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Ajout Readme + Modif mineures interface

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Unknown 2018-08-10 16:57:26 +02:00
parent 1c72d90e9a
commit 3300e0efd3
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cmake_minimum_required(VERSION 2.8.3)
project(asift_matching)
## Find catkin macros and libraries
## if COMPONENTS list like find_package(catkin REQUIRED COMPONENTS xyz)
## is used, also find other catkin packages
find_package(cmake_modules)
IF(cmake_modules_FOUND)
find_package(Eigen REQUIRED)
ELSE()
find_package(Eigen3 REQUIRED)
set(Eigen_INCLUDE_DIRS ${EIGEN3_INCLUDE_DIRS})
ENDIF()
FIND_PACKAGE(OpenMP)
if (OPENMP_FOUND)
SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}")
endif (OPENMP_FOUND)
IF(MSVC)
ADD_DEFINITIONS(/arch:SSE2)
ENDIF(MSVC)
find_package(catkin REQUIRED COMPONENTS
roscpp
tf
rospy
pcl_conversions
pcl_ros
sensor_msgs
)
## Uncomment this if the package has a setup.py. This macro ensures
## modules and global scripts declared therein get installed
## See http://ros.org/doc/api/catkin/html/user_guide/setup_dot_py.html
catkin_python_setup()
## Compile as C++11, supported in ROS Kinetic and newer
#add_compile_options(-std=c++11)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O2 -ftree-vectorize -funroll-loops -L/usr/X11R6/lib -lm -lpthread -lX11 -std=c++11")
################################################
## Declare ROS messages, services and actions ##
################################################
## To declare and build messages, services or actions from within this
## package, follow these steps:
## * Let MSG_DEP_SET be the set of packages whose message types you use in
## your messages/services/actions (e.g. std_msgs, actionlib_msgs, ...).
## * In the file package.xml:
## * add a build_depend tag for "message_generation"
## * add a build_depend and a exec_depend tag for each package in MSG_DEP_SET
## * If MSG_DEP_SET isn't empty the following dependency has been pulled in
## but can be declared for certainty nonetheless:
## * add a exec_depend tag for "message_runtime"
## * In this file (CMakeLists.txt):
## * add "message_generation" and every package in MSG_DEP_SET to
## find_package(catkin REQUIRED COMPONENTS ...)
## * add "message_runtime" and every package in MSG_DEP_SET to
## catkin_package(CATKIN_DEPENDS ...)
## * uncomment the add_*_files sections below as needed
## and list every .msg/.srv/.action file to be processed
## * uncomment the generate_messages entry below
## * add every package in MSG_DEP_SET to generate_messages(DEPENDENCIES ...)
## Generate messages in the 'msg' folder
#add_message_files(
# FILES
#)
## Generate services in the 'srv' folder
# add_service_files(
# FILES
# Service1.srv
# Service2.srv
# )
## Generate actions in the 'action' folder
# add_action_files(
# FILES
# Action1.action
# Action2.action
# )
## Generate added messages and services with any dependencies listed here
#generate_messages(
# DEPENDENCIES
# std_msgs
# geometry_msgs
#)
###################################
## catkin specific configuration ##
###################################
## The catkin_package macro generates cmake config files for your package
## Declare things to be passed to dependent projects
## LIBRARIES: libraries you create in this project that dependent projects also need
## CATKIN_DEPENDS: catkin_packages dependent projects also need
## DEPENDS: system dependencies of this project that dependent projects also need
catkin_package(
#CATKIN_DEPENDS roscpp tf message_runtime
#INCLUDE_DIRS include
)
###########
## Build ##
###########
set(ASIFT_srcs
src/numerics1.cpp src/frot.cpp src/splines.cpp src/fproj.cpp
src/library.cpp src/flimage.cpp src/filter.cpp
src/demo_lib_sift.cpp src/compute_asift_keypoints.cpp
src/compute_asift_matches.cpp
src/orsa.cpp
src/ASIFT_matcher.cpp
src/ROS_matcher.cpp
src/ROS_matcher_node.cpp
)
## Specify additional locations of header files
## Your package locations should be listed before other locations
include_directories(
.
#include
${catkin_INCLUDE_DIRS}
${Eigen_INCLUDE_DIRS}
)
## Declare a C++ executable
## With catkin_make all packages are built within a single CMake context
## The recommended prefix ensures that target names across packages don't collide
add_executable(ASIFT_matcher ${ASIFT_srcs})
target_link_libraries(ASIFT_matcher
${catkin_LIBRARIES}
${Eigen_LIBRARIES}
X11
)
#add_custom_command(TARGET ASIFT_matcher PRE_BUILD
# COMMAND ${CMAKE_COMMAND} -E copy_directory
# ${CMAKE_SOURCE_DIR}/book_training $<TARGET_FILE_DIR:ASIFT_matcher>)
## Add cmake target dependencies of the executable
## same as for the library above
# add_dependencies(${PROJECT_NAME}_node ${${PROJECT_NAME}_EXPORTED_TARGETS} ${catkin_EXPORTED_TARGETS})
#add_dependencies(ASIFT_matcher ${${PROJECT_NAME}_EXPORTED_TARGETS} ${catkin_EXPORTED_TARGETS})
## Specify libraries to link a library or executable target against
# target_link_libraries(${PROJECT_NAME}_node
# ${catkin_LIBRARIES}
# )
#############
## Install ##
#############
## Mark executable scripts (Python etc.) for installation
## in contrast to setup.py, you can choose the destination
#install(PROGRAMS
#scripts/basic_controls.py
#scripts/cube.py
#scripts/menu.py
#scripts/simple_marker.py
#DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION}
#)
## Mark executables and/or libraries for installation
#install(TARGETS
# ASIFT_matcher
# ARCHIVE DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION}
# LIBRARY DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION}
# RUNTIME DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION}
#)
## Mark cpp header files for installation
# install(DIRECTORY include/${PROJECT_NAME}/
# DESTINATION ${CATKIN_PACKAGE_INCLUDE_DESTINATION}
# FILES_MATCHING PATTERN "*.h"
# PATTERN ".svn" EXCLUDE
# )
## Mark other files for installation (e.g. launch and bag files, etc.)
# install(FILES
# # myfile1
# # myfile2
# DESTINATION ${CATKIN_PACKAGE_SHARE_DESTINATION}
# )
#############
## Testing ##
#############
## Add gtest based cpp test target and link libraries
# catkin_add_gtest(${PROJECT_NAME}-test test/test_beginner_tutorials.cpp)
# if(TARGET ${PROJECT_NAME}-test)
# target_link_libraries(${PROJECT_NAME}-test ${PROJECT_NAME})
# endif()
## Add folders to be run by python nosetests
# catkin_add_nosetests(test)

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cmake_minimum_required(VERSION 2.8.3)
project(asift_matching)
## Find catkin macros and libraries
## if COMPONENTS list like find_package(catkin REQUIRED COMPONENTS xyz)
## is used, also find other catkin packages
find_package(cmake_modules)
IF(cmake_modules_FOUND)
find_package(Eigen REQUIRED)
ELSE()
find_package(Eigen3 REQUIRED)
set(Eigen_INCLUDE_DIRS ${EIGEN3_INCLUDE_DIRS})
ENDIF()
FIND_PACKAGE(OpenMP)
if (OPENMP_FOUND)
SET(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} ${OpenMP_CXX_FLAGS}")
endif (OPENMP_FOUND)
IF(MSVC)
ADD_DEFINITIONS(/arch:SSE2)
ENDIF(MSVC)
find_package(catkin REQUIRED COMPONENTS
roscpp
tf
rospy
pcl_conversions
pcl_ros
sensor_msgs
)
#find_package(Eigen3 REQUIRED)
#find_package(cmake_modules REQUIRED)
## Compile as C++11, supported in ROS Kinetic and newer
#add_compile_options(-std=c++11)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O2 -ftree-vectorize -funroll-loops -L/usr/X11R6/lib -lm -lpthread -lX11 -std=c++11")
################################################
## Declare ROS messages, services and actions ##
################################################
## To declare and build messages, services or actions from within this
## package, follow these steps:
## * Let MSG_DEP_SET be the set of packages whose message types you use in
## your messages/services/actions (e.g. std_msgs, actionlib_msgs, ...).
## * In the file package.xml:
## * add a build_depend tag for "message_generation"
## * add a build_depend and a exec_depend tag for each package in MSG_DEP_SET
## * If MSG_DEP_SET isn't empty the following dependency has been pulled in
## but can be declared for certainty nonetheless:
## * add a exec_depend tag for "message_runtime"
## * In this file (CMakeLists.txt):
## * add "message_generation" and every package in MSG_DEP_SET to
## find_package(catkin REQUIRED COMPONENTS ...)
## * add "message_runtime" and every package in MSG_DEP_SET to
## catkin_package(CATKIN_DEPENDS ...)
## * uncomment the add_*_files sections below as needed
## and list every .msg/.srv/.action file to be processed
## * uncomment the generate_messages entry below
## * add every package in MSG_DEP_SET to generate_messages(DEPENDENCIES ...)
## Generate messages in the 'msg' folder
#add_message_files(
# FILES
#)
## Generate services in the 'srv' folder
# add_service_files(
# FILES
# Service1.srv
# Service2.srv
# )
## Generate actions in the 'action' folder
# add_action_files(
# FILES
# Action1.action
# Action2.action
# )
## Generate added messages and services with any dependencies listed here
#generate_messages(
# DEPENDENCIES
# std_msgs
# geometry_msgs
#)
###################################
## catkin specific configuration ##
###################################
## The catkin_package macro generates cmake config files for your package
## Declare things to be passed to dependent projects
## LIBRARIES: libraries you create in this project that dependent projects also need
## CATKIN_DEPENDS: catkin_packages dependent projects also need
## DEPENDS: system dependencies of this project that dependent projects also need
catkin_package(
#CATKIN_DEPENDS roscpp tf message_runtime
#INCLUDE_DIRS include
)
###########
## Build ##
###########
set(ASIFT_srcs
src/numerics1.cpp src/frot.cpp src/splines.cpp src/fproj.cpp
src/library.cpp src/flimage.cpp src/filter.cpp
src/demo_lib_sift.cpp src/compute_asift_keypoints.cpp
src/compute_asift_matches.cpp
src/orsa.cpp
src/ASIFT_matcher.cpp
src/ROS_matcher.cpp
src/ROS_matcher_node.cpp
)
## Specify additional locations of header files
## Your package locations should be listed before other locations
include_directories(
.
#include
${catkin_INCLUDE_DIRS}
${Eigen_INCLUDE_DIRS}
)
## Declare a C++ executable
## With catkin_make all packages are built within a single CMake context
## The recommended prefix ensures that target names across packages don't collide
add_executable(ASIFT_matcher ${ASIFT_srcs})
target_link_libraries(ASIFT_matcher
${catkin_LIBRARIES}
${Eigen_LIBRARIES}
X11
)
#add_custom_command(TARGET ASIFT_matcher PRE_BUILD
# COMMAND ${CMAKE_COMMAND} -E copy_directory
# ${CMAKE_SOURCE_DIR}/book_training $<TARGET_FILE_DIR:ASIFT_matcher>)
## Add cmake target dependencies of the executable
## same as for the library above
# add_dependencies(${PROJECT_NAME}_node ${${PROJECT_NAME}_EXPORTED_TARGETS} ${catkin_EXPORTED_TARGETS})
#add_dependencies(ASIFT_matcher ${${PROJECT_NAME}_EXPORTED_TARGETS} ${catkin_EXPORTED_TARGETS})
## Specify libraries to link a library or executable target against
# target_link_libraries(${PROJECT_NAME}_node
# ${catkin_LIBRARIES}
# )
#############
## Install ##
#############
## Mark executable scripts (Python etc.) for installation
## in contrast to setup.py, you can choose the destination
#install(PROGRAMS
#scripts/basic_controls.py
#scripts/cube.py
#scripts/menu.py
#scripts/simple_marker.py
#DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION}
#)
## Mark executables and/or libraries for installation
#install(TARGETS
# ASIFT_matcher
# ARCHIVE DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION}
# LIBRARY DESTINATION ${CATKIN_PACKAGE_LIB_DESTINATION}
# RUNTIME DESTINATION ${CATKIN_PACKAGE_BIN_DESTINATION}
#)
## Mark cpp header files for installation
# install(DIRECTORY include/${PROJECT_NAME}/
# DESTINATION ${CATKIN_PACKAGE_INCLUDE_DESTINATION}
# FILES_MATCHING PATTERN "*.h"
# PATTERN ".svn" EXCLUDE
# )
## Mark other files for installation (e.g. launch and bag files, etc.)
# install(FILES
# # myfile1
# # myfile2
# DESTINATION ${CATKIN_PACKAGE_SHARE_DESTINATION}
# )
#############
## Testing ##
#############
## Add gtest based cpp test target and link libraries
# catkin_add_gtest(${PROJECT_NAME}-test test/test_beginner_tutorials.cpp)
# if(TARGET ${PROJECT_NAME}-test)
# target_link_libraries(${PROJECT_NAME}-test ${PROJECT_NAME})
# endif()
## Add folders to be run by python nosetests
# catkin_add_nosetests(test)

