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| #!/usr/bin/env python | |
| from __future__ import annotations | |
| import pathlib | |
| import tarfile | |
| import gradio as gr | |
| from model import AppModel | |
| DESCRIPTION = '''# [ViTPose](https://github.com/ViTAE-Transformer/ViTPose) | |
| Related app: [https://huggingface.co/spaces/Gradio-Blocks/ViTPose](https://huggingface.co/spaces/Gradio-Blocks/ViTPose) | |
| ''' | |
| def extract_tar() -> None: | |
| if pathlib.Path('mmdet_configs/configs').exists(): | |
| return | |
| with tarfile.open('mmdet_configs/configs.tar') as f: | |
| f.extractall('mmdet_configs') | |
| extract_tar() | |
| model = AppModel() | |
| with gr.Blocks(css='style.css') as demo: | |
| gr.Markdown(DESCRIPTION) | |
| with gr.Row(): | |
| with gr.Column(): | |
| input_video = gr.Video(label='Input Video', | |
| format='mp4', | |
| elem_id='input_video') | |
| detector_name = gr.Dropdown(label='Detector', | |
| choices=list( | |
| model.det_model.MODEL_DICT.keys()), | |
| value=model.det_model.model_name) | |
| pose_model_name = gr.Dropdown( | |
| label='Pose Model', | |
| choices=list(model.pose_model.MODEL_DICT.keys()), | |
| value=model.pose_model.model_name) | |
| det_score_threshold = gr.Slider(label='Box Score Threshold', | |
| minimum=0, | |
| maximum=1, | |
| step=0.05, | |
| value=0.5) | |
| max_num_frames = gr.Slider(label='Maximum Number of Frames', | |
| minimum=1, | |
| maximum=300, | |
| step=1, | |
| value=60) | |
| predict_button = gr.Button('Predict') | |
| pose_preds = gr.Variable() | |
| paths = sorted(pathlib.Path('videos').rglob('*.mp4')) | |
| gr.Examples(examples=[[path.as_posix()] for path in paths], | |
| inputs=input_video) | |
| with gr.Column(): | |
| result = gr.Video(label='Result', format='mp4', elem_id='result') | |
| vis_kpt_score_threshold = gr.Slider( | |
| label='Visualization Score Threshold', | |
| minimum=0, | |
| maximum=1, | |
| step=0.05, | |
| value=0.3) | |
| vis_dot_radius = gr.Slider(label='Dot Radius', | |
| minimum=1, | |
| maximum=10, | |
| step=1, | |
| value=4) | |
| vis_line_thickness = gr.Slider(label='Line Thickness', | |
| minimum=1, | |
| maximum=10, | |
| step=1, | |
| value=2) | |
| redraw_button = gr.Button('Redraw') | |
| detector_name.change(fn=model.det_model.set_model, inputs=detector_name) | |
| pose_model_name.change(fn=model.pose_model.set_model, | |
| inputs=pose_model_name) | |
| predict_button.click(fn=model.run, | |
| inputs=[ | |
| input_video, | |
| detector_name, | |
| pose_model_name, | |
| det_score_threshold, | |
| max_num_frames, | |
| vis_kpt_score_threshold, | |
| vis_dot_radius, | |
| vis_line_thickness, | |
| ], | |
| outputs=[ | |
| result, | |
| pose_preds, | |
| ]) | |
| redraw_button.click(fn=model.visualize_pose_results, | |
| inputs=[ | |
| input_video, | |
| pose_preds, | |
| vis_kpt_score_threshold, | |
| vis_dot_radius, | |
| vis_line_thickness, | |
| ], | |
| outputs=result) | |
| demo.queue(max_size=10).launch() | |