EasyPoseis a human pose estimation algorithm library based on Python and Onnxruntime, which integrates commonly used human pose estimation algorithms and can be used after installation. The original intention of developing EasyPose is to enable developers to easily use various human pose estimation algorithms for their own tasks. Therefore, EasyPose has fewer third-party dependencies and is more convenient to use. When developing a human pose estimation application using EasyPose, the program automatically downloads the corresponding weight file from the server and only requires less than ten lines of code to obtain the algorithm's prediction results.
Major features
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Simple operation
EasyPose can quickly call various human pose estimation algorithms with simple commands and supports custom models, greatly facilitating developers to quickly use algorithms.
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Supports multiple models
EasyPose supports various human pose estimation models, including HRNet and RTMPose, as well as human detection models such as RTMDet and YOLOv8.
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Fast speed
The model implements the GPU version and can quickly call algorithms within the GPU.
1、Install anaconda and create a new virtual environment
conda create -n easypose python=3.82、Clone the repository and install EasyPose
git clone https://github.com/Dominic23331/EasyPose.git
pip install -v -e .
3、Verify installation
import easypose as ep
print(ep.pose_model_list())
| Model | Input Size | AP | PARAMS | GFLOPS |
|---|---|---|---|---|
| RTMDet-tiny | 640x640 | 41.1 | 4.8 | 8.1 |
| RTMDet-s | 640x640 | 44.6 | 8.89 | 14.8 |
| YOLOv8-n | 640x640 | 37.3 | 3.2 | 8.7 |
| YOLOv8-s | 640x640 | 44.9 | 11.2 | 28.6 |
The above model is taken from mmdetection and ultralytics.
| Model | Input Size | AP | AR |
|---|---|---|---|
| RTMPose-tiny | 256x192 | 68.2 | 73.6 |
| RTMPose-s | 256x192 | 71.6 | 76.8 |
| RTMPose-m | 256x192 | 74.6 | 79.5 |
| RTMPose-l | 256x192 | 75.8 | 80.6 |
| ResNet50-SimCC | 256x192 | 72.1 | 78.1 |
| ResNet50-Heatmap | 256x192 | 72.0 | 77.5 |
| HRNet-Heatmap | 256x192 | 74.9 | 80.4 |
| HRNet-Dark | 256x192 | 75.7 | 80.7 |
| Hourglass | 256x192 | 72.6 | 78.0 |
| Lite-HRNet-Heatmap | 256x192 | 64.2 | 70.5 |
| MobileNetv2-Heatmap | 256x192 | 64.8 | 70.9 |
| MobileNetv2-SimCC | 256x192 | 62.0 | 67.8 |
The above model is taken from mmpose。
1、Importing the EasyPose and OpenCV libraries
import easypose as ep
import cv22、Instantiating the TopDown model
model = ep.TopDown('rtmpose_s', 'SimCC', 'rtmdet_s')3、Using the predict function to predict input images
image = cv2.imread('img.jpg')
poses = model.predict(image)4、Draw key points of the human body in the image
image = ep.draw_keypoints(image, poses)EasyPose follows Apache 2.0 open source license.
- Add more TopDown human pose estimation algorithms
- Add some one-stage human pose estimation algorithms
- Optimize the speed of existing models and add quantitative models
- Writing instructional documents
- Publish wheel files in pypi
- Support whole body pose estimation algorithms
- Support MPII dataset
- Suppoty animal pose estimation algorithms