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

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
}