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dlib
Commit ad6c87b0 authored by Davis King's avatar Davis King
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Merge github.com:davisking/dlib

parents 1ab34825 d4cbaecd
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Showing with 517 additions and 253 deletions
.hgtags
View file @ ad6c87b0
......@@ -21,3 +21,4 @@ a6c2b16111b8023dbded7299dcc7e6acd26671b8 v18.8
4de62892e10850e8f0205b4857cf48b31fd730c8 v18.9
5a14394843c04628990857e5db94ff6bc43c2da0 v18.10
dd8e950033d5026373acce9ed4b2ffb85908d3b5 v18.11
4e3941b13ca859f788853cfcef9973ac4b161e65 v18.12
dlib/CMakeLists.txt
View file @ ad6c87b0
......@@ -12,6 +12,9 @@ set(CMAKE_LEGACY_CYGWIN_WIN32 0) # Remove when CMake >= 2.8.4 is required
# Suppress cmake warnings about changes in new versions.
if(COMMAND cmake_policy)
cmake_policy(SET CMP0003 NEW)
if (POLICY CMP0054)
cmake_policy(SET CMP0054 NEW)
endif()
endif()
......
dlib/add_python_module
View file @ ad6c87b0
......@@ -20,10 +20,13 @@
# A list of various paths you need to search on windows since people install
# boost in a bunch of different places.
set(CMAKE_PREFIX_PATH ${CMAKE_PREFIX_PATH}
"C:/local/boost_1_*"
"C:/Program Files (x86)/boost/boost_1_*"
"C:/Program Files/boost/boost_1_*")
set(BOOST_LIBRARYDIR "C:/local/boost_1_*/lib32-msvc-*")
C:/local/boost_*
C:/Program\ Files\ \(x86\)/boost/boost_*
C:/Program\ Files/boost/boost_*
)
set(BOOST_LIBRARYDIR ${BOOST_LIBRARYDIR} $ENV{BOOST_LIBRARYDIR}
C:/local/boost_*/lib32-msvc-*
)
......@@ -32,8 +35,10 @@ set(BOOST_LIBRARYDIR "C:/local/boost_1_*/lib32-msvc-*")
#SET(Boost_USE_STATIC_RUNTIME OFF)
set(Boost_NO_BOOST_CMAKE ON)
set(BOOST_LIBRARYDIR /usr/lib/x86_64-linux-gnu/)
if (NOT WIN32)
set(BOOST_LIBRARYDIR ${BOOST_LIBRARYDIR} $ENV{BOOST_LIBRARYDIR}
/usr/lib/x86_64-linux-gnu/)
endif()
if (PYTHON3)
FIND_PACKAGE(Boost 1.41.0 COMPONENTS python-py34 REQUIRED)
FIND_PACKAGE(PythonLibs 3.4 REQUIRED)
......
dlib/cmake
View file @ ad6c87b0
......@@ -3,6 +3,9 @@ cmake_minimum_required(VERSION 2.6.4)
set(CMAKE_LEGACY_CYGWIN_WIN32 0) # Remove when CMake >= 2.8.4 is required
if (POLICY CMP0054)
cmake_policy(SET CMP0054 NEW)
endif()
# Don't add dlib if it's already been added to the cmake project
if (NOT TARGET dlib)
......
dlib/image_processing/shape_predictor.h
View file @ ad6c87b0
......@@ -315,10 +315,6 @@ namespace dlib
unsigned long num_parts (
) const
/*!
ensures
- returns the number of points in the shape
!*/
{
return initial_shape.size()/2;
}
......@@ -328,13 +324,6 @@ namespace dlib
const image_type& img,
const rectangle& rect
) const
/*!
ensures
- runs the tree regressor on the detection rect inside img and returns a
full_object_detection DET such that:
- DET.get_rect() == rect
- DET.num_parts() == num_parts()
!*/
{
using namespace impl;
matrix<float,0,1> current_shape = initial_shape;
......
dlib/image_processing/shape_predictor_abstract.h
View file @ ad6c87b0
......@@ -270,7 +270,7 @@ namespace dlib
of the box. So a padding of 0.5 would cause the algorithm to sample
pixels from a box that was 2x2, effectively multiplying the area pixels
are sampled from by 4. Similarly, setting the padding to -0.2 would
cause it to sample from a box 0.8x0.8 in size.
cause it to sample from a box 0.6x0.6 in size.
