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