Line Counter

v2

Class: LineCounterBlockV2 (there are multiple versions of this block)

Source: inference.core.workflows.core_steps.analytics.line_counter.v2.LineCounterBlockV2

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 LineCounter is an analytics block designed to count objects passing the line. The block requires detections to be tracked (i.e. each object must have unique tracker_id assigned, which persists between frames)

Type identifier

Use the following identifier in step "type" field: roboflow_core/line_counter@v2to add the block as as step in your workflow.

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
line_segment	List[Any]	Line consisting of exactly two points. For line [[0, 100], [100, 100]], objects entering from the bottom will count as IN..	✅
triggering_anchor	str	The point on the detection that must cross the line to be counted..	✅

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 Line Counter in version v2.

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

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Line Counter in version v2 has.

Bindings

• input

  • image (image): not available.
  • detections (Union[object_detection_prediction, instance_segmentation_prediction]): Model predictions to count line crossings for..
  • line_segment (list_of_values): Line consisting of exactly two points. For line [[0, 100], [100, 100]], objects entering from the bottom will count as IN..
  • triggering_anchor (string): The point on the detection that must cross the line to be counted..

• output

  • count_in (integer): Integer value.
  • count_out (integer): Integer value.
  • detections_in (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.
  • detections_out (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 Line Counter in version v2

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/line_counter@v2",
    "image": "<block_does_not_provide_example>",
    "detections": "$steps.object_detection_model.predictions",
    "line_segment": [
        [
            0,
            50
        ],
        [
            500,
            50
        ]
    ],
    "triggering_anchor": "CENTER"
}

v1

Class: LineCounterBlockV1 (there are multiple versions of this block)

Source: inference.core.workflows.core_steps.analytics.line_counter.v1.LineCounterBlockV1

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 LineCounter is an analytics block designed to count objects passing the line. The block requires detections to be tracked (i.e. each object must have unique tracker_id assigned, which persists between frames)

Type identifier

Use the following identifier in step "type" field: roboflow_core/line_counter@v1to add the block as as step in your workflow.

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
line_segment	List[Any]	Line consisting of exactly two points. For line [[0, 100], [100, 100]], objects entering from the bottom will count as IN..	✅
triggering_anchor	str	The point on the detection that must cross the line to be counted..	✅

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 Line Counter in version v1.

• inputs: Anthropic Claude, YOLO-World Model, LMM, Model Monitoring Inference Aggregator, Roboflow Dataset Upload, Time in Zone, Byte Tracker, Segment Anything 2 Model, Slack Notification, Instance Segmentation Model, Roboflow Dataset Upload, Detection Offset, Google Vision OCR, Dimension Collapse, Byte Tracker, Detections Stabilizer, Detections Transformation, CSV Formatter, VLM as Detector, Google Gemini, OpenAI, PTZ Tracking (ONVIF).md), Path Deviation, Dynamic Zone, Roboflow Custom Metadata, Line Counter, Stitch OCR Detections, Multi-Label Classification Model, Local File Sink, Velocity, Overlap Filter, VLM as Classifier, Clip Comparison, Email Notification, Keypoint Detection Model, Size Measurement, Perspective Correction, Detections Consensus, Path Deviation, Moondream2, Bounding Rectangle, OpenAI, Detections Filter, Detections Merge, Buffer, Detections Stitch, Instance Segmentation Model, CogVLM, OpenAI, Twilio SMS Notification, Dynamic Crop, Detections Classes Replacement, Florence-2 Model, Template Matching, Time in Zone, Webhook Sink, VLM as Detector, Florence-2 Model, LMM For Classification, Object Detection Model, Llama 3.2 Vision, Clip Comparison, Single-Label Classification Model, OCR Model, Byte Tracker, Object Detection Model
• outputs: Blur Visualization, Triangle Visualization, Anthropic Claude, Dominant Color, Trace Visualization, Label Visualization, Pixel Color Count, Absolute Static Crop, Image Preprocessing, Keypoint Detection Model, Byte Tracker, Image Threshold, Reference Path Visualization, Slack Notification, Stability AI Outpainting, Instance Segmentation Model, Identify Outliers, Detection Offset, Stability AI Inpainting, Byte Tracker, Detections Stabilizer, Circle Visualization, Keypoint Visualization, Image Blur, SIFT Comparison, PTZ Tracking (ONVIF).md), Line Counter Visualization, Dynamic Zone, Image Slicer, Stitch OCR Detections, Crop Visualization, Corner Visualization, Pixelate Visualization, Image Slicer, Mask Visualization, Color Visualization, Polygon Visualization, Email Notification, Keypoint Detection Model, Perspective Correction, Detections Consensus, Bounding Box Visualization, Instance Segmentation Model, Twilio SMS Notification, Detections Classes Replacement, Halo Visualization, Dot Visualization, Classification Label Visualization, Webhook Sink, SIFT Comparison, Object Detection Model, Ellipse Visualization, Image Contours, Identify Changes, Grid Visualization, Stitch Images, Byte Tracker, Object Detection Model

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Line Counter in version v1 has.

Bindings

• input

  • metadata (video_metadata): not available.
  • detections (Union[object_detection_prediction, instance_segmentation_prediction]): Predictions.
  • line_segment (list_of_values): Line consisting of exactly two points. For line [[0, 100], [100, 100]], objects entering from the bottom will count as IN..
  • triggering_anchor (string): The point on the detection that must cross the line to be counted..

• output

  • count_in (integer): Integer value.
  • count_out (integer): Integer value.

Example JSON definition of step Line Counter in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/line_counter@v1",
    "metadata": "<block_does_not_provide_example>",
    "detections": "$steps.object_detection_model.predictions",
    "line_segment": [
        [
            0,
            50
        ],
        [
            500,
            50
        ]
    ],
    "triggering_anchor": "CENTER"
}