Size Measurement

Class: SizeMeasurementBlockV1

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

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

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[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
    ]
}