-items: center; justify-content: space-between;\r\n padding: 18px 28px; margin-bottom: 24px;\r\n background: var(--glass);\r\n backdrop-filter: blur(24px); -webkit-backdrop-filter: blur(24px);\r\n border: 1px solid var(--border-glow); border-radius: var(--radius);\r\n }\r\n .header-left { display: flex; align-items: center; gap: 14px; }\r\n .logo-icon {\r\n width: 44px; height: 44px;\r\n background: linear-gradient(135deg, var(--accent), var(--accent2));\r\n border-radius: 11px; font-size: 22px;\r\n display: flex; align-items: center; justify-content: center;\r\n box-shadow: 0 0 28px rgba(0, 240, 255, 0.35);\r\n animation: logoPulse 3s ease-in-out infinite;\r\n }\r\n @keyframes logoPulse {\r\n 0%, 100% { box-shadow: 0 0 22px rgba(0, 240, 255, 0.3); }\r\n 50% { box-shadow: 0 0 44px rgba(0, 240, 255, 0.6); }\r\n }\r\n .header h1 {\r\n font-family: 'Orbitron', sans-serif; font-size: 20px; font-weight: 700; letter-spacing: 3px;\r\n background: linear-gradient(90deg, var(--accent), var(--accent2));\r\n -webkit-background-clip: text; -webkit-text-fill-color: transparent; background-clip: text;\r\n }\r\n .header-sub { font-size: 10px; color: var(--text-secondary); letter-spacing: 2px; text-transform: uppercase; }\r\n .header-right { display: flex; align-items: center; gap: 18px; }\r\n .header-date {\r\n font-size: 10px; color: var(--text-secondary); letter-spacing: 1px;\r\n text-align: right;\r\n }\r\n .header-date .date-val {\r\n font-family: 'Orbitron', sans-serif; font-size: 14px; color: var(--accent); display: block;\r\n }\r\n .live-chip {\r\n display: flex; align-items: center; gap: 8px;\r\n font-size: 9px; letter-spacing: 1.5px; padding: 5px 12px; border-radius: 12px;\r\n font-weight: 600; text-transform: uppercase;\r\n background: rgba(0, 255, 136, 0.1); color: var(--accent4);\r\n border: 1px solid rgba(0, 255, 136, 0.2);\r\n }\r\n .live-chip .dot {\r\n width: 7px; height: 7px; border-radius: 50%;\r\n background: var(--accent4); box-shadow: 0 0 8px var(--accent4);\r\n animation: blink 1.5s ease-in-out infinite;\r\n }\r\n @keyframes blink { 0%, 100% { opacity: 1; } 50% { opacity: 0.25; } }\r\n \r\n .theme-toggle {\r\n background: none; border: 1px solid var(--cell-border);\r\n border-radius: 8px; padding: 6px 10px; cursor: pointer;\r\n font-size: 16px; line-height: 1;\r\n transition: border-color 0.3s, background 0.3s;\r\n }\r\n .theme-toggle:hover { border-color: var(--accent); background: var(--cell-bg); }\r\n \r\n /* Section title */\r\n .section-title {\r\n font-family: 'Orbitron', sans-serif; font-size: 13px; font-weight: 600; letter-spacing: 2px;\r\n margin-bottom: 16px;\r\n background: linear-gradient(90deg, var(--accent), var(--accent2));\r\n -webkit-background-clip: text; -webkit-text-fill-color: transparent; background-clip: text;\r\n display: inline-block;\r\n }\r\n \r\n /* Summary Cards */\r\n .summary-cards {\r\n display: grid;\r\n grid-template-columns: repeat(4, 1fr);\r\n gap: 16px;\r\n margin-bottom: 24px;\r\n }\r\n .s-card {\r\n background: var(--glass);\r\n backdrop-filter: blur(24px); -webkit-backdrop-filter: blur(24px);\r\n border: 1px solid var(--border-glow); border-radius: var(--radius);\r\n padding: 20px 22px;\r\n transition: border-color 0.3s, transform 0.2s;\r\n position: relative; overflow: hidden;\r\n }\r\n .s-card:hover { border-color: rgba(0, 240, 255, 0.3); transform: translateY(-2px); }\r\n .s-card .s-icon {\r\n width: 42px; height: 42px; border-radius: 12px;\r\n display: flex; align-items: center; justify-content: center;\r\n font-size: 18px; margin-bottom: 14px;\r\n }\r\n .s-card .s-icon.live { background: linear-gradient(135deg, var(--accent4), #059669); color: #fff; }\r\n .s-card .s-icon.primary { background: linear-gradient(135deg, var(--accent), var(--accent2)); color: #000; }\r\n .s-card .s-icon.warm { background: linear-gradient(135deg, var(--accent2), var(--accent3)); color: #fff; }\r\n .s-card .s-icon.success { background: linear-gradient(135deg, var(--accent), var(--accent4)); color: #000; }\r\n .s-card .s-chip {\r\n font-size: 8px; text-transform: uppercase; letter-spacing: 2px; padding: 3px 10px;\r\n border-radius: 10px; font-weight: 600;\r\n position: absolute; top: 16px; right: 16px;\r\n }\r\n .s-card .s-chip.live-chip-tag {\r\n background: rgba(0, 255, 136, 0.1); color: var(--accent4);\r\n border: 1px solid rgba(0, 255, 136, 0.2);\r\n }\r\n .s-card .s-chip.live-chip-tag .ldot {\r\n width: 6px; height: 6px; border-radius: 50%; display: inline-block;\r\n background: var(--accent4); margin-right: 4px;\r\n animation: blink 1.5s ease-in-out infinite;\r\n }\r\n .s-card .s-chip.info {\r\n background: rgba(0, 240, 255, 0.08); color: var(--accent);\r\n border: 1px solid rgba(0, 240, 255, 0.15);\r\n }\r\n .s-card .s-val {\r\n font-family: 'Orbitron', sans-serif; font-size: 32px; font-weight: 700;\r\n background: linear-gradient(180deg, #fff 0%, var(--accent) 100%);\r\n -webkit-background-clip: text; -webkit-text-fill-color: transparent; background-clip: text;\r\n margin-bottom: 4px;\r\n }\r\n .s-card .s-label { font-size: 11px; color: var(--text-secondary); }\r\n .s-card .s-sub { font-size: 9px; color: var(--text-secondary); margin-top: 4px; }\r\n \r\n .hide-me { display: none; }\r\n \r\n /* Video Feed Panel */\r\n .video-panel {\r\n background: var(--glass);\r\n backdrop-filter: blur(24px); -webkit-backdrop-filter: blur(24px);\r\n border: 1px solid var(--border-glow); border-radius: var(--radius);\r\n padding: 12px; margin-bottom: 24px;\r\n position: relative; overflow: hidden;\r\n min-height: 300px;\r\n display: flex; flex-direction: column;\r\n }\r\n .video-header {\r\n display: flex; align-items: center; justify-content: space-between;\r\n margin-bottom: 10px; padding: 0 12px;\r\n }\r\n .video-toggle {\r\n background: none; border: 1px solid var(--cell-border);\r\n border-radius: 8px; padding: 6px 14px; cursor: pointer;\r\n font-family: 'JetBrains Mono', monospace;\r\n font-size: 10px; letter-spacing: 1px;\r\n color: var(--text-secondary);\r\n transition: border-color 0.3s, color 0.3s;\r\n }\r\n .video-toggle:hover { border-color: var(--accent); color: var(--text-primary); }\r\n .video-toggle.active { border-color: var(--accent4); color: var(--accent4); }\r\n .video-container {\r\n flex: 1; display: flex; align-items: center; justify-content: center;\r\n background: #000; border-radius: 10px;\r\n overflow: hidden; position: relative;\r\n min-height: 280px;\r\n }\r\n .video-container img {\r\n max-width: 100%; max-height: 100%;\r\n display: block;\r\n }\r\n .video-placeholder {\r\n text-align: center; color: var(--text-secondary);\r\n padding: 40px;\r\n }\r\n .video-placeholder .vp-icon {\r\n font-size: 48px; opacity: 0.15; margin-bottom: 