| 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() |
|
|
