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