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:
S3 Sink,Email Notification,Keypoint Detection Model,Morphological Transformation,VLM As Detector,Path Deviation,SAM 3,Clip Comparison,Qwen-VL,Twilio SMS/MMS Notification,YOLO-World Model,Line Counter,Time in Zone,Polygon Zone Visualization,MoonshotAI Kimi,Stitch OCR Detections,OpenAI-Compatible LLM,VLM As Detector,OpenAI,Heatmap Visualization,Keypoint Visualization,Email Notification,Seg Preview,Llama 3.2 Vision,Stability AI Image Generation,Google Vision OCR,Anthropic Claude,Camera Focus,Label Visualization,SAM 3,Instance Segmentation Model,Path Deviation,Overlap Filter,Local File Sink,Google Gemini,Motion Detection,Byte Tracker,Background Color Visualization,Mask Edge Snap,Instance Segmentation Model,Qwen 3.5 API,Google Gemini,Polygon Visualization,Moondream2,Velocity,SIFT Comparison,Grid Visualization,Detection Event Log,Florence-2 Model,Time in Zone,OCR Model,VLM As Classifier,Detections Filter,Detections Merge,Detections Stabilizer,LMM For Classification,Keypoint Detection Model,Image Preprocessing,SIFT,Roboflow Dataset Upload,Dynamic Zone,Corner Visualization,Stability AI Outpainting,Segment Anything 2 Model,Halo Visualization,Multi-Label Classification Model,Qwen3.5-VL,Time in Zone,Detections List Roll-Up,Blur Visualization,Distance Measurement,Morphological Transformation,Trace Visualization,VLM As Classifier,Stitch OCR Detections,Gaze Detection,Reference Path Visualization,Halo Visualization,Model Comparison Visualization,Dot Visualization,JSON Parser,Pixel Color Count,Background Subtraction,Text Display,Detections Combine,Bounding Rectangle,ByteTrack Tracker,Absolute Static Crop,CSV Formatter,Florence-2 Model,Byte Tracker,Identify Outliers,Icon Visualization,Mask Area Measurement,Object Detection Model,Perspective Correction,SAM 3,BoT-SORT Tracker,Stability AI Inpainting,Image Convert Grayscale,Object Detection Model,Line Counter,QR Code Generator,OpenRouter,Model Monitoring Inference Aggregator,OpenAI,Llama 3.2 Vision,Image Threshold,OC-SORT Tracker,Anthropic Claude,Dynamic Crop,Detections Consensus,Size Measurement,Clip Comparison,Contrast Enhancement,Bounding Box Visualization,Depth Estimation,Detection Offset,Keypoint Detection Model,Image Contours,EasyOCR,Relative Static Crop,Polygon Visualization,Google Gemma API,Template Matching,Qwen 3.6 API,Image Blur,Per-Class Confidence Filter,Anthropic Claude,Triangle Visualization,Object Detection Model,Roboflow Custom Metadata,SIFT Comparison,OpenAI,Slack Notification,Image Stack,Pixelate Visualization,Stitch Images,Single-Label Classification Model,Instance Segmentation Model,OpenAI,Buffer,Image Slicer,Line Counter Visualization,Image Slicer,Detections Classes Replacement,LMM,Roboflow Dataset Upload,Detections Transformation,Color Visualization,Google Gemini,Classification Label Visualization,Camera Focus,Camera Calibration,Detections Stitch,Byte Tracker,Ellipse Visualization,PTZ Tracking (ONVIF),Identify Changes,SORT Tracker,Mask Visualization,GLM-OCR,Crop Visualization,Circle Visualization,CogVLM,Dimension Collapse,SAM2 Video Tracker,Contrast Equalization,Roboflow Vision Events,Webhook Sink,Twilio SMS Notification,MoonshotAI Kimi,Google Gemma - outputs:
Keypoint Detection Model,Clip Comparison,Morphological Transformation,SAM 3,Qwen-VL,VLM As Detector,Email Notification,Twilio SMS/MMS Notification,YOLO-World Model,MoonshotAI Kimi,Polygon Zone Visualization,OpenAI,VLM As Detector,Heatmap Visualization,Keypoint Visualization,Llama 3.2 Vision,Anthropic Claude,Stability AI Image Generation,Google Vision OCR,Seg Preview,Camera Focus,Label Visualization,SAM 3,Instance Segmentation Model,Qwen3.5,Multi-Label Classification Model,SmolVLM2,Google Gemini,Motion Detection,Background Color Visualization,Mask Edge Snap,Instance Segmentation Model,Qwen 3.5 API,Google Gemini,Polygon Visualization,Moondream2,SIFT Comparison,Florence-2 Model,Barcode Detection,Time in Zone,Single-Label Classification Model,OCR Model,VLM As Classifier,Qwen2.5-VL,Detections Stabilizer,LMM For Classification,Keypoint Detection Model,SIFT,Image Preprocessing,Roboflow Dataset Upload,Corner Visualization,Stability AI Outpainting,Segment Anything 2 Model,Multi-Label Classification Model,Halo Visualization,Qwen3-VL,Qwen3.5-VL,Semantic Segmentation Model,Blur Visualization,Perception Encoder Embedding Model,Morphological Transformation,Trace Visualization,VLM As Classifier,Gaze Detection,Reference Path Visualization,Halo Visualization,Model Comparison Visualization,Dot Visualization,Pixel Color Count,Background Subtraction,QR Code Detection,Text Display,ByteTrack Tracker,Absolute Static Crop,Florence-2 Model,Byte Tracker,Icon Visualization,Object Detection Model,Perspective Correction,SAM 3,BoT-SORT Tracker,Stability AI Inpainting,Image Convert Grayscale,Object Detection Model,OpenRouter,OpenAI,Llama 3.2 Vision,Image Threshold,OC-SORT Tracker,Anthropic Claude,Dynamic Crop,Clip Comparison,Dominant Color,Contrast Enhancement,Bounding Box Visualization,Depth Estimation,Keypoint Detection Model,CLIP Embedding Model,Image Contours,EasyOCR,Relative Static Crop,Multi-Label Classification Model,Polygon Visualization,Google Gemma API,Qwen 3.6 API,Template Matching,Single-Label Classification Model,Image Blur,Anthropic Claude,Triangle Visualization,Object Detection Model,OpenAI,Image Stack,Pixelate Visualization,Single-Label Classification Model,OpenAI,Instance Segmentation Model,Buffer,Stitch Images,Image Slicer,Line Counter Visualization,Image Slicer,Semantic Segmentation Model,LMM,Roboflow Dataset Upload,Color Visualization,Google Gemini,Classification Label Visualization,Camera Focus,Camera Calibration,Detections Stitch,Ellipse Visualization,SORT Tracker,Mask Visualization,GLM-OCR,Crop Visualization,Circle Visualization,CogVLM,SAM2 Video Tracker,Contrast Equalization,Roboflow Vision Events,MoonshotAI Kimi,Google Gemma
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,object_detection_prediction,rle_instance_segmentation_prediction,keypoint_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 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
}