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

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
}