YoloZone is a powerful computer vision toolkit built on YOLOv8, providing intuitive interfaces for object detection, pose estimation, and object tracking. It simplifies complex computer vision tasks with easy-to-use APIs and comprehensive documentation.
- Detect and classify objects in images and videos
- Support for custom models and multiple detection strategies
- Real-time processing capabilities
- Configurable confidence thresholds
- Advanced human pose detection and keypoint analysis
- Real-time pose tracking
- 17-point keypoint detection
- Angle and distance measurements between keypoints
- Support for multiple people in frame
- Robust object tracking across video frames
- Motion pattern analysis
- Trajectory data generation
- Line crossing detection
- Multi-object tracking
pip install yolozone
from yolozone import Objects
# Initialize detector
detector = Objects()
# Detect objects in an image
results = detector.detect('image.jpg')
# Process video stream
detector.process_video('video.mp4', output='output.mp4')
from yolozone import Pose
# Initialize pose estimator
pose = Pose()
# Detect poses in an image
results = pose.detect('image.jpg')
# Get keypoints
for detection in results:
keypoints = pose.get_keypoints(detection)
print(f"Found person with {len(keypoints)} keypoints")
from yolozone import Tracker
# Initialize tracker
tracker = Tracker()
# Track objects in video
tracks = tracker.track_video('video.mp4')
# Analyze motion patterns
for track in tracks:
motion = tracker.analyze_motion(track)
print(f"Track {track.id}: {motion.pattern}")
The pose estimation module uses the following 17 keypoints:
ID | Keypoint | ID | Keypoint |
---|---|---|---|
0 | Nose | 9 | Left Wrist |
1 | Left Eye | 10 | Right Wrist |
2 | Right Eye | 11 | Left Hip |
3 | Left Ear | 12 | Right Hip |
4 | Right Ear | 13 | Left Knee |
5 | Left Shoulder | 14 | Right Knee |
6 | Right Shoulder | 15 | Left Ankle |
7 | Left Elbow | 16 | Right Ankle |
8 | Right Elbow |
Visit our comprehensive documentation for:
- Detailed API references
- Code examples
- Implementation guides
- Best practices
- Troubleshooting tips
- Python 3.7+
- ultralytics (YOLOv8)
- opencv-python
- numpy
Contributions are welcome! Please feel free to submit a Pull Request.
This project is licensed under the MIT License - see the LICENSE file for details.
- Nushan Kodikara
- GitHub: @nushankodikara
- LinkedIn: Nushan Kodikara
- Email: [email protected]