NonlinearLeastSquares (version 2.0.2, 2023-June-14) |
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NonlinearLeastSquares.py
Version: 2.0.2
Author: Avinash Kak (kak@purdue.edu)
Date: 2023-June-14
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View the main module file in your browser
View the code for OptimizedSurfaceFit for optimized surface fitting to noisy height data
View the code for ProjectiveCamera for camera modeling
CHANGES:
Version 2.0.2:
This version includes additional functionality in the ProjectiveCamera class
for constructing flyby camera-image sequences of virtual objects in the world
coordinate frame. See the script flyby_photos_of_3D_planar_shape.py in the
ExamplesStructureFromCameraMotion directory of the distribution for how you
can generate flyby images of a planar object in the YZ-plane.
Version 2.0.1:
Cleaned up the code to make it work with the current version of the matplotlib
library that is used for displaying the results. I have also removed the use
of the "is" and "is not" comparison operators for numeric and string literals
since such use has now been deprecated. Also added additional comment blocks
to the main functions in the module file.
Version 2.0.0:
This version includes sparse bundle adjustment (SBA) to speed up code
execution when using nonlinear least-squares for solving
structure-from-camera-motion problems with uncalibrated cameras. SBA exploits
the sparseness of the Jacobian encountered in such problems. This version
also includes improvements to the Levenberg-Marquardt code itself. The most
important improvement consists of how the "mu" factor (renamed "alambda" in
this module) is initialized. Version 2.0.0 also provides additional methods
for the visualization of the results.
Version 1.5.0:
This version adds a new class, ProjectiveCamera, to the module for
demonstrating how nonlinear least-squares can be used for estimating structure
of a scene from the data recorded with a calibrated camera in motion. For a
simulated demonstration, you can create calibrated cameras from a generic
instance of the ProjectiveCamera class that is then subject to various
translational and rotational transformations.
Version 1.1.1:
This version includes constructor options to control the size and the position
of the graphics for displaying the results of nonlinear least-squares
optimization.
Version 1.1:
This version fixes a bug in the synthetic data generator function used for
illustrating the functionality of the NonlinearLeastSquares class. Changes
also made to the information that is printed out when the module is run in the
debug mode.
Version 1.0:
In the form of a class named NonlinearLeastSquares, this module provides a
domain agnostic implementation of nonlinear least-squares algorithms
(gradient-descent and Levenberg-Marquardt) for fitting a model to observed
data. Typically, the model involves several parameters and each observed data
element can be expressed as a function of those parameters plus noise. The
goal of nonlinear least-squares is to estimate the best values for the
parameters given all of the observed data. In order to illustrate how to use
the NonlinearLeastSquares class, the module also comes with another class,
OptimizeSurfaceFit, whose job is to fit the best surface (of a specified
analytical form) to noisy height data over the XY-plane. The model in this
case is the analytical description of the surface and the goal of nonlinear
least-squares is to estimate the best values for the parameters in the
analytical description.
USAGE FOR OPTIMAL SURFACE FITTING:
The module includes a domain specific class, OptimizedSurfaceFit, for
demonstrating how nonlinear least-squares in the form of the
Levenberg-Marquardt algorithm can be used for optimally fitting analytically
specified surfaces to noisy data over a plane.
For surface fitting, you need to create an instance of the
NonlinearLeastSquares class as shown in line (A) below. In addition, you need
to create an instance of a domain-specific class such as OptimizedSurfaceFit
as is shown at line (B) below. Note that in the example shown in line (B),
the domain-specific class has two duties: (1) to synthetically generate noisy
height data; and (2) to call on NonlinearLeastSquares to fit an optimized
model to the synthetically generated data.
optimizer = NonlinearLeastSquares( #(A)
max_iterations = 400,
delta_for_jacobian = 0.000001,
delta_for_step_size = 0.0001, # only needed for GD
)
surface_fitter = OptimizedSurfaceFit( #(B)
gen_data_synthetically = True,
datagen_functional = "7.8*(x - 0.5)**3 + 2.2*(y - 0.5)**2",
data_array_size = (16,16),
how_much_noise_for_synthetic_data = 0.7,
model_functional = "a*(x-b)**3 + c*(y-d)**2",
initial_param_values = {'a':2.0, 'b':0.4, 'c':0.8, 'd':0.4},
display_needed = True,
)
surface_fitter.set_constructor_options_for_optimizer(optimizer) #(C)
result = surface_fitter.calculate_best_fitting_surface('lm') #(D)
or
result = surface_fitter.calculate_best_fitting_surface('gd') #(E)
In line (C), it is the job of the OptimizedSurfaceFit instance as constructed
in line (B) to export the domain-related information (after it is packaged
under the hood into a domain-agnostic form) to the instance of
NonlinearLeastSquares that was constructed in line (A). The information that
the OptimizedSurfaceFit instance conveys to the NonlinearLeastSquares instance
includes:
1) The observed noisy data vector. This vector is denoted X in the
NonlinearLeastSquares class. The same notation is used for the
observed data vector in the usage-example class OptimizedSurfaceFit.
2) The initial values to be used for the parameters of the model.
3) A vector of the predicted values for each of the data elements in X,
all in functional form involving the parameters of the model. This
vector is denoted Fvec in the NonlinearLeastSquares class.
4) The display function to be used for plotting the partial and the final
results if at all such results can be displayed in the form of a 2D or
3D graphic with Python's matplotlib library.
Finally, the statement in line (D) lets you call on the Levenberg-Marquardt
algorithm for estimating the model parameters. If you would rather use the
more basic gradient-descent algorithm for that purpose, your call will look
like what is shown in line (E).
See the script
leven_marq.py
in the ExamplesOptimizedSurfaceFit subdirectory of the distribution for
further information on what calls you need to sequence together for optimally
estimating the parameters of an analytically specified surface that you want
to fit to noisy data.
USAGE FOR ESTIMATING THE SCENE STRUCTURE WITH A CALIBRATED CAMERA IN MOTION:
You again need to create an instance of the NonlinearLeastSquares class as
shown in line (F) below. In addition, you need to create an instance of a
class like ProjectiveCamera as shown at line (G) below. If you are writing
your own code for camera modeling, it would be best if you subclass your code
from the ProjectiveCamera class in the module.
optimizer = NonlinearLeastSquares.NonlinearLeastSquares( #(F)
max_iterations = 400,
delta_for_jacobian = 0.000001,
)
camera = ProjectiveCamera.ProjectiveCamera( #(G)
camera_type = 'projective',
alpha_x = 1000.0,
alpha_y = 1000.0,
x0 = 300.0,
y0 = 250.0,
)
camera.initialize()
world_points = camera.make_world_points_random(30)
world_points_xformed = camera.apply_transformation_to_generic_world_points(world_points, ..... )
##
## all_pixels = Move the camera to different positions and
## collect the pixels
##
## Now construct the X vector and the corresponding prediction
## vector (Fvec) for nonlinear least-squares:
camera.construct_X_vector( all_pixels ) #(H)
camera.construct_parameter_vec_for_calibrated_cameras() #(I)
camera.construct_Fvec_for_calibrated_cameras(camera_params_dict) #(J)
result = camera.get_scene_structure_from_camera_motion('lm') #(K)
where the returned result will include the optimal estimations for the scene
parameters. Note that the parameter vector constructed in line (I) defines
the hyperplane over which is defined the cost function. The goal of nonlinear
least-squares is return that point on the parameter hyperplane where the cost
function takes on the least value. Line (J) constructs the prediction vector
in terms of the parameters used in line (I). The argument 'lm' in line (K)
stands for "Levenberg-Marquardt".