!*/
void set_feature_pool_region_padding (
......
dlib/image_transforms/interpolation.h
View file @ ad6c87b0
......@@ -1621,7 +1621,7 @@ namespace dlib
}
// now make an image pyramid
dlib::array<image_type1> levels(max_depth);
dlib::array<image_type2> levels(max_depth);
if (levels.size() != 0)
pyr(img,levels[0]);
for (unsigned long i = 1; i < levels.size(); ++i)
......
dlib/image_transforms/morphological_operations.h
View file @ ad6c87b0
......@@ -6,6 +6,7 @@
#include "../pixel.h"
#include "thresholding.h"
#include "morphological_operations_abstract.h"
#include "assign_image.h"
namespace dlib
{
......@@ -662,6 +663,182 @@ namespace dlib
}
// ----------------------------------------------------------------------------------------
// ----------------------------------------------------------------------------------------
namespace impl
{
template <typename image_type>
inline bool should_remove_pixel (
const image_type& img,
long r,
long c,
int iter
)
{
unsigned int p2 = img[r-1][c];
unsigned int p3 = img[r-1][c+1];
unsigned int p4 = img[r][c+1];
unsigned int p5 = img[r+1][c+1];
unsigned int p6 = img[r+1][c];
unsigned int p7 = img[r+1][c-1];
unsigned int p8 = img[r][c-1];
unsigned int p9 = img[r-1][c-1];
int A = (p2 == 0 && p3 == 255) + (p3 == 0 && p4 == 255) +
(p4 == 0 && p5 == 255) + (p5 == 0 && p6 == 255) +
(p6 == 0 && p7 == 255) + (p7 == 0 && p8 == 255) +
(p8 == 0 && p9 == 255) + (p9 == 0 && p2 == 255);
int B = p2 + p3 + p4 + p5 + p6 + p7 + p8 + p9;
int m1 = iter == 0 ? (p2 * p4 * p6) : (p2 * p4 * p8);
int m2 = iter == 0 ? (p4 * p6 * p8) : (p2 * p6 * p8);
// Decide if we should remove the pixel img[r][c].
return (A == 1 && (B >= 2*255 && B <= 6*255) && m1 == 0 && m2 == 0);
}
template <typename image_type>
inline void add_to_remove (
std::vector<point>& to_remove,
array2d<unsigned char>& marker,
const image_type& img,
long r,
long c,
int iter
)
{
if (marker[r][c]&&should_remove_pixel(img,r,c,iter))
{
to_remove.push_back(point(c,r));
marker[r][c] = 0;
}
}
template <typename image_type>
inline bool is_bw_border_pixel(
const image_type& img,
long r,
long c
)
{
unsigned int p2 = img[r-1][c];
unsigned int p3 = img[r-1][c+1];
unsigned int p4 = img[r][c+1];
unsigned int p5 = img[r+1][c+1];
unsigned int p6 = img[r+1][c];
unsigned int p7 = img[r+1][c-1];
unsigned int p8 = img[r][c-1];
unsigned int p9 = img[r-1][c-1];
int B = p2 + p3 + p4 + p5 + p6 + p7 + p8 + p9;
// If you are on but at least one of your neighbors isn't.
return B<8*255 && img[r][c];
}
inline void add_if(
std::vector<point>& to_check2,
const array2d<unsigned char>& marker,
long c,
long r
)
{
if (marker[r][c])
to_check2.push_back(point(c,r));
}
} // end namespace impl
// ----------------------------------------------------------------------------------------
template <
typename image_type
>
void skeleton(
image_type& img_
)
{
/*
The implementation of this function is based on the paper
"A fast parallel algorithm for thinning digital patterns” by T.Y. Zhang and C.Y. Suen.
and also the excellent discussion of it at:
http://opencv-code.com/quick-tips/implementation-of-thinning-algorithm-in-opencv/
*/
typedef typename image_traits<image_type>::pixel_type pixel_type;
// This function only works on grayscale images
COMPILE_TIME_ASSERT(pixel_traits<pixel_type>::grayscale);
using namespace impl;
// Note that it's important to zero the border for 2 reasons. First, it allows
// thinning to being at the border of the image. But more importantly, it causes
// the mask to have a border of 0 pixels as well which we use later to avoid
// indexing outside the image inside add_to_remove().
zero_border_pixels(img_,1,1);
image_view<image_type> img(img_);
// We use the marker to keep track of pixels we have committed to removing but
// haven't yet removed from img.
array2d<unsigned char> marker(img.nr(), img.nc());
assign_image(marker, img);
// Begin by making a list of the pixels on the borders of binary blobs.