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<launch>
<!--Matcher Parameters/-->
<param name="tracked_object" value="6DOF"/>
<param name="num_tilt" type="int" value="2"/>
<param name="std_dev_filter_coeff" type="double" value="2"/>
<!--Matcher References/-->
<param name="reference_path" value="$(find asift_matching)/reference_data/"/>
<param name="reference_txt_path" value="$(find asift_matching)/reference_data/book_references.txt"/>
<rosparam param="reference_data">[
"train_image_000.png",
"train_image_001.png"]
</rosparam>
<!--Topics/-->
<param name="object_center_topic" value="/object_center"/>
<param name="image_topic" value="/camera/rgb/image_raw"/>
<param name="pointcloud_topic" value="/camera/depth_registered/points"/>
<node name="ASIFT_matching" pkg="asift_matching" type="ASIFT_matcher" output="screen"/>
</launch>

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<launch>
<!--Matcher Parameters/-->
<param name="tracked_object" value="6DOF"/>
<param name="num_tilt" type="int" value="2"/>
<param name="std_dev_filter_coeff" type="float" value="2"/>
<!--Matcher References/-->
<param name="reference_path" value="$(find asift_matching)/reference_data/"/>
<param name="reference_txt_path" value="$(find asift_matching)/reference_data/book_references.txt"/>
<rosparam param="reference_data">[
"train_image_000.png",
"train_image_001.png"]
</rosparam>
<!--Topics/-->
<param name="object_center_topic" value="/object_center"/>
<param name="image_topic" value="/camera/rgb/image_raw"/>
<param name="pointcloud_topic" value="/camera/depth_registered/points"/>
<node name="ASIFT_matching" pkg="asift_matching" type="ASIFT_matcher" output="screen"/>
</launch>

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<?xml version="1.0"?>
<package>
<name>asift_matching</name>
<version>0.0.0</version>
<description>ASIFT based matching package</description>
<!-- One maintainer tag required, multiple allowed, one person per tag -->
<!-- Example: -->
<!-- <maintainer email="jane.doe@example.com">Jane Doe</maintainer> -->
<maintainer email="antoineharle@etu.upmc.fr">Antoine Harle</maintainer>
<!-- One license tag required, multiple allowed, one license per tag -->
<!-- Commonly used license strings: -->
<!-- BSD, MIT, Boost Software License, GPLv2, GPLv3, LGPLv2.1, LGPLv3 -->
<license>DREAM</license>
<!-- Url tags are optional, but multiple are allowed, one per tag -->
<!-- Optional attribute type can be: website, bugtracker, or repository -->
<!-- Example: -->
<!-- <url type="website">http://wiki.ros.org/rviz_interface</url> -->
<!-- Author tags are optional, multiple are allowed, one per tag -->
<!-- Authors do not have to be maintainers, but could be -->
<!-- Example: -->
<!-- <author email="jane.doe@example.com">Jane Doe</author> -->
<!-- The *depend tags are used to specify dependencies -->
<!-- Dependencies can be catkin packages or system dependencies -->
<!-- Examples: -->
<!-- Use depend as a shortcut for packages that are both build and exec dependencies -->
<!-- <depend>roscpp</depend> -->
<!-- Note that this is equivalent to the following: -->
<!-- <build_depend>roscpp</build_depend> -->
<!-- <exec_depend>roscpp</exec_depend> -->
<!-- Use build_depend for packages you need at compile time: -->
<!-- <build_depend>message_generation</build_depend> -->
<!-- Use build_export_depend for packages you need in order to build against this package: -->
<!-- <build_export_depend>message_generation</build_export_depend> -->
<!-- Use buildtool_depend for build tool packages: -->
<!-- <buildtool_depend>catkin</buildtool_depend> -->
<!-- Use exec_depend for packages you need at runtime: -->
<!-- <exec_depend>message_runtime</exec_depend> -->
<!-- Use test_depend for packages you need only for testing: -->
<!-- <test_depend>gtest</test_depend> -->
<!-- Use doc_depend for packages you need only for building documentation: -->
<!-- <doc_depend>doxygen</doc_depend> -->
<buildtool_depend>catkin</buildtool_depend>
<build_depend>roscpp</build_depend>
<build_depend>tf</build_depend>
<build_depend>pcl_conversions</build_depend>
<build_depend>pcl_ros</build_depend>
<build_depend>sensor_msgs</build_depend>
<build_depend>libpcl-all-dev</build_depend>
<run_depend>libpcl-all</run_depend>
<run_depend>pcl_conversions</run_depend>
<run_depend>pcl_ros</run_depend>
<run_depend>roscpp</run_depend>
<run_depend>sensor_msgs</run_depend>
<run_depend>roscpp</run_depend>
<run_depend>tf</run_depend>
<!--run_depend>message_runtime</run_depend-->
<export>
</export>
</package>

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<?xml version="1.0"?>
<package>
<name>asift_matching</name>
<version>0.0.0</version>
<description>ASIFT based matching package</description>
<!-- One maintainer tag required, multiple allowed, one person per tag -->
<!-- Example: -->
<!-- <maintainer email="jane.doe@example.com">Jane Doe</maintainer> -->
<maintainer email="blue@todo.todo">blue</maintainer>
<!-- One license tag required, multiple allowed, one license per tag -->
<!-- Commonly used license strings: -->
<!-- BSD, MIT, Boost Software License, GPLv2, GPLv3, LGPLv2.1, LGPLv3 -->
<license>DREAM</license>
<!-- Url tags are optional, but multiple are allowed, one per tag -->
<!-- Optional attribute type can be: website, bugtracker, or repository -->
<!-- Example: -->
<!-- <url type="website">http://wiki.ros.org/rviz_interface</url> -->
<!-- Author tags are optional, multiple are allowed, one per tag -->
<!-- Authors do not have to be maintainers, but could be -->
<!-- Example: -->
<!-- <author email="jane.doe@example.com">Jane Doe</author> -->
<!-- The *depend tags are used to specify dependencies -->
<!-- Dependencies can be catkin packages or system dependencies -->
<!-- Examples: -->
<!-- Use depend as a shortcut for packages that are both build and exec dependencies -->
<!-- <depend>roscpp</depend> -->
<!-- Note that this is equivalent to the following: -->
<!-- <build_depend>roscpp</build_depend> -->
<!-- <exec_depend>roscpp</exec_depend> -->
<!-- Use build_depend for packages you need at compile time: -->
<!-- <build_depend>message_generation</build_depend> -->
<!-- Use build_export_depend for packages you need in order to build against this package: -->
<!-- <build_export_depend>message_generation</build_export_depend> -->
<!-- Use buildtool_depend for build tool packages: -->
<!-- <buildtool_depend>catkin</buildtool_depend> -->
<!-- Use exec_depend for packages you need at runtime: -->
<!-- <exec_depend>message_runtime</exec_depend> -->
<!-- Use test_depend for packages you need only for testing: -->
<!-- <test_depend>gtest</test_depend> -->
<!-- Use doc_depend for packages you need only for building documentation: -->
<!-- <doc_depend>doxygen</doc_depend> -->
<buildtool_depend>catkin</buildtool_depend>
<build_depend>roscpp</build_depend>
<build_depend>tf</build_depend>
<build_depend>pcl_conversions</build_depend>
<build_depend>pcl_ros</build_depend>
<build_depend>sensor_msgs</build_depend>
<build_depend>libpcl-all-dev</build_depend>
<run_depend>libpcl-all</run_depend>
<run_depend>pcl_conversions</run_depend>
<run_depend>pcl_ros</run_depend>
<run_depend>roscpp</run_depend>
<run_depend>sensor_msgs</run_depend>
<run_depend>roscpp</run_depend>
<run_depend>tf</run_depend>
<!--run_depend>message_runtime</run_depend-->
<export>
</export>
</package>

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asift_matching/reference_data/train_image_005.yml~ Normal file
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Book cMo for training image 005
rows: 4
cols: 4
data:
- [-0.2314201876, -0.9583649151, 0.1672763771, 0.09835545579]
- [0.7484075924, -0.06552319445, 0.6599945353, -0.0974700766]
- [-0.6215551242, 0.2779269699, 0.7324109687, 0.5499983612]
- [0; 0; 0; 1]

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Book cMo for training image 008
rows: 4
cols: 4
data:
- [0.02063568325, -0.5653102458, -0.8246202123, 0.0403687505]
- [0.8210674394, 0.4801939642, -0.3086454546, -0.1745029756]
- [0.5704580865, -0.6706996964, 0.4740669666, 0.4630312508]
- [0, 0, 0, 1]

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Book cMo for training image 007
rows: 4
cols: 4
data:
- [-0.03609085509, -0.3148440768, 0.9484569877, 0.04713881051]
- [-0.8006242946, 0.5771011583, 0.1611055304, 0.02971868344]
- [-0.5980787482, -0.7535432704, -0.2728998912, 0.6240615433]
- [0, 0, 0, 1]

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#!/usr/bin/python
# -*- coding: utf-8 -*-
## ! DO NOT MANUALLY INVOKE THIS setup.py, USE CATKIN INSTEAD
from distutils.core import setup
from catkin_pkg.python_setup import generate_distutils_setup
# fetch values from package.xml
setup_args = generate_distutils_setup(
packages=['asift_matching'],
package_dir={'': 'src'},
)
setup(**setup_args)

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#!/usr/bin/python
# -*- coding: utf-8 -*-
## ! DO NOT MANUALLY INVOKE THIS setup.py, USE CATKIN INSTEAD
from distutils.core import setup
from catkin_pkg.python_setup import generate_distutils_setup
# fetch values from package.xml
setup_args = generate_distutils_setup(
packages=['babbling_modules_emulator'],
package_dir={'': 'src'},
)
setup(**setup_args)