See the script
sfm_with_calibrated_cameras_translations_only.py
in the ExamplesStructureFromCameraMotion subdirectory of the distribution for
further information on what calls you need to sequence together for estimating
the structure of a scene that is being viewed with a moving camera.
USING SPARSE BUNDLE ADJUSTMENT FOR ESTIMATING THE SCENE STRUCTURE WITH AN UNCALIBRATED CAMERA IN MOTION:
You would again need to construct an instance of the NonlinearLeastSquares and
an instance of the ProjectiveCamera class as shown in lines (F) and (G) above.
But now you would need to replace the code in lines (H) through (K) by:
camera.construct_X_vector_for_bundle_adjustment( all_pixels ) #(L)
camera.construct_parameter_vec_for_uncalibrated_cameras_with_rodrigues_rotations()
#(M)
camera.construct_Fvec_for_bundle_adjustment() #(O)
result = camera.get_scene_structure_from_camera_motion_with_bundle_adjustment()
#(P)
The construction of the observation vector X in line (L) is specific to sparse
bundle adjustment --- in the sense that you first want to list the pixels in
all the cameras for the first world point, then you want to list the pixels in
all the cameras for the second world point, and so on. Ordering the observed
data in this fashion is necessary to create the sort of block sparsity in the
Jacobian that is exploited in SBA.
Note that we assume that the intrinsic parameters of the camera are known
already. So the goal of the bundle adjustment is to estimate the six
extrinsic parameters, three for translation of the camera and three (in the
form of Rodrigues rotations) for the rotation.
See the script
bundle_adjust_sfm_with_uncalibrated_cameras_translations_only.py
in the ExamplesStructureFromCameraMotion subdirectory of the distribution for
further information on what calls you need to sequence together for estimating
both the camera parameters and the scene structure for a scene that is being
viewed with a moving uncalibrated camera.
INTRODUCTION:
Nonlinear least-squares is a widely used set of algorithms for fitting a model
to noisy data through the minimization of a cost function. The observed data
is represented as a vector (denoted X in this module) and how the model would
predict each element of X by another vector that is denoted Fvec. As you
would expect, each element of Fvec is a function of the parameters of the
model. The goal of nonlinear least-squares is to find the best possible
values for the parameters, best in the sense of minimizing a cost function
that is almost always the square of the vector norm of the difference between
X and Fvec.
The simplest approach to solving a nonlinear least-squares problem is Gradient
Descent (GD). The mental imagery that best explains GD is to think of all of
the model parameters as constituting a hyperplane and the cost function as a
surface over the hyperplane. Gradient descent consists of starting at some
point in the hyperplane, looking straight up at the surface, and walking in
the direction of the steepest descent on the surface. At each point on the
hyperplane, you make the size of your next step proportional to the value of
the gradient on the cost-function surface. Assuming there are no local minima
to trap your progress, GD will eventually take you to the point on the
hyperplane that is directly below the global minimum for the cost function.
Even when getting trapped in a local minimum is not an issue (because, let's
say, you made a good choice for the starting point), the basic
gradient-descent algorithm suffers from the shortcoming that the closer you
get to the minimum, the smaller your steps will be, and, therefore, the slower
your progress toward the destination.
One way to get round this problem with gradient-descent is to use the
Gauss-Newton formula to make a direct jump to the minimum --- assuming you are
already sufficiently close to it. However, should you inadvertently try to
make a Gauss-Newton jump when still far from the minimum, your algorithm could
become numerically unstable and crash.
The algorithm that does a good job of combining the numerical robustness of
gradient-descent with the speed of Gauss-Newton, while making sure that the
latter is not invoked too early, is the Levenberg-Marquardt (LM)
algorithm. Given a start point on the hyperplane spanned by the model
parameters, LM starts by taking a GD step. In subsequent iterations, before
committing itself to a step, LM checks whether or not that step can safely be
taken with GN. If not, it steers the path toward GD. However, if the
condition for taking the GN step safely is satisfied, it goes ahead with that.
In this manner, LM can get to the minimum in a small number of steps, often
under 10, whereas for the same problem and the same start point, GD might take
more than a hundred.
The NonlinearLeastSquares class in this module provides implementations for
both the basic Gradient Descent (GD) algorithm and the more efficient
Levenberg-Marquardt algorithm for getting to the minimum of a cost function.
Starting with Version 2.0.0. this class also includes an SBA (Sparse Bundle
Adjustment) variant of the Levenberg-Marquardt algorithm for optimal
computation of both the structure and the camera parameters with the data
collected by an uncalibrated camera in motion.
From a programming standpoint, the most notable feature of
NonlinearLeastSquares is that it is domain agnostic. That is, you should be
able to use NonlinearLeastSquares for solving any problem that requires a GD
or an LM solution for finding the optimum values for a set of model parameters
through the minimization of a cost function.
The fact that NonlinearLeastSquares is generic implies that you have to write
a class of your own for the specific domain in which you wish to use
NonlinearLeastSquares. The domain specific class that you create must export
to NonlinearLeastSquares values for the following options in its constructor:
-- The observed data vector. This vector is denoted X in the
NonlinearLeastSquares class.
-- The initial values to be used for the parameters of the model.
-- A vector of the predicted values for each of the data elements in X, all in
functional form involving the parameters of the model. This vector is
denoted Fvec in the NonlinearLeastSquares class.
-- The display function to be used for plotting the partial and the final
results if at all such results can be displayed in the form of a 2D or 3D
graphic with Python's matplotlib library.
And, if you wish for NonlinearLeastSquares to use your analytically specified
partial derivatives in the Jacobian matrix that it needs for the next-step
calculations, your domain-specific class must also export that matrix to
NonlinearLeastSquares. In the absence of a user-supplied Jacobian matrix,
NonlinearLeastSquares estimates it numerically. [See the implementation code
for the OptimizedSurfaceFit class supplied with this module --- that class is
an example of a domain-specific class that uses NonlinearLeastSquares for
carrying out the needed optimization --- for how your own domain-specific
class can construct a Jacobian matrix in a functional form and supply it to
NonlinearLeastSquares.]
The NonlinearLeastSquares class provides several setter methods that your own
domain-specific class can use to convey the above-mentioned information to
NonlinearLeastSquares.
To illustrate how you may wish to write your domain specific class, this
module also comes with two additional classes named OptimizedSurfaceFit and
ProjectiveCamera, the first for fitting surfaces to noisy data over an
xy-plane and the second for estimating the structure of a 3D scene from the
data recorded by a camera in motion.