std::vector<point> to_remove, to_check, to_check2;
for (int r = 1; r < img.nr()-1; r++)
{
for (int c = 1; c < img.nc()-1; c++)
{
if (is_bw_border_pixel(img, r, c))
{
to_check.push_back(point(c,r));
}
}
}
// Now start iteratively looking at the border pixels and removing them.
while(to_check.size() != 0)
{
for (int iter = 0; iter <= 1; ++iter)
{
// Check which pixels we should remove
to_remove.clear();
for (unsigned long i = 0; i < to_check.size(); ++i)
{
long r = to_check[i].y();
long c = to_check[i].x();
add_to_remove(to_remove, marker, img, r, c, iter);
}
for (unsigned long i = 0; i < to_check2.size(); ++i)
{
long r = to_check2[i].y();
long c = to_check2[i].x();
add_to_remove(to_remove, marker, img, r, c, iter);
}
// Now remove those pixels. Also add their neighbors into the "to check"
// pixel list for the next iteration.
for (unsigned long i = 0; i < to_remove.size(); ++i)
{
long r = to_remove[i].y();
long c = to_remove[i].x();
// remove the pixel
img[r][c] = 0;
add_if(to_check2, marker, c-1, r-1);
add_if(to_check2, marker, c, r-1);
add_if(to_check2, marker, c+1, r-1);
add_if(to_check2, marker, c-1, r);
add_if(to_check2, marker, c+1, r);
add_if(to_check2, marker, c-1, r+1);
add_if(to_check2, marker, c, r+1);
add_if(to_check2, marker, c+1, r+1);
}
}
to_check.clear();
to_check.swap(to_check2);
}
}
// ----------------------------------------------------------------------------------------
// ----------------------------------------------------------------------------------------
}
......
dlib/image_transforms/morphological_operations_abstract.h
View file @ ad6c87b0
......@@ -283,6 +283,30 @@ namespace dlib
- calls binary_complement(img,img);
!*/
// ----------------------------------------------------------------------------------------
template <
typename image_type
>
void skeleton(
image_type& img
);
/*!
requires
- image_type is an object that implement the interface defined in
dlib/image_processing/generic_image.h
- img must contain a grayscale pixel type.
- all pixels in img are set to either on_pixel or off_pixel.
(i.e. it must be a binary image)
ensures
- This function computes the skeletonization of img and stores the result in
#img. That is, given a binary image, we progressively thin the binary blobs
(composed of on_pixel values) until only a single pixel wide skeleton of the
original blobs remains.
- #img.nc() == img.nc()
- #img.nr() == img.nr()
!*/
// ----------------------------------------------------------------------------------------
}
......
dlib/image_transforms/spatial_filtering.h
View file @ ad6c87b0
......@@ -1217,7 +1217,7 @@ namespace dlib
typename in_image_type,
typename out_image_type
>
void gaussian_blur (
rectangle gaussian_blur (
const in_image_type& in_img,
out_image_type& out_img,
double sigma = 1,
......@@ -1241,7 +1241,7 @@ namespace dlib
ptype scale = sum(filt);
scale = scale*scale;
spatially_filter_image_separable(in_img, out_img, filt, filt, scale);
return spatially_filter_image_separable(in_img, out_img, filt, filt, scale);
}
......
dlib/image_transforms/spatial_filtering_abstract.h
View file @ ad6c87b0
......@@ -353,7 +353,7 @@ namespace dlib
typename in_image_type,
typename out_image_type
>
void gaussian_blur (
rectangle gaussian_blur (
const in_image_type& in_img,
out_image_type& out_img,
double sigma = 1,
......@@ -384,6 +384,8 @@ namespace dlib
inside the image are set to zero.
- #out_img.nc() == in_img.nc()
- #out_img.nr() == in_img.nr()
- returns a rectangle which indicates what pixels in #out_img are considered
non-border pixels and therefore contain output from the filter.
!*/
// ----------------------------------------------------------------------------------------
......
dlib/matlab/CMakeLists.txt
View file @ ad6c87b0
cmake_minimum_required(VERSION 2.8.4)
PROJECT(mex_functions)
include(cmake_mex_wrapper)
include(../cmake)
# Compile the example_mex_function.cpp file and link it to dlib. Note
# that you can give a list of things to link to here. E.g.
# add_mex_function(some_other_mex_function pthread dlib fftw)
add_mex_function(example_mex_function dlib)
add_mex_function(example_mex_callback dlib)
cmake_minimum_required(VERSION 2.8.4)
PROJECT(mex_functions)
include(cmake_mex_wrapper)
include(../cmake)
# Compile the example_mex_function.cpp file and link it to dlib. Note
# that you can give a list of things to link to here. E.g.
# add_mex_function(some_other_mex_function pthread dlib fftw)
add_mex_function(example_mex_function dlib)
add_mex_function(example_mex_callback dlib)
dlib/matlab/README.txt
View file @ ad6c87b0
This folder contains a set of tools which make it easy to create MATLAB mex
functions. To understand how they work, you should read the
example_mex_function.cpp and example_mex_callback.cpp examples.
