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Return how many measurements we have from point observations
SYNOPSIS
m = mrcal.cameramodel('xxx.cameramodel')
optimization_inputs = m.optimization_inputs()
x = mrcal.optimizer_callback(**optimization_inputs)[1]
Nmeas = mrcal.num_measurements_points( **optimization_inputs)
i_meas0 = mrcal.measurement_index_points(0, **optimization_inputs)
x_points_all = x[i_meas0:i_meas0+Nmeas]
The optimization algorithm tries to minimize the norm of a "measurements" vector
x. The optimizer doesn't know or care about the meaning of each element of this
vector, but for later analysis, it is useful to know what's what. The
mrcal.num_measurements_...() functions report how many measurements are produced
by particular items.
THIS function reports how many measurements come from the observations of
discrete points. When solving structure-from-motion problems, most if not all of
the measurements will come from these observations. These are stored
contiguously.
In order to determine the layout, we need quite a bit of context. If we have the
full set of inputs to the optimization function, we can pass in those (as shown
in the example above). Or we can pass the individual arguments that are needed
(see ARGUMENTS section for the full list). If the optimization inputs and
explicitly-given arguments conflict about the size of some array, the explicit
arguments take precedence. If any array size is not specified, it is assumed to
be 0. Thus most arguments are optional.
ARGUMENTS
- **kwargs: if the optimization inputs are available, they can be passed-in as
kwargs. These inputs contain everything this function needs to operate. If we
don't have these, then the rest of the variables will need to be given
- lensmodel: string specifying the lensmodel we're using (this is always
'LENSMODEL_...'). The full list of valid models is returned by
mrcal.supported_lensmodels(). This is required if we're not passing in the
optimization inputs
- do_optimize_intrinsics_core
do_optimize_intrinsics_distortions
do_optimize_extrinsics
do_optimize_frames
do_optimize_calobject_warp
do_apply_regularization
optional booleans; default to True. These specify what we're optimizing. See
the documentation for mrcal.optimize() for details
- Ncameras_intrinsics
Ncameras_extrinsics
Nframes
Npoints
Npoints_fixed
Nobservations_board
Nobservations_point
calibration_object_width_n
calibration_object_height_n
optional integers; default to 0. These specify the sizes of various arrays in
the optimization. See the documentation for mrcal.optimize() for details
RETURNED VALUE
The integer reporting how many elements of the measurement vector x come from
observations of discrete points
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