Mask Visualization

Class: MaskVisualizationBlockV1

Source: inference.core.workflows.core_steps.visualizations.mask.v1.MaskVisualizationBlockV1

The MaskVisualization block uses a detected polygon from an instance segmentation to draw a mask using sv.MaskAnnotator.

Type identifier

Use the following identifier in step "type" field: roboflow_core/mask_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..	✅
opacity	float	Transparency of the Mask overlay..	✅

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 Mask Visualization in version v1.

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

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Mask 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 (instance_segmentation_prediction): Predictions.
  • 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..
  • opacity (float_zero_to_one): Transparency of the Mask overlay..

• output

  • image (image): Image in workflows.

Example JSON definition of step Mask Visualization in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/mask_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",
    "opacity": 0.5
}