METHODS:
Constructing an instance of NonlinearLeastSquares:
optimizer = NonlinearLeastSquares(
X = None,
Fvec = None,
num_measurements = None,
num_parameters = None,
initial_params_dict = None,
initial_param_values_file = None,
jacobian_functionals_array = None,
delta_for_jacobian = 0.000001,
delta_for_step_size = 0.0001,
max_iterations = 200,
)
In most usage scenarios, though, you are likely to call
NonlinearLeastSquares directly with just the following constructor options
and let your own domain specific class set the other options at run time.
That is, in your own code, you will first create an instance of
NonlinearLeastSquares through the following call to its constructor:
optimizer = NonlinearLeastSquares(
max_iterations = 400,
delta_for_jacobian = 0.000001,
)
and subsequently have your own domain-specific class call the various
setter methods of NonlinearLeastSquares for giving values to its other
constructor options. As to which setter methods your own class should
call is presented in the rest of this section.
CONSTRUCTOR OPTIONS:
debug, debug2:
When set to True, it prints out a lot of information at each iteration
of the nonlinear least-squares algorithm invoked.
display_function:
If the problem you are trying to solve with nonlinear least-squares
allows for the result of optimization to be visualized, use this
constructor option to supply a reference to the function you would
like to be used for such visualization.
Fvec:
Fvec_BA:
These must be set to a numpy vector (actually a numpy matrix with just
one column) whose elements are the "predictors" for the corresponding
observed values in the X and the X_BA vectors, respectively. (We use
the symbol 'X' to denote the vector of measured data, as you will soon
see. X_BA does the same for the bundle-adjustment variant of the basic
Levenberg-Marquardt algorithm.) Each element of Fvec and Fvec_BA is,
in general, a function of all of the model parameters.
initial_params_dict:
This is set to a dictionary whose keys are the model parameters and
whose values the initial values for the model parameters. The initial
values for the model parameters specify the point in the parameter
hyperplane where you want to start the descent to the minimum.
initial_param_values_file:
If your problem involves a very large number of parameters, it may be
more convenient to place all their initial values in a text file.
Each parameter and its initial value must be in a line all by itself.
See the file "initial_params_gd2.txt" in the Examples directory for
how this file needs to be formatted.
jacobian_functionals_array:
If you wish to specify the partial derivatives of the functional
elements in the Fvec vector with respect to the model parameters, you
can supply those as a numpy chararray through this constructor option.
num_measurements:
This is simply the number of data values (meaning the number of
measurements) in X.
num_parameters:
This is the number of model parameters that you wish to calculate with
nonlinear least-squares.
X:
X_BA:
This variable must be set to a numpy vector (actually a numpy matrix
with just one column) whose elements constitute the observed data.
The number of elements in X would equal the number of observed data
elements that you are using for calculating the optimum values of the
model parameters. Whereas the variable X stores the observations for
any regular application of the Levenberg-Marquardt algorithm, the
variable X_BA stores the observed data for the case when you want to
use the bundle-adjustment variant of Levenberg-Marquardt. Whereas the
generic Levenberg-Marquardt places no constraints on how the
observations are arranged in the vector X, that is not the case with
the bundle-adjustment variant of the same algorithm. Both X and M_BA
must be a numpy vector, meaning a numpy matrix with just one column.
METHODS:
(1) grad_descent():
This is the implementation of the basic gradient-descent algorithm.
(2) leven_marq():
This is the implementation of the Levenberg-Marquardt algorithm mentioned
in the Introduction section.
(3) set_debug():
This allow your own domain-specific class to set the 'debug' attribute of
an instance of NonlinearLeastSquares.
(4) set_display_function():
Some problem domains allow the result of a nonlinear least-squares
calculation to be displayed with 2D or 3D graphics. For example, if the
goal is to fit an analytical form (in the form of, say, a polynomial) to
noisy height data over a flat plane and you use the nonlinear
least-squares algorithm to calculate the best values for the parameters
of the analytical form, you should be able to visualize the quality of
your results by displaying both the original noisy data and the model you
fit to the data. When such a visualization of the results is possible,
you can pass the definition of the your display function through this
setter function.
(5) set_Fvec():
set_Fvec_BA()
Each of these methods expects for its main argument a numpy matrix with a
single column whose each element must be a functional form that predicts
the corresponding element in the measurement vector X or X_BA. These
functional forms will be functions of the model parameters. The first of
the two methods is for the regular implementation of the
Levenberg-Marquardt algorithm and the second for the bundle-adjustment
variant of the same.
(6) set_initial_params():
This method expects for its main argument a dictionary of <key,value>
pairs in which the keys are the model parameters and the the
corresponding values the initial values to be given to those parameters.
(7) set_jacobian_functionals_array():
This method expects for its argument an Nxp matrix of functionals for the
partial derivatives needed for the Jacobian matrix. N is the number of
data elements in the X vector and p is the number of parameters in the
model. To elaborate, if you are using nonlinear least-squares to fit an
optimal surface to noisy height values over the xy-plane, your X vector
will be a single-column numpy matrix and each row of this vector would
correspond to one height value at some (x,y) point. The corresponding row
in the argument jacobian_functionals_array contains p functionals, with
each functional being a partial derivative of the model functional (with
its x and y set according to where the height was recorded) with respect
to the parameter corresponding to the column index.
(8) set_num_measurements():
This method sets the number of data elements in the X vector in the
instance of the NonlinearLeastSquares on which the method is invoked.
(9) set_num_parameters():
This method sets the number of model parameters that will be used in the
nonlinear least-squares optimization.
(10) set_X():
set_X_BA():
These two methods set the data vector, in the form of a numpy matrix
consisting of only one column, in an instance of NonlinearLeastSquares.
The first of these for the regular implementation of the
Levenberg-Marquardt algorithm and the second for the bundle-adjustment
version of the same. This is the data to which you which you want to fit
a given model and you want NonlinearLeastSquares to estimate the best
values for the parameters of the model.
OPTIMIZED SURFACE FITTING:
This section presents a class named OptimizedSurfaceFit to illustrate how you
can use the functionality of NonlinearLeastSquares in your own code. The goal
of OptimizedSurfaceFit is to fit a model surface to noisy height data over the
xy-plane in the xyz-coordinate frame, with the model surface being described
by a polynomial function. Here are some examples of such polynomials:
"a*(x-b)**2 + c*(y-d)**2 + e"
"a*x**2 + c*y**2"
"a*x + b*y + c"
where the value returned by the polynomial for given values of the coordinate
pair (x,y) is the height above the xy-plane at that point. Given the sort of
a model surface shown above, the problem becomes one of optimally estimating
the value for the model parameters from the noisy observed data. If vector X
represents the measured data over a set of (x,y) points in the form of a
vector of observations, we can now write for the cost function:
d^2 = || X - Fvec ||^2
where Fvec is a vector of the predictions as dictated by the model at each of
the (x,y) point. That is, each element of the Fvec vector is a prediction for
the corresponding element of the measurement vector X. The quantity d^2 is
the square of the vector norm of the prediction error, meaning the difference
between the observations in X and the predictions in Fvec. Given X and Fvec
vectors, We can call on NonlinearLeastSquares to help us find the best values
for the parameters of the model surface.
