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@v2
to 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:
Detection Offset
,Line Counter
,CogVLM
,Detections Filter
,VLM as Detector
,Slack Notification
,Time in Zone
,Local File Sink
,Path Deviation
,YOLO-World Model
,Clip Comparison
,Instance Segmentation Model
,Roboflow Custom Metadata
,Perspective Correction
,Moondream2
,OpenAI
,Twilio SMS Notification
,OCR Model
,Multi-Label Classification Model
,Google Vision OCR
,Dimension Collapse
,Clip Comparison
,Stitch OCR Detections
,OpenAI
,Time in Zone
,Single-Label Classification Model
,Webhook Sink
,Google Gemini
,Roboflow Dataset Upload
,Byte Tracker
,Size Measurement
,Detections Transformation
,Byte Tracker
,Florence-2 Model
,Email Notification
,Detections Stabilizer
,Detections Classes Replacement
,Object Detection Model
,Template Matching
,LMM
,LMM For Classification
,Detections Consensus
,Roboflow Dataset Upload
,Overlap Filter
,Dynamic Crop
,Instance Segmentation Model
,CSV Formatter
,Florence-2 Model
,Detections Merge
,Dynamic Zone
,Velocity
,Model Monitoring Inference Aggregator
,Keypoint Detection Model
,Buffer
,Detections Stitch
,Bounding Rectangle
,Byte Tracker
,Segment Anything 2 Model
,Object Detection Model
,Llama 3.2 Vision
,VLM as Detector
,Anthropic Claude
,Path Deviation
,VLM as Classifier
- outputs:
Detection Offset
,Blur Visualization
,Line Counter
,Stability AI Inpainting
,Detections Filter
,Time in Zone
,Dot Visualization
,Background Color Visualization
,Path Deviation
,Trace Visualization
,Roboflow Custom Metadata
,Perspective Correction
,Color Visualization
,Distance Measurement
,Circle Visualization
,Pixelate Visualization
,Stitch OCR Detections
,Triangle Visualization
,Halo Visualization
,Label Visualization
,Time in Zone
,Roboflow Dataset Upload
,Byte Tracker
,Line Counter
,Size Measurement
,Byte Tracker
,Corner Visualization
,Detections Transformation
,Florence-2 Model
,Detections Stabilizer
,Detections Classes Replacement
,Detections Consensus
,Roboflow Dataset Upload
,Overlap Filter
,Dynamic Crop
,Polygon Visualization
,Florence-2 Model
,Detections Merge
,Ellipse Visualization
,Mask Visualization
,Dynamic Zone
,Velocity
,Model Monitoring Inference Aggregator
,Detections Stitch
,Segment Anything 2 Model
,Byte Tracker
,Bounding Box Visualization
,Bounding Rectangle
,Model Comparison Visualization
,Path Deviation
,Crop 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 ifobject_detection_prediction
or 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@v1
to 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:
Detection Offset
,Line Counter
,CogVLM
,Detections Filter
,VLM as Detector
,Slack Notification
,Time in Zone
,Local File Sink
,Path Deviation
,YOLO-World Model
,Clip Comparison
,Instance Segmentation Model
,Roboflow Custom Metadata
,Perspective Correction
,Moondream2
,OpenAI
,Twilio SMS Notification
,OCR Model
,Multi-Label Classification Model
,Google Vision OCR
,Dimension Collapse
,Clip Comparison
,Stitch OCR Detections
,OpenAI
,Time in Zone
,Single-Label Classification Model
,Webhook Sink
,Google Gemini
,Roboflow Dataset Upload
,Byte Tracker
,Size Measurement
,Detections Transformation
,Byte Tracker
,Florence-2 Model
,Email Notification
,Detections Stabilizer
,Detections Classes Replacement
,Object Detection Model
,Template Matching
,LMM
,LMM For Classification
,Detections Consensus
,Roboflow Dataset Upload
,Overlap Filter
,Dynamic Crop
,Instance Segmentation Model
,CSV Formatter
,Florence-2 Model
,Detections Merge
,Dynamic Zone
,Velocity
,Model Monitoring Inference Aggregator
,Keypoint Detection Model
,Buffer
,Detections Stitch
,Bounding Rectangle
,Byte Tracker
,Segment Anything 2 Model
,Object Detection Model
,Llama 3.2 Vision
,VLM as Detector
,Anthropic Claude
,Path Deviation
,VLM as Classifier
- outputs:
Detection Offset
,Blur Visualization
,Line Counter
,Stability AI Inpainting
,Detections Filter
,Time in Zone
,Dot Visualization
,Background Color Visualization
,Path Deviation
,Trace Visualization
,Roboflow Custom Metadata
,Perspective Correction
,Color Visualization
,Distance Measurement
,Circle Visualization
,Pixelate Visualization
,Stitch OCR Detections
,Triangle Visualization
,Halo Visualization
,Label Visualization
,Time in Zone
,Roboflow Dataset Upload
,Byte Tracker
,Line Counter
,Size Measurement
,Byte Tracker
,Corner Visualization
,Detections Transformation
,Florence-2 Model
,Detections Stabilizer
,Detections Classes Replacement
,Detections Consensus
,Roboflow Dataset Upload
,Overlap Filter
,Dynamic Crop
,Polygon Visualization
,Florence-2 Model
,Detections Merge
,Ellipse Visualization
,Mask Visualization
,Dynamic Zone
,Velocity
,Model Monitoring Inference Aggregator
,Detections Stitch
,Segment Anything 2 Model
,Byte Tracker
,Bounding Box Visualization
,Bounding Rectangle
,Model Comparison Visualization
,Path Deviation
,Crop 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 ifobject_detection_prediction
or 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"
}