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

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
}