Reading Videos in OpenCV using cv2.VideoWriter Module in Python

Introduction:

OpenCV is a computer vision and machine learning library that has a wide range of applications in fields such as facial recognition, object detection, and image processing. The cv2.VideoWriter module in OpenCV provides functionality for writing videos. In this blog post, we will learn how to read videos in OpenCV using cv2.VideoWriter module in Python.

Prerequisites:

• Basic understanding of computer vision
• Knowledge of Python programming language
• Familiarity with the OpenCV library

Reading Videos in OpenCV using cv2.VideoWriter module in Python: The cv2.VideoWriter module in OpenCV provides a simple interface for reading videos. To read a video in OpenCV, we need to create a cv2.VideoCapture object and open a video file or a camera stream. The following code demonstrates how to read a video in OpenCV using the cv2. VideoCapture module.

import cv2

# Create a VideoCapture object
cap = cv2.VideoCapture('video.mp4')

# Check if the video was opened successfully
if not cap.isOpened():
    print("Error opening the video")

# Read the video frame-by-frame
while True:
    # Read the frame
    ret, frame = cap.read()

    # Break the loop if the video has ended
    if not ret:
        break

    # Display the frame
    cv2.imshow('Frame', frame)

    # Exit the loop if the 'q' key is pressed
    if cv2.waitKey(25) & 0xFF == ord('q'):
        break

# Release the VideoCapture object
cap.release()

# Close all windows
cv2.destroyAllWindows()

In the code above, we create a cv2.VideoCapture object using the cv2.VideoCapture('video.mp4') line. We then check if the video was opened successfully using the cap.isOpened() method. If the video was not opened successfully, an error message is printed.

The video is read frame-by-frame using the cap.read() method. The cap.read() method returns two values: ret and frame. The ret value is True if the frame was read successfully, and False if the video has ended. The frame value is the current frame in the video.

We display each frame using the cv2.imshow('Frame', frame) line. The cv2.imshow('Frame', frame) line displays the frame image in a window with the title 'Frame'. We then wait for 25 milliseconds for the next frame to be displayed using the cv2.waitKey(25) line.

Finally, we release the cv2.VideoCapture object using the cap.release() line, and close all windows using the cv2.destroyAllWindows() line.

follow https://medium.com/@korlakuntasaikamal10/gray-scale-and-color-images-using-opencv-eda0c13c292a to understand grayscale and color images.