Metadata-Version: 2.2
Name: amr_x2_pose
Version: 0.1.1
Author: AMR
Author-email: your.email@example.com
License: MIT
Keywords: pose-estimation 3d-pose mediapipe computer-vision
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Topic :: Scientific/Engineering :: Image Processing
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
Requires-Dist: opencv-python
Requires-Dist: mediapipe
Requires-Dist: numpy
Requires-Dist: matplotlib
Requires-Dist: filterpy
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: keywords
Dynamic: license
Dynamic: requires-dist
Dynamic: requires-python

# amr_x2_pose

amr_x2_pose is a Python package for robust human pose estimation, featuring adaptive frame preprocessing and occlusion-aware landmark recovery to enhance accuracy in real-time applications.

## Features
- **Real-time 3D Pose Estimation** – Enables accurate tracking of human posture.
- **Advanced Occlusion Handling** – Recovers hidden landmarks with intelligent interpolation.
- **Multi-Hypothesis Tracking** – Improves stability by evaluating multiple pose possibilities.
- **Adaptive Image Preprocessing** – Enhances frames dynamically for better pose detection.
- **Anatomical Constraints Enforcement** – Ensures natural movement and joint positioning.
- **Temporal Smoothing & Consistency Checks** – Reduces flickering and ensures smooth tracking.

## Installation
To install amr_x2_pose, simply use:
```bash
pip install amr_x2_pose
````
## Usage

# Initialize pose estimation
from amr_x2_pose import occluded_landmarks_handler

# Basic usage with webcam
occluded_landmarks_handler(
    use_webcam=True,
    camera_index=0,
    show_pose=True
)

# Advanced usage with custom settings
```python
from amr_x2_pose import occluded_landmarks_handler

# Basic usage with webcam
occluded_landmarks_handler(
    use_webcam=True,
    camera_index=0,
    show_pose=True
)

# Advanced usage with custom settings
occluded_landmarks_handler(
    use_webcam=False,
    video_path="path/to/video.mp4",
    device='gpu',
    target_fps=30,
    enable_adaptive_preprocessing=True,
    output_video_path="output.mp4",
    extract_landmarks=True,
    save_landmarks_to_file="landmarks.json"
)
```

## Configuration
amr_x2_pose provides a flexible configuration file (settings.py) where you can customize:

- **Image Processing Parameters** – Adjust brightness, contrast, and sharpening.  
- **Tracking Settings** – Modify visibility thresholds and confidence decay rates.  
- **Occlusion Handling Thresholds** – Fine-tune how the model deals with missing landmarks.  
- **Visualization Options** – Enable or disable 2D/3D rendering.  
## Documentation
For a detailed API reference and additional usage examples, please visit our [Documentation](Replace with actual link).  

## Performance Optimization
- **CPU Mode** – Suitable for standard applications, maintaining 30 FPS with minimal requirements.  
- **GPU Mode** – Recommended for high-resolution video processing and multi-person tracking.  
- **Adaptive Preprocessing** – Dynamically adjusts image settings to improve tracking accuracy.  

## Common Use Cases
- **Real-Time Motion Capture** – Track human movements with webcam-based pose detection.  
- **Video Analysis** – Process pre-recorded videos for in-depth pose analytics.  
- **Research Applications** – Extract landmark data for AI models and biomechanical studies.  

## Troubleshooting
### Common Issues & Solutions  

#### Poor Tracking Quality  
- Ensure proper lighting.  
- Adjust `VISIBILITY_THRESHOLD` in settings.  
- Enable adaptive preprocessing.  

#### Performance Lag  
- Switch to GPU mode if available.  
- Reduce target FPS or resolution.  
- Optimize image preprocessing settings.  

## Contributing
We welcome contributions! Please see our contribution guidelines for more details.  

**Rendered Output (Python Syntax Highlighting):**  


