Polygon Zone Visualization¶
Class: PolygonZoneVisualizationBlockV1
Source: inference.core.workflows.core_steps.visualizations.polygon_zone.v1.PolygonZoneVisualizationBlockV1
Draw polygon zones on an image to visualize monitoring areas, displaying colored polygon overlays for zone-based detection and counting workflows that track objects within irregular, custom-defined regions.
How This Block Works¶
This block takes an image and polygon zone coordinates (a list of points defining a polygon shape) and draws a filled polygon overlay to visualize the monitoring zone. The block:
- Takes an image and polygon zone coordinates (a list of points: [(x1, y1), (x2, y2), (x3, y3), ...]) as input
- Creates a filled polygon mask from the zone coordinates using the specified color
- Overlays the filled polygon onto the image with the specified opacity, creating a semi-transparent zone visualization
- Returns an annotated image with the polygon zone overlay on the original image
The block visualizes polygon zones used to define irregular monitoring areas for detection, counting, or tracking workflows. The polygon is drawn as a filled shape between the specified points, creating a closed region that can represent any custom area shape (unlike rectangular bounding boxes). This allows for flexible zone definitions that match real-world boundaries, such as specific floor areas, irregular regions of interest, or complex monitoring zones. The zone overlay is semi-transparent, allowing the underlying image details to remain visible while clearly indicating the monitoring area. Note: This block should typically be placed before other visualization blocks in the workflow, as the polygon zone provides a background reference layer for object detection visualizations.
Common Use Cases¶
- Zone Detection Visualization: Visualize polygon zones for object detection or counting workflows where objects are tracked within irregular, custom-defined areas, displaying the monitoring boundaries clearly
- Area-Based Monitoring: Display polygon zones for area-based monitoring applications such as occupancy tracking, people counting in specific regions, or object presence detection within defined spaces
- Custom Region Visualization: Visualize custom monitoring regions that don't fit rectangular boundaries, such as irregular floor areas, complex room layouts, or specific zones within larger spaces
- Security and Surveillance: Display polygon zones for security monitoring, access control, or surveillance workflows where specific areas need to be visually marked and monitored
- Retail and Business Analytics: Visualize polygon zones for foot traffic analysis, customer movement tracking, or space utilization monitoring in retail, hospitality, or business intelligence applications
- Real-Time Zone Monitoring: Create visual overlays for real-time monitoring dashboards, live video feeds, or monitoring interfaces where polygon zones need to be clearly visible to indicate monitored areas
Connecting to Other Blocks¶
The annotated image from this block can be connected to:
- Zone detection or counting blocks to receive polygon zone coordinates that are visualized
- Other visualization blocks (e.g., Bounding Box Visualization, Label Visualization, Polygon Visualization) to add object detection annotations on top of the polygon zone visualization
- Data storage blocks (e.g., Local File Sink, CSV Formatter, Roboflow Dataset Upload) to save images with polygon zone visualizations for documentation, reporting, or analysis
- Webhook blocks to send visualized results with polygon zones to external systems, APIs, or web applications for display in dashboards or monitoring tools
- Notification blocks (e.g., Email Notification, Slack Notification) to send annotated images with polygon zones as visual evidence in alerts or reports
