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| #!/usr/bin/env python | |
| from __future__ import annotations | |
| import pathlib | |
| import tarfile | |
| import gradio as gr | |
| from model import AppDetModel, AppPoseModel | |
| DESCRIPTION = '# [ViTPose](https://github.com/ViTAE-Transformer/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() | |
| det_model = AppDetModel() | |
| pose_model = AppPoseModel() | |
| with gr.Blocks(css='style.css') as demo: | |
| gr.Markdown(DESCRIPTION) | |
| with gr.Box(): | |
| gr.Markdown('## Step 1') | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Row(): | |
| input_image = gr.Image(label='Input Image', type='numpy') | |
| with gr.Row(): | |
| detector_name = gr.Dropdown( | |
| label='Detector', | |
| choices=list(det_model.MODEL_DICT.keys()), | |
| value=det_model.model_name) | |
| with gr.Row(): | |
| detect_button = gr.Button('Detect') | |
| det_preds = gr.Variable() | |
| with gr.Column(): | |
| with gr.Row(): | |
| detection_visualization = gr.Image( | |
| label='Detection Result', | |
| type='numpy', | |
| elem_id='det-result') | |
| with gr.Row(): | |
| vis_det_score_threshold = gr.Slider( | |
| label='Visualization Score Threshold', | |
| minimum=0, | |
| maximum=1, | |
| step=0.05, | |
| value=0.5) | |
| with gr.Row(): | |
| redraw_det_button = gr.Button(value='Redraw') | |
| with gr.Row(): | |
| paths = sorted(pathlib.Path('images').rglob('*.jpg')) | |
| example_images = gr.Examples(examples=[[path.as_posix()] | |
| for path in paths], | |
| inputs=input_image) | |
| with gr.Box(): | |
| gr.Markdown('## Step 2') | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Row(): | |
| pose_model_name = gr.Dropdown( | |
| label='Pose Model', | |
| choices=list(pose_model.MODEL_DICT.keys()), | |
| value=pose_model.model_name) | |
| det_score_threshold = gr.Slider(label='Box Score Threshold', | |
| minimum=0, | |
| maximum=1, | |
| step=0.05, | |
| value=0.5) | |
| with gr.Row(): | |
| predict_button = gr.Button('Predict') | |
| pose_preds = gr.Variable() | |
| with gr.Column(): | |
| with gr.Row(): | |
| pose_visualization = gr.Image(label='Result', | |
| type='numpy', | |
| elem_id='pose-result') | |
| with gr.Row(): | |
| vis_kpt_score_threshold = gr.Slider( | |
| label='Visualization Score Threshold', | |
| minimum=0, | |
| maximum=1, | |
| step=0.05, | |
| value=0.3) | |
| with gr.Row(): | |
| vis_dot_radius = gr.Slider(label='Dot Radius', | |
| minimum=1, | |
| maximum=10, | |
| step=1, | |
| value=4) | |
| with gr.Row(): | |
| vis_line_thickness = gr.Slider(label='Line Thickness', | |
| minimum=1, | |
| maximum=10, | |
| step=1, | |
| value=2) | |
| with gr.Row(): | |
| redraw_pose_button = gr.Button('Redraw') | |
| detector_name.change(fn=det_model.set_model, | |
| inputs=detector_name, | |
| outputs=None) | |
| detect_button.click(fn=det_model.run, | |
| inputs=[ | |
| detector_name, | |
| input_image, | |
| vis_det_score_threshold, | |
| ], | |
| outputs=[ | |
| det_preds, | |
| detection_visualization, | |
| ]) | |
| redraw_det_button.click(fn=det_model.visualize_detection_results, | |
| inputs=[ | |
| input_image, | |
| det_preds, | |
| vis_det_score_threshold, | |
| ], | |
| outputs=detection_visualization) | |
| pose_model_name.change(fn=pose_model.set_model, | |
| inputs=pose_model_name, | |
| outputs=None) | |
| predict_button.click(fn=pose_model.run, | |
| inputs=[ | |
| pose_model_name, | |
| input_image, | |
| det_preds, | |
| det_score_threshold, | |
| vis_kpt_score_threshold, | |
| vis_dot_radius, | |
| vis_line_thickness, | |
| ], | |
| outputs=[ | |
| pose_preds, | |
| pose_visualization, | |
| ]) | |
| redraw_pose_button.click(fn=pose_model.visualize_pose_results, | |
| inputs=[ | |
| input_image, | |
| pose_preds, | |
| vis_kpt_score_threshold, | |
| vis_dot_radius, | |
| vis_line_thickness, | |
| ], | |
| outputs=pose_visualization) | |
| demo.queue(max_size=10).launch() | |