A typical call to OptimizedSurfaceFit's constructor looks like:
surface_fitter = OptimizedSurfaceFit(
gen_data_synthetically = True,
datagen_functional = "7.8*(x - 0.5)**3 + 2.2*(y - 0.5)**2",
data_array_size = (16,16),
how_much_noise_for_synthetic_data = 0.7,
model_functional = "a*(x-b)**3 + c*(y-d)**2",
initial_param_values = {'a':2.0, 'b':0.4, 'c':0.8, 'd':0.4},
display_needed = True,
display_size = (12,8),
display_position = (500,300),
debug = True,
)
or, if you wish to also supply the partial derivatives of the model functional
that can be used by OptimizedSurfaceFit for specifying the the Jacobian matrix
to NonlinearLeastSquares, like
surface_fitter = OptimizedSurfaceFit(
gen_data_synthetically = True,
datagen_functional = "7.8*(x - 0.5)**3 + 2.2*(y - 0.5)**2",
data_array_size = (16,16),
how_much_noise_for_synthetic_data = 0.5,
model_functional = "a*(x-b)**3 + c*(y-d)**2",
initial_param_values = {'a':2.0, 'b':0.4, 'c':0.8, 'd':0.4},
partials_for_jacobian = {'a':'(x-b)**2',
'b':'-2*a*(x-b)',
'c':'(y-d)**2',
'd':'-2*c*(y-d)'},
display_needed = True,
display_size = (12,8),
display_position = (500,300),
)
With regard to the constructor option 'partials_for_jacobian', it is important
to realize that what is passed to OptimizedSurfaceFit's constructor is not
directly the Nxp Jacobian matrix (where N is the number of observations in X
and p the number of parameters in the model). Instead, it is a set of partial
derivatives of the model functional with respect to the parameters of the
functional. However, OptimizedSurfaceFit knows how to translate into an Nxp
numpy chararray of the functionals needed for the Jacobian.
The constructor options for the OptimizedSurfaceFit class:
data_array_size:
The synthetic height that is generated by OptimizedSurfaceFit is over a
unit square in the xy-plane. Both the x and the y coordinates of this
square range over the interval 0.0 to 1.0. If you set this constructor
option to, say, (16,16), the unit square will be sampled over a 16x16
grid.
datagen_functional:
When gen_data_synthetically is set to True, you must supply an
algebraic expression in the form of a string that OptimizedSurfaceFit
can use to generate the height data. Here is an example of what such
a string looks like:
"7.8*(x - 0.5)**3 + 2.2*(y - 0.5)**2"
You can call any function inside the string that the Python math
library knows about.
debug:
This flag is passed on to NonlinearLeastSquares. When set to True, it
causes that class to display useful information during each iteration
of the nonlinear least-squares algorithm.
display_needed:
Fitting optimal surfaces to height data lends itself well to 3D
visualization. So if you'd like to see the the surfaces that
correspond to the optimal values for the model parameters, set this
constructor option to True.
display_position:
It is set to a tuple of two integers, with the first integer
specifying the horizontal coordinate and the second the vertical
coordinate of the upper left corner of the display. These two
coordinate values are with respect to the upper left corner of your
terminal screen. Horizontal coordinates are positive to the right and
vertical coordinates positive pointing down. Setting this constructor
parameter is optional. If not set, matplotlib will use its default
values.
display_size:
It is set to a tuple of two integers, with the first integer
specifying the width and the second the height of the display.
Setting this constructor parameter is optional. If not set,
matplotlib will use its default values.
gen_data_synthetically:
If set to True, OptimizedSurfaceFit can generate the measurement
height data for your synthetically according to the function you
specify through the constructor option 'datagen_functional'.
how_much_noise_for_synthetic_data:
This option controls the amount of noise that is added to the height
data generated according to the datagen_functional. The best way to
give a meaningful value to this construction option is to set to some
fraction of the largest coefficient in the datagen_functional.
initial_param_values:
Through this option, you can transmit to OptimizedSurfaceFit your
initial guesses for the values of the parameters in the model
functional. If you cannot think of a guess, you try setting all the
parameters to zero. OptimizedSurfaceFit conveys your initial values
for the parameters to the NonlinearLeastSquares class. You express the
initial values in the form of a dictionary, whole keys are the name of
the parameters and whose values the initial values to be given to those
parameters.
initial_param_values_file:
If the number of parameters in the problem you are addressing is large,
it may be more convenient to supply the initial values for the
parameters through a text file.
measured_data_file:
If the amount of measured data is large, it may be more convenient to
feed it into the module through a text file.
model_functional:
This is the algebraic expression that we want to fit to the noisy
height data. OptimizedSurfaceFit will call on NonlinearLeastSquares to
estimate the best values for the parameters of this algebraic
expression. For example, if the model_functional is "a*(x-b)**3 +
c*(y-d)**2", the NonlinearLeastSquares will find the best possible
values for the parameters a, b, c, and d --- best in the sense of
minimizing the cost function described previously.
model_functional_file:
If the model functional is too long and/or too complex to be specified
as an option directly in a call to the constructor, you can also place
it in a text file through the model_functional_file option.
optimizer:
Through this constructor option, you can have an instance variable of
the same name to hold a reference to an instance of
NonlinearLeastSquares.
partials_for_jacobian:
Although the NonlinearLeastSquares class can numerically estimate the
partial derivatives of the element of the Fvec vector with respect to
the model parameters, with this option you can supply your own
analytical forms for the partial derivatives that OptimizedSurfaceFit
can convert into a Jacobian matrix before transmitting it to
NonlinearLeastSquares.
Here are the methods defined for OptimizedSurfaceFit:
(1) construct_Fvec():
This method constructs the Fvec vector that NonlinearLeastSquares
needs for comparing with the measurement vector X. Each element of
Fvec is a prediction for the corresponding element of X and this
prediction is a functional form involving model parameters.
(2) construct_jacobian_array_in_functional_form():
This method is used only when the user supplies analytical forms for
the partial derivatives of the model functional with respect to each
of the model parameters. (When the user does not supply such partial
derivatives, NonlinearLeastSquares estimates the Jacobian through
numerical approximations.)
(3) display_function()
The problem addressed by OptimizedSurfaceFit lends itself well to
visualization of the quality of the results returned by
NonlinearLeastSquares. With the definition for this method that is
provided, you can see how well the model parameters estimated by
NonlinearLeastSquares fit the noisy height data.
(4) gen_data():
This method generates the height data over the xy-plane according to
the analytical form that is supplied to it as its main argument. We
refer to this analytical form as the 'model functional'.
(5) get_initial_params_from_file():
If you want to use model functions that have a large number of
parameters, it might be easier to place their values in a text file
and have OptimizedSurfaceFit get it from the file by using this
method.
