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