Polygon Visualization

Class: PolygonVisualizationBlockV1

Source: inference.core.workflows.core_steps.visualizations.polygon.v1.PolygonVisualizationBlockV1

Draw polygon outlines around detected objects that follow the exact shape of object masks, providing precise boundary visualization for instance segmentation results.

How This Block Works

This block takes an image and instance segmentation predictions (which include segmentation masks) and draws polygon outlines that precisely follow the shape of each detected object. The block:

1. Takes an image and instance segmentation predictions as input (predictions must include mask data)
2. Converts segmentation masks to polygon coordinates that trace the object boundaries
3. Applies color styling based on the selected color palette, with colors assigned by class, index, or track ID
4. Draws polygon outlines with the specified thickness using the PolygonAnnotator
5. Returns an annotated image with polygon outlines overlaid on the original image

The block extracts the exact shape of each object from its segmentation mask and draws polygon outlines that follow these precise boundaries. This provides much more accurate visualization than bounding boxes, as polygons conform to the actual object shape rather than enclosing them in rectangles. If mask data is not available, the block falls back to drawing bounding boxes. The polygon outlines can be customized with different thickness values and color palettes, allowing you to clearly distinguish between different objects or object classes.

Common Use Cases

• Precise Object Boundary Visualization: Visualize the exact shape and boundaries of segmented objects for applications requiring accurate object outlines, such as medical imaging, manufacturing quality control, or precise measurement workflows
• Instance Segmentation Model Validation: Verify and debug instance segmentation model performance by visualizing how well polygon predictions match object boundaries, identify segmentation errors, and validate mask quality
• Irregular Shape Analysis: Visualize objects with irregular or non-rectangular shapes (e.g., people, animals, complex machinery parts) where bounding boxes would be inaccurate or misleading
• Overlapping Object Visualization: Clearly show object boundaries when multiple objects overlap, as polygons accurately represent each object's shape without the ambiguity of overlapping bounding boxes
• Shape-Based Quality Control: Inspect object shapes and boundaries in manufacturing, agriculture, or quality assurance workflows where precise object contours are critical for defect detection or classification
• Scientific and Medical Imaging: Visualize segmented regions in medical imaging, microscopy, or scientific analysis where accurate boundary representation is essential for measurement, analysis, or diagnosis

Connecting to Other Blocks

The annotated image from this block can be connected to:

• Other visualization blocks (e.g., Label Visualization, Mask Visualization, Bounding Box Visualization) to combine polygon outlines with additional annotations for comprehensive visualization
• Data storage blocks (e.g., Local File Sink, CSV Formatter, Roboflow Dataset Upload) to save annotated images with polygon outlines for documentation, reporting, or training data validation
• Webhook blocks to send visualized results with polygon outlines to external systems, APIs, or web applications for display in dashboards or analysis tools
• Notification blocks (e.g., Email Notification, Slack Notification) to send annotated images with polygon outlines as visual evidence in alerts or reports
• Video output blocks to create annotated video streams or recordings with polygon outlines for live monitoring, tracking, or post-processing analysis

Type identifier

Use the following identifier in step "type" field: roboflow_core/polygon_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..	✅
color_palette	str	Select a color palette for the visualised elements..	✅
palette_size	int	Specify the number of colors in the palette. This applies when using custom or Matplotlib palettes..	✅
custom_colors	List[str]	Define a list of custom colors for bounding boxes in HEX format..	✅
color_axis	str	Choose how bounding box colors are assigned..	✅
thickness	int	Thickness of the polygon outline in pixels. Higher values create thicker, more visible outlines..	✅

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 Visualization in version v1.

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

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Polygon 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..
  • predictions (Union[rle_instance_segmentation_prediction, instance_segmentation_prediction]): Instance segmentation predictions containing mask data. The block converts masks to polygon outlines that follow the exact shape of each detected object..
  • color_palette (string): Select a color palette for the visualised elements..
  • palette_size (integer): Specify the number of colors in the palette. This applies when using custom or Matplotlib palettes..
  • custom_colors (list_of_values): Define a list of custom colors for bounding boxes in HEX format..
  • color_axis (string): Choose how bounding box colors are assigned..
  • thickness (integer): Thickness of the polygon outline in pixels. Higher values create thicker, more visible outlines..

• output

  • image (image): Image in workflows.

Example JSON definition of step Polygon Visualization in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/polygon_visualization@v1",
    "image": "$inputs.image",
    "copy_image": true,
    "predictions": "$steps.instance_segmentation_model.predictions",
    "color_palette": "DEFAULT",
    "palette_size": 10,
    "custom_colors": [
        "#FF0000",
        "#00FF00",
        "#0000FF"
    ],
    "color_axis": "CLASS",
    "thickness": 2
}