yolov8-oprncv可视化结果，并将其显示在窗口中_yolov8 result可视化

流式传输源循环for

下面是一个使用 OpenCV （） 和 YOLOv8 对视频帧运行推理的 Python 脚本。此脚本假定您已经安装了必要的软件包（ 和 ）。cv2opencv-pythonultralytics

import cv2
from ultralytics import YOLO
 
# Load the YOLOv8 model
model = YOLO('yolov8n.pt')
 
# Open the video file
video_path = "path/to/your/video/file.mp4"
cap = cv2.VideoCapture(video_path)
 
# Loop through the video frames
while cap.isOpened():
    # Read a frame from the video
    success, frame = cap.read()
 
    if success:
        # Run YOLOv8 inference on the frame
        results = model(frame)
 
        # Visualize the results on the frame
        annotated_frame = results[0].plot()
 
        # Display the annotated frame
        cv2.imshow("YOLOv8 Inference", annotated_frame)
 
        # Break the loop if 'q' is pressed
        if cv2.waitKey(1) & 0xFF == ord("q"):
            break
    else:
        # Break the loop if the end of the video is reached
        break
 
# Release the video capture object and close the display window
cap.release()
cv2.destroyAllWindows()

此脚本将对视频的每一帧运行预测，可视化结果，并将其显示在窗口中。可以通过按“q”退出循环。