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/*
* Image matching using Affine-SIFT algorithm.
* Allow to find matching keypoints, filter, compute ROI & center of an object in an image, after having built the references (with at least 1 image).
* @author : antoine.harle@etu.upmc.Fr
* Reference: J.M. Morel and G.Yu, ASIFT: A New Framework for Fully Affine Invariant Image
* Comparison, SIAM Journal on Imaging Sciences, vol. 2, issue 2, pp. 438-469, 2009.
* Reference: ASIFT online demo (You can try ASIFT with your own images online.)
* http://www.ipol.im/pub/algo/my_affine_sift/
*/
#include "ASIFT_matcher.hpp"
//Default constructor
ASIFT_matcher::ASIFT_matcher(): _nb_refs(0), _total_num_matchings(0), _resize_imgs(false), _showDebug(false), _showInfo(true)
{
default_sift_parameters(_siftParam);
}
/* Constuctor from keypoints references (.txt)
* ref_path : path to a text file with keypoints reference following the convention of saveReference function.
*/
ASIFT_matcher::ASIFT_matcher(const char* ref_path): ASIFT_matcher()
{
if(!loadReferences(ref_path))
{
std::cerr<<"Error : Failed to load references"<<std::endl;
}
}
// ASIFT_matcher::~ASIFT_matcher()
// {
// }
/*
* Add a reference image.
* image_path : path to the image file (support the same format than CImg / ImageMagick).
* num_tilts : Number of virtual tilts applied to the image. More tilts equal to better matching but slower process. Default : 1 (no tilt). Recommended : 8.
* Return true if the reference was loaded with success.
*/
bool ASIFT_matcher::addReference(const char* image_path, unsigned int num_tilts)
{
///// Read input
// float * iarr1;
// size_t w1, h1;
// if (NULL == (iarr1 = read_png_f32_gray(image_path, &w1, &h1))) {
// std::cerr << "Unable to load image file " << image_path << std::endl;
// return false;
// }
// std::vector<float> ipixels1(iarr1, iarr1 + w1 * h1);
// free(iarr1); /*memcheck*/
// cout<<"Size : "<<w1<<"/"<<h1<<" - "<<ipixels1.size()<<endl;
cimg_library::CImg<float> image;
try
{
image.assign(image_path);
}
catch(cimg_library::CImgIOException)
{
std::cerr << "Unable to load image file " << image_path << std::endl;
return false;
}
//Convert to grayscale
cimg_library::CImg<float> gray(image.width(), image.height(), 1, 1, 0);
cimg_forXY(image,x,y) {
// Separation of channels
int R = (int)image(x,y,0,0);
int G = (int)image(x,y,0,1);
int B = (int)image(x,y,0,2);
// Arithmetic addition of channels for gray
// int grayValue = (int)(0.33*R + 0.33*G + 0.33*B);
// Real weighted addition of channels for gray
int grayValueWeight = (int)(0.299*R + 0.587*G + 0.114*B);
// saving píxel values into image information
// gray(x,y,0,0) = grayValue;
gray(x,y,0,0) = grayValueWeight;
}
std::vector<float> ipixels1;
size_t w1=gray.width(), h1=gray.height();
ipixels1.assign(gray.begin(), gray.end());
if(_showInfo)
std::cout<<"Building reference from "<< image_path << std::endl;
return addReference(ipixels1, w1, h1, num_tilts);
}
/*
* Add a reference image.
* image : Gray scale image. Image size must be equal to width * height.
* w : Width of the image.
* h : Height of the image.
* num_tilts : Number of virtual tilts applied to the image. More tilts equal to better matching but slower process. Default : 1 (no tilt). Recommended : 8.
* Return true if the reference was loaded with success.
*/
bool ASIFT_matcher::addReference(const vector<float>& image, unsigned int w, unsigned int h, unsigned int num_tilts)
{
if(image.size()!=w*h)
{
cerr<<"Error : Input image size doesn't correspond with parameters"<<endl;
return false;
}
int w1=w, h1=h;
vector<float> ipixels1 = image;
///// Resize the images to area wS*hW in remaining the apsect-ratio
///// Resize if the resize flag is not set or if the flag is set unequal to 0
float wS = IM_X;
float hS = IM_Y;
float zoom1=0;
int wS1=0, hS1=0;
vector<float> ipixels1_zoom;
if(_resize_imgs)
{
if(_showInfo)
cout << "WARNING: The input image is resized to " << wS << "x" << hS << " for ASIFT. " << endl
<< " But the results will be normalized to the original image size." << endl << endl;
float InitSigma_aa = 1.6;
float fproj_p, fproj_bg;
char fproj_i;
float *fproj_x4, *fproj_y4;
int fproj_o;
fproj_o = 3;
fproj_p = 0;
fproj_i = 0;
fproj_bg = 0;
fproj_x4 = 0;
fproj_y4 = 0;
float areaS = wS * hS;
// Resize image 1
float area1 = w1 * h1;
zoom1 = sqrt(area1/areaS);
wS1 = (int) (w1 / zoom1);
hS1 = (int) (h1 / zoom1);
int fproj_sx = wS1;
int fproj_sy = hS1;
float fproj_x1 = 0;
float fproj_y1 = 0;
float fproj_x2 = wS1;
float fproj_y2 = 0;
float fproj_x3 = 0;
float fproj_y3 = hS1;
/* Anti-aliasing filtering along vertical direction */
if ( zoom1 > 1 )
{
float sigma_aa = InitSigma_aa * zoom1 / 2;
GaussianBlur1D(ipixels1,w1,h1,sigma_aa,1);
GaussianBlur1D(ipixels1,w1,h1,sigma_aa,0);
}
// simulate a tilt: subsample the image along the vertical axis by a factor of t.
ipixels1_zoom.resize(wS1*hS1);
fproj (ipixels1, ipixels1_zoom, w1, h1, &fproj_sx, &fproj_sy, &fproj_bg, &fproj_o, &fproj_p,
&fproj_i , fproj_x1 , fproj_y1 , fproj_x2 , fproj_y2 , fproj_x3 , fproj_y3, fproj_x4, fproj_y4);
}
else
{
ipixels1_zoom.resize(w1*h1);
ipixels1_zoom = ipixels1;
wS1 = w1;
hS1 = h1;
zoom1 = 1;
}
///// Compute ASIFT keypoints
asift_keypoints keys;
int num_keys = 0;
time_t tstart, tend;
tstart = time(0);
num_keys = compute_asift_keypoints(ipixels1_zoom, wS1, hS1, num_tilts, _showDebug, keys, _siftParam);
tend = time(0);
//Save data
_im_refs.push_back(ipixels1_zoom);
_size_refs.push_back(make_pair(wS1,hS1));
_zoom_refs.push_back(zoom1);
_num_keys.push_back(num_keys);
_num_tilts.push_back(num_tilts);
_keys.push_back(keys);
_nb_refs++;
if(_showInfo)
cout<<"Reference built in "<< difftime(tend, tstart) << " seconds." << endl
<<" "<< num_keys <<" ASIFT keypoints found."<< endl;
return true;
}
/*
* Perform matching between an image and the references.
* image_path : path to the image file (support the same format than CImg / ImageMagick).
* num_tilts : Number of virtual tilts applied to the image. More tilts equal to better matching but slower process. Default : 1 (no tilt). Recommended : 8.
* Return number of matching keypoints found.
*/
unsigned int ASIFT_matcher::match(const char* image_path, unsigned int num_tilts)
{
if(_nb_refs<=0)
{
std::cerr<<"ASIFT_matcher Error : Trying to match without reference"<<std::endl;
return 0;
}
///// Read input
// float * iarr1;
// size_t w1, h1;
// if (NULL == (iarr1 = read_png_f32_gray(image_path, &w1, &h1))) {
// std::cerr << "Unable to load image file " << image_path << std::endl;
// return 1;
// }
// std::vector<float> ipixels1(iarr1, iarr1 + w1 * h1);
// free(iarr1); /*memcheck*/
cimg_library::CImg<float> image;
try
{
image.assign(image_path);
}
catch(cimg_library::CImgIOException)
{
std::cerr << "Unable to load image file " << image_path << std::endl;
return 0;
}
//Convert to grayscale
cimg_library::CImg<float> gray(image.width(), image.height(), 1, 1, 0);
cimg_forXY(image,x,y) {
// Separation of channels
int R = (int)image(x,y,0,0);
int G = (int)image(x,y,0,1);
int B = (int)image(x,y,0,2);
// Arithmetic addition of channels for gray
// int grayValue = (int)(0.33*R + 0.33*G + 0.33*B);
// Real weighted addition of channels for gray
int grayValueWeight = (int)(0.299*R + 0.587*G + 0.114*B);
// saving píxel values into image information
// gray(x,y,0,0) = grayValue;
gray(x,y,0,0) = grayValueWeight;
}
vector<float> ipixels1;
size_t w1=gray.width(), h1=gray.height();
ipixels1.assign(gray.begin(), gray.end());
if(_showInfo)
std::cout<<"Matching from "<<image_path<<std::endl;
return match(ipixels1, w1, h1, num_tilts);
}
/*
* Perform matching between an image and the references.
* image : Gray scale image. Image size must be equal to width * height.
* w : Width of the image.
* h : Height of the image.
* num_tilts : Number of virtual tilts applied to the image. More tilts equal to better matching but slower process. Default : 1 (no tilt). Recommended : 8.
* Return number of matching keypoints found.
*/
unsigned int ASIFT_matcher::match(const vector<float>& image, unsigned int w, unsigned int h, unsigned int num_tilts)
{
if(image.size()!=w*h)
{
cerr<<"Error : Input image size doesn't correspond with parameters"<<endl;
return 0;
}
int w1=w, h1=h;
vector<float> ipixels1 = image;
///// Resize the images to area wS*hW in remaining the apsect-ratio
///// Resize if the resize flag is not set or if the flag is set unequal to 0
float wS = IM_X;
float hS = IM_Y;
float zoom1=0;
int wS1=0, hS1=0;
vector<float> ipixels1_zoom;
if(_resize_imgs)
{
if(_showInfo)
cout << "WARNING: The input image is resized to " << wS << "x" << hS << " for ASIFT. " << endl
<< " But the results will be normalized to the original image size." << endl << endl;
float InitSigma_aa = 1.6;
float fproj_p, fproj_bg;
char fproj_i;
float *fproj_x4, *fproj_y4;
int fproj_o;
fproj_o = 3;
fproj_p = 0;
fproj_i = 0;
fproj_bg = 0;
fproj_x4 = 0;
fproj_y4 = 0;
float areaS = wS * hS;
// Resize image 1
float area1 = w1 * h1;
zoom1 = sqrt(area1/areaS);
wS1 = (int) (w1 / zoom1);
hS1 = (int) (h1 / zoom1);
int fproj_sx = wS1;
int fproj_sy = hS1;
float fproj_x1 = 0;
float fproj_y1 = 0;
float fproj_x2 = wS1;
float fproj_y2 = 0;
float fproj_x3 = 0;
float fproj_y3 = hS1;
/* Anti-aliasing filtering along vertical direction */
if ( zoom1 > 1 )
{
float sigma_aa = InitSigma_aa * zoom1 / 2;
GaussianBlur1D(ipixels1,w1,h1,sigma_aa,1);
GaussianBlur1D(ipixels1,w1,h1,sigma_aa,0);
}
// simulate a tilt: subsample the image along the vertical axis by a factor of t.
ipixels1_zoom.resize(wS1*hS1);
fproj (ipixels1, ipixels1_zoom, w1, h1, &fproj_sx, &fproj_sy, &fproj_bg, &fproj_o, &fproj_p,
&fproj_i , fproj_x1 , fproj_y1 , fproj_x2 , fproj_y2 , fproj_x3 , fproj_y3, fproj_x4, fproj_y4);
}
else
{
ipixels1_zoom.resize(w1*h1);
ipixels1_zoom = ipixels1;
wS1 = w1;
hS1 = h1;
zoom1 = 1;
}
///// Compute ASIFT keypoints
asift_keypoints keys;
int num_keys = 0;
time_t tstart, tend;
tstart = time(0);
num_keys = compute_asift_keypoints(ipixels1_zoom, wS1, hS1, num_tilts, _showDebug, keys, _siftParam);
tend = time(0);
if(_showInfo)
cout<< "Keypoints computation accomplished in " << difftime(tend, tstart) << " seconds." << endl
<<" "<< num_keys <<" ASIFT keypoints found."<< endl;
//// Match ASIFT keypoints
_total_num_matchings=0;
for(unsigned int i = 0; i<_nb_refs;i++)
{
int num_matchings = 0;
matchingslist matchings;
if(_showInfo)
cout << "Matching the keypoints..." << endl;
tstart = time(0);
try
{
num_matchings = compute_asift_matches(num_tilts, _num_tilts[i], w, h, _size_refs[i].first, _size_refs[i].second, _showDebug, keys, _keys[i], matchings, _siftParam);
}
catch(const bad_alloc& ba)
{
cerr<<"ERROR: ASIFT_matcher::match - ";
cerr << ba.what() << endl;
}
// cout<< _keys[i].size()<< " " << _keys[i][0].size() <<" "<< _keys[i][0][0].size()<<endl;
tend = time(0);
if(_showInfo)
cout << "Keypoints matching accomplished in " << difftime(tend, tstart) << " seconds." << endl;
_num_matchings.push_back(num_matchings);
_total_num_matchings += num_matchings;
_matchings.push_back(matchings);
}
return _total_num_matchings;
}
/*
* Compute the bounding rectangle of the matching keypoints.
* Match function must have been called before and found at least one matching keypoint.
* x : X-coordinate of the upper-left point.
* y : Y-coordinate of the upper-left point.
* h : Height of the rectangle.
* w : Width of the rectangle.
* Return true if the ROI was succesfully found and arguments modified.
*/
bool ASIFT_matcher::computeROI(int& x, int& y, unsigned int& h, unsigned int& w) const
{
if(getNbMatch()==0)
{
cerr<<"Error : cannot compute ROI without matchs"<<endl;
return false;
}
pair<int,int> upLe, doRi; //UpLeft / DownRight
//Initialisation
for(unsigned int i=0;i<_matchings.size();i++)
{
if(getNbMatchs()[i]!=0)
{
upLe = make_pair(_matchings[i][0].first.x,_matchings[i][0].first.y);
doRi = make_pair(_matchings[i][0].first.x,_matchings[i][0].first.y);
}
}
//Compute ROI
for(unsigned int i=0;i<_matchings.size();i++)
{
for(unsigned int j=0;j<_matchings[i].size();j++)
{
keypoint kp = _matchings[i][j].first;
if(kp.x<upLe.first)
upLe.first = kp.x;
if(kp.y<upLe.second)
upLe.second=kp.y;
if(kp.x>doRi.first)
doRi.first=kp.x;
if(kp.y>doRi.second)
doRi.second=kp.y;
}
}
x=upLe.first; //Système de coordonée ? (devrait etre bon)
y=upLe.second;
h=doRi.second-y;
w=doRi.first-x;
return true;
}
/*
* Compute the centroid of the matching keypoints.
* Match function must have been called before and found at least one matching keypoint.
* cx : X-coordinate of the centroid.
* cy : Y-coordinate of the centroid.
* Return true if the ROI was succesfully found and arguments modified.
*/
bool ASIFT_matcher::computeCenter(int& cx, int& cy) const
{
if(getNbMatch()==0)
{
cerr<<"Error : cannot compute Center without matchs"<<endl;
return false;
}
unsigned int total_kp =0;
cx=0;cy=0;
for(unsigned int i=0;i<_matchings.size();i++)
{
for(unsigned int j=0;j<_matchings[i].size();j++)
{
keypoint kp = _matchings[i][j].first;
cx+=kp.x;
cy+=kp.y;
}
total_kp += _matchings[i].size();
}
cx/=total_kp;
cy/=total_kp;
return true;
}
/*
* Perform a standard deviation filtering on the matching keypoints.
* Match function must have been called before and found at least one matching keypoint.
* threshold : Filtering coefficient. 1-Keep 68% of the keypoints / 2-Keep 95% of the keypoints / 3-Keep 99% of the keypoints. Default : 2.
* Return true if the filtering is done.
*/
bool ASIFT_matcher::distFilter(float threshold)
{
if(_showInfo)
cout<<"filtering keypoint..."<<endl;
if(getNbMatch()==0)
{
cerr<<"Error : cannot filter points without matchs"<<endl;
return false;
}
//Compute standard deviation
int cx, cy;
unsigned int total_kp =0;
unsigned int euc_dist, dist_avg =0, dist_var=0, std_dev;
vector< vector< int > > kp_euc_dist;
if(computeCenter(cx,cy))
{
// cout<<"Center : "<<cx<<" / "<<cy<<endl;
//Compute means/average distance to center + euclidian distances to center for each keypoint
for(unsigned int i=0;i<_matchings.size();i++)
{
vector<int> temp_euc_dist;
for(unsigned int j=0;j<_matchings[i].size();j++)
{
keypoint kp = _matchings[i][j].first;
euc_dist =sqrt((kp.x-cx)*(kp.x-cx)+(kp.y-cy)+(kp.y-cy));
dist_avg+=euc_dist;
temp_euc_dist.push_back(euc_dist);
}
total_kp += _matchings[i].size();
kp_euc_dist.push_back(temp_euc_dist);
}
dist_avg/=total_kp;
// cout<<"Dist avg: "<<dist_avg<<endl;
//Compute variance
for(unsigned int i=0;i<_matchings.size();i++)
{
for(unsigned int j=0;j<_matchings[i].size();j++)
{
euc_dist =kp_euc_dist[i][j];
dist_var+=(euc_dist-dist_avg)*(euc_dist-dist_avg);
}
}
dist_var/=total_kp;
//Compute standard deviation
std_dev=sqrt(dist_var);
// cout<<"Standard Deviation : "<<std_dev<<endl;
//Filter
vector< matchingslist > filtered_match;
for(unsigned int i=0;i<_matchings.size();i++)
{
matchingslist new_match;
for(unsigned int j=0;j<_matchings[i].size();j++)
{
euc_dist =kp_euc_dist[i][j];
if(euc_dist<dist_avg+threshold*std_dev) //Filtering Condition
{
new_match.push_back(_matchings[i][j]);
}
}
filtered_match.push_back(new_match);
_num_matchings[i]=new_match.size();
}
//Update number of remaining points
_total_num_matchings = 0;
for(unsigned int i=0; i<_num_matchings.size();i++)
_total_num_matchings+=_num_matchings[i];
//Save filtered matchs
_matchings = filtered_match;
return true;
}
return false;
}
/*
* Save reference data necessary for the matching.
* ref_path : path were the reference data will be saved (.txt).
* Follow a modified convention of David Lowe (SIFT keypoints) :
* - Number of reference.
* - Number of keypoints in the reference / Length of the descriptors (128) / Width of the reference / Height of the reference / Number of tilts.
* -
* - Keypoints (row, col, scale, orientation, desciptor (128 integers)).
* Return true if the reference data was successfully saved.
*/
bool ASIFT_matcher::saveReferences(const char* ref_path) const
{
// Write all the keypoints (row, col, scale, orientation, desciptor (128 integers))
std::ofstream file_key1(ref_path);
if (file_key1.is_open())
{
file_key1<<_nb_refs<<" "<<std::endl;
for(unsigned int j=0; j<_keys.size();j++)
{
asift_keypoints kps =_keys[j];
// Follow the same convention of David Lowe:
// the first line contains the number of keypoints and the length of the desciptors (128)
// Added number of tilts
// Added sizes (* zoom_ref useful ?)
file_key1 << _num_keys[j] << " " << VecLength << " " <<_size_refs[j].first*_zoom_refs[j]<<" "<<_size_refs[j].second*_zoom_refs[j]<<" "<<_num_tilts[j] << " "<<std::endl; //<<_num_tilts[j] << " "
for (int tt = 0; tt < (int) kps.size(); tt++) //kps.size = num_tilt
{
file_key1<<kps[tt].size()<<" "<<std::endl;
for (int rr = 0; rr < (int) kps[tt].size(); rr++)
{
file_key1<<kps[tt][rr].size()<<" "<<std::endl;
keypointslist::iterator ptr = kps[tt][rr].begin();
for(int i=0; i < (int) kps[tt][rr].size(); i++, ptr++)
{
file_key1 << _zoom_refs[j]*ptr->x << " " << _zoom_refs[j]*ptr->y << " " << _zoom_refs[j]*ptr->scale << " " << ptr->angle;
for (int ii = 0; ii < (int) VecLength; ii++)
{
file_key1 << " " << ptr->vec[ii];
}
file_key1 << std::endl;
}
}
}
// file_key1<<std::endl;
}
}
else
{
std::cerr << "Unable to open the file :"<<ref_path;
return false;
}
file_key1.close();
return true;
}
/*
* Load reference data necessary for the matching.
* ref_path : path from were the reference data will be loaded (.txt).
* Follow a modified convention of David Lowe (SIFT keypoints) :
* - Number of reference.
* - Number of keypoints in the reference / Length of the descriptors (128) / Width of the reference / Height of the reference / Number of tilts.
* -
* - Keypoints (row, col, scale, orientation, desciptor (128 integers)).
* Return true if the reference data was successfully loaded.
*/
bool ASIFT_matcher::loadReferences(const char* ref_path)
{
std::ifstream ref_file(ref_path);
std::string line, tmp;
std::stringstream iss;
pair<int,int> img_size_tmp;
if (ref_file.is_open())
{
std::getline(ref_file, line);
std::string::size_type sz;
// _nb_refs = std::stoi(line, &sz); //C++11
_nb_refs = atoi(line.c_str());
_keys = std::vector<asift_keypoints>(_nb_refs);
_num_keys = std::vector< int >(_nb_refs);
_size_refs= std::vector< pair<int,int> >(_nb_refs);
_num_tilts = std::vector< int >(_nb_refs);
_zoom_refs = std::vector<float>(_nb_refs,1);
for(unsigned int i = 0; i<_nb_refs;i++)
{
std::getline(ref_file, line);
std::stringstream iss(line);
std::getline(iss,tmp,' ');
_num_keys[i]=atoi(tmp.c_str());
std::getline(iss,tmp,' ');
if(VecLength!=atoi(tmp.c_str()))
{
std::cerr<<"Error VecLength doesn't correspond..."<<std::endl;
return false;
}
std::getline(iss,tmp,' ');
img_size_tmp.first=atoi(tmp.c_str());
std::getline(iss,tmp,' ');
img_size_tmp.second=atoi(tmp.c_str());
_size_refs[i]=img_size_tmp;
std::getline(iss,tmp,' ');
_num_tilts[i]=atoi(tmp.c_str());
asift_keypoints nakp(_num_tilts[i]);
for(unsigned int j =0; j<(unsigned int)_num_tilts[i];j++)
{
std::getline(ref_file, line);
std::stringstream iss(line);
int veclist_size_tmp = atoi(line.c_str());
std::vector< keypointslist > vkpl(veclist_size_tmp);
for(unsigned int k =0; k<(unsigned int)veclist_size_tmp;k++)
{
std::getline(ref_file, line);
std::stringstream iss(line);
int list_size_tmp = atoi(line.c_str());
keypointslist list(list_size_tmp);
for(unsigned int l =0; l<(unsigned int)list_size_tmp;l++)
{
keypoint nkp;
std::getline(ref_file, line);
std::stringstream iss(line);
// if(j==0)
// cout<<line<<endl;
std::getline(iss,tmp,' ');
// std::stof(nb_ref, nkp.x); //C++11
nkp.x=atof(tmp.c_str());
// if(j<5)
// cout<<"x : "<<nkp.x<<endl;
std::getline(iss,tmp,' ');
nkp.y=atof(tmp.c_str());
// if(j<5)
// cout<<"y : "<<nkp.y<<endl;
std::getline(iss,tmp,' ');
nkp.scale=atof(tmp.c_str());
// if(j<5)
// cout<<"Scale : "<<nkp.scale<<endl;
std::getline(iss,tmp,' ');
nkp.angle=atof(tmp.c_str());
for(unsigned int m=0; m<(int) VecLength; m++)
{
std::getline(iss,tmp,' ');
nkp.vec[m]=atof(tmp.c_str());
}
list[l]=nkp;
}
vkpl[k]=list;
}
nakp[j]=vkpl;
}
_keys[i]=nakp;
// std::getline(ref_file, line);
}
}
else
{
std::cerr << "Unable to open the file :"<<ref_path;
return false;
}
ref_file.close();
return true;
}
// bool ASIFT_matcher::saveReferences(const char* ref_path) const
// {
// // Write all the keypoints (row, col, scale, orientation, desciptor (128 integers))
// std::ofstream file_key1(ref_path);
// if (file_key1.is_open())
// {
// file_key1<<_nb_refs<<" "<<std::endl;
// for(unsigned int j=0; j<_keys.size();j++)
// {
// asift_keypoints kps =_keys[j];
// // Follow the same convention of David Lowe:
// // the first line contains the number of keypoints and the length of the desciptors (128)
// // Added number of tilts
// // Added sizes
// file_key1 << _num_keys[j] << " " << VecLength << " " <<_size_refs[j].first<<" "<<_size_refs[j].second<<" "<<std::endl; //<<_num_tilts[j] << " "
// for (int tt = 0; tt < (int) kps.size(); tt++) //kps.size = num_tilt
// {
// for (int rr = 0; rr < (int) kps[tt].size(); rr++)
// {
// keypointslist::iterator ptr = kps[tt][rr].begin();
// for(int i=0; i < (int) kps[tt][rr].size(); i++, ptr++)
// {
// file_key1 << _zoom_refs[j]*ptr->x << " " << _zoom_refs[j]*ptr->y << " " << _zoom_refs[j]*ptr->scale << " " << ptr->angle;
// for (int ii = 0; ii < (int) VecLength; ii++)
// {
// file_key1 << " " << ptr->vec[ii];
// }
// file_key1 << std::endl;
// }
// }
// }
// // file_key1<<std::endl;
// }
// }
// else
// {
// std::cerr << "Unable to open the file :"<<ref_path;
// return false;
// }
// file_key1.close();
// return true;
// }
//Load reference data necessary for the matching
//Doesn't load references images or Sift parameters
//TODO : split data between different tilts
// bool ASIFT_matcher::loadReferences(const char* ref_path)
// {
// std::ifstream ref_file(ref_path);
// std::string line, tmp;
// std::stringstream iss;
// pair<int,int> size_tmp;
// if (ref_file.is_open())
// {
// std::getline(ref_file, line);
// std::string::size_type sz;
// // _nb_refs = std::stoi(line, &sz); //C++11
// _nb_refs = atoi(line.c_str());
// _keys = std::vector<asift_keypoints>(_nb_refs);
// _num_keys = std::vector< int >(_nb_refs);
// _size_refs= std::vector< pair<int,int> >(_nb_refs);
// _num_tilts = std::vector< int >(_nb_refs,1);
// _zoom_refs = std::vector<float>(_nb_refs,1);
// for(unsigned int i = 0; i<_nb_refs;i++)
// {
// std::getline(ref_file, line);
// std::stringstream iss(line);
// std::getline(iss,tmp,' ');
// _num_keys[i]=atoi(tmp.c_str());
// std::getline(iss,tmp,' ');
// if(VecLength!=atoi(tmp.c_str()))
// {
// std::cerr<<"Error VecLength doesn't correspond..."<<std::endl;
// return false;
// }
// std::getline(iss,tmp,' ');
// size_tmp.first=atoi(tmp.c_str());
// std::getline(iss,tmp,' ');
// size_tmp.second=atoi(tmp.c_str());
// _size_refs[i]=size_tmp;
// std::getline(iss,tmp,' ');
// _num_tilts[i]=atoi(tmp.c_str());
// keypointslist list;
// for(unsigned int j =0; j<(unsigned int)_num_keys[i];j++)
// {
// keypoint nkp;
// std::getline(ref_file, line);
// std::stringstream iss(line);
// // if(j==0)
// // cout<<line<<endl;
// std::getline(iss,tmp,' ');
// // std::stof(nb_ref, nkp.x); //C++11
// nkp.x=atof(tmp.c_str());
// // if(j<5)
// // cout<<"x : "<<nkp.x<<endl;
// std::getline(iss,tmp,' ');
// nkp.y=atof(tmp.c_str());
// // if(j<5)
// // cout<<"y : "<<nkp.y<<endl;
// std::getline(iss,tmp,' ');
// nkp.scale=atof(tmp.c_str());
// // if(j<5)
// // cout<<"Scale : "<<nkp.scale<<endl;
// std::getline(iss,tmp,' ');
// nkp.angle=atof(tmp.c_str());
// for(unsigned int k=0; k<(int) VecLength; k++)
// {
// std::getline(iss,tmp,' ');
// nkp.vec[k]=atof(tmp.c_str());
// }
// list.push_back(nkp);
// // if(j<5)
// // {
// // cout<<"x : "<<list[j].x<<endl;
// // cout<<"y : "<<list[j].y<<endl;
// // cout<<"Scale : "<<list[j].scale<<endl;
// // }
// }
// std::vector< keypointslist > vkps(1,list);
// asift_keypoints akps(1,vkps);
// _keys[i]=akps;
// // std::getline(ref_file, line);
// }
// }
// else
// {
// std::cerr << "Unable to open the file :"<<ref_path;
// return false;
// }
// ref_file.close();
// return true;
// }
/*
* Assignation operator.
* m : ASIFT matcher object to copy.
*/
ASIFT_matcher& ASIFT_matcher::operator=(const ASIFT_matcher& m)
{
_nb_refs=m.getNbRef();
_im_refs=m.getRefImgs();
_size_refs=m.getSizeRef();
_zoom_refs=m.getZoomRef();
_num_keys=m.getNumKeys();
_num_tilts=m.getNumTilts();
_keys=m.getKeys();
_total_num_matchings=m.getNbMatch();
_num_matchings=getNbMatchs();
_matchings=m.getMatch();
_siftParam=m.getSiftPar();
_resize_imgs=m.isResizingImg();
_showDebug=m.isShowingDebug();
return *this;
}
//Debugging function : print content form the ASIFT matcher.
void ASIFT_matcher::print() const
{
for(unsigned int i=0; i< _keys.size();i++)
{
cout<<"Ref :"<<i<<" - Nb tilts : "<<_num_tilts[i]<< " - Nb keys : "<< _num_keys[i]<<endl;
for(unsigned int j=0; j<_keys[i].size();j++)
{
cout<<" Tilt "<<j<<" - size :"<<_keys[i][j].size()<<endl;
for(unsigned int k=0; k<_keys[i][j].size();k++)
{
cout<<" "<<k<<" - size :"<<_keys[i][j][k].size()<<endl;
double sx=0,sy=0,ss=0,sa=0, sv=0;
for(unsigned int l=0; l<_keys[i][j][k].size();l++)
{
sx+=_keys[i][j][k][l].x;
sy+=_keys[i][j][k][l].y;
ss+=_keys[i][j][k][l].scale;
sa+=_keys[i][j][k][l].angle;
for(unsigned int v=0;v<VecLength;v++)
{
sv+=_keys[i][j][k][l].vec[v];
}
// cout<<" "<<sx<<"-"<<sy<<"-"<<ss<<"-"<<sa<<"-"<<sv<<endl;
}
cout<<" "<<sx<<"-"<<sy<<"-"<<ss<<"-"<<sa<<"-"<<sv<<endl;
}
}
}
}

