image_changer.py

import numpy as np
import cv2
import sys

filename = '[path_to_file]'

def resize_image(image):
    scale_ratio = 0.3
    width = int(image.shape[1] * scale_ratio)
    height = int(image.shape[0] * scale_ratio)
    new_dimensions = (width, height)
    resized = cv2.resize(image, new_dimensions, interpolation = cv2.INTER_AREA)
    
    return resized

def find_contours(image):
    contoured_image = image
    gray = cv2.cvtColor(contoured_image, cv2.COLOR_BGR2GRAY) 
    edged = cv2.Canny(gray, 30, 100)
    contours, hierarchy = cv2.findContours(edged, 
    cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
    cv2.drawContours(contoured_image, contours, contourIdx=-1, color=1, thickness=1)
    cv2.imshow('Image after contouring', contoured_image)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

    return contoured_image

def color_quantization(image, K=4):
    Z = image.reshape((-1, 3))
    # Convert image to numpy float32
    Z = np.float32(Z)
    
    # Define criteria and apply kmeans
    criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10000, 0.0001)
    compactness, label, center = cv2.kmeans(Z, K, None, criteria, 1, cv2.KMEANS_RANDOM_CENTERS)
    
    # Convert to uint8 and apply to the original image
    center = np.uint8(center)
    res = center[label.flatten()]
    res2 = res.reshape((image.shape))
    
    return res2
    
if __name__ == "__main__":
    image = cv2.imread(filename)
    resized_image = resize_image(image)
    coloured = color_quantization(resized_image)
    contoured = find_contours(coloured)
    final_image = contoured
    save_q = input("Save the image? [y]/[n] ")

    if save_q == "y":
        cv2.imwrite("cartoonized_"+ filename, final_image)
        print("Image saved!")