- Video output blocks to create annotated video streams or recordings with polygon zone visualizations for live monitoring, zone visualization, or post-processing analysis
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 zone coordinates in the format [[(x1, y1), (x2, y2), (x3, y3), ...], ...] defining one or more polygon shapes. Each zone must consist of more than 2 points to form a valid polygon. The polygon is drawn as a filled shape connecting these points in order, creating a closed region. Typically connected from zone detection or counting blocks that define monitoring areas.. | ✅ |
color |
str |
Color of the polygon zone overlay. Can be specified as a color name (e.g., 'WHITE', 'RED'), hex color code (e.g., '#5bb573', '#FFFFFF'), or RGB format (e.g., 'rgb(255, 255, 255)'). The polygon is filled with this color and overlaid with the specified opacity.. | ✅ |
opacity |
float |
Opacity of the polygon zone overlay, ranging from 0.0 (fully transparent) to 1.0 (fully opaque). Controls how transparent the polygon zone appears over the image. Lower values create more transparent zones that blend with the background, while higher values create more opaque, visible zones. Typical values range from 0.2 to 0.5 for balanced visibility where both the zone and underlying image are visible.. | ✅ |
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:
Roboflow Asset Library Attributes,MoonshotAI Kimi,Image Blur,Reference Path Visualization,SIFT Comparison,PTZ Tracking (ONVIF),Event Writer,Slack Notification,Halo Visualization,VLM As Classifier,Image Stack,Clip Comparison,Google Gemma,Qwen 3.6 API,Dot Visualization,Label Visualization,Background Color Visualization,Llama 3.2 Vision,Email Notification,Pixelate Visualization,OpenAI-Compatible LLM,Google Gemini,JSON Parser,Anthropic Claude,OpenAI,Trace Visualization,Llama 3.2 Vision,Clip Comparison,Camera Focus,OpenAI,GLM-OCR,PLC ModbusTCP,Buffer,MQTT Writer,SIFT Comparison,Webhook Sink,CSV Formatter,Image Contours,Local File Sink,Motion Detection,Google Gemini,MoonshotAI Kimi,Polygon Visualization,Dimension Collapse,SIFT,Classification Label Visualization,Multi-Label Classification Model,Keypoint Detection Model,Keypoint Visualization,Icon Visualization,Dynamic Crop,Stability AI Inpainting,Bounding Box Visualization,Polygon Zone Visualization,Stability AI Outpainting,Crop Visualization,Image Convert Grayscale,Mask Visualization,Halo Visualization,PLC EthernetIP,Text Display,Morphological Transformation,Anthropic Claude,VLM As Classifier,Roboflow Dataset Upload,VLM As Detector,Detections Consensus,Object Detection Model,Ellipse Visualization,Size Measurement,Circle Visualization,Twilio SMS Notification,S3 Sink,Email Notification,Camera Focus,Identify Changes,Image Slicer,LMM For Classification,OCR Model,Heatmap Visualization,Google Gemma API,OpenAI,Stitch Images,Identify Outliers,Morphological Transformation,EasyOCR,Current Time,Blur Visualization,Stitch OCR Detections,Detections List Roll-Up,Florence-2 Model,Google Gemini,Corner Visualization,OpenRouter,Model Comparison Visualization,Model Monitoring Inference Aggregator,Google Vision OCR,Image Threshold,LMM,Single-Label Classification Model,Polygon Visualization,Stability AI Image Generation,Line Counter Visualization,CogVLM,Relative Static Crop,Qwen3.5-VL,Grid Visualization,Image Preprocessing,Stitch OCR Detections,Anthropic Claude,OPC UA Writer Sink,Color Visualization,Dynamic Zone,Triangle Visualization,QR Code Generator,Contrast Enhancement,Roboflow Dataset Upload,Absolute Static Crop,Qwen 3.5 API,Background Subtraction,OpenAI,Image Slicer,Qwen-VL,Florence-2 Model,Perspective Correction,Twilio SMS/MMS Notification,Roboflow Vision Events,Microsoft SQL Server Sink,Instance Segmentation Model,Depth Estimation,Roboflow Custom Metadata,Contrast Equalization,Camera Calibration,VLM As Detector - outputs:
MoonshotAI Kimi,Image Blur,SmolVLM2,Reference Path Visualization,Event Writer,SAM2 Video Tracker,VLM As Classifier,Halo Visualization,CLIP Embedding Model,Image Stack,Clip Comparison,Google Gemma,Qwen 3.6 API,Object Detection Model,Dot Visualization,Label Visualization,Background Color Visualization,Llama 3.2 Vision,SAM 3 Interactive,Pixelate Visualization,Qwen3-VL,Google Gemini,Track Class Lock,Anthropic Claude,OpenAI,Trace Visualization,Llama 3.2 Vision,ByteTrack Tracker,Clip Comparison,Camera Focus,GLM-OCR,OpenAI,Qwen3.5,Buffer,QR Code Detection,SIFT Comparison,Image Contours,Motion Detection,Google Gemini,MoonshotAI Kimi,Polygon Visualization,SIFT,Classification Label Visualization,Multi-Label Classification Model,Instance Segmentation Model,Keypoint Detection Model,Template Matching,Keypoint Visualization,Instance Segmentation Model,Icon Visualization,Seg Preview,Dynamic Crop,Stability AI Inpainting,Bounding Box Visualization,BoT-SORT Tracker,Multi-Label Classification Model,Polygon Zone Visualization,Crop Visualization,Stability AI Outpainting,Image Convert Grayscale,Mask Visualization,Halo Visualization,Detections Stitch,SORT Tracker,Barcode Detection,Text Display,Anthropic Claude,Morphological Transformation,VLM As Classifier,Roboflow Dataset Upload,VLM As Detector,Object Detection Model,Ellipse Visualization,Keypoint Detection Model,SAM3 Video Tracker,SAM 3,Circle Visualization,Semantic Segmentation Model,Email Notification,Camera Focus,Single-Label Classification Model,SAM 3,Image Slicer,LMM For Classification,Dominant Color,OCR Model,Heatmap Visualization,Google Gemma API,OpenAI,Stitch Images,Morphological Transformation,EasyOCR,Single-Label Classification Model,YOLO-World Model,Blur Visualization,Moondream2,Florence-2 Model,Google Gemini,Corner Visualization,OpenRouter,Detections Stabilizer,Pixel Color Count,Model Comparison Visualization,SAM 3,Google Vision OCR,Byte Tracker,Image Threshold,Instance Segmentation Model,LMM,Single-Label Classification Model,Polygon Visualization,Segment Anything 2 Model,Time in Zone,Mask Edge Snap,Line Counter Visualization,Stability AI Image Generation,CogVLM,Relative Static Crop,Qwen3.5-VL,Image Preprocessing,Gaze Detection,Anthropic Claude,Color Visualization,Triangle Visualization,Roboflow Dataset Upload,Contrast Enhancement,Absolute Static Crop,Qwen 3.5 API,Background Subtraction,Multi-Label Classification Model,OC-SORT Tracker,OpenAI,Image Slicer,Semantic Segmentation Model,Qwen-VL,Florence-2 Model,Perspective Correction,Roboflow Vision Events,Twilio SMS/MMS Notification,Perception Encoder Embedding Model,Instance Segmentation Model,Depth Estimation,Contrast Equalization,Camera Calibration,VLM As Detector,Qwen2.5-VL,Keypoint Detection Model,Object Detection Model
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 zone coordinates in the format [[(x1, y1), (x2, y2), (x3, y3), ...], ...] defining one or more polygon shapes. Each zone must consist of more than 2 points to form a valid polygon. The polygon is drawn as a filled shape connecting these points in order, creating a closed region. Typically connected from zone detection or counting blocks that define monitoring areas..color(string): Color of the polygon zone overlay. Can be specified as a color name (e.g., 'WHITE', 'RED'), hex color code (e.g., '#5bb573', '#FFFFFF'), or RGB format (e.g., 'rgb(255, 255, 255)'). The polygon is filled with this color and overlaid with the specified opacity..opacity(float_zero_to_one): Opacity of the polygon zone overlay, ranging from 0.0 (fully transparent) to 1.0 (fully opaque). Controls how transparent the polygon zone appears over the image. Lower values create more transparent zones that blend with the background, while higher values create more opaque, visible zones. Typical values range from 0.2 to 0.5 for balanced visibility where both the zone and underlying image are visible..
-
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
}