Icon Visualization

Class: IconVisualizationBlockV1

Source: inference.core.workflows.core_steps.visualizations.icon.v1.IconVisualizationBlockV1

The IconVisualization block draws icons on an image using Supervision's sv.IconAnnotator. It supports two modes: 1. Static Mode: Position an icon at a fixed location (e.g., for watermarks) 2. Dynamic Mode: Position icons based on detection coordinates

Type identifier

Use the following identifier in step "type" field: roboflow_core/icon_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..	✅
mode	str	Mode for placing icons: 'static' for fixed position (watermark), 'dynamic' for detection-based.	✅
icon_width	int	Width of the icon in pixels.	✅
icon_height	int	Height of the icon in pixels.	✅
position	str	Position relative to detection for dynamic mode.	✅
x_position	int	X coordinate for static mode. Positive values from left edge, negative from right edge.	✅
y_position	int	Y coordinate for static mode. Positive values from top edge, negative from bottom edge.	✅

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 Icon Visualization in version v1.

• inputs: Byte Tracker, Distance Measurement, Time in Zone, Dot Visualization, Blur Visualization, Perspective Correction, OpenAI, Keypoint Detection Model, EasyOCR, Stability AI Outpainting, VLM as Classifier, Twilio SMS Notification, Google Vision OCR, Roboflow Dataset Upload, Email Notification, Instance Segmentation Model, Image Convert Grayscale, Llama 3.2 Vision, Bounding Box Visualization, Detections Stabilizer, Reference Path Visualization, Image Preprocessing, Local File Sink, OpenAI, Image Slicer, Detections Stitch, SIFT Comparison, Velocity, Object Detection Model, Stability AI Image Generation, SIFT Comparison, Roboflow Custom Metadata, Model Comparison Visualization, Dynamic Zone, Line Counter, Time in Zone, Relative Static Crop, Polygon Visualization, Slack Notification, JSON Parser, Triangle Visualization, YOLO-World Model, Detections Classes Replacement, Bounding Rectangle, Circle Visualization, Label Visualization, VLM as Detector, Google Gemini, Detections Merge, Path Deviation, CSV Formatter, Detections Consensus, Moondream2, Segment Anything 2 Model, Polygon Zone Visualization, VLM as Classifier, Identify Outliers, LMM For Classification, Morphological Transformation, Corner Visualization, LMM, Pixel Color Count, PTZ Tracking (ONVIF).md), Florence-2 Model, Grid Visualization, Image Threshold, Florence-2 Model, Halo Visualization, Multi-Label Classification Model, CogVLM, Detections Combine, Detection Offset, Byte Tracker, Line Counter Visualization, Stitch OCR Detections, VLM as Detector, Keypoint Detection Model, Identify Changes, Camera Focus, SIFT, Clip Comparison, Image Slicer, Keypoint Visualization, Template Matching, OCR Model, Line Counter, Instance Segmentation Model, Dynamic Crop, Roboflow Dataset Upload, Mask Visualization, Background Color Visualization, Webhook Sink, Camera Calibration, Depth Estimation, QR Code Generator, Trace Visualization, Time in Zone, Detections Transformation, Contrast Equalization, Byte Tracker, Overlap Filter, Crop Visualization, Object Detection Model, Pixelate Visualization, Model Monitoring Inference Aggregator, Gaze Detection, Anthropic Claude, Image Contours, OpenAI, Path Deviation, Detections Filter, Classification Label Visualization, Image Blur, Absolute Static Crop, Stability AI Inpainting, Icon Visualization, Ellipse Visualization, Color Visualization, Single-Label Classification Model, Stitch Images
• 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 Icon 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..
  • icon (image): The icon image to place on the input image (PNG with transparency recommended).
  • mode (string): Mode for placing icons: 'static' for fixed position (watermark), 'dynamic' for detection-based.
  • predictions (Union[object_detection_prediction, instance_segmentation_prediction, keypoint_detection_prediction]): Model predictions to place icons on (required for dynamic mode).
  • icon_width (integer): Width of the icon in pixels.
  • icon_height (integer): Height of the icon in pixels.
  • position (string): Position relative to detection for dynamic mode.
  • x_position (integer): X coordinate for static mode. Positive values from left edge, negative from right edge.
  • y_position (integer): Y coordinate for static mode. Positive values from top edge, negative from bottom edge.

• output

  • image (image): Image in workflows.

Example JSON definition of step Icon Visualization in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/icon_visualization@v1",
    "image": "$inputs.image",
    "copy_image": true,
    "icon": "$inputs.icon",
    "mode": "static",
    "predictions": "$steps.object_detection_model.predictions",
    "icon_width": 64,
    "icon_height": 64,
    "position": "TOP_CENTER",
    "x_position": 10,
    "y_position": 10
}