Reference Path Visualization¶
Class: ReferencePathVisualizationBlockV1
Draw a static reference path on an image to visualize an expected or ideal route, displaying a predefined polyline path that can be compared against actual object trajectories for path deviation analysis and route compliance monitoring.
How This Block Works¶
This block takes an image and reference path coordinates (a list of points defining a path) and draws a static polyline path representing an expected route or ideal trajectory. The block:
- Takes an image and reference path coordinates (a list of points: [(x1, y1), (x2, y2), (x3, y3), ...]) as input
- Converts the coordinate list into a polyline path connecting the points in sequence
- Draws the reference path as a polyline using the specified color and thickness
- Returns an annotated image with the reference path overlaid on the original image
The block visualizes a static, predefined reference path that represents where objects should ideally move or what route they should follow. Unlike Trace Visualization (which draws dynamic paths based on actual tracked object movement), Reference Path Visualization draws a fixed path that remains constant. This reference path serves as a baseline for comparison, allowing you to visualize the expected route alongside actual object trajectories. The path is drawn as a continuous line connecting all the specified points, creating a visual guide for route compliance, path deviation analysis, or navigation workflows. This block is commonly used with Path Deviation analytics blocks to visually display the reference path that actual object trajectories will be compared against.
Common Use Cases¶
- Path Deviation Visualization: Visualize a reference path alongside actual object trajectories to compare expected routes against actual movement for path deviation detection, route compliance monitoring, or navigation validation workflows
- Route Planning and Navigation: Display predefined routes, navigation paths, or expected travel routes that objects should follow for route planning, navigation systems, or waypoint visualization applications
- Compliance and Safety Monitoring: Visualize expected paths for safety monitoring, compliance workflows, or route validation where objects need to follow specific paths (e.g., vehicles on designated lanes, robots on expected routes)
- Industrial and Logistics Applications: Display reference paths for conveyor systems, automated guided vehicles (AGVs), or manufacturing workflows where objects must follow predefined routes for process control or quality assurance
- Security and Access Control: Visualize expected movement paths for security monitoring, access control, or surveillance workflows where deviations from expected routes need to be identified
- Training and Documentation: Display reference paths in training materials, documentation, or demonstrations to show expected object behavior, routes, or movement patterns for educational or reference purposes
Connecting to Other Blocks¶
The annotated image from this block can be connected to:
- Path Deviation analytics blocks to compare tracked object trajectories against the visualized reference path for deviation analysis
- Other visualization blocks (e.g., Trace Visualization, Bounding Box Visualization, Label Visualization) to combine reference path visualization with actual object tracking visualizations for comprehensive path comparison
- Tracking blocks (e.g., Byte Tracker) where the reference path can serve as a visual baseline for comparing actual tracked object trajectories
- Data storage blocks (e.g., Local File Sink, CSV Formatter, Roboflow Dataset Upload) to save images with reference paths for documentation, reporting, or analysis
- Webhook blocks to send visualized results with reference paths to external systems, APIs, or web applications for display in dashboards or monitoring tools
- Notification blocks (e.g., Email Notification, Slack Notification) to send annotated images with reference paths as visual evidence in alerts or reports
- Video output blocks to create annotated video streams or recordings with reference paths for live monitoring, path visualization, or post-processing analysis
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 coordinates in the format [(x1, y1), (x2, y2), (x3, y3), ...] defining the expected or ideal route. The path is drawn as a polyline connecting these points in sequence, creating a continuous line representing the reference trajectory. Typically connected from Path Deviation analytics blocks or defined manually as an expected route. Must contain at least two points to form a valid path.. | ✅ |
color |
str |
Color of the reference path. Can be specified as a color name (e.g., 'WHITE', 'GREEN', 'BLUE'), hex color code (e.g., '#5bb573', '#FFFFFF'), or RGB format (e.g., 'rgb(91, 181, 115)'). The reference path is drawn in this color with the specified thickness.. | ✅ |
thickness |
int |
Thickness of the reference path line in pixels. Controls how thick the reference path appears. Higher values create thicker, more visible paths, while lower values create thinner, more subtle paths. Must be greater than or equal to zero. Typical values range from 1 to 5 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:
