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: VLM as Detector, Byte Tracker, Google Vision OCR, Overlap Filter, SAM 3, Detections Stabilizer, Detections Filter, LMM For Classification, VLM as Classifier, Detections Combine, Model Monitoring Inference Aggregator, Segment Anything 2 Model, Template Matching, Moondream2, Velocity, OCR Model, Florence-2 Model, Detections Transformation, EasyOCR, Gaze Detection, Florence-2 Model, Detection Offset, Slack Notification, Clip Comparison, Instance Segmentation Model, OpenAI, Byte Tracker, Line Counter, PTZ Tracking (ONVIF).md), Object Detection Model, Keypoint Detection Model, Google Gemini, Email Notification, Llama 3.2 Vision, Byte Tracker, Dynamic Zone, YOLO-World Model, Email Notification, Time in Zone, OpenAI, CogVLM, Roboflow Custom Metadata, Stitch OCR Detections, Detections Stitch, Time in Zone, CSV Formatter, VLM as Detector, OpenAI, Detections Classes Replacement, Perspective Correction, Twilio SMS Notification, Single-Label Classification Model, Seg Preview, Roboflow Dataset Upload, Roboflow Dataset Upload, Webhook Sink, Camera Focus, Instance Segmentation Model, Multi-Label Classification Model, Time in Zone, Detections Merge, Path Deviation, Anthropic Claude, LMM, Google Gemini, Cosine Similarity, Dynamic Crop, Bounding Rectangle, Path Deviation, Detections Consensus, Local File Sink, Identify Changes, Object Detection Model
• outputs: Byte Tracker, Classification Label Visualization, Detections Stabilizer, SIFT Comparison, Circle Visualization, Image Contours, Image Preprocessing, Ellipse Visualization, Triangle Visualization, Stitch Images, Stability AI Inpainting, QR Code Generator, Image Slicer, Dot Visualization, Morphological Transformation, Reference Path Visualization, Halo Visualization, SIFT Comparison, Polygon Visualization, Image Slicer, Detection Offset, Slack Notification, Instance Segmentation Model, Byte Tracker, PTZ Tracking (ONVIF).md), Color Visualization, Object Detection Model, Keypoint Detection Model, Label Visualization, Email Notification, Trace Visualization, Byte Tracker, Dynamic Zone, Email Notification, Corner Visualization, Mask Visualization, Stability AI Outpainting, Stitch OCR Detections, Blur Visualization, Dominant Color, Crop Visualization, Grid Visualization, Detections Classes Replacement, Perspective Correction, Twilio SMS Notification, Absolute Static Crop, Webhook Sink, Bounding Box Visualization, Line Counter Visualization, Instance Segmentation Model, Icon Visualization, Image Blur, Pixelate Visualization, Image Threshold, Keypoint Detection Model, Anthropic Claude, Identify Outliers, Pixel Color Count, Detections Consensus, Keypoint Visualization, Identify Changes, 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
}