pyPixelateE.py

import cv2
import numpy as np
from multiprocessing import Pool, cpu_count
from sys import argv

PADDING = 2000
GRID_LINES = True

def makePixelate(img, output_img, imheight, imwidth, width, height, block_size, fileName):
    for i in range(0, imheight // block_size):
        for j in range(0, imwidth // block_size):

            x, x2 = j * width, (j + 1) * width
            y, y2 = i * height, (i + 1) * height

            a = img[y:y2, x:x2]
            a = np.array(a)

            mean_color = np.mean(a, axis=(0, 1))
            mean_color = tuple([int(mean_color[0]), int(mean_color[1]), int(mean_color[2])])
                
            if GRID_LINES:
                a = np.ones((height-2, width-2, 3), np.uint8)*mean_color
                a = cv2.copyMakeBorder(a, 1, 1, 1, 1, borderType=cv2.BORDER_CONSTANT,value=0)
            else:
                a = np.ones((height, width, 3), np.uint8)*mean_color

            output_img[y:y2, x:x2] = a
    
    lab= cv2.cvtColor(output_img, cv2.COLOR_BGR2LAB)
    l, a, b = cv2.split(lab)
    clahe = cv2.createCLAHE(clipLimit=1.0, tileGridSize=(8,8))
    cl = clahe.apply(l)
    limg = cv2.merge((cl,a,b))
    output_img = cv2.cvtColor(limg, cv2.COLOR_LAB2BGR)
    cv2.imwrite(fileName, output_img)

def pixelate(img, block_size, fileName):
    img = cv2.imread(img)
    img = cv2.resize(img, (PADDING,PADDING), interpolation = cv2.INTER_LINEAR)
    imwidth, imheight = img.shape[:2]

    height = imheight // (imheight // block_size)
    width = imwidth // (imwidth // block_size)
    
    output_img = np.ones((PADDING,PADDING,3), np.uint8)*255

    pool = Pool(cpu_count())
    pool.starmap(makePixelate, [(img, output_img, imheight, imwidth, width, height, block_size, fileName)])
    pool.close()

if __name__ == "__main__":
    if len(argv) != 4:
        print("Usage: python3 pyPixelateE.py <image> <block size (can be divided with PADDING)> <output image (e.g. 1.jpg)>")
        exit(1)
    pixelate(argv[1], int(argv[2]), argv[3])