Polygon Visualization

v1

Class: PolygonVisualizationBlockV1 (there are multiple versions of this block)

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

Warning: This block has multiple versions. Please refer to the specific version for details. You can learn more about how versions work here: Versioning

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

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[instance_segmentation_prediction, rle_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
}

v1

Class: PolygonVisualizationBlockV2 (there are multiple versions of this block)

Source: inference.core.workflows.core_steps.visualizations.polygon.v2.PolygonVisualizationBlockV2

Warning: This block has multiple versions. Please refer to the specific version for details. You can learn more about how versions work here: Versioning

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@v2to 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: Triangle Visualization, Detections Stitch, Ellipse Visualization, Detections Classes Replacement, Florence-2 Model, Blur Visualization, Anthropic Claude, Google Gemini, Motion Detection, Keypoint Visualization, Pixelate Visualization, Size Measurement, Image Slicer, SIFT Comparison, Line Counter, Dynamic Zone, Distance Measurement, Clip Comparison, SAM 3, Image Slicer, CSV Formatter, EasyOCR, Object Detection Model, Anthropic Claude, Detections Combine, Google Gemini, Pixel Color Count, Background Color Visualization, Image Convert Grayscale, Camera Calibration, Time in Zone, Image Preprocessing, VLM As Detector, PTZ Tracking (ONVIF).md), Detections Transformation, SIFT, Stability AI Outpainting, Stitch OCR Detections, Detection Offset, Trace Visualization, OpenAI, Icon Visualization, Dot Visualization, Time in Zone, Email Notification, Instance Segmentation Model, Path Deviation, Camera Focus, Contrast Equalization, Segment Anything 2 Model, Text Display, Detections Filter, Reference Path Visualization, Image Threshold, Perspective Correction, Image Contours, Multi-Label Classification Model, Detection Event Log, Local File Sink, Identify Changes, Google Vision OCR, Crop Visualization, Detections List Roll-Up, Google Gemini, Webhook Sink, Classification Label Visualization, VLM As Classifier, Seg Preview, OpenAI, Stability AI Image Generation, Roboflow Dataset Upload, Morphological Transformation, LMM, Halo Visualization, Camera Focus, Llama 3.2 Vision, Model Comparison Visualization, VLM As Detector, Stitch OCR Detections, Roboflow Dataset Upload, Line Counter Visualization, Label Visualization, SIFT Comparison, QR Code Generator, Email Notification, Buffer, Slack Notification, Detections Stabilizer, Path Deviation, Corner Visualization, Florence-2 Model, SAM 3, OpenAI, Bounding Box Visualization, Keypoint Detection Model, Anthropic Claude, Background Subtraction, Polygon Visualization, Image Blur, VLM As Classifier, Relative Static Crop, Clip Comparison, Heatmap Visualization, CogVLM, Mask Visualization, Twilio SMS Notification, Instance Segmentation Model, OpenAI, OCR Model, Stitch Images, Dynamic Crop, Model Monitoring Inference Aggregator, Circle Visualization, Color Visualization, Dimension Collapse, Velocity, Twilio SMS/MMS Notification, Depth Estimation, LMM For Classification, Roboflow Custom Metadata, Bounding Rectangle, Grid Visualization, Polygon Zone Visualization, Polygon Visualization, Halo Visualization, JSON Parser, SAM 3, Stability AI Inpainting, Time in Zone, Identify Outliers, Detections Consensus, Template Matching, Absolute Static Crop, Single-Label Classification Model, Line Counter
• outputs: Triangle Visualization, Detections Stitch, Roboflow Dataset Upload, Ellipse Visualization, Morphological Transformation, LMM, Florence-2 Model, Blur Visualization, CLIP Embedding Model, Anthropic Claude, Halo Visualization, Google Gemini, Camera Focus, Llama 3.2 Vision, Motion Detection, Model Comparison Visualization, VLM As Detector, Keypoint Visualization, Qwen3-VL, Pixelate Visualization, Image Slicer, Roboflow Dataset Upload, Line Counter Visualization, Keypoint Detection Model, SmolVLM2, Label Visualization, SIFT Comparison, Clip Comparison, Buffer, SAM 3, Detections Stabilizer, Object Detection Model, EasyOCR, Image Slicer, Corner Visualization, Florence-2 Model, Object Detection Model, SAM 3, QR Code Detection, Anthropic Claude, OpenAI, Google Gemini, Perception Encoder Embedding Model, Bounding Box Visualization, Keypoint Detection Model, Anthropic Claude, Pixel Color Count, Background Subtraction, Background Color Visualization, Image Convert Grayscale, Camera Calibration, Polygon Visualization, Image Blur, VLM As Classifier, Relative Static Crop, Clip Comparison, Heatmap Visualization, CogVLM, Mask Visualization, Image Preprocessing, VLM As Detector, Instance Segmentation Model, OpenAI, OCR Model, SIFT, Stitch Images, Single-Label Classification Model, Moondream2, Stability AI Outpainting, Dynamic Crop, Circle Visualization, Byte Tracker, OpenAI, Trace Visualization, Color Visualization, Qwen2.5-VL, Icon Visualization, YOLO-World Model, Dot Visualization, Time in Zone, Email Notification, Instance Segmentation Model, Camera Focus, Twilio SMS/MMS Notification, Contrast Equalization, Depth Estimation, Segment Anything 2 Model, LMM For Classification, Text Display, Dominant Color, Reference Path Visualization, Image Threshold, Perspective Correction, Multi-Label Classification Model, Image Contours, Polygon Zone Visualization, Polygon Visualization, Halo Visualization, Google Vision OCR, SAM 3, Stability AI Inpainting, Crop Visualization, Template Matching, Google Gemini, Barcode Detection, VLM As Classifier, Classification Label Visualization, Seg Preview, OpenAI, Absolute Static Crop, Multi-Label Classification Model, Single-Label Classification Model, Gaze Detection, Stability AI Image Generation

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[instance_segmentation_prediction, rle_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@v2",
    "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
}