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| import gradio as gr | |
| from backend import visualize_image | |
| # gradio inputs | |
| image_input = gr.components.Image(type="pil", label="Input Image") | |
| color_mode_select = gr.components.Radio(choices=["Black/white", "Random", "Segmentation"], label="Color Mode", value="Segmentation") | |
| mode_dropdown = gr.components.Dropdown(choices=["Trees", "Buildings", "Both"], label="Detection Mode", value="Both") | |
| tree_threshold_slider = gr.components.Slider(minimum=0, maximum=1, step=0.1, value=0.7, label='Set confidence threshold "%" for trees') | |
| building_threshold_slider = gr.components.Slider(minimum=0, maximum=1, step=0.1, value=0.7, label='Set confidence threshold "%" for buildings') | |
| # gradio outputs | |
| output_image = gr.components.Image(type="pil", label="Output Image") | |
| title = "Aerial Image Segmentation" | |
| description = "An instance segmentation demo for identifying boundaries of buildings and trees in aerial images using DETR (End-to-End Object Detection) model with MaskRCNN-101 backbone" | |
| # gradio interface | |
| interface = gr.Interface( | |
| fn=visualize_image, | |
| inputs=[image_input, mode_dropdown, tree_threshold_slider, building_threshold_slider, color_mode_select], | |
| outputs=output_image, | |
| title=title, | |
| description=description | |
| ) | |
| interface.launch(debug=True) | |