Circle Visualization¶
Class: CircleVisualizationBlockV1
Source: inference.core.workflows.core_steps.visualizations.circle.v1.CircleVisualizationBlockV1
The CircleVisualization block draws a circle around detected
objects in an image using Supervision's sv.CircleAnnotator.
Type identifier¶
Use the following identifier in step "type" field: roboflow_core/circle_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.. | ✅ |
thickness |
int |
Thickness of the lines 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 Circle Visualization in version v1.
- inputs:
Segment Anything 2 Model,Image Slicer,Stability AI Inpainting,Clip Comparison,Perspective Correction,Object Detection Model,Roboflow Custom Metadata,Object Detection Model,SIFT Comparison,Detection Offset,Grid Visualization,Ellipse Visualization,SIFT,VLM as Detector,CogVLM,Image Contours,OpenAI,Absolute Static Crop,Camera Focus,Trace Visualization,Multi-Label Classification Model,VLM as Detector,Dot Visualization,Google Vision OCR,Clip Comparison,Identify Outliers,Polygon Zone Visualization,Roboflow Dataset Upload,Identify Changes,VLM as Classifier,Gaze Detection,Classification Label Visualization,Corner Visualization,Byte Tracker,Llama 3.2 Vision,Dynamic Crop,Reference Path Visualization,Line Counter,Label Visualization,Detections Stabilizer,Mask Visualization,Triangle Visualization,Line Counter Visualization,Template Matching,Dynamic Zone,Detections Transformation,Model Monitoring Inference Aggregator,Time in Zone,Blur Visualization,Line Counter,Anthropic Claude,Webhook Sink,Instance Segmentation Model,SIFT Comparison,Time in Zone,Instance Segmentation Model,Slack Notification,Detections Filter,Stitch OCR Detections,Pixelate Visualization,Path Deviation,OpenAI,Relative Static Crop,Detections Consensus,Twilio SMS Notification,Dimension Collapse,VLM as Classifier,Roboflow Dataset Upload,Keypoint Detection Model,Google Gemini,Model Comparison Visualization,Halo Visualization,JSON Parser,Crop Visualization,Byte Tracker,Image Blur,Distance Measurement,Circle Visualization,Velocity,Buffer,Keypoint Detection Model,Image Preprocessing,Background Color Visualization,Bounding Rectangle,Pixel Color Count,Size Measurement,Florence-2 Model,Bounding Box Visualization,Byte Tracker,Local File Sink,Image Slicer,Florence-2 Model,LMM For Classification,Stitch Images,Stability AI Image Generation,Image Threshold,Detections Stitch,OCR Model,LMM,Keypoint Visualization,Email Notification,Color Visualization,Path Deviation,Single-Label Classification Model,YOLO-World Model,CSV Formatter,Image Convert Grayscale,Detections Classes Replacement,Polygon Visualization - outputs:
Segment Anything 2 Model,Image Slicer,Stability AI Inpainting,Clip Comparison,Perspective Correction,Object Detection Model,Object Detection Model,SIFT Comparison,CogVLM,Ellipse Visualization,SIFT,VLM as Detector,Image Contours,Multi-Label Classification Model,OpenAI,Absolute Static Crop,Camera Focus,Trace Visualization,CLIP Embedding Model,Multi-Label Classification Model,VLM as Detector,Dot Visualization,Google Vision OCR,Clip Comparison,Polygon Zone Visualization,Gaze Detection,Roboflow Dataset Upload,VLM as Classifier,Classification Label Visualization,Corner Visualization,Llama 3.2 Vision,Dynamic Crop,Reference Path Visualization,Detections Stabilizer,Label Visualization,Mask Visualization,Triangle Visualization,Template Matching,Line Counter Visualization,Dominant Color,Barcode Detection,Blur Visualization,Anthropic Claude,Instance Segmentation Model,Time in Zone,Instance Segmentation Model,Pixelate Visualization,OpenAI,Relative Static Crop,VLM as Classifier,Keypoint Detection Model,Roboflow Dataset Upload,Google Gemini,Model Comparison Visualization,Halo Visualization,Crop Visualization,Qwen2.5-VL,Image Blur,Circle Visualization,Buffer,Keypoint Detection Model,Image Preprocessing,Background Color Visualization,QR Code Detection,Pixel Color Count,Florence-2 Model,Single-Label Classification Model,Bounding Box Visualization,Byte Tracker,Florence-2 Model,Image Slicer,LMM For Classification,Stitch Images,Stability AI Image Generation,Image Threshold,Detections Stitch,OCR Model,LMM,Keypoint Visualization,Color Visualization,Single-Label Classification Model,YOLO-World Model,Image Convert Grayscale,Polygon Visualization
Input and Output Bindings¶
The available connections depend on its binding kinds. Check what binding kinds
Circle 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(Union[instance_segmentation_prediction,keypoint_detection_prediction,object_detection_prediction]): Model predictions to visualize..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 lines in pixels..
-
output
image(image): Image in workflows.
Example JSON definition of step Circle Visualization in version v1
{
"name": "<your_step_name_here>",
"type": "roboflow_core/circle_visualization@v1",
"image": "$inputs.image",
"copy_image": true,
"predictions": "$steps.object_detection_model.predictions",
"color_palette": "DEFAULT",
"palette_size": 10,
"custom_colors": [
"#FF0000",
"#00FF00",
"#0000FF"
],
"color_axis": "CLASS",
"thickness": 2
}