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

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
}