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

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[instance_segmentation_prediction, object_detection_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
}