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