Halo Visualization

Class: HaloVisualizationBlockV1

Source: inference.core.workflows.core_steps.visualizations.halo.v1.HaloVisualizationBlockV1

The HaloVisualization block uses a detected polygon from an instance segmentation to draw a halo using sv.HaloAnnotator.

Type identifier

Use the following identifier in step "type" field: roboflow_core/halo_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..	✅
opacity	float	Transparency of the halo overlay..	✅
kernel_size	int	Size of the average pooling kernel used for creating the halo..	✅

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 Halo Visualization in version v1.

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

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Halo 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 (instance_segmentation_prediction): Predictions.
  • 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..
  • opacity (float_zero_to_one): Transparency of the halo overlay..
  • kernel_size (integer): Size of the average pooling kernel used for creating the halo..

• output

  • image (image): Image in workflows.

Example JSON definition of step Halo Visualization in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/halo_visualization@v1",
    "image": "$inputs.image",
    "copy_image": true,
    "predictions": "$steps.instance_segmentation_model.predictions",
    "color_palette": "DEFAULT",
    "palette_size": 10,
    "custom_colors": [
        "#FF0000",
        "#00FF00",
        "#0000FF"
    ],
    "color_axis": "CLASS",
    "opacity": 0.8,
    "kernel_size": 40
}