Comment on page
Color Quantizer and Clustering
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.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 Title: Color Quantizer and Clustering
- Node ID: OP_NODE_COLOR_QUANTIZATION
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.
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.
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.
- 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.
- 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.Last modified 4mo ago