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