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