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

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
}