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:
Keypoint Visualization,Twilio SMS/MMS Notification,OPC UA Writer Sink,Qwen 3.6 API,SIFT Comparison,Grid Visualization,PLC Writer,Llama 3.2 Vision,JSON Parser,Absolute Static Crop,Roboflow Visual Search Classifier,Image Threshold,Polygon Zone Visualization,CogVLM,MQTT Writer,Contrast Equalization,Google Gemini,Detections List Roll-Up,OCR Model,GLM-OCR,Background Subtraction,Contrast Enhancement,Reference Path Visualization,Twilio SMS Notification,Google Gemma API,Instance Segmentation Model,PTZ Tracking (ONVIF),VLM As Detector,Florence-2 Model,Dynamic Crop,Halo Visualization,Image Blur,LMM,Ellipse Visualization,OpenAI,Florence-2 Model,LMM For Classification,Event Writer,OpenAI,Google Gemma,Keypoint Detection Model,Buffer,Slack Notification,Heatmap Visualization,Email Notification,Circle Visualization,Perspective Correction,Camera Focus,Crop Visualization,Polygon Visualization,Detections Consensus,Multi-Label Classification Model,Stability AI Inpainting,OpenAI-Compatible LLM,VLM As Classifier,Object Detection Model,PLC ModbusTCP,Image Slicer,Google Gemini,Roboflow Vision Events,Size Measurement,Local File Sink,Dot Visualization,Line Counter Visualization,Depth Estimation,Stitch OCR Detections,PP-OCR,Blur Visualization,Clip Comparison,EasyOCR,Anthropic Claude,PLC EthernetIP,CSV Formatter,Color Visualization,Dynamic Zone,Image Convert Grayscale,Motion Detection,Stitch OCR Detections,Single-Label Classification Model,Qwen 3.5 API,Trace Visualization,Icon Visualization,Current Time,PLC Reader,Model Comparison Visualization,Camera Focus,QR Code Generator,Google Gemini,Image Stack,Qwen-VL,Mask Visualization,MoonshotAI Kimi,Microsoft SQL Server Sink,Text Display,Stability AI Image Generation,Google Vision OCR,Webhook Sink,OpenAI,VLM As Detector,Identify Outliers,Pixelate Visualization,Stitch Images,S3 Sink,Classification Label Visualization,MoonshotAI Kimi,Polygon Visualization,Dimension Collapse,Anthropic Claude,Roboflow Dataset Upload,Morphological Transformation,Bounding Box Visualization,VLM As Classifier,Image Contours,Image Slicer,Stability AI Outpainting,Anthropic Claude,Roboflow Custom Metadata,Camera Calibration,Roboflow Asset Library Attributes,Morphological Transformation,Relative Static Crop,Background Color Visualization,Model Monitoring Inference Aggregator,Clip Comparison,Corner Visualization,GeoTag Detection,SIFT,Roboflow Visual Search,OpenRouter,Identify Changes,Llama 3.2 Vision,Qwen3.5-VL,Email Notification,Triangle Visualization,OpenAI,Roboflow Dataset Upload,Image Preprocessing,SIFT Comparison,Halo Visualization,Label Visualization - outputs:
Keypoint Visualization,Twilio SMS/MMS Notification,Keypoint Detection Model,Perception Encoder Embedding Model,Qwen 3.6 API,Object Detection Model,SAM 3,Llama 3.2 Vision,Absolute Static Crop,Roboflow Visual Search Classifier,Image Threshold,Polygon Zone Visualization,CogVLM,BoT-SORT Tracker,Contrast Equalization,Google Gemini,OCR Model,GLM-OCR,Background Subtraction,Contrast Enhancement,Reference Path Visualization,Google Gemma API,Instance Segmentation Model,Mask Edge Snap,VLM As Detector,Florence-2 Model,Halo Visualization,Dynamic Crop,SAM 3 Interactive,LMM,Image Blur,Seg Preview,SAM 3,Ellipse Visualization,OpenAI,Florence-2 Model,LMM For Classification,Time in Zone,Semantic Segmentation Model,Instance Segmentation Model,OpenAI,Event Writer,Single-Label Classification Model,Google Gemma,Keypoint Detection Model,Buffer,Heatmap Visualization,Semantic Segmentation Model,SORT Tracker,Email Notification,Circle Visualization,Perspective Correction,Camera Focus,Byte Tracker,Crop Visualization,Keypoint Detection Model,Polygon Visualization,ByteTrack Tracker,Multi-Label Classification Model,Stability AI Inpainting,SAM 3,Object Detection Model,VLM As Classifier,Google Gemini,Image Slicer,Roboflow Vision Events,Multi-Label Classification Model,Qwen3.5,Object Detection Model,SAM3 Video Tracker,Dot Visualization,QR Code Detection,Line Counter Visualization,Instance Segmentation Model,Depth Estimation,PP-OCR,Blur Visualization,EasyOCR,Clip Comparison,Anthropic Claude,Segment Anything 2 Model,Color Visualization,Qwen2.5-VL,Qwen3-VL,Image Convert Grayscale,Motion Detection,Single-Label Classification Model,Qwen 3.5 API,Trace Visualization,Icon Visualization,Model Comparison Visualization,Camera Focus,Google Gemini,Detections Stitch,Image Stack,Instance Segmentation Model,Qwen-VL,Mask Visualization,MoonshotAI Kimi,Text Display,Gaze Detection,Stability AI Image Generation,Google Vision OCR,OpenAI,VLM As Detector,Pixelate Visualization,Stitch Images,Classification Label Visualization,MoonshotAI Kimi,Polygon Visualization,Anthropic Claude,Roboflow Dataset Upload,Morphological Transformation,Bounding Box Visualization,SmolVLM2,Template Matching,VLM As Classifier,Image Contours,Image Slicer,Stability AI Outpainting,Anthropic Claude,Multi-Label Classification Model,SAM2 Video Tracker,Barcode Detection,Camera Calibration,Morphological Transformation,Detections Stabilizer,Relative Static Crop,Background Color Visualization,Clip Comparison,GeoTag Detection,Corner Visualization,SIFT,Track Class Lock,Dominant Color,Moondream2,Roboflow Visual Search,OpenRouter,Qwen3.5-VL,Llama 3.2 Vision,CLIP Embedding Model,Single-Label Classification Model,Triangle Visualization,OpenAI,Pixel Color Count,OC-SORT Tracker,Roboflow Dataset Upload,Image Preprocessing,SIFT Comparison,YOLO-World Model,Halo Visualization,Label 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 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
}