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

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[rgb_color, string]): 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
}