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:
Template Matching,Morphological Transformation,Classification Label Visualization,Crop Visualization,Stability AI Outpainting,Blur Visualization,Detections Transformation,Reference Path Visualization,OpenAI,YOLO-World Model,Detections Classes Replacement,Anthropic Claude,Camera Focus,Track Class Lock,Instance Segmentation Model,Mask Edge Snap,Model Comparison Visualization,Florence-2 Model,Trace Visualization,Background Color Visualization,JSON Parser,Label Visualization,Image Convert Grayscale,Florence-2 Model,Text Display,Qwen-VL,Llama 3.2 Vision,Image Blur,Keypoint Detection Model,Absolute Static Crop,Velocity,CSV Formatter,Gaze Detection,Keypoint Detection Model,LMM,OC-SORT Tracker,Qwen 3.5 API,Qwen 3.6 API,Camera Focus,SORT Tracker,Line Counter,VLM As Detector,Clip Comparison,Detections Stitch,Google Gemma API,Contrast Enhancement,Halo Visualization,Color Visualization,Morphological Transformation,MoonshotAI Kimi,Stitch OCR Detections,Event Writer,Stability AI Inpainting,Bounding Rectangle,Roboflow Asset Library Attributes,Microsoft SQL Server Sink,Time in Zone,OpenAI,Roboflow Vision Events,Identify Outliers,Mask Area Measurement,Detection Offset,CogVLM,Detections Consensus,Object Detection Model,OPC UA Writer Sink,Dynamic Crop,Path Deviation,Byte Tracker,Bounding Box Visualization,Detections Combine,Qwen3.5-VL,SAM 3,OpenAI,SIFT Comparison,Time in Zone,OCR Model,Single-Label Classification Model,Slack Notification,OpenRouter,Detection Event Log,SIFT Comparison,Pixelate Visualization,Google Vision OCR,SAM3 Video Tracker,Google Gemma,Dynamic Zone,Halo Visualization,Stitch OCR Detections,GLM-OCR,Image Threshold,SAM 3 Interactive,Stitch Images,Twilio SMS/MMS Notification,Icon Visualization,VLM As Classifier,MoonshotAI Kimi,ByteTrack Tracker,Google Gemini,Byte Tracker,Webhook Sink,Instance Segmentation Model,QR Code Generator,Path Deviation,MQTT Writer,Ellipse Visualization,Object Detection Model,Keypoint Detection Model,BoT-SORT Tracker,Dot Visualization,Perspective Correction,Instance Segmentation Model,Seg Preview,Per-Class Confidence Filter,Roboflow Dataset Upload,Detections Stabilizer,Detections Merge,SIFT,Google Gemini,EasyOCR,Local File Sink,SAM 3,Triangle Visualization,Contrast Equalization,Time in Zone,Polygon Visualization,OpenAI,SAM2 Video Tracker,Heatmap Visualization,Detections List Roll-Up,Google Gemini,LMM For Classification,VLM As Detector,Identify Changes,Llama 3.2 Vision,Polygon Visualization,Email Notification,Image Stack,Mask Visualization,Anthropic Claude,Detections Filter,Distance Measurement,PTZ Tracking (ONVIF),Keypoint Visualization,Background Subtraction,Multi-Label Classification Model,Overlap Filter,Twilio SMS Notification,Email Notification,Image Slicer,Image Contours,Line Counter Visualization,Image Preprocessing,Byte Tracker,SAM 3,VLM As Classifier,Depth Estimation,Pixel Color Count,Motion Detection,Current Time,Corner Visualization,Polygon Zone Visualization,Camera Calibration,Roboflow Dataset Upload,Grid Visualization,Stability AI Image Generation,Moondream2,Segment Anything 2 Model,S3 Sink,Circle Visualization,Image Slicer,Roboflow Custom Metadata,Relative Static Crop,Instance Segmentation Model,Model Monitoring Inference Aggregator,OpenAI-Compatible LLM,Object Detection Model,Anthropic Claude,Line Counter - outputs:
Template Matching,Morphological Transformation,Classification Label Visualization,Crop Visualization,Stability AI Outpainting,Blur Visualization,Reference Path Visualization,OpenAI,YOLO-World Model,Anthropic Claude,Camera Focus,Track Class Lock,Instance Segmentation Model,Mask Edge Snap,Model Comparison Visualization,Florence-2 Model,Trace Visualization,SmolVLM2,Label Visualization,Image Convert Grayscale,Florence-2 Model,Text Display,Llama 3.2 Vision,Qwen-VL,Image Blur,Keypoint Detection Model,Absolute Static Crop,Gaze Detection,Keypoint Detection Model,LMM,OC-SORT Tracker,QR Code Detection,Qwen 3.5 API,Qwen 3.6 API,Qwen2.5-VL,Camera Focus,SORT Tracker,VLM As Detector,Qwen3-VL,Multi-Label Classification Model,Detections Stitch,Clip Comparison,Google Gemma API,Contrast Enhancement,Halo Visualization,MoonshotAI Kimi,Color Visualization,Morphological Transformation,Event Writer,Buffer,Stability AI Inpainting,Time in Zone,OpenAI,Roboflow Vision Events,Dominant Color,CogVLM,Object Detection Model,Semantic Segmentation Model,Dynamic Crop,Byte Tracker,Bounding Box Visualization,Qwen3.5-VL,Clip Comparison,SAM 3,OpenAI,Single-Label Classification Model,OCR Model,OpenRouter,SIFT Comparison,Pixelate Visualization,Google Vision OCR,SAM3 Video Tracker,Google Gemma,CLIP Embedding Model,Halo Visualization,GLM-OCR,Image Threshold,SAM 3 Interactive,Stitch Images,Twilio SMS/MMS Notification,VLM As Classifier,Icon Visualization,MoonshotAI Kimi,ByteTrack Tracker,Google Gemini,Single-Label Classification Model,Single-Label Classification Model,Instance Segmentation Model,Ellipse Visualization,Anthropic Claude,Object Detection Model,Keypoint Detection Model,BoT-SORT Tracker,Dot Visualization,Perspective Correction,Instance Segmentation Model,Seg Preview,Roboflow Dataset Upload,Detections Stabilizer,SIFT,Google Gemini,EasyOCR,SAM 3,Triangle Visualization,Contrast Equalization,Polygon Visualization,OpenAI,SAM2 Video Tracker,Heatmap Visualization,Perception Encoder Embedding Model,Google Gemini,LMM For Classification,VLM As Detector,Llama 3.2 Vision,Multi-Label Classification Model,Image Stack,Polygon Visualization,Mask Visualization,Anthropic Claude,Barcode Detection,Keypoint Visualization,Background Subtraction,Multi-Label Classification Model,Email Notification,Semantic Segmentation Model,Image Slicer,Image Contours,Line Counter Visualization,Image Preprocessing,SAM 3,VLM As Classifier,Depth Estimation,Pixel Color Count,Motion Detection,Qwen3.5,Roboflow Dataset Upload,Corner Visualization,Camera Calibration,Segment Anything 2 Model,Polygon Zone Visualization,Stability AI Image Generation,Moondream2,Circle Visualization,Image Slicer,Relative Static Crop,Instance Segmentation Model,Object Detection Model,Background Color Visualization
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,keypoint_detection_prediction,rle_instance_segmentation_prediction,object_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
}