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

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

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 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"
}