Ellipse Visualization

Class: EllipseVisualizationBlockV1

Source: inference.core.workflows.core_steps.visualizations.ellipse.v1.EllipseVisualizationBlockV1

The EllipseVisualization block draws ellipses that highlight detected objects in an image using Supervision's sv.EllipseAnnotator.

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 lines in pixels..	✅
start_angle	int	Starting angle of the ellipse in degrees..	✅
end_angle	int	Ending angle of the ellipse in degrees..	✅

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

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[object_detection_prediction, keypoint_detection_prediction, 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 lines in pixels..
  • start_angle (integer): Starting angle of the ellipse in degrees..
  • end_angle (integer): Ending angle of the ellipse in degrees..

• 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
}