Estimate human poses in images
Generate detailed pose estimates from images
Detect and highlight key joints in an image
Analyze images to detect human poses
Generate pose estimates for humans, vehicles, and animals in images
Analyzez une vidéo de danse et affichez les poses 3D
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Detect and annotate poses in images
Detect... human poses in images
Mediapipe, OpenCV, CVzone simple pose detection
Using our method, given a support image and skeleton we can
Poser TF is a pose estimation tool designed to identify and analyze human body poses within images. It leverages advanced AI technology to detect body keypoints and provide accurate pose estimates. The tool is particularly useful for applications in fitness, gaming, and computer vision, where understanding human movement and posture is essential.
• Real-Time Pose Estimation: Analyze poses in real-time or from static images.
• High Accuracy: Utilizes state-of-the-art models like TensorFlow for precise keypoint detection.
• Multi-Pose Detection: Detect multiple human poses in a single image.
• Customizable: Adjust settings to suit specific use cases or environments.
• Lightweight: Optimized for performance without compromising on accuracy.
• Cross-Platform Compatibility: Supports multiple platforms and frameworks.
• Integration with TensorFlow: Seamlessly integrates with TensorFlow workflows for advanced customization.
Install Poser TF:
pip install posertf
Import the Library:
from posertf import PoseEstimator
Load an Image:
Detect Poses:
estimator = PoseEstimator()
poses = estimator.detect(image)
Visualize Results:
Review Output:
What file formats does Poser TF support?
Poser TF supports common image formats such as JPG, PNG, and BMP.
Can Poser TF handle images with occluded or partially visible poses?
Yes, Poser TF uses advanced algorithms to handle occlusions and partial visibility, though accuracy may vary depending on the severity of occlusion.
How can I improve the accuracy of pose detection?
Ensure high-quality input images and validate that the environment aligns with the model's training data. Additionally, fine-tuning the model with custom datasets can improve accuracy for specific use cases.