Path Deviation¶
v2¶
Class: PathDeviationAnalyticsBlockV2 (there are multiple versions of this block)
Source: inference.core.workflows.core_steps.analytics.path_deviation.v2.PathDeviationAnalyticsBlockV2
Warning: This block has multiple versions. Please refer to the specific version for details. You can learn more about how versions work here: Versioning
The PathDeviationAnalyticsBlock is an analytics block designed to measure the Frechet distance
of tracked objects from a user-defined reference path. The block requires detections to be tracked
(i.e. each object must have a unique tracker_id assigned, which persists between frames).
Type identifier¶
Use the following identifier in step "type" field: roboflow_core/path_deviation_analytics@v2to add the block as
as step in your workflow.
Properties¶
| Name | Type | Description | Refs |
|---|---|---|---|
name |
str |
Enter a unique identifier for this step.. | ❌ |
triggering_anchor |
str |
Triggering anchor. Allowed values: CENTER, CENTER_LEFT, CENTER_RIGHT, TOP_CENTER, TOP_LEFT, TOP_RIGHT, BOTTOM_LEFT, BOTTOM_CENTER, BOTTOM_RIGHT, CENTER_OF_MASS. | ✅ |
reference_path |
List[Any] |
Reference path in a format [(x1, y1), (x2, y2), (x3, y3), ...]. | ✅ |
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 Path Deviation in version v2.
- inputs:
Twilio SMS Notification,Object Detection Model,Slack Notification,VLM as Detector,LMM,Clip Comparison,Local File Sink,Detections Transformation,Anthropic Claude,Detections Stitch,OCR Model,Path Deviation,Detections Merge,YOLO-World Model,Detection Offset,Google Gemini,Roboflow Dataset Upload,Dimension Collapse,Stitch OCR Detections,Line Counter,Perspective Correction,Byte Tracker,Detections Consensus,Path Deviation,OpenAI,OpenAI,Time in Zone,Detections Filter,Webhook Sink,Clip Comparison,Time in Zone,Dynamic Crop,Template Matching,CSV Formatter,Byte Tracker,Detections Classes Replacement,Florence-2 Model,Instance Segmentation Model,VLM as Classifier,Keypoint Detection Model,Dynamic Zone,Instance Segmentation Model,CogVLM,Buffer,Email Notification,Object Detection Model,Florence-2 Model,Model Monitoring Inference Aggregator,Bounding Rectangle,Velocity,Single-Label Classification Model,Llama 3.2 Vision,Google Vision OCR,VLM as Detector,Roboflow Dataset Upload,Multi-Label Classification Model,LMM For Classification,Roboflow Custom Metadata,Byte Tracker,Segment Anything 2 Model,Detections Stabilizer,Size Measurement - outputs:
Circle Visualization,Background Color Visualization,Corner Visualization,Bounding Box Visualization,Trace Visualization,Label Visualization,Detections Transformation,Crop Visualization,Detections Stitch,Path Deviation,Detections Merge,Dot Visualization,Detection Offset,Roboflow Dataset Upload,Model Comparison Visualization,Stability AI Inpainting,Pixelate Visualization,Line Counter,Stitch OCR Detections,Perspective Correction,Detections Consensus,Byte Tracker,Path Deviation,Detections Stabilizer,Distance Measurement,Mask Visualization,Time in Zone,Detections Filter,Color Visualization,Time in Zone,Dynamic Crop,Halo Visualization,Polygon Visualization,Byte Tracker,Detections Classes Replacement,Florence-2 Model,Dynamic Zone,Florence-2 Model,Triangle Visualization,Model Monitoring Inference Aggregator,Bounding Rectangle,Velocity,Roboflow Dataset Upload,Roboflow Custom Metadata,Byte Tracker,Line Counter,Segment Anything 2 Model,Ellipse Visualization,Size Measurement,Blur Visualization
Input and Output Bindings¶
The available connections depend on its binding kinds. Check what binding kinds
Path Deviation in version v2 has.
Bindings
-
input
image(image): not available.detections(Union[instance_segmentation_prediction,object_detection_prediction]): Predictions.triggering_anchor(string): Triggering anchor. Allowed values: CENTER, CENTER_LEFT, CENTER_RIGHT, TOP_CENTER, TOP_LEFT, TOP_RIGHT, BOTTOM_LEFT, BOTTOM_CENTER, BOTTOM_RIGHT, CENTER_OF_MASS.reference_path(list_of_values): Reference path in a format [(x1, y1), (x2, y2), (x3, y3), ...].
-
output
path_deviation_detections(Union[object_detection_prediction,instance_segmentation_prediction]): Prediction with detected bounding boxes in form of sv.Detections(...) object ifobject_detection_predictionor Prediction with detected bounding boxes and segmentation masks in form of sv.Detections(...) object ifinstance_segmentation_prediction.
