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Python OpenCV | cv2.cvtColor() method

Last Updated : 11 Aug, 2025

cv2.cvtColor() is an OpenCV function that converts an image from one color space to another.

It supports over 150 color conversion methods, but in most cases, only a few (like BGR↔GRAY or BGR↔RGB) are used frequently in real-world projects.

Different tasks require different representations of an image:

• Grayscale: Simplifies image analysis and reduces processing time.
• HSV (Hue, Saturation, Value): Useful for color-based segmentation and object tracking.
• LAB, YCrCb, RGB, etc.: Used in specialized applications like skin tone detection or advanced image enhancement.

cv2.cvtColor(src, code[, dst[, dstCn]])

Parameters:

• src: input image whose color space is to be changed.
• code: color space conversion code (e.g., cv2.COLOR_BGR2GRAY).
• dst(Optional): Output image of the same size and depth as src.
• dstCn(Optional): Number of channels in destination image. If 0, it’s derived automatically.

• OpenCV reads images in BGR format, not RGB.
• When displaying images using Matplotlib, you may need to convert BGR -> RGB for correct colors.
• Always choose a color conversion code that matches your source image format.

For the examples, we are using below image:

Here’s a simple Python code using OpenCV to read an image, convert it to grayscale and display it in a window.

import cv2
src = cv2.imread(r'logo.png')  # Read the image

# Convert to Grayscale
gray_image = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)

# Display
cv2.imshow("Grayscale Image", gray_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

Output

Explanation:

• cv2.cvtColor(src, cv2.COLOR_BGR2GRAY): Converts image to grayscale.
• cv2.imshow("Grayscale Image", gray_image): Displays grayscale image in a window.
• cv2.waitKey(0): Waits for a key press to keep window open.
• cv2.destroyAllWindows(): Closes all OpenCV windows.

Here’s a simple Python code using OpenCV to read an image, convert it from BGR to HSV color space and display the result.

import cv2
src = cv2.imread(r'logo.png')

# Convert to HSV
hsv_image = cv2.cvtColor(src, cv2.COLOR_BGR2HSV)

cv2.imshow("HSV Image", hsv_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

Output

Explanation:

• cv2.cvtColor(src, cv2.COLOR_BGR2HSV): Converts BGR image to HSV color space.
• cv2.imshow("HSV Image", hsv_image): Displays HSV image in a window titled "HSV Image".

This Python code reads an image using OpenCV, converts it from BGR to RGB color format and then displays it using Matplotlib.

import cv2
import matplotlib.pyplot as plt
src = cv2.imread(r'logo.png')

# Convert from BGR to RGB
rgb_image = cv2.cvtColor(src, cv2.COLOR_BGR2RGB)

# Display with Matplotlib
plt.imshow(rgb_image)
plt.title("RGB Image for Matplotlib")
plt.axis('off')
plt.show()

Output

Explanation:

• cv2.cvtColor(src, cv2.COLOR_BGR2RGB): Converts image from BGR to RGB format for correct color display in Matplotlib.
• plt.imshow(rgb_image): Displays the RGB image using Matplotlib.
• plt.axis('off'): Hides the axis for a cleaner view.

Let’s see some of the most commonly used OpenCV color conversion codes:

• cv2.COLOR_BGR2GRAY BGR: Grayscale
• cv2.COLOR_BGR2RGB BGR: RGB
• cv2.COLOR_BGR2HSV BGR: HSV
• cv2.COLOR_BGR2LAB BGR: LAB color space
• cv2.COLOR_BGR2YCrCb BGR: YCrCb (used in compression & skin detection)

Different color spaces are preferred for specific computer vision tasks because they highlight certain image features better. Let’s see some real-world applications of these conversions:

• Grayscale: Face detection, OCR (text recognition)
• HSV: Object tracking (e.g., detecting a colored ball)
• LAB: Color enhancement, skin tone detection
• YCrCb: Video compression, human skin detection

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