To compile them, you can use CMake. In particular, from this folder execute
these commands:
mkdir build
cd build
cmake ..
cmake --build . --config release --target install
That should build the mex files on any platform.
Note that on windows you will probably need to tell CMake to use a 64bit
version of visual studio. You can do this by using a command like:
cmake -G "Visual Studio 10 Win64" ..
instead of
cmake ..
This folder contains a set of tools which make it easy to create MATLAB mex
functions. To understand how they work, you should read the
example_mex_function.cpp and example_mex_callback.cpp examples.
To compile them, you can use CMake. In particular, from this folder execute
these commands:
mkdir build
cd build
cmake ..
cmake --build . --config release --target install
That should build the mex files on any platform.
Note that on windows you will probably need to tell CMake to use a 64bit
version of visual studio. You can do this by using a command like:
cmake -G "Visual Studio 10 Win64" ..
instead of
cmake ..
dlib/matlab/call_matlab.h
View file @ ad6c87b0
This diff is collapsed.
dlib/matlab/cmake_mex_wrapper
View file @ ad6c87b0
# This file figured out where MATLAB is and then defines a macro, add_mex_function(name)
# which when called instructs CMake to build a mex file from a file called name.cpp. Note
# that additional library dependencies can be added like this: add_mex_function(name lib1 dlib libetc).
# That is, just add more libraries after the name and they will be build into the mex file.
cmake_minimum_required(VERSION 2.8.4)
# Find MATLAB's include directory and needed libraries
find_program(MATLAB_EXECUTABLE matlab PATHS
"C:/Program Files/MATLAB/*/bin"
"C:/Program Files (x86)/MATLAB/*/bin"
)
# Resolve symbolic links to try and get the real path to the MATLAB executable
get_filename_component(MATLAB_EXECUTABLE ${MATLAB_EXECUTABLE} REALPATH)
# Now get MATLAB root directory
get_filename_component(MATLAB_HOME ${MATLAB_EXECUTABLE} PATH)
get_filename_component(MATLAB_HOME ${MATLAB_HOME} PATH)
set(MATLAB_LIB_FOLDERS
"${MATLAB_HOME}/extern/lib/win64/microsoft"
"${MATLAB_HOME}/bin/glnxa64"
)
# Find the MATLAB libraries that need to get linked into the mex file
if (WIN32)
find_library(MATLAB_MEX_LIBRARY libmex PATHS ${MATLAB_LIB_FOLDERS} )
find_library(MATLAB_MX_LIBRARY libmx PATHS ${MATLAB_LIB_FOLDERS} )
find_library(MATLAB_ENG_LIBRARY libeng PATHS ${MATLAB_LIB_FOLDERS} )
else()
find_library(MATLAB_MEX_LIBRARY mex PATHS ${MATLAB_LIB_FOLDERS} )
find_library(MATLAB_MX_LIBRARY mx PATHS ${MATLAB_LIB_FOLDERS} )
find_library(MATLAB_ENG_LIBRARY eng PATHS ${MATLAB_LIB_FOLDERS} )
endif()
set(MATLAB_LIBRARIES ${MATLAB_MEX_LIBRARY} ${MATLAB_MX_LIBRARY} ${MATLAB_ENG_LIBRARY})
INCLUDE_DIRECTORIES("${MATLAB_HOME}/extern/include")
# Determine the path to cmake_mex_wrapper file so we can add it to the include search path..
string(REGEX REPLACE "cmake_mex_wrapper$" "" dlib_matlab_binding_path ${CMAKE_CURRENT_LIST_FILE})
INCLUDE_DIRECTORIES("${dlib_matlab_binding_path}")
# Determine the path to dlib so we can add it to the include search path.
string(REGEX REPLACE "cmake_mex_wrapper$" "" dlib_path ${CMAKE_CURRENT_LIST_FILE})
INCLUDE_DIRECTORIES(${dlib_path}/../..)
