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| import gradio as gr | |
| from gradio.outputs import Label | |
| import cv2 | |
| from ultralytics import YOLO | |
| model = YOLO('best.pt') | |
| path = [['pothole_screenshot.png']] | |
| def show_preds(image_path): | |
| image = cv2.imread(image_path) | |
| outputs = model.predict(source=image_path, return_outputs=True) | |
| for image_id, result in enumerate(outputs): | |
| print(result['det']) | |
| for i, det in enumerate(result['det']): | |
| print(det) | |
| cv2.rectangle( | |
| image, | |
| (int(det[0]), int(det[1])), | |
| (int(det[2]), int(det[3])), | |
| color=(0, 0, 255), | |
| thickness=2, | |
| lineType=cv2.LINE_AA | |
| ) | |
| return cv2.cvtColor(image, cv2.COLOR_BGR2RGB) | |
| gr_interface = gr.Interface( | |
| fn=show_preds, | |
| inputs=gr.inputs.Image(type="filepath", label="Input Image"), | |
| outputs=gr.outputs.Image(type="numpy", label="Output Image"), | |
| title="Pothole detector", | |
| examples=path, | |
| ) | |
| gr_interface.launch(inline=False, share=False, debug=True) |