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

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
}