Resizing a Video with OpenCV: A Comprehensive Guide for Beginners

Learn how to resize a video using OpenCV, the most popular computer vision library. Follow our step-by-step guide with code examples to change the resolution or aspect ratio of your videos. Start exploring the world of video processing and computer vision today!

Welcome to this tutorial on how to resize a video with OpenCV, one of the most widely used computer vision libraries. Resizing a video involves changing its resolution or aspect ratio and is an important task in many applications such as video compression, streaming, and display.

In this tutorial, we will explore how to resize a video with OpenCV. We will discuss the theory behind resizing and provide multiple code examples to illustrate the concept.

Theory Resizing a video involves changing its size, aspect ratio, or both. This can be achieved using various techniques such as nearest neighbor interpolation, bilinear interpolation, and bicubic interpolation.

Nearest neighbor interpolation involves copying the nearest pixel value to the new location. Bilinear interpolation involves taking a weighted average of the four nearest pixel values, while bicubic interpolation involves taking a weighted average of the sixteen nearest pixel values.

OpenCV provides a range of functions and algorithms to resize a video. These include the resize() function, which can perform nearest neighbor, bilinear, and bicubic interpolation, and the VideoWriter() class, which can write the resized video to a file.

Now that we have a basic understanding of the theory, let’s move on to the code examples.

Code Examples We will use Python for our examples, but the concept applies to other programming languages supported by OpenCV.

First, let’s start by importing the necessary libraries:

import cv2
import numpy as np

Next, let’s load a sample video file:

cap = cv2.VideoCapture('sample_video.mp4')

Resizing a Video

To resize a video, we can use the following code:

while True:
    ret, frame = cap.read()
    if not ret:
        break

    # Get the current frame size
    height, width, _ = frame.shape

    # Resize the frame
    scale_percent = 50
    new_width = int(width * scale_percent / 100)
    new_height = int(height * scale_percent / 100)
    dim = (new_width, new_height)
    resized = cv2.resize(frame, dim, interpolation = cv2.INTER_AREA)

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

    # Wait for a key press
    k = cv2.waitKey(30) & 0xff
    if k == 27:
        break

In the above code, we first read each frame from the video using the read() function. We then get the current frame size using the shape attribute of the frame.

Next, we define the desired size for the resized video using a percentage scale factor. We then calculate the new width and height of the frame using the scale factor and resize the frame using the resize() function with the desired interpolation method.

Finally, we display the resized frame using the imshow() function and wait for a key press using the waitKey() function.

Saving the Resized Video

To save the resized video to a file, we can use the following code:

fourcc = cv2.VideoWriter_fourcc(*'mp4v')
out = cv2.VideoWriter('resized_video.mp4', fourcc, 30.0, dim)

while True:
    ret, frame = cap.read()
    if not ret:
        break

    # Resize the frame
    resized = cv2.resize(frame, dim, interpolation = cv2.INTER_AREA)

    # Write the resized frame to the output video
    out.write(resized)

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

    # Wait for a key press
    k = cv2.waitKey(30) & 0xff
    if k == 27:
        break

# Release the video capture and writer and close all windows
cap.release()
out.release()
cv2.destroyAllWindows()

In the above code, we first define the codec to be used for the output video using the VideoWriter_fourcc() function. We then create a new VideoWriter() object with the desired output file name, codec, frame rate, and dimensions.

We then loop through the video frames, resize each frame using the resize() function with the desired interpolation method, write the resized frame to the output video using the write() function, and display the resized frame using the imshow() function.

Finally, we release the video capture and writer using the release() method and close all windows using the destroyAllWindows() method.

Conclusion

In this tutorial, we’ve explored how to resize a video with OpenCV, one of the most widely used computer vision libraries. We discussed the theory behind resizing and provided multiple code examples to illustrate the concept.

We hope that this tutorial has been helpful and informative for beginners and those looking to explore the world of computer vision and video processing. For further information, please refer to the OpenCV documentation and explore the different image and video processing techniques and their applications.