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

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
}