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/*
* Image matching using Affine-SIFT algorithm.
* Allow to find matching keypoints, filter, compute ROI & center of an object in an image, after having built the references (with at least 1 image).
* @author : antoine.harle@etu.upmc.Fr
* Reference: J.M. Morel and G.Yu, ASIFT: A New Framework for Fully Affine Invariant Image
* Comparison, SIAM Journal on Imaging Sciences, vol. 2, issue 2, pp. 438-469, 2009.
* Reference: ASIFT online demo (You can try ASIFT with your own images online.)
* http://www.ipol.im/pub/algo/my_affine_sift/
*/
#ifndef ASIFTMATCHER_HPP
#define ASIFTMATCHER_HPP
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <time.h>
#include <vector>
#include <sstream>
#ifdef _OPENMP
#include <omp.h>
#endif
#include "demo_lib_sift.h"
// #include "io_png/io_png.h"
#include "library.h"
#include "frot.h"
#include "fproj.h"
#include "compute_asift_keypoints.h"
#include "compute_asift_matches.h"
#include "CImg.h" //Need ImageMagick package
# define IM_X 800
# define IM_Y 600
using namespace std;
typedef vector< vector< keypointslist > > asift_keypoints;
class ASIFT_matcher
{
public:
ASIFT_matcher();//Default constructor.
ASIFT_matcher(const char* ref_path); //Constuctor from keypoints references (.txt).
ASIFT_matcher(const ASIFT_matcher& matcher) { *this = matcher;} //Copy constructor.
// virtual ~ASIFT_matcher();
bool addReference(const char* image_path, unsigned int num_tilts =1); //Add a reference image.
bool addReference(const vector<float>& image, unsigned int w, unsigned int h, unsigned int num_tilts =1); //Add a reference image.
unsigned int match(const char* image_path, unsigned int num_tilts =1); //Perform matching between an image and the references.
unsigned int match(const vector<float>& image, unsigned int w, unsigned int h, unsigned int num_tilts =1); //Perform matching between an image and the references.
bool computeROI(int& x, int& y, unsigned int& h, unsigned int& w) const; //Compute the bounding rectangle of the mathcing keypoints.
bool computeCenter(int& cx, int& cy) const; //Compute the centroid of the matching keypoints.
bool distFilter(float threshold =2); //Perform a standard deviation filtering on the matching keypoints.
bool saveReferences(const char* ref_path) const; //Save reference data necessary for the matching.
bool loadReferences(const char* ref_path); //Load reference data necessary for the matching.
ASIFT_matcher& operator=(const ASIFT_matcher& m); //Assignation operator.
//Setter/Getter
unsigned int getNbRef() const{ return _nb_refs;}
const vector< vector< float > >& getRefImgs() const{ return _im_refs;}
const vector< pair<int,int> >& getSizeRef() const{ return _size_refs;}
const vector<float>& getZoomRef() const{ return _zoom_refs;}
const std::vector<int>& getNumKeys() const{ return _num_keys;}
const std::vector<int>& getNumTilts() const{ return _num_tilts;}
const std::vector< asift_keypoints >& getKeys() const{ return _keys;}
const vector < unsigned int >& getNbMatchs() const{ return _num_matchings;}
unsigned int getNbMatch() const{ return _total_num_matchings;}
const vector< matchingslist >& getMatch() const{ return _matchings;}
vector< matchingslist >& getMatch(){ return _matchings;}
const siftPar& getSiftPar() const{ return _siftParam;}
void setSiftPar(const siftPar &newSiftPar){ _siftParam = newSiftPar;}
bool isResizingImg() const{ return _resize_imgs;}
void resizeImg(bool resize_imgs){ _resize_imgs=resize_imgs;}
bool isShowingDebug() const{ return _showDebug;}
void showDebug(bool showDebug){ _showDebug=showDebug;}
bool isShowingInfo() const{ return _showInfo;}
void showInfo(bool showInfo){ _showInfo=showInfo;}
void print() const; //Debugging function : print content form the ASIFT matcher.
protected:
//Reference Images
unsigned int _nb_refs;//Number of reference images
vector< vector< float > > _im_refs; //Reference images used for matching
vector< pair<int,int> > _size_refs; //Width/Height
vector<float> _zoom_refs; //Zoom coeffs
//Reference ASIFT Keypoints
vector< int > _num_keys; //Number of keypoint/reference
vector< int > _num_tilts; //Number of tilts/reference (Speed VS Precision)
vector< asift_keypoints > _keys; //Reference keypoints
//Matchs
unsigned int _total_num_matchings; //Number of matching keypoints.
vector < unsigned int > _num_matchings; //Number of match/reference
vector< matchingslist > _matchings; //Matching keypoints
siftPar _siftParam; //SIFT parameters
//Flags
bool _resize_imgs;//Resize images to IM_X/IM_Y ?
bool _showDebug;//Show debugging messages ?
bool _showInfo; //Show info messages
};
#endif

