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

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
}