Polygon Visualization

Class: PolygonVisualizationBlockV1

Source: inference.core.workflows.core_steps.visualizations.polygon.v1.PolygonVisualizationBlockV1

The PolygonVisualization block uses a detections from an instance segmentation to draw polygons around objects using sv.PolygonAnnotator.

Type identifier

Use the following identifier in step "type" field: roboflow_core/polygon_visualization@v1to add the block as as step in your workflow.

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
copy_image	bool	Enable this option to create a copy of the input image for visualization, preserving the original. Use this when stacking multiple visualizations..	✅
color_palette	str	Select a color palette for the visualised elements..	✅
palette_size	int	Specify the number of colors in the palette. This applies when using custom or Matplotlib palettes..	✅
custom_colors	List[str]	Define a list of custom colors for bounding boxes in HEX format..	✅
color_axis	str	Choose how bounding box colors are assigned..	✅
thickness	int	Thickness of the outline in pixels..	✅

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 Polygon Visualization in version v1.

• inputs: PTZ Tracking (ONVIF).md), Local File Sink, Dot Visualization, Stability AI Inpainting, Reference Path Visualization, Bounding Rectangle, VLM as Classifier, VLM as Classifier, Stability AI Outpainting, Distance Measurement, Multi-Label Classification Model, QR Code Generator, Path Deviation, Time in Zone, Line Counter Visualization, Detections Classes Replacement, Size Measurement, Ellipse Visualization, Roboflow Custom Metadata, Dimension Collapse, Identify Outliers, Detections Combine, Background Color Visualization, Polygon Zone Visualization, CSV Formatter, Roboflow Dataset Upload, Contrast Equalization, EasyOCR, Object Detection Model, Image Slicer, Dynamic Zone, Google Gemini, Florence-2 Model, Google Vision OCR, Image Threshold, SIFT Comparison, Identify Changes, Detections Consensus, Image Preprocessing, Icon Visualization, OCR Model, Roboflow Dataset Upload, Detections Filter, Seg Preview, Detection Offset, Absolute Static Crop, Pixelate Visualization, Clip Comparison, Image Blur, Buffer, Perspective Correction, Relative Static Crop, Line Counter, Florence-2 Model, Pixel Color Count, VLM as Detector, LMM For Classification, Llama 3.2 Vision, Detections Stitch, Line Counter, Time in Zone, Clip Comparison, LMM, SIFT, Halo Visualization, Model Monitoring Inference Aggregator, Image Convert Grayscale, Anthropic Claude, Triangle Visualization, Depth Estimation, Image Contours, Mask Visualization, Keypoint Detection Model, Image Slicer, CogVLM, Model Comparison Visualization, Stitch OCR Detections, Twilio SMS Notification, Time in Zone, Template Matching, Single-Label Classification Model, Polygon Visualization, Corner Visualization, Crop Visualization, Stitch Images, Blur Visualization, Dynamic Crop, Detections Stabilizer, Detections Transformation, Camera Focus, Email Notification, Segment Anything 2 Model, VLM as Detector, Instance Segmentation Model, OpenAI, Color Visualization, Velocity, Classification Label Visualization, Label Visualization, OpenAI, Circle Visualization, Keypoint Visualization, Trace Visualization, Camera Calibration, SIFT Comparison, Instance Segmentation Model, Morphological Transformation, JSON Parser, Bounding Box Visualization, OpenAI, Path Deviation, Grid Visualization, Slack Notification, Webhook Sink, Stability AI Image Generation
• outputs: Barcode Detection, Dot Visualization, Stability AI Inpainting, Reference Path Visualization, VLM as Classifier, CLIP Embedding Model, Object Detection Model, VLM as Classifier, Stability AI Outpainting, Perception Encoder Embedding Model, Multi-Label Classification Model, Line Counter Visualization, Ellipse Visualization, Polygon Zone Visualization, Background Color Visualization, Roboflow Dataset Upload, Contrast Equalization, EasyOCR, Object Detection Model, Image Slicer, Qwen2.5-VL, Google Gemini, Byte Tracker, Florence-2 Model, Gaze Detection, Google Vision OCR, Image Threshold, SIFT Comparison, Image Preprocessing, Icon Visualization, OCR Model, YOLO-World Model, Roboflow Dataset Upload, Clip Comparison, Absolute Static Crop, Pixelate Visualization, Buffer, Image Blur, Relative Static Crop, Perspective Correction, Florence-2 Model, Pixel Color Count, VLM as Detector, Single-Label Classification Model, LMM For Classification, Llama 3.2 Vision, Detections Stitch, LMM, Clip Comparison, SIFT, Multi-Label Classification Model, Halo Visualization, SmolVLM2, Image Convert Grayscale, Anthropic Claude, Triangle Visualization, Mask Visualization, Depth Estimation, Keypoint Detection Model, Image Contours, Image Slicer, CogVLM, Model Comparison Visualization, Template Matching, Time in Zone, QR Code Detection, Single-Label Classification Model, Moondream2, Polygon Visualization, Corner Visualization, Crop Visualization, Stitch Images, Blur Visualization, Keypoint Detection Model, Dynamic Crop, Detections Stabilizer, Instance Segmentation Model, OpenAI, Segment Anything 2 Model, Camera Focus, VLM as Detector, Color Visualization, Classification Label Visualization, Label Visualization, OpenAI, Circle Visualization, Keypoint Visualization, Trace Visualization, Camera Calibration, Instance Segmentation Model, Morphological Transformation, OpenAI, Bounding Box Visualization, Dominant Color, Seg Preview, Stability AI Image Generation

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Polygon Visualization in version v1 has.

Bindings

• input

  • image (image): The image to visualize on..
  • copy_image (boolean): Enable this option to create a copy of the input image for visualization, preserving the original. Use this when stacking multiple visualizations..
  • predictions (instance_segmentation_prediction): Predictions.
  • color_palette (string): Select a color palette for the visualised elements..
  • palette_size (integer): Specify the number of colors in the palette. This applies when using custom or Matplotlib palettes..
  • custom_colors (list_of_values): Define a list of custom colors for bounding boxes in HEX format..
  • color_axis (string): Choose how bounding box colors are assigned..
  • thickness (integer): Thickness of the outline in pixels..

• output

  • image (image): Image in workflows.

Example JSON definition of step Polygon Visualization in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/polygon_visualization@v1",
    "image": "$inputs.image",
    "copy_image": true,
    "predictions": "$steps.instance_segmentation_model.predictions",
    "color_palette": "DEFAULT",
    "palette_size": 10,
    "custom_colors": [
        "#FF0000",
        "#00FF00",
        "#0000FF"
    ],
    "color_axis": "CLASS",
    "thickness": 2
}