diff --git a/.oc/session.json b/.oc/session.json index b785756..f5ed06d 100644 --- a/.oc/session.json +++ b/.oc/session.json @@ -2,8 +2,8 @@ "info": { "id": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3", "slug": "tidy-meadow", - "projectID": "45e61044497a123d750492db8a5bb735bc6664ea", - "directory": "/opt/bytetrack-counter", + "projectID": "e3575fc21512b6d62de15178f14e83993418c7cb", + "directory": "/home/dsutanto/MyEnv/Codes/CPSP/bytetrack-counter", "path": "", "title": "Add version printing to compiled binary", "agent": "build", @@ -17,19 +17,19 @@ "deletions": 0, "files": 0 }, - "cost": 0.10242953700000004, + "cost": 0.579444157, "tokens": { - "input": 186493, - "output": 8386, - "reasoning": 6093, + "input": 724743, + "output": 71484, + "reasoning": 40808, "cache": { - "read": 2402304, + "read": 45927424, "write": 0 } }, "time": { "created": 1783308022494, - "updated": 1785285181158 + "updated": 1785425566855 } }, "messages": [ @@ -39,14 +39,14 @@ "time": { "created": 1783308022565 }, + "summary": { + "diffs": [] + }, "agent": "build", "model": { "providerID": "deepseek", "modelID": "deepseek-v4-pro" }, - "summary": { - "diffs": [] - }, "id": "msg_f3570f325001LLIc39xgDMbjmi", "sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3" }, @@ -62,8 +62,20 @@ }, { "info": { - "parentID": "msg_f3570f325001LLIc39xgDMbjmi", "role": "assistant", + "time": { + "created": 1783308022603, + "completed": 1783308053394 + }, + "error": { + "name": "MessageAbortedError", + "data": { + "message": "Aborted" + } + }, + "parentID": "msg_f3570f325001LLIc39xgDMbjmi", + "modelID": "deepseek-v4-pro", + "providerID": "deepseek", "mode": "build", "agent": "build", "path": { @@ -80,25 +92,13 @@ "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", + "snapshot": "8dd1d5b751ba93c645ddb4a0e14c3d75f17a6197", "id": "prt_f3570f728001vJVIia8AVAav6d", "sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3", "messageID": "msg_f3570f34b001YbHLZypL941I6t" @@ -116,10 +116,16 @@ }, { "type": "tool", - "tool": "task", "callID": "call_00_uxwAWf2fzO77kQGQlsAz6862", + "tool": "task", "state": { - "title": "Explore codebase structure", + "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" + }, + "error": "Tool execution aborted", "metadata": { "parentSessionId": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3", "sessionId": "ses_0ca8ef7acffe5gGEvQ17HVlkkJ", @@ -129,17 +135,10 @@ }, "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", @@ -163,11 +162,6 @@ "time": { "created": 1785284228706 }, - "agent": "build", - "model": { - "providerID": "deepseek", - "modelID": "deepseek-v4-pro" - }, "summary": { "diffs": [ { @@ -179,6 +173,11 @@ } ] }, + "agent": "build", + "model": { + "providerID": "deepseek", + "modelID": "deepseek-v4-pro" + }, "id": "msg_fab3b7662001asy7eTmLMm4mEq", "sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3" }, @@ -194,8 +193,14 @@ }, { "info": { - "parentID": "msg_fab3b7662001asy7eTmLMm4mEq", "role": "assistant", + "time": { + "created": 1785284228748, + "completed": 1785284234332 + }, + "parentID": "msg_fab3b7662001asy7eTmLMm4mEq", + "modelID": "deepseek-v4-pro", + "providerID": "deepseek", "mode": "build", "agent": "build", "path": { @@ -209,24 +214,18 @@ "output": 212, "reasoning": 19, "cache": { - "write": 0, - "read": 1920 + "read": 1920, + "write": 0 } }, - "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", + "snapshot": "e0e1414b68e2544fb2d71b977882e90620947d43", "id": "prt_fab3b8129001XaHaEJfKlPR5qP", "sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3", "messageID": "msg_fab3b768b0016CKiVP10E6vAsf" @@ -244,14 +243,15 @@ }, { "type": "tool", - "tool": "read", "callID": "call_00_jK4A2K3Ongbsd2KIA1vj5882", + "tool": "read", "state": { "status": "completed", "input": { "filePath": "/opt/bytetrack-counter" }, "output": "/opt/bytetrack-counter\ndirectory\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(23 entries)\n", + "title": "", "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, @@ -289,7 +289,6 @@ "truncated": false } }, - "title": "", "time": { "start": 1785284232983, "end": 1785284233008 @@ -301,8 +300,8 @@ }, { "type": "tool", - "tool": "glob", "callID": "call_01_KmjrUVbZ4X4d86sYSS0Z3134", + "tool": "glob", "state": { "status": "completed", "input": { @@ -310,11 +309,11 @@ "path": "/opt/bytetrack-counter" }, "output": "No files found", + "title": "", "metadata": { "count": 0, "truncated": false }, - "title": "", "time": { "start": 1785284233315, "end": 1785284233359 @@ -326,8 +325,8 @@ }, { "type": "tool", - "tool": "glob", "callID": "call_02_RlzVEwcdJxWQh4j5pGt87999", + "tool": "glob", "state": { "status": "completed", "input": { @@ -335,11 +334,11 @@ "path": "/opt/bytetrack-counter" }, "output": "No files found", + "title": "", "metadata": { "count": 0, "truncated": false }, - "title": "", "time": { "start": 1785284233696, "end": 1785284233757 @@ -351,8 +350,8 @@ }, { "type": "tool", - "tool": "glob", "callID": "call_03_iaTsWXV0yjAFZjEUANHm3432", + "tool": "glob", "state": { "status": "completed", "input": { @@ -360,11 +359,11 @@ "path": "/opt/bytetrack-counter" }, "output": "No files found", + "title": "", "metadata": { "count": 0, "truncated": false }, - "title": "", "time": { "start": 1785284234073, "end": 1785284234118 @@ -375,20 +374,20 @@ "messageID": "msg_fab3b768b0016CKiVP10E6vAsf" }, { + "type": "step-finish", "reason": "tool-calls", "snapshot": "e0e1414b68e2544fb2d71b977882e90620947d43", - "type": "step-finish", + "cost": 0.00354438, "tokens": { "total": 9821, "input": 7670, "output": 212, "reasoning": 19, "cache": { - "write": 0, - "read": 1920 + "read": 1920, + "write": 0 } }, - "cost": 0.00354438, "id": "prt_fab3b8c01001oRRWNmyUZU2kVd", "sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3", "messageID": "msg_fab3b768b0016CKiVP10E6vAsf" @@ -397,8 +396,14 @@ }, { "info": { - "parentID": "msg_fab3b7662001asy7eTmLMm4mEq", "role": "assistant", + "time": { + "created": 1785284234339, + "completed": 1785284237633 + }, + "parentID": "msg_fab3b7662001asy7eTmLMm4mEq", + "modelID": "deepseek-v4-pro", + "providerID": "deepseek", "mode": "build", "agent": "build", "path": { @@ -412,24 +417,18 @@ "output": 248, "reasoning": 26, "cache": { - "write": 0, - "read": 9728 + "read": 9728, + "write": 0 } }, - "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", + "snapshot": "e0e1414b68e2544fb2d71b977882e90620947d43", "id": "prt_fab3b8e95001CCejmuIy60hMBE", "sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3", "messageID": "msg_fab3b8c630019oax7kHIohIWBa" @@ -447,14 +446,15 @@ }, { "type": "tool", - "tool": "read", "callID": "call_00_Q5AQBhUEOQySx1cZSzWo0273", + "tool": "read", "state": { "status": "completed", "input": { "filePath": "/opt/bytetrack-counter/README.md" }, "output": "/opt/bytetrack-counter/README.md\nfile\n\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://: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", + "title": "README.md", "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, @@ -469,7 +469,6 @@ "truncated": false } }, - "title": "README.md", "time": { "start": 1785284235996, "end": 1785284236128 @@ -481,14 +480,15 @@ }, { "type": "tool", - "tool": "read", "callID": "call_01_FixcLZLgw20u0fGSN3BT6049", + "tool": "read", "state": { "status": "completed", "input": { "filePath": "/opt/bytetrack-counter/requirements.txt" }, "output": "/opt/bytetrack-counter/requirements.txt\nfile\n\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", + "title": "requirements.txt", "metadata": { "preview": "numpy<2\nrknn-toolkit-lite2\nopencv-python\nflask\npython-dotenv\nopenpyxl", "truncated": false, @@ -503,7 +503,6 @@ "truncated": false } }, - "title": "requirements.txt", "time": { "start": 1785284236303, "end": 1785284236315 @@ -515,14 +514,15 @@ }, { "type": "tool", - "tool": "read", "callID": "call_02_EA0ldEx8QS3NNn0tFRBT8644", + "tool": "read", "state": { "status": "completed", "input": { "filePath": "/opt/bytetrack-counter/DEPLOY.md" }, "output": "/opt/bytetrack-counter/DEPLOY.md\nfile\n\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$//' .env\n19: \n20: # 3. Venv + services\n21: chmod +x setup-venv.sh install-services.sh\n22: sudo ./setup-venv.sh\n23: sudo ./install-services.sh\n24: ```\n25: \n26: Dashboard: `http://:5000`\n27: \n28: ---\n29: \n30: ## YOLO11n TensorRT engine (one-time)\n31: \n32: On the Jetson (must match `IMGSZ` / `HALF` in `.env`):\n33: \n34: ```bash\n35: source /opt/jetson-counter/venv/bin/activate\n36: export PYTHONNOUSERSITE=1\n37: \n38: yolo export model=/media/jetson/DATA/yolo11n.pt format=engine half=True imgsz=416 device=0\n39: ```\n40: \n41: Verify classes:\n42: \n43: ```bash\n44: PYTHONNOUSERSITE=1 python -c \"\n45: from ultralytics import YOLO\n46: m = YOLO('/media/jetson/DATA/yolo11n.engine')\n47: print(m.names)\n48: \"\n49: ```\n50: \n51: Expect `ayam` and `talenan`.\n52: \n53: ---\n54: \n55: ## Direct camera RTSP\n56: \n57: Set in `.env`:\n58: \n59: ```env\n60: SOURCE=rtsp://user:pass@192.168.x.x:554/stream1\n61: ```\n62: \n63: Test before install:\n64: \n65: ```bash\n66: ffplay -rtsp_transport tcp -t 5 \"$SOURCE\"\n67: nc -zv 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", + "title": "DEPLOY.md", "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$//' .env\n\n# 3. Venv + services", "truncated": false, @@ -537,7 +537,6 @@ "truncated": false } }, - "title": "DEPLOY.md", "time": { "start": 1785284236566, "end": 1785284236618 @@ -549,14 +548,15 @@ }, { "type": "tool", - "tool": "read", "callID": "call_03_XcAXNMUkJ5Y7aLBm0Igk4940", + "tool": "read", "state": { "status": "completed", "input": { "filePath": "/opt/bytetrack-counter/.gitignore" }, "output": "/opt/bytetrack-counter/.gitignore\nfile\n\n1: # Byte-compiled / optimized / DLL files\n2: __pycache__/\n3: *.py[codz]\n4: *$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", + "title": ".gitignore", "metadata": { "preview": "# Byte-compiled / optimized / DLL files\n__pycache__/\n*.py[codz]\n*$py.class\n\n# C extensions\n*.so\n\n# Distribution / packaging\n.Python\nbuild/\ndevelop-eggs/\ndist/\ndownloads/\neggs/\n.eggs/\nlib/\nlib64/\nparts/\nsdist/", "truncated": false, @@ -571,7 +571,6 @@ "truncated": false } }, - "title": ".gitignore", "time": { "start": 1785284236844, "end": 1785284236858 @@ -583,14 +582,15 @@ }, { "type": "tool", - "tool": "read", "callID": "call_04_8kLRyFQlLLrl8gWMDuZu8872", + "tool": "read", "state": { "status": "completed", "input": { "filePath": "/opt/bytetrack-counter/setup-venv.sh" }, "output": "/opt/bytetrack-counter/setup-venv.sh\nfile\n\n1: #!