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@v1
to 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
,Depth Estimation
,Classification Label Visualization
,Camera Calibration
,Stitch OCR Detections
,LMM For Classification
,Clip Comparison
,CSV Formatter
,Instance Segmentation Model
,Stitch Images
,Line Counter
,CogVLM
,Image Slicer
,Roboflow Dataset Upload
,Absolute Static Crop
,Twilio SMS Notification
,Distance Measurement
,SIFT Comparison
,Florence-2 Model
,OpenAI
,Label Visualization
,Path Deviation
,Single-Label Classification Model
,Roboflow Dataset Upload
,Dimension Collapse
,Detections Stitch
,Bounding Box Visualization
,Llama 3.2 Vision
,Model Comparison Visualization
,Slack Notification
,Object Detection Model
,Pixel Color Count
,VLM as Classifier
,Bounding Rectangle
,Detections Consensus
,Grid Visualization
,Dynamic Zone
,Image Convert Grayscale
,Halo Visualization
,Detection Offset
,Buffer
,Triangle Visualization
,Model Monitoring Inference Aggregator
,Time in Zone
,Keypoint Detection Model
,Reference Path Visualization
,Perspective Correction
,Dynamic Crop
,Detections Transformation
,Camera Focus
,Identify Changes
,VLM as Detector
,Detections Classes Replacement
,Time in Zone
,Florence-2 Model
,Local File Sink
,Path Deviation
,VLM as Detector
,OpenAI
,Google Vision OCR
,Stability AI Image Generation
,Ellipse Visualization
,Size Measurement
,SIFT
,Blur Visualization
,Circle Visualization
,Dot Visualization
,Image Blur
,Background Color Visualization
,Multi-Label Classification Model
,Color Visualization
,Detections Filter
,Pixelate Visualization
,Identify Outliers
,Clip Comparison
,Google Gemini
,Stability AI Inpainting
,VLM as Classifier
,Polygon Zone Visualization
,Relative Static Crop
,Detections Stabilizer
,OCR Model
,Keypoint Visualization
,Template Matching
,Mask Visualization
,Velocity
,Image Preprocessing
,Line Counter Visualization
,Roboflow Custom Metadata
,Anthropic Claude
,Webhook Sink
,SIFT Comparison
,Trace Visualization
,JSON Parser
,Instance Segmentation Model
,Corner Visualization
,Polygon Visualization
,Segment Anything 2 Model
,Crop Visualization
,Email Notification
,Image Contours
,Image Slicer
,Line Counter
,Image Threshold
- outputs:
LMM
,Depth Estimation
,Classification Label Visualization
,Camera Calibration
,LMM For Classification
,Clip Comparison
,Instance Segmentation Model
,Stitch Images
,CogVLM
,Image Slicer
,Roboflow Dataset Upload
,Absolute Static Crop
,Multi-Label Classification Model
,Object Detection Model
,Florence-2 Model
,QR Code Detection
,OpenAI
,Label Visualization
,Single-Label Classification Model
,Qwen2.5-VL
,Roboflow Dataset Upload
,Detections Stitch
,Bounding Box Visualization
,Llama 3.2 Vision
,Model Comparison Visualization
,Pixel Color Count
,Object Detection Model
,VLM as Classifier
,Moondream2
,Image Convert Grayscale
,Halo Visualization
,Buffer
,Triangle Visualization
,Time in Zone
,Keypoint Detection Model
,Reference Path Visualization
,Perspective Correction
,Dynamic Crop
,Camera Focus
,VLM as Detector
,Florence-2 Model
,OpenAI
,Google Vision OCR
,VLM as Detector
,Stability AI Image Generation
,Ellipse Visualization
,SIFT
,Blur Visualization
,Circle Visualization
,Image Blur
,Dot Visualization
,CLIP Embedding Model
,Background Color Visualization
,Gaze Detection
,Multi-Label Classification Model
,Single-Label Classification Model
,Byte Tracker
,Color Visualization
,Pixelate Visualization
,Dominant Color
,Clip Comparison
,Google Gemini
,Keypoint Detection Model
,Stability AI Inpainting
,VLM as Classifier
,Polygon Zone Visualization
,Relative Static Crop
,Detections Stabilizer
,OCR Model
,Keypoint Visualization
,Template Matching
,Mask Visualization
,SmolVLM2
,Image Preprocessing
,Line Counter Visualization
,Anthropic Claude
,SIFT Comparison
,YOLO-World Model
,Trace Visualization
,Instance Segmentation Model
,Corner Visualization
,Polygon Visualization
,Segment Anything 2 Model
,Barcode Detection
,Crop Visualization
,Image Contours
,Image Slicer
,Image Threshold
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
}