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:
Absolute Static Crop,VLM As Classifier,Qwen-VL,Triangle Visualization,EasyOCR,Model Comparison Visualization,Detection Event Log,Stitch OCR Detections,Llama 3.2 Vision,Heatmap Visualization,Stitch Images,Keypoint Visualization,Webhook Sink,Anthropic Claude,Google Gemini,GLM-OCR,QR Code Generator,VLM As Detector,PLC ModbusTCP,Twilio SMS/MMS Notification,Multi-Label Classification Model,Line Counter,Distance Measurement,Ellipse Visualization,Pixelate Visualization,Corner Visualization,Buffer,Template Matching,Llama 3.2 Vision,JSON Parser,Qwen 3.5 API,LMM,Roboflow Visual Search,OpenAI-Compatible LLM,Morphological Transformation,Color Visualization,Roboflow Dataset Upload,Dimension Collapse,Morphological Transformation,Camera Calibration,Motion Detection,Google Vision OCR,Clip Comparison,Bounding Box Visualization,OpenAI,Halo Visualization,Identify Changes,Contrast Equalization,CSV Formatter,Model Monitoring Inference Aggregator,MQTT Writer,PLC Reader,Image Threshold,Blur Visualization,Line Counter Visualization,Google Gemini,Background Color Visualization,Microsoft SQL Server Sink,Single-Label Classification Model,Background Subtraction,VLM As Classifier,Identify Outliers,Twilio SMS Notification,OPC UA Writer Sink,Polygon Zone Visualization,Instance Segmentation Model,Google Gemma API,Reference Path Visualization,Image Stack,Stability AI Inpainting,Crop Visualization,Anthropic Claude,Image Convert Grayscale,Dynamic Zone,Camera Focus,Image Slicer,Florence-2 Model,Polygon Visualization,MoonshotAI Kimi,Dynamic Crop,Qwen 3.6 API,Event Writer,Perspective Correction,Line Counter,Polygon Visualization,Clip Comparison,OCR Model,Roboflow Visual Search Classifier,Circle Visualization,Slack Notification,CogVLM,Contrast Enhancement,PLC EthernetIP,Grid Visualization,Local File Sink,Florence-2 Model,LMM For Classification,OpenAI,Roboflow Custom Metadata,Qwen3.5-VL,Image Blur,Label Visualization,Object Detection Model,Anthropic Claude,Pixel Color Count,Roboflow Vision Events,Image Preprocessing,Trace Visualization,Email Notification,SIFT Comparison,Stability AI Outpainting,Detections Consensus,PTZ Tracking (ONVIF),Dot Visualization,Current Time,Roboflow Asset Library Attributes,Keypoint Detection Model,Halo Visualization,Classification Label Visualization,Mask Visualization,Image Contours,Camera Focus,OpenRouter,S3 Sink,Stability AI Image Generation,VLM As Detector,Google Gemma,Relative Static Crop,Roboflow Dataset Upload,OpenAI,Image Slicer,Stitch OCR Detections,Google Gemini,Email Notification,Text Display,Size Measurement,Depth Estimation,Icon Visualization,OpenAI,GeoTag Detection,SIFT Comparison,SIFT,PLC Writer,Detections List Roll-Up,MoonshotAI Kimi - outputs:
Absolute Static Crop,VLM As Classifier,Qwen-VL,Gaze Detection,Triangle Visualization,EasyOCR,Model Comparison Visualization,OC-SORT Tracker,YOLO-World Model,Track Class Lock,Llama 3.2 Vision,Anthropic Claude,Heatmap Visualization,Stitch Images,Keypoint Visualization,Detections Stabilizer,Google Gemini,Dominant Color,MoonshotAI Kimi,GLM-OCR,Instance Segmentation Model,VLM As Detector,SAM2 Video Tracker,Multi-Label Classification Model,Twilio SMS/MMS Notification,Ellipse Visualization,Single-Label Classification Model,SmolVLM2,Keypoint Detection Model,Pixelate Visualization,Corner Visualization,Buffer,Template Matching,Llama 3.2 Vision,Qwen2.5-VL,Qwen 3.5 API,LMM,Semantic Segmentation Model,Roboflow Visual Search,Morphological Transformation,Color Visualization,Roboflow Dataset Upload,Morphological Transformation,Camera Calibration,Motion Detection,Google Vision OCR,Clip Comparison,Bounding Box Visualization,OpenAI,Halo Visualization,SAM3 Video Tracker,Object Detection Model,Contrast Equalization,Instance Segmentation Model,Multi-Label Classification Model,Image Threshold,Blur Visualization,Line Counter Visualization,Google Gemini,Single-Label Classification Model,Background Subtraction,Object Detection Model,Instance Segmentation Model,SAM 3,QR Code Detection,Polygon Zone Visualization,Google Gemma API,Keypoint Detection Model,Image Stack,Reference Path Visualization,SAM 3 Interactive,Qwen3-VL,Stability AI Inpainting,Multi-Label Classification Model,Perception Encoder Embedding Model,Crop Visualization,Anthropic Claude,Image Convert Grayscale,Camera Focus,Image Slicer,Florence-2 Model,Polygon Visualization,MoonshotAI Kimi,Dynamic Crop,Qwen 3.6 API,Event Writer,Perspective Correction,Segment Anything 2 Model,Polygon Visualization,Clip Comparison,OCR Model,Roboflow Visual Search Classifier,Circle Visualization,CogVLM,Contrast Enhancement,Mask Edge Snap,Barcode Detection,Time in Zone,LMM For Classification,Qwen3.5-VL,Florence-2 Model,OpenAI,SORT Tracker,ByteTrack Tracker,Label Visualization,Image Blur,Seg Preview,Object Detection Model,Anthropic Claude,Pixel Color Count,Roboflow Vision Events,Image Preprocessing,Trace Visualization,Email Notification,SIFT Comparison,Stability AI Outpainting,Keypoint Detection Model,Dot Visualization,Halo Visualization,SAM 3,Classification Label Visualization,Mask Visualization,Image Contours,Camera Focus,Single-Label Classification Model,OpenRouter,Moondream2,Stability AI Image Generation,VLM As Detector,Google Gemma,CLIP Embedding Model,Relative Static Crop,Roboflow Dataset Upload,OpenAI,Image Slicer,Google Gemini,BoT-SORT Tracker,Byte Tracker,SAM 3,Detections Stitch,Semantic Segmentation Model,Text Display,Qwen3.5,Depth Estimation,Icon Visualization,OpenAI,GeoTag Detection,SIFT,Instance Segmentation Model,Background Color Visualization,VLM As Classifier
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
}