eroncero
432f0378bf
- Add Flask application with MJPEG video streaming - Implement OpenCV DNN face detection module - Add zone-based entry/exit tracking with cooldown mechanism - Create web interface with real-time WebSocket updates - Add model download script and comprehensive README - Include OpenCV DNN model files for face detection
124 lines
4.5 KiB
Markdown
124 lines
4.5 KiB
Markdown
# People Counter Web App
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A real-time web application that uses USB camera face detection to count people entering and leaving a room, with zone-based entry/exit tracking.
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## Features
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- Real-time face detection using OpenCV DNN face detector
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- Zone-based entry/exit tracking (left zone = entry, right zone = exit)
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- Live video streaming via MJPEG
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- Real-time count updates via WebSocket
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- Visual indicators for overpopulation warnings
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- Clean, modern web interface
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## Prerequisites
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- Python 3.7 or higher
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- USB camera connected to your computer
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- Linux/Windows/macOS
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## Installation
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1. **Clone or download this repository**
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2. **Install Python dependencies:**
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```bash
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pip install -r requirements.txt
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```
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3. **Download the face detection model files:**
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```bash
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python download_models.py
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```
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This will download the required OpenCV DNN model files to the `models/` directory.
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**Note:** If the automatic download fails, you can manually download:
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- `deploy.prototxt` from: https://github.com/opencv/opencv/blob/master/samples/dnn/face_detector/deploy.prototxt
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- `res10_300x300_ssd_iter_140000.caffemodel` from: https://github.com/opencv/opencv_3rdparty/dnn_samples_face_detector_20170830/res10_300x300_ssd_iter_140000.caffemodel
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Place both files in the `models/` directory.
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## Usage
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1. **Make sure your USB camera is connected**
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2. **Run the Flask application:**
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```bash
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python app.py
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```
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3. **Open your web browser and navigate to:**
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```
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http://localhost:5000
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```
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4. **The application will:**
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- Display live video feed from your camera
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- Detect faces in real-time
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- Count people entering (left zone) and exiting (right zone)
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- Display current occupancy and statistics
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- Show visual warnings when occupancy is high
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## Configuration
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You can modify the following settings in `app.py` and `camera.py`:
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- **Camera index:** Change `camera_index=0` to use a different camera
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- **Frame processing rate:** Adjust `process_every_n_frames` (default: 3) to balance performance and accuracy
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- **Face detection confidence:** Modify `face_confidence` threshold (default: 0.5)
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- **Zone boundaries:** Adjust `entry_zone_percent` and `exit_zone_percent` in `zone_tracker.py`
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- **Cooldown period:** Change `cooldown_seconds` to prevent double-counting (default: 2.0 seconds)
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- **Maximum occupancy:** Update `MAX_OCCUPANCY` in `static/js/main.js` for overpopulation warnings
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## How It Works
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1. **Camera Capture:** The camera module captures frames from your USB camera
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2. **Face Detection:** OpenCV DNN detects faces in the video frames
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3. **Zone Tracking:** The zone tracker determines which zone each face is in:
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- **Left 40%** = Entry zone (green)
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- **Right 40%** = Exit zone (red)
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- **Center 10%** = Buffer zone (ignored to prevent false counts)
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4. **Counting Logic:** People are counted when they appear in entry/exit zones with a cooldown period to prevent double-counting
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5. **Real-time Updates:** Counts are sent to the web interface via WebSocket for live updates
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## Project Structure
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```
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PeopleCounter/
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├── app.py # Flask main application
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├── camera.py # Camera capture wrapper
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├── face_detector.py # Face detection module
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├── zone_tracker.py # Zone-based tracking logic
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├── download_models.py # Script to download model files
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├── requirements.txt # Python dependencies
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├── models/ # OpenCV DNN model files
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│ ├── deploy.prototxt
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│ └── res10_300x300_ssd_iter_140000.caffemodel
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├── templates/
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│ └── index.html # Main web interface
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└── static/
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├── css/
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│ └── style.css # Styling
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└── js/
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└── main.js # Client-side JavaScript
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```
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## Troubleshooting
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- **Camera not found:** Make sure your camera is connected and try changing the `camera_index` in `camera.py`
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- **Model files missing:** Run `python download_models.py` to download required files
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- **Slow performance:** Increase `process_every_n_frames` value or reduce video resolution
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- **Double counting:** Increase the `cooldown_seconds` value in `zone_tracker.py`
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## Security & Privacy
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- All processing runs locally (no cloud storage)
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- No face recognition - only detection (no personal identification)
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- Video frames processed in memory, not stored
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- Optional: You can modify the code to blur faces for display if privacy is a concern
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## License
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This project is open source and available for use and modification.
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