import gradio as gr import cv2 from ultralytics import YOLO model = YOLO('best.pt') path = [['pothole1.jpg'], ['pothole2.jpg'], ['pothole3.jpg'],['pothole4.jpg']] import cv2 def resize_image(image_path): # Read the image using OpenCV img = cv2.imread(image_path) # Resize the image to 512x512 resized_img = cv2.resize(img, (512, 512), interpolation = cv2.INTER_LINEAR) return resized_img def prediction1(image_path): #image = resize_image(image_path) image = cv2.imread(image_path) outputs = model.predict(source=image_path) results = outputs[0].cpu().numpy() for i, det in enumerate(results.boxes.xyxy): cv2.rectangle( image, (int(det[0]), int(det[1])), (int(det[2]), int(det[3])), color=(0,255, 0), thickness=1, lineType=cv2.LINE_AA, ) return cv2.cvtColor(image, cv2.COLOR_BGR2RGB) inputs_image = [ gr.components.Image(type="filepath", label="Input Image"), ] outputs_image = [ gr.components.Image(type="numpy", label="Output Image"), ] interface_image = gr.Interface( fn=prediction1, inputs=inputs_image, outputs=outputs_image, title="Pothole detection", description="Detects potholes in images", #cache_examples=True, examples=path ) interface_image.launch()