import cv2
import numpy as np

def measure_stripe_width(image_path):
    # 1. Load the image in grayscale
    img = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)

    if img is None:
        print(f"Error: Could not load image from {image_path}")
        return

    # 2. Check if the image has already been binarized, otherwise apply thresholding
    # For clean black/white images, we can assume a simple global threshold is sufficient
    # We invert the colors so that the features we are interested in (stripes) are white (255)
    _, binary_img = cv2.threshold(img, 128, 255, cv2.THRESH_BINARY)
    
    # Optional: Display the binary image to verify
    # cv2.imshow('Binary Image', binary_img)
    # cv2.waitKey(0)
    # cv2.destroyAllWindows()

    # 3. Analyze a horizontal line (a row) to find the stripe widths
    # We'll take a row from the middle of the image for robustness
    row_index = binary_img.shape[0] // 2
    row = binary_img[row_index, :]

    # 4. Find the horizontal positions where the color changes (edges)
    # We look for where the pixel value changes from one column to the next
    # A change happens where the difference is non-zero
    edges = np.where(np.abs(np.diff(row)) > 0)[0] + 1 
    
    # Add the image boundaries as 'edges' for the first and last stripe measurement
    edges = np.insert(edges, 0, 0)
    edges = np.append(edges, img.shape[1])

    # 5. Calculate the width of each stripe
    stripe_widths = np.diff(edges)

    # 6. Separate widths for black and white stripes
    # Stripes start at column 0. If the first pixel (row[0]) is black (0), 
    # then the widths are [Black, White, Black, White, ...]
    # If the first pixel is white (255), then the widths are [White, Black, White, Black, ...]
    
    is_first_stripe_white = row[0] == 255
    
    if is_first_stripe_white:
        white_widths = stripe_widths[::2]  # First, third, fifth, etc.
        black_widths = stripe_widths[1::2] # Second, fourth, sixth, etc.
    else:
        black_widths = stripe_widths[::2]  # First, third, fifth, etc.
        white_widths = stripe_widths[1::2] # Second, fourth, sixth, etc.

    # 7. Calculate and print the average widths
    avg_white_width = np.mean(white_widths) if white_widths.size > 0 else 0
    avg_black_width = np.mean(black_widths) if black_widths.size > 0 else 0

    print(f"Total stripes detected (alternating colors): {len(stripe_widths)}")
    print(f"Average White Stripe Width: {avg_white_width:.2f} pixels")
    print(f"Average Black Stripe Width: {avg_black_width:.2f} pixels")
    print(f"All Stripe Widths (alternating): {stripe_widths}")

    for s in stripe_widths:
        print(s)

measure_stripe_width("images/images-001.png")

# Example usage: Replace 'path/to/your/image.png' with the actual path
# The image should be a clear pattern of vertical black and white stripes.
# measure_stripe_width('path/to/your/image.png') 
# You'll need an image to test this.