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/*
* ROS wrapper for the ASIFT_matcher object.
* Track an object described in the references in a RGBD stream and publish it's center.
* @author : antoine.harle@etu.upmc.Fr
* @see : ASIFT_matcher.cpp/.hpp, asift_match.launch
*/
#include "ROS_matcher.hpp"
//TODO : Regler le problème du chergements des images
ROS_matcher::ROS_matcher(): _status(MATCHER_STATUS_WAITING_INIT)
{
std::string center_topic, image_topic, pointcloud_topic, reference_txt_path, reference_path;
std::vector<std::string> reference_data_paths;
//Load Param
_nh.param<std::string>("tracked_object", tracked_object, "Object");
_nh.param<int>("num_tilt", _num_tilt, 8);
_nh.param<float>("std_dev_filter_coeff", _filter_coeff, 0);
_nh.param<std::string>("object_center_topic", center_topic,"/ASIFT_matcher/object_center");
_nh.param<std::string>("image_topic", image_topic,"/camera/rgb/image_raw");
_nh.param<std::string>("pointcloud_topic", pointcloud_topic,"/camera/depth_registered/points");
//Publisher
_center_pub = _nh.advertise<rviz_interface::NamedPoint>(center_topic, 1);
//Subscriber
// info_sub = new message_filters::Subscriber<sensor_msgs::CameraInfo>(_nh, "camera/rgb/camera_info", 1);
_image_sub = new message_filters::Subscriber<sensor_msgs::Image>(_nh, image_topic, 1);
_pointcloud_sub= new message_filters::Subscriber<sensor_msgs::PointCloud2>(_nh, pointcloud_topic, 1);
Timesync = new message_filters::Synchronizer<MySyncPolicy>(MySyncPolicy(2), *_image_sub, *_pointcloud_sub);
Timesync-> registerCallback(boost::bind(&ROS_matcher::cameraCallback, this, _1, _2));
matcher.showInfo(false);
//Load References
_nh.param<std::string>("reference_path", reference_path); //BUG sur reference path ?
ROS_INFO("Ref path : %s", reference_path.c_str());
if(_nh.getParam("reference_txt_path", reference_txt_path) && matcher.loadReferences(reference_txt_path.c_str()))
{
ROS_INFO("Loaded reference from %s",reference_txt_path.c_str());
}
else if(_nh.getParam("reference_data", reference_data_paths))
{
for(unsigned int i=0; i<reference_data_paths.size(); i++)
{
matcher.addReference((reference_path+reference_data_paths[i]).c_str(),_num_tilt);
ROS_INFO("Loaded reference from %s",(reference_path+reference_data_paths[i]).c_str());
}
}
else
{
ROS_WARN("No reference data to initialize matcher.");
}
if(matcher.getNbRef()>0)
{
_status = MATCHER_STATUS_IDLE;
ROS_INFO("Matcher Ready ! (%d references)",matcher.getNbRef());
}
}
ROS_matcher::~ROS_matcher()
{
// delete info_sub;
delete _image_sub;
delete _pointcloud_sub;
// delete Timesync;
}
void ROS_matcher::cameraCallback(const sensor_msgs::Image::ConstPtr& image_msg, const sensor_msgs::PointCloud2::ConstPtr& pointcloud_msg)
{
// ROS_INFO("Callback");
if(_status == MATCHER_STATUS_IDLE)
{
unsigned int width = image_msg->width, height = image_msg->height;
std::vector<float> image(height*width);
// ROS_INFO("Image size : %d - %d", height, width);
//Conversion en niveau de gris
if(image_msg->encoding == "yuv422")
{
for(unsigned int i=0; i<height*width; i++)
{
image[i]=image_msg->data[2*i+1];
}
}
else
{
ROS_WARN("Encoding doesn't correspond to yuv422");
return;
}
//Matching
if(matcher.getNbRef()<1)
{
ROS_INFO("Building reference...");
matcher.addReference(image, width, height, _num_tilt);
}
else
{
// ROS_INFO("Matching...");
_status = MATCHER_STATUS_PROCESSING;
ROS_INFO("Matching...");
int nb_match=0;
nb_match = matcher.match(image, width, height, _num_tilt);
ROS_INFO("Match : %d", nb_match);
//Recherche du point 3D
if(nb_match>0)
{
//Publish 3D point
int cx, cy;
// geometry_msgs::PointStamped center_msg;
rviz_interface::NamedPoint center_msg;
matcher.distFilter(_filter_coeff);
ROS_INFO("Filtered points : %d", matcher.getNbMatch());
if(matcher.computeCenter(cx, cy))
{
//Conversions des donnée d'entrée en PCL
pcl::PCLPointCloud2 pcl_pc2;
pcl_conversions::toPCL(*pointcloud_msg,pcl_pc2);
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);
pcl::fromPCLPointCloud2(pcl_pc2,*cloud);
// ROS_INFO("PointCloud Size : %d / %d", cloud->height, cloud->width);
ROS_INFO("Center %f / %f",(float)cx/width,(float)cy/height);
if(cloud->isOrganized())
{
pcl::PointXYZ center = cloud->at(cx,cy);
if(!isnan(center.x) && !isnan(center.y) && !isnan(center.z))
{
center_msg.name = tracked_object;
center_msg.header.frame_id = image_msg->header.frame_id;
center_msg.point.x=center.x;
center_msg.point.y=center.y;
center_msg.point.z=center.z;
_center_pub.publish(center_msg);
}
else
{
ROS_WARN("NaN Values");
}
}
else
{
ROS_WARN("Pointcloud isn't organized");
}
}
else
{
ROS_WARN("Failed to compute center");
}
}
_status = MATCHER_STATUS_IDLE;
}
}
else
{
ROS_INFO("Matcher not ready to process");
}
}

