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: Mask Visualization, Classification Label Visualization, Detections Consensus, Instance Segmentation Model, Detections Merge, Webhook Sink, Multi-Label Classification Model, Email Notification, QR Code Generator, VLM As Detector, LMM, SAM 3, Detection Offset, Corner Visualization, Image Convert Grayscale, Stability AI Outpainting, Segment Anything 2 Model, Halo Visualization, JSON Parser, Object Detection Model, Trace Visualization, Google Vision OCR, Instance Segmentation Model, CSV Formatter, Text Display, Stitch Images, Google Gemini, Local File Sink, Slack Notification, VLM As Classifier, Roboflow Dataset Upload, PTZ Tracking (ONVIF).md), Color Visualization, Dot Visualization, Polygon Visualization, Object Detection Model, Anthropic Claude, Byte Tracker, Contrast Equalization, Identify Changes, Detections Classes Replacement, Velocity, Moondream2, SIFT Comparison, Halo Visualization, Florence-2 Model, Blur Visualization, Label Visualization, Twilio SMS/MMS Notification, Ellipse Visualization, OpenAI, SIFT, Model Monitoring Inference Aggregator, Single-Label Classification Model, Detections List Roll-Up, OpenAI, Image Threshold, Background Color Visualization, Model Comparison Visualization, OpenAI, Keypoint Detection Model, Gaze Detection, Polygon Visualization, Twilio SMS Notification, SAM 3, Bounding Box Visualization, OCR Model, Overlap Filter, Icon Visualization, Time in Zone, Google Gemini, Florence-2 Model, Roboflow Dataset Upload, Anthropic Claude, Dynamic Zone, Dynamic Crop, VLM As Detector, Google Gemini, Path Deviation, Image Blur, Line Counter, Byte Tracker, Stability AI Inpainting, Template Matching, Image Contours, Path Deviation, Morphological Transformation, Triangle Visualization, Bounding Rectangle, Detections Stitch, Relative Static Crop, Detections Filter, Camera Calibration, Grid Visualization, Detections Stabilizer, Camera Focus, Image Slicer, Detections Combine, LMM For Classification, Line Counter Visualization, Llama 3.2 Vision, Keypoint Detection Model, Distance Measurement, SIFT Comparison, Camera Focus, Time in Zone, Background Subtraction, Image Slicer, Circle Visualization, Seg Preview, Identify Outliers, Clip Comparison, Email Notification, Byte Tracker, Image Preprocessing, SAM 3, Depth Estimation, Time in Zone, Line Counter, CogVLM, Absolute Static Crop, Roboflow Custom Metadata, EasyOCR, Stitch OCR Detections, Perspective Correction, Anthropic Claude, Pixelate Visualization, Stability AI Image Generation, Reference Path Visualization, Keypoint Visualization, VLM As Classifier, Detection Event Log, Polygon Zone Visualization, YOLO-World Model, Stitch OCR Detections, Crop Visualization, Pixel Color Count, Motion Detection, OpenAI, Detections Transformation
• outputs: Anthropic Claude, Mask Visualization, Classification Label Visualization, Instance Segmentation Model, Multi-Label Classification Model, Email Notification, Dynamic Crop, CLIP Embedding Model, VLM As Detector, VLM As Detector, Google Gemini, Multi-Label Classification Model, LMM, SAM 3, Image Blur, Corner Visualization, Image Convert Grayscale, Byte Tracker, Stability AI Outpainting, SmolVLM2, Segment Anything 2 Model, Halo Visualization, Stability AI Inpainting, Object Detection Model, Template Matching, Single-Label Classification Model, Image Contours, Trace Visualization, Google Vision OCR, Morphological Transformation, Triangle Visualization, Instance Segmentation Model, Clip Comparison, Detections Stitch, Relative Static Crop, Text Display, Stitch Images, Google Gemini, Camera Calibration, Detections Stabilizer, VLM As Classifier, Roboflow Dataset Upload, Camera Focus, Color Visualization, Dot Visualization, Image Slicer, Polygon Visualization, Object Detection Model, Anthropic Claude, LMM For Classification, Line Counter Visualization, Keypoint Detection Model, Buffer, Llama 3.2 Vision, Contrast Equalization, SIFT Comparison, Camera Focus, Perception Encoder Embedding Model, Dominant Color, Time in Zone, Background Subtraction, Image Slicer, Circle Visualization, Moondream2, Seg Preview, Halo Visualization, Florence-2 Model, Blur Visualization, Qwen3-VL, Twilio SMS/MMS Notification, Label Visualization, Barcode Detection, Clip Comparison, Ellipse Visualization, OpenAI, QR Code Detection, SIFT, Image Preprocessing, SAM 3, Single-Label Classification Model, OpenAI, Image Threshold, Background Color Visualization, Model Comparison Visualization, Depth Estimation, OpenAI, Motion Detection, Keypoint Detection Model, CogVLM, Absolute Static Crop, Gaze Detection, EasyOCR, Perspective Correction, Qwen2.5-VL, Anthropic Claude, Pixelate Visualization, Reference Path Visualization, Stability AI Image Generation, Keypoint Visualization, SAM 3, Polygon Visualization, VLM As Classifier, Bounding Box Visualization, Polygon Zone Visualization, OCR Model, YOLO-World Model, Icon Visualization, Crop Visualization, Pixel Color Count, Google Gemini, OpenAI, Florence-2 Model, Roboflow Dataset Upload

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[instance_segmentation_prediction, object_detection_prediction, rle_instance_segmentation_prediction, keypoint_detection_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
}