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