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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()
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