12px;\r\n }\r\n .video-placeholder .vp-text {\r\n font-size: 11px; letter-spacing: 1px;\r\n }\r\n .video-placeholder .vp-hint {\r\n font-size: 9px; margin-top: 8px; opacity: 0.5;\r\n }\r\n .video-error-overlay {\r\n position: absolute; inset: 0;\r\n display: flex; align-items: center; justify-content: center;\r\n background: rgba(0,0,0,0.7);\r\n color: var(--accent3); font-size: 11px;\r\n letter-spacing: 1px;\r\n pointer-events: none; opacity: 0; transition: opacity 0.5s;\r\n }\r\n .video-error-overlay.visible { opacity: 1; }\r\n .video-status {\r\n font-size: 9px; letter-spacing: 1px;\r\n display: flex; align-items: center; gap: 6px;\r\n padding: 4px 10px; border-radius: 6px;\r\n }\r\n .video-status.connected {\r\n color: var(--accent4); background: rgba(0, 255, 136, 0.06);\r\n }\r\n .video-status.waiting {\r\n color: var(--text-secondary); background: var(--cell-bg);\r\n }\r\n \r\n /* Charts + Stats layout */\r\n .charts-row {\r\n display: grid;\r\n grid-template-columns: 2fr 1fr;\r\n gap: 20px;\r\n margin-bottom: 24px;\r\n }\r\n .panel {\r\n background: var(--glass);\r\n backdrop-filter: blur(24px); -webkit-backdrop-filter: blur(24px);\r\n border: 1px solid var(--border-glow); border-radius: var(--radius);\r\n padding: 24px 26px;\r\n position: relative; overflow: hidden;\r\n }\r\n \r\n .chart-header {\r\n display: flex; align-items: center; justify-content: space-between;\r\n margin-bottom: 20px; flex-wrap: wrap; gap: 12px;\r\n }\r\n .chart-filters { display: flex; gap: 6px; }\r\n .chart-filters button {\r\n font-family: 'JetBrains Mono', monospace; font-size: 10px;\r\n letter-spacing: 1px; padding: 6px 14px; border-radius: 7px;\r\n border: 1px solid var(--cell-border);\r\n background: var(--cell-bg); color: var(--text-secondary);\r\n cursor: pointer; transition: all 0.3s;\r\n }\r\n .chart-filters button:hover { border-color: var(--accent); color: var(--text-primary); }\r\n .chart-filters button.active {\r\n background: linear-gradient(135deg, var(--accent), var(--accent2));\r\n color: #000; border-color: transparent; font-weight: 600;\r\n }\r\n \r\n .chart-container { position: relative; height: 320px; }\r\n \r\n /* Quick Stats */\r\n .quick-stat {\r\n display: flex; align-items: center; justify-content: space-between;\r\n padding: 14px 16px; margin-bottom: 10px;\r\n background: var(--cell-bg);\r\n border: 1px solid var(--cell-border); border-radius: 10px;\r\n transition: border-color 0.3s;\r\n }\r\n .quick-stat:hover { border-color: var(--border-glow); }\r\n .quick-stat .qs-left { display: flex; align-items: center; gap: 12px; }\r\n .quick-stat .qs-icon {\r\n width: 38px; height: 38px; border-radius: 10px;\r\n display: flex; align-items: center; justify-content: center;\r\n font-size: 15px;\r\n }\r\n .quick-stat .qs-icon.primary { background: linear-gradient(135deg, var(--accent), var(--accent2)); color: #000; }\r\n .quick-stat .qs-icon.warm { background: linear-gradient(135deg, var(--accent2), var(--accent3)); color: #fff; }\r\n .quick-stat .qs-icon.success { background: linear-gradient(135deg, var(--accent), var(--accent4)); color: #000; }\r\n .quick-stat .qs-label { font-size: 10px; color: var(--text-secondary); }\r\n .quick-stat .qs-sub { font-size: 8px; color: var(--text-secondary); }\r\n .quick-stat .qs-val {\r\n font-family: 'Orbitron', sans-serif; font-size: 18px; font-weight: 700;\r\n color: var(--text-primary);\r\n }\r\n \r\n .recent-section { margin-top: 20px; padding-top: 20px; border-top: 1px solid var(--divider); }\r\n .recent-section .rs-title {\r\n font-size: 10px; text-transform: uppercase; letter-spacing: 3px;\r\n color: var(--text-secondary); margin-bottom: 14px;\r\n }\r\n .recent-item {\r\n display: flex; align-items: center; justify-content: space-between;\r\n padding: 10px 14px; margin-bottom: 6px;\r\n background: var(--cell-bg);\r\n border: 1px solid var(--cell-border); border-radius: 9px;\r\n transition: border-color 0.2s;\r\n }\r\n .recent-item:hover { border-color: var(--border-glow); }\r\n .recent-item .ri-left { display: flex; align-items: center; gap: 10px; }\r\n .recent-item .ri-badge {\r\n width: 32px; height: 32px; border-radius: 8px;\r\n display: flex; align-items: center; justify-content: center;\r\n font-family: 'Orbitron', sans-serif; font-size: 10px; font-weight: 700;\r\n background: linear-gradient(135deg, var(--accent), var(--accent2));\r\n color: #000;\r\n }\r\n .recent-item .ri-date { font-size: 10px; color: var(--text-primary); }\r\n .recent-item .ri-time { font-size: 8px; color: var(--text-secondary); }\r\n .recent-item .ri-count {\r\n font-family: 'Orbitron', sans-serif; font-size: 13px; font-weight: 700;\r\n color: var(--text-primary);\r\n }\r\n .recent-item .ri-dur { font-size: 8px; color: var(--text-secondary); text-align: right; }\r\n \r\n /* Table Panel */\r\n .table-panel {\r\n background: var(--glass);\r\n backdrop-filter: blur(24px); -webkit-backdrop-filter: blur(24px);\r\n border: 1px solid var(--border-glow); border-radius: var(--radius);\r\n padding: 24px 26px;\r\n margin-bottom: 24px;\r\n }\r\n .table-toolbar {\r\n display: flex; align-items: center; justify-content: space-between;\r\n margin-bottom: 20px; flex-wrap: wrap; gap: 12px;\r\n }\r\n .table-toolbar .toolbar-group { display: flex; align-items: center; gap: 8px; }\r\n \r\n .table-toolbar input[type=\"date\"] {\r\n font-family: 'JetBrains Mono', monospace; font-size: 11px;\r\n background: var(--input-bg); color: var(--text-primary);\r\n border: 1px solid var(--cell-border); border-radius: 7px;\r\n padding: 7px 12px; outline: none;\r\n transition: border-color 0.3s;\r\n }\r\n .table-toolbar input[type=\"date\"]:focus { border-color: var(--accent); }\r\n \r\n .btn {\r\n font-family: 'Orbitron', sans-serif; font-size: 10px; letter-spacing: 2px;\r\n padding: 8px 16px; border-radius: 7px; cursor: pointer; font-weight: 600;\r\n border: none; transition: opacity 0.3s, box-shadow 0.3s;\r\n }\r\n .btn-primary {\r\n background: linear-gradient(135deg, var(--accent), var(--accent2));\r\n color: #000;\r\n }\r\n .btn-primary:hover { opacity: 0.85; box-shadow: 0 0 20px rgba(0, 240, 255, 0.3); }\r\n .btn-ghost {\r\n background: var(--cell-bg); color: var(--text-secondary);\r\n border: 1px solid var(--cell-border);\r\n }\r\n .btn-ghost:hover { border-color: var(--accent); color: var(--text-primary); }\r\n .btn-export-btn {\r\n background: linear-gradient(135deg, var(--accent), var(--accent4));\r\n color: #000;\r\n }\r\n .btn-export-btn:hover { opacity: 0.85; box-shadow: 0 0 16px rgba(0, 255, 136, 0.3); }\r\n \r\n table {\r\n width: 100%; border-collapse: collapse;\r\n }\r\n table thead th {\r\n font-size: 8px; text-transform: uppercase; letter-spacing: 2px;\r\n color: