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

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
}