Dominant Color

Class: DominantColorBlockV1

Source: inference.core.workflows.core_steps.classical_cv.dominant_color.v1.DominantColorBlockV1

Extract the dominant color from an input image using K-means clustering.

This block identifies the most prevalent color in an image. Processing time is dependant on color complexity and image size. Most images should complete in under half a second.

The output is a list of RGB values representing the dominant color, making it easy to use in further processing or visualization tasks.

Note: The block operates on the assumption that the input image is in RGB format.

Type identifier

Use the following identifier in step "type" field: roboflow_core/dominant_color@v1to add the block as as step in your workflow.

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
color_clusters	int	Number of dominant colors to identify. Higher values increase precision but may slow processing..	✅
max_iterations	int	Max number of iterations to perform. Higher values increase precision but may slow processing..	✅
target_size	int	Sets target for the smallest dimension of the downsampled image in pixels. Lower values increase speed but may reduce precision..	✅

The Refs column marks possibility to parametrise the property with dynamic values available in workflow runtime. See Bindings for more info.

Available Connections

Compatible Blocks

Check what blocks you can connect to Dominant Color in version v1.

• inputs: Halo Visualization, Mask Visualization, Dynamic Crop, Label Visualization, Color Visualization, Polygon Zone Visualization, Stitch Images, Pixelate Visualization, Image Threshold, Relative Static Crop, Corner Visualization, Distance Measurement, Stability AI Image Generation, Pixel Color Count, Circle Visualization, Polygon Visualization, Model Comparison Visualization, Depth Estimation, Absolute Static Crop, Camera Calibration, Perspective Correction, Blur Visualization, Stability AI Inpainting, Grid Visualization, Image Preprocessing, Line Counter, Image Blur, Classification Label Visualization, Camera Focus, Image Slicer, Trace Visualization, Bounding Box Visualization, Stability AI Outpainting, Crop Visualization, SIFT Comparison, Reference Path Visualization, SIFT, Line Counter, Keypoint Visualization, SIFT Comparison, Ellipse Visualization, Triangle Visualization, Line Counter Visualization, Image Slicer, Dot Visualization, Image Convert Grayscale, Template Matching, Background Color Visualization, Image Contours
• outputs: Dynamic Crop, Pixel Color Count

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Dominant Color in version v1 has.

Bindings

• input

  • image (image): The input image for this step..
  • color_clusters (integer): Number of dominant colors to identify. Higher values increase precision but may slow processing..
  • max_iterations (integer): Max number of iterations to perform. Higher values increase precision but may slow processing..
  • target_size (integer): Sets target for the smallest dimension of the downsampled image in pixels. Lower values increase speed but may reduce precision..

• output

  • rgb_color (rgb_color): RGB color.

Example JSON definition of step Dominant Color in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/dominant_color@v1",
    "image": "$inputs.image",
    "color_clusters": 4,
    "max_iterations": 100,
    "target_size": 100
}