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:
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
}