Stitch Images,Image Threshold,Email Notification,Corner Visualization,Image Blur,Ellipse Visualization,OpenAI,Roboflow Dataset Upload,Object Detection Model,Depth Estimation,Stitch OCR Detections,Dimension Collapse,Absolute Static Crop,JSON Parser,EasyOCR,CogVLM,Google Gemini,Stability AI Image Generation,Grid Visualization,Dynamic Crop,Image Slicer,Image Preprocessing,Relative Static Crop,SIFT,Morphological Transformation,Instance Segmentation Model,Line Counter Visualization,Trace Visualization,LMM For Classification,Detection Event Log,Halo Visualization,Dot Visualization,GLM-OCR,Model Monitoring Inference Aggregator,Roboflow Custom Metadata,Pixel Color Count,Pixelate Visualization,Circle Visualization,Image Convert Grayscale,Icon Visualization,QR Code Generator,S3 Sink,Keypoint Detection Model,Twilio SMS Notification,Halo Visualization,Camera Focus,SIFT Comparison,Anthropic Claude,OCR Model,Polygon Visualization,Text Display,Detections Consensus,Reference Path Visualization,Identify Changes,Llama 3.2 Vision,CSV Formatter,Crop Visualization,Roboflow Dataset Upload,Mask Visualization,Heatmap Visualization,Webhook Sink,Label Visualization,Classification Label Visualization,Detections List Roll-Up,Google Vision OCR,Florence-2 Model,Florence-2 Model,VLM As Detector,Polygon Zone Visualization,Stability AI Inpainting,VLM As Classifier,Google Gemini,Perspective Correction,Camera Calibration,Anthropic Claude,OpenAI,VLM As Detector,OpenAI,Distance Measurement,PTZ Tracking (ONVIF),Qwen3.5-VL,Background Color Visualization,Template Matching,Anthropic Claude,Size Measurement,Email Notification,Background Subtraction,Contrast Equalization,SIFT Comparison,Multi-Label Classification Model,Keypoint Visualization,Line Counter,Stitch OCR Detections,LMM,Identify Outliers,Line Counter,Color Visualization,Motion Detection,Dynamic Zone,Single-Label Classification Model,OpenAI,Roboflow Vision Events,Local File Sink,VLM As Classifier,Twilio SMS/MMS Notification,Clip Comparison,Buffer,Triangle Visualization,Clip Comparison,Blur Visualization,Bounding Box Visualization,Camera Focus,Polygon Visualization,Google Gemini,Image Slicer,Image Contours,Model Comparison Visualization,Stability AI Outpainting,Slack Notification - outputs:
Stitch Images,Image Threshold,Corner Visualization,Image Blur,Ellipse Visualization,OpenAI,Roboflow Dataset Upload,Object Detection Model,Barcode Detection,Depth Estimation,Gaze Detection,EasyOCR,Absolute Static Crop,Multi-Label Classification Model,CogVLM,Google Gemini,Stability AI Image Generation,Time in Zone,Dynamic Crop,Instance Segmentation Model,Image Slicer,Image Preprocessing,SIFT,Relative Static Crop,Morphological Transformation,Line Counter Visualization,Trace Visualization,LMM For Classification,Halo Visualization,Dot Visualization,ByteTrack Tracker,GLM-OCR,Keypoint Detection Model,Pixel Color Count,Pixelate Visualization,Circle Visualization,Semantic Segmentation Model,Image Convert Grayscale,Icon Visualization,Keypoint Detection Model,Detections Stabilizer,Halo Visualization,Camera Focus,Anthropic Claude,SAM 3,OC-SORT Tracker,OCR Model,Polygon Visualization,Text Display,Qwen2.5-VL,Reference Path Visualization,Instance Segmentation Model,Llama 3.2 Vision,Qwen3-VL,Crop Visualization,Roboflow Dataset Upload,Mask Visualization,Moondream2,CLIP Embedding Model,SORT Tracker,Heatmap Visualization,Label Visualization,Google Vision OCR,Byte Tracker,Classification Label Visualization,Florence-2 Model,Segment Anything 2 Model,Florence-2 Model,VLM As Detector,Polygon Zone Visualization,Stability AI Inpainting,VLM As Classifier,Google Gemini,SAM 3,Perspective Correction,Anthropic Claude,OpenAI,Camera Calibration,VLM As Detector,OpenAI,Qwen3.5-VL,Template Matching,Background Color Visualization,Anthropic Claude,Email Notification,Background Subtraction,SIFT Comparison,Contrast Equalization,Multi-Label Classification Model,Keypoint Visualization,LMM,SmolVLM2,Single-Label Classification Model,Perception Encoder Embedding Model,SAM 3,Motion Detection,Seg Preview,Single-Label Classification Model,Color Visualization,Object Detection Model,OpenAI,Dominant Color,Roboflow Vision Events,QR Code Detection,VLM As Classifier,Detections Stitch,Clip Comparison,YOLO-World Model,Triangle Visualization,Buffer,Clip Comparison,Twilio SMS/MMS Notification,Bounding Box Visualization,Blur Visualization,Camera Focus,Polygon Visualization,Google Gemini,Image Slicer,Image Contours,Model Comparison Visualization,Stability AI Outpainting
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 coordinates in the format [(x1, y1), (x2, y2), (x3, y3), ...] defining the expected or ideal route. The path is drawn as a polyline connecting these points in sequence, creating a continuous line representing the reference trajectory. Typically connected from Path Deviation analytics blocks or defined manually as an expected route. Must contain at least two points to form a valid path..color(string): Color of the reference path. Can be specified as a color name (e.g., 'WHITE', 'GREEN', 'BLUE'), hex color code (e.g., '#5bb573', '#FFFFFF'), or RGB format (e.g., 'rgb(91, 181, 115)'). The reference path is drawn in this color with the specified thickness..thickness(integer): Thickness of the reference path line in pixels. Controls how thick the reference path appears. Higher values create thicker, more visible paths, while lower values create thinner, more subtle paths. Must be greater than or equal to zero. Typical values range from 1 to 5 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
}