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