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