ADD_DEFINITIONS(-DMATLAB_MEX_FILE)
# Determine the path to our CMakeLists.txt file. This is the file that
# includeded the one you are reading right now. So here we make it so that
# when you run the install target it will copy the compiled mex files into the
# same folder as the parent CMakeLists.txt file.
string(REGEX REPLACE "CMakeLists.txt$" "" install_dir ${CMAKE_PARENT_LIST_FILE})
set(CMAKE_INSTALL_PREFIX "${install_dir}")
set(CMAKE_INSTALL_SYSTEM_RUNTIME_DESTINATION "${install_dir}")
INCLUDE(InstallRequiredSystemLibraries)
MACRO(add_mex_function name )
ADD_LIBRARY(${name} MODULE ${name}.cpp )
# Change the output file extension to a mex extension.
if (WIN32)
set_target_properties(${name} PROPERTIES SUFFIX ".mexw64")
elseif(APPLE)
set_target_properties(${name} PROPERTIES SUFFIX ".mexmaci64")
else()
set_target_properties(${name} PROPERTIES SUFFIX ".mexa64")
endif()
set_target_properties(${name} PROPERTIES PREFIX "")
TARGET_LINK_LIBRARIES(${name} ${MATLAB_LIBRARIES} ${ARGN})
install(TARGETS ${name} DESTINATION "${install_dir}")
ENDMACRO()
# This file figures out where MATLAB is and then defines a macro, add_mex_function(name)
# which when called instructs CMake to build a mex file from a file called name.cpp. Note
# that additional library dependencies can be added like this: add_mex_function(name lib1 dlib libetc).
# That is, just add more libraries after the name and they will be build into the mex file.
cmake_minimum_required(VERSION 2.8.4)
# Find MATLAB's include directory and needed libraries
find_program(MATLAB_EXECUTABLE matlab PATHS
"C:/Program Files/MATLAB/*/bin"
"C:/Program Files (x86)/MATLAB/*/bin"
)
# Resolve symbolic links to try and get the real path to the MATLAB executable
get_filename_component(MATLAB_EXECUTABLE ${MATLAB_EXECUTABLE} REALPATH)
# Now get MATLAB root directory
get_filename_component(MATLAB_HOME ${MATLAB_EXECUTABLE} PATH)
get_filename_component(MATLAB_HOME ${MATLAB_HOME} PATH)
set(MATLAB_LIB_FOLDERS
"${MATLAB_HOME}/extern/lib/win64/microsoft"
"${MATLAB_HOME}/bin/glnxa64"
)
# Find the MATLAB libraries that need to get linked into the mex file
if (WIN32)
find_library(MATLAB_MEX_LIBRARY libmex PATHS ${MATLAB_LIB_FOLDERS} )
find_library(MATLAB_MX_LIBRARY libmx PATHS ${MATLAB_LIB_FOLDERS} )
find_library(MATLAB_ENG_LIBRARY libeng PATHS ${MATLAB_LIB_FOLDERS} )
else()
find_library(MATLAB_MEX_LIBRARY mex PATHS ${MATLAB_LIB_FOLDERS} )
find_library(MATLAB_MX_LIBRARY mx PATHS ${MATLAB_LIB_FOLDERS} )
find_library(MATLAB_ENG_LIBRARY eng PATHS ${MATLAB_LIB_FOLDERS} )
endif()
set(MATLAB_LIBRARIES ${MATLAB_MEX_LIBRARY} ${MATLAB_MX_LIBRARY} ${MATLAB_ENG_LIBRARY})
INCLUDE_DIRECTORIES("${MATLAB_HOME}/extern/include")
# Determine the path to cmake_mex_wrapper file so we can add it to the include search path..
string(REGEX REPLACE "cmake_mex_wrapper$" "" dlib_matlab_binding_path ${CMAKE_CURRENT_LIST_FILE})
INCLUDE_DIRECTORIES("${dlib_matlab_binding_path}")
# Determine the path to dlib so we can add it to the include search path.
string(REGEX REPLACE "cmake_mex_wrapper$" "" dlib_path ${CMAKE_CURRENT_LIST_FILE})
INCLUDE_DIRECTORIES(${dlib_path}/../..)
