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: Blur Visualization, Triangle Visualization, Anthropic Claude, Dominant Color, Trace Visualization, Label Visualization, LMM, Distance Measurement, Model Monitoring Inference Aggregator, Roboflow Dataset Upload, Pixel Color Count, Absolute Static Crop, Image Preprocessing, Relative Static Crop, Image Threshold, Reference Path Visualization, Slack Notification, Stability AI Outpainting, SIFT, Roboflow Dataset Upload, Google Vision OCR, Stability AI Inpainting, Background Color Visualization, CSV Formatter, Circle Visualization, Image Blur, Keypoint Visualization, VLM as Detector, Google Gemini, OpenAI, Image Convert Grayscale, SIFT Comparison, Line Counter Visualization, Model Comparison Visualization, Roboflow Custom Metadata, Image Slicer, Stitch OCR Detections, Crop Visualization, Corner Visualization, Line Counter, Multi-Label Classification Model, Pixelate Visualization, Local File Sink, Image Slicer, Mask Visualization, VLM as Classifier, Clip Comparison, Color Visualization, Polygon Visualization, Email Notification, Keypoint Detection Model, Perspective Correction, Camera Calibration, OpenAI, Bounding Box Visualization, Camera Focus, CogVLM, Instance Segmentation Model, Twilio SMS Notification, OpenAI, Dynamic Crop, Depth Estimation, Halo Visualization, Florence-2 Model, Dot Visualization, Template Matching, Classification Label Visualization, Webhook Sink, SIFT Comparison, Stability AI Image Generation, Florence-2 Model, LMM For Classification, Ellipse Visualization, Image Contours, Llama 3.2 Vision, Line Counter, Single-Label Classification Model, Grid Visualization, Stitch Images, OCR Model, Object Detection Model, Polygon Zone Visualization
• outputs: Polygon Visualization, Triangle Visualization, Anthropic Claude, Dominant Color, Email Notification, Trace Visualization, Blur Visualization, Keypoint Detection Model, Label Visualization, Perspective Correction, Detections Consensus, Pixel Color Count, Absolute Static Crop, Image Preprocessing, Keypoint Detection Model, Byte Tracker, Image Threshold, Bounding Box Visualization, Reference Path Visualization, Object Detection Model, Slack Notification, Stability AI Outpainting, Instance Segmentation Model, Identify Outliers, Twilio SMS Notification, Instance Segmentation Model, Detection Offset, Detections Classes Replacement, Halo Visualization, Stability AI Inpainting, Dot Visualization, Byte Tracker, Detections Stabilizer, Classification Label Visualization, Webhook Sink, SIFT Comparison, Circle Visualization, Keypoint Visualization, Image Blur, Object Detection Model, SIFT Comparison, PTZ Tracking (ONVIF).md), Line Counter Visualization, Ellipse Visualization, Dynamic Zone, Image Contours, Image Slicer, Stitch OCR Detections, Crop Visualization, Corner Visualization, Identify Changes, Grid Visualization, Pixelate Visualization, Stitch Images, Image Slicer, Byte Tracker, Mask Visualization, Color 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

  • image (image): The input image for this step..
  • target_color (Union[string, rgb_color]): 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 (integer): Tolerance for color matching..

• 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
}