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