Example JSON definition of step Path Deviation in version v2
{
"name": "<your_step_name_here>",
"type": "roboflow_core/path_deviation_analytics@v2",
"image": "<block_does_not_provide_example>",
"detections": "$steps.object_detection_model.predictions",
"triggering_anchor": "CENTER",
"reference_path": "$inputs.expected_path"
}
v1¶
Class: PathDeviationAnalyticsBlockV1 (there are multiple versions of this block)
Source: inference.core.workflows.core_steps.analytics.path_deviation.v1.PathDeviationAnalyticsBlockV1
Warning: This block has multiple versions. Please refer to the specific version for details. You can learn more about how versions work here: Versioning
The PathDeviationAnalyticsBlock is an analytics block designed to measure the Frechet distance
of tracked objects from a user-defined reference path. The block requires detections to be tracked
(i.e. each object must have a unique tracker_id assigned, which persists between frames).
Type identifier¶
Use the following identifier in step "type" field: roboflow_core/path_deviation_analytics@v1to add the block as
as step in your workflow.
Properties¶
| Name | Type | Description | Refs |
|---|---|---|---|
name |
str |
Enter a unique identifier for this step.. | ❌ |
triggering_anchor |
str |
Point on the detection that will be used to calculate the Frechet distance.. | ✅ |
reference_path |
List[Any] |
Reference path in a format [(x1, y1), (x2, y2), (x3, y3), ...]. | ✅ |
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 Path Deviation in version v1.
- inputs:
Twilio SMS Notification,Object Detection Model,Slack Notification,VLM as Detector,LMM,Clip Comparison,Local File Sink,Detections Transformation,Anthropic Claude,Detections Stitch,OCR Model,Path Deviation,Detections Merge,YOLO-World Model,Detection Offset,Google Gemini,Roboflow Dataset Upload,Dimension Collapse,Stitch OCR Detections,Line Counter,Perspective Correction,Byte Tracker,Detections Consensus,Path Deviation,OpenAI,OpenAI,Time in Zone,Detections Filter,Webhook Sink,Clip Comparison,Time in Zone,Dynamic Crop,Template Matching,CSV Formatter,Byte Tracker,Detections Classes Replacement,Florence-2 Model,Instance Segmentation Model,VLM as Classifier,Keypoint Detection Model,Dynamic Zone,Instance Segmentation Model,CogVLM,Buffer,Email Notification,Object Detection Model,Florence-2 Model,Model Monitoring Inference Aggregator,Bounding Rectangle,Velocity,Single-Label Classification Model,Llama 3.2 Vision,Google Vision OCR,VLM as Detector,Roboflow Dataset Upload,Multi-Label Classification Model,LMM For Classification,Roboflow Custom Metadata,Byte Tracker,Segment Anything 2 Model,Detections Stabilizer,Size Measurement - outputs:
Circle Visualization,Background Color Visualization,Corner Visualization,Bounding Box Visualization,Trace Visualization,Label Visualization,Detections Transformation,Crop Visualization,Detections Stitch,Path Deviation,Detections Merge,Dot Visualization,Detection Offset,Roboflow Dataset Upload,Model Comparison Visualization,Stability AI Inpainting,Pixelate Visualization,Line Counter,Stitch OCR Detections,Perspective Correction,Detections Consensus,Byte Tracker,Path Deviation,Detections Stabilizer,Distance Measurement,Mask Visualization,Time in Zone,Detections Filter,Color Visualization,Time in Zone,Dynamic Crop,Halo Visualization,Polygon Visualization,Byte Tracker,Detections Classes Replacement,Florence-2 Model,Dynamic Zone,Florence-2 Model,Triangle Visualization,Model Monitoring Inference Aggregator,Bounding Rectangle,Velocity,Roboflow Dataset Upload,Roboflow Custom Metadata,Byte Tracker,Line Counter,Segment Anything 2 Model,Ellipse Visualization,Size Measurement,Blur Visualization
Input and Output Bindings¶
The available connections depend on its binding kinds. Check what binding kinds
Path Deviation in version v1 has.
Bindings
-
input
metadata(video_metadata): not available.detections(Union[instance_segmentation_prediction,object_detection_prediction]): Predictions.triggering_anchor(string): Point on the detection that will be used to calculate the Frechet distance..reference_path(list_of_values): Reference path in a format [(x1, y1), (x2, y2), (x3, y3), ...].
-
output
path_deviation_detections(Union[object_detection_prediction,instance_segmentation_prediction]): Prediction with detected bounding boxes in form of sv.Detections(...) object ifobject_detection_predictionor Prediction with detected bounding boxes and segmentation masks in form of sv.Detections(...) object ifinstance_segmentation_prediction.
Example JSON definition of step Path Deviation in version v1
{
"name": "<your_step_name_here>",
"type": "roboflow_core/path_deviation_analytics@v1",
"metadata": "<block_does_not_provide_example>",
"detections": "$steps.object_detection_model.predictions",
"triggering_anchor": "CENTER",
"reference_path": "$inputs.expected_path"
}