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#!/usr/bin/env python
from __future__ import annotations
import os
import pathlib
import shlex
import subprocess
import tarfile
if os.getenv("SYSTEM") == "spaces":
subprocess.run(shlex.split("pip install click==7.1.2"))
subprocess.run(shlex.split("pip install typer==0.9.4"))
import mim
mim.uninstall("mmcv-full", confirm_yes=True)
mim.install("mmcv-full==1.5.0", is_yes=True)
subprocess.call(shlex.split("pip uninstall -y opencv-python"))
subprocess.call(shlex.split("pip uninstall -y opencv-python-headless"))
subprocess.call(shlex.split("pip install opencv-python-headless==4.8.0.74"))
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.State()
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,
)
if __name__ == "__main__":
demo.queue(max_size=10).launch()
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