Icon Visualization

Class: IconVisualizationBlockV1

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

Place custom icon images on images either at fixed positions (static mode) or dynamically positioned on detected objects (dynamic mode), useful for watermarks, labels, badges, or visual markers.

How This Block Works

This block takes an image and optionally detection predictions, then places a custom icon image on the image. The block supports two modes:

Static Mode (for watermarks and fixed positioning): 1. Takes an image and an icon image as input 2. Places the icon at fixed x and y coordinates on the image 3. Supports negative coordinates for positioning from the right or bottom edges 4. Returns an annotated image with the icon at the specified static location

Dynamic Mode (for detection-based positioning): 1. Takes an image, an icon image, and detection predictions as input 2. Positions the icon on each detected object based on the selected anchor point (center, corners, edges, or center of mass) 3. Places the icon at the same position relative to each detection 4. Returns an annotated image with icons overlaid on detected objects

The block supports PNG images with transparency (alpha channel), allowing icons to blend naturally with the background. Icons can be resized to any width and height, making them suitable for various use cases from small badges to large watermarks. In static mode, icons are placed at fixed coordinates, making it ideal for watermarks or branding. In dynamic mode, icons automatically follow detected objects, making it useful for labeling, categorizing, or marking detected items with custom visual indicators.

Common Use Cases

• Watermarks and Branding: Place logos, watermarks, or branding elements at fixed positions (static mode) on images or videos for content protection, copyright marking, or brand identification
• Object Labeling with Icons: Place custom icons on detected objects (dynamic mode) to categorize, label, or mark objects with visual indicators (e.g., warning icons on unsafe objects, category icons for products, status badges)
• Visual Status Indicators: Display status icons (e.g., checkmarks, warning signs, information badges) on detected objects based on classification results, confidence levels, or custom logic for quick visual feedback
• Product Marking and Categorization: Place category icons, product type indicators, or custom markers on detected products in retail, e-commerce, or inventory management workflows
• Custom Annotation Systems: Create custom annotation workflows with specialized icons for quality control, defect marking, or compliance tracking in manufacturing or inspection workflows
• Interactive UI Elements: Add icon-based visual elements to images or videos for user interfaces, dashboards, or interactive applications where custom icons provide intuitive visual cues

Connecting to Other Blocks

The annotated image from this block can be connected to:

• Other visualization blocks (e.g., Label Visualization, Bounding Box Visualization, Polygon Visualization) to combine icon placement with additional annotations for comprehensive visualization
• Data storage blocks (e.g., Local File Sink, CSV Formatter, Roboflow Dataset Upload) to save images with icons for documentation, reporting, or archiving
• Webhook blocks to send visualized results with icons to external systems, APIs, or web applications for display in dashboards or monitoring tools
• Notification blocks (e.g., Email Notification, Slack Notification) to send annotated images with icons as visual evidence in alerts or reports
• Video output blocks to create annotated video streams or recordings with icons for live monitoring, tracking visualization, or post-processing analysis

