Distance Measurement

Class: DistanceMeasurementBlockV1

Source: inference.core.workflows.core_steps.classical_cv.distance_measurement.v1.DistanceMeasurementBlockV1

Calculate the distance between two bounding boxes on a 2D plane, leveraging a perpendicular camera view and either a reference object or a pixel-to-unit scaling ratio for precise measurements.

Type identifier

Use the following identifier in step "type" field: roboflow_core/distance_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..	❌
object_1_class_name	str	The class name of the first object..	❌
object_2_class_name	str	The class name of the second object..	❌
reference_axis	str	The axis along which the distance will be measured..	❌
calibration_method	str	Select how to calibrate the measurement of distance between objects..	❌
reference_object_class_name	str	The class name of the reference object..	✅
reference_width	float	Width of the reference object in centimeters.	✅
reference_height	float	Height of the reference object in centimeters.	✅
pixel_ratio	float	The pixel-to-centimeter ratio of the input image, i.e. 1 centimeter = 100 pixels..	✅

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 Distance Measurement in version v1.

• inputs: Bounding Rectangle, Anthropic Claude, Multi-Label Classification Model, Single-Label Classification Model, LMM, Email Notification, Llama 3.2 Vision, Instance Segmentation Model, CSV Formatter, VLM as Detector, Webhook Sink, Dynamic Crop, Camera Focus, Time in Zone, Clip Comparison, Google Vision OCR, Byte Tracker, Twilio SMS Notification, Segment Anything 2 Model, Detection Offset, Model Monitoring Inference Aggregator, Florence-2 Model, LMM For Classification, Florence-2 Model, Detections Filter, Detections Transformation, YOLO-World Model, Roboflow Dataset Upload, Identify Changes, CogVLM, OCR Model, Template Matching, VLM as Detector, Path Deviation, Detections Consensus, OpenAI, Stitch OCR Detections, Cosine Similarity, Google Gemini, Detections Merge, Velocity, Local File Sink, Detections Stitch, Dynamic Zone, Instance Segmentation Model, Roboflow Custom Metadata, OpenAI, VLM as Classifier, Detections Classes Replacement, Detections Stabilizer, Path Deviation, Slack Notification, Object Detection Model, Gaze Detection, Time in Zone, Roboflow Dataset Upload, Keypoint Detection Model, Perspective Correction, Moondream2, Byte Tracker, Byte Tracker, Line Counter, Object Detection Model
• outputs: Identify Outliers, Classification Label Visualization, Webhook Sink, Mask Visualization, Twilio SMS Notification, Absolute Static Crop, Detection Offset, Image Blur, Identify Changes, Circle Visualization, Crop Visualization, SIFT Comparison, Stitch OCR Detections, Image Preprocessing, Pixel Color Count, Detections Stabilizer, Stitch Images, Bounding Box Visualization, Keypoint Detection Model, Dominant Color, Perspective Correction, SIFT Comparison, Color Visualization, Slack Notification, Ellipse Visualization, Reference Path Visualization, Blur Visualization, Anthropic Claude, Pixelate Visualization, Email Notification, Instance Segmentation Model, Keypoint Visualization, Byte Tracker, Grid Visualization, Trace Visualization, Image Threshold, Polygon Visualization, Detections Consensus, Triangle Visualization, Keypoint Detection Model, Halo Visualization, Dot Visualization, Dynamic Zone, Instance Segmentation Model, Object Detection Model, Image Contours, Corner Visualization, Line Counter Visualization, Image Slicer, Byte Tracker, Image Slicer, Byte Tracker, Label Visualization, Object Detection Model

Input and Output Bindings

The available connections depend on its binding kinds. Check what binding kinds Distance Measurement in version v1 has.

Bindings

• input

  • predictions (Union[object_detection_prediction, instance_segmentation_prediction]): The output of a detection model describing the bounding boxes that will be used to measure the objects..
  • reference_object_class_name (string): The class name of the reference object..
  • reference_width (float): Width of the reference object in centimeters.
  • reference_height (float): Height of the reference object in centimeters.
  • pixel_ratio (float): The pixel-to-centimeter ratio of the input image, i.e. 1 centimeter = 100 pixels..

• output

  • distance_cm (integer): Integer value.
  • distance_pixel (integer): Integer value.

Example JSON definition of step Distance Measurement in version v1

{
    "name": "<your_step_name_here>",
    "type": "roboflow_core/distance_measurement@v1",
    "predictions": "$steps.model.predictions",
    "object_1_class_name": "car",
    "object_2_class_name": "person",
    "reference_axis": "vertical",
    "calibration_method": "<block_does_not_provide_example>",
    "reference_object_class_name": "reference-object",
    "reference_width": 2.5,
    "reference_height": 2.5,
    "pixel_ratio": 100
}