var(--text-secondary); text-align: left;\r\n padding-bottom: 12px; border-bottom: 1px solid var(--divider);\r\n }\r\n table tbody td {\r\n padding: 12px 0; font-size: 11px;\r\n border-bottom: 1px solid var(--divider);\r\n color: var(--text-primary);\r\n }\r\n .table-row tr { transition: background 0.2s; cursor: pointer; }\r\n .table-row tr:hover { background: var(--cell-bg); }\r\n \r\n .row-date { display: flex; align-items: center; gap: 10px; }\r\n .row-date .rd-icon {\r\n width: 36px; height: 36px; border-radius: 10px;\r\n display: flex; align-items: center; justify-content: center;\r\n font-size: 13px;\r\n background: var(--cell-bg); color: var(--text-secondary);\r\n border: 1px solid var(--cell-border);\r\n }\r\n .row-date .rd-icon.today {\r\n background: linear-gradient(135deg, var(--accent), var(--accent2));\r\n color: #000; border: none;\r\n }\r\n .badge {\r\n display: inline-flex; align-items: center; padding: 3px 10px;\r\n border-radius: 10px; font-size: 9px; font-weight: 600; letter-spacing: 1px;\r\n }\r\n .badge-batches { background: rgba(0, 240, 255, 0.08); color: var(--accent); }\r\n .badge-green { background: rgba(0, 255, 136, 0.1); color: var(--accent4); }\r\n .badge-gray { background: var(--cell-bg); color: var(--text-secondary); }\r\n .link-text { font-size: 10px; letter-spacing: 1px; cursor: pointer; transition: color 0.2s; }\r\n .link-text:hover { color: var(--accent2); }\r\n \r\n /* Modal */\r\n .modal-overlay {\r\n position: fixed; inset: 0; z-index: 100;\r\n background: rgba(0, 0, 0, 0.6);\r\n backdrop-filter: blur(8px); -webkit-backdrop-filter: blur(8px);\r\n display: flex; align-items: center; justify-content: center;\r\n padding: 20px;\r\n }\r\n .modal-overlay.hidden { display: none; }\r\n .modal-content {\r\n background: var(--bg-surface);\r\n backdrop-filter: blur(24px); -webkit-backdrop-filter: blur(24px);\r\n border: 1px solid var(--border-glow); border-radius: var(--radius);\r\n padding: 28px 30px;\r\n width: 100%; max-width: 900px; max-height: 85vh; overflow-y: auto;\r\n transform: scale(0.95); opacity: 0;\r\n transition: transform 0.2s ease-out, opacity 0.2s ease-out;\r\n }\r\n .modal-content.open { transform: scale(1); opacity: 1; }\r\n .modal-header {\r\n display: flex; align-items: center; justify-content: space-between;\r\n margin-bottom: 20px; padding-bottom: 16px;\r\n border-bottom: 1px solid var(--divider);\r\n }\r\n .modal-header h2 {\r\n font-family: 'Orbitron', sans-serif; font-size: 16px; letter-spacing: 2px;\r\n background: linear-gradient(90deg, var(--accent), var(--accent2));\r\n -webkit-background-clip: text; -webkit-text-fill-color: transparent; background-clip: text;\r\n }\r\n .modal-header .close-btn {\r\n background: none; border: 1px solid var(--cell-border); color: var(--text-secondary);\r\n width: 32px; height: 32px; border-radius: 8px; cursor: pointer;\r\n font-size: 16px; display: flex; align-items: center; justify-content: center;\r\n transition: border-color 0.3s, color 0.3s;\r\n }\r\n .modal-header .close-btn:hover { border-color: var(--accent3); color: var(--accent3); }\r\n .modal-subtitle { font-size: 10px; color: var(--text-secondary); letter-spacing: 1px; margin-top: 4px; }\r\n \r\n .modal-stats {\r\n display: grid; grid-template-columns: repeat(3, 1fr); gap: 12px;\r\n margin-bottom: 20px;\r\n }\r\n .modal-stat {\r\n background: var(--cell-bg);\r\n border: 1px solid var(--cell-border); border-radius: 10px;\r\n padding: 16px; text-align: center;\r\n }\r\n .modal-stat .ms-val {\r\n font-family: 'Orbitron', sans-serif; font-size: 24px; font-weight: 700;\r\n color: var(--text-primary);\r\n }\r\n .modal-stat .ms-label { font-size: 9px; color: var(--text-secondary); letter-spacing: 1px; margin-top: 4px; }\r\n .modal-table-wrap { max-height: 380px; overflow-y: auto; }\r\n \r\n /* Footer */\r\n .footer {\r\n margin-top: 24px; text-align: center;\r\n font-size: 9px; letter-spacing: 2px; color: var(--text-secondary);\r\n text-transform: uppercase;\r\n }\r\n \r\n /* Scrollbar */\r\n ::-webkit-scrollbar { width: 6px; }\r\n ::-webkit-scrollbar-track { background: transparent; }\r\n ::-webkit-scrollbar-thumb { background: var(--text-secondary); border-radius: 3px; }\r\n \r\n /* Responsive */\r\n @media (max-width: 1200px) {\r\n .summary-cards { grid-template-columns: repeat(2, 1fr); }\r\n .charts-row { grid-template-columns: 1fr; }\r\n }\r\n @media (max-width: 768px) {\r\n .container { padding: 16px 12px; }\r\n .summary-cards { grid-template-columns: 1fr; }\r\n .header { flex-direction: column; gap: 10px; padding: 14px 20px; }\r\n .header-left { flex-direction: column; text-align: center; }\r\n .header-right { flex-wrap: wrap; justify-content: center; }\r\n .s-card .s-val { font-size: 24px; }\r\n .panel { padding: 18px 16px; }\r\n .modal-content { padding: 18px 16px; max-width: 95vw; }\r\n .modal-stats { grid-template-columns: 1fr; }\r\n .table-toolbar { flex-direction: column; align-items: flex-start; }\r\n }\r\n @media (max-width: 480px) {\r\n .container { padding: 10px 8px; }\r\n .header { padding: 12px 14px; border-radius: 12px; }\r\n .header h1 { font-size: 16px; letter-spacing: 2px; }\r\n .logo-icon { width: 36px; height: 36px; font-size: 18px; border-radius: 9px; }\r\n .s-card { padding: 16px; border-radius: 14px; }\r\n .s-card .s-val { font-size: 22px; }\r\n .panel { padding: 14px 12px; border-radius: 14px; }\r\n .chart-container { height: 240px; }\r\n .footer { font-size: 8px; letter-spacing: 1px; }\r\n }\r\n </style>\r\n </head>\r\n <body>\r\n \r\n <div class=\"bg-grid\"></div>\r\n <div class=\"orb orb-1\"></div>\r\n <div class=\"orb orb-2\"></div>\r\n <div class=\"orb orb-3\"></div>\r\n \r\n <div class=\"container\">\r\n <!-- Header -->\r\n <div class=\"header\">\r\n <div class=\"header-left\">\r\n <div class=\"logo-icon\">◆</div>\r\n <div>\r\n <h1>ZenAI APC Module</h1>\r\n <div class=\"header-sub\">Edge Nano counter — YOLO RockChip</div>\r\n </div>\r\n </div>\r\n <div class=\"header-right\">\r\n <div class=\"header-date\">\r\n <span class=\"date-val\" id=\"current-date\">--</span>\r\n Counting Day\r\n </div>\r\n <div class=\"live-chip\">\r\n <span class=\"dot\"></span>\r\n <span>{{ site_name }}</span>\r\n </div>\r\n <button class=\"theme-toggle\" id=\"themeToggle\" onclick=\"toggleTheme()\" title=\"Toggle theme\">☀️</button>\r\n </div>\r\n </div>\r\n \r\n <!