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/*
* ROS wrapper for the ASIFT_matcher object.
* Track an object described in the references in a RGBD stream and publish it's center.
* @author : antoine.harle@etu.upmc.Fr
* @see : ASIFT_matcher.cpp/.hpp, asift_match.launch
*/
#ifndef ROSMATCHER_HPP
#define ROSMATCHER_HPP
#include <ros/ros.h>
#include <tf/tf.h>
#include <message_filters/subscriber.h>
#include <message_filters/synchronizer.h>
#include <message_filters/sync_policies/approximate_time.h>
#include <sensor_msgs/CameraInfo.h>
#include <sensor_msgs/PointCloud2.h>
#include <sensor_msgs/Image.h>
#include <geometry_msgs/PointStamped.h>
#include <rviz_interface/NamedPoint.h>
#include <pcl_conversions/pcl_conversions.h>
#include <pcl/impl/point_types.hpp>
#include "ASIFT_matcher.hpp"
typedef message_filters::sync_policies::ApproximateTime<sensor_msgs::Image, sensor_msgs::PointCloud2> MySyncPolicy;
enum MATCHER_STATUS{
MATCHER_STATUS_IDLE=0,
MATCHER_STATUS_PROCESSING,
MATCHER_STATUS_WAITING_INIT};
class ROS_matcher
{
protected:
ros::NodeHandle _nh;
//Publisher ROS
ros::Publisher _center_pub;
//Subscriber ROS
// message_filters::Subscriber<sensor_msgs::CameraInfo>* info_sub;
message_filters::Subscriber<sensor_msgs::Image>* _image_sub;
message_filters::Subscriber<sensor_msgs::PointCloud2>* _pointcloud_sub;
message_filters::Synchronizer<MySyncPolicy>* Timesync;
//Matcher
int _num_tilt; //Number of tilts
float _filter_coeff; //Filter parameter
ASIFT_matcher matcher; //Matcher
MATCHER_STATUS _status; //Matcher status
std::string tracked_object; //Name of the tracked object.
public:
ROS_matcher();
~ROS_matcher();
void cameraCallback(const sensor_msgs::Image::ConstPtr& image_msg, const sensor_msgs::PointCloud2::ConstPtr& pointcloud_msg);
};
#endif

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#include "ROS_matcher.hpp"
int main(int argc, char *argv[])
{
ros::init(argc, argv, "ROS_matcher");
ROS_matcher ros_matcher;
ros::spin();
return 0;
}