/usr/bin/env bash\n2: # One-time venv for edge Jetson counter — NVIDIA torch required (not PyPI).\n3: set -euo pipefail\n4: \n5: INSTALL_DIR=\"${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 \"${VENV_DIR}/bin/python\" -c \"\n34: import torch\n35: from ultralytics import YOLO\n36: import cv2\n37: import flask\n38: print('venv ok | torch', torch.__version__, '| cuda', torch.cuda.is_available())\n39: \"\n40: \n41: echo \"\"\n42: echo \"If torchvision import fails for ultralytics, copy/build torchvision into venv.\"\n43: echo \"Next: cp config.env.example .env && nano .env && sudo ./install-services.sh\"\n\n(End of file - total 43 lines)\n", + "title": "setup-venv.sh", "metadata": { "preview": "#!/usr/bin/env bash\n# One-time venv for edge Jetson counter — NVIDIA torch required (not PyPI).\nset -euo pipefail\n\nINSTALL_DIR=\"${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 \"${VENV_DIR}\"\nfi", "truncated": false, @@ -605,7 +605,6 @@ "truncated": false } }, - "title": "setup-venv.sh", "time": { "start": 1785284237140, "end": 1785284237252 @@ -617,14 +616,15 @@ }, { "type": "tool", - "tool": "read", "callID": "call_05_tsusidwd3qAQiCqmM8XU8836", + "tool": "read", "state": { "status": "completed", "input": { "filePath": "/opt/bytetrack-counter/install-services.sh" }, "output": "/opt/bytetrack-counter/install-services.sh\nfile\n\n1: #!/usr/bin/env bash\n2: # Install edge Jetson counter + dashboard; disable legacy MQTT frigate-counter.\n3: # Run on the Jetson: sudo ./install-services.sh\n4: set -euo pipefail\n5: \n6: INSTALL_DIR=\"${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 $1}'):5000\"\n\n(End of file - total 68 lines)\n", + "title": "install-services.sh", "metadata": { "preview": "#!/usr/bin/env bash\n# Install edge Jetson counter + dashboard; disable legacy MQTT frigate-counter.\n# Run on the Jetson: sudo ./install-services.sh\nset -euo pipefail\n\nINSTALL_DIR=\"${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, @@ -639,7 +639,6 @@ "truncated": false } }, - "title": "install-services.sh", "time": { "start": 1785284237384, "end": 1785284237428 @@ -650,20 +649,20 @@ "messageID": "msg_fab3b8c630019oax7kHIohIWBa" }, { + "type": "step-finish", "reason": "tool-calls", "snapshot": "e0e1414b68e2544fb2d71b977882e90620947d43", - "type": "step-finish", + "cost": 0.000391964, "tokens": { "total": 10274, "input": 272, "output": 248, "reasoning": 26, "cache": { - "write": 0, - "read": 9728 + "read": 9728, + "write": 0 } }, - "cost": 0.000391964, "id": "prt_fab3b98f4001tSAip2xPIP13qH", "sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3", "messageID": "msg_fab3b8c630019oax7kHIohIWBa" @@ -672,8 +671,14 @@ }, { "info": { - "parentID": "msg_fab3b7662001asy7eTmLMm4mEq", "role": "assistant", + "time": { + "created": 1785284237640, + "completed": 1785284240946 + }, + "parentID": "msg_fab3b7662001asy7eTmLMm4mEq", + "modelID": "deepseek-v4-pro", + "providerID": "deepseek", "mode": "build", "agent": "build", "path": { @@ -687,24 +692,18 @@ "output": 214, "reasoning": 16, "cache": { - "write": 0, - "read": 10240 + "read": 10240, + "write": 0 } }, - "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", + "snapshot": "e0e1414b68e2544fb2d71b977882e90620947d43", "id": "prt_fab3b9be5001YEAxOxjPaxN0rA", "sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3", "messageID": "msg_fab3b9948001NqWLx9PXClyyv8" @@ -722,14 +721,15 @@ }, { "type": "tool", - "tool": "read", "callID": "call_00_z5R05gvWfSAWc2BWe9nR5485", + "tool": "read", "state": { "status": "completed", "input": { "filePath": "/opt/bytetrack-counter/counter_live.py" }, "output": "/opt/bytetrack-counter/counter_live.py\nfile\n\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", + "title": "counter_live.py", "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, @@ -744,7 +744,6 @@ "truncated": false } }, - "title": "counter_live.py", "time": { "start": 1785284239384, "end": 1785284239564 @@ -756,14 +755,15 @@ }, { "type": "tool", - "tool": "read", "callID": "call_01_EH0xKXBAcnACHOs2YfUx7328", + "tool": "read", "state": { "status": "completed", "input": { "filePath": "/opt/bytetrack-counter/batch_store.py" }, "output": "/opt/bytetrack-counter/batch_store.py\nfile\n\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", + "title": "batch_store.py", "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, @@ -778,7 +778,6 @@ "truncated": false } }, - "title": "batch_store.py", "time": { "start": 1785284239603, "end": 1785284239654 @@ -790,14 +789,15 @@ }, { "type": "tool", - "tool": "read", "callID": "call_02_GngfrJznz8i7yllztxzQ2349", + "tool": "read", "state": { "status": "completed", "input": { "filePath": "/opt/bytetrack-counter/counter_dashboard.py" }, "output": "/opt/bytetrack-counter/counter_dashboard.py\nfile\n\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/\")\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/\")\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", + "title": "counter_dashboard.py", "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, @@ -812,7 +812,6 @@ "truncated": false } }, - "title": "counter_dashboard.py", "time": { "start": 1785284239884, "end": 1785284239971 @@ -824,14 +823,15 @@ }, { "type": "tool", - "tool": "read", "callID": "call_03_FLmzGWGb68M2KNx7MD5C8536", + "tool": "read", "state": { "status": "completed", "input": { "filePath": "/opt/bytetrack-counter/config.env.example" }, "output": "/opt/bytetrack-counter/config.env.example\nfile\n\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", + "title": "config.env.example", "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, @@ -846,7 +846,6 @@ "truncated": false } }, - "title": "config.env.example", "time": { "start": 1785284240169, "end": 1785284240183 @@ -858,14 +857,15 @@ }, { "type": "tool", - "tool": "read", "callID": "call_04_y5rZw0rEq6o3tZU2iuyD7373", + "tool": "read", "state": { "status": "completed", "input": { "filePath": "/opt/bytetrack-counter/counter_live_rknn.py" }, "output": "/opt/bytetrack-counter/counter_live_rknn.py\nfile\n\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", + "title": "counter_live_rknn.py", "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, @@ -880,7 +880,6 @@ "truncated": false } }, - "title": "counter_live_rknn.py", "time": { "start": 1785284240529, "end": 1785284240709 @@ -891,20 +890,20 @@ "messageID": "msg_fab3b9948001NqWLx9PXClyyv8" }, { + "type": "step-finish", "reason": "tool-calls", "snapshot": "e0e1414b68e2544fb2d71b977882e90620947d43", - "type": "step-finish", + "cost": 0.00258274, "tokens": { "total": 15862, "input": 5392, "output": 214, "reasoning": 16, "cache": { - "write": 0, - "read": 10240 + "read": 10240, + "write": 0 } }, - "cost": 