ADD_DEFINITIONS(-DMATLAB_MEX_FILE)
# Determine the path to our CMakeLists.txt file. This is the file that
# includeded the one you are reading right now. So here we make it so that
# when you run the install target it will copy the compiled mex files into the
# same folder as the parent CMakeLists.txt file.
string(REGEX REPLACE "CMakeLists.txt$" "" install_dir ${CMAKE_PARENT_LIST_FILE})
set(CMAKE_INSTALL_PREFIX "${install_dir}")
set(CMAKE_INSTALL_SYSTEM_RUNTIME_DESTINATION "${install_dir}")
INCLUDE(InstallRequiredSystemLibraries)
MACRO(add_mex_function name )
ADD_LIBRARY(${name} MODULE ${name}.cpp )
# Change the output file extension to a mex extension.
if (WIN32)
set_target_properties(${name} PROPERTIES SUFFIX ".mexw64")
elseif(APPLE)
set_target_properties(${name} PROPERTIES SUFFIX ".mexmaci64")
else()
set_target_properties(${name} PROPERTIES SUFFIX ".mexa64")
endif()
set_target_properties(${name} PROPERTIES PREFIX "")
TARGET_LINK_LIBRARIES(${name} ${MATLAB_LIBRARIES} ${ARGN})
install(TARGETS ${name} DESTINATION "${install_dir}")
ENDMACRO()
dlib/matlab/example_mex_callback.cpp
View file @ ad6c87b0
#include "call_matlab.h"
#include "dlib/matrix.h"
using namespace dlib;
using namespace std;
/*
This mex function takes a MATLAB function handle, calls it, and
returns the results.
For example, you can call this function in MATLAB like so:
A = magic(3)
y = example_mex_callback(A, @(x)x+x)
This will result in y containing the value 2*A.
*/
void mex_function (
const matrix<double>& A,
const function_handle& f,
matrix<double>& result
)
{
// The f argument to this function is a function handle passed from MATLAB. To
// call it we use the following syntax:
call_matlab(f, A, returns(result));
// This is equivalent to result = f(A). Therefore, the returns(variable) syntax
// is used to indicate which variables are outputs of the function.
// Another thing we can do is call MATLAB functions based on their string name
// rather than a function_handle. Here is an example of calling eigs().
matrix<double> m(2,2);
m = 1,2,
3,4;
matrix<double> v,d;
// This is equivalent to [v,d] = eigs(m);
call_matlab("eigs", m, returns(v), returns(d));
cout << "eigenvectors: \n" << v << endl;
cout << "eigenvalues: \n" << d << endl;
}
// #including this brings in all the mex boiler plate needed by MATLAB.
#include "mex_wrapper.cpp"
// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
#include "call_matlab.h"
#include "dlib/matrix.h"
using namespace dlib;
using namespace std;
/*
This mex function takes a MATLAB function handle, calls it, and
returns the results.
For example, you can call this function in MATLAB like so:
A = magic(3)
y = example_mex_callback(A, @(x)x+x)
This will result in y containing the value 2*A.
*/
void mex_function (
const matrix<double>& A,
const function_handle& f,
matrix<double>& result
)
{
// The f argument to this function is a function handle passed from MATLAB. To
// call it we use the following syntax:
call_matlab(f, A, returns(result));
// This is equivalent to result = f(A). Therefore, the returns(variable) syntax
// is used to indicate which variables are outputs of the function.
// Another thing we can do is call MATLAB functions based on their string name
// rather than a function_handle. Here is an example of calling eigs().
matrix<double> m(2,2);
m = 1,2,
3,4;
matrix<double> v,d;
// This is equivalent to [v,d] = eigs(m);
call_matlab("eigs", m, returns(v), returns(d));
cout << "eigenvectors: \n" << v << endl;
cout << "eigenvalues: \n" << d << endl;
}
// #including this brings in all the mex boiler plate needed by MATLAB.
#include "mex_wrapper.cpp"
dlib/matlab/example_mex_function.cpp
View file @ ad6c87b0
#include "dlib/matrix.h"
using namespace dlib;
using namespace std;
/*!
This file defines a function callable from MATLAB once you mex it.
It computes the same thing as the following MATLAB function:
function [A, B] = example_mex_function(x, y, some_number)
A = x+y;
B = sum(sum(x+y));
disp(['some_number: ' num2str(some_number)])
end
VALID INPUT AND OUTPUT ARGUMENTS
The mex wrapper can handle the following kinds of input and output arguments:
- Types corresponding to a MATLAB matrix
- a dlib::matrix containing any kind of scalar value.
- a dlib::array2d containing any kind of scalar value.
- a dlib::vector containing any kind of scalar value.
- a dlib::point
- RGB color images
- dlib::array2d<dlib::rgb_pixel> can be used to represent
MATLAB uint8 MxNx3 images.