-- Summary Cards -->\r\n <div class=\"summary-cards\">\r\n <div class=\"s-card\" id=\"card-live\">\r\n <span class=\"s-chip live-chip-tag\"><span class=\"ldot\"></span>LIVE</span>\r\n <div class=\"s-icon live\"><i class=\"fas fa-bolt\"></i></div>\r\n <div class=\"s-val\" id=\"live-count\">--</div>\r\n <div class=\"s-label\">Current Batch Count</div>\r\n <div class=\"s-sub\">Batch #<span id=\"live-batch-number\">--</span> · <span id=\"live-last-detection\">--</span></div>\r\n </div>\r\n <div class=\"s-card\">\r\n <span class=\"s-chip info\">Today</span>\r\n <div class=\"s-icon primary\"><i class=\"fas fa-calendar-day\"></i></div>\r\n <div class=\"s-val\" id=\"today-count\">--</div>\r\n <div class=\"s-label\">Total Count Today</div>\r\n <div class=\"s-sub\" id=\"today-batches\">-- batches</div>\r\n </div>\r\n <div class=\"s-card\">\r\n <span class=\"s-chip info\">Yesterday</span>\r\n <div class=\"s-icon warm\"><i class=\"fas fa-history\"></i></div>\r\n <div class=\"s-val\" id=\"yesterday-count\">--</div>\r\n <div class=\"s-label\">Total Count Yesterday</div>\r\n <div class=\"s-sub\" id=\"yesterday-batches\">-- batches</div>\r\n </div>\r\n <div class=\"s-card\">\r\n <span class=\"s-chip info\">Average</span>\r\n <div class=\"s-icon success\"><i class=\"fas fa-chart-line\"></i></div>\r\n <div class=\"s-val\" id=\"avg-count\">--</div>\r\n <div class=\"s-label\">Average Per Day</div>\r\n <div class=\"s-sub\" id=\"total-days\">-- days recorded</div>\r\n </div>\r\n </div>\r\n \r\n <!-- Best Day (hidden) -->\r\n <div class=\"hide-me\" id=\"best-day-data\">\r\n <span id=\"best-count\">--</span>\r\n <span id=\"best-date\">--</span>\r\n </div>\r\n \r\n <!-- Live Video Feed -->\r\n <div class=\"video-panel\" id=\"video-panel\">\r\n <div class=\"video-header\">\r\n <span class=\"section-title\" style=\"margin-bottom:0;\">◉ Live Camera Feed</span>\r\n <div style=\"display:flex;align-items:center;gap:10px;\">\r\n <span class=\"video-status waiting\" id=\"video-status\">\r\n <span style=\"width:6px;height:6px;border-radius:50%;background:var(--text-secondary);\"></span>\r\n waiting...\r\n </span>\r\n <button class=\"video-toggle\" id=\"videoToggle\" onclick=\"toggleVideo()\">STREAM OFF</button>\r\n </div>\r\n </div>\r\n <div class=\"video-container\" id=\"video-container\">\r\n <div class=\"video-placeholder\" id=\"video-placeholder\">\r\n <div class=\"vp-icon\">▦</div>\r\n <div class=\"vp-text\">Live Feed Unavailable</div>\r\n <div class=\"vp-hint\">Enable <code>LIVE_STREAM_ENABLED=true</code> in counter .env</div>\r\n </div>\r\n <img id=\"live-video-img\" alt=\"Live Feed\" style=\"display:none;\" onerror=\"onVideoError()\" onload=\"onVideoLoad()\" />\r\n <div class=\"video-error-overlay\" id=\"video-error-overlay\">Connection lost — retrying...</div>\r\n </div>\r\n </div>\r\n \r\n <!-- Charts + Quick Stats -->\r\n <div class=\"charts-row\">\r\n <div class=\"panel\">\r\n <div class=\"chart-header\">\r\n <span class=\"section-title\">◉ Daily Trends</span>\r\n <div class=\"chart-filters\">\r\n <button class=\"active\" onclick=\"loadChartData(7)\" data-days=\"7\">7D</button>\r\n <button onclick=\"loadChartData(14)\" data-days=\"14\">14D</button>\r\n <button onclick=\"loadChartData(30)\" data-days=\"30\">30D</button>\r\n </div>\r\n </div>\r\n <div class=\"chart-container\">\r\n <canvas id=\"mainChart\"></canvas>\r\n </div>\r\n </div>\r\n <div class=\"panel\">\r\n <span class=\"section-title\">◉ Quick Stats</span>\r\n <div style=\"margin-top:16px;\">\r\n <div class=\"quick-stat\">\r\n <div class=\"qs-left\">\r\n <div class=\"qs-icon primary\"><i class=\"fas fa-layer-group\"></i></div>\r\n <div>\r\n <div class=\"qs-label\">Grand Total</div>\r\n <div class=\"qs-sub\">All time</div>\r\n </div>\r\n </div>\r\n <span class=\"qs-val\" id=\"grand-total\">--</span>\r\n </div>\r\n <div class=\"quick-stat\">\r\n <div class=\"qs-left\">\r\n <div class=\"qs-icon warm\"><i class=\"fas fa-cubes\"></i></div>\r\n <div>\r\n <div class=\"qs-label\">Total Batches</div>\r\n <div class=\"qs-sub\">All time</div>\r\n </div>\r\n </div>\r\n <span class=\"qs-val\" id=\"grand-batches\">--</span>\r\n </div>\r\n <div class=\"quick-stat\">\r\n <div class=\"qs-left\">\r\n <div class=\"qs-icon success\"><i class=\"fas fa-percentage\"></i></div>\r\n <div>\r\n <div class=\"qs-label\">Avg per Batch</div>\r\n <div class=\"qs-sub\">Overall average</div>\r\n </div>\r\n </div>\r\n <span class=\"qs-val\" id=\"avg-per-batch\">--</span>\r\n </div>\r\n </div>\r\n \r\n <div class=\"recent-section\">\r\n <div class=\"rs-title\">◉ Recent Activity</div>\r\n <div id=\"recent-batches\">\r\n <!-- Populated by JS -->\r\n </div>\r\n </div>\r\n </div>\r\n </div>\r\n \r\n <!-- Daily Records Table -->\r\n <div class=\"table-panel\">\r\n <div class=\"table-toolbar\">\r\n <div>\r\n <span class=\"section-title\" style=\"margin-bottom:0;\">◉ Daily Records</span>\r\n <div style=\"font-size:10px;color:var(--text-secondary);letter-spacing:1px;\">Click on a row to view batch details</div>\r\n </div>\r\n <div class=\"toolbar-group\">\r\n <input type=\"date\" id=\"date-filter\">\r\n <button class=\"btn btn-primary\" onclick=\"filterByDate()\">FILTER</button>\r\n <button class=\"btn btn-ghost\" onclick=\"resetFilter()\">RESET</button>\r\n <button class=\"btn btn-export-btn\" onclick=\"exportDayDetail()\">EXPORT EXCEL</button>\r\n </div>\r\n </div>\r\n <div style=\"overflow-x:auto;\">\r\n <table>\r\n <thead>\r\n <tr>\r\n <th style=\"padding-left:4px;\">Date</th>\r\n <th>Total Count</th>\r\n <th>Batches</th>\r\n <th>Avg/Batch</th>\r\n <th>Status</th>\r\n <th style=\"padding-right:4px;\">Action</th>\r\n </tr>\r\n </thead>\r\n <tbody id=\"daily-table-body\">\r\n <!-- Populated by JS -->\r\n </tbody>\r\n </table>\r\n </div>\r\n </div>\r\n \r\n <div class=\"footer\">Edge Nano Counter by ZenAi</div>\r\n </div>\r\n \r\n <!-- Modal -->\r\n <div class=\"modal-overlay hidden\" id=\"detail-modal\">\r\n <div class=\"modal-content\" id=\"modal-content\">\r\n <div class=\"modal-header\">\r\n <div>\r\n <h2 id=\"modal-title\">Batch Details</h2>\r\n <div class=\"modal-subtitle\" id=\"modal-subtitle\">--</div>\r\n </div>\r\n <button class=\"close-btn\" onclick=\"closeModal()\">×</button>\r\n </div>\r\n <div class=\"modal-stats\">\r\n <div class=\"modal-stat\">\r\n <div class=\"ms-val\" id=\"modal-total\">--</div>\r\n <div class=\"ms-label\">Total Count</div>\r\n </div>\r\n <div class=\"modal-stat\">\r\n <div class=\"ms-val\" id=\"modal-batches\">--</div>\r\n <div class=\"ms-label\">Total Batches</div>\r\n </div>\r\n <div class=\"modal-stat\">\r\n <div class=\"ms-val\" id=\"modal-avg\">--</div>\r\n <div class=\"ms-label\">Avg Duration (min)</div>\r\n </div>\r\n </div>\r\n <div class=\"modal-table-wrap\">\r\n <table>\r\n <thead>\r\n <tr>\r\n <th style=\"padding-left:4px;\">Batch #</th>\r\n <th>Count</th>\r\n <th>Start</th>\r\n <th>End</th>\r\n <th>Duration</th>\r\n </tr>\r\n </thead>\r\n <tbody id=\"modal-table-body\">\r\n <!