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

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[object_detection_prediction, list_of_values, 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[string, list_of_values]): 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
    ]
}