Posterize Image

Reduce the number of colors in an image to create a poster-like effect with flat areas of color.

Request Parameters

Response

Returns the posterized image in the same format as the input file.

Example Request

    curl -X POST "https://oyyi.xyz/api/image/posterize"   -H "accept: application/json" \
  -H "Content-Type: multipart/form-data" \
  -F "file=@image.jpg" \
  -F "levels=4" \
  --output posterized.jpg
  

Example with Python

    import requests

url = "https://oyyi.xyz/api/image/posterize"

files = {
    'file': open('image.jpg', 'rb')
}

data = {
    'levels': 4
}

response = requests.post(url, files=files, data=data)

# Save the posterized image
with open('posterized.jpg', 'wb') as f:
    f.write(response.content)
  

Example with JavaScript

    // Using fetch API
const formData = new FormData();
formData.append('file', fileInput.files[0]);
formData.append('levels', '4');

fetch('https://oyyi.xyz/api/image/posterize', {
  method: 'POST',
  body: formData
})
.then(response => response.blob())
.then(blob => {
  // Create a URL for the blob
  const url = URL.createObjectURL(blob);

  // Create a link to download the image
  const a = document.createElement('a');
  a.href = url;
  a.download = 'posterized.jpg';
  a.click();

  // Clean up by revoking the URL
  URL.revokeObjectURL(url);
})
.catch(error => console.error('Error:', error));
  

Posterization Methods

How It Works

Posterization reduces the continuous gradation of tones in an image to a smaller number of distinct tones:

• Uniform method: Each color channel (R, G, B) is divided into the specified number of evenly spaced levels. For example, with 4 levels, the possible values would be 0, 85, 170, and 255.
• K-means method: The algorithm analyzes the image to find the most common or representative colors, then maps each pixel to the closest of these colors.

The result is an image with fewer colors and more distinct color boundaries, creating a stylized, poster-like effect.

Level Examples

Error Responses

Notes

• Posterization is applied to all color channels (RGB) but does not affect the alpha channel (transparency) if present.
• Lower level values create more dramatic, stylized effects.
• The k-means method is more computationally intensive but often produces more visually pleasing results.
• The maximum file size allowed is 10MB.
• Posterization can significantly reduce file size when saving to formats like PNG, as there are fewer unique colors to encode.

Use Cases

• Creating retro or vintage poster-style artwork
• Simplifying complex images for graphic design purposes
• Preparing images for screen printing or other printing techniques with limited color capabilities
• Creating stylized visual effects for social media or digital art
• Reducing file size by limiting the color palette
• Creating base images for further digital painting or illustration