---
title: "OmniCalQualityMetric"
description: "API documentation for OmniCalQualityMetric"
url: https://developer.disguise.one/python-api/docs/omnicalqualitymetric/
---

# OmniCalQualityMetric

Aggregate quality metrics to compare similar OmniCal calibrations

Note:

-   The calculated values should only be used to assess the same or very  
    similar calibration setups. For example, adding or removing a camera or  
    projector will make the quality metrics technically incompatible with  
    earlier collected metrics.  
    However, there is no mechanism to stop users from making these  
    comparisons. Such a mechanism could be based on known information like  
    the list of cameras, projectors and their resolutions. But it might make  
    the OmniCal Score less useful.  
    Overall, users mainly want to know: Has it worked? The OmniCal Score is  
    slightly more informative than raw RMS errors, but as a single value it  
    is never enough to assess the result. Therefore Disguise always requires  
    users to check the actual projection coverage and alignment in the real world.
-   All aggregate values are calculated using a normalised data range.  
    This reduces systematic bias errors in case e.g. some projectors have a  
    different pixel resolution than others.  
    The returned value will be multiplied by the highest pixel width.
-   OmniCal provides a single RMS error value for each camera and projector.  
    This RMS error is already an aggregate value over all inlier points.  
    Gathering another aggregate over several such aggregate values is  
    mathematically a bit questionable, see:  
    [https://stats.stackexchange.com/questions/490683/aggregating-error-metrics-like-rmse-for-multiple-time-series](https://stats.stackexchange.com/questions/490683/aggregating-error-metrics-like-rmse-for-multiple-time-series)  
    In practice we do need some kind of aggregate, though, and we don’t want  
    to pass in the whole set of all individual errors. This is not only  
    impractical, but it would also lead to bias towards e.g. cameras that  
    have more inlier data points.  
    Taking the mean / average over a list of RMS errors would reduce the impact  
    of outliers / high errors, because better RMS values will compensate:  
    aggregate(rms1 … rms8) < rms(err11 … err8N)  
    Calculating the RMS over a list of RMS errors is typically higher than  
    the RMS over all individual errors:  
    rms(rms1 … rms8) > rms(err11 … err8N)  
    However the “RMS of RMS errors” is much closer to the “RMS of individual  
    values”, and better reflects higher individual errors to the users.  
    So we will use RMS(RMS) as a more reliable aggregate metric.

Base class: _\_BlipValue_

## Properties

#### **cameraAggregateRMS** : _float_ _(Read-Only)_

Aggregate reprojection RMS error (in pixels) across all cameras. For information only: good values do not guarantee success. A value smaller than 1 is ideal.

_UserName: Aggregate camera RMS error_

#### **omniCalScore** : _float_ _(Read-Only)_

OmniCal score. Based on aggregate projector error and quality/practicality assessment of results. Only this score should be used for comparisons between calibrations.

_UserName: OmniCal score_

#### **projectorAggregateRMS** : _float_ _(Read-Only)_

Aggregate reprojection RMS error (in pixels) across all projectors. For information only: good values do not guarantee success. A value smaller than 1 is ideal, depending on projector resolution and audience viewing distance.

_UserName: Aggregate projector RMS error_