- Types corresponding to a MATLAB scalar
- any kind of scalar value, e.g. double, int, etc.
- Types corresponding to a MATLAB string
- std::string
- Types corresponding to a MATLAB cell array
- a std::vector or dlib::array containing any of the above
types of objects or std::vector or dlib::array objects.
!*/
// You can also define default values for your input arguments. So
// here we say that if the user in MATLAB doesn't provide the "some_number"
// then it will get a value of 3.141.
#define ARG_5_DEFAULT 3.141
// Make a function named mex_function() and put your code inside it.
// Note that the return type should be void. Use non-const reference
// arguments to return outputs. Finally, mex_function() must have no
// more than 10 arguments.
void mex_function (
const matrix<double>& x,
const matrix<double>& y,
matrix<double>& out1,
double& out2,
double some_number
)
{
out1 = x + y;
out2 = sum(x+y);
// we can also use cout to print things as usual:
cout << "some_number: "<< some_number << endl;
}
// #including this brings in all the mex boiler plate needed by MATLAB.
#include "mex_wrapper.cpp"
// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
#include "dlib/matrix.h"
using namespace dlib;
using namespace std;
/*!
This file defines a function callable from MATLAB once you mex it.
It computes the same thing as the following MATLAB function:
function [A, B] = example_mex_function(x, y, some_number)
A = x+y;
B = sum(sum(x+y));
disp(['some_number: ' num2str(some_number)])
end
VALID INPUT AND OUTPUT ARGUMENTS
The mex wrapper can handle the following kinds of input and output arguments:
- Types corresponding to a MATLAB matrix
- a dlib::matrix containing any kind of scalar value.
- a dlib::array2d containing any kind of scalar value.
- a dlib::vector containing any kind of scalar value.
- a dlib::point
- RGB color images
- dlib::array2d<dlib::rgb_pixel> can be used to represent
MATLAB uint8 MxNx3 images.
- Types corresponding to a MATLAB scalar
- any kind of scalar value, e.g. double, int, etc.
- Types corresponding to a MATLAB string
- std::string
- Types corresponding to a MATLAB cell array
- a std::vector or dlib::array containing any of the above
types of objects or std::vector or dlib::array objects.
!*/
// You can also define default values for your input arguments. So
// here we say that if the user in MATLAB doesn't provide the "some_number"
// then it will get a value of 3.141.
#define ARG_5_DEFAULT 3.141
// Make a function named mex_function() and put your code inside it.
// Note that the return type should be void. Use non-const reference
// arguments to return outputs. Finally, mex_function() must have no
// more than 10 arguments.
void mex_function (
const matrix<double>& x,
const matrix<double>& y,
matrix<double>& out1,
double& out2,
double some_number
)
{
out1 = x + y;
out2 = sum(x+y);
// we can also use cout to print things as usual:
cout << "some_number: "<< some_number << endl;
}
// #including this brings in all the mex boiler plate needed by MATLAB.
#include "mex_wrapper.cpp"
dlib/matlab/mex_wrapper.cpp
View file @ ad6c87b0
This diff is collapsed.
dlib/test/image.cpp
View file @ ad6c87b0
......@@ -1800,6 +1800,16 @@ namespace
for (int i = 0; i < 100; ++i)
test_separable_filtering_center<float>(rnd);
{
print_spinner();
matrix<unsigned char> img(40,80);
assign_all_pixels(img, 255);
skeleton(img);
DLIB_TEST(sum(matrix_cast<int>(mat(img)))/255 == 40);
draw_line(img, point(20,19), point(59,19), 00);
DLIB_TEST(sum(matrix_cast<int>(mat(img))) == 0);
}
}
} a;
......
docs/docs/imaging.xml
View file @ ad6c87b0
......@@ -161,6 +161,7 @@
<item>suppress_non_maximum_edges</item>
<item>threshold_image</item>
<item>auto_threshold_image</item>
<item>hough_transform</item>
</section>
<section>
......@@ -175,6 +176,7 @@
<item>binary_union</item>
<item>binary_difference</item>
<item>binary_complement</item>
<item>skeleton</item>
</section>
<section>
......@@ -240,7 +242,6 @@
<item>zero_border_pixels</item>
<item>integral_image</item>
<item>integral_image_generic</item>
<item>hough_transform</item>
</section>
......@@ -274,6 +275,20 @@
</component>
<!-- ************************************************************************* -->
<component>
<name>skeleton</name>
<file>dlib/image_transforms.h</file>
<spec_file link="true">dlib/image_transforms/morphological_operations_abstract.h</spec_file>
<description>
This function computes the skeletonization of an image. That is,
given a binary image, we progressively thin the binary blobs
until only a single pixel wide skeleton of the original blobs
remains.
