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| import gradio as gr | |
| import cv2 | |
| import requests | |
| import os | |
| from ultralytics import YOLO | |
| file_urls = ["https://www.dropbox.com/scl/fi/i582tgw95r0f8i8cssmx0/images.jpg?rlkey=fa3d74yaj0bh941jo67n0elns&dl=0" | |
| # 'https://www.dropbox.com/scl/fi/z4tnnills03s1o4evbqpl/download.jpg?rlkey=gmh63kexnnjcva6ahzo2wfmsd&dl=0' | |
| ] | |
| 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('anuj_best.pt') | |
| path = [] | |
| # ,['image/i4.png'],['image/i5.png'],['image/i6.png'],['image/i7.png'],['image/i8.png'],['image/i9.png'],['image/i10.png'],['image/i11.png'], | |
| # ['image/i12.png'],['image/i13.png'],['image/i14.png'],['image/i15.png'],['image/i16.png'],['image/i17.png'],['image/i18.png'],['image/i19.png'], | |
| # ['image/i20.png'],['image/i21.png'],['image/i22.png'],['image/i23.png'],['image/i24.png'],['image/i25.png'],['image/i26.png'],['image/i27.png'],['image/i28.png']] | |
| # 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="Airport Luggage Weapon 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(), | |
| ] | |
| outputs_video = [ | |
| gr.components.Image(), | |
| ] | |
| interface_video = gr.Interface( | |
| fn=show_preds_video, | |
| inputs=inputs_video, | |
| outputs=outputs_video, | |
| title="Airport Luggage Weapon Detector", | |
| cache_examples=False, | |
| ) | |
| MORE = """ ## TRY Other Models | |
|  | |
| ### Instant Image: 4k images in 5 Second -> https://huggingface.co/spaces/KingNish/Instant-Image | |
| """ | |
| gr.Markdown(MORE) | |
| gr.TabbedInterface( | |
| [interface_image, interface_video], | |
| tab_names=['Image inference', 'Video inference'] | |
| ).queue().launch() |