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from __future__ import annotations |
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import os |
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import pathlib |
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import shlex |
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import subprocess |
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if os.getenv("SYSTEM") == "spaces": |
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subprocess.run(shlex.split("pip install click==7.1.2")) |
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subprocess.run(shlex.split("pip install typer==0.9.4")) |
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import mim |
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mim.uninstall("mmcv-full", confirm_yes=True) |
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mim.install("mmcv-full==1.5.0", is_yes=True) |
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subprocess.run(shlex.split("pip uninstall -y opencv-python")) |
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subprocess.run(shlex.split("pip uninstall -y opencv-python-headless")) |
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subprocess.run(shlex.split("pip install opencv-python-headless==4.8.0.74")) |
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with open("patch") as f: |
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subprocess.run(shlex.split("patch -p1"), cwd="CBNetV2", stdin=f) |
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subprocess.run("mv palette.py CBNetV2/mmdet/core/visualization/".split()) |
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import gradio as gr |
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from model import Model |
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DESCRIPTION = "# [CBNetV2](https://github.com/VDIGPKU/CBNetV2)" |
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model = Model() |
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with gr.Blocks(css="style.css") as demo: |
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gr.Markdown(DESCRIPTION) |
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with gr.Row(): |
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with gr.Column(): |
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with gr.Row(): |
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input_image = gr.Image(label="Input Image", type="numpy") |
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with gr.Row(): |
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detector_name = gr.Dropdown( |
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label="Detector", choices=list(model.models.keys()), value=model.model_name |
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) |
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with gr.Row(): |
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detect_button = gr.Button("Detect") |
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detection_results = gr.State() |
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with gr.Column(): |
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with gr.Row(): |
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detection_visualization = gr.Image(label="Detection Result", type="numpy") |
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with gr.Row(): |
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visualization_score_threshold = gr.Slider( |
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label="Visualization Score Threshold", minimum=0, maximum=1, step=0.05, value=0.3 |
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) |
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with gr.Row(): |
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redraw_button = gr.Button("Redraw") |
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with gr.Row(): |
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paths = sorted(pathlib.Path("images").rglob("*.jpg")) |
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gr.Examples(examples=[[path.as_posix()] for path in paths], inputs=input_image) |
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detector_name.change(fn=model.set_model_name, inputs=detector_name) |
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detect_button.click( |
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fn=model.detect_and_visualize, |
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inputs=[ |
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input_image, |
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visualization_score_threshold, |
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], |
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outputs=[ |
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detection_results, |
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detection_visualization, |
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], |
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) |
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redraw_button.click( |
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fn=model.visualize_detection_results, |
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inputs=[ |
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input_image, |
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detection_results, |
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visualization_score_threshold, |
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], |
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outputs=detection_visualization, |
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) |
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if __name__ == "__main__": |
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demo.queue(max_size=10).launch() |
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