Pixel Color Count¶
Class: PixelationCountBlockV1
Source: inference.core.workflows.core_steps.classical_cv.pixel_color_count.v1.PixelationCountBlockV1
Count the number of pixels that match a specific color within a given tolerance.
Type identifier¶
Use the following identifier in step "type" field: roboflow_core/pixel_color_count@v1to add the block as
as step in your workflow.
Properties¶
| Name | Type | Description | Refs |
|---|---|---|---|
name |
str |
Enter a unique identifier for this step.. | ❌ |
target_color |
Union[Tuple[int, int, int], str] |
Target color to count in the image. Can be a hex string (like '#431112') RGB string (like '(128, 32, 64)') or a RGB tuple (like (18, 17, 67)).. | ✅ |
tolerance |
int |
Tolerance for color matching.. | ✅ |
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 Pixel Color Count in version v1.
- inputs:
Circle Visualization,Background Color Visualization,Corner Visualization,Twilio SMS Notification,Slack Notification,VLM as Detector,LMM,Polygon Zone Visualization,Camera Focus,Image Slicer,Line Counter,Image Blur,Dot Visualization,Google Gemini,Roboflow Dataset Upload,Stability AI Inpainting,Pixelate Visualization,Line Counter,OpenAI,Distance Measurement,Image Convert Grayscale,Absolute Static Crop,Stability AI Image Generation,Dominant Color,Webhook Sink,Color Visualization,Image Threshold,Halo Visualization,Polygon Visualization,VLM as Classifier,CogVLM,Camera Calibration,Email Notification,Classification Label Visualization,Single-Label Classification Model,Llama 3.2 Vision,Google Vision OCR,Roboflow Dataset Upload,Ellipse Visualization,Pixel Color Count,Bounding Box Visualization,Object Detection Model,Line Counter Visualization,Image Preprocessing,Trace Visualization,Label Visualization,Local File Sink,Image Slicer,Anthropic Claude,Crop Visualization,OCR Model,Relative Static Crop,Model Comparison Visualization,Stitch OCR Detections,Perspective Correction,OpenAI,Mask Visualization,Clip Comparison,Dynamic Crop,Template Matching,CSV Formatter,Florence-2 Model,Instance Segmentation Model,Keypoint Detection Model,Image Contours,SIFT,SIFT Comparison,Reference Path Visualization,Florence-2 Model,Triangle Visualization,Model Monitoring Inference Aggregator,SIFT Comparison,Keypoint Visualization,Multi-Label Classification Model,LMM For Classification,Roboflow Custom Metadata,Grid Visualization,Stitch Images,Blur Visualization - outputs:
Circle Visualization,Corner Visualization,Bounding Box Visualization,Twilio SMS Notification,Slack Notification,Line Counter Visualization,Image Preprocessing,Object Detection Model,Keypoint Detection Model,Trace Visualization,Label Visualization,Image Slicer,Image Slicer,Image Blur,Anthropic Claude,Crop Visualization,Identify Outliers,Dot Visualization,Detection Offset,Pixelate Visualization,Stitch OCR Detections,Byte Tracker,Perspective Correction,Detections Consensus,Dominant Color,Absolute Static Crop,Mask Visualization,Webhook Sink,Color Visualization,Image Threshold,Halo Visualization,Polygon Visualization,Byte Tracker,Ellipse Visualization,Instance Segmentation Model,Keypoint Detection Model,Image Contours,Dynamic Zone,Instance Segmentation Model,Email Notification,SIFT Comparison,Object Detection Model,Reference Path Visualization,Classification Label Visualization,Triangle Visualization,SIFT Comparison,Keypoint Visualization,Identify Changes,Byte Tracker,Grid Visualization,Detections Stabilizer,Stitch Images,Pixel Color Count,Blur Visualization
Input and Output Bindings¶
The available connections depend on its binding kinds. Check what binding kinds
Pixel Color Count in version v1 has.
Bindings
-
input
-
output
matching_pixels_count(integer): Integer value.
Example JSON definition of step Pixel Color Count in version v1
{
"name": "<your_step_name_here>",
"type": "roboflow_core/pixel_color_count@v1",
"image": "$inputs.image",
"target_color": "#431112",
"tolerance": 10
}