Trace Visualization

Class: TraceVisualizationBlockV1

Source: inference.core.workflows.core_steps.visualizations.trace.v1.TraceVisualizationBlockV1

The TraceVisualization block draws tracker results on an image using Supervision's sv.TraceAnnotator.

Type identifier

Use the following identifier in step "type" field: roboflow_core/trace_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 label..	✅
trace_length	int	Maximum number of historical tracked objects positions to display..	✅
thickness	int	Thickness of the track visualization line..	✅

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 Trace Visualization in version v1.

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

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Trace 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, 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): The anchor position for placing the label..
  • trace_length (integer): Maximum number of historical tracked objects positions to display..
  • thickness (integer): Thickness of the track visualization line..

• output

  • image (image): Image in workflows.

Example JSON definition of step Trace Visualization in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/trace_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",
    "trace_length": 30,
    "thickness": 1
}