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