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