Reading video with OpenCV in Computer Vision - ML Experiment: Train & Evaluate

Experiment - Reading video with OpenCV

Problem:You want to read a video file frame by frame using OpenCV to process or analyze each frame.

Current Metrics:The current code reads the video but sometimes skips frames or crashes if the video file path is incorrect.

Issue:The video reading is not robust; it does not handle errors well and does not display frames smoothly.

Your Task

Improve the video reading code to handle errors gracefully and display each frame smoothly until the video ends or the user stops it.

You must use OpenCV (cv2) for video reading and display.

Do not use any other video processing libraries.

Keep the code simple and easy to understand.

Hint 1

Hint 2

Hint 3

Hint 4

Solution

Computer Vision

import cv2

# Path to the video file
video_path = 'sample_video.mp4'

# Create a VideoCapture object
cap = cv2.VideoCapture(video_path)

# Check if video opened successfully
if not cap.isOpened():
    print(f'Error: Cannot open video file {video_path}')
    exit()

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

    # If frame reading was not successful, end of video is reached
    if not ret:
        print('Reached end of video or cannot read the frame.')
        break

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

    # Wait for 25 ms and check if user pressed 'q' to quit
    if cv2.waitKey(25) & 0xFF == ord('q'):
        print('Video playback stopped by user.')
        break

# Release the video capture object and close windows
cap.release()
cv2.destroyAllWindows()

Added a check to confirm the video file opened successfully.

Used a loop to read frames until the video ends.

Added a condition to break the loop if frame reading fails.

Displayed each frame with cv2.imshow and used cv2.waitKey(25) for smooth playback.

Allowed user to stop playback by pressing 'q'.

Released resources properly after video ends or stops.

Results Interpretation

Before: The video reading code sometimes crashed or skipped frames and did not handle errors.

After: The improved code checks for errors, reads frames continuously, displays them smoothly, and allows user control to stop playback.

Proper error handling and frame-by-frame reading with controlled display timing are essential for smooth and robust video processing using OpenCV.

Bonus Experiment

Try modifying the code to convert each video frame to grayscale before displaying it.

💡 Hint

Use cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) to convert the frame to grayscale.