Reference Path Visualization

Class: ReferencePathVisualizationBlockV1

Source: inference.core.workflows.core_steps.visualizations.reference_path.v1.ReferencePathVisualizationBlockV1

The Reference Path Visualization block draws reference path in the image. To be used in combination with Path deviation block - to display the reference path.

Type identifier

Use the following identifier in step "type" field: roboflow_core/reference_path_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..	✅
reference_path	List[Any]	Reference path in a format [(x1, y1), (x2, y2), (x3, y3), ...].	✅
color	str	Color of the zone..	✅
thickness	int	Thickness of the lines 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 Reference Path Visualization in version v1.

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

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Reference Path 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..
  • reference_path (list_of_values): Reference path in a format [(x1, y1), (x2, y2), (x3, y3), ...].
  • color (string): Color of the zone..
  • thickness (integer): Thickness of the lines in pixels..

• output

  • image (image): Image in workflows.

Example JSON definition of step Reference Path Visualization in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/reference_path_visualization@v1",
    "image": "$inputs.image",
    "copy_image": true,
    "reference_path": "$inputs.expected_path",
    "color": "WHITE",
    "thickness": 2
}