| import gradio as gr | |
| from annotator.util import resize_image, HWC3 | |
| model_canny = None | |
| def canny(img, res, l, h): | |
| img = resize_image(HWC3(img), res) | |
| global model_canny | |
| if model_canny is None: | |
| from annotator.canny import apply_canny | |
| model_canny = apply_canny | |
| result = model_canny(img, l, h) | |
| return [result] | |
| model_hed = None | |
| def hed(img, res): | |
| img = resize_image(HWC3(img), res) | |
| global model_hed | |
| if model_hed is None: | |
| from annotator.hed import apply_hed | |
| model_hed = apply_hed | |
| result = model_hed(img) | |
| return [result] | |
| model_mlsd = None | |
| def mlsd(img, res, thr_v, thr_d): | |
| img = resize_image(HWC3(img), res) | |
| global model_mlsd | |
| if model_mlsd is None: | |
| from annotator.mlsd import apply_mlsd | |
| model_mlsd = apply_mlsd | |
| result = model_mlsd(img, thr_v, thr_d) | |
| return [result] | |
| model_midas = None | |
| def midas(img, res, a): | |
| img = resize_image(HWC3(img), res) | |
| global model_midas | |
| if model_midas is None: | |
| from annotator.midas import apply_midas | |
| model_midas = apply_midas | |
| results = model_midas(img, a) | |
| return results | |
| model_openpose = None | |
| def openpose(img, res, has_hand): | |
| img = resize_image(HWC3(img), res) | |
| global model_openpose | |
| if model_openpose is None: | |
| from annotator.openpose import apply_openpose | |
| model_openpose = apply_openpose | |
| result, _ = model_openpose(img, has_hand) | |
| return [result] | |
| model_uniformer = None | |
| def uniformer(img, res): | |
| img = resize_image(HWC3(img), res) | |
| global model_uniformer | |
| if model_uniformer is None: | |
| from annotator.uniformer import apply_uniformer | |
| model_uniformer = apply_uniformer | |
| result = model_uniformer(img) | |
| return [result] | |
| block = gr.Blocks().queue() | |
| with block: | |
| 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") | |
| with gr.Column(): | |
| gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto") | |
| run_button.click(fn=canny, inputs=[input_image, resolution, low_threshold, high_threshold], outputs=[gallery]) | |
| 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") | |
| with gr.Column(): | |
| gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto") | |
| run_button.click(fn=hed, inputs=[input_image, resolution], outputs=[gallery]) | |
| 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") | |
| with gr.Column(): | |
| gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto") | |
| run_button.click(fn=mlsd, inputs=[input_image, resolution, value_threshold, distance_threshold], outputs=[gallery]) | |
| 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") | |
| with gr.Column(): | |
| gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto") | |
| run_button.click(fn=midas, inputs=[input_image, resolution, alpha], outputs=[gallery]) | |
| 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") | |
| with gr.Column(): | |
| gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto") | |
| run_button.click(fn=openpose, inputs=[input_image, resolution, hand], outputs=[gallery]) | |
| 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") | |
| with gr.Column(): | |
| gallery = gr.Gallery(label="Generated images", show_label=False).style(height="auto") | |
| run_button.click(fn=uniformer, inputs=[input_image, resolution], outputs=[gallery]) | |
| block.launch(server_name='0.0.0.0') | |