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