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