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

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
}