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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 = [ |
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'https://www.google.com/url?sa=i&url=https%3A%2F%2Fwww.researchgate.net%2Ffigure%2Fray-of-baggage-showing-different-objects-with-different-densities-III-ALGORITHEM_fig2_305264591&psig=AOvVaw1s-qOqWplhsbjliHEq4bqo&ust=1699106526616000&source=images&cd=vfe&opi=89978449&ved=0CBIQjRxqFwoTCNCBs5n_p4IDFQAAAAAdAAAAABAJ', |
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'https://www.google.com/url?sa=i&url=https%3A%2F%2Fbitrefine.group%2Faboutcompany%2Fnews%2F251-computer-system-has-learned-to-recognize-x-ray-images-and-alerts-if-it-sees-illegal-items&psig=AOvVaw1s-qOqWplhsbjliHEq4bqo&ust=1699106526616000&source=images&cd=vfe&opi=89978449&ved=0CBIQjRxqFwoTCNCBs5n_p4IDFQAAAAAdAAAAABAR' |
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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('airport_scaner.pt') |
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path = [['image_0.jpg'], ['image_1.jpg']] |
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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() |