-- Populated by JS -->\r\n </tbody>\r\n </table>\r\n </div>\r\n </div>\r\n </div>\r\n \r\n <script>\r\n // --- Theme ---\r\n (function() {\r\n const saved = localStorage.getItem('theme');\r\n if (saved) document.documentElement.setAttribute('data-theme', saved);\r\n updateToggleIcon();\r\n })();\r\n \r\n function toggleTheme() {\r\n const el = document.documentElement;\r\n const current = el.getAttribute('data-theme');\r\n const next = current === 'light' ? '' : 'light';\r\n if (next) el.setAttribute('data-theme', next);\r\n else el.removeAttribute('data-theme');\r\n localStorage.setItem('theme', next || 'dark');\r\n updateToggleIcon();\r\n }\r\n \r\n function updateToggleIcon() {\r\n const btn = document.getElementById('themeToggle');\r\n if (!btn) return;\r\n btn.textContent = document.documentElement.getAttribute('data-theme') === 'light' ? '🌙' : '☀️';\r\n }\r\n \r\n let mainChart = null;\r\n let videoActive = false;\r\n let videoErrorTimer = null;\r\n \r\n document.addEventListener('DOMContentLoaded', () => {\r\n loadSummary();\r\n loadCurrentBatch();\r\n loadChartData(7);\r\n loadRecentBatches();\r\n resetFilter();\r\n \r\n setInterval(() => {\r\n loadSummary();\r\n loadRecentBatches();\r\n }, 30000);\r\n \r\n setInterval(() => {\r\n loadCurrentBatch();\r\n- }, 2000);\r\n+ }, 200);\r\n });\r\n \r\n async function loadCurrentBatch() {\r\n try {\r\n const res = await fetch('/api/current-batch');\r\n if (!res.ok) throw new Error('No active batch');\r\n const data = await res.json();\r\n \r\n document.getElementById('live-count').textContent = data.count.toLocaleString();\r\n document.getElementById('live-batch-number').textContent = data.batch_number || '--';\r\n \r\n if (data.last_detection_time) {\r\n const lastDet = new Date(data.last_detection_time);\r\n document.getElementById('live-last-detection').textContent =\r\n lastDet.toLocaleTimeString('en-US', { hour12: false, hour: '2-digit', minute: '2-digit', second: '2-digit' });\r\n } else {\r\n document.getElementById('live-last-detection').textContent = 'waiting...';\r\n }\r\n } catch (err) {\r\n document.getElementById('live-count').textContent = '0';\r\n document.getElementById('live-batch-number').textContent = '--';\r\n document.getElementById('live-last-detection').textContent = 'no active batch';\r\n }\r\n }\r\n \r\n async function loadSummary() {\r\n try {\r\n const res = await fetch('/api/summary');\r\n const data = await res.json();\r\n \r\n document.getElementById('current-date').textContent = data.today.date;\r\n document.getElementById('today-count').textContent = data.today.total_count.toLocaleString();\r\n document.getElementById('today-batches').textContent = `${data.today.total_batches} batches`;\r\n \r\n document.getElementById('yesterday-count').textContent = data.yesterday.total_count.toLocaleString();\r\n document.getElementById('yesterday-batches').textContent = `${data.yesterday.total_batches} batches`;\r\n \r\n document.getElementById('avg-count').textContent = Number(data.average_per_day).toLocaleString();\r\n document.getElementById('total-days').textContent = `${data.all_time.total_days} days recorded`;\r\n \r\n document.getElementById('best-count').textContent = data.best_day.count.toLocaleString();\r\n document.getElementById('best-date').textContent = data.best_day.date || '--';\r\n \r\n document.getElementById('grand-total').textContent = data.all_time.grand_total.toLocaleString();\r\n document.getElementById('grand-batches').textContent = data.all_time.grand_batches.toLocaleString();\r\n \r\n const avgBatch = data.all_time.grand_batches > 0\r\n ? (data.all_time.grand_total / data.all_time.grand_batches).toFixed(1)\r\n : 0;\r\n document.getElementById('avg-per-batch').textContent = avgBatch;\r\n \r\n } catch (err) {\r\n console.error('Failed to load summary:', err);\r\n }\r\n }\r\n \r\n async function loadChartData(days) {\r\n document.querySelectorAll('.chart-filters button').forEach(btn => {\r\n btn.classList.remove('active');\r\n if (parseInt(btn.dataset.days) === days) btn.classList.add('active');\r\n });\r\n \r\n try {\r\n const res = await fetch(`/api/daily-data?days=${days}`);\r\n const data = await res.json();\r\n \r\n const labels = data.map(d => {\r\n const date = new Date(d.date);\r\n return date.toLocaleDateString('en-US', { weekday: 'short', day: 'numeric', month: 'short' });\r\n });\r\n const counts = data.map(d => d.total_count);\r\n const batches = data.map(d => d.total_batches);\r\n \r\n if (mainChart) mainChart.destroy();\r\n \r\n const ctx = document.getElementById('mainChart').getContext('2d');\r\n const isDark = !document.documentElement.getAttribute('data-theme') ||\r\n document.documentElement.getAttribute('data-theme') !== 'light';\r\n const gridColor = isDark ? 'rgba(255,255,255,0.05)' : 'rgba(0,0,0,0.05)';\r\n const textColor = isDark ? '#6b7394' : '#5a6088';\r\n \r\n mainChart = new Chart(ctx, {\r\n type: 'bar',\r\n data: {\r\n labels: labels,\r\n datasets: [\r\n {\r\n label: 'Total Count',\r\n data: counts,\r\n backgroundColor: 'rgba(0, 240, 255, 0.7)',\r\n borderColor: 'rgba(0, 240, 255, 1)',\r\n borderWidth: 2,\r\n borderRadius: 8,\r\n yAxisID: 'y'\r\n },\r\n {\r\n label: 'Batches',\r\n data: batches,\r\n type: 'line',\r\n borderColor: 'rgba(255, 45, 120, 1)',\r\n backgroundColor: 'rgba(255, 45, 120, 0.05)',\r\n borderWidth: 3,\r\n pointRadius: 4,\r\n pointBackgroundColor: 'rgba(255, 45, 120, 1)',\r\n tension: 0.4,\r\n yAxisID: 'y1'\r\n }\r\n ]\r\n },\r\n options: {\r\n responsive: true,\r\n maintainAspectRatio: false,\r\n interaction: { mode: 'index', intersect: false },\r\n plugins: {\r\n legend: {\r\n position: 'top',\r\n labels: { usePointStyle: true, padding: 20, font: { size: 11 }, color: textColor }\r\n },\r\n tooltip: {\r\n backgroundColor: 'rgba(12, 16, 32, 0.95)',\r\n padding: 12,\r\n cornerRadius: 8,\r\n titleFont: { size: 12 },\r\n bodyFont: { size: 11 }\r\n }\r\n },\r\n scales: {\r\n x: {\r\n grid: { display: false },\r\n ticks: { font: { size: 10 }, color: textColor }\r\n },\r\n y: {\r\n type: 'linear',\r\n display: true,\r\n position: 'left',\r\n grid: { color: gridColor },\r\n ticks: { color: textColor, font: { size: 10 } },\r\n title: { display: true, text: 'Count', color: textColor }\r\n },\r\n y1: {\r\n type: 'linear',\r\n display: true,\r\n position: 'right',\r\n grid: { display: false },\r\n ticks: { color: textColor, font: { size: 10 } },\r\n title: { display: true, text: 'Batches', color: textColor }\r\n }\r\n }\r\n }\r\n });\r\n } catch (err) {\r\n console.error('Failed to load chart:', err);\r\n }\r\n }\r\n \r\n async function loadRecentBatches() {\r\n try {\r\n const res = await fetch('/api/recent-batches?limit=5');\r\n const batches = await res.json();\r\n \r\n const container = document.getElementById('recent-batches');\r\n if (!batches || batches.length === 0) {\r\n container.innerHTML = '<div class=\"recent-item\" style=\"justify-content:center;color:var(--text-secondary);font-size:10px;\">No batches recorded yet</div>';\r\n return;\r\n }\r\n container.innerHTML = batches.map(b => `\r\n <div class=\"recent-item\">\r\n <div class=\"ri-left\">\r\n <div class=\"ri-badge\">#${b.batch_number}</div>\r\n <div>\r\n <div class=\"ri-date\">${b.date}</div>\r\n <div class=\"ri-time\">${formatTime(b.start_time)} - ${formatTime(b.end_time)}</div>\r\n </div>\r\n </div>\r\n <div style=\"text-align:right;\">\r\n <div class=\"ri-count\">${b.count.toLocaleString()}</div>\r\n <div class=\"ri-dur\">${b.duration_minutes}m</div>\r\n </div>\r\n </div>\r\n `).join('');\r\n } catch (err) {\r\n console.error('Failed to load recent batches:', err);\r\n }\r\n }\r\n \r\n async function loadAllDates() {\r\n try {\r\n const res = await fetch('/api/available-dates');\r\n const dates = await res.json();\r\n renderDailyTable(dates);\r\n } catch (err) {\r\n console.error('Failed to load dates:', err);\r\n }\r\n }\r\n \r\n function resetFilter() {\r\n document.getElementById('date-filter').value = '';\r\n loadAllDates();\r\n }\r\n \r\n function renderDailyTable(dates) {\r\n const tbody = document.getElementById('daily-table-body');\r\n const currentDate = document.getElementById('current-date').textContent;\r\n \r\n if (dates.length === 0) {\r\n tbody.innerHTML = `\r\n <tr>\r\n <td colspan=\"6\" style=\"text-align:center;padding:32px;color:var(--text-secondary);\">\r\n <div style=\"font-size:32px;opacity:0.2;margin-bottom:8px;\">□</div>\r\n <div style=\"font-size:10px;letter-spacing:1px;\">NO DATA AVAILABLE</div>\r\n </td>\r\n </tr>`;\r\n return;\r\n }\r\n \r\n tbody.innerHTML = dates.map(d => {\r\n const isToday = d.date === currentDate;\r\n const avgPerBatch = d.total_batches > 0 ? (d.total_count / d.total_batches).toFixed(1) : 0;\r\n \r\n return `\r\n <tr onclick=\"showDayDetail('${d.date}')\" style=\"cursor:pointer;\">\r\n <td style=\"padding-left:4px;\">\r\n <div class=\"row-date\">\r\n <div class=\"rd-icon${isToday ? ' today' : ''}\"><i class=\"fas fa-calendar\"></i></div>\r\n <div>\r\n <div style=\"font-size:11px;color:var(--text-primary);\">${d.date}</div>\r\n ${isToday ? '<div style=\"font-size:8px;color:var(--accent);letter-spacing:1px;\">Today</div>' : ''}\r\n </div>\r\n </div>\r\n </td>\r\n <td><span style=\"font-family:'Orbitron',sans-serif;font-size:15px;font-weight:700;\">${d.total_count.toLocaleString()}</span></td>\r\n <td><span class=\"badge badge-batches\">${d.total_batches} batch${d.total_batches !== 1 ? 'es' : ''}</span></td>\r\n <td style=\"color:var(--text-secondary);\">${avgPerBatch}</td>\r\n <td>\r\n <span class=\"badge ${isToday ? 'badge-green' : 'badge-gray'}\">\r\n ${isToday ? '<span style=\"width:5px;height:5px;border-radius:50%;background:var(--accent4);display:inline-block;margin-right:5px;animation:blink 1.5s ease-in-out infinite;\"></span>Active' : 'Completed'}\r\n </span>\r\n </td>\r\n <td style=\"padding-right:4px;\">\r\n <span class=\"link-text\" style=\"color:var(--accent);\"><i class=\"fas fa-eye\" style=\"margin-right:4px;\"></i>View</span>\r\n </td>\r\n </tr>`;\r\n }).join('');\r\n }\r\n \r\n async function showDayDetail(date) {\r\n try {\r\n const res = await fetch(`/api/day-detail/${date}`);\r\n const data = await res.json();\r\n \r\n document.getElementById('modal-title').textContent = `Batch Details - ${date}`;\r\n document.getElementById('modal-subtitle').textContent = `${data.total_batches} batches, ${data.total_count.toLocaleString()} total objects`;\r\n document.getElementById('modal-total').textContent = data.total_count.toLocaleString();\r\n document.getElementById('modal-batches').textContent = data.total_batches;\r\n document.getElementById('modal-avg').textContent = data.avg_duration_minutes;\r\n \r\n const tbody = document.getElementById('modal-table-body');\r\n tbody.innerHTML = data.batches.map(b => `\r\n <tr style=\"border-bottom:1px solid var(--divider);\">\r\n <td style=\"padding:10px 0;padding-left:4px;\">\r\n <span style=\"display:inline-flex;align-items:center;justify-content:center;width:32px;height:32px;border-radius:8px;font-family:'Orbitron',sans-serif;font-size:10px;font-weight:700;background:linear-gradient(135deg,var(--accent),var(--accent2));color:#000;\">\r\n ${b.batch_number}\r\n </span>\r\n </td>\r\n <td style=\"padding:10px 0;font-weight:600;\">${b.count.toLocaleString()}</td>\r\n <td style=\"padding:10px 0;color:var(--text-secondary);\">${formatTime(b.start_time)}</td>\r\n <td style=\"padding:10px 0;color:var(--text-secondary);\">${formatTime(b.end_time)}</td>\r\n <td style=\"padding:10px 0;color:var(--text-secondary);\">${b.duration_minutes}m</td>\r\n </tr>\r\n `).join('');\r\n \r\n const modal = document.getElementById('detail-modal');\r\n const content = document.getElementById('modal-content');\r\n modal.classList.remove('hidden');\r\n requestAnimationFrame(() => {\r\n content.classList.add('open');\r\n });\r\n \r\n } catch (err) {\r\n console.error('Failed to load day detail:', err);\r\n }\r\n }\r\n \r\n function closeModal() {\r\n const modal = document.getElementById('detail-modal');\r\n const content = document.getElementById('modal-content');\r\n content.classList.remove('open');\r\n setTimeout(() => modal.classList.add('hidden'), 200);\r\n }\r\n \r\n function filterByDate() {\r\n const date = document.getElementById('date-filter').value;\r\n if (!date) return;\r\n \r\n fetch('/api/available-dates')\r\n .then(r => r.json())\r\n .then(dates => {\r\n const filtered = dates.filter(d => d.date === date);\r\n renderDailyTable(filtered);\r\n });\r\n }\r\n \r\n function formatTime(isoString) {\r\n if (!isoString) return '--';\r\n const date = new Date(isoString);\r\n return date.toLocaleTimeString('en-US', { hour12: false, hour: '2-digit', minute: '2-digit' });\r\n }\r\n \r\n document.addEventListener('keydown', (e) => {\r\n if (e.key === 'Escape') closeModal();\r\n });\r\n \r\n // --- Live Video Feed ---\r\n \r\n function toggleVideo() {\r\n const btn = document.getElementById('videoToggle');\r\n const img = document.getElementById('live-video-img');\r\n const placeholder = document.getElementById('video-placeholder');\r\n const status = document.getElementById('video-status');\r\n \r\n videoActive = !videoActive;\r\n \r\n if (videoActive) {\r\n resetFilter();\r\n img.src = '/api/live-video?' + Date.now();\r\n img.style.display = 'block';\r\n if (placeholder) placeholder.style.display = 'none';\r\n btn.textContent = 'STREAM ON';\r\n btn.classList.add('active');\r\n status.innerHTML = '<span style=\"width:6px;height:6px;border-radius:50%;background:var(--accent4);animation:blink 