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.. | ❌ |
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:
Roboflow Custom Metadata,Time in Zone,Anthropic Claude,OpenAI,Model Monitoring Inference Aggregator,Local File Sink,Webhook Sink,Dynamic Crop,Detection Event Log,Florence-2 Model,Dynamic Zone,Detections Combine,Google Vision OCR,EasyOCR,Moondream2,Byte Tracker,OCR Model,Anthropic Claude,Size Measurement,Clip Comparison,Detections Stabilizer,Twilio SMS Notification,Roboflow Dataset Upload,Stitch OCR Detections,VLM As Classifier,CogVLM,Instance Segmentation Model,LMM For Classification,VLM As Detector,Object Detection Model,Google Gemini,OpenAI,Single-Label Classification Model,Path Deviation,Detections Stitch,Detections Consensus,Mask Area Measurement,YOLO-World Model,Byte Tracker,PTZ Tracking (ONVIF),OpenAI,Slack Notification,Detections Filter,Detection Offset,Roboflow Dataset Upload,Dimension Collapse,Detections List Roll-Up,Keypoint Detection Model,Email Notification,Twilio SMS/MMS Notification,Segment Anything 2 Model,Byte Tracker,Anthropic Claude,Instance Segmentation Model,Stitch OCR Detections,Seg Preview,Email Notification,LMM,Florence-2 Model,Bounding Rectangle,SAM 3,Overlap Filter,Detections Merge,VLM As Detector,Clip Comparison,Detections Classes Replacement,Velocity,Template Matching,Perspective Correction,Path Deviation,Motion Detection,CSV Formatter,Camera Focus,Llama 3.2 Vision,Detections Transformation,Google Gemini,Line Counter,SAM 3,SAM 3,Time in Zone,Buffer,Multi-Label Classification Model,OpenAI,Qwen3.5-VL,Time in Zone,Google Gemini,Object Detection Model - outputs:
Time in Zone,Halo Visualization,Anthropic Claude,Halo Visualization,Trace Visualization,Grid Visualization,Ellipse Visualization,Webhook Sink,Corner Visualization,Polygon Zone Visualization,Circle Visualization,Florence-2 Model,Keypoint Visualization,Anthropic Claude,Size Measurement,Clip Comparison,Roboflow Dataset Upload,Cache Set,Reference Path Visualization,VLM As Classifier,Instance Segmentation Model,LMM For Classification,VLM As Detector,Object Detection Model,Google Gemini,Classification Label Visualization,OpenAI,Label Visualization,Mask Visualization,Path Deviation,VLM As Classifier,Line Counter,YOLO-World Model,Detections Consensus,Bounding Box Visualization,OpenAI,Roboflow Dataset Upload,Triangle Visualization,Keypoint Detection Model,Email Notification,Dot Visualization,Detections List Roll-Up,Twilio SMS/MMS Notification,Anthropic Claude,Instance Segmentation Model,Email Notification,Seg Preview,Florence-2 Model,SAM 3,VLM As Detector,Clip Comparison,Keypoint Detection Model,Crop Visualization,Detections Classes Replacement,Perspective Correction,Path Deviation,Motion Detection,Line Counter Visualization,Llama 3.2 Vision,Color Visualization,Google Gemini,Line Counter,SAM 3,Time in Zone,SAM 3,Buffer,OpenAI,Time in Zone,Google Gemini,Polygon Visualization,Object Detection Model,Polygon 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[object_detection_prediction,instance_segmentation_prediction]): Model predictions to measure the dimensions of..reference_predictions(Union[list_of_values,object_detection_prediction,instance_segmentation_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
]
}