Size Measurement

Class: SizeMeasurementBlockV1

Source: inference.core.workflows.core_steps.classical_cv.size_measurement.v1.SizeMeasurementBlockV1

The [Size Measurement Block](https://www.

How This Block Works

youtube.com/watch?v=FQY7TSHfZeI) calculates the dimensions of objects relative to a reference object. It uses one model to detect the reference object and another to detect the objects to measure. The block outputs the dimensions of the objects in terms of the reference object.

• Reference Object: This is the known object used as a baseline for measurements. Its dimensions are known and used to scale the measurements of other objects.
• Object to Measure: This is the object whose dimensions are being calculated. The block measures these dimensions relative to the reference object.

Block Usage

To use the Size Measurement Block, follow these steps:

1. Select Models: Choose a model to detect the reference object and another model to detect the objects you want to measure.
2. Configure Inputs: Provide the predictions from both models as inputs to the block.
3. Set Reference Dimensions: Specify the known dimensions of the reference object in the format 'width,height' or as a tuple (width, height).
4. Run the Block: Execute the block to calculate the dimensions of the detected objects relative to the reference object.

Example

Imagine you have a scene with a calibration card and several packages. The calibration card has known dimensions of 5.0 inches by 3.0 inches. You want to measure the dimensions of packages in the scene.

• Reference Object: Calibration card with dimensions 5.0 inches (width) by 3.0 inches (height).
• Objects to Measure: Packages detected in the scene.

The block will use the known dimensions of the calibration card to calculate the dimensions of each package. For example, if a package is detected with a width of 100 pixels and a height of 60 pixels, and the calibration card is detected with a width of 50 pixels and a height of 30 pixels, the block will calculate the package's dimensions as:

• Width: (100 pixels / 50 pixels) * 5.0 inches = 10.0 inches
• Height: (60 pixels / 30 pixels) * 3.0 inches = 6.0 inches

This allows you to obtain the real-world dimensions of the packages based on the reference object's known size.

Watch the video tutorial

Type identifier

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

Properties

Name	Type	Description	Refs
name	str	Enter a unique identifier for this step..	❌
reference_predictions	List[Any]	Reference object used to calculate the dimensions of the specified objects. If multiple objects are provided, the highest confidence prediction will be used..	✅
reference_dimensions	Union[List[float], Tuple[float, float], str]	Dimensions of the reference object in desired units, (e.g. inches). Will be used to convert the pixel dimensions of the other objects to real-world units..	✅

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 Size Measurement in version v1.

• inputs: LMM, Twilio SMS Notification, Moondream2, Roboflow Custom Metadata, Detections Filter, Detections Classes Replacement, Detections Merge, Llama 3.2 Vision, Velocity, Template Matching, Detections List Roll-Up, Bounding Rectangle, Buffer, OpenAI, Email Notification, SAM 3, Detection Event Log, EasyOCR, Google Gemini, Roboflow Dataset Upload, Dimension Collapse, Twilio SMS/MMS Notification, Instance Segmentation Model, Detections Transformation, VLM as Detector, Single-Label Classification Model, Detection Offset, Clip Comparison, Google Vision OCR, CogVLM, Path Deviation, Stitch OCR Detections, Multi-Label Classification Model, Segment Anything 2 Model, VLM as Detector, LMM For Classification, Dynamic Zone, SAM 3, Seg Preview, Slack Notification, OpenAI, Local File Sink, Anthropic Claude, Time in Zone, Google Gemini, Webhook Sink, Perspective Correction, Overlap Filter, Florence-2 Model, Detections Consensus, Instance Segmentation Model, Object Detection Model, Anthropic Claude, VLM as Classifier, CSV Formatter, Byte Tracker, Size Measurement, Path Deviation, Line Counter, Time in Zone, Google Gemini, Motion Detection, Florence-2 Model, Object Detection Model, Detections Stitch, Byte Tracker, Keypoint Detection Model, Camera Focus, YOLO-World Model, Clip Comparison, OCR Model, Byte Tracker, OpenAI, SAM 3, Detections Stabilizer, Roboflow Dataset Upload, Dynamic Crop, Email Notification, PTZ Tracking (ONVIF).md), Model Monitoring Inference Aggregator, Time in Zone, Detections Combine, Anthropic Claude, OpenAI
• outputs: Color Visualization, Cache Set, Llama 3.2 Vision, Circle Visualization, Reference Path Visualization, Detections List Roll-Up, OpenAI, Buffer, Email Notification, SAM 3, Halo Visualization, Google Gemini, Trace Visualization, Roboflow Dataset Upload, Twilio SMS/MMS Notification, Instance Segmentation Model, VLM as Detector, Classification Label Visualization, Clip Comparison, Path Deviation, VLM as Detector, LMM For Classification, Triangle Visualization, SAM 3, Ellipse Visualization, Seg Preview, OpenAI, Mask Visualization, Anthropic Claude, Polygon Visualization, Google Gemini, Polygon Zone Visualization, Time in Zone, Label Visualization, Webhook Sink, Line Counter Visualization, Perspective Correction, Florence-2 Model, Detections Consensus, Instance Segmentation Model, Anthropic Claude, Object Detection Model, VLM as Classifier, Keypoint Detection Model, Grid Visualization, Size Measurement, VLM as Classifier, Path Deviation, Line Counter, Time in Zone, Google Gemini, Motion Detection, Florence-2 Model, Object Detection Model, Line Counter, Keypoint Detection Model, Dot Visualization, YOLO-World Model, Crop Visualization, Clip Comparison, Bounding Box Visualization, SAM 3, Roboflow Dataset Upload, Keypoint Visualization, Email Notification, Time in Zone, Anthropic Claude, Corner Visualization, OpenAI

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Size Measurement in version v1 has.

Bindings

• input

  • object_predictions (Union[object_detection_prediction, instance_segmentation_prediction]): Model predictions to measure the dimensions of..
  • reference_predictions (Union[list_of_values, object_detection_prediction, instance_segmentation_prediction]): Reference object used to calculate the dimensions of the specified objects. If multiple objects are provided, the highest confidence prediction will be used..
  • reference_dimensions (Union[list_of_values, string]): Dimensions of the reference object in desired units, (e.g. inches). Will be used to convert the pixel dimensions of the other objects to real-world units..

• output

  • dimensions (list_of_values): List of values of any type.

Example JSON definition of step Size Measurement in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/size_measurement@v1",
    "object_predictions": "$segmentation.object_predictions",
    "reference_predictions": "$segmentation.reference_predictions",
    "reference_dimensions": [
        4.5,
        3.0
    ]
}