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

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
}