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:
Segment Anything 2 Model,Detections Filter,Slack Notification,Stitch OCR Detections,Clip Comparison,Perspective Correction,Object Detection Model,Path Deviation,Roboflow Custom Metadata,Detections Consensus,Object Detection Model,Twilio SMS Notification,Dimension Collapse,VLM as Classifier,Detection Offset,Roboflow Dataset Upload,Google Gemini,CogVLM,VLM as Detector,OpenAI,Byte Tracker,Velocity,Buffer,Keypoint Detection Model,Multi-Label Classification Model,VLM as Detector,Google Vision OCR,Clip Comparison,Bounding Rectangle,Roboflow Dataset Upload,Size Measurement,Florence-2 Model,Byte Tracker,Llama 3.2 Vision,Byte Tracker,Florence-2 Model,Local File Sink,Detections Stabilizer,LMM For Classification,Webhook Sink,Template Matching,Dynamic Zone,Detections Transformation,Detections Stitch,OCR Model,LMM,Time in Zone,Model Monitoring Inference Aggregator,Path Deviation,Email Notification,YOLO-World Model,Line Counter,Single-Label Classification Model,Anthropic Claude,Instance Segmentation Model,CSV Formatter,Time in Zone,Instance Segmentation Model,Detections Classes Replacement,OpenAI - outputs:
Segment Anything 2 Model,Detections Filter,Stitch OCR Detections,Stability AI Inpainting,Pixelate Visualization,Perspective Correction,Path Deviation,Roboflow Custom Metadata,Detections Consensus,Detection Offset,Roboflow Dataset Upload,Ellipse Visualization,Model Comparison Visualization,Halo Visualization,Crop Visualization,Byte Tracker,Trace Visualization,Distance Measurement,Circle Visualization,Velocity,Dot Visualization,Background Color Visualization,Bounding Rectangle,Roboflow Dataset Upload,Size Measurement,Florence-2 Model,Byte Tracker,Corner Visualization,Bounding Box Visualization,Florence-2 Model,Byte Tracker,Dynamic Crop,Line Counter,Detections Stabilizer,Label Visualization,Mask Visualization,Triangle Visualization,Dynamic Zone,Detections Transformation,Detections Stitch,Model Monitoring Inference Aggregator,Time in Zone,Color Visualization,Path Deviation,Blur Visualization,Line Counter,Time in Zone,Detections Classes Replacement,Polygon 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:
Segment Anything 2 Model,Detections Filter,Slack Notification,Stitch OCR Detections,Clip Comparison,Perspective Correction,Object Detection Model,Path Deviation,Roboflow Custom Metadata,Detections Consensus,Object Detection Model,Twilio SMS Notification,Dimension Collapse,VLM as Classifier,Detection Offset,Roboflow Dataset Upload,Google Gemini,CogVLM,VLM as Detector,OpenAI,Byte Tracker,Velocity,Buffer,Keypoint Detection Model,Multi-Label Classification Model,VLM as Detector,Google Vision OCR,Clip Comparison,Bounding Rectangle,Roboflow Dataset Upload,Size Measurement,Florence-2 Model,Byte Tracker,Llama 3.2 Vision,Byte Tracker,Florence-2 Model,Local File Sink,Detections Stabilizer,LMM For Classification,Webhook Sink,Template Matching,Dynamic Zone,Detections Transformation,Detections Stitch,OCR Model,LMM,Time in Zone,Model Monitoring Inference Aggregator,Path Deviation,Email Notification,YOLO-World Model,Line Counter,Single-Label Classification Model,Anthropic Claude,Instance Segmentation Model,CSV Formatter,Time in Zone,Instance Segmentation Model,Detections Classes Replacement,OpenAI - outputs:
Segment Anything 2 Model,Detections Filter,Stitch OCR Detections,Stability AI Inpainting,Pixelate Visualization,Perspective Correction,Path Deviation,Roboflow Custom Metadata,Detections Consensus,Detection Offset,Roboflow Dataset Upload,Ellipse Visualization,Model Comparison Visualization,Halo Visualization,Crop Visualization,Byte Tracker,Trace Visualization,Distance Measurement,Circle Visualization,Velocity,Dot Visualization,Background Color Visualization,Bounding Rectangle,Roboflow Dataset Upload,Size Measurement,Florence-2 Model,Byte Tracker,Corner Visualization,Bounding Box Visualization,Florence-2 Model,Byte Tracker,Dynamic Crop,Line Counter,Detections Stabilizer,Label Visualization,Mask Visualization,Triangle Visualization,Dynamic Zone,Detections Transformation,Detections Stitch,Model Monitoring Inference Aggregator,Time in Zone,Color Visualization,Path Deviation,Blur Visualization,Line Counter,Time in Zone,Detections Classes Replacement,Polygon 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"
}