(6) get_measured_data_from_text_file():
If you would like to supply the height data through a text file
(rather than have the class generate it automatically for you), then
this is the method to call for reading in the data from the file. The
method assumes that that individual data elements are separated by
whitespace characters (space, tab, newline, etc.). Since
OptimizedSurfaceFit knows about the size of the array both along the
x-coordinate and along the y-coordinate, it knows how to interpret the
data in the text file.
(7) get_model_functional_from_file():
If the model functional is too long, you can get OptimizedSurfaceFit
to read it from a text file by using this option.
(8) set_constructor_options_for_optimizer()
The responsibility of this method is to take all of the user supplied
information and reconstitute it into a form that is needed by
NonlinearLeastSquares taking into account the peculiarities of your
domain.
ESTIMATING SCENE STRUCTURE WHEN USING A CALIBRATED CAMERA IN MOTION:
This section presents a class named ProjectiveCamera to illustrate how you can
use the functionality of NonlinearLeastSquares in your own code for estimating
the structure of a 3D scene from the data recorded by a calibrated camera in
motion.
To create a simulated structure-from-camera-motion demonstration with this
module, you must first create an instance the ProjectiveCamera class. A
typical call to ProjectiveCamera's constructor looks like:
camera = ProjectiveCamera.ProjectiveCamera(
camera_type = 'projective',
alpha_x = 1000.0,
alpha_y = 1000.0,
x0 = 300.0,
y0 = 250.0,
)
camera.initialize()
This returns a camera whose optic axis is aligned with the world-Z axis and
whose image plane is parallel to the world-XY plane. The parameters 'alpha_x'
and 'alpha_y' are for the focal length of the camera in terms of the image
sampling intervals along the x-axis and along the y-axis, respectively. The
parameters 'x0' and 'y0' are for the coordinates of the point in the camera
image plane where the optic axis penetrates the image plane with respect to
the origin in the image plane (which is usually a corner of the image).
world_points = camera.make_world_points_for_triangle()
world_points_xformed = camera.apply_transformation_to_generic_world_points( world_points, (0,0,0), (0.0,0.0,5000.0), 1.0)
which generates a triangle defined by its three vertices from a method defined
for the ProjectiveCamera class and then moves the scene triangle along the
optic axis of the camera (the world-Z axis) by 5000 units. After the
transformation, the three vertices are at the coordinates (3000,3000,5000),
(4000,3000,5000), and (4000,5000,5000).
Subsequently, you must move the camera to different positions and orientations
and use the camera matrix constructed by the ProjectiveCamera instance to
project the world triangle into the camera images. You are going to need the
following two methods defined for the ProjectiveCamera class for these camera
motions:
rotate_previously_initialized_camera_around_x_axis( theta_x_delta )
translate_a_previously_initialized_camera( (0.0,y_motion_delta,0.0) )
At each camera position/orientation achieved with the above two methods, you
can record the pixels with the following call:
pixels = camera.get_pixels_for_a_sequence_of_world_points( world_points_xformed )
Subsequently, you must make the following call:
construct_X_vector( all_pixels )
where 'all_pixels' is the set of all the pixel recorded in all the positions
of the camera.
You would also need to create a Prediction Vector, Fvec, for the observed data
whose elements are predictor functionals in terms of the scene parameters that
need to be estimated. This is achieved with a call like:
construct_Fvec_for_calibrated_cameras( camera_params_dict )
Now you are ready to call the following method:
get_scene_structure_from_camera_motion('lm')
which will invoke the Levenberg-Marquardt method on the NonlinearLeastSquares
class to estimate the scene structure.
The constructor options for the ProjectiveCamera class:
camera_type:
You can only set this constructor option to 'projective' in the current
version of the module. Eventually, I intend to include 'orthographic'
as another possibility for this option.
alpha_x:
alpha_y:
These options are for the focal length in terms of the image sampling
intervals used along the image x-axis and along the image y-axis,
respectively.
x0:
y0:
These options are for the coordinates of the point in the camera image
plane where the optic axis penetrates the image plane with respect to
the origin in the image plane (which is usually a corner of the image).
camera_rotation:
Using the (roll,pitch,yaw) convention you can specify the rotation for
the camera in the constructor itself. However, for experimenting with
structure-from-camera-motion experiments, it is easier to first
construct a camera in its generic pose and to then call the rotate and
translate methods on it in order to move to a different position and
orientation.
camera_translation:
Using a triple to indicate displacements along the world-X, world-Y,
and world-Z, you can specify a translation for the camera in the
constructor itself. However, for experimenting with
structure-from-camera-motion experiments, it is easier to first
construct a camera in its generic pose and to then call the rotate and
translate methods on it in order to move to a different position and
orientation.
Here are the methods defined for ProjectiveCamera:
(1) add_new_camera_to_list_of_cameras():
You will find this utility method useful for enumerating all the
different camera positions you will be using in a simulated
structure-from-camera-motion experiment.
(2) apply_transformation_to_generic_world_points()
After you have constructed a scene object (typically just a simple
shape like a triangle or a tetrahedron), you can call on this method
to change its position and the pose in the world frame. The method
takes FOUR arguments: (1) The scene structure in the form of a list of
homogeneous coordinates for the world points on the object. (2) The
first is a triple that specifies the rotation using the
(roll,pitch,yaw) convention. (3) The second argument is a triple for
the displacement along the world-X, world-Y, and world-Z
coordinates. (4) The scale factor by which you want to expand or
shrink the scene object.
(3) construct_Fvec_for_calibrated_cameras(camera_params_dict)
This method constructs the prediction vector Fvec vector that
NonlinearLeastSquares needs for comparing with the measurement vector
X. Each element of Fvec is a prediction for the corresponding element
of X and this prediction is a functional form involving the structure
parameters.
(4) construct_parameter_vec_for_calibrated_cameras()
This method constructs an ordered list of the SYMBOLIC NAMES to be
used for each of the coordinates for the scene points that need to be
estimated. This list looks like "['X_0', 'Y_0', 'Z_0', 'X_1', 'Y_1',
'Z_1', 'X_2' ......]"
(5) construct_structure_ground_truth()
This method packages the scene world points in a way that makes it
convenient to output in your terminal window the estimated coordinates
for the scene points, the ground-truth value for those coordinates,
and the initial guesses supplied for them.
(6) construct_X_vector(all_pixels)
As mentioned in the Introduction, we use the notation X to represent a
vector of all the observed data. For a structure-from-camera-motion
problem, the observed data consists of all the pixels in all of the
camera positions. This method orders the x- and the y-coordinates of
all the recorded pixels in the same fashion as the order given to the
scene points in world-3D.
(7) display_structure()
This method is used to display in the form of a Matplotlib figure the
following three things simultaneously: the estimated scene structure,
the actual world points used for the scene object, and the initial
guesses supplied for those coordinates to the nonlinear least-squares
algorithm. The three parameters for this method are named
'structure_points_estimated', 'world_points_xformed', and
'initial_values_supplied'.
(8) get_all_cameras()
This utility method is convenient for getting hold of all the cameras
that supplied the data for solving the structure-from- camera-motion
problem. We consider an instance of ProjectiveCamera at each of its
positions in world-3D as a distinct camera. So if you move the camera
to, say, 20 different locations, you are in effect using 20 cameras.
