| import gradio as gr | |
| import PIL.Image as Image | |
| from ultralytics import ASSETS, YOLO | |
| import cv2 | |
| model = YOLO("best.pt") | |
| def predict_image(img, conf_threshold=0.25, iou_threshold=0.45): | |
| """Predicts objects in an image using a YOLOv8 model with adjustable confidence and IOU thresholds.""" | |
| results = model.predict( | |
| source=img, | |
| conf=conf_threshold, | |
| iou=iou_threshold, | |
| show_labels=True, | |
| show_conf=True, | |
| ) | |
| for r in results: | |
| im_array = r.plot() | |
| im = Image.fromarray(im_array[..., ::-1]) | |
| return im | |
| iface = gr.Interface( | |
| fn=predict_image, | |
| inputs=[ | |
| gr.Image(type="pil", label="Upload Image"), | |
| gr.Slider(minimum=0, maximum=1, value=0.25, label="Confidence threshold"), | |
| gr.Slider(minimum=0, maximum=1, value=0.45, label="IoU threshold"), | |
| ], | |
| outputs=gr.Image(type="pil", label="Result"), | |
| title="Roadvis🛣️ Gradio!", | |
| description="Upload images for inference.", | |
| examples=[ | |
| ["Assets/pothole1.png", 0.25, 0.45], | |
| ["Assets/pothole2.webp", 0.25, 0.45], | |
| ], | |
| ) | |
| if __name__ == "__main__": | |
| iface.launch() |