0.00258274, "id": "prt_fab3ba5d8001gigTvJdY0dDaxc", "sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3", "messageID": "msg_fab3b9948001NqWLx9PXClyyv8" @@ -913,8 +912,14 @@ }, { "info": { - "parentID": "msg_fab3b7662001asy7eTmLMm4mEq", "role": "assistant", + "time": { + "created": 1785284240955, + "completed": 1785284247206 + }, + "parentID": "msg_fab3b7662001asy7eTmLMm4mEq", + "modelID": "deepseek-v4-pro", + "providerID": "deepseek", "mode": "build", "agent": "build", "path": { @@ -928,24 +933,18 @@ "output": 234, "reasoning": 22, "cache": { - "write": 0, - "read": 15744 + "read": 15744, + "write": 0 } }, - "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", + "snapshot": "e0e1414b68e2544fb2d71b977882e90620947d43", "id": "prt_fab3ba9cf001LT6TNHAWViUNp6", "sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3", "messageID": "msg_fab3ba63b001ubMqr9d9Qa8oQb" @@ -963,14 +962,15 @@ }, { "type": "tool", - "tool": "read", "callID": "call_00_f8jmNYi8WXfxjMO9KPiv3266", + "tool": "read", "state": { "status": "completed", "input": { "filePath": "/opt/bytetrack-counter/bytetrack-counter.service" }, "output": "/opt/bytetrack-counter/bytetrack-counter.service\nfile\n\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", + "title": "bytetrack-counter.service", "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, @@ -985,7 +985,6 @@ "truncated": false } }, - "title": "bytetrack-counter.service", "time": { "start": 1785284245737, "end": 1785284245772 @@ -997,14 +996,15 @@ }, { "type": "tool", - "tool": "read", "callID": "call_01_aFvuJpL4hnlAmNDM3Iox5868", + "tool": "read", "state": { "status": "completed", "input": { "filePath": "/opt/bytetrack-counter/bytetrack-counter-dashboard.service" }, "output": "/opt/bytetrack-counter/bytetrack-counter-dashboard.service\nfile\n\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", + "title": "bytetrack-counter-dashboard.service", "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, @@ -1019,7 +1019,6 @@ "truncated": false } }, - "title": "bytetrack-counter-dashboard.service", "time": { "start": 1785284245977, "end": 1785284246024 @@ -1031,8 +1030,8 @@ }, { "type": "tool", - "tool": "read", "callID": "call_02_P90DorXhzpjuuJ4Hn5zo1862", + "tool": "read", "state": { "status": "completed", "input": { @@ -1040,6 +1039,7 @@ "limit": 60 }, "output": "/opt/bytetrack-counter/counter_live_rknn_bytetrack.py\nfile\n\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", + "title": "counter_live_rknn_bytetrack.py", "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, @@ -1054,7 +1054,6 @@ "truncated": true } }, - "title": "counter_live_rknn_bytetrack.py", "time": { "start": 1785284246442, "end": 1785284246461 @@ -1066,14 +1065,15 @@ }, { "type": "tool", - "tool": "read", "callID": "call_03_5IekWxo8eZuCKMxXI4mn0963", + "tool": "read", "state": { "status": "completed", "input": { "filePath": "/opt/bytetrack-counter/uninstall-services.sh" }, "output": "/opt/bytetrack-counter/uninstall-services.sh\nfile\n\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/${unit}.service\"\n15: done\n16: \n17: systemctl daemon-reload\n18: echo \"Removed jetson-counter and jetson-counter-dashboard services.\"\n\n(End of file - total 18 lines)\n", + "title": "uninstall-services.sh", "metadata": { "preview": "#!/usr/bin/env bash\n# Remove Jetson edge counter systemd services.