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// Copyright (c) 2008-2011, Guoshen Yu <yu@cmap.polytechnique.fr>
// Copyright (c) 2008-2011, Jean-Michel Morel <morel@cmla.ens-cachan.fr>
//
// WARNING:
// This file implements an algorithm possibly linked to the patent
//
// Jean-Michel Morel and Guoshen Yu, Method and device for the invariant
// affine recognition recognition of shapes (WO/2009/150361), patent pending.
//
// This file is made available for the exclusive aim of serving as
// scientific tool to verify of the soundness and
// completeness of the algorithm description. Compilation,
// execution and redistribution of this file may violate exclusive
// patents rights in certain countries.
// The situation being different for every country and changing
// over time, it is your responsibility to determine which patent
// rights restrictions apply to you before you compile, use,
// modify, or redistribute this file. A patent lawyer is qualified
// to make this determination.
// If and only if they don't conflict with any patent terms, you
// can benefit from the following license terms attached to this
// file.
//
// This program is provided for scientific and educational only:
// you can use and/or modify it for these purposes, but you are
// not allowed to redistribute this work or derivative works in
// source or executable form. A license must be obtained from the
// patent right holders for any other use.
//
//
//*------------------------ compute_asift_keypoints -------------------------*/
// Compute the ASIFT keypoints on the input image.
//
// Please report bugs and/or send comments to Guoshen Yu yu@cmap.polytechnique.fr
//
// Reference: J.M. Morel and G.Yu, ASIFT: A New Framework for Fully Affine Invariant Image
// Comparison, SIAM Journal on Imaging Sciences, vol. 2, issue 2, pp. 438-469, 2009.
// Reference: ASIFT online demo (You can try ASIFT with your own images online.)
// http://www.ipol.im/pub/algo/my_affine_sift/
/*---------------------------------------------------------------------------*/
#include <assert.h>
#include <stdlib.h>
#include <stdio.h>
#include <math.h>
#include <time.h>
#include "compute_asift_keypoints.h"
#ifdef _OPENMP
#include <omp.h>
#endif
#define ABS(x) (((x) > 0) ? (x) : (-(x)))
/* InitSigma gives the amount of smoothing applied to the image at the
first level of each octave. In effect, this determines the sampling
needed in the image domain relative to amount of smoothing. Good
values determined experimentally are in the range 1.2 to 1.8.
*/
/* float InitSigma_aa = 1.0;*/
static float InitSigma_aa = 1.6;
#define round(x) ((x)>=0?(long)((x)+0.5):(long)((x)-0.5))
/* Gaussian convolution kernels are truncated at this many sigmas from
the center. While it is more efficient to keep this value small,
experiments show that for consistent scale-space analysis it needs
a value of about 3.0, at which point the Gaussian has fallen to
only 1% of its central value. A value of 2.0 greatly reduces
keypoint consistency, and a value of 4.0 is better than 3.0.
*/
const float GaussTruncate1 = 4.0;
/* --------------------------- Blur image --------------------------- */
/* Same as ConvBuffer, but implemented with loop unrolling for increased
speed. This is the most time intensive routine in keypoint detection,
so deserves careful attention to efficiency. Loop unrolling simply
sums 5 multiplications at a time to allow the compiler to schedule
operations better and avoid loop overhead. This almost triples
speed of previous version on a Pentium with gcc.
*/
void ConvBufferFast(float *buffer, float *kernel, int rsize, int ksize)
{
int i;
float *bp, *kp, *endkp;
float sum;
for (i = 0; i < rsize; i++) {
sum = 0.0;
bp = &buffer[i];
kp = &kernel[0];
endkp = &kernel[ksize];
/* Loop unrolling: do 5 multiplications at a time. */
// while (kp + 4 < endkp) {
// sum += (double) bp[0] * (double) kp[0] + (double) bp[1] * (double) kp[1] + (double) bp[2] * (double) kp[2] +
// (double) bp[3] * (double) kp[3] + (double) bp[4] * (double) kp[4];
// bp += 5;
// kp += 5;
// }
// /* Do 2 multiplications at a time on remaining items. */
// while (kp + 1 < endkp) {
// sum += (double) bp[0] * (double) kp[0] + (double) bp[1] * (double) kp[1];
// bp += 2;
// kp += 2;
// }
// /* Finish last one if needed. */
// if (kp < endkp) {
// sum += (double) *bp * (double) *kp;
// }
while (kp < endkp) {
sum += *bp++ * *kp++;
}
buffer[i] = sum;
}
}
/* Convolve image with the 1-D kernel vector along image rows. This
is designed to be as efficient as possible. Pixels outside the
image are set to the value of the closest image pixel.
*/
void ConvHorizontal(vector<float>& image, int width, int height, float *kernel, int ksize)
{
int rows, cols, r, c, i, halfsize;
float buffer[4000];
vector<float> pixels(width*height);
rows = height;
cols = width;
halfsize = ksize / 2;
pixels = image;
assert(cols + ksize < 4000);
for (r = 0; r < rows; r++) {
/* Copy the row into buffer with pixels at ends replicated for
half the mask size. This avoids need to check for ends
within inner loop. */
for (i = 0; i < halfsize; i++)
buffer[i] = pixels[r*cols];
for (i = 0; i < cols; i++)
buffer[halfsize + i] = pixels[r*cols+i];
for (i = 0; i < halfsize; i++)
buffer[halfsize + cols + i] = pixels[r*cols+cols-1];
ConvBufferFast(buffer, kernel, cols, ksize);
for (c = 0; c < cols; c++)
pixels[r*cols+c] = buffer[c];
}
image = pixels;
}
/* Same as ConvHorizontal, but apply to vertical columns of image.
*/
void ConvVertical(vector<float>& image, int width, int height, float *kernel, int ksize)
{
int rows, cols, r, c, i, halfsize;
float buffer[4000];
vector<float> pixels(width*height);
rows = height;
cols = width;
halfsize = ksize / 2;
pixels = image;
assert(rows + ksize < 4000);
for (c = 0; c < cols; c++) {
for (i = 0; i < halfsize; i++)
buffer[i] = pixels[c];
for (i = 0; i < rows; i++)
buffer[halfsize + i] = pixels[i*cols+c];
for (i = 0; i < halfsize; i++)
buffer[halfsize + rows + i] = pixels[(rows - 1)*cols+c];
ConvBufferFast(buffer, kernel, rows, ksize);
for (r = 0; r < rows; r++)
pixels[r*cols+c] = buffer[r];
}
image = pixels;
}
/* 1D Convolve image with a Gaussian of width sigma and store result back
in image. This routine creates the Gaussian kernel, and then applies
it in horizontal (flag_dir=0) OR vertical directions (flag_dir!=0).
*/
void GaussianBlur1D(vector<float>& image, int width, int height, float sigma, int flag_dir)
{
float x, kernel[100], sum = 0.0;
int ksize, i;
/* The Gaussian kernel is truncated at GaussTruncate sigmas from
center. The kernel size should be odd.
*/
ksize = (int)(2.0 * GaussTruncate1 * sigma + 1.0);
ksize = MAX(3, ksize); /* Kernel must be at least 3. */
if (ksize % 2 == 0) /* Make kernel size odd. */
ksize++;
assert(ksize < 100);
/* Fill in kernel values. */
for (i = 0; i <= ksize; i++) {
x = i - ksize / 2;
kernel[i] = exp(- x * x / (2.0 * sigma * sigma));
sum += kernel[i];
}
/* Normalize kernel values to sum to 1.0. */
for (i = 0; i < ksize; i++)
kernel[i] /= sum;
if (flag_dir == 0)
{
ConvHorizontal(image, width, height, kernel, ksize);
}
else
{
ConvVertical(image, width, height, kernel, ksize);
}
}
void compensate_affine_coor1(float *x0, float *y0, int w1, int h1, float t1, float t2, float Rtheta)
{
float x_ori, y_ori;
float x_tmp, y_tmp;
float x1 = *x0;
float y1 = *y0;
Rtheta = Rtheta*PI/180;
if ( Rtheta <= PI/2 )
{
x_ori = 0;
y_ori = w1 * sin(Rtheta) / t1;
}
else
{
x_ori = -w1 * cos(Rtheta) / t2;
y_ori = ( w1 * sin(Rtheta) + h1 * sin(Rtheta-PI/2) ) / t1;
}
float sin_Rtheta = sin(Rtheta);
float cos_Rtheta = cos(Rtheta);
/* project the coordinates of im1 to original image before tilt-rotation transform */
/* Get the coordinates with respect to the 'origin' of the original image before transform */
x1 = x1 - x_ori;
y1 = y1 - y_ori;
/* Invert tilt */
x1 = x1 * t2;
y1 = y1 * t1;
/* Invert rotation (Note that the y direction (vertical) is inverse to the usual concention. Hence Rtheta instead of -Rtheta to inverse the rotation.) */
x_tmp = cos_Rtheta*x1 - sin_Rtheta*y1;
y_tmp = sin_Rtheta*x1 + cos_Rtheta*y1;
x1 = x_tmp;
y1 = y_tmp;
*x0 = x1;
*y0 = y1;
}
/* -------------- MAIN FUNCTION ---------------------- */
int compute_asift_keypoints(vector<float>& image, int width, int height, int num_of_tilts, int verb, vector< vector< keypointslist > >& keys_all, siftPar &siftparameters)
// Compute ASIFT keypoints in the input image.
// Input:
// image: input image
// width, height: width and height of the input image.
// num_of_tilts: number of tilts to simulate.
// verb: 1/0 --> show/don not show verbose messages. (1 for debugging)
// keys_all (output): ASIFT keypoints. It is a 2D matrix with varying rows and columns. Each entry keys_all[tt][rr]
// stores the SIFT keypoints calculated on the image with the simulated tilt index tt and simulated rotation index rr (see the code below). In the coordinates of the keypoints,
// the affine distortions have been compensated.
// siftparameters: SIFT parameters.
//
// Output: the number of keypoints
{
vector<float> image_t, image_tmp1, image_tmp;
float t_min, t_k;
int num_tilt, tt, num_rot_t2, rr;
int fproj_o;
float fproj_p, fproj_bg;
char fproj_i;
float *fproj_x4, *fproj_y4;
// float frot_b=0;
float frot_b=128;
char *frot_k;
int counter_sim=0, num_sim;
int flag_dir = 1;
float BorderFact=6*sqrt(2.);
int num_keys_total=0;
fproj_o = 3;
fproj_p = 0;
fproj_i = 0;
fproj_bg = 0;
fproj_x4 = 0;
fproj_y4 = 0;
frot_k = 0;
num_rot_t2 = 10;
t_min = 1;
t_k = sqrt(2.);
num_tilt = num_of_tilts;
if ( num_tilt < 1)
{
printf("Number of tilts num_tilt should be equal or larger than 1. \n");
exit(-1);
}
image_tmp1 = image;
/* Calculate the number of simulations, and initialize keys_all */
keys_all = std::vector< vector< keypointslist > >(num_tilt);
for (tt = 1; tt <= num_tilt; tt++)
{
float t = t_min * pow(t_k, tt-1);
if ( t == 1 )
{
counter_sim ++;
keys_all[tt-1] = std::vector< keypointslist >(1);
}
else
{
int num_rot1 = round(num_rot_t2*t/2);
if ( num_rot1%2 == 1 )
{
num_rot1 = num_rot1 + 1;
}
num_rot1 = num_rot1 / 2;
counter_sim += num_rot1;
keys_all[tt-1] = std::vector< keypointslist >(num_rot1);
}
}
num_sim = counter_sim;
if ( verb )
{
printf("%d affine simulations will be performed. \n", num_sim);
}
counter_sim = 0;
/* Affine simulation (rotation+tilt simulation) */
// Loop on tilts.
#ifdef _OPENMP
omp_set_nested(1);
#endif
#pragma omp parallel for private(tt)
for (tt = 1; tt <= num_tilt; tt++)
{
float t = t_min * pow(t_k, tt-1);
float t1 = 1;
float t2 = 1/t;
// If tilt t = 1, do not simulate rotation.
if ( t == 1 )
{
// copy the image from vector to array as compute_sift_keypoints uses only array.
float *image_tmp1_float = new float[width*height];
for (int cc = 0; cc < width*height; cc++)
image_tmp1_float[cc] = image_tmp1[cc];
compute_sift_keypoints(image_tmp1_float,keys_all[tt-1][0],width,height,siftparameters);
delete[] image_tmp1_float;
}
else
{
// The number of rotations to simulate under the current tilt.
int num_rot1 = round(num_rot_t2*t/2);
if ( num_rot1%2 == 1 )
{
num_rot1 = num_rot1 + 1;
}
num_rot1 = num_rot1 / 2;
float delta_theta = PI/num_rot1;
// Loop on rotations.
#pragma omp parallel for private(rr)
for ( int rr = 1; rr <= num_rot1; rr++ )
{
float theta = delta_theta * (rr-1);
theta = theta * 180 / PI;
vector<float> image_t;
int width_r, height_r;
// simulate a rotation: rotate the image with an angle theta. (the outside of the rotated image are padded with the value frot_b)
frot(image, image_t, width, height, &width_r, &height_r, &theta, &frot_b , frot_k);
/* Tilt */
int width_t = (int) (width_r * t1);
int height_t = (int) (height_r * t2);
int fproj_sx = width_t;
int fproj_sy = height_t;
float fproj_x1 = 0;
float fproj_y1 = 0;
float fproj_x2 = width_t;
float fproj_y2 = 0;
float fproj_x3 = 0;
float fproj_y3 = height_t;
/* Anti-aliasing filtering along vertical direction */
/* sigma_aa = InitSigma_aa * log2(t);*/
float sigma_aa = InitSigma_aa * t / 2;
GaussianBlur1D(image_t,width_r,height_r,sigma_aa,flag_dir);
// simulate a tilt: subsample the image along the vertical axis by a factor of t.
vector<float> image_tmp(width_t*height_t);
fproj (image_t, image_tmp, width_r, height_r, &fproj_sx, &fproj_sy, &fproj_bg, &fproj_o, &fproj_p, &fproj_i , fproj_x1 , fproj_y1 , fproj_x2 , fproj_y2 , fproj_x3 , fproj_y3, fproj_x4, fproj_y4);
vector<float> image_tmp1 = image_tmp;
if ( verb )
{
printf("Rotation theta = %.2f, Tilt t = %.2f. w=%d, h=%d, sigma_aa=%.2f, \n", theta, t, width_t, height_t, sigma_aa);
}
float *image_tmp1_float = new float[width_t*height_t];
for (int cc = 0; cc < width_t*height_t; cc++)
image_tmp1_float[cc] = image_tmp1[cc];
// compute SIFT keypoints on simulated image.
keypointslist keypoints;
keypointslist keypoints_filtered;
compute_sift_keypoints(image_tmp1_float,keypoints,width_t,height_t,siftparameters);
delete[] image_tmp1_float;
/* check if the keypoint is located on the boundary of the parallelogram (i.e., the boundary of the distorted input image). If so, remove it to avoid boundary artifacts. */
if ( keypoints.size() != 0 )
{
for ( int cc = 0; cc < (int) keypoints.size(); cc++ )
{
float x0, y0, x1, y1, x2, y2, x3, y3 ,x4, y4, d1, d2, d3, d4, scale1, theta1, sin_theta1, cos_theta1, BorderTh;
x0 = keypoints[cc].x;
y0 = keypoints[cc].y;
scale1= keypoints[cc].scale;
theta1 = theta * PI / 180;
sin_theta1 = sin(theta1);
cos_theta1 = cos(theta1);
/* the coordinates of the 4 submits of the parallelogram */
if ( theta <= 90 )
{
x1 = height * sin_theta1;
y1 = 0;
y2 = width * sin_theta1;
x3 = width * cos_theta1;
x4 = 0;
y4 = height * cos_theta1;
x2 = x1 + x3;
y3 = y2 + y4;
/* note that the vertical direction goes from top to bottom!!!
The calculation above assumes that the vertical direction goes from the bottom to top. Thus the vertical coordinates need to be reversed!!! */
y1 = y3 - y1;
y2 = y3 - y2;
y4 = y3 - y4;
y3 = 0;
y1 = y1 * t2;
y2 = y2 * t2;
y3 = y3 * t2;
y4 = y4 * t2;
}
else
{
y1 = -height * cos_theta1;
x2 = height * sin_theta1;
x3 = 0;
y3 = width * sin_theta1;
x4 = -width * cos_theta1;
y4 = 0;
x1 = x2 + x4;
y2 = y1 + y3;
/* note that the vertical direction goes from top to bottom!!!
The calculation above assumes that the vertical direction goes from the bottom to top. Thus the vertical coordinates need to be reversed!!! */
y1 = y2 - y1;
y3 = y2 - y3;
y4 = y2 - y4;
y2 = 0;
y1 = y1 * t2;
y2 = y2 * t2;
y3 = y3 * t2;
y4 = y4 * t2;
}
/* the distances from the keypoint to the 4 sides of the parallelogram */
d1 = ABS((x2-x1)*(y1-y0)-(x1-x0)*(y2-y1)) / sqrt((x2-x1)*(x2-x1)+(y2-y1)*(y2-y1));
d2 = ABS((x3-x2)*(y2-y0)-(x2-x0)*(y3-y2)) / sqrt((x3-x2)*(x3-x2)+(y3-y2)*(y3-y2));
d3 = ABS((x4-x3)*(y3-y0)-(x3-x0)*(y4-y3)) / sqrt((x4-x3)*(x4-x3)+(y4-y3)*(y4-y3));
d4 = ABS((x1-x4)*(y4-y0)-(x4-x0)*(y1-y4)) / sqrt((x1-x4)*(x1-x4)+(y1-y4)*(y1-y4));
BorderTh = BorderFact*scale1;
if (!((d1<BorderTh) || (d2<BorderTh) || (d3<BorderTh) || (d4<BorderTh) ))
{
// Normalize the coordinates of the matched points by compensate the simulate affine transformations
compensate_affine_coor1(&x0, &y0, width, height, 1/t2, t1, theta);
keypoints[cc].x = x0;
keypoints[cc].y = y0;
keypoints_filtered.push_back(keypoints[cc]);
}
}
}
keys_all[tt-1][rr-1] = keypoints_filtered;
}
}
}
{
for (tt = 0; tt < (int) keys_all.size(); tt++)
for (rr = 0; rr < (int) keys_all[tt].size(); rr++)
{
num_keys_total += (int) keys_all[tt][rr].size();
}
// printf("%d ASIFT keypoints are detected. \n", num_keys_total);
}
return num_keys_total;
}

