Lab Color Space Based Color Detection

This function reads an image from a specified path, converts the image to the Lab color space, detects areas within a specific color range in the Lab space, and draws the detected color areas on the original image.

Technology Stack : opencv-python, numpy, skimage

Code Type : Image processing

Code Difficulty : Intermediate

                
                    
def random_color_detection(image_path):
    import cv2
    import numpy as np
    from skimage.color import rgb2lab

    # Read the image
    image = cv2.imread(image_path)

    # Convert the image to Lab color space
    lab_image = rgb2lab(image)

    # Define the range for the color we want to detect
    lower_color = np.array([20, 100, 100])
    upper_color = np.array([30, 255, 255])

    # Create a mask for the color range
    mask = cv2.inRange(lab_image, lower_color, upper_color)

    # Find contours in the mask
    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    # Draw contours on the original image
    cv2.drawContours(image, contours, -1, (0, 255, 0), 3)

    # Display the image
    cv2.imshow('Detected Color', image)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

    return image