main.py

import cv2
import numpy as np

img = cv2.imread('messi.jpg')

img_gb = cv2.GaussianBlur(img, (7, 7) ,0)
img_mb = cv2.medianBlur(img_gb, 5)
img_bf = cv2.bilateralFilter(img_mb, 5, 80, 80)

# laplace filter to detect the edge

img_lp_im = cv2.Laplacian(img, cv2.CV_8U, ksize=5)
img_lp_gb = cv2.Laplacian(img_gb, cv2.CV_8U, ksize=5)
img_lp_mb = cv2.Laplacian(img_mb, cv2.CV_8U, ksize=5)
img_lp_al = cv2.Laplacian(img_bf, cv2.CV_8U, ksize=5)

img_lp_im_grey = cv2.cvtColor(img_lp_im, cv2.COLOR_BGR2GRAY)
img_lp_gb_grey = cv2.cvtColor(img_lp_gb, cv2.COLOR_BGR2GRAY)
img_lp_mb_grey = cv2.cvtColor(img_lp_mb, cv2.COLOR_BGR2GRAY)
img_lp_al_grey = cv2.cvtColor(img_lp_al, cv2.COLOR_BGR2GRAY)

_, EdgeImage = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)

blur_im = cv2.GaussianBlur(img_lp_im_grey, (5, 5), 0)
blur_gb = cv2.GaussianBlur(img_lp_gb_grey, (5, 5), 0)
blur_mb = cv2.GaussianBlur(img_lp_mb_grey, (5, 5), 0)
blur_al = cv2.GaussianBlur(img_lp_al_grey, (5, 5), 0)
# Apply a threshold (Otsu)
_, tresh_im = cv2.threshold(blur_im, 245, 255,cv2.THRESH_BINARY +  cv2.THRESH_OTSU)
_, tresh_gb = cv2.threshold(blur_gb, 245, 255,cv2.THRESH_BINARY + cv2.THRESH_OTSU)
_, tresh_mb = cv2.threshold(blur_mb, 245, 255,cv2.THRESH_BINARY + cv2.THRESH_OTSU)
_, tresh_al = cv2.threshold(blur_al, 245, 255,cv2.THRESH_BINARY + cv2.THRESH_OTSU)


inverted_original = cv2.subtract(255, tresh_im)
inverted_GaussianBlur = cv2.subtract(255, tresh_gb)
inverted_MedianBlur = cv2.subtract(255, tresh_mb)
inverted_Bilateral = cv2.subtract(255, tresh_al)

img_reshaped = img.reshape((-1,3))
img_reshaped = np.float32(img_reshaped)
# Set the Kmeans criteria
criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 10, 1.0)
K = 8
# Apply Kmeans
_, label, center = cv2.kmeans(img_reshaped, K, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS)
center = np.uint8(center)
res = center[label.flatten()]
# Reshape it back to an image
img_Kmeans = res.reshape((img.shape))

div = 64
img_bins = img // div * div + div // 2

# Convert the mask image back to color 
inverted_Bilateral = cv2.cvtColor(inverted_Bilateral, cv2.COLOR_GRAY2RGB)
# Combine the edge image and the binned image
cartoon_Bilateral = cv2.bitwise_and(inverted_Bilateral, img_bins)
# Save the image
cv2.imwrite('Cartoonimage.png', cartoon_Bilateral)