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