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