Circle Visualization

Class: CircleVisualizationBlockV1

Source: inference.core.workflows.core_steps.visualizations.circle.v1.CircleVisualizationBlockV1

Draw circular outlines around detected objects, providing an alternative to rectangular bounding boxes with a softer, more rounded visualization style.

How This Block Works

This block takes an image and detection predictions and draws circular outlines around each detected object. The block:

1. Takes an image and predictions as input
2. Calculates the center point and size for each detection based on its bounding box
3. Applies color styling based on the selected color palette, with colors assigned by class, index, or track ID
4. Draws circular outlines around each detected object using Supervision's CircleAnnotator
5. Applies the specified circle thickness to control the line width of the circular outlines
6. Returns an annotated image with circular outlines overlaid on the original image

The block draws circles that are typically centered on each detection's bounding box, with the circle size determined by the detection dimensions. Circles provide a softer, more organic visual style compared to rectangular bounding boxes, while still clearly marking the location and extent of detected objects. Unlike dot visualization (which marks specific points), circle visualization draws full circular outlines that encompass the detected objects, making it useful when you want a rounded geometric shape that's less angular than bounding boxes but more prominent than small dot markers.

Common Use Cases

• Soft Geometric Visualization: Use circular outlines instead of rectangular bounding boxes for a softer, more organic visual style in presentations, dashboards, or user interfaces where rounded shapes are preferred
• Object Highlighting with Rounded Shapes: Highlight detected objects with circular outlines when working with circular or spherical objects (e.g., balls, coins, circular logos, round products) where circles naturally fit the object shape
• Aesthetic Visualization Alternatives: Create visually distinct annotations compared to standard bounding boxes for design purposes, artistic visualizations, or when circular shapes better match the overall design aesthetic
• Detection Visualization with Variation: Provide an alternative visualization style to bounding boxes for comparison, experimentation, or when multiple visualization types are used together to distinguish different detection sets
• User Interface Design: Use circular outlines in user interfaces, mobile apps, or interactive displays where rounded shapes are more visually appealing or match design guidelines
• Scientific and Medical Imaging: Visualize detections with circular outlines in scientific or medical imaging contexts where rounded shapes may be more appropriate than angular bounding boxes

Connecting to Other Blocks

The annotated image from this block can be connected to:

• Other visualization blocks (e.g., Label Visualization, Dot Visualization, Bounding Box Visualization) to combine circular outlines with additional annotations for comprehensive visualization
• Data storage blocks (e.g., Local File Sink, CSV Formatter, Roboflow Dataset Upload) to save annotated images with circular outlines for documentation, reporting, or analysis
• Webhook blocks to send visualized results with circular outlines to external systems, APIs, or web applications for display in dashboards or monitoring tools
• Notification blocks (e.g., Email Notification, Slack Notification) to send annotated images with circular outlines as visual evidence in alerts or reports
• Video output blocks to create annotated video streams or recordings with circular outlines for live monitoring, tracking visualization, or post-processing analysis

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 circle outline in pixels. Higher values create thicker, more visible circular outlines..	✅

