Color Quantizer and Clustering

This function block allows users to apply color quantization to an image using the K-means clustering algorithm. It helps in reducing the number of colors in an image to a specified level (K), enhancing the visual representation by clustering similar colors together.

📥 Inputs

Image Any The input image that you want to apply color quantization to.

📤 Outputs

Image Any The output image after color quantization, where similar colors are grouped together.

Clustered Colors (B,G,R) This output provides the RGB values of the clustered colors found in the processed image, allowing for further reference or analysis.

🕹️ Controls

K Coefficient A slider that allows you to adjust the value of K, which determines the number of clusters (or colors) in the quantization process. Moving the slider changes the number of unique colors in the output image.

🎨 Features

Dynamic Color Reduction Users can interactively adjust the K coefficient to see how the color representation changes in real time.

Color Analysis The block provides insight into the dominant colors present in the image after quantization, aiding in color analysis tasks.

📝 Usage Instructions

1. Connect Input Image: Connect any image to the Image Any input to apply color quantization.

2. Adjust Color Clusters: Use the K Coefficient slider to specify the number of clusters you want to create. Values typically range from 1 to 8, depending on your needs.

3. Evaluate: Run the block to see the quantized image and the clustered color values, which will be produced as outputs.

📊 Evaluation

Upon evaluation, this function block processes the input image and produces a quantized version alongside the RGB values of clustered colors, facilitating color reduction and analysis tasks.

💡 Tips and Tricks

Choosing K Effectively

A smaller K value will yield a more abstract image, while a larger K value retains more detail. Experiment with different K values to find the best visual representation for your specific image.

Post-processing

After quantization, consider applying an Image Filter or Auto Contrast to further enhance the visual quality of the quantized image.

Use with Other Blocks

You can link the output of this block to an Image Logger to analyze saved colors or pass the quantized image to other processing blocks like Apply Mask to isolate specific clustered colors.

🛠️ Troubleshooting

No Output Image

If you're not seeing any output image, check to ensure that the input image is properly connected and not empty. Invalid or corrupt images can lead to no output being generated.

Unexpected Colors in Output

If the output colors seem unexpected, try adjusting the K coefficient. The clustering algorithm may not yield satisfactory results for certain images if K is not set appropriately.