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

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[string, rgb_color]): 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
}