Ellipse Visualization¶
Class: EllipseVisualizationBlockV1
Source: inference.core.workflows.core_steps.visualizations.ellipse.v1.EllipseVisualizationBlockV1
Draw elliptical outlines around detected objects, providing oval-shaped annotations that can be customized to full ellipses or partial arcs, offering more flexible geometry than circular visualizations.
How This Block Works¶
This block takes an image and detection predictions and draws elliptical (oval-shaped) outlines around each detected object. The block:
- Takes an image and predictions as input
- Identifies bounding box coordinates for each detected object
- Calculates the center point and dimensions (width and height) for each detection based on its bounding box
- Applies color styling based on the selected color palette, with colors assigned by class, index, or track ID
- Draws elliptical outlines around each detected object using Supervision's EllipseAnnotator
- Applies the specified thickness to control the line width of the elliptical outlines
- Uses start and end angle parameters to draw either full ellipses or partial elliptical arcs
- Returns an annotated image with elliptical outlines overlaid on the original image
The block draws ellipses that are typically fitted to each detection's bounding box, creating oval-shaped outlines that adapt to the object's aspect ratio (unlike circles which are always round). Ellipses provide more flexible geometry than circles, as they can represent both round and elongated objects more accurately. The start and end angle parameters allow you to draw full ellipses (360 degrees) or partial arcs, providing additional visual style options. Unlike circle visualization (which always draws complete circles), ellipse visualization can draw partial arcs, creating distinctive visual markers while still clearly indicating object locations.
Common Use Cases¶
- Oval and Elongated Object Highlighting: Highlight detected objects with elliptical outlines when objects are oval-shaped or elongated (e.g., vehicles, elongated products, elliptical objects) where ellipses provide a better fit than circular or rectangular shapes
- Flexible Geometric Visualization: Use elliptical shapes as a more geometrically flexible alternative to circles or bounding boxes, adapting to object aspect ratios for more accurate visual representation
- Partial Arc Annotations: Draw partial elliptical arcs (using start and end angles) to create distinctive, stylized visual markers that indicate object locations with a unique visual style
- Aesthetic Visualization Alternatives: Create visually distinct annotations compared to standard bounding boxes or circles for design purposes, artistic visualizations, or when elliptical shapes better match design aesthetics
- Object Shape Adaptation: Use ellipses when objects have non-square aspect ratios where circular outlines would be less accurate, providing better visual fit for rectangular or elongated objects
- Design and UI Applications: Integrate elliptical outlines into user interfaces, dashboards, or interactive applications where oval shapes provide a softer, more organic visual style than angular rectangles
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 elliptical 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 elliptical outlines for documentation, reporting, or analysis
- Webhook blocks to send visualized results with elliptical 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 elliptical outlines as visual evidence in alerts or reports
- Video output blocks to create annotated video streams or recordings with elliptical outlines for live monitoring, tracking visualization, or post-processing analysis
Type identifier¶
Use the following identifier in step "type" field: roboflow_core/ellipse_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 ellipse outline in pixels. Higher values create thicker, more visible elliptical outlines.. | ✅ |
start_angle |
int |
Starting angle for drawing the ellipse arc in degrees. Used together with end_angle to control whether a full ellipse (360 degrees) or partial arc is drawn. Angles are measured from the positive x-axis (0 degrees = right, 90 degrees = down, 180 degrees = left, 270 degrees = up).. | ✅ |
end_angle |
int |
Ending angle for drawing the ellipse arc in degrees. Used together with start_angle to control whether a full ellipse (360 degrees) or partial arc is drawn. To draw a full ellipse, set end_angle = start_angle + 360 (or equivalent). Angles are measured from the positive x-axis (0 degrees = right, 90 degrees = down, 180 degrees = left, 270 degrees = up).. | ✅ |
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 Ellipse Visualization in version v1.
