| import gradio as gr | |
| import cv2 | |
| import requests | |
| import os | |
| from PIL import Image | |
| 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): | |
| if not os.path.exists(save_name): | |
| with requests.get(url, stream=True) as r: | |
| r.raise_for_status() | |
| with open(save_name, 'wb') as f: | |
| for chunk in r.iter_content(chunk_size=8192): | |
| f.write(chunk) | |
| for i, url in enumerate(file_urls): | |
| save_name = "video.mp4" if 'mp4' in url else f"image_{i}.jpg" | |
| download_file(url, save_name) | |
| model = YOLO('yolov8n_epoch20_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 det in 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="Detector - Pothole", | |
| examples=path, | |
| cache_examples=False, | |
| ) | |
| def show_preds_video(video_path): | |
| cap = cv2.VideoCapture(video_path) | |
| while cap.isOpened(): | |
| ret, frame = cap.read() | |
| if not ret: | |
| break | |
| frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) | |
| frame_pil = Image.fromarray(frame_rgb) | |
| outputs = model.predict(source=frame_pil) | |
| results = outputs[0].cpu().numpy() | |
| for det in results.boxes.xyxy: | |
| cv2.rectangle( | |
| frame, | |
| (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, cv2.COLOR_BGR2RGB) | |
| cap.release() | |
| inputs_video = [ | |
| gr.components.Video(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="Detect Pothole", | |
| examples=video_path, | |
| cache_examples=False, | |
| ) | |
| gr.TabbedInterface( | |
| [interface_image, interface_video], | |
| tab_names=['Image inference', 'Video inference'] | |
| ).queue().launch() | |