Polygon Visualization¶
Class: PolygonVisualizationBlockV1
Source: inference.core.workflows.core_steps.visualizations.polygon.v1.PolygonVisualizationBlockV1
The PolygonVisualization
block uses a detections from an
instance segmentation to draw polygons around objects using
sv.PolygonAnnotator
.
Type identifier¶
Use the following identifier in step "type"
field: roboflow_core/polygon_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.. | ✅ |
thickness |
int |
Thickness of the outline in pixels.. | ✅ |
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 Polygon Visualization
in version v1
.
- inputs:
Identify Changes
,Detection Offset
,Camera Focus
,Line Counter
,Polygon Zone Visualization
,Detections Filter
,Slack Notification
,Time in Zone
,Local File Sink
,Grid Visualization
,Image Convert Grayscale
,Trace Visualization
,Instance Segmentation Model
,Absolute Static Crop
,Roboflow Custom Metadata
,Perspective Correction
,OpenAI
,Distance Measurement
,Circle Visualization
,Dimension Collapse
,Clip Comparison
,Image Slicer
,OpenAI
,Triangle Visualization
,Halo Visualization
,Line Counter
,Size Measurement
,Corner Visualization
,Email Notification
,Detections Classes Replacement
,Object Detection Model
,LMM
,Template Matching
,Detections Consensus
,Roboflow Dataset Upload
,Dynamic Crop
,VLM as Classifier
,Depth Estimation
,Dynamic Zone
,Velocity
,Model Monitoring Inference Aggregator
,Buffer
,Stitch Images
,Segment Anything 2 Model
,Llama 3.2 Vision
,Model Comparison Visualization
,Anthropic Claude
,Crop Visualization
,Blur Visualization
,SIFT Comparison
,CogVLM
,Image Threshold
,Stability AI Inpainting
,VLM as Detector
,Relative Static Crop
,Image Preprocessing
,Keypoint Visualization
,Background Color Visualization
,Path Deviation
,Pixel Color Count
,Clip Comparison
,Color Visualization
,Twilio SMS Notification
,OCR Model
,Multi-Label Classification Model
,Classification Label Visualization
,Google Vision OCR
,Camera Calibration
,Pixelate Visualization
,Stitch OCR Detections
,Label Visualization
,Image Slicer
,Time in Zone
,Reference Path Visualization
,JSON Parser
,Webhook Sink
,Identify Outliers
,Roboflow Dataset Upload
,Line Counter Visualization
,Single-Label Classification Model
,Google Gemini
,Image Blur
,Detections Transformation
,Florence-2 Model
,SIFT Comparison
,Detections Stabilizer
,LMM For Classification
,Image Contours
,Polygon Visualization
,Instance Segmentation Model
,CSV Formatter
,SIFT
,Florence-2 Model
,Ellipse Visualization
,Mask Visualization
,Keypoint Detection Model
,Bounding Rectangle
,Bounding Box Visualization
,Dot Visualization
,Detections Stitch
,Stability AI Image Generation
,VLM as Detector
,Path Deviation
,VLM as Classifier
- outputs:
Camera Focus
,Single-Label Classification Model
,Polygon Zone Visualization
,YOLO-World Model
,Image Convert Grayscale
,Instance Segmentation Model
,Trace Visualization
,Absolute Static Crop
,Perspective Correction
,OpenAI
,Circle Visualization
,Clip Comparison
,Image Slicer
,OpenAI
,Triangle Visualization
,Halo Visualization
,QR Code Detection
,Gaze Detection
,Corner Visualization
,Object Detection Model
,Template Matching
,LMM
,Roboflow Dataset Upload
,VLM as Classifier
,Dynamic Crop
,Depth Estimation
,Buffer
,Stitch Images
,Segment Anything 2 Model
,Object Detection Model
,Llama 3.2 Vision
,Anthropic Claude
,Model Comparison Visualization
,Keypoint Detection Model
,Crop Visualization
,Blur Visualization
,CLIP Embedding Model
,CogVLM
,Dominant Color
,Image Threshold
,Stability AI Inpainting
,SmolVLM2
,VLM as Detector
,Relative Static Crop
,Image Preprocessing
,Pixel Color Count
,Background Color Visualization
,Barcode Detection
,Keypoint Visualization
,Clip Comparison
,Color Visualization
,Moondream2
,OCR Model
,Multi-Label Classification Model
,Classification Label Visualization
,Google Vision OCR
,Pixelate Visualization
,Camera Calibration
,Image Slicer
,Label Visualization
,Time in Zone
,Reference Path Visualization
,Single-Label Classification Model
,Google Gemini
,Roboflow Dataset Upload
,Line Counter Visualization
,Qwen2.5-VL
,Byte Tracker
,Multi-Label Classification Model
,Image Blur
,Florence-2 Model
,SIFT Comparison
,Detections Stabilizer
,LMM For Classification
,Image Contours
,Instance Segmentation Model
,Polygon Visualization
,SIFT
,Florence-2 Model
,Ellipse Visualization
,Mask Visualization
,Keypoint Detection Model
,Detections Stitch
,Bounding Box Visualization
,Dot Visualization
,Stability AI Image Generation
,VLM as Detector
,VLM as Classifier
Input and Output Bindings¶
The available connections depend on its binding kinds. Check what binding kinds
Polygon 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..thickness
(integer
): Thickness of the outline in pixels..
-
output
image
(image
): Image in workflows.
Example JSON definition of step Polygon Visualization
in version v1
{
"name": "<your_step_name_here>",
"type": "roboflow_core/polygon_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",
"thickness": 2
}