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| import importlib | |
| import os | |
| import gradio as gr | |
| from annotator.util import resize_image, HWC3 | |
| config = { | |
| "canny": "CannyDetector", | |
| "hed": "HedDetector", | |
| "mlsd": "MLSDProcessor", | |
| "midas": "MidasProcessor", | |
| "openpose": "OpenposeDetector", | |
| "uniformer": "UniformerDetector" | |
| } | |
| package_annotator = "annotator" | |
| def process_image(cls: str, img, res, *kwargs): | |
| img = resize_image(HWC3(img), res) | |
| # load_model() | |
| module_imp = importlib.import_module(package_annotator) | |
| model = getattr(module_imp, cls) | |
| image_processor = model() | |
| result = image_processor(img, *kwargs) | |
| if type(result) == tuple: | |
| return result | |
| return [result] | |
| def process(cls): | |
| def process_fc(img, res, *args): | |
| return process_image(cls, img, res, *args) | |
| return process_fc | |
| block = gr.Blocks().queue() | |
| examples = [os.path.join(os.path.dirname(__file__), "examples/demo.jpeg")] | |
| with block: | |
| with gr.Tab("Canny Edge"): | |
| with gr.Row(): | |
| gr.Markdown("## Canny Edge") | |
| with gr.Row(): | |
| with gr.Column(): | |
| input_image = gr.Image(source='upload', type="numpy") | |
| low_threshold = gr.Slider(label="low_threshold", minimum=1, maximum=255, value=100, step=1) | |
| high_threshold = gr.Slider(label="high_threshold", minimum=1, maximum=255, value=200, step=1) | |
| resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=512, step=64) | |
| run_button = gr.Button(label="Run") | |
| gr.Examples(examples, input_image) | |
| with gr.Column(): | |
| gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto") | |
| run_button.click(fn=process(config["canny"]), inputs=[input_image, resolution, low_threshold, high_threshold], | |
| outputs=[gallery]) | |
| with gr.Tab("HED Edge"): | |
| with gr.Row(): | |
| gr.Markdown("## HED Edge") | |
| with gr.Row(): | |
| with gr.Column(): | |
| input_image = gr.Image(source='upload', type="numpy") | |
| resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=512, step=64) | |
| run_button = gr.Button(label="Run") | |
| gr.Examples(examples, input_image) | |
| with gr.Column(): | |
| gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto") | |
| run_button.click(fn=process(config["hed"]), inputs=[input_image, resolution], outputs=[gallery]) | |
| with gr.Tab("MLSD Edge"): | |
| with gr.Row(): | |
| gr.Markdown("## MLSD Edge") | |
| with gr.Row(): | |
| with gr.Column(): | |
| input_image = gr.Image(source='upload', type="numpy") | |
| value_threshold = gr.Slider(label="value_threshold", minimum=0.01, maximum=2.0, value=0.1, step=0.01) | |
| distance_threshold = gr.Slider(label="distance_threshold", minimum=0.01, maximum=20.0, value=0.1, | |
| step=0.01) | |
| resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=384, step=64) | |
| run_button = gr.Button(label="Run") | |
| gr.Examples(examples, input_image) | |
| with gr.Column(): | |
| gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto") | |
| run_button.click(fn=process(config["mlsd"]), | |
| inputs=[input_image, resolution, value_threshold, distance_threshold], | |
| outputs=[gallery]) | |
| with gr.Tab("MIDAS Depth and Normal"): | |
| with gr.Row(): | |
| gr.Markdown("## MIDAS Depth and Normal") | |
| with gr.Row(): | |
| with gr.Column(): | |
| input_image = gr.Image(source='upload', type="numpy") | |
| alpha = gr.Slider(label="alpha", minimum=0.1, maximum=20.0, value=6.2, step=0.01) | |
| resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=384, step=64) | |
| run_button = gr.Button(label="Run") | |
| gr.Examples(examples, input_image) | |
| with gr.Column(): | |
| gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto") | |
| run_button.click(fn=process(config["midas"]), inputs=[input_image, resolution, alpha], outputs=[gallery]) | |
| with gr.Tab("Openpose"): | |
| with gr.Row(): | |
| gr.Markdown("## Openpose") | |
| with gr.Row(): | |
| with gr.Column(): | |
| input_image = gr.Image(source='upload', type="numpy") | |
| hand = gr.Checkbox(label='detect hand', value=False) | |
| resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=512, step=64) | |
| run_button = gr.Button(label="Run") | |
| gr.Examples(examples, input_image) | |
| with gr.Column(): | |
| gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto") | |
| run_button.click(fn=process(config["openpose"]), inputs=[input_image, resolution, hand], outputs=[gallery]) | |
| with gr.Tab("Uniformer Segmentation"): | |
| with gr.Row(): | |
| gr.Markdown("## Uniformer Segmentation") | |
| with gr.Row(): | |
| with gr.Column(): | |
| input_image = gr.Image(source='upload', type="numpy") | |
| resolution = gr.Slider(label="resolution", minimum=256, maximum=1024, value=512, step=64) | |
| run_button = gr.Button(label="Run") | |
| gr.Examples(examples, input_image) | |
| with gr.Column(): | |
| gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto") | |
| run_button.click(fn=process(config["uniformer"]), inputs=[input_image, resolution], outputs=[gallery]) | |
| block.launch() | |