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:
- Select Models: Choose a model to detect the reference object and another model to detect the objects you want to measure.
- Configure Inputs: Provide the predictions from both models as inputs to the block.
- Set Reference Dimensions: Specify the known dimensions of the reference object in the format 'width,height' or as a tuple (width, height).
- 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.
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.. | โ |
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.. | โ |
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.. | โ |
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:
Email Notification,OpenAI,Time in Zone,Object Detection Model,Roboflow Dataset Upload,Stitch OCR Detections,EasyOCR,Dimension Collapse,CogVLM,Google Gemini,Time in Zone,Dynamic Crop,Instance Segmentation Model,Velocity,Detections Combine,LMM For Classification,Detection Event Log,ByteTrack Tracker,GLM-OCR,Model Monitoring Inference Aggregator,Roboflow Custom Metadata,S3 Sink,Detections Classes Replacement,Keypoint Detection Model,Twilio SMS Notification,Detections Stabilizer,Camera Focus,Anthropic Claude,SAM 3,OC-SORT Tracker,OCR Model,Detections Consensus,Instance Segmentation Model,Llama 3.2 Vision,CSV Formatter,Roboflow Dataset Upload,Detection Offset,SORT Tracker,Webhook Sink,Byte Tracker,Detections List Roll-Up,Google Vision OCR,Byte Tracker,Florence-2 Model,Segment Anything 2 Model,Florence-2 Model,Overlap Filter,Google Gemini,SAM 3,Perspective Correction,Anthropic Claude,OpenAI,VLM As Detector,OpenAI,PTZ Tracking (ONVIF),Bounding Rectangle,Qwen3.5-VL,Template Matching,Anthropic Claude,Size Measurement,Email Notification,Multi-Label Classification Model,Line Counter,Time in Zone,Path Deviation,Detections Filter,Stitch OCR Detections,LMM,Detections Merge,Detections Transformation,Byte Tracker,SAM 3,Seg Preview,Dynamic Zone,Motion Detection,Single-Label Classification Model,Object Detection Model,OpenAI,Roboflow Vision Events,Local File Sink,VLM As Classifier,Mask Area Measurement,Detections Stitch,YOLO-World Model,Clip Comparison,Buffer,Clip Comparison,Twilio SMS/MMS Notification,Google Gemini,Path Deviation,Moondream2,VLM As Detector,Slack Notification - outputs:
Email Notification,Corner Visualization,Ellipse Visualization,OpenAI,Time in Zone,Object Detection Model,Roboflow Dataset Upload,Google Gemini,Time in Zone,Grid Visualization,Instance Segmentation Model,Line Counter Visualization,Trace Visualization,LMM For Classification,Halo Visualization,Dot Visualization,Keypoint Detection Model,Circle Visualization,Detections Classes Replacement,Keypoint Detection Model,Halo Visualization,Anthropic Claude,SAM 3,Detections Consensus,Polygon Visualization,Reference Path Visualization,Instance Segmentation Model,Llama 3.2 Vision,Crop Visualization,Roboflow Dataset Upload,Mask Visualization,Webhook Sink,Label Visualization,Cache Set,Classification Label Visualization,Detections List Roll-Up,Florence-2 Model,Florence-2 Model,Polygon Zone Visualization,VLM As Classifier,Google Gemini,SAM 3,Perspective Correction,OpenAI,Anthropic Claude,VLM As Detector,OpenAI,Size Measurement,Anthropic Claude,Email Notification,Keypoint Visualization,Line Counter,Time in Zone,Path Deviation,Line Counter,SAM 3,Motion Detection,Seg Preview,Color Visualization,Object Detection Model,VLM As Classifier,Clip Comparison,YOLO-World Model,Buffer,Triangle Visualization,Clip Comparison,Twilio SMS/MMS Notification,Bounding Box Visualization,Polygon Visualization,Google Gemini,Path Deviation,VLM As Detector
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[list_of_values,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
]
}