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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.Group():
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.State()
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.Group():
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.State()
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)
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)
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,
)
if __name__ == "__main__":
demo.queue(max_size=10).launch()
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