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

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
}