Dot Visualization

Class: DotVisualizationBlockV1

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

The DotVisualization block draws dots on an image at specific coordinates based on provided detections using Supervision's sv.DotAnnotator.

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	The anchor position for placing the dot..	✅
radius	int	Radius of the dot in pixels..	✅
outline_thickness	int	Thickness of the outline of the dot in pixels..	✅

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

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, 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): The anchor position for placing the dot..
  • radius (integer): Radius of the dot in pixels..
  • outline_thickness (integer): Thickness of the outline of the dot in pixels..

• 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
}