\n# Run on the Jetson: sudo ./uninstall-services.sh\nset -euo pipefail\n\nif [[ \"$(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, @@ -1088,7 +1088,6 @@ "truncated": false } }, - "title": "uninstall-services.sh", "time": { "start": 1785284246730, "end": 1785284246773 @@ -1100,14 +1099,15 @@ }, { "type": "tool", - "tool": "read", "callID": "call_04_XaTxhp7G94JyiAbveNio1027", + "tool": "read", "state": { "status": "completed", "input": { "filePath": "/opt/bytetrack-counter/templates" }, "output": "/opt/bytetrack-counter/templates\ndirectory\n\ndashboard.html\n\n(1 entries)\n", + "title": "templates", "metadata": { "preview": "dashboard.html", "truncated": false, @@ -1123,7 +1123,6 @@ "truncated": false } }, - "title": "templates", "time": { "start": 1785284246986, "end": 1785284246993 @@ -1134,20 +1133,20 @@ "messageID": "msg_fab3ba63b001ubMqr9d9Qa8oQb" }, { + "type": "step-finish", "reason": "tool-calls", "snapshot": "e0e1414b68e2544fb2d71b977882e90620947d43", - "type": "step-finish", + "cost": 0.015093282, "tokens": { "total": 50054, "input": 34054, "output": 234, "reasoning": 22, "cache": { - "write": 0, - "read": 15744 + "read": 15744, + "write": 0 } }, - "cost": 0.015093282, "id": "prt_fab3bbe4c0019nHaPhmiqAhdOR", "sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3", "messageID": "msg_fab3ba63b001ubMqr9d9Qa8oQb" @@ -1156,8 +1155,14 @@ }, { "info": { - "parentID": "msg_fab3b7662001asy7eTmLMm4mEq", "role": "assistant", + "time": { + "created": 1785284247247, + "completed": 1785284255745 + }, + "parentID": "msg_fab3b7662001asy7eTmLMm4mEq", + "modelID": "deepseek-v4-pro", + "providerID": "deepseek", "mode": "build", "agent": "build", "path": { @@ -1171,24 +1176,18 @@ "output": 148, "reasoning": 435, "cache": { - "write": 0, - "read": 50048 + "read": 50048, + "write": 0 } }, - "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", + "snapshot": "e0e1414b68e2544fb2d71b977882e90620947d43", "id": "prt_fab3bc1c2001kavY1kjaybuBjC", "sessionID": "ses_0ca8f0d22ffe6PSdRVjrsVqYP3", "messageID": "msg_fab3bbece001uI2KIIvs954jfg" @@ -1206,8 +1205,8 @@ }, { "type": "tool", - "tool": "read", "callID": "call_00_9HbQKv8N5nsoY1OWjyrB5032", + "tool": "read", "state": { "status": "completed", "input": { @@ -1215,6 +1214,7 @@ "limit": 20 }, "output": "/opt/bytetrack-counter/templates/dashboard.html\nfile\n\n1: \n2: \n3: \n4: \n5: \n6: ZenAI APC Dashboard\n7: \n8: \n9: \n549: \n550: \n551: \n552: \n553: \n554: \n555: \n556: \n557: \n558: \n559: \n560: \n561: ◆\n562: \n563: ZenAI APC Module\n564: Edge Nano counter — YOLO RockChip\n565: \n566: \n567: \n568: \n569: --\n570: Counting Day\n571: \n572: \n573: \n574: {{ site_name }}\n575: \n576: ☀️\n577: \n578: \n579: \n580: \n581: \n582: \n583: LIVE\n584: \n585: --\n586: Current Batch Count\n587: Batch #-- · --\n588: \n589: \n590: Today\n591: \n592: --\n593: Total Count Today\n594: -- batches\n595: \n596: \n597: Yesterday\n598: \n599: --\n600: Total Count Yesterday\n601: -- batches\n602: \n603: \n604: Average\n605: \n606: --\n607: Average Per Day\n608: -- days recorded\n609: \n610: \n611: \n612: \n613: \n614: --\n615: --\n616: \n617: \n618: \n619: \n620: \n621: ◉ Live Camera Feed\n622: \n623: \n624: \n625: waiting...\n626: \n627: STREAM OFF\n628: \n629: \n630: \n631: \n632: ▦\n633: Live Feed Unavailable\n634: Enable LIVE_STREAM_ENABLED=true in counter .env\n635: \n636: \n637: Connection lost — retrying...