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' mode places the icon at fixed x,y coordinates (useful for watermarks or fixed-position elements). 'dynamic' mode places icons on detected objects based on their positions (useful for object labeling or categorization)..	✅
icon_width	int	Width of the icon in pixels. The icon image will be resized to this width while maintaining aspect ratio if height is also specified..	✅
icon_height	int	Height of the icon in pixels. The icon image will be resized to this height while maintaining aspect ratio if width is also specified..	✅
position	str	Anchor position for placing icons relative to each detection's bounding box (dynamic mode only). Options include: CENTER (center of box), corners (TOP_LEFT, TOP_RIGHT, BOTTOM_LEFT, BOTTOM_RIGHT), edge midpoints (TOP_CENTER, CENTER_LEFT, CENTER_RIGHT, BOTTOM_CENTER), or CENTER_OF_MASS (center of mass of the object)..	✅
x_position	int	X coordinate for static mode positioning. Positive values position from the left edge of the image. Negative values position from the right edge (e.g., -10 places the icon 10 pixels from the right edge)..	✅
y_position	int	Y coordinate for static mode positioning. Positive values position from the top edge of the image. Negative values position from the bottom edge (e.g., -10 places the icon 10 pixels from the 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: Clip Comparison, Morphological Transformation, Motion Detection, Email Notification, Detections Stitch, Anthropic Claude, Detections Merge, Pixel Color Count, Keypoint Detection Model, Reference Path Visualization, Stitch OCR Detections, Camera Focus, Stability AI Image Generation, Stitch Images, Stability AI Outpainting, Time in Zone, Bounding Rectangle, Roboflow Dataset Upload, Depth Estimation, Detections Transformation, CogVLM, Identify Outliers, JSON Parser, Local File Sink, SAM 3, Dynamic Crop, Time in Zone, Moondream2, Dot Visualization, Triangle Visualization, Crop Visualization, PTZ Tracking (ONVIF).md), Twilio SMS Notification, Perspective Correction, Twilio SMS/MMS Notification, EasyOCR, Pixelate Visualization, Detections Consensus, OpenAI, Roboflow Dataset Upload, Single-Label Classification Model, Object Detection Model, SIFT Comparison, Contrast Equalization, Byte Tracker, Halo Visualization, Model Comparison Visualization, Slack Notification, Byte Tracker, Dynamic Zone, Image Contours, Background Color Visualization, Image Blur, Mask Visualization, Google Vision OCR, Color Visualization, Corner Visualization, Path Deviation, Template Matching, Line Counter Visualization, Ellipse Visualization, Icon Visualization, Velocity, Image Slicer, Detections Stabilizer, Absolute Static Crop, Stability AI Inpainting, SAM 3, Distance Measurement, Relative Static Crop, SIFT, CSV Formatter, Detections Filter, Blur Visualization, Instance Segmentation Model, Florence-2 Model, Google Gemini, LMM, Instance Segmentation Model, Polygon Zone Visualization, Keypoint Visualization, Roboflow Custom Metadata, Camera Focus, Multi-Label Classification Model, Detection Offset, Image Threshold, LMM For Classification, Anthropic Claude, Email Notification, Gaze Detection, Overlap Filter, Image Slicer, OpenAI, Detection Event Log, YOLO-World Model, Google Gemini, Image Preprocessing, VLM as Detector, Florence-2 Model, Image Convert Grayscale, Time in Zone, Byte Tracker, OCR Model, Seg Preview, Path Deviation, SAM 3, Detections List Roll-Up, Grid Visualization, Google Gemini, Object Detection Model, Line Counter, Trace Visualization, QR Code Generator, Camera Calibration, Webhook Sink, VLM as Detector, Background Subtraction, Bounding Box Visualization, Label Visualization, OpenAI, Circle Visualization, VLM as Classifier, Llama 3.2 Vision, Classification Label Visualization, OpenAI, Segment Anything 2 Model, Detections Combine, Detections Classes Replacement, Model Monitoring Inference Aggregator, Line Counter, VLM as Classifier, Polygon Visualization, SIFT Comparison, Keypoint Detection Model, Identify Changes, Text Display
• outputs: Instance Segmentation Model, Clip Comparison, Florence-2 Model, Morphological Transformation, Google Gemini, LMM, Instance Segmentation Model, Motion Detection, Email Notification, Detections Stitch, Polygon Zone Visualization, Keypoint Visualization, Camera Focus, Anthropic Claude, Multi-Label Classification Model, Pixel Color Count, Image Threshold, LMM For Classification, Keypoint Detection Model, Anthropic Claude, Gaze Detection, Reference Path Visualization, Camera Focus, Stability AI Image Generation, Stitch Images, Stability AI Outpainting, Image Slicer, SmolVLM2, OpenAI, Roboflow Dataset Upload, Depth Estimation, YOLO-World Model, Google Gemini, CogVLM, Image Preprocessing, VLM as Detector, Florence-2 Model, Image Convert Grayscale, SAM 3, Byte Tracker, Dynamic Crop, Time in Zone, Perception Encoder Embedding Model, Moondream2, Triangle Visualization, Dot Visualization, OCR Model, Seg Preview, Crop Visualization, Twilio SMS/MMS Notification, Perspective Correction, EasyOCR, SAM 3, Google Gemini, Object Detection Model, Text Display, Trace Visualization, Pixelate Visualization, OpenAI, CLIP Embedding Model, Camera Calibration, Roboflow Dataset Upload, Buffer, Barcode Detection, Object Detection Model, Single-Label Classification Model, QR Code Detection, VLM as Detector, Background Subtraction, Bounding Box Visualization, Contrast Equalization, Model Comparison Visualization, Halo Visualization, Label Visualization, OpenAI, Circle Visualization, Qwen2.5-VL, Image Contours, Image Blur, Background Color Visualization, Mask Visualization, Dominant Color, VLM as Classifier, Google Vision OCR, Llama 3.2 Vision, Color Visualization, Corner Visualization, Classification Label Visualization, Single-Label Classification Model, OpenAI, Segment Anything 2 Model, Clip Comparison, Template Matching, Line Counter Visualization, Icon Visualization, Ellipse Visualization, Image Slicer, Detections Stabilizer, Absolute Static Crop, VLM as Classifier, Polygon Visualization, SIFT Comparison, Stability AI Inpainting, Qwen3-VL, SAM 3, Keypoint Detection Model, Relative Static Crop, SIFT, Blur Visualization, Multi-Label Classification 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 format with transparency (alpha channel) is recommended for best results, as it allows the icon to blend naturally with the background. The icon will be resized to the specified width and height..
  • mode (string): Mode for placing icons. 'static' mode places the icon at fixed x,y coordinates (useful for watermarks or fixed-position elements). 'dynamic' mode places icons on detected objects based on their positions (useful for object labeling or categorization)..
  • predictions (Union[rle_instance_segmentation_prediction, object_detection_prediction, keypoint_detection_prediction, instance_segmentation_prediction]): Model predictions to place icons on (required for dynamic mode). Icons will be positioned on each detected object based on the selected position anchor point..
  • icon_width (integer): Width of the icon in pixels. The icon image will be resized to this width while maintaining aspect ratio if height is also specified..
  • icon_height (integer): Height of the icon in pixels. The icon image will be resized to this height while maintaining aspect ratio if width is also specified..
  • position (string): Anchor position for placing icons relative to each detection's bounding box (dynamic mode only). Options include: CENTER (center of box), corners (TOP_LEFT, TOP_RIGHT, BOTTOM_LEFT, BOTTOM_RIGHT), edge midpoints (TOP_CENTER, CENTER_LEFT, CENTER_RIGHT, BOTTOM_CENTER), or CENTER_OF_MASS (center of mass of the object)..
  • x_position (integer): X coordinate for static mode positioning. Positive values position from the left edge of the image. Negative values position from the right edge (e.g., -10 places the icon 10 pixels from the right edge)..
  • y_position (integer): Y coordinate for static mode positioning. Positive values position from the top edge of the image. Negative values position from the bottom edge (e.g., -10 places the icon 10 pixels from the 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
}