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

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
}