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#!/usr/bin/env python
from __future__ import annotations
import os
import pathlib
import shlex
import subprocess
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.run(shlex.split("pip uninstall -y opencv-python"))
subprocess.run(shlex.split("pip uninstall -y opencv-python-headless"))
subprocess.run(shlex.split("pip install opencv-python-headless==4.8.0.74"))
with open("patch") as f:
subprocess.run(shlex.split("patch -p1"), cwd="CBNetV2", stdin=f)
subprocess.run("mv palette.py CBNetV2/mmdet/core/visualization/".split())
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.State()
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)
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,
)
if __name__ == "__main__":
demo.queue(max_size=10).launch()
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