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

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
}