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

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

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"
}