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