CartoonifyMe script by ChatGPT4

CartoonifyMe_by_chatgpt-4.py

# pip install opencv-python
# pip install opencv-python-headless
# pip install numpy
# pip install scikit-learn


import base64
import cv2
import numpy as np
from PIL import Image
from io import BytesIO
from sklearn.cluster import KMeans

def cartoonize_image(image, num_colors=16, edge_thickness=3):
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    gray = cv2.medianBlur(gray, 7)
    edges = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 9, 9)
    
    if edge_thickness > 1:
        kernel = np.ones((edge_thickness, edge_thickness), np.uint8)
        edges = cv2.dilate(edges, kernel, iterations=1)

    color = cv2.bilateralFilter(image, 9, 250, 250)
    color = quantize_colors(color, num_colors)
    
    cartoon = cv2.bitwise_and(color, color, mask=edges)
    return cartoon

def quantize_colors(image, num_colors):
    data = np.float32(image).reshape((-1, 3))
    kmeans = KMeans(n_clusters=num_colors)
    labels = kmeans.fit_predict(data)
    centers = kmeans.cluster_centers_.astype("uint8")
    segmented_image = centers[labels.flatten()].reshape(image.shape)
    return segmented_image

def base64_to_image(base64_string):
    img_data = base64.b64decode(base64_string)
    return np.array(Image.open(BytesIO(img_data)))

def image_to_base64(image):
    img_pil = Image.fromarray(image)
    buffer = BytesIO()
    img_pil.save(buffer, format="PNG")
    return base64.b64encode(buffer.getvalue()).decode("utf-8")

# Replace this with your base64 string
base64_string = "your_base_64_image"

image = base64_to_image(base64_string)
cartoon_image = cartoonize_image(image, num_colors=16, edge_thickness=3)
cartoon_base64_string = image_to_base64(cartoon_image)

print(cartoon_base64_string)