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