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