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