1.5s ease-in-out infinite;\"></span> connecting...';\r\n status.className = 'video-status waiting';\r\n } else {\r\n img.src = '';\r\n img.style.display = 'none';\r\n if (placeholder) placeholder.style.display = '';\r\n btn.textContent = 'STREAM OFF';\r\n btn.classList.remove('active');\r\n status.innerHTML = '<span style=\"width:6px;height:6px;border-radius:50%;background:var(--text-secondary);\"></span> stopped';\r\n status.className = 'video-status waiting';\r\n hideVideoError();\r\n }\r\n }\r\n \r\n function onVideoLoad() {\r\n const status = document.getElementById('video-status');\r\n status.innerHTML = '<span style=\"width:6px;height:6px;border-radius:50%;background:var(--accent4);box-shadow:0 0 6px var(--accent4);animation:blink 1.5s ease-in-out infinite;\"></span> live';\r\n status.className = 'video-status connected';\r\n hideVideoError();\r\n }\r\n \r\n function onVideoError() {\r\n const overlay = document.getElementById('video-error-overlay');\r\n if (overlay) overlay.classList.add('visible');\r\n if (videoErrorTimer) clearTimeout(videoErrorTimer);\r\n videoErrorTimer = setTimeout(() => {\r\n if (videoActive) {\r\n const img = document.getElementById('live-video-img');\r\n img.src = '/api/live-video?' + Date.now();\r\n }\r\n }, 2000);\r\n }\r\n \r\n function hideVideoError() {\r\n const overlay = document.getElementById('video-error-overlay');\r\n if (overlay) overlay.classList.remove('visible');\r\n if (videoErrorTimer) { clearTimeout(videoErrorTimer); videoErrorTimer = null; }\r\n }\r\n </script>\r\n \r\n <script>\r\n async function exportDayDetail() {\r\n try {\r\n const date = document.getElementById('date-filter')?.value;\r\n const url = date ? `/api/export-day-csv/${date}` : '/api/export-daily-csv';\r\n const res = await fetch(url);\r\n if (!res.ok) throw new Error('Export failed');\r\n const blob = await res.blob();\r\n const disposition = res.headers.get('Content-Disposition');\r\n const filename = disposition\r\n ? decodeURIComponent(disposition.replace(/.*filename=\"?([^\";]+)\"?.*/, '$1'))\r\n : `export_${date || 'all'}.xlsx`;\r\n const a = document.createElement('a');\r\n a.href = URL.createObjectURL(blob);\r\n a.download = filename;\r\n document.body.appendChild(a);\r\n a.click();\r\n document.body.removeChild(a);\r\n URL.revokeObjectURL(a.href);\r\n } catch (err) {\r\n alert('Export error: ' + err.message);\r\n }\r\n }\r\n </script>\r\n \r\n </body>\r\n </html>\r\n",
|
||
"additions": 1,
|
||
"deletions": 1,
|
||
"status": "modified"
|
||
}
|
||
]
|
||
},
|
||
"id": "msg_fab49d2270011Pr7ZD751np3Fa",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"type": "text",
|
||
"text": "ok implement it",
|
||
"id": "prt_fab49d22a001Ax1Mfl8wFJpJWS",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab49d2270011Pr7ZD751np3Fa"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"parentID": "msg_fab49d2270011Pr7ZD751np3Fa",
|
||
"role": "assistant",
|
||
"mode": "build",
|
||
"agent": "build",
|
||
"path": {
|
||
"cwd": "/opt/bytetrack-counter",
|
||
"root": "/opt/bytetrack-counter"
|
||
},
|
||
"cost": 0.00046603,
|
||
"tokens": {
|
||
"total": 75824,
|
||
"input": 166,
|
||
"output": 119,
|
||
"reasoning": 19,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 75520
|
||
}
|
||
},
|
||
"modelID": "deepseek-v4-pro",
|
||
"providerID": "deepseek",
|
||
"time": {
|
||
"created": 1785285169747,
|
||
"completed": 1785285172957
|
||
},
|
||
"finish": "tool-calls",
|
||
"id": "msg_fab49d2530015rx5ZkBXau2tmC",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"snapshot": "903513119f5f892d9f5642024f6839426e6caf93",
|
||
"type": "step-start",
|
||
"id": "prt_fab49d6ad001sHU39mF1ngcB0Z",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab49d2530015rx5ZkBXau2tmC"
|
||
},
|
||
{
|
||
"type": "reasoning",
|
||
"text": "The user wants me to change the polling interval from 2000ms to 200ms.",
|
||
"time": {
|
||
"start": 1785285171121,
|
||
"end": 1785285171651
|
||
},
|
||
"id": "prt_fab49d7b1001T8IYXFFNg0Yrdn",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab49d2530015rx5ZkBXau2tmC"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "edit",
|
||
"callID": "call_00_kyUacSJi8Bt9GNEA9H8g9941",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"filePath": "/opt/bytetrack-counter/templates/dashboard.html",
|
||
"oldString": " setInterval(() => {\n loadCurrentBatch();\n }, 2000);",
|
||
"newString": " setInterval(() => {\n loadCurrentBatch();\n }, 200);"
|
||
},
|
||
"output": "Edit applied successfully.",
|
||
"metadata": {
|
||
"diagnostics": {},
|
||
"diff": "Index: /opt/bytetrack-counter/templates/dashboard.html\n===================================================================\n--- /opt/bytetrack-counter/templates/dashboard.html\n+++ /opt/bytetrack-counter/templates/dashboard.html\n@@ -817,9 +817,9 @@\n }, 30000);\n \n setInterval(() => {\n loadCurrentBatch();\n- }, 2000);\n+ }, 200);\n });\n \n async function loadCurrentBatch() {\n try {\n",
|
||
"filediff": {
|
||
"file": "/opt/bytetrack-counter/templates/dashboard.html",
|
||
"patch": "Index: /opt/bytetrack-counter/templates/dashboard.html\n===================================================================\n--- /opt/bytetrack-counter/templates/dashboard.html\n+++ /opt/bytetrack-counter/templates/dashboard.html\n@@ -817,9 +817,9 @@\n }, 30000);\n \n setInterval(() => {\n loadCurrentBatch();\n- }, 2000);\n+ }, 200);\n });\n \n async function loadCurrentBatch() {\n try {\n",
|
||
"additions": 1,
|
||
"deletions": 1
|
||
},
|
||
"truncated": false
|
||
},
|
||
"title": "templates/dashboard.html",
|
||
"time": {
|
||
"start": 1785285172323,
|
||
"end": 1785285172332
|
||
}
|
||
},
|
||
"id": "prt_fab49d9ce001zXqv3Zat3iNcof",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab49d2530015rx5ZkBXau2tmC"
|
||
},
|
||
{
|
||
"reason": "tool-calls",
|
||
"snapshot": "eb77f5d97d1d69405e7cdcf97de530ec015198a1",
|
||
"type": "step-finish",
|
||
"tokens": {
|
||
"total": 75824,
|
||
"input": 166,
|
||
"output": 119,
|
||
"reasoning": 19,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 75520
|
||
}
|
||
},
|
||
"cost": 0.00046603,
|
||
"id": "prt_fab49ddbd0012EO1CyZg7JjzlD",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab49d2530015rx5ZkBXau2tmC"
|
||
},
|
||
{
|
||
"type": "patch",
|
||
"hash": "903513119f5f892d9f5642024f6839426e6caf93",
|
||
"files": [
|
||
"/opt/bytetrack-counter/templates/dashboard.html"
|
||
],
|
||
"id": "prt_fab49ded7001DHeKN3h74d1O0f",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab49d2530015rx5ZkBXau2tmC"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"parentID": "msg_fab49d2270011Pr7ZD751np3Fa",
|
||
"role": "assistant",
|
||
"mode": "build",
|
||
"agent": "build",
