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Color Quantizer and Clustering

Color Quantization and Clustering Node Documentation
The Color Quantization and Clustering node in AugeLab Studio performs color quantization and clustering on an input image. It reduces the number of colors in the image and groups similar colors together using the K-means clustering algorithm.
Node Overview
The Color Quantization and Clustering node reduces the number of colors in an image and groups similar colors together using the K-means clustering algorithm. It produces two outputs: the quantized image and the clustered colors.
Node Properties
Node Title: Color Quantizer and Clustering
Node ID: OP_NODE_COLOR_QUANTIZATION
Inputs
The Color Quantization and Clustering node has the following input socket:
Image: The input image to perform color quantization and clustering. Connect an image to this socket.
Outputs
The Color Quantization and Clustering node has the following output sockets:
Quantized Image: The quantized version of the input image with a reduced number of colors.
Clustered Colors (B,G,R): The RGB values of the clustered colors in the image.
Node Configuration
The Color Quantization and Clustering node provides the following configuration options:
K Coefficient: Adjusts the number of colors to quantize and cluster. Higher values result in more colors.
Usage
1.Drag and drop the Color Quantization and Clustering node from the node library onto the canvas in AugeLab Studio.
2.Connect the input image to the Image input socket of the Color Quantization and Clustering node.
3.Adjust the K Coefficient slider to set the number of colors for quantization and clustering. Higher values result in more colors.
4.Run the pipeline.
5.The Color Quantization and Clustering node will perform color quantization and clustering on the input image.
6.The quantized version of the image with a reduced number of colors will be available at the Quantized Image output socket.
7.The RGB values of the clustered colors will be available at the Clustered Colors (B,G,R) output socket.
Notes
The Color Quantization and Clustering node is useful for reducing the number of colors in an image and grouping similar colors together.
Adjust the K Coefficient to control the number of colors for quantization and clustering. Higher values result in more colors.
The node utilizes the K-means clustering algorithm to group similar colors together.
The quantized image output represents the input image with a reduced number of colors.
The clustered colors output provides the RGB values of the colors obtained from clustering.
Experiment with different values of the K Coefficient to achieve the desired level of color reduction and clustering.
That concludes the documentation for the Color Quantization and Clustering node in AugeLab Studio. This node provides a powerful tool for reducing the number of colors in an image and grouping similar colors together using the K-means clustering algorithm.

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