add cartoonify images tutorial

Author: x4nth055

README.md

@@ -94,6 +94,7 @@ This is a repository of all the tutorials of [The Python Code](https://www.thepy
         - [How to Edit Images using InstructPix2Pix in Python](https://www.thepythoncode.com/article/edit-images-using-instruct-pix2pix-with-huggingface). ([code](machine-learning/edit-images-instruct-pix2pix))
         - [How to Upscale Images using Stable Diffusion in Python](https://www.thepythoncode.com/article/upscale-images-using-stable-diffusion-x4-upscaler-huggingface). ([code](machine-learning/stable-diffusion-upscaler))
         - [Real-Time Vehicle Detection, Tracking and Counting in Python](https://thepythoncode.com/article/real-time-vehicle-tracking-and-counting-with-yolov8-opencv). ([code](https://github.com/python-dontrepeatyourself/Real-Time-Vehicle-Detection-Tracking-and-Counting-in-Python/))
+        - [How to Cartoonify Images in Python](https://thepythoncode.com/article/make-a-cartoonifier-with-opencv-in-python). ([code](machine-learning/cartoonify-images))
     - [Building a Speech Emotion Recognizer using Scikit-learn](https://www.thepythoncode.com/article/building-a-speech-emotion-recognizer-using-sklearn). ([code](machine-learning/speech-emotion-recognition))
     - [How to Convert Speech to Text in Python](https://www.thepythoncode.com/article/using-speech-recognition-to-convert-speech-to-text-python). ([code](machine-learning/speech-recognition))
     - [Top 8 Python Libraries For Data Scientists and Machine Learning Engineers](https://www.thepythoncode.com/article/top-python-libraries-for-data-scientists).

machine-learning/cartoonify-images/README.md

@@ -0,0 +1 @@
+# [How to Cartoonify Images in Python](https://thepythoncode.com/article/make-a-cartoonifier-with-opencv-in-python)

machine-learning/cartoonify-images/cartooned_hazrard2.jpg

@@ -0,0 +1,44 @@
+import cv2, argparse, sys
+
+
+# In this function, we accept an image and convert it to a cartoon form.
+def cartoonizer(image_name):
+    # Load the image to cartoonize.
+    image_to_animate = cv2.imread(image_name)
+
+    # Apply a bilateral filter to smoothen the image while preserving edges.
+    smoothened_image = cv2.bilateralFilter(image_to_animate, d=9, sigmaColor=75, sigmaSpace=75)
+
+    # Convert image to gray and create an edge mask using adaptive thresholding.
+    gray_image = cv2.cvtColor(smoothened_image, cv2.COLOR_BGR2GRAY)
+    edges = cv2.adaptiveThreshold(gray_image, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 9, 9)
+
+    # Combine the smoothened image and the edge mask to create a cartoon-like effect.
+    to_cartoon = cv2.bitwise_and(smoothened_image, smoothened_image, mask=edges)
+
+    # Save the cartoon image in our current directory. A new Image would be generated in your current directory.
+    cartooned_image = f"cartooned_{image_name}"
+    cv2.imwrite(cartooned_image, to_cartoon)
+
+    # Display the result.
+    cv2.imshow("Cartooned Image", to_cartoon)
+    cv2.waitKey(0)
+    cv2.destroyAllWindows()
+
+
+# In this function, we accept user's argument from the terminal. -i or --image to specify the image.
+def get_image_argument():
+    parser = argparse.ArgumentParser(description="Please specify an image to 'cartoonify'.")
+    parser.add_argument('-i', '--image', help="Please use -h or --help to see usage.", dest='image')
+    argument = parser.parse_args()
+
+    if not argument.image:
+        print("[-] Please specify an image. Use --help to see usage.")
+        sys.exit()  # Exit the program
+
+    return argument
+
+
+# We get the user's input (image) from the terminal and pass it into cartoonizer function.
+image_args = get_image_argument()
+cartoonizer(image_args.image)

machine-learning/cartoonify-images/cartoonifier.py

@@ -0,0 +1 @@
+opencv-python