(9) get_pixels_for_a_sequence_of_world_points()
For any given camera position, this method applies the corresponding
camera matrix to each world point, which must be in homogeneous
coordinates, in the sequence of world points supplied to the method as
its argument.
(10) get_scene_structure_from_camera_motion('lm')
get_scene_structure_from_camera_motion_with_bundle_adjustment()
You must call the first of these two methods for estimating the scene
structure for the calibrated cameras case after you have collected all
the pixel data from all the different positions of the camera.
Obviously, before you can call this method, you would need to
construct the observation vector X from the pixel data the predictor
vector Fvec from the parameters of the cameras at each of their
positions. And you call the second method for doing the same for the
case of uncalibrated cameras if you want to use the bundle-adjustment
version of the Levenberg-Marquardt algorithm.
(11) initialize()
This method packs the constructor options supplied to the
ProjectiveCamera constructor in the form of the camera's intrinsic
parameter matrix K. If a translation and/or a rotation is specified
for the camera through the constructor, those are also incorporated in
the 3x4 camera matrix P put together by this method.
(12) make_world_points_for_triangle()
This method returns a scene object that consists of a triangle in
world 3D. I have found a triangle defined by its three world points to
be convenient for testing the basic logic of the algorithm for solving
a structure-from-camera-motion problem. The triangle returned by this
method can be subject to any orientation changing and position
changing transformation.
(13) make_world_points_from_tetrahedron_generic()
Like the previous method, this method returns world points on a
tetrahedron in world 3D that you can subsequently use for your
simulated structure-from-moving-camera experiment.
(14) print_camera_matrix()
This utility method is convenient for displaying the 3x4 camera matrix
for any or all of the positions of the camera.
(15) rotate_previously_initialized_camera_around_world_X_axis()
This method incrementally rotates the camera clockwise around the
world-X axis by an angle 'theta' in degrees that is supplied to the
method as its argument.
(16) rotate_previously_initialized_camera_around_world_Y_axis()
This method incrementally rotates the camera clockwise around the
world-Y axis by an angle 'theta' in degrees that is supplied to the
method as its argument.
(17) set_constructor_options_for_optimizer( optimizer )
set_constructor_options_for_optimizer_BA( optimizer )
A ProjectiveCamera instance uses these method to pass on to
NonlinearLeastSquares all the information needed by the latter (such
as the observed data vector X or X_BA and the prediction vector Fvec
or Fvec_BA) for constructing an optimum estimate of the scene
structure. The argument 'optimizer' that this method takes is an
instance of NonlinearLeastSquares.
(18) set_initial_values_for_params()
Every nonlinear least-squares algorithm needs a starting guess for
whatever it is that is being estimated. In most cases, you would
construct a random guess for the parameters and supply those values to
this method in the form of a dictionary in which each key is the
symbolic name of one of the parameters being estimated and the value a
random guess for the parameter.
(19) set_params_list( params_arranged_list )
You will use this method to pass on to the instance of
ProjectiveCamera an ordered list of the parameters you want estimated
with nonlinear least-squares.
(20) translate_a_previously_initialized_camera()
This method incrementally displaces the camera by 'translation' that
is supplied to it as its argument. The argument 'translation' consists
of a triple of real numbers that stand for a displacement along the
world-X, along the world-Y, and along the world-Z.
(21) displayImage(self, argimage, file_name_for_save="")
This function, added in Version 2.0.2, is for constructing a camera
image of an object defined virtually in the world XYZ frame. I havce
used this function in the script flyby_photos_of_3D_planar_shape.py
in the ExamplesStructureFromCameraMotion directory for constructing
flyby sequence of images of a planar scene in the YZ-plane of the
world frame.
ESTIMATING SCENE STRUCTURE AND CAMERA PARAMETERS WHEN USING AN UNCALIBRATED CAMERA IN MOTION:
The problem of scene construction becomes a lot more challenging when the
camera in motion is uncalibrated. When I say uncalibrated, I mean
uncalibrated with respect to its extrinsic parameters. We assume in all
cases in this document that the intrinsic parameters of the camera are known.
What makes structure estimation more complicated in this case is that each new
camera position adds 6 additional variables to the overall parameter space, 3
for translation and 3 for rotation as expressed by the Rodrigues parameters.
Let's say you have M camera positions and N structure points. That would call
for (6*M + 3*N) parameters to be estimated by the Levenberg-Marquardt
algorithm because each camera has six external pose parameters associated with
it and each structure point is defined by its three world coordinates.
Assuming that you can see all of the structure points in all the cameras, all
of the pixels recorded in all of the camera position would constitute a 2*N*M
dimensional observation vector X. Therefore, your Jacobian will be of size
[(2*N*M) x (6*M+3*N)]. For an example, say you have 20 camera positions and
100 structure points, your Jacobian will be of size 4000x420. Calculating a
Jacobian of this size and multiplying it by its transpose could take many more
than several minutes on a run of the mill machine. And having to calculate
the Jacobian multiple times in the iterative framework of nonlinear
least-squares estimation could test your patience as you are waiting for the
results.
Fortunately, this otherwise long execution time can be significantly shortened
if you take advantage of the sparsity of the Jacobian when using uncalibrated
cameras. As to why the Jacobian is sparse, consider the fact that each row of
the Jacobian is a partial derivative of the prediction for one observation
with respect to ALL the parameters. Obviously, when you take the partial
derivative of the prediction function for a pixel in one specific camera with
respect to the parameters of all other cameras, all those partial derivatives
will be zero. The implementations of the Levenberg-Marquardt algorithm that
take advantage of the sparsity of the Jacobian are commonly referred to as
"Sparse Bundle Adjustment" or just "Bundle Adjustment".
This logic that this module uses for exploiting the sparsity of the Jacobian
is based on the paper "SBA: A Software Package for Generic Sparse Bundle
Adjustment" by Manolis Lourakis and Antonis Argyros that appeared in ACM
Transactions on Mathematical Software, March 2009.
In honor of these two authors, I have named all of the block Jacobian
submatrices that take partial derivatives of the predictors with respect to
the camera parameters as the Argyros matrices. And I have named all of the
block Jacobian matrices that do the same with respect to the structure
variables as Lourakis matrices.
In this module, the "bundle-adjustment" version of Levenberg-Marquardt
algorithm can be found in the method
bundle_adjust()
of the NonlinearLeastSquares class. As you will notice, the observation
vector that I called X in the implementation of the method leven_marq() is now
called X_BA in the implementation of the method bundle_adjust(). The reason
has to do with the fact that the basic Levenberg-Marquardt algorithm that you
see in leven_marq() does not care how the observed data is arranged in the
vector X. Typically, in my personal computer vision code for multi-camera
situations, I arrange the data by the camera. That is, I group together all
of the pixels recorded in one camera, followed by all of the pixels in the
second camera, and so on. That is how the observed data (and, therefore, also
the prediction vector Fvec) is arranged in the method leven_marq().
