Practical Color Detection

import cv2
import numpy as np

def colour_detector(image_path, target_colour="red"):
    """
    Detects objects of a specific colour and draws bounding boxes.
    Returns the processed image and detection count.
    """
    image = cv2.imread(image_path)
    hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)

    colour_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_bgr = {"red": (0,0,255), "green": (0,255,0),
                  "blue": (255,0,0), "yellow": (0,255,255)}

    # Build mask
    mask = np.zeros(hsv.shape[:2], dtype=np.uint8)
    for lower, upper in colour_ranges.get(target_colour, []):
        mask |= cv2.inRange(hsv, lower, upper)

    # Clean with morphology
    k = np.ones((5, 5), np.uint8)
    mask = cv2.morphologyEx(mask, cv2.MORPH_OPEN, k)
    mask = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, k)

    # Find contours of detected regions
    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    result = image.copy()
    detection_count = 0
    for contour in contours:
        area = cv2.contourArea(contour)
        if area > 500:  # Ignore tiny detections
            x, y, w, h = cv2.boundingRect(contour)
            color_bgr = colour_bgr.get(target_colour, (0,255,0))
            cv2.rectangle(result, (x,y), (x+w, y+h), color_bgr, 2)
            cv2.putText(result, f"{target_colour} ({area:.0f}px)",
                       (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, color_bgr, 1)
            detection_count += 1

    cv2.imwrite(f"detected_{target_colour}.jpg", result)
    print(f"Found {detection_count} {target_colour} object(s)")
    return result, detection_count

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