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

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
}