Color Detection

import cv2
import numpy as np

def detect_colour(image_path, colour_name):
    """Detect a specific colour in an image using HSV masking."""

    image = cv2.imread(image_path)
    hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)

    # Define HSV ranges for different colours
    ranges = {
        "red": [
            (np.array([0, 100, 100]),   np.array([10, 255, 255])),
            (np.array([160, 100, 100]), np.array([179, 255, 255])),
        ],
        "green": [(np.array([35, 100, 100]), np.array([85, 255, 255]))],
        "blue":  [(np.array([100, 100, 100]), np.array([130, 255, 255]))],
        "yellow":[(np.array([20, 100, 100]), np.array([35, 255, 255]))],
    }

    colour = colour_name.lower()
    if colour not in ranges:
        print(f"Colour '{colour}' not in database")
        return

    # Create mask by combining all ranges for this colour
    mask = np.zeros(hsv.shape[:2], dtype=np.uint8)
    for lower, upper in ranges[colour]:
        mask |= cv2.inRange(hsv, lower, upper)

    # Count detected pixels
    detected_pixels = cv2.countNonZero(mask)
    total_pixels = mask.shape[0] * mask.shape[1]
    percentage = detected_pixels / total_pixels * 100

    print(f"Colour '{colour}' detected:")
    print(f"  Pixels: {detected_pixels:,} / {total_pixels:,}")
    print(f"  Coverage: {percentage:.2f}% of image")

    # Apply mask to original image
    result = cv2.bitwise_and(image, image, mask=mask)
    cv2.imwrite(f"detected_{colour}.jpg", result)
    cv2.imwrite(f"mask_{colour}.jpg", mask)

detect_colour("photo.jpg", "blue")