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