|
||
"path": {
|
||
"cwd": "/opt/bytetrack-counter",
|
||
"root": "/opt/bytetrack-counter"
|
||
},
|
||
"cost": 0.000562223,
|
||
"tokens": {
|
||
"total": 76138,
|
||
"input": 63,
|
||
"output": 148,
|
||
"reasoning": 151,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 75776
|
||
}
|
||
},
|
||
"modelID": "deepseek-v4-pro",
|
||
"providerID": "deepseek",
|
||
"time": {
|
||
"created": 1785285172973,
|
||
"completed": 1785285179158
|
||
},
|
||
"finish": "tool-calls",
|
||
"id": "msg_fab49deed0014Ou1kedtkISA7A",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"snapshot": "eb77f5d97d1d69405e7cdcf97de530ec015198a1",
|
||
"type": "step-start",
|
||
"id": "prt_fab49e2c2001smNt5XaBUEfO36",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab49deed0014Ou1kedtkISA7A"
|
||
},
|
||
{
|
||
"type": "reasoning",
|
||
"text": "Now update the SPEC.md — this was not previously a task, but it changes observable behavior. I should note this in §V or §T. Actually, this is a behavioral improvement, not a spec change. But I should mark something in §T about it being done. Let me add a completed task or just update the relevant task.\n\nLooking at §T, there's no task for this. But I think I should add one marking it as done since it was a user-requested change.\n\nActually, let me just note it — it's a small enough change that doesn't need a full task row. The spec is about the architecture, not every UI tweak. But let me add a T11 in §T as done.",
|
||
"time": {
|
||
"start": 1785285174832,
|
||
"end": 1785285177689
|
||
},
|
||
"id": "prt_fab49e6300016gJhNB6JExJWG0",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab49deed0014Ou1kedtkISA7A"
|
||
},
|
||
{
|
||
"type": "tool",
|
||
"tool": "edit",
|
||
"callID": "call_00_AeLcScWxXDAtMNf3vdyD1434",
|
||
"state": {
|
||
"status": "completed",
|
||
"input": {
|
||
"filePath": "/opt/bytetrack-counter/SPEC.md",
|
||
"newString": "T10|.|decay inf_ms toward 0 when inference skipped — stale display misleads operator|\nT11|x|reduce live-counter poll interval 2000→200ms for real-time feel|",
|
||
"oldString": "T10|.|decay inf_ms toward 0 when inference skipped — stale display misleads operator|"
|
||
},
|
||
"output": "Edit applied successfully.",
|
||
"metadata": {
|
||
"diagnostics": {},
|
||
"diff": "Index: /opt/bytetrack-counter/SPEC.md\n===================================================================\n--- /opt/bytetrack-counter/SPEC.md\n+++ /opt/bytetrack-counter/SPEC.md\n@@ -120,8 +120,9 @@\n T7|.|add dashboard health-check endpoint (no `/health` or `/api/status` exists)|\n T8|.|add model checks on startup — model class names ! validated against CLASS_AYAM/CLASS_TALENAN (TensorRT variant validates; RKNN variants do not)|\n T9|.|reset prev_gray on stream reconnect — stale gray ref causes crash or false motion|V19,B1\n T10|.|decay inf_ms toward 0 when inference skipped — stale display misleads operator|\n+T11|x|reduce live-counter poll interval 2000→200ms for real-time feel|\n ```\n \n ## §B — Bugs\n \n",
|
||
"filediff": {
|
||
"file": "/opt/bytetrack-counter/SPEC.md",
|
||
"patch": "Index: /opt/bytetrack-counter/SPEC.md\n===================================================================\n--- /opt/bytetrack-counter/SPEC.md\n+++ /opt/bytetrack-counter/SPEC.md\n@@ -120,8 +120,9 @@\n T7|.|add dashboard health-check endpoint (no `/health` or `/api/status` exists)|\n T8|.|add model checks on startup — model class names ! validated against CLASS_AYAM/CLASS_TALENAN (TensorRT variant validates; RKNN variants do not)|\n T9|.|reset prev_gray on stream reconnect — stale gray ref causes crash or false motion|V19,B1\n T10|.|decay inf_ms toward 0 when inference skipped — stale display misleads operator|\n+T11|x|reduce live-counter poll interval 2000→200ms for real-time feel|\n ```\n \n ## §B — Bugs\n \n",
|
||
"additions": 1,
|
||
"deletions": 0
|
||
},
|
||
"truncated": false
|
||
},
|
||
"title": "SPEC.md",
|
||
"time": {
|
||
"start": 1785285178885,
|
||
"end": 1785285178907
|
||
}
|
||
},
|
||
"id": "prt_fab49f15d001em1v9ZqJH9eOkf",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab49deed0014Ou1kedtkISA7A"
|
||
},
|
||
{
|
||
"reason": "tool-calls",
|
||
"snapshot": "558750bc9ad57dfdd325cc3eb3ba64f27edd67ee",
|
||
"type": "step-finish",
|
||
"tokens": {
|
||
"total": 76138,
|
||
"input": 63,
|
||
"output": 148,
|
||
"reasoning": 151,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 75776
|
||
}
|
||
},
|
||
"cost": 0.000562223,
|
||
"id": "prt_fab49f6c3001g4Vjq2NNZLYNl7",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab49deed0014Ou1kedtkISA7A"
|
||
},
|
||
{
|
||
"type": "patch",
|
||
"hash": "eb77f5d97d1d69405e7cdcf97de530ec015198a1",
|
||
"files": [
|
||
"/opt/bytetrack-counter/SPEC.md"
|
||
],
|
||
"id": "prt_fab49f712001ZloLrbeA5gcf4Y",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab49deed0014Ou1kedtkISA7A"
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"info": {
|
||
"parentID": "msg_fab49d2270011Pr7ZD751np3Fa",
|
||
"role": "assistant",
|
||
"mode": "build",
|
||
"agent": "build",
|
||
"path": {
|
||
"cwd": "/opt/bytetrack-counter",
|
||
"root": "/opt/bytetrack-counter"
|
||
},
|
||
"cost": 0.000350001,
|
||
"tokens": {
|
||
"total": 76178,
|
||
"input": 121,
|
||
"output": 25,
|
||
"reasoning": 0,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 76032
|
||
}
|
||
},
|
||
"modelID": "deepseek-v4-pro",
|
||
"providerID": "deepseek",
|
||
"time": {
|
||
"created": 1785285179171,
|
||
"completed": 1785285181138
|
||
},
|
||
"finish": "stop",
|
||
"id": "msg_fab49f723001QJsoXYXC5OokKi",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3"
|
||
},
|
||
"parts": [
|
||
{
|
||
"snapshot": "558750bc9ad57dfdd325cc3eb3ba64f27edd67ee",
|
||
"type": "step-start",
|
||
"id": "prt_fab49fa81001LhGfeOnN6FvjC4",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab49f723001QJsoXYXC5OokKi"
|
||
},
|
||
{
|
||
"type": "text",
|
||
"text": "Done. `templates/dashboard.html:820` — polling interval `2000` → `200`ms.",
|
||
"time": {
|
||
"start": 1785285180681,
|
||
"end": 1785285181004
|
||
},
|
||
"id": "prt_fab49fd09001Wkb0FWmp5uJfsi",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab49f723001QJsoXYXC5OokKi"
|
||
},
|
||
{
|
||
"reason": "stop",
|
||
"snapshot": "558750bc9ad57dfdd325cc3eb3ba64f27edd67ee",
|
||
"type": "step-finish",
|
||
"tokens": {
|
||
"total": 76178,
|
||
"input": 121,
|
||
"output": 25,
|
||
"reasoning": 0,
|
||
"cache": {
|
||
"write": 0,
|
||
"read": 76032
|
||
}
|
||
},
|
||
"cost": 0.000350001,
|
||
"id": "prt_fab49fe84001o6b83FRG9MeWnz",
|
||
"sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3",
|
||
"messageID": "msg_fab49f723001QJsoXYXC5OokKi"
|
||
}
|
||
]
|
||
}
|
||
]
|
||
}
|