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: Bounding Rectangle, Anthropic Claude, Multi-Label Classification Model, Single-Label Classification Model, LMM, Email Notification, Llama 3.2 Vision, Dimension Collapse, Instance Segmentation Model, CSV Formatter, VLM as Detector, Webhook Sink, Dynamic Crop, Time in Zone, Clip Comparison, Google Vision OCR, Byte Tracker, Twilio SMS Notification, Buffer, Segment Anything 2 Model, Detection Offset, Model Monitoring Inference Aggregator, Florence-2 Model, LMM For Classification, Florence-2 Model, Detections Filter, Detections Transformation, YOLO-World Model, Roboflow Dataset Upload, CogVLM, OCR Model, Clip Comparison, Template Matching, VLM as Detector, Path Deviation, Detections Consensus, OpenAI, Stitch OCR Detections, Google Gemini, Detections Merge, Velocity, Local File Sink, Detections Stitch, Dynamic Zone, Size Measurement, Instance Segmentation Model, Roboflow Custom Metadata, OpenAI, VLM as Classifier, Detections Classes Replacement, Detections Stabilizer, Path Deviation, Slack Notification, Object Detection Model, Time in Zone, Roboflow Dataset Upload, Keypoint Detection Model, Perspective Correction, Moondream2, Byte Tracker, Byte Tracker, Line Counter, Object Detection Model
• outputs: Bounding Rectangle, Blur Visualization, Pixelate Visualization, Background Color Visualization, Dynamic Crop, Mask Visualization, Time in Zone, Byte Tracker, Segment Anything 2 Model, Detection Offset, Model Monitoring Inference Aggregator, Florence-2 Model, Florence-2 Model, Detections Filter, Detections Transformation, Roboflow Dataset Upload, Circle Visualization, Crop Visualization, Trace Visualization, Path Deviation, Triangle Visualization, Detections Consensus, Stitch OCR Detections, Polygon Visualization, Stability AI Inpainting, Halo Visualization, Dot Visualization, Velocity, Detections Merge, Detections Stitch, Dynamic Zone, Size Measurement, Roboflow Custom Metadata, Detections Classes Replacement, Detections Stabilizer, Label Visualization, Path Deviation, Line Counter, Corner Visualization, Time in Zone, Model Comparison Visualization, Bounding Box Visualization, Roboflow Dataset Upload, Perspective Correction, Byte Tracker, Byte Tracker, Line Counter, Distance Measurement, Color Visualization, Ellipse 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[object_detection_prediction, instance_segmentation_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 if object_detection_prediction or Prediction with detected bounding boxes and segmentation masks in form of sv.Detections(...) object if instance_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: Bounding Rectangle, Anthropic Claude, Multi-Label Classification Model, Single-Label Classification Model, LMM, Email Notification, Llama 3.2 Vision, Dimension Collapse, Instance Segmentation Model, CSV Formatter, VLM as Detector, Webhook Sink, Dynamic Crop, Time in Zone, Clip Comparison, Google Vision OCR, Byte Tracker, Twilio SMS Notification, Buffer, Segment Anything 2 Model, Detection Offset, Model Monitoring Inference Aggregator, Florence-2 Model, LMM For Classification, Florence-2 Model, Detections Filter, Detections Transformation, YOLO-World Model, Roboflow Dataset Upload, CogVLM, OCR Model, Clip Comparison, Template Matching, VLM as Detector, Path Deviation, Detections Consensus, OpenAI, Stitch OCR Detections, Google Gemini, Detections Merge, Velocity, Local File Sink, Detections Stitch, Dynamic Zone, Size Measurement, Instance Segmentation Model, Roboflow Custom Metadata, OpenAI, VLM as Classifier, Detections Classes Replacement, Detections Stabilizer, Path Deviation, Slack Notification, Object Detection Model, Time in Zone, Roboflow Dataset Upload, Keypoint Detection Model, Perspective Correction, Moondream2, Byte Tracker, Byte Tracker, Line Counter, Object Detection Model
• outputs: Bounding Rectangle, Blur Visualization, Pixelate Visualization, Background Color Visualization, Dynamic Crop, Mask Visualization, Time in Zone, Byte Tracker, Segment Anything 2 Model, Detection Offset, Model Monitoring Inference Aggregator, Florence-2 Model, Florence-2 Model, Detections Filter, Detections Transformation, Roboflow Dataset Upload, Circle Visualization, Crop Visualization, Trace Visualization, Path Deviation, Triangle Visualization, Detections Consensus, Stitch OCR Detections, Polygon Visualization, Stability AI Inpainting, Halo Visualization, Dot Visualization, Velocity, Detections Merge, Detections Stitch, Dynamic Zone, Size Measurement, Roboflow Custom Metadata, Detections Classes Replacement, Detections Stabilizer, Label Visualization, Path Deviation, Line Counter, Corner Visualization, Time in Zone, Model Comparison Visualization, Bounding Box Visualization, Roboflow Dataset Upload, Perspective Correction, Byte Tracker, Byte Tracker, Line Counter, Distance Measurement, Color Visualization, Ellipse 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[object_detection_prediction, instance_segmentation_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 if object_detection_prediction or Prediction with detected bounding boxes and segmentation masks in form of sv.Detections(...) object if instance_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"
}