Update app.py
Browse files
app.py
CHANGED
@@ -33,7 +33,7 @@ for i, url in enumerate(file_urls):
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model = YOLO('best.
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path = [['image_0.jpg'], ['image_1.jpg'], ['image_2.jpg'], ['image_3.jpg']]
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# path = [['IMG_7612.JPG'], ['IMG_7678.JPG'], ['all_33.jpg'], ['all_80.jpg'],
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@@ -49,7 +49,7 @@ classes = ['alligator_cracking', 'longitudinal_cracking', 'potholes', 'ravelling
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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, agnostic_nms=True, conf=0.25, iou=0.4, imgsz=
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results = outputs[0].cpu().numpy()
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re_boxes = results.boxes.data.tolist()
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@@ -117,7 +117,7 @@ def show_preds_video(video_path):
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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, agnostic_nms=True, conf=0.25, iou=0.4, imgsz=
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results = outputs[0].cpu().numpy()
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re_boxes = results.boxes.data.tolist()
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)
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model = YOLO('best.pt', task='detect')
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path = [['image_0.jpg'], ['image_1.jpg'], ['image_2.jpg'], ['image_3.jpg']]
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# path = [['IMG_7612.JPG'], ['IMG_7678.JPG'], ['all_33.jpg'], ['all_80.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, agnostic_nms=True, conf=0.25, iou=0.4, imgsz=640)
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results = outputs[0].cpu().numpy()
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re_boxes = results.boxes.data.tolist()
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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, agnostic_nms=True, conf=0.25, iou=0.4, imgsz=640)
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results = outputs[0].cpu().numpy()
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re_boxes = results.boxes.data.tolist()
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