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@v1
to 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:
Blur Visualization
,Camera Focus
,SIFT Comparison
,Line Counter
,Image Threshold
,Polygon Zone Visualization
,Stability AI Inpainting
,Relative Static Crop
,Image Preprocessing
,Keypoint Visualization
,Background Color Visualization
,Grid Visualization
,Pixel Color Count
,Image Convert Grayscale
,Trace Visualization
,Absolute Static Crop
,Color Visualization
,Perspective Correction
,Distance Measurement
,Classification Label Visualization
,Circle Visualization
,Camera Calibration
,Pixelate Visualization
,Image Slicer
,Label Visualization
,Halo Visualization
,Triangle Visualization
,Reference Path Visualization
,Image Slicer
,Line Counter Visualization
,Line Counter
,Image Blur
,Corner Visualization
,SIFT Comparison
,Template Matching
,Image Contours
,Dynamic Crop
,Polygon Visualization
,Depth Estimation
,SIFT
,Ellipse Visualization
,Mask Visualization
,Stitch Images
,Bounding Box Visualization
,Dot Visualization
,Stability AI Image Generation
,Model Comparison Visualization
,Crop 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
}