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