Dot Visualization

Class: DotVisualizationBlockV1

Source: inference.core.workflows.core_steps.visualizations.dot.v1.DotVisualizationBlockV1

Draw circular dots on an image to mark specific points on detected objects, with customizable position, size, color, and outline styling.

How This Block Works

This block takes an image and detection predictions and draws circular dot markers at specified anchor positions on each detected object. The block:

1. Takes an image and predictions as input
2. Determines the dot position for each detection based on the selected anchor point (center, corners, edges, or center of mass)
3. Applies color styling based on the selected color palette, with colors assigned by class, index, or track ID
4. Draws circular dots with the specified radius and optional outline thickness using Supervision's DotAnnotator
5. Returns an annotated image with dots overlaid on the original image

The block supports various position options including the center of the bounding box, any of the four corners, edge midpoints, or the center of mass (useful for objects with irregular shapes). Dots can be customized with different sizes (radius), optional outlines for better visibility, and various color palettes. This provides a minimal, clean visualization style that marks detection locations without the visual clutter of full bounding boxes, making it ideal for dense scenes or when you need to highlight specific points of interest.

Common Use Cases

• Minimal Object Marking: Mark detected objects with small dots instead of bounding boxes for cleaner, less cluttered visualizations when working with dense scenes or many detections
• Point of Interest Highlighting: Mark specific anchor points (corners, center, center of mass) on detected objects for applications like object tracking, pose estimation, or spatial analysis
• Tracking Visualization: Use dots to visualize object trajectories or tracking IDs over time, creating a cleaner alternative to bounding boxes for tracking workflows
• Crowd Counting and Density Analysis: Mark people or objects with dots to visualize density patterns, crowd distribution, or object counts without overlapping bounding boxes
• Keypoint and Landmark Marking: Mark specific points on objects (such as the center of mass for irregular shapes) for physics simulations, measurement workflows, or spatial relationship analysis
• Minimal UI Overlays: Create clean, unobtrusive visual overlays for user interfaces, dashboards, or mobile applications where full bounding boxes would be too visually intrusive

Connecting to Other Blocks

The annotated image from this block can be connected to:

• Other visualization blocks (e.g., Bounding Box Visualization, Label Visualization, Trace Visualization) to combine dot markers with additional annotations for comprehensive visualization
• Data storage blocks (e.g., Local File Sink, CSV Formatter, Roboflow Dataset Upload) to save annotated images with dot markers for documentation, reporting, or analysis
• Webhook blocks to send visualized results with dot markers 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 dot markers as visual evidence in alerts or reports
• Video output blocks to create annotated video streams or recordings with dot markers for live monitoring, tracking visualization, or post-processing analysis

Type identifier

Use the following identifier in step "type" field: roboflow_core/dot_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..	✅
color_palette	str	Select a color palette for the visualised elements..	✅
palette_size	int	Specify the number of colors in the palette. This applies when using custom or Matplotlib palettes..	✅
custom_colors	List[str]	Define a list of custom colors for bounding boxes in HEX format..	✅
color_axis	str	Choose how bounding box colors are assigned..	✅
position	str	Anchor position for placing the dot relative to each detection's bounding box. 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, useful for irregular shapes)..	✅
radius	int	Radius of the dot in pixels. Higher values create larger, more visible dots..	✅
outline_thickness	int	Thickness of the dot outline in pixels. Set to 0 for no outline (filled dots only). Higher values create thicker outlines around the dot for better visibility against varying backgrounds..	✅

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 Dot Visualization in version v1.

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

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Dot 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..
  • predictions (Union[object_detection_prediction, instance_segmentation_prediction, rle_instance_segmentation_prediction, keypoint_detection_prediction]): Model predictions to visualize..
  • color_palette (string): Select a color palette for the visualised elements..
  • palette_size (integer): Specify the number of colors in the palette. This applies when using custom or Matplotlib palettes..
  • custom_colors (list_of_values): Define a list of custom colors for bounding boxes in HEX format..
  • color_axis (string): Choose how bounding box colors are assigned..
  • position (string): Anchor position for placing the dot relative to each detection's bounding box. 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, useful for irregular shapes)..
  • radius (integer): Radius of the dot in pixels. Higher values create larger, more visible dots..
  • outline_thickness (integer): Thickness of the dot outline in pixels. Set to 0 for no outline (filled dots only). Higher values create thicker outlines around the dot for better visibility against varying backgrounds..

• output

  • image (image): Image in workflows.

Example JSON definition of step Dot Visualization in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/dot_visualization@v1",
    "image": "$inputs.image",
    "copy_image": true,
    "predictions": "$steps.object_detection_model.predictions",
    "color_palette": "DEFAULT",
    "palette_size": 10,
    "custom_colors": [
        "#FF0000",
        "#00FF00",
        "#0000FF"
    ],
    "color_axis": "CLASS",
    "position": "CENTER",
    "radius": 4,
    "outline_thickness": 2
}