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

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
}