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