| import mmpose | |
| import os | |
| from mmpose.apis import MMPoseInferencer | |
| print("[INFO]: Imported modules!") | |
| import gradio as gr | |
| import numpy as np | |
| inferencer = MMPoseInferencer('human') | |
| print("[INFO]: Downloaded models!") | |
| def poses(photo): | |
| result_generator = inferencer(photo, | |
| vis_out_dir =".", | |
| return_vis=True, | |
| thickness=2) | |
| print("[INFO]: Visualizing results!") | |
| print(os.listdir()) | |
| print(result_generator[0]) | |
| #print("[INFO]: Type of vis is ", type(result_generator['visualization'])) | |
| #print("[INFO]: Vis is ", type(result_generator['visualization'])) | |
| # The MMPoseInferencer API employs a lazy inference approach, | |
| # creating a prediction generator when given input | |
| #result = next(result_generator) | |
| return "000000.mp4" | |
| # # 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' | |
| # ) | |
| def run(): | |
| #https://github.com/open-mmlab/mmpose/blob/main/docs/en/user_guides/inference.md | |
| demo = gr.Interface(fn=poses, | |
| inputs=gr.Video(source="webcam"), | |
| outputs=gr.Video()) | |
| demo.launch(server_name="0.0.0.0", server_port=7860) | |
| if __name__ == "__main__": | |
| run() |