import cv2

num_down = 2 
# number of downsampling steps

num_bilateral = 7 # number of bilateral filtering steps

img_rgb = cv2.imread("myCat.jpg")

# downsample image using Gaussian pyramid

img_color = img_rgb

for _ in range(num_down):

   img_color = cv2.pyrDown(img_color)

# repeatedly apply small bilateral filter instead of

# applying one large filter

for _ in range(num_bilateral):

img_color = cv2.bilateralFilter(img_color, d=9, sigmaColor=9, sigmaSpace=7)

# upsample image to original size

for _ in range(num_down):

   img_color = cv2.pyrUp(img_color)

#STEP 2 & 3

#Use median filter to reduce noise

# convert to grayscale and apply median blur

img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_RGB2GRAY)

img_blur = cv2.medianBlur(img_gray, 7)

#STEP 4

#Use adaptive thresholding to create an edge mask

# detect and enhance edges

img_edge = cv2.adaptiveThreshold(img_blur, 255,

   cv2.ADAPTIVE_THRESH_MEAN_C,

   cv2.THRESH_BINARY,

   blockSize=9,

   C=2)

# Step 5

# Combine color image with edge mask & display picture

# convert back to color, bit-AND with color image

img_edge = cv2.cvtColor(img_edge, cv2.COLOR_GRAY2RGB)

img_cartoon = cv2.bitwise_and(img_color, img_edge)

# display

cv2.imshow("myCat_cartoon", img_cartoon)