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

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
}