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import gradio as gr |
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import cv2 |
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import requests |
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import os |
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from ultralytics import YOLO |
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file_urls = ["https://www.dropbox.com/scl/fi/i582tgw95r0f8i8cssmx0/images.jpg?rlkey=fa3d74yaj0bh941jo67n0elns&dl=0" |
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] |
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def download_file(url, save_name): |
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url = url |
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if not os.path.exists(save_name): |
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file = requests.get(url) |
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open(save_name, 'wb').write(file.content) |
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for i, url in enumerate(file_urls): |
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if 'mp4' in file_urls[i]: |
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download_file( |
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file_urls[i], |
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f"video.mp4" |
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) |
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else: |
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download_file( |
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file_urls[i], |
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f"image_{i}.jpg" |
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) |
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model = YOLO('best.pt') |
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path = [['images.jpg'],['images_1.png'],['image/i1.png'],['image/i2.png'],['image/i3.png'],['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']] |
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def show_preds_image(image_path): |
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image = cv2.imread(image_path) |
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outputs = model.predict(source=image_path) |
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results = outputs[0].cpu().numpy() |
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for i, det in enumerate(results.boxes.xyxy): |
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cv2.rectangle( |
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image, |
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(int(det[0]), int(det[1])), |
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(int(det[2]), int(det[3])), |
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color=(0, 0, 255), |
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thickness=2, |
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lineType=cv2.LINE_AA |
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) |
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return cv2.cvtColor(image, cv2.COLOR_BGR2RGB) |
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inputs_image = [ |
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gr.components.Image(type="filepath", label="Input Image"), |
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] |
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outputs_image = [ |
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gr.components.Image(type="numpy", label="Output Image"), |
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] |
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interface_image = gr.Interface( |
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fn=show_preds_image, |
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inputs=inputs_image, |
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outputs=outputs_image, |
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title="Airport Luggage Weapon Detector app", |
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examples=path, |
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cache_examples=False, |
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) |
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def show_preds_video(video_path): |
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cap = cv2.VideoCapture(video_path) |
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while(cap.isOpened()): |
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ret, frame = cap.read() |
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if ret: |
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frame_copy = frame.copy() |
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outputs = model.predict(source=frame) |
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results = outputs[0].cpu().numpy() |
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for i, det in enumerate(results.boxes.xyxy): |
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cv2.rectangle( |
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frame_copy, |
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(int(det[0]), int(det[1])), |
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(int(det[2]), int(det[3])), |
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color=(0, 0, 255), |
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thickness=2, |
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lineType=cv2.LINE_AA |
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) |
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yield cv2.cvtColor(frame_copy, cv2.COLOR_BGR2RGB) |
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inputs_video = [ |
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gr.components.Video(), |
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] |
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outputs_video = [ |
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gr.components.Image(), |
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] |
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interface_video = gr.Interface( |
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fn=show_preds_video, |
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inputs=inputs_video, |
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outputs=outputs_video, |
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title="Airport Luggage Weapon Detector", |
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cache_examples=False, |
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) |
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gr.TabbedInterface( |
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[interface_image, interface_video], |
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tab_names=['Image inference', 'Video inference'] |
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).queue().launch() |
