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

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[rle_instance_segmentation_prediction, object_detection_prediction, keypoint_detection_prediction, instance_segmentation_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
}