# supertracker
[](https://pypi.python.org/pypi/supertracker)
**An easy-to-use library for implementing various multi-object tracking algorithms.**
## Features
- Simple and intuitive interface for multi-object tracking
- Multiple tracking algorithms supported:
- ByteTrack
- DeepSORT (coming soon)
- SORT (coming soon)
- OCSORT (coming soon)
- BoT-SORT (coming soon)
- Support for custom detection formats
- Easy integration with existing detection pipelines
- Modular design for adding new trackers
## Documentation
Visit our [documentation](https://Hirai-Labs.github.io/supertracker) to find:
- Detailed API reference
- Configuration guides for each tracker
- Performance benchmarks
- Integration examples with popular detection models
- Advanced usage tutorials
- Troubleshooting guide
## Installation
```bash
pip install supertracker
```
## Examples
```python
import cv2
from ultralytics import YOLO
from supertracker import ByteTrack
from supertracker import Detections
# Initialize YOLO model and tracker
model = YOLO('yolov8n.pt') # or your custom model
tracker = ByteTrack(
track_activation_threshold=0.25,
lost_track_buffer=30,
frame_rate=30
)
# Initialize video capture
cap = cv2.VideoCapture(0) # or video file path
while True:
ret, frame = cap.read()
if not ret:
break
# Run YOLO detection
results = model(frame)[0]
# Convert YOLO results to Detections format
detections = Detections(
xyxy=results.boxes.xyxy.cpu().numpy(),
confidence=results.boxes.conf.cpu().numpy(),
class_id=results.boxes.cls.cpu().numpy().astype(int)
)
# Update tracker
tracked_objects = tracker.update_with_detections(detections)
# Visualize results
for i in range(len(tracked_objects)):
box = tracked_objects.xyxy[i].astype(int)
track_id = tracked_objects.tracker_id[i]
class_id = tracked_objects.class_id[i]
conf = tracked_objects.confidence[i]
# Draw bounding box
cv2.rectangle(frame, (box[0], box[1]), (box[2], box[3]), (0, 255, 0), 2)
# Draw label with class name, track ID and confidence
label = f"#{track_id} {model.names[class_id]} {conf:.2f}"
cv2.putText(frame, label, (box[0], box[1] - 10),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)
cv2.imshow("Tracking", frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
```
## Contributing
Contributions are welcome! Here's how you can help:
1. Fork the repository
2. Create your feature branch (`git checkout -b feature/amazing-feature`)
3. Commit your changes (`git commit -m 'Add some amazing feature'`)
4. Push to the branch (`git push origin feature/amazing-feature`)
5. Open a Pull Request
## License
This project is licensed under the MIT License - see the LICENSE file for details.
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"description": "# supertracker\n\n[](https://pypi.python.org/pypi/supertracker)\n\n**An easy-to-use library for implementing various multi-object tracking algorithms.**\n\n## Features\n\n- Simple and intuitive interface for multi-object tracking\n- Multiple tracking algorithms supported:\n - ByteTrack\n - DeepSORT (coming soon)\n - SORT (coming soon)\n - OCSORT (coming soon)\n - BoT-SORT (coming soon)\n- Support for custom detection formats\n- Easy integration with existing detection pipelines\n- Modular design for adding new trackers\n\n## Documentation\nVisit our [documentation](https://Hirai-Labs.github.io/supertracker) to find:\n- Detailed API reference\n- Configuration guides for each tracker\n- Performance benchmarks\n- Integration examples with popular detection models\n- Advanced usage tutorials\n- Troubleshooting guide\n\n## Installation\n\n```bash\npip install supertracker\n```\n\n## Examples\n\n```python\nimport cv2\nfrom ultralytics import YOLO\nfrom supertracker import ByteTrack\nfrom supertracker import Detections\n\n# Initialize YOLO model and tracker\nmodel = YOLO('yolov8n.pt') # or your custom model\ntracker = ByteTrack(\n track_activation_threshold=0.25,\n lost_track_buffer=30,\n frame_rate=30\n)\n\n# Initialize video capture\ncap = cv2.VideoCapture(0) # or video file path\n\nwhile True:\n ret, frame = cap.read()\n if not ret:\n break\n\n # Run YOLO detection\n results = model(frame)[0]\n \n # Convert YOLO results to Detections format\n detections = Detections(\n xyxy=results.boxes.xyxy.cpu().numpy(),\n confidence=results.boxes.conf.cpu().numpy(),\n class_id=results.boxes.cls.cpu().numpy().astype(int)\n )\n \n # Update tracker\n tracked_objects = tracker.update_with_detections(detections)\n\n # Visualize results\n for i in range(len(tracked_objects)):\n box = tracked_objects.xyxy[i].astype(int)\n track_id = tracked_objects.tracker_id[i]\n class_id = tracked_objects.class_id[i]\n conf = tracked_objects.confidence[i]\n \n # Draw bounding box\n cv2.rectangle(frame, (box[0], box[1]), (box[2], box[3]), (0, 255, 0), 2)\n \n # Draw label with class name, track ID and confidence\n label = f\"#{track_id} {model.names[class_id]} {conf:.2f}\"\n cv2.putText(frame, label, (box[0], box[1] - 10),\n cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 2)\n\n cv2.imshow(\"Tracking\", frame)\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n\ncap.release()\ncv2.destroyAllWindows()\n```\n\n## Contributing\n\nContributions are welcome! Here's how you can help:\n\n1. Fork the repository\n2. Create your feature branch (`git checkout -b feature/amazing-feature`)\n3. Commit your changes (`git commit -m 'Add some amazing feature'`)\n4. Push to the branch (`git push origin feature/amazing-feature`)\n5. Open a Pull Request\n\n## License\n\nThis project is licensed under the MIT License - see the LICENSE file for details.\n",
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