| import mmpose | |
| print(mmpose.__version__) | |
| from mmpose.apis import MMPoseInferencer | |
| inferencer = MMPoseInferencer('human') | |
| print("[INFO]: Imported modules!!") | |
| import gradio as gr | |
| def greet(photo): | |
| print("[INFO]: Downloaded models!") | |
| result_generator = inferencer(photo) | |
| print("[INFO]: Visualizing results!") | |
| vis, pred = next(result_generator) | |
| return vis | |
| # # specify detection model by alias | |
| # # the available aliases include 'human', 'hand', 'face', 'animal', | |
| # # as well as any additional aliases defined in mmdet | |
| # inferencer = MMPoseInferencer( | |
| # # suppose the pose estimator is trained on custom dataset | |
| # pose2d='custom_human_pose_estimator.py', | |
| # pose2d_weights='custom_human_pose_estimator.pth', | |
| # det_model='human' | |
| # ) | |
| if __name__ == '__main__': | |
| demo = gr.Interface(fn=greet, inputs=gr.Image(source="webcam"), outputs=gr.Image()) | |
| demo.launch() |