53
asift_matching/src/compute_asift_keypoints.h Executable file
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@ -0,0 +1,53 @@
// Copyright (c) 2008-2011, Guoshen Yu <yu@cmap.polytechnique.fr>
// Copyright (c) 2008-2011, Jean-Michel Morel <morel@cmla.ens-cachan.fr>
//
// WARNING:
// This file implements an algorithm possibly linked to the patent
//
// Jean-Michel Morel and Guoshen Yu, Method and device for the invariant
// affine recognition recognition of shapes (WO/2009/150361), patent pending.
//
// This file is made available for the exclusive aim of serving as
// scientific tool to verify of the soundness and
// completeness of the algorithm description. Compilation,
// execution and redistribution of this file may violate exclusive
// patents rights in certain countries.
// The situation being different for every country and changing
// over time, it is your responsibility to determine which patent
// rights restrictions apply to you before you compile, use,
// modify, or redistribute this file. A patent lawyer is qualified
// to make this determination.
// If and only if they don't conflict with any patent terms, you
// can benefit from the following license terms attached to this
// file.
//
// This program is provided for scientific and educational only:
// you can use and/or modify it for these purposes, but you are
// not allowed to redistribute this work or derivative works in
// source or executable form. A license must be obtained from the
// patent right holders for any other use.
//
//
//*------------------------ compute_asift_keypoints -------------------------*/
// Compute the ASIFT keypoints on the input image.
//
// Please report bugs and/or send comments to Guoshen Yu yu@cmap.polytechnique.fr
//
// Reference: J.M. Morel and G.Yu, ASIFT: A New Framework for Fully Affine Invariant Image
// Comparison, SIAM Journal on Imaging Sciences, vol. 2, issue 2, pp. 438-469, 2009.
// Reference: ASIFT online demo (You can try ASIFT with your own images online.)
// http://www.ipol.im/pub/algo/my_affine_sift/
/*---------------------------------------------------------------------------*/
#include "library.h"
#include "demo_lib_sift.h"
#include "frot.h"
#include "fproj.h"
#include <vector>
using namespace std;
int compute_asift_keypoints(vector<float>& image, int width, int height, int num_of_tilts, int verb, vector< vector< keypointslist > >& keys_all, siftPar &siftparameters);
void GaussianBlur1D(vector<float>& image, int width, int height, float sigma, int flag_dir);

791
asift_matching/src/compute_asift_matches.cpp Executable file
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@ -0,0 +1,791 @@
// Copyright (c) 2008-2011, Guoshen Yu <yu@cmap.polytechnique.fr>
// Copyright (c) 2008-2011, Jean-Michel Morel <morel@cmla.ens-cachan.fr>
//
// WARNING:
// This file implements an algorithm possibly linked to the patent
//
// Jean-Michel Morel and Guoshen Yu, Method and device for the invariant
// affine recognition recognition of shapes (WO/2009/150361), patent pending.
//
// This file is made available for the exclusive aim of serving as
// scientific tool to verify of the soundness and
// completeness of the algorithm description. Compilation,
// execution and redistribution of this file may violate exclusive
// patents rights in certain countries.
// The situation being different for every country and changing
// over time, it is your responsibility to determine which patent
// rights restrictions apply to you before you compile, use,
// modify, or redistribute this file. A patent lawyer is qualified
// to make this determination.
// If and only if they don't conflict with any patent terms, you
// can benefit from the following license terms attached to this
// file.
//
// This program is provided for scientific and educational only:
// you can use and/or modify it for these purposes, but you are
// not allowed to redistribute this work or derivative works in
// source or executable form. A license must be obtained from the
// patent right holders for any other use.
//
//
//*------------------------ compute_asift_matches-- -------------------------*/
// Match the ASIFT keypoints.
//
// Please report bugs and/or send comments to Guoshen Yu yu@cmap.polytechnique.fr
//
// Reference: J.M. Morel and G.Yu, ASIFT: A New Framework for Fully Affine Invariant Image
// Comparison, SIAM Journal on Imaging Sciences, vol. 2, issue 2, pp. 438-469, 2009.
// Reference: ASIFT online demo (You can try ASIFT with your own images online.)
// http://www.ipol.im/pub/algo/my_affine_sift/
/*---------------------------------------------------------------------------*/
#include <assert.h>
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <time.h>
#ifdef _OPENMP
#include <omp.h>
#endif
#include "compute_asift_matches.h"
#include "libMatch/match.h"
#include "orsa.h"
#define round(x) ((x)>=0?(long)((x)+0.5):(long)((x)-0.5))
/* Remove the repetitive matches that appear in different simulations and retain only one */
void unique_match1(matchingslist &seg_in, matchingslist &seg_out, vector< vector <float> > &Minfoall_in, vector< vector <float> > &Minfoall_out)
{
int i_in, i_out;
float x1_in, x2_in, y1_in, y2_in, x1_out, x2_out, y1_out, y2_out;
int flag_unique;
float d1, d2;
int Th2 = 2;
seg_out.push_back(seg_in[0]);
Minfoall_out.push_back(Minfoall_in[0]);
/* For other matches */
if ( seg_in.size() > 1 )
{
/* check if a match is unique. if yes, copy */
/* Bug fix by Xiaoyu Sun (Sichuan university) (Dec 13, 2015) */
/* Original version
matchingslist::iterator ptr_in = seg_in.begin();
for ( i_in = 1; i_in < (int) seg_in.size(); i_in++, ptr_in++ )
*/
/* Bug fixed */
matchingslist::iterator ptr_in = seg_in.begin();
ptr_in++;
for ( i_in = 1; i_in < (int) seg_in.size(); i_in++, ptr_in++ )
{
x1_in = ptr_in->first.x;
y1_in = ptr_in->first.y;
x2_in = ptr_in->second.x;
y2_in = ptr_in->second.y;
flag_unique = 1;
matchingslist::iterator ptr_out = seg_out.begin();
for ( i_out = 0; i_out < (int) seg_out.size(); i_out++, ptr_out++ )
{
x1_out = ptr_out->first.x;
y1_out = ptr_out->first.y;
x2_out = ptr_out->second.x;
y2_out = ptr_out->second.y;
d1 = (x1_in - x1_out)*(x1_in - x1_out) + (y1_in - y1_out)*(y1_in - y1_out);
d2 = (x2_in - x2_out)*(x2_in - x2_out) + (y2_in - y2_out)*(y2_in - y2_out);
if ( ( d1 <= Th2) && ( d2 <= Th2) )
{
flag_unique = 0;
continue;
}
}
if ( flag_unique == 1 )
{
seg_out.push_back(seg_in[i_in]);
Minfoall_out.push_back(Minfoall_in[i_in]);
}
}
}
}
/* Remove the ALL one-to-multiple matches. */
void clean_match1(matchingslist &seg_in, matchingslist &seg_out, vector< vector <float> > &Minfoall_in, vector< vector <float> > &Minfoall_out)
{
int i1, i2;
float x1_in, x2_in, y1_in, y2_in, x1_out, x2_out, y1_out, y2_out;
// Guoshen Yu, 2010.09.22, Windows version
// int flag_unique[seg_in.size()];
int tmp_size = seg_in.size();
int *flag_unique = new int[tmp_size];
int sum_flag=0;
float d1, d2;
int Th1 = 1;
int Th2 = 4;
for ( i1 = 0; i1 < (int) seg_in.size(); i1++ )
{
flag_unique[i1] = 1;
}
/* Set the flag of redundant matches to 0. */
matchingslist::iterator ptr_in = seg_in.begin();
for ( i1 = 0; i1 < (int) seg_in.size() - 1; i1++, ptr_in++ )
{
x1_in = ptr_in->first.x;
y1_in = ptr_in->first.y;
x2_in = ptr_in->second.x;
y2_in = ptr_in->second.y;
matchingslist::iterator ptr_out = ptr_in+1;
for ( i2 = i1 + 1; i2 < (int) seg_in.size(); i2++, ptr_out++ )
{
x1_out = ptr_out->first.x;
y1_out = ptr_out->first.y;
x2_out = ptr_out->second.x;
y2_out = ptr_out->second.y;
d1 = (x1_in - x1_out)*(x1_in - x1_out) + (y1_in - y1_out)*(y1_in - y1_out);
d2 = (x2_in - x2_out)*(x2_in - x2_out) + (y2_in - y2_out)*(y2_in - y2_out);
/* If redundant, set flags of both elements to 0.*/
if ( ( ( d1 <= Th1) && ( d2 > Th2) ) || ( ( d1 > Th2) && ( d2 <= Th1) ) )
{
flag_unique[i1] = 0;
flag_unique[i2] = 0;
}
}
}
for ( i1 = 0; i1 < (int) seg_in.size(); i1++ )
{
sum_flag += flag_unique[i1];
}
/* Copy the matches that are not redundant */
if ( sum_flag > 0 )
{
for ( i1 = 0; i1 < (int) seg_in.size(); i1++ )
{
if ( flag_unique[i1] == 1 )
{
seg_out.push_back