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| import gradio as gr | |
| import cv2 | |
| import requests | |
| import os | |
| from ultralytics import YOLO | |
| file_urls = [ | |
| 'https://www.dropbox.com/s/b5g97xo901zb3ds/pothole_example.jpg?dl=1', | |
| 'https://www.dropbox.com/s/86uxlxxlm1iaexa/pothole_screenshot.png?dl=1', | |
| 'https://www.dropbox.com/s/7sjfwncffg8xej2/video_7.mp4?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", | |
| 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], | |
| tab_names=['Image inference'] | |
| ).queue().launch() |