Video color correction using MoviePy

In video editing and production, achieving the right color balance and visual appeal is crucial to set a video's intended mood and message. One tool that has gained popularity for its compatibility and ease of use in color correction is MoviePy. For video editing, understanding how to color-correct video using MoviePy can significantly enhance the quality of your videos.

What is MoviePy?

MoviePy is a Python library that provides functionalities for video editing, manipulation, and processing. It is built on top of other libraries like NumPy, Matplotlib, and OpenCV, making it a powerful tool for handling video-related tasks. MoviePy simplifies complex video editing processes by offering a user-friendly interface and a wide range of functions for tasks like cutting, concatenating, filtering, and color correction.

Applications of MoviePy

MoviePy finds applications in various domains, including:

Video production: MoviePy can create and edit videos for diverse purposes, such as commercials, short films, social media content, and more.
Educational content: It is a valuable tool for educators who want to create engaging video lessons and tutorials.
Visual effects: MoviePy's features can be utilized to add visual effects, filters, and transformations to videos.

Program overview

The program we will demonstrate is the video color correction process using MoviePy. Here's a quick overview of the code:

Import necessary libraries, including MoviePy and OpenCV.
Load the video clip from the specified file path.
Define the desired brightness factor for color adjustment.
Apply color correction and contrast adjustments to the video clip.

Libraries used

The libraries we use in this program are:

MoviePy

OpenCV

For moviepy:

import cv2
from moviepy.editor import VideoFileClip, ColorClip, CompositeVideoClip
import moviepy.video.fx.all as vfx

video_path_1 = "clip.mp4"
video_clip_1 = VideoFileClip(video_path_1)

brightness_factor = 0.5  # Adjust as needed (higher values increase brightness)

color_adjusted_clip = video_clip_1.fx(vfx.colorx, brightness_factor)
final_clip = color_adjusted_clip.fx(vfx.lum_contrast, lum=50, contrast=0.8, contrast_thr=127)

for frame in final_clip.iter_frames(fps=final_clip.fps):
    frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)  # Convert from RGB to BGR
    cv2.imshow("Final Clip", frame)
    
    # Check for the 'q' key press to exit
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
cv2.destroyAllWindows()

Code explanation

Line 1–3: Import the required libraries that are cv2 which is used for computer vision operations and moviepy to handle video editing tasks and apply effects.
Line 5: Define the path of the input video file.
Line 6: Load the input video clip using VideoFileClip from MoviePy.
Line 8: Set the desired brightness factor for color adjustment. This factor determines the amount of brightness added to the video.
Line 10: Apply color correction to the video clip using the colorx effect from vfx (video effects) module. This effect adjusts the color channels of the video based on the provided brightness factor.
Line 11: Further apply contrast adjustments using the lum_contrast effect.
- lum parameter controls the brightness of the video
- contrast parameter adjusts the contrast
- contrast_thr determines the threshold for contrast adjustment.
Line 13–20: Iterate through the frames of the final clip and display them using OpenCV.
- Check for the 'q' key press to exit the loop and close the OpenCV window.

Let's take a small quiz to better understand the implemented program.