| import gradio as gr | |
| import torch | |
| from ultralyticsplus import YOLO, render_result | |
| def yoloFunc(image: gr.inputs.Image = None, | |
| image_size: int = 640, | |
| conf_threshold: float = 0.4, | |
| iou_threshold: float = 0.5): | |
| model_path = 'best.pt' | |
| model = YOLO(model_path) | |
| results = model.predict(image, | |
| image_size=image_size, | |
| conf_threshold=conf_threshold, | |
| iou_threshold=iou_threshold | |
| ) | |
| box = results[0].boxes | |
| render = render_result(model=model, image=image, results=results[0]) | |
| return render | |
| inputs = [ | |
| gr.inputs.Image(type='filepath', label="Input Image"), | |
| gr.inputs.Slider(minimum=320, maximum=1024, default=640, step=32, label="Image Size"), | |
| gr.inputs.Slider(minimum=0.1, maximum=1.0, default=0.4, steps=0.05, label="Confidence Threshold"), | |
| gr.inputs.Slider(minimum=0.1, maximum=1.0, default=0.5, steps=0.05, label="IOU Threshold") | |
| ] | |
| outputs = gr.outputs.Image(type='filepath', label="Output Image") | |
| title = "Pothole Detection" | |
| yolo_app = gr.Interface( | |
| fn=yoloFunc, | |
| inputs=inputs, | |
| outputs=outputs, | |
| title=title, | |
| ) | |
| yolo_app.launch(debug=True, enable_queue=True) |