- inputs:
SAM 3,Image Preprocessing,Time in Zone,Image Slicer,Anthropic Claude,Dynamic Crop,Mask Area Measurement,BoT-SORT Tracker,Bounding Box Visualization,Object Detection Model,Mask Edge Snap,Path Deviation,Absolute Static Crop,SIFT Comparison,Stitch Images,Stitch OCR Detections,OpenAI,Instance Segmentation Model,Email Notification,Stability AI Inpainting,Frame Delay,Distance Measurement,EasyOCR,Llama 3.2 Vision,Track Class Lock,Florence-2 Model,Gaze Detection,Roboflow Custom Metadata,Dynamic Zone,Auto Rotate on Edges,YOLO-World Model,Byte Tracker,Detections Transformation,Keypoint Detection Model,Stability AI Outpainting,Model Comparison Visualization,Slack Notification,Detections Classes Replacement,Line Counter Visualization,Camera Calibration,Byte Tracker,PLC Reader,Single-Label Classification Model,Clip Comparison,VLM As Detector,CogVLM,SORT Tracker,Camera Focus,Corner Visualization,Ellipse Visualization,PP-OCR,Morphological Transformation,Detections List Roll-Up,Anthropic Claude,Roboflow Visual Search,Color Visualization,Instance Segmentation Model,OpenAI,Triangle Visualization,Time in Zone,Detection Event Log,Detections Stabilizer,Object Detection Model,Image Contours,SAM 3,Image Threshold,SAM 3,Detections Merge,Current Time,Roboflow Visual Search Classifier,QR Code Generator,OpenAI-Compatible LLM,Florence-2 Model,Polygon Zone Visualization,Stitch OCR Detections,Roboflow Asset Library Attributes,Path Deviation,GeoTag Detection,Microsoft SQL Server Sink,Moondream2,VLM As Classifier,Camera Focus,Image Convert Grayscale,Label Visualization,Detection Offset,Stability AI Image Generation,Llama 3.2 Vision,VLM As Detector,Instance Segmentation Model,Line Counter,MQTT Writer,Roboflow Dataset Upload,Local File Sink,Identify Outliers,VLM As Classifier,Bounding Rectangle,Event Writer,Google Gemini,Depth Estimation,Seg Preview,Byte Tracker,Google Gemini,OpenAI,Trace Visualization,Twilio SMS Notification,Pixel Color Count,SAM 3 Interactive,PLC EthernetIP,Detections Filter,LMM For Classification,Object Detection Model,Velocity,Webhook Sink,Halo Visualization,Buffer,Mask Visualization,Template Matching,Pixelate Visualization,Twilio SMS/MMS Notification,MoonshotAI Kimi,Dot Visualization,Multi-Label Classification Model,Image Stack,OPC UA Writer Sink,Google Gemini,OC-SORT Tracker,Keypoint Visualization,Dimension Collapse,LMM,Detections Combine,Image Slicer,PTZ Tracking (ONVIF),Time in Zone,OCR Model,Circle Visualization,Contrast Enhancement,Relative Static Crop,SAM2 Video Tracker,ByteTrack Tracker,Morphological Transformation,Per-Class Confidence Filter,Email Notification,Halo Visualization,Clip Comparison,Cosmos 3,Polygon Visualization,Qwen-VL,PLC Writer,Google Gemma,Crop Visualization,Qwen 3.5 API,Model Monitoring Inference Aggregator,Keypoint Detection Model,Size Measurement,Icon Visualization,Heatmap Visualization,Motion Detection,Google Gemma API,Detections Consensus,Instance Segmentation Model,CSV Formatter,Image Blur,Segment Anything 2 Model,Background Color Visualization,Grid Visualization,Detections Stitch,SAM3 Video Tracker,Blur Visualization,GLM-OCR,Anthropic Claude,Reference Path Visualization,Classification Label Visualization,Google Vision OCR,Perspective Correction,Background Subtraction,Polygon Visualization,Contrast Equalization,SIFT,Qwen 3.6 API,Text Display,JSON Parser,MoonshotAI Kimi,OpenRouter,Qwen3.5-VL,Roboflow Vision Events,Identify Changes,Keypoint Detection Model,SIFT Comparison,OpenAI,PLC ModbusTCP,Overlap Filter,S3 Sink,Line Counter,Roboflow Dataset Upload - outputs:
