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

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
}