How to use OpenCV for video processing and analysis?

Video processing and analysis using OpenCV is a common task in computer vision, covering multiple aspects such as video reading, processing, saving, and analysis. Below are the basic steps for video processing and analysis using OpenCV:

1. Video reads:

   a. Use OpenCV's VideoCapture class to open a video file or camera. The VideoCapture class provides functionality for accessing video streams.

   b. Use the read() method to read the image of each frame.

2. Video processing:

   a. Apply image processing algorithms such as image filtering, edge detection, object detection, object tracking, etc. to the image of each frame.

   b. The processed image can be displayed in real time through OpenCV's imshow() method, or saved to a video file.

3. Video save:

   a. Create a video writing object using OpenCV's VideoWriter class.

   b. Write the processed image frames to the video one by one.

   c. Release the video write object to complete video saving.

4. Video analysis:

   a. Frame-by-frame processing and analysis of the video.

   b. Video content can be analyzed using algorithms such as image processing, object detection, tracking, optical flow estimation, action recognition, etc.

Below is a simple code example demonstrating how to use OpenCV for video processing and analysis:

import cv2

# 打开视频文件
cap = cv2.VideoCapture('video.mp4')

# 获取视频帧率和大小
fps = cap.get(cv2.CAP_PROP_FPS)
frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))

# 创建视频写入对象
out = cv2.VideoWriter('output_video.avi', cv2.VideoWriter_fourcc('M','J','P','G'), fps, (frame_width, frame_height))

while cap.isOpened():
    # 读取视频帧
    ret, frame = cap.read()

    if ret:
        # 在视频帧上进行图像处理或分析，例如目标检测或边缘检测
        # processed_frame = ...

        # 显示处理后的视频帧
        cv2.imshow('Processed Frame', processed_frame)

        # 将处理后的视频帧写入输出视频文件
        out.write(processed_frame)

        # 按q键退出
        if cv2.waitKey(1) & 0xFF == ord('q'):
            break
    else:
        break

# 释放资源
cap.release()
out.release()
cv2.destroyAllWindows()

Thank you for liking the article, welcome to pay attention to Wei

❤Public account [AI Technology Planet] Reply (123)

Free prostitution supporting materials + 60G entry-advanced AI resource pack + technical questions and answers + full version video

Contains: deep learning neural network + CV computer vision learning (two major frameworks pytorch/tensorflow + source code courseware notes) + NLP, etc.

In practical applications, the effect and performance of video processing and analysis depends on the quality of the selected algorithms and parameters. Appropriate methods and parameters can be selected according to specific application requirements to achieve high-quality video processing and analysis.