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#!/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()
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