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

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
}