{"id":24880676,"url":"https://github.com/aryansk/human-counting-system","last_synced_at":"2026-04-19T13:32:11.396Z","repository":{"id":272934197,"uuid":"918224637","full_name":"aryansk/Human-Counting-System","owner":"aryansk","description":"HumanVision-Detector is a real-time human detection system built with OpenCV and HOG (Histogram of Oriented Gradients) descriptor. 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Live video stream analysis\n  - Image file processing\n  - Multiple person detection\n  - Accurate counting mechanism\n- **Visualization**\n  - Bounding box rendering\n  - Person count display\n  - Status indicators\n  - Progress tracking\n\n### 🎯 Detection Capabilities\n- **Input Handling**\n  - Video stream support\n  - Image file processing\n  - Camera feed integration\n  - Batch processing\n- **Output Options**\n  - Annotated video saving\n  - Processed image export\n  - Real-time display\n  - Statistics logging\n\n## 🛠 Technical Architecture\n\n### System Flow\n```mermaid\ngraph TD\n    A[Input Source] --\u003e B[Frame Processor]\n    B --\u003e C[HOG Detector]\n    C --\u003e D[Person Detection]\n    D --\u003e E[Counting System]\n    E --\u003e F[Visualization]\n    F --\u003e G[Output Handler]\n    G --\u003e H1[Video Output]\n    G --\u003e H2[Image Output]\n```\n\n### Dependencies\n```python\n# requirements.txt\nopencv-python\u003e=4.5.0\nnumpy\u003e=1.19.0\nimutils\u003e=0.5.4\nargparse\u003e=1.4.0\n```\n\n## 💻 Installation \u0026 Setup\n\n### System Requirements\n- **Minimum Specifications**\n  - Python 3.6+\n  - 4GB RAM\n  - 2GB storage\n- **Recommended Specifications**\n  - Python 3.8+\n  - 8GB RAM\n  - 4GB storage\n  - CUDA-compatible GPU\n\n### Quick Start\n```bash\n# Clone repository\ngit clone https://github.com/yourusername/HumanVision-Detector.git\n\n# Navigate to project\ncd HumanVision-Detector\n\n# Create virtual environment\npython -m venv venv\nsource venv/bin/activate  # Linux/Mac\n.\\venv\\Scripts\\activate   # Windows\n\n# Install dependencies\npip install -r requirements.txt\n```\n\n### Configuration\n```python\n# config.py\nCONFIG = {\n    'detection': {\n        'winStride': (4, 4),\n        'padding': (8, 8),\n        'scale': 1.03,\n        'confidence_threshold': 0.5\n    },\n    'processing': {\n        'frame_width': 640,\n        'frame_height': 480,\n        'fps': 30\n    },\n    'output': {\n        'show_boxes': True,\n        'show_count': True,\n        'save_output': True\n    }\n}\n```\n\n## 🚀 Usage Guide\n\n### Core Implementation\n```python\nfrom human_detector import HumanDetector\n\n# Initialize detector\ndetector = HumanDetector(config=CONFIG)\n\n# Process image\ndef process_image(image_path):\n    result = detector.detect_from_image(image_path)\n    detector.save_output(result, 'output_image.jpg')\n\n# Process video\ndef process_video(video_path):\n    detector.detect_from_video(video_path,\n                             output_path='output_video.avi')\n```\n\n### Advanced Usage\n```python\n# Custom detection parameters\ndetector.set_parameters(\n    winStride=(4, 4),\n    padding=(8, 8),\n    scale=1.05\n)\n\n# Real-time camera feed\ndetector.start_live_detection(\n    camera_id=0,\n    display_output=True\n)\n\n# Batch processing\ndetector.process_batch(\n    input_directory='input_folder',\n    output_directory='output_folder'\n)\n```\n\n## 🔍 Implementation Details\n\n### Detection Pipeline\n```python\nclass HumanDetector:\n    def __init__(self, config):\n        self.hog = cv2.HOGDescriptor()\n        self.hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())\n        self.config = config\n\n    def detect_people(self, frame):\n        \"\"\"\n        Detects people in a given frame using HOG descriptor.\n        \n        Args:\n            frame (numpy.ndarray): Input frame\n            \n        Returns:\n            tuple: Detected regions and weights\n        \"\"\"\n        # Resize frame for optimal performance\n        frame = imutils.resize(frame, \n                             width=self.config['processing']['frame_width'])\n        \n        # Detect people\n        regions, weights = self.hog.detectMultiScale(\n            frame,\n            winStride=self.config['detection']['winStride'],\n            padding=self.config['detection']['padding'],\n            scale=self.config['detection']['scale']\n        )\n        \n        return regions, weights\n\n    def draw_detections(self, frame, regions):\n        \"\"\"\n        Draws bounding boxes around detected people.\n        \n        Args:\n            frame (numpy.ndarray): Input frame\n            regions (numpy.ndarray): Detected regions\n            \n        Returns:\n            numpy.ndarray: Annotated frame\n        \"\"\"\n        for (x, y, w, h) in regions:\n            cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)\n        \n        cv2.putText(frame, f'People: {len(regions)}', \n                   (10, 30), cv2.FONT_HERSHEY_SIMPLEX, \n                   1, (0, 255, 0), 2)\n        \n        return frame\n```\n\n## ⚡ Performance Optimization\n\n### Processing Techniques\n- Frame resizing for speed\n- Stride optimization\n- Scale factor tuning\n- GPU acceleration (when available)\n\n### Benchmarks\n| Resolution | FPS | CPU Usage | Memory Usage |\n|------------|-----|-----------|--------------|\n| 640x480    | 25  | 45%      | 250MB        |\n| 1280x720   | 15  | 65%      | 450MB        |\n| 1920x1080  | 8   | 85%      | 750MB        |\n\n## 👨‍💻 Development\n\n### Project Structure\n```\nHumanVision-Detector/\n├── data/\n│   ├── samples/\n│   └── test_cases/\n├── src/\n│   ├── detector.py\n│   ├── visualizer.py\n│   └── utils.py\n├── tests/\n│   └── test_detector.py\n├── config.py\n├── requirements.txt\n└── README.md\n```\n\n### Testing\n```bash\n# Run all tests\npython -m pytest\n\n# Run specific test file\npython -m pytest tests/test_detector.py\n\n# Run with coverage\npython -m pytest --cov=src\n```\n\n## 🤝 Contributing\n\n### Development Process\n1. Fork repository\n2. Create feature branch\n3. Implement changes\n4. Add tests\n5. Submit pull request\n\n### Code Style Guidelines\n- Follow PEP 8\n- Document all functions\n- Write comprehensive tests\n- Maintain clean code structure\n\n## 📄 License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## 🙏 Acknowledgments\n\n- OpenCV community\n- HOG descriptor developers\n- Computer vision researchers\n- Open source contributors\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faryansk%2Fhuman-counting-system","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Faryansk%2Fhuman-counting-system","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Faryansk%2Fhuman-counting-system/lists"}