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

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
}