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