| import gradio as gr | |
| from demo import automask_image_app, automask_video_app, sahi_autoseg_app | |
| def image_app(): | |
| with gr.Blocks(): | |
| with gr.Row(): | |
| with gr.Column(): | |
| seg_automask_image_file = gr.Image(type="filepath").style(height=260) | |
| with gr.Row(): | |
| with gr.Column(): | |
| seg_automask_image_model_type = gr.Dropdown( | |
| choices=[ | |
| "vit_h", | |
| "vit_l", | |
| "vit_b", | |
| ], | |
| value="vit_l", | |
| label="Model Type", | |
| ) | |
| seg_automask_image_min_area = gr.Number( | |
| value=0, | |
| label="Min Area", | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| seg_automask_image_points_per_side = gr.Slider( | |
| minimum=0, | |
| maximum=32, | |
| step=2, | |
| value=16, | |
| label="Points per Side", | |
| ) | |
| seg_automask_image_points_per_batch = gr.Slider( | |
| minimum=0, | |
| maximum=64, | |
| step=2, | |
| value=64, | |
| label="Points per Batch", | |
| ) | |
| seg_automask_image_predict = gr.Button(value="Generator") | |
| with gr.Column(): | |
| output_image = gr.Image() | |
| seg_automask_image_predict.click( | |
| fn=automask_image_app, | |
| inputs=[ | |
| seg_automask_image_file, | |
| seg_automask_image_model_type, | |
| seg_automask_image_points_per_side, | |
| seg_automask_image_points_per_batch, | |
| seg_automask_image_min_area, | |
| ], | |
| outputs=[output_image], | |
| ) | |
| def video_app(): | |
| with gr.Blocks(): | |
| with gr.Row(): | |
| with gr.Column(): | |
| seg_automask_video_file = gr.Video().style(height=260) | |
| with gr.Row(): | |
| with gr.Column(): | |
| seg_automask_video_model_type = gr.Dropdown( | |
| choices=[ | |
| "vit_h", | |
| "vit_l", | |
| "vit_b", | |
| ], | |
| value="vit_l", | |
| label="Model Type", | |
| ) | |
| seg_automask_video_min_area = gr.Number( | |
| value=1000, | |
| label="Min Area", | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| seg_automask_video_points_per_side = gr.Slider( | |
| minimum=0, | |
| maximum=32, | |
| step=2, | |
| value=16, | |
| label="Points per Side", | |
| ) | |
| seg_automask_video_points_per_batch = gr.Slider( | |
| minimum=0, | |
| maximum=64, | |
| step=2, | |
| value=64, | |
| label="Points per Batch", | |
| ) | |
| seg_automask_video_predict = gr.Button(value="Generator") | |
| with gr.Column(): | |
| output_video = gr.Video() | |
| seg_automask_video_predict.click( | |
| fn=automask_video_app, | |
| inputs=[ | |
| seg_automask_video_file, | |
| seg_automask_video_model_type, | |
| seg_automask_video_points_per_side, | |
| seg_automask_video_points_per_batch, | |
| seg_automask_video_min_area, | |
| ], | |
| outputs=[output_video], | |
| ) | |
| def sahi_app(): | |
| with gr.Blocks(): | |
| with gr.Row(): | |
| with gr.Column(): | |
| sahi_image_file = gr.Image(type="filepath").style(height=260) | |
| sahi_autoseg_model_type = gr.Dropdown( | |
| choices=[ | |
| "vit_h", | |
| "vit_l", | |
| "vit_b", | |
| ], | |
| value="vit_l", | |
| label="Sam Model Type", | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| sahi_model_type = gr.Dropdown( | |
| choices=[ | |
| "yolov5", | |
| "yolov8", | |
| ], | |
| value="yolov5", | |
| label="Detector Model Type", | |
| ) | |
| sahi_image_size = gr.Slider( | |
| minimum=0, | |
| maximum=1600, | |
| step=32, | |
| value=640, | |
| label="Image Size", | |
| ) | |
| sahi_overlap_width = gr.Slider( | |
| minimum=0, | |
| maximum=1, | |
| step=0.1, | |
| value=0.2, | |
| label="Overlap Width", | |
| ) | |
| sahi_slice_width = gr.Slider( | |
| minimum=0, | |
| maximum=640, | |
| step=32, | |
| value=256, | |
| label="Slice Width", | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| sahi_model_path = gr.Dropdown( | |
| choices=[ | |
| "yolov5l.pt", | |
| "yolov5l6.pt", | |
| "yolov8l.pt", | |
| "yolov8x.pt" | |
| ], | |
| value="yolov5l6.pt", | |
| label="Detector Model Path", | |
| ) | |
| sahi_conf_th = gr.Slider( | |
| minimum=0, | |
| maximum=1, | |
| step=0.1, | |
| value=0.2, | |
| label="Confidence Threshold", | |
| ) | |
| sahi_overlap_height = gr.Slider( | |
| minimum=0, | |
| maximum=1, | |
| step=0.1, | |
| value=0.2, | |
| label="Overlap Height", | |
| ) | |
| sahi_slice_height = gr.Slider( | |
| minimum=0, | |
| maximum=640, | |
| step=32, | |
| value=256, | |
| label="Slice Height", | |
| ) | |
| sahi_image_predict = gr.Button(value="Generator") | |
| with gr.Column(): | |
| output_image = gr.Image() | |
| sahi_image_predict.click( | |
| fn=sahi_autoseg_app, | |
| inputs=[ | |
| sahi_image_file, | |
| sahi_autoseg_model_type, | |
| sahi_model_type, | |
| sahi_model_path, | |
| sahi_conf_th, | |
| sahi_image_size, | |
| sahi_slice_height, | |
| sahi_slice_width, | |
| sahi_overlap_height, | |
| sahi_overlap_width, | |
| ], | |
| outputs=[output_image], | |
| ) | |
| def metaseg_app(): | |
| app = gr.Blocks() | |
| with app: | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Tab("Image"): | |
| image_app() | |
| with gr.Tab("Video"): | |
| video_app() | |
| with gr.Tab("SAHI"): | |
| sahi_app() | |
| app.queue(concurrency_count=1) | |
| app.launch(debug=True, enable_queue=True) | |
| if __name__ == "__main__": | |
| metaseg_app() | |