SAM 3,Image Preprocessing,Single-Label Classification Model,Anthropic Claude,Image Slicer,CLIP Embedding Model,Dynamic Crop,BoT-SORT Tracker,Bounding Box Visualization,Mask Edge Snap,Object Detection Model,QR Code Detection,Absolute Static Crop,SIFT Comparison,Stitch Images,OpenAI,Instance Segmentation Model,Email Notification,Stability AI Inpainting,Frame Delay,EasyOCR,Llama 3.2 Vision,Track Class Lock,Florence-2 Model,Gaze Detection,Auto Rotate on Edges,YOLO-World Model,Dominant Color,Keypoint Detection Model,Stability AI Outpainting,Model Comparison Visualization,Line Counter Visualization,Camera Calibration,Single-Label Classification Model,Clip Comparison,VLM As Detector,CogVLM,SORT Tracker,Corner Visualization,PP-OCR,Camera Focus,Ellipse Visualization,Morphological Transformation,Anthropic Claude,Roboflow Visual Search,Instance Segmentation Model,Color Visualization,OpenAI,Time in Zone,Triangle Visualization,Detections Stabilizer,Object Detection Model,Image Contours,SAM 3,Image Threshold,SAM 3,Barcode Detection,Roboflow Visual Search Classifier,Qwen2.5-VL,Florence-2 Model,Semantic Segmentation Model,SmolVLM2,Polygon Zone Visualization,GeoTag Detection,Moondream2,VLM As Classifier,Camera Focus,Image Convert Grayscale,Label Visualization,Llama 3.2 Vision,Stability AI Image Generation,VLM As Detector,Perception Encoder Embedding Model,Instance Segmentation Model,Roboflow Dataset Upload,VLM As Classifier,Google Gemini,Event Writer,Semantic Segmentation Model,Depth Estimation,Seg Preview,Byte Tracker,Google Gemini,OpenAI,Trace Visualization,Pixel Color Count,SAM 3 Interactive,LMM For Classification,Object Detection Model,Halo Visualization,Buffer,Mask Visualization,Template Matching,Twilio SMS/MMS Notification,Pixelate Visualization,MoonshotAI Kimi,Dot Visualization,Multi-Label Classification Model,Image Stack,Google Gemini,OC-SORT Tracker,Keypoint Visualization,LMM,Image Slicer,OCR Model,Circle Visualization,Contrast Enhancement,Relative Static Crop,SAM2 Video Tracker,ByteTrack Tracker,Morphological Transformation,Halo Visualization,Clip Comparison,Cosmos 3,Polygon Visualization,Qwen-VL,Google Gemma,Crop Visualization,Qwen 3.5 API,Qwen3-VL,Qwen3.5,Keypoint Detection Model,Single-Label Classification Model,Icon Visualization,Heatmap Visualization,Motion Detection,Multi-Label Classification Model,Google Gemma API,Instance Segmentation Model,SAM3 Video Tracker,Image Blur,Background Color Visualization,Detections Stitch,Segment Anything 2 Model,Blur Visualization,GLM-OCR,Anthropic Claude,Reference Path Visualization,Classification Label Visualization,Google Vision OCR,Background Subtraction,Perspective Correction,Polygon Visualization,Contrast Equalization,SIFT,Qwen 3.6 API,Text Display,MoonshotAI Kimi,OpenRouter,Qwen3.5-VL,Roboflow Vision Events,OpenAI,Keypoint Detection Model,Multi-Label Classification Model,Roboflow Dataset Upload
Input and Output Bindings¶
The available connections depend on its binding kinds. Check what binding kinds
Ellipse 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 ellipse outline in pixels. Higher values create thicker, more visible elliptical outlines..start_angle(integer): Starting angle for drawing the ellipse arc in degrees. Used together with end_angle to control whether a full ellipse (360 degrees) or partial arc is drawn. Angles are measured from the positive x-axis (0 degrees = right, 90 degrees = down, 180 degrees = left, 270 degrees = up)..end_angle(integer): Ending angle for drawing the ellipse arc in degrees. Used together with start_angle to control whether a full ellipse (360 degrees) or partial arc is drawn. To draw a full ellipse, set end_angle = start_angle + 360 (or equivalent). Angles are measured from the positive x-axis (0 degrees = right, 90 degrees = down, 180 degrees = left, 270 degrees = up)..
-
output
image(image): Image in workflows.
Example JSON definition of step Ellipse Visualization in version v1
{
"name": "<your_step_name_here>",
"type": "roboflow_core/ellipse_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,
"start_angle": -45,
"end_angle": 235
}