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