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