Dot Visualization
Draw dots on an image at specific coordinates based on provided detections.
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:
Takes an image and predictions as input
Determines the dot position for each detection based on the selected anchor point (center, corners, edges, or center of mass)
Applies color styling based on the selected color palette, with colors assigned by class, index, or track ID
Draws circular dots with the specified radius and optional outline thickness using Supervision's DotAnnotator
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@v1 to add the block as a 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.
Input and Output Bindings
The available connections depend on its binding kinds. Check what binding kinds Dot Visualization in version v1 has.
Input and output 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[instance_segmentation_prediction,keypoint_detection_prediction,object_detection_prediction,rle_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.
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