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

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[instance_segmentation_prediction, object_detection_prediction]): Model predictions to measure the dimensions of..
  • reference_predictions (Union[instance_segmentation_prediction, list_of_values, object_detection_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
    ]
}