Unfortunately, that ordering of the data does not work for sparse bundle
adjustment. To maximally exploit sparse bundle adjustment, you must order the
observation vector so that your first group together the pixels for what we
may refer to as the first structure point, to be followed by the pixels in all
the cameras for the second structure point, and so on. Because this ordering
is critical to the SBA algorithm described in the paper by Lourakis and
Argyros, I have denoted the observation vector X_BA in the method
bundle_adjust(). The order in which the predictor functionals are placed in
the predictor vector Fvec must correspond to the order used in X_BA. So the
prediction vector in bundle_adjust() is named Fvec_BA.
About how to invoke the bundle-adjustment variant of Levenberg-Marquardt in
your own code, note the following methods of the ProjectiveCamera class that
are specially meant for that purpose:
(1) construct_X_vector_for_bundle_adjustment()
It is this method's job to arrange the observed data (meaning the
pixels in the camera images) in the special order that is needed by
the bundle-adjustment algorithm.
(2) construct_parameter_vec_for_uncalibrated_cameras_with_rodrigues_rotations()
When using uncalibrated cameras, the parameter vector must include
three translational and three rotational parameters for each camera.
These are in addition to the three (X,Y,Z) parameters for each
structure point being tracked in the cameras. The job of this method
is to synthesize this parameter vector.
(3) construct_Fvec_for_bundle_adjustment()
The ordering used for the observed data (meaning the pixels in the
cameras) in the X_BA vector must also be used in the prediction
vector when you use the bundle-adjustment variant of the basic
Levenberg-Marquardt algorithm. This method creates the needed
prediction vector.
(4) get_scene_structure_from_camera_motion_with_bundle_adjustment()
It is this method of the ProjectiveCamera class that invokes the
bundle_adjust() method of the NonlinearLeastSquares class.
THE ExamplesOptimizedSurfaceFit DIRECTORY:
See the 'ExamplesOptimizedSurfaceFit' directory in the distribution for
examples of how you can use the NonlinearLeastSquares class for solving
optimization problems. These examples are based on the domain specific class
OptimizedSurfaceFit that knows about fitting model surfaces to noisy height
data over a flat plane. You will see the following four scripts in this
directory:
leven_marq.py
grad_descent.py
leven_marq_with_partial_derivatives.py
grad_descent_with_partial_derivatives.py
For the first two scripts, the NonlinearLeastSquares instance used will
estimate the needed Jacobian matrix through appropriate numerical
approximation formulas applied to the elements of the Fvec vector. On the
other hand, for the third and the fourth scripts, your own domain-specific
class must construct the Jacobian matrix, in the form of an array of
functions. In the case of the domain-specific class OptimizedSurfaceFit that
comes with this module, this Jacobian matrix is constructed from the
user-supplied partial derivatives for the model functional.
In order to become familiar with the NonlinearLeastSquares class, you might
wish to play with the four scripts listed above by:
-- Trying different functional forms for the 'datagen_functional' for
different shaped surfaces.
When you change the algebraic form of 'datagen_functional' for the
OptimizedSurfaceFit class, make sure that you also change the algebraic
form supplied for 'model_functional'. Note that nonlinear least-squares
can only calculate the parameters of a model functional that best fit the
noisy height data; it cannot conjure up a new mathematical form for the
surface. So the basic mathematical form of the 'model_functional' must be
the same as that of the 'datagen_functional'.
-- Trying different degrees of noise.
As mentioned elsewhere, when you supply a numerical value for the
constructor option 'how_much_noise_for_synthetic_data' for the
OptimizedSurfaceFit class, the number you enter should be in proportion to
the largest numerical coefficient in the 'datagen' functional. Change this
numerical value and see what happens to the quality of the final results.
-- Try different values for the initial values of the model parameters.
Since, depending on where the search for the optimum solution is started,
all nonlinear least-squares methods can get trapped in a local minimum, see
what happens when you change these initial values.
-- Try different algebraic expressions for the 'model_functional' constructor
option for the OptimizedSurfaceFit class. But note that if you change the
algebraic form of this functional, you must also change the algebraic form
of the 'datagen_functional' option.
-- Try running the example with and without the partial derivatives that are
supplied through the 'partials_for_jacobian' option for the
OptimizedSurfaceFit class.
THE ExamplesStructureFromCameraMotion DIRECTORY:
See the 'ExamplesStructureFromCameraMotion' directory in the distribution for
the following three example scripts that show how you can use the
NonlinearLeastSquares module for estimating the structure of a 3D scene from
the images recorded by a moving camera:
sfm_with_calibrated_cameras_translations_only.py
sfm_with_uncalibrated_cameras_translations_only.py
bundle_adjust_sfm_with_uncalibrated_cameras_translations_only.py
The first script listed above is for estimating the scene structure with a
calibrated camera in motion. As you play with this method, make sure you
change the level of noise in the initial values supplied for the structure
parameters to be estimated. As you will see, the method works even when the
initial values for the parameters are far from their true values. Note that
the ProjectiveCamera class makes it easy to specify calibrated cameras. The
constructor of the class first gives you a camera for which you can specify
the internal and the external parameters through the constructor
options. Subsequently, you can apply translational and rotational
transformations to the camera to move it to different locations in world 3D.
Since the 3x4 camera matrices for all these positions of the camera are known,
you end up with a set of fully calibrated cameras for experimenting with
structure-from-motion simulations.
The second and the third scripts listed above are for the case of uncalibrated
cameras, with the former a straightforward application of the
Levenberg-Marquardt algorithm and the latter a bundle-adjustment variant of
the same. Logically, both these methods must return identical answers. (If
you encounter a case when the two do not return the same answer, please send a
bug report to me. I'd appreciate that very much.)
Just to give you an idea of the speed-up you will get with bundle-adjustment,
when I run the second script listed above on my laptop, it takes about 15
minutes for the number of structure points and the number of camera positions
used in that script. For exactly the same number of structure points and the
camera positions, the third script takes only a couple of minutes. You can
only imagine the speed-up you will get with a C-based library for sparse
bundle adjustment --- such as the "sba" library mentioned in the paper by
Lourakis and Argyros that I cited earlier in this documentation page.
Starting with Version 2.0.2, an additional script in this directory is
flyby_photos_of_3D_planar_shape.py
that allows you to create a flyby sequence of images by "flying" a virtual
camera (created as an instance of the ProjectiveCamera class) over a scene in
the world XYZ frame. The specific example in the above script is for a planar
shape in the world YZ-plane.
CAVEAT
Note the bundle-adjustment variant of the Levenberg-Marquardt algorithm that
you see in the bundle-adjust() method of the NonlinearLeastSquares module is
meant for just educational purposes. Being pure Python, it cannot compete
with highly optimized C-based code you will see in, say, the "sba" library
mentioned in the article by Lourakis and Argyros that I have cited earlier in
this documentation. So if your needs for nonlinear least-squares for
estimating the structure and the camera parameters are primarily of the
production variety, go directly to those publicly available libraries.
