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| import gradio as gr | |
| from gradio.outputs import Label | |
| 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' | |
| ] | |
| 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): | |
| download_file( | |
| file_urls[i], | |
| f"image_{i}.jpg" | |
| ) | |
| model = YOLO('best.pt') | |
| path = [['image_0.jpg'], ['image_1.jpg']] | |
| def show_preds(image_path): | |
| image = cv2.imread(image_path) | |
| outputs = model.predict(source=image_path, return_outputs=True) | |
| for image_id, result in enumerate(outputs): | |
| print(result['det']) | |
| for i, det in enumerate(result['det']): | |
| print(det) | |
| 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) | |
| gr_interface = gr.Interface( | |
| fn=show_preds, | |
| inputs=gr.inputs.Image(type="filepath", label="Input Image"), | |
| outputs=gr.outputs.Image(type="numpy", label="Output Image"), | |
| title="Pothole detector", | |
| examples=path, | |
| cache_examples=False, | |
| # live=True, | |
| ) | |
| gr_interface.launch(debug=True, enable_queue=True) |