import gradio as gr import cv2 import requests import os from ultralytics import YOLO file_urls = [ 'https://www.dropbox.com/scl/fi/jlx3gmwzoo214bdc5j0vq/images-13-15.jpeg?rlkey=ccam5oi3udwj8t19s91lfb75r&dl=1', 'https://www.dropbox.com/scl/fi/j9b1kbreq6rl70w7k066d/images-13-14.jpeg?rlkey=rur6rkmvp4e8iai04hv9uip7r&dl=1', 'https://www.dropbox.com/scl/fi/ys17sb2glhe9mvdm6vy1h/video_7.mp4?rlkey=pvlo141n52m3t5294l5jn6c54&dl=1' ] def download_file(url, save_name): url = url if not os.path.exists(save_name): file = requests.get(url) open(save_name, 'wb').write(file.content) for i, url in enumerate(file_urls): if 'mp4' in file_urls[i]: download_file( file_urls[i], f"video.mp4" ) else: download_file( file_urls[i], f"image_{i}.jpg" ) model = YOLO('best.pt') path = [['image_0.jpg'], ['image_1.jpg']] video_path = [['video.mp4']] def show_preds_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, 0, 255), thickness=2, 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=show_preds_image, inputs=inputs_image, outputs=outputs_image, title="Pothole detector app", examples=path, cache_examples=False, ) def show_preds_video(video_path): cap = cv2.VideoCapture(video_path) while(cap.isOpened()): ret, frame = cap.read() if ret: frame_copy = frame.copy() outputs = model.predict(source=frame) results = outputs[0].cpu().numpy() for i, det in enumerate(results.boxes.xyxy): cv2.rectangle( frame_copy, (int(det[0]), int(det[1])), (int(det[2]), int(det[3])), color=(0, 0, 255), thickness=2, lineType=cv2.LINE_AA ) yield cv2.cvtColor(frame_copy, cv2.COLOR_BGR2RGB) inputs_video = [ gr.components.Video(type="filepath", label="Input Video"), ] outputs_video = [ gr.components.Image(type="numpy", label="Output Image"), ] interface_video = gr.Interface( fn=show_preds_video, inputs=inputs_video, outputs=outputs_video, title="Pothole detector", examples=video_path, cache_examples=False, ) gr.TabbedInterface( [interface_image, interface_video], tab_names=['Image inference', 'Video inference'] ).queue().launch()