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

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[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): 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
}