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YoloZone

YoloZone

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.

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Features

Object Detection

  • Detect and classify objects in images and videos
  • Support for custom models and multiple detection strategies
  • Real-time processing capabilities
  • Configurable confidence thresholds

Pose Estimation

  • 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

Object Tracking

  • Robust object tracking across video frames
  • Motion pattern analysis
  • Trajectory data generation
  • Line crossing detection
  • Multi-object tracking

Installation

pip install yolozone

Quick Start

Object Detection

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')

Pose Estimation

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")

Object Tracking

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}")

Keypoint Reference

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

Documentation

Visit our comprehensive documentation for:

  • Detailed API references
  • Code examples
  • Implementation guides
  • Best practices
  • Troubleshooting tips

Requirements

  • Python 3.7+
  • ultralytics (YOLOv8)
  • opencv-python
  • numpy

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Author

References

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