Polygon Zone Visualization

Class: PolygonZoneVisualizationBlockV1

Source: inference.core.workflows.core_steps.visualizations.polygon_zone.v1.PolygonZoneVisualizationBlockV1

The PolygonZoneVisualization block draws polygon zone in an image with a specified color and opacity. Please note: zones will be drawn on top of image passed to this block, this block should be placed before other visualization blocks in the workflow.

Type identifier

Use the following identifier in step "type" field: roboflow_core/polygon_zone_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..	✅
zone	List[Any]	Polygon zones (one for each batch) in a format [[(x1, y1), (x2, y2), (x3, y3), ...], ...]; each zone must consist of more than 2 points.	✅
color	str	Color of the zone..	✅
opacity	float	Transparency of the Mask overlay..	✅

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 Zone Visualization in version v1.

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

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Polygon Zone 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..
  • zone (list_of_values): Polygon zones (one for each batch) in a format [[(x1, y1), (x2, y2), (x3, y3), ...], ...]; each zone must consist of more than 2 points.
  • color (string): Color of the zone..
  • opacity (float_zero_to_one): Transparency of the Mask overlay..

• output

  • image (image): Image in workflows.

Example JSON definition of step Polygon Zone Visualization in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/polygon_zone_visualization@v1",
    "image": "$inputs.image",
    "copy_image": true,
    "zone": "$inputs.zones",
    "color": "WHITE",
    "opacity": 0.3
}