amr-x2-pose 0.1.1

No project description provided

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:

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

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):