</description>
</component>
<!-- ************************************************************************* -->
<component>
......
docs/docs/index.xml
View file @ ad6c87b0
......@@ -216,6 +216,7 @@
<li><a href="other.html#serialize">Serialization support</a></li>
<li>Many <a href="other.html#memory_manager">memory manager</a> objects that implement
different memory pooling strategies</li>
<li>A tool that lets you easily <a href="other.html#MATLAB">call C++ from MATLAB</a></li>
</ul>
</li>
</ul>
......
docs/docs/other.xml
View file @ ad6c87b0
......@@ -103,8 +103,9 @@
</section>
<section>
<name>Testing</name>
<name>Other</name>
<item>dlib_testing_suite</item>
<item>MATLAB</item>
</section>
</top>
......@@ -1008,6 +1009,21 @@
</component>
<!-- ************************************************************************* -->
<component>
<name>MATLAB</name>
<description>
dlib contains a tool that makes it easy to call C++ code from MATLAB. It's
documented in the examples in the dlib/matlab folder. In particular, the
<a href="dlib/matlab/example_mex_function.cpp.html">dlib/matlab/example_mex_function.cpp</a> and
<a href="dlib/matlab/example_mex_callback.cpp.html">dlib/matlab/example_mex_callback.cpp</a> examples.
You can also easily compile these files using CMake. See the instructions in the README file
in the dlib/matlab folder for further details.
</description>
</component>
<!-- ************************************************************************* -->
<component>
......
docs/docs/release_notes.xml
View file @ ad6c87b0
......@@ -12,12 +12,25 @@
<current>
New Features:
- Upgraded fft() and ifft() to support 2D matrices.
- Added hough_transform
- Added skeleton() for finding the skeletonization of a binary image.
- Added distance_to_line(), clip_line_to_rectangle(), min_point(), and max_point().
- Added a simple API for calling C++ from MATLAB.
Non-Backwards Compatible Changes:
Bug fixes:
- Fixed a compile time error that could happen when calling fft() for
certain input types.
- Fixed a compile time error that prevented auto_threshold_image() from
being used.
- Fixed name clashes with new version of Boost.
- Changed Python pickling code so it works with Python 3.
- Fixed CMake compile time error related to finding fftw.
Other:
- Made extract_image_chips() much faster when extracting unscaled image chips.
</current>
<!-- ************************************************************************************** -->
......
docs/docs/term_index.xml
View file @ ad6c87b0
......@@ -676,6 +676,7 @@
<term file="metaprogramming.html" name="DLIB_CASSERT" include="dlib/assert.h"/>
<term file="metaprogramming.html" name="COMPILE_TIME_ASSERT" include="dlib/assert.h"/>
<term file="metaprogramming.html" name="DLIB_ASSERT_HAS_STANDARD_LAYOUT" include="dlib/assert.h"/>
<term file="other.html" name="MATLAB" />
<term file="other.html" name="TIME_THIS" include="dlib/time_this.h"/>
<term link="other.html#timing code blocks" name="print" include="dlib/timing.h"/>
<term file="other.html" name="timing code blocks" include="dlib/timing.h"/>
......@@ -1151,6 +1152,7 @@
<term file="containers.html" name="tuple" include="dlib/tuple.h"/>
<term file="dlib/type_safe_union/type_safe_union_kernel_abstract.h.html"
name="bad_type_safe_union_cast" include="dlib/type_safe_union.h"/>
<term file="containers.html" name="type_safe_union" include="dlib/type_safe_union.h"/>
<term file="containers.html" name="array2d" include="dlib/array2d.h"/>
......@@ -1287,6 +1289,7 @@
<term file="imaging.html" name="integral_image" include="dlib/image_transforms.h"/>
<term file="imaging.html" name="integral_image_generic" include="dlib/image_transforms.h"/>
<term file="imaging.html" name="hough_transform" include="dlib/image_transforms.h"/>
<term file="imaging.html" name="skeleton" include="dlib/image_transforms.h"/>
<term file="imaging.html" name="hessian_pyramid" include="dlib/image_keypoint.h"/>
<term file="imaging.html" name="compute_dominant_angle" include="dlib/image_keypoint.h"/>
<term file="imaging.html" name="draw_surf_points" include="dlib/image_keypoint/draw_surf_points.h"/>
......
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