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