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: Contrast Equalization, Clip Comparison, VLM as Detector, Detections Transformation, Polygon Visualization, Image Blur, SIFT Comparison, Text Display, SIFT, Moondream2, Google Vision OCR, Pixelate Visualization, Time in Zone, VLM as Classifier, Detection Offset, Detections Filter, Instance Segmentation Model, Perspective Correction, Halo Visualization, Image Threshold, Path Deviation, Keypoint Detection Model, CSV Formatter, Florence-2 Model, Twilio SMS Notification, Detections Stabilizer, Image Convert Grayscale, Corner Visualization, Dynamic Zone, Identify Changes, Icon Visualization, SAM 3, Detections Consensus, Multi-Label Classification Model, Detections Stitch, Dynamic Crop, Bounding Box Visualization, YOLO-World Model, Detection Event Log, Detections Classes Replacement, Blur Visualization, Camera Calibration, Line Counter, Path Deviation, OpenAI, Camera Focus, Trace Visualization, CogVLM, Image Slicer, Absolute Static Crop, Dot Visualization, Label Visualization, Slack Notification, Google Gemini, Object Detection Model, LMM For Classification, Stitch OCR Detections, OpenAI, Classification Label Visualization, Stitch OCR Detections, Byte Tracker, Twilio SMS/MMS Notification, Velocity, Gaze Detection, Anthropic Claude, Clip Comparison, VLM as Detector, Webhook Sink, Llama 3.2 Vision, SIFT Comparison, Anthropic Claude, Local File Sink, QR Code Generator, Time in Zone, Email Notification, Roboflow Dataset Upload, Motion Detection, Model Comparison Visualization, Camera Focus, PTZ Tracking (ONVIF).md), LMM, Byte Tracker, Mask Visualization, SAM 3, Relative Static Crop, Anthropic Claude, Object Detection Model, Detections Merge, Keypoint Detection Model, Circle Visualization, Seg Preview, EasyOCR, Stability AI Inpainting, Reference Path Visualization, Time in Zone, Detections Combine, Ellipse Visualization, Crop Visualization, Overlap Filter, Line Counter, Image Preprocessing, Detections List Roll-Up, Background Subtraction, Segment Anything 2 Model, Image Contours, Image Slicer, Depth Estimation, Pixel Color Count, Stitch Images, VLM as Classifier, Model Monitoring Inference Aggregator, Instance Segmentation Model, Line Counter Visualization, Morphological Transformation, Polygon Zone Visualization, Single-Label Classification Model, Email Notification, Keypoint Visualization, OCR Model, Roboflow Custom Metadata, Google Gemini, Distance Measurement, OpenAI, Color Visualization, Size Measurement, Byte Tracker, Identify Outliers, Buffer, Florence-2 Model, Google Gemini, JSON Parser, Grid Visualization, Template Matching, OpenAI, Dimension Collapse, Bounding Rectangle, Background Color Visualization, Stability AI Outpainting, Roboflow Dataset Upload, SAM 3, Triangle Visualization, Stability AI Image Generation
• outputs: Contrast Equalization, Llama 3.2 Vision, Clip Comparison, Anthropic Claude, VLM as Detector, Polygon Visualization, Image Blur, SIFT Comparison, SmolVLM2, CLIP Embedding Model, Roboflow Dataset Upload, Text Display, Motion Detection, SIFT, Model Comparison Visualization, Camera Focus, Moondream2, LMM, Qwen3-VL, Single-Label Classification Model, Google Vision OCR, SAM 3, Anthropic Claude, Relative Static Crop, Mask Visualization, Object Detection Model, Keypoint Detection Model, Circle Visualization, Seg Preview, EasyOCR, Pixelate Visualization, Stability AI Inpainting, Multi-Label Classification Model, Time in Zone, VLM as Classifier, Reference Path Visualization, Instance Segmentation Model, Perspective Correction, Halo Visualization, Image Threshold, Ellipse Visualization, Crop Visualization, Keypoint Detection Model, Florence-2 Model, Detections Stabilizer, Image Convert Grayscale, Perception Encoder Embedding Model, Corner Visualization, Image Preprocessing, Barcode Detection, Icon Visualization, SAM 3, Background Subtraction, Segment Anything 2 Model, Qwen2.5-VL, Image Slicer, Image Contours, Depth Estimation, Multi-Label Classification Model, Pixel Color Count, Detections Stitch, Stitch Images, QR Code Detection, Dynamic Crop, Bounding Box Visualization, Anthropic Claude, VLM as Classifier, YOLO-World Model, Instance Segmentation Model, Line Counter Visualization, Blur Visualization, Morphological Transformation, Camera Calibration, Polygon Zone Visualization, Single-Label Classification Model, Email Notification, Stability AI Image Generation, Dominant Color, OCR Model, Keypoint Visualization, Google Gemini, OpenAI, Camera Focus, Trace Visualization, CogVLM, OpenAI, Image Slicer, Absolute Static Crop, Color Visualization, Dot Visualization, Label Visualization, Buffer, Florence-2 Model, Google Gemini, Google Gemini, Object Detection Model, LMM For Classification, Template Matching, OpenAI, OpenAI, Classification Label Visualization, Background Color Visualization, Stability AI Outpainting, Byte Tracker, SAM 3, Twilio SMS/MMS Notification, Roboflow Dataset Upload, Gaze Detection, Clip Comparison, Triangle Visualization, VLM as Detector

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, rle_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 circle outline in pixels. Higher values create thicker, more visible circular outlines..

• 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
}