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

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, instance_segmentation_prediction, keypoint_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
}