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