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| #!/usr/bin/env python | |
| from __future__ import annotations | |
| import os | |
| import pathlib | |
| import subprocess | |
| import tarfile | |
| if os.getenv('SYSTEM') == 'spaces': | |
| import mim | |
| mim.uninstall('mmcv-full', confirm_yes=True) | |
| mim.install('mmcv-full==1.5.2', 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 | |
| DESCRIPTION = '''# MMDetection | |
| This is an unofficial demo for [https://github.com/open-mmlab/mmdetection](https://github.com/open-mmlab/mmdetection). | |
| <img id="overview" alt="overview" src="https://user-images.githubusercontent.com/12907710/137271636-56ba1cd2-b110-4812-8221-b4c120320aa9.png" /> | |
| ''' | |
| DEFAULT_MODEL_TYPE = 'detection' | |
| DEFAULT_MODEL_NAMES = { | |
| 'detection': 'YOLOX-l', | |
| 'instance_segmentation': 'QueryInst (R-50-FPN)', | |
| 'panoptic_segmentation': 'MaskFormer (R-50)', | |
| } | |
| DEFAULT_MODEL_NAME = DEFAULT_MODEL_NAMES[DEFAULT_MODEL_TYPE] | |
| def extract_tar() -> None: | |
| if pathlib.Path('mmdet_configs/configs').exists(): | |
| return | |
| with tarfile.open('mmdet_configs/configs.tar') as f: | |
| f.extractall('mmdet_configs') | |
| 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]) | |
| extract_tar() | |
| model = AppModel(DEFAULT_MODEL_NAME) | |
| 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.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(list( | |
| model.DETECTION_MODEL_DICT.keys()), | |
| 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('*.jpg')) | |
| example_images = gr.Dataset(components=[input_image], | |
| samples=[[path.as_posix()] | |
| for path in paths]) | |
| 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.queue().launch(show_api=False) | |