proitlab
010f6e1493
- Add engine_utils for TensorRT compatibility verification, auto-recompilation from .pt models, and YAML auto-updates - Add mortality detection pipeline (mortality.py, test_run_mortality.sh, mortality_config.yaml) - Add multi-execution batch modes (parallel_processes, tensor_batching, hybrid) in batch_runner.py - Add daily test run automation scripts and video processing runners - Add dashboard REST API, live stream endpoints, and web UI templates - Clean up git tracking by ignoring __pycache__, .pyc, and build artifacts
241 lines
7.2 KiB
Markdown
241 lines
7.2 KiB
Markdown
# Quick Start Guide
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This guide gets a new developer up and running from scratch.
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---
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## 1. Prerequisites
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- Python 3.10+
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- `ffmpeg` on PATH (for video compression)
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- NVIDIA GPU + CUDA drivers (optional but recommended for inference speed)
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---
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## 2. First-Time Setup
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```bash
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# Clone / copy the project folder, then enter it
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cd chicken-counting-sukawarna-det
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# Create a virtual environment and install all dependencies
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python3 -m venv venv
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venv/bin/pip install --upgrade pip
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venv/bin/pip install -r requirements.txt
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```
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> **Note**: If moving the project from another machine, always recreate the venv.
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> Do NOT copy the `venv/` folder — it contains absolute paths baked in from the source machine.
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---
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## 3. Project Layout
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```text
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chicken-counting-sukawarna-det/
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├── configs/
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│ ├── cycle7_batch_optimized.yaml ← Main batch processing config
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│ ├── cycle7_batch.yaml ← Alternate batch config
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│ ├── mortality_config.yaml ← Mortality (carcass) detection config
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│ ├── cameras/example_camera.yaml ← Single-camera run config template
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│ └── trackers/botsort_chicken.yaml ← BoT-SORT tracker settings
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├── db/
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│ └── chicken_counts.db ← SQLite database (auto-created)
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├── models/ ← Place your .pt / .onnx / .engine files here
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│ ├── chicken-detection-model-v26n-300e-best-2026-05-02-NEW.onnx
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│ └── chicken-detection-model-v26n-seg-300e-best-2026-04-18.pt ← Used by mortality
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├── src/chicken_counter/ ← Main Python package
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├── templates/ ← Dashboard HTML
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├── dashboard.py ← Live API + Dashboard server
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├── start_dashboard.sh ← Portable dashboard launcher ← USE THIS
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├── test_run_mortality.sh ← Run mortality detection
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├── test_run.sh ← Run batch processing all dates
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└── requirements.txt
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```
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---
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## 4. Model Setup
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Put your model files in the `models/` directory.
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| Purpose | File |
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| :--- | :--- |
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| Batch video counting | `chicken-detection-model-v26n-300e-best-2026-05-02-NEW.onnx` (speed) or `.pt` (accuracy) |
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| Mortality detection | `chicken-detection-model-v26n-seg-300e-best-2026-04-18.pt` |
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Update `configs/mortality_config.yaml` and `configs/cycle7_batch_optimized.yaml` if using different filenames.
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---
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## 5. Running the Systems
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### A — Batch Video Processing (Daily Chicken Count)
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Place input videos under the `VIDEOS` folder adjacent to the project:
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```text
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../VIDEOS/cycle7/kandang-atas/
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2026-06-18/
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kandang_1_camera_1_2026-06-18_120056.mp4
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kandang_1_camera_2_2026-06-18_120456.mp4
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...
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```
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Then run:
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```bash
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# Run all dates (multi-process mode)
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./test_run.sh
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# Run all dates (Tensor Batching mode)
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./test_run_tensor_batch.sh
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# Run all dates (Hybrid mode: Threaded CPU + Batched GPU)
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./test_run_hybrid.sh
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# Run a specific date (4 cameras in parallel)
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PYTHONPATH=src venv/bin/python -m chicken_counter.cli batch \
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--config configs/cycle7_batch_optimized.yaml \
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--date 2026-06-18
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# Run a specific date using Model-Level Tensor Batching
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PYTHONPATH=src venv/bin/python -m chicken_counter.cli batch \
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--config configs/cycle7_batch_optimized.yaml \
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--date 2026-06-18 \
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--mode tensor_batching
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# Run a specific date using Hybrid Execution Mode
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PYTHONPATH=src venv/bin/python -m chicken_counter.cli batch \
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--config configs/cycle7_batch_optimized.yaml \
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--date 2026-06-18 \
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--mode hybrid
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# Run with video output enabled
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./test_run_video_2026-06-18.sh
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```
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Output is saved in `../VIDEOS/cycle7/kandang-atas/2026-06-18/output/`.
