count_curtain_hooks.py

import cv2

def count_curtain_hooks(image_path):
    # Load and check image
    image = cv2.imread(image_path)
    if image is None:
        raise ValueError("Could not load image.")

    # Preprocessing: Convert to grayscale, apply Gaussian blur, and detect edges
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    blurred = cv2.GaussianBlur(gray, (5, 5), 0)
    edges = cv2.Canny(blurred, 30, 80)

    # Find contours and filter based on area
    contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
    min_contour_area = 200  # Adjust based on hook size
    filtered_contours = [contour for contour in contours if cv2.contourArea(contour) > min_contour_area]

    # Create debug image with all contours (before filtering)
    debug_image = image.copy()
    cv2.drawContours(debug_image, contours, -1, (255, 0, 0), 1)
    cv2.imshow("All Contours (Before Filtering)", debug_image)

    # Draw filtered contours with labels on the result image
    result_image = image.copy()
    for i, contour in enumerate(filtered_contours):
        # Draw contour and label it with the index
        cv2.drawContours(result_image, [contour], -1, (0, 255, 0), 2)
        M = cv2.moments(contour)
        cX = int(M["m10"] / M["m00"]) if M["m00"] != 0 else 0
        cY = int(M["m01"] / M["m00"]) if M["m00"] != 0 else 0
        cv2.putText(result_image, str(i + 1), (cX, cY), cv2.FONT_HERSHEY_SIMPLEX, 2, (255, 0, 0), 5)

    # Display result
    cv2.imshow("Labeled Hooks", result_image)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

    return len(filtered_contours)

# Example usage
image_path = "curtain_hooks.jpg"
num_hooks = count_curtain_hooks(image_path)
print(f"Number of hooks detected: {num_hooks}")