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

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
}