flatLine.py

flatLine.py

#https://github.com/Kazuhito00/XDoG-OpenCV-Sample
import os
import cv2
import numpy as np

def DoG_filter(image, kernel_size=0, sigma=1.0, k_sigma=2.0, gamma=1.5):
    g1 = cv2.GaussianBlur(image, (kernel_size, kernel_size), sigma)
    g2 = cv2.GaussianBlur(image, (kernel_size, kernel_size), sigma * k_sigma)
    return g1 - gamma * g2

def XDoG_filter(image, kernel_size=0, sigma=1.4, k_sigma=1.6, epsilon=0, phi=10, gamma=0.98):
    epsilon /= 255
    dog = DoG_filter(image, kernel_size, sigma, k_sigma, gamma)
    dog /= dog.max()
    e = 1 + np.tanh(phi * (dog - epsilon))
    e[e >= 1] = 1
    return (e * 255).astype('uint8')

def binarize_image(image):
    _, binarized = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
    return binarized

def skeletonize_and_dilate_image(image):
    inverted = cv2.bitwise_not(image)
    skeleton = cv2.ximgproc.thinning(inverted, thinningType=cv2.ximgproc.THINNING_ZHANGSUEN)
    return cv2.bitwise_not(skeleton)

def process_XDoG(input_dir, output_dir, kernel_size=0, sigma=1.4, k_sigma=1.6, epsilon=0, phi=10, gamma=0.98):
    if not os.path.exists(output_dir):
        os.makedirs(output_dir)
    for filename in os.listdir(input_dir):
        if filename.lower().endswith((".jpg", ".jpeg", ".png", "webp")):
            image_path = os.path.join(input_dir, filename)
            image = cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)
            if image is None:
                print(f"Failed to load {filename}")
                continue
            xdog_image = XDoG_filter(image, kernel_size, sigma, k_sigma, epsilon, phi, gamma)
            binarized_image = binarize_image(xdog_image)
            final_image = skeletonize_and_dilate_image(binarized_image)
            
            # Invert black and white colors
            final_image = 255 - final_image

            output_path = os.path.join(output_dir, os.path.splitext(filename)[0] + '.webp')
            cv2.imwrite(output_path, final_image)
            print(f"Processed {filename} and saved as {output_path}")

if __name__ == '__main__':
    input_dir = "E:/desktop/part_b_c/partc"
    output_dir = "E:/desktop/part_c_x"
    process_XDoG(input_dir, output_dir)