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

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
}