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