OpenCV Image Color Detection and Visualization

This function uses the OpenCV library to detect red, green, and blue regions in an image and draws corresponding rectangles on the original image.

Technology Stack : OpenCV, NumPy, Python

Code Type : Image processing

Code Difficulty : Intermediate

                
                    
def random_color_detection(image_path):
    import cv2
    import numpy as np
    import random

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

    # Convert the image to HSV color space
    hsv_image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)

    # Define range of colors to detect
    colors = {
        'red': ([0, 120, 70], [10, 255, 255]),
        'green': ([36, 25, 25], [86, 255, 255]),
        'blue': ([100, 150, 0], [140, 255, 255])
    }

    # Initialize list to store the detected colors
    detected_colors = []

    # Loop through each color and detect it
    for color, (lower, upper) in colors.items():
        # Create a mask for the current color
        mask = cv2.inRange(hsv_image, lower, upper)

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

        # Loop through each contour and draw it on the original image
        for contour in contours:
            if cv2.contourArea(contour) > 100:  # Filter out small contours
                (x, y, w, h) = cv2.boundingRect(contour)
                cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
                detected_colors.append(color)

    # Display the image with detected colors
    cv2.imshow('Detected Colors', image)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

    return detected_colors