app.py

#!/usr/bin/env python

from __future__ import annotations

import argparse
import os
import pathlib
import subprocess

if os.getenv("SYSTEM") == "spaces":
    import mim

    mim.uninstall("mmcv-full", confirm_yes=True)
    mim.install("mmcv-full==1.6.1", is_yes=True)

    subprocess.call("pip uninstall -y opencv-python".split())
    subprocess.call("pip uninstall -y opencv-python-headless".split())
    subprocess.call("pip install opencv-python-headless==4.5.5.64".split())

import cv2
import gradio as gr
import numpy as np

from model import AppModel

## Edit and 
DESCRIPTION = """# MMDetection
This is an unofficial demo for [https://github.com/open-mmlab/mmdetection](https://github.com/open-mmlab/mmdetection).
"""
FOOTER = '<img id="visitor-badge" src="https://visitor-badge.glitch.me/badge?page_id=hf-technical-mmdetection" alt="visitor badge" />'

DEFAULT_MODEL_TYPE = "detection"
DEFAULT_MODEL_NAMES = {
    "detection": "faster_rcnn"
}
DEFAULT_MODEL_NAME = DEFAULT_MODEL_NAMES[DEFAULT_MODEL_TYPE]


def parse_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser()
    parser.add_argument("--device", type=str, default="cpu")
    parser.add_argument("--theme", type=str)
    parser.add_argument("--share", action="store_true")
    parser.add_argument("--port", type=int)
    parser.add_argument("--disable-queue", dest="enable_queue", action="store_false")
    return parser.parse_args()



def update_input_image(image: np.ndarray) -> dict:
    if image is None:
        return gr.Image.update(value=None)
    scale = 1500 / max(image.shape[:2])
    if scale < 1:
        image = cv2.resize(image, None, fx=scale, fy=scale)
    return gr.Image.update(value=image)


def update_model_name(model_type: str) -> dict:
    model_dict = getattr(AppModel, f"{model_type.upper()}_MODEL_DICT")
    model_names = list(model_dict.keys())
    model_name = DEFAULT_MODEL_NAMES[model_type]
    return gr.Dropdown.update(choices=model_names, value=model_name)


def update_visualization_score_threshold(model_type: str) -> dict:
    return gr.Slider.update(visible=model_type != "panoptic_segmentation")


def update_redraw_button(model_type: str) -> dict:
    return gr.Button.update(visible=model_type != "panoptic_segmentation")


def set_example_image(example: list) -> dict:
    return gr.Image.update(value=example[0])


def main():
    args = parse_args()
    model = AppModel(DEFAULT_MODEL_NAME, args.device)

    with gr.Blocks(theme=args.theme, 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.Group():
                    with gr.Row():
                        model_type = gr.Radio(
                            list(DEFAULT_MODEL_NAMES.keys()),
                            value=DEFAULT_MODEL_TYPE,
                            label="Model Type",
                        )
                    with gr.Row():
                        model_name = gr.Dropdown(
                            model.model_list(),
                            value=DEFAULT_MODEL_NAME,
                            label="Model",
                        )
                with gr.Row():
                    run_button = gr.Button(value="Run")
                    prediction_results = gr.Variable()
            with gr.Column():
                with gr.Row():
                    visualization = gr.Image(label="Result", type="numpy")
                with gr.Row():
                    visualization_score_threshold = gr.Slider(
                        0,
                        1,
                        step=0.05,
                        value=0.3,
                        label="Visualization Score Threshold",
                    )
                with gr.Row():
                    redraw_button = gr.Button(value="Redraw")

        with gr.Row():
            paths = sorted(pathlib.Path("images").rglob("*.jpeg"))
            example_images = gr.Dataset(
                components=[input_image], samples=[[path.as_posix()] for path in paths]
            )

        gr.Markdown(FOOTER)

        input_image.change(
            fn=update_input_image, inputs=input_image, outputs=input_image
        )

        model_type.change(fn=update_model_name, inputs=model_type, outputs=model_name)
        model_type.change(
            fn=update_visualization_score_threshold,
            inputs=model_type,
            outputs=visualization_score_threshold,
        )
        model_type.change(
            fn=update_redraw_button, inputs=model_type, outputs=redraw_button
        )

        model_name.change(fn=model.set_model, inputs=model_name, outputs=None)
        run_button.click(
            fn=model.run,
            inputs=[
                model_name,
                input_image,
                visualization_score_threshold,
            ],
            outputs=[
                prediction_results,
                visualization,
            ],
        )
        redraw_button.click(
            fn=model.visualize_detection_results,
            inputs=[
                input_image,
                prediction_results,
                visualization_score_threshold,
            ],
            outputs=visualization,
        )
        example_images.click(
            fn=set_example_image, inputs=example_images, outputs=input_image
        )

    demo.launch(
        enable_queue=args.enable_queue,
        server_port=args.port,
        share=args.share,
    )


if __name__ == "__main__":
    main()