\n638: \n639: \n640: \n641: \n642: \n643: \n644: \n645: ◉ Daily Trends\n646: \n647: 7D\n648: 14D\n649: 30D\n650: \n651: \n652: \n653: \n654: \n655: \n656: \n657: ◉ Quick Stats\n658: \n659: \n660: \n661: \n662: \n663: Grand Total\n664: All time\n665: \n666: \n667: --\n668: \n669: \n670: \n671: \n672: \n673: Total Batches\n674: All time\n675: \n676: \n677: --\n678: \n679: \n680: \n681: \n682: \n683: Avg per Batch\n684: Overall average\n685: \n686: \n687: --\n688: \n689: \n690: \n691: \n692: ◉ Recent Activity\n693: \n694: \n695: \n696: \n697: \n698: \n699: \n700: \n701: \n702: \n703: \n704: ◉ Daily Records\n705: Click on a row to view batch details\n706: \n707: \n708: \n709: FILTER\n710: RESET\n711: EXPORT EXCEL\n712: \n713: \n714: \n715: \n716: \n717: \n718: Date\n719: Total Count\n720: Batches\n721: Avg/Batch\n722: Status\n723: Action\n724: \n725: \n726: \n727: \n728: \n729: \n730: \n731: \n732: \n733: \n734: \n735: \n736: \n737: \n738: \n739: \n740: \n741: Batch Details\n742: --\n743: \n744: ×\n745: \n746: \n747: \n748: --\n749: Total Count\n750: \n751: \n752: --\n753: Total Batches\n754: \n755: \n756: --\n757: Avg Duration (min)\n758: \n759: \n760: \n761: \n762: \n763: \n764: Batch #\n765: Count\n766: Start\n767: End\n768: Duration\n769: \n770: \n\n(Showing lines 521-770 of 1217. Use offset=771 to continue.)\n", + "metadata": { + "preview": " @media (max-width: 1200px) {\n .summary-cards { grid-template-columns: repeat(2, 1fr); }\n .charts-row { grid-template-columns: 1fr; }\n }\n @media (max-width: 768px) {\n .container { padding: 16px 12px; }\n .summary-cards { grid-template-columns: 1fr; }\n .header { flex-direction: column; gap: 10px; padding: 14px 20px; }\n .header-left { flex-direction: column; text-align: center; }\n .header-right { flex-wrap: wrap; justify-content: center; }\n .s-card .s-val { font-size: 24px; }\n .panel { padding: 18px 16px; }\n .modal-content { padding: 18px 16px; max-width: 95vw; }\n .modal-stats { grid-template-columns: 1fr; }\n .table-toolbar { flex-direction: column; align-items: flex-start; }\n }\n @media (max-width: 480px) {\n .container { padding: 10px 8px; }\n .header { padding: 12px 14px; border-radius: 12px; }\n .header h1 { font-size: 16px; letter-spacing: 2px; }", + "truncated": true, + "loaded": [], + "display": { + "type": "file", + "path": "/home/dsutanto/MyEnv/Codes/CPSP/bytetrack-counter/templates/dashboard.html", + "text": " @media (max-width: 1200px) {\n .summary-cards { grid-template-columns: repeat(2, 1fr); }\n .charts-row { grid-template-columns: 1fr; }\n }\n @media (max-width: 768px) {\n .container { padding: 16px 12px; }\n .summary-cards { grid-template-columns: 1fr; }\n .header { flex-direction: column; gap: 10px; padding: 14px 20px; }\n .header-left { flex-direction: column; text-align: center; }\n .header-right { flex-wrap: wrap; justify-content: center; }\n .s-card .s-val { font-size: 24px; }\n .panel { padding: 18px 16px; }\n .modal-content { padding: 18px 16px; max-width: 95vw; }\n .modal-stats { grid-template-columns: 1fr; }\n .table-toolbar { flex-direction: column; align-items: flex-start; }\n }\n @media (max-width: 480px) {\n .container { padding: 10px 8px; }\n .header { padding: 12px 14px; border-radius: 12px; }\n .header h1 { font-size: 16px; letter-spacing: 2px; }\n .logo-icon { width: 36px; height: 36px; font-size: 18px; border-radius: 9px; }\n .s-card { padding: 16px; border-radius: 14px; }\n .s-card .s-val { font-size: 22px; }\n .panel { padding: 14px 12px; border-radius: 14px; 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LIVE_STREAM_ENABLED=true