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---
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### B — Mortality Detection (Carcass Photo Scanning)
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Place input photos in `../VIDEOS/cycle7/kandang-atas/mortality/`.
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```bash
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# Run with default config
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./test_run_mortality.sh
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# Override confidence threshold
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CONF=0.65 ./test_run_mortality.sh
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# Run on a specific image
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./test_run_mortality.sh /path/to/photo.jpg
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```
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Output annotated images are saved as `output_<original_name>.jpg` in the same directory.
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A `mortality_report.json` is also saved there with full detection data.
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#### Key config options in `configs/mortality_config.yaml`
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| Setting | Description |
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| :--- | :--- |
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| `conf` | Detection confidence threshold (0.0–1.0) |
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| `iou` | IoU NMS threshold |
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| `min_box_area_px` | Minimum bounding box area in pixels |
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| `dedupe_radius_px` | Centroid deduplication radius in pixels |
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| `two_pass` | Enable 2x Detect & Refine pipeline |
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| `classes` | `[0]` = chicken only; ignores background/text/equipment |
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---
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### C — Dashboard & API Server
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```bash
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# Start the dashboard (auto-discovers mortality directory)
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./start_dashboard.sh
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# Custom port and directories
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PORT=9090 ./start_dashboard.sh
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# Multiple mortality directories
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MORTALITY_DIRS="/path/to/mortality1,/path/to/mortality2" ./start_dashboard.sh
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```
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Open in browser: **http://localhost:8080**
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Available API endpoints:
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| Endpoint | Description |
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| :--- | :--- |
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| `GET /api/status` | Live system status, active counting cameras & latest date |
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| `GET /api/cameras` | Live camera list from `/dev/shm` |
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| `GET /api/db/summary` | Total chickens, days, hours across all dates |
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| `GET /api/db/history` | Per-date summary, newest first |
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| `GET /api/db/date/<YYYY-MM-DD>` | Per-camera breakdown for a specific date |
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| `GET /api/db/camera/<id>` | History for a specific camera (CC1, CC2...) |
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| `GET /api/db/location/<name>` | Summary and history for a location |
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| `GET /api/mortality/latest` | Latest mortality detection report (JSON) |
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| `GET /api/mortality/history` | All mortality reports, newest first |
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| `GET /api/mortality/image/<filename>` | Serve annotated output JPEG by filename |
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| `GET /shm/<cam>/stats.json` | Live pipeline stats for a running camera |
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| `GET /shm/<cam>/frame.jpg` | Live frame snapshot from a running camera |
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See `API.md` for full response schemas.
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---
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### D — Install as a System Service (Auto-Start)
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```bash
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# Copy the service file and adjust WorkingDirectory / User if needed
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sudo cp chicken-dashboard.service /etc/systemd/system/
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# Enable and start
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sudo systemctl daemon-reload
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sudo systemctl enable chicken-dashboard
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sudo systemctl start chicken-dashboard
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# Check status
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sudo systemctl status chicken-dashboard
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```
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The service reads `start_dashboard.sh`, so it also auto-discovers the mortality directory.
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---
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## 6. Database
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Results are automatically written to `db/chicken_counts.db` when `db_path` is set in the batch YAML. To store results manually from a JSON report:
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```bash
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PYTHONPATH=src venv/bin/python store_results.py \
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../VIDEOS/cycle7/kandang-atas/2026-06-18/output/counts_2026-06-18.json \
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--location kandang-atas \
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--db db/chicken_counts.db
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```
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Export to Excel:
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```bash
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PYTHONPATH=src venv/bin/python export_excel_report.py
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```
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---
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## 7. Moving the Project to Another Machine
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1. Delete the `venv/` folder before copying:
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```bash
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rm -rf venv/
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```
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2. Copy the entire project folder to the new machine.
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3. Update the `WorkingDirectory` and `ExecStart` in `chicken-dashboard.service` to the new path.
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4. Recreate the venv on the new machine:
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```bash
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python3 -m venv venv
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venv/bin/pip install -r requirements.txt
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```
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5. All configs and Python code use relative paths and will work without any other changes.
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