INSTALLATION:
The NonlinearLeastSquares class was packaged using setuptools. For
installation, execute the following command-line in the source directory (this
is the directory that contains the setup.py file after you have downloaded and
uncompressed the package):
sudo python setup.py install
and/or
sudo python3 setup.py install
On Linux distributions, this will install the module file at a location that
looks like
/usr/local/lib/python3.6/dist-packages/
If you do not have root access, you have the option of working directly off
the directory in which you downloaded the software by simply placing the
following statements at the top of your scripts that use the
NonlinearLeastSquares class:
import sys
sys.path.append( "pathname_to_NonlinearLeastSquares_directory" )
To uninstall the module, simply delete the source directory, locate where the
NonlinearLeastSquares module was installed with "locate NonlinearLeastSquares"
and delete those files. As mentioned above, the full pathname to the
installed version is likely to look like
/usr/local/lib/python2.7/dist-packages/NonlinearLeastSquares*
If you want to carry out a non-standard install of the NonlinearLeastSquares
module, look up the on-line information on Disutils by pointing your browser
to
http://docs.python.org/dist/dist.html
BUGS:
Please notify the author if you encounter any bugs. When sending email,
please place the string 'NonlinearLeastSquares' in the subject line.
ABOUT THE AUTHOR:
Not too long ago, the author, Avinash Kak, finished a 17-year long "Objects
Trilogy Project" with the publication of the last book "Designing with
Objects" by John-Wiley. If interested, visit his web page at Purdue to find
out what this project was all about. You might like "Designing with Objects"
especially if you enjoyed reading Harry Potter as a kid (or even as an adult,
for that matter).
For all issues related to this module, contact the author at kak@purdue.edu
If you send email, please place the string "NonlinearLeastSquares" in your
subject line to get past the author's spam filter.
COPYRIGHT:
Python Software Foundation License
Copyright 2023 Avinash Kak
@endofdocs
Classes |
| | |
- builtins.object
-
- NonlinearLeastSquares
class NonlinearLeastSquares(builtins.object) |
| |
NonlinearLeastSquares(*args, **kwargs)
|
| |
Methods defined here:
- __init__(self, *args, **kwargs)
- constructor
- bundle_adjust(self, *args, **kwargs)
- This is an implementation of the "bundle adjustment" version of the Levenberg-Marquardt algorithm for nonlinear
least-squares. Bundle adjustment takes advantage of the sparsity of the Jacobian that one sees in
applications such as estimating the scene structure with the images recorded with uncalibrated cameras.
The implementation shown here is based on the now celebrated paper "SBA: A Software Package for Generic
Sparse Bundle Adjustment" by Manolis Lourakis and Antonis Argyros that appeared in ACM Transactions on
Mathematical Software, March 2009.
This function is used in the following scripts
bundle_adjust_sfm_with_uncalibrated_cameras_translations_only.py
in the ExamplesStructureFromCameraMotion directory of the distribution.
Note that since bundle_adjust() is written in a generic manner, it is not called directly by the example
scripts listed above. As shown in the scripts, you need to first construct an instance of the class
ProjectiveCamera that is a co-class of the main module class NonlinearLeastSquares in the distribution.
- grad_descent(self)
- This is an implementation of the basic gradient descent algorithm for nonlinear least-squares as described
in my Lecture 13 notes at the lecture-notes website for Purdue University's ECE661: Computer Vision
- leven_marq(self)
- This is an implementation of the Levenberg-Marquardt algorithm for nonlinear least-squares as described
in my Lecture 13 notes at the lecture-notes website for Purdue University's ECE661: Computer Vision
This function is used the following scripts in the ExamplesOptimizedSurfaceFit directory of the distro:
leven_marq.py
leven_marq_with_partial_derivatives.py
It is important to note that the above two scripts do NOT call leven_marq() function directly. AS
stated in the main document page for the NonlinearLeastSquares module, the code in the file
NonlinearLeastSquares.py is written in a domain agnostic manner. So you need a domain adaptation
class that knows how to package the arguments for calling leven_marq(). For the case of the two scripts
listed above, that domain-specific class in the distro is OptimizedSurfaceFit. It is a co-class of
the main module class NonlinearLeastSquares in the distribution.
The function leven_marq() defined here is ALSO used in the following two scripts
sfm_with_calibrated_cameras_translations_only.py
sfm_with_uncalibrated_cameras_translations_only.py
in the ExamplesStructureFromCameraMotion directory of the distribution. Again, these example scripts
do NOT directly call the leven_marq() function. As shown in the two scripts, they must first construct
an instance of the ProjectiveCamera class that knows how to bundle the arguments together for calling
the leven_marq() function.
- leven_marq_v1_5(self)
- This is the implementation of the leven_marq() function as it existed in Version 1.5. On account of the
fact that I made significant changes to this function in Version 2.0.0 of the module, I have retained the
old version for the old-time users of my module.
- set_Fvec(self, Fvector)
- This method supplies the NonlinearLeastSquares class with the prediction vector
whose each element is a functional form of the prediction in the observed data vector
X. Note that Fvec is a column vector --- meaning a numpy matrix with just one column.
- set_Fvec_BA(self, Fvector_BA)
- You need to call this method for providing the NonlinearLeastSquares class with the
prediction vector if you are going to be using the bundle-adjustment capabilities
of the class.
- set_X(self, X)
- set_X_BA(self, X_BA)
- set_debug(self, debug)
- set_display_function(self, display_function)
- set_initial_params(self, initial_params_dict)
- set_jacobian_functionals_array(self, jacobian_functionals_array)
- This method expects for its argument an Nxp matrix of functionals for the partial
derivatives needed for the Jacobian matrix. N is the number of measurements in
the X vector and p is the number of parameters in the model. If you are using
nonlinear least-squares to fit optimal surfaces to noisy measurements over the
xy-plane, each element of the X vector would correspond to one such measurement at
some (x,y) coordinates. And an element the argument jacobian_functionals_array chararray
would correspond to the partial derivative of the model functional that already
has incorporated the (x,y) coordinates corresponding to that row and that is
a partial derivative of the model with respect to the parameter corresponding to
the column.
- set_num_measurements(self, how_many_measurements)
- set_num_parameters(self, how_many_parameters)
- set_params_arranged_list(self, params_list)
- set_params_ordered_list(self, params_list)
- set_problem(self, prob)
- If you are using this module to solve structure from camera motion (sfm) problems, use this method
to set 'self.problem' to 'sfm_N' where 'N' is the number of world points you are tracking. This
is needed because sfm needs the specialized display function defined for the ProjectiveCamera class.
Data descriptors defined here:
- __dict__
- dictionary for instance variables (if defined)
- __weakref__
- list of weak references to the object (if defined)
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Data |
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__author__ = 'Avinash Kak (kak@purdue.edu)'
__copyright__ = '(C) 2023 Avinash Kak. Python Software Foundation.'
__date__ = '2023-June-14'
__url__ = 'https://engineering.purdue.edu/kak/distNonlinearLeastSquares/NonlinearLeastSquares-2.0.2.html'
__version__ = '2.0.2' |
Author |
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Avinash Kak (kak@purdue.edu) |