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

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
}