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