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#!/usr/bin/env python
from __future__ import annotations
import pathlib
import gradio as gr
from model import Model
DESCRIPTION = '# [CBNetV2](https://github.com/VDIGPKU/CBNetV2)'
model = Model()
with gr.Blocks(css='style.css') as demo:
gr.Markdown(DESCRIPTION)
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(model.models.keys()),
value=model.model_name)
with gr.Row():
detect_button = gr.Button('Detect')
detection_results = gr.Variable()
with gr.Column():
with gr.Row():
detection_visualization = gr.Image(label='Detection Result',
type='numpy')
with gr.Row():
visualization_score_threshold = gr.Slider(
label='Visualization Score Threshold',
minimum=0,
maximum=1,
step=0.05,
value=0.3)
with gr.Row():
redraw_button = gr.Button('Redraw')
with gr.Row():
paths = sorted(pathlib.Path('images').rglob('*.jpg'))
gr.Examples(examples=[[path.as_posix()] for path in paths],
inputs=input_image)
detector_name.change(fn=model.set_model_name,
inputs=[detector_name],
outputs=None)
detect_button.click(fn=model.detect_and_visualize,
inputs=[
input_image,
visualization_score_threshold,
],
outputs=[
detection_results,
detection_visualization,
])
redraw_button.click(fn=model.visualize_detection_results,
inputs=[
input_image,
detection_results,
visualization_score_threshold,
],
outputs=[detection_visualization])
demo.queue(max_size=10).launch()
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