Mon Jun 26 05:12:51 PM CST 2023
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demo.py
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from ultralytics import YOLO
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# from ultralytics.yolo.utils.benchmarks import benchmark 。
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import cv2
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# Load a model
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# model = YOLO("yolov8n.yaml") # build a new model from scratch
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model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training)
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# Use the model
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# model.train(data="coco128.yaml", epochs=3,workers=0) # train the model,workers=0 if windows
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# metrics = model.val() # evaluate model performance on the validation set
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'''
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results = model("bus.jpg") # predict on an image
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print(results)
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# '''
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img_path = "./image/"
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# results = model.predict(img_path, save=True,conf=0.5) # device=0 by default, conf:置信度阈值
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# results = model.predict(img_path,save=True,classes=[0,2],conf=0.5) # i.e. classes=0,classes=[0,3,4]
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# save detection results *
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# results = model.predict(img_path,save=True,save_txt=True,classes=0,conf=0.4)
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# predict video
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video_path = "./video/1.mp4"
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cap = cv2.VideoCapture(0)
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# Loop through the video frames
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while cap.isOpened():
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# Read a frame from the video
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success, frame = cap.read()
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if success:
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# Run YOLOv8 inference on the frame
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results = model(frame)
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# Visualize the results on the frame
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annotated_frame = results[0].plot()
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# Display the annotated frame
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cv2.imshow("YOLOv8 Inference", annotated_frame)
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# Break the loop if 'q' is pressed
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if cv2.waitKey(1) & 0xFF == ord("q"):
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break
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else:
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# Break the loop if the end of the video is reached
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break
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# Release the video capture object and close the display window
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cap.release()
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cv2.destroyAllWindows()
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detect_image.py
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detect_video.py
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