Image Cartoonizer

A cartoonization project that takes an image provided by the user and applies filters and transformations to create a cartoon-like version of the original image. This is be done using various image processing techniques and algorithms provided by OpenCV library in Python.

• Ability to upload images from the user's device
• Real-time preview of the cartoonized image
• Option to save or share the cartoonized image
• User-friendly interface and easy-to-use controls

We have used OpenCV library in Python for applying all the image processing and masking options:

• Reading the image

  We extract the image from the request object of the Flask server and process it using Pillow and then convert it into a numpy array for further processing as cv2.imread() cannot take an image(file) as an input parameter

  file = request.files['image']
  image = Image.open(file)
  img_np = np.array(image)

• Creating an edge mask

  Cartoons usually contain a distinguishable edge. We can detect the edge in an image by using the cv2.adaptiveThreshold() function.

  def edge_mask(img, line_size, blur_value):
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    gray_blur = cv2.medianBlur(gray, blur_value)
    
    return cv2.adaptiveThreshold(
        gray_blur,
        255,
        cv2.ADAPTIVE_THRESH_MEAN_C,
        cv2.THRESH_BINARY,
        line_size,
        blur_value,
    )

• Decreasing the number of colors in the image

  A drawing has fewer colors than a photo. Therefore, we use color quantization to reduce the number of colors in the photo. We use K-Means clustering algorithm which is provided by the OpenCV library.

    # K-Means clustering algo, we use a k-value of 9 here
    ret, label, center = cv2.kmeans(
      data, 
      k, 
      None, 
      criteria, 
      10, 
      cv2.KMEANS_RANDOM_CENTERS
    )
    
    center = np.uint8(center)
    result = center[label.flatten()]
    result = result.reshape(img.shape) # Final image

  Then we blur the image for redusing some of the noise. We choose the bilateralFilter for blurring the image as it replaces the intensity of each pixel with a weighted average of intensity values from nearby pixels.

  blurred = cv2.bilateralFilter(
      img_np, 
      d=7, 
      sigmaColor=200, 
      sigmaSpace=200
    )

• Combining edge mask with original image

  Lastly, we combine the edge mask with the blurred image. To do so, use the cv2.bitwise_and method.

  cartoon = cv2.bitwise_and(blurred, blurred, mask=edges)

  POST /api/cartoonize

Takes the file uploaded by the user in the frontend and converts it into an input buffer using the numpy library in Python and then applies image processing and blurring techniques provided by OpenCV.

Request object: The file uploaded by the user is sent in the request object to the server. This is done in the frontend by using the FormData object and Axios package.

const formData = new FormData();
formData.append('image', selectedImage);
await axios.request({
  method: 'POST',
  url: 'http://localhost:5000/api/cartoonize',
  data: formData,  //the image is attached in the formData variable
  headers: {
    'Content-Type': 'multipart/form-data',
  },
  withCredentials: true,
  responseType: 'arraybuffer', //response type of the server route
})

Response object: The cartoonized image is converted to array buffer of bytes and send in the Response object having the property mimetype='image/jpeg'.

  img_byte_arr = io.BytesIO() # convert to bytes buffer
  cartoon.save(img_byte_arr, format='JPEG')
  img_byte_arr = img_byte_arr.getvalue() # get the value of the converted filetype

  return Response(img_byte_arr, mimetype='image/jpeg') # Flask Response object

The project is built with Python and JavaScript(React)

  cd /any-folder
  git clone https://github.com/DarthSalad/image-cartoonizer.git

• Go into the folder to build the virtual environment for running the Python web app through Flask

  cd image-cartoonizer
  python -m venv ./venv

• Activate the virtual environment
  • Windows (PowerShell)

  ./venv/Scripts/Activate.ps1

  • Linux

  source ./venv/bin/activate

• Install the dependencies

pip install -r "requirements.txt"

• Run the app

python main.py

• Go into the web-app folder

cd web-app

• Run the npm command to install all the dependencies

npm i

• Run the React app

npm start

• @DarthSalad
• @karteek-02
• @shiyo2002