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