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#!/usr/bin/env python
from __future__ import annotations
import argparse
import pathlib
import tarfile
import gradio as gr
from model import AppModel
DESCRIPTION = '''# ViTPose
This is an unofficial demo for [https://github.com/ViTAE-Transformer/ViTPose](https://github.com/ViTAE-Transformer/ViTPose).
Related app: [https://huggingface.co/spaces/Gradio-Blocks/ViTPose](https://huggingface.co/spaces/Gradio-Blocks/ViTPose)
'''
FOOTER = '<img id="visitor-badge" alt="visitor badge" src="https://visitor-badge.glitch.me/badge?page_id=hysts.vitpose_video" />'
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument('--device', type=str, default='cpu')
parser.add_argument('--theme', type=str)
parser.add_argument('--share', action='store_true')
parser.add_argument('--port', type=int)
parser.add_argument('--disable-queue',
dest='enable_queue',
action='store_false')
return parser.parse_args()
def set_example_video(example: list) -> dict:
return gr.Video.update(value=example[0])
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')
def main():
args = parse_args()
extract_tar()
model = AppModel(device=args.device)
with gr.Blocks(theme=args.theme, 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')
with gr.Group():
detector_name = gr.Dropdown(
list(model.det_model.MODEL_DICT.keys()),
value=model.det_model.model_name,
label='Detector')
pose_model_name = gr.Dropdown(
list(model.pose_model.MODEL_DICT.keys()),
value=model.pose_model.model_name,
label='Pose Model')
det_score_threshold = gr.Slider(
0,
1,
step=0.05,
value=0.5,
label='Box Score Threshold')
max_num_frames = gr.Slider(
1,
300,
step=1,
value=60,
label='Maximum Number of Frames')
predict_button = gr.Button(value='Predict')
pose_preds = gr.Variable()
paths = sorted(pathlib.Path('videos').rglob('*.mp4'))
example_videos = gr.Dataset(components=[input_video],
samples=[[path.as_posix()]
for path in paths])
with gr.Column():
with gr.Group():
result = gr.Video(label='Result',
format='mp4',
elem_id='result')
vis_kpt_score_threshold = gr.Slider(
0,
1,
step=0.05,
value=0.3,
label='Visualization Score Threshold')
vis_dot_radius = gr.Slider(1,
10,
step=1,
value=4,
label='Dot Radius')
vis_line_thickness = gr.Slider(1,
10,
step=1,
value=2,
label='Line Thickness')
redraw_button = gr.Button(value='Redraw')
gr.Markdown(FOOTER)
detector_name.change(fn=model.det_model.set_model,
inputs=detector_name,
outputs=None)
pose_model_name.change(fn=model.pose_model.set_model,
inputs=pose_model_name,
outputs=None)
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)
example_videos.click(fn=set_example_video,
inputs=example_videos,
outputs=input_video)
demo.launch(
enable_queue=args.enable_queue,
server_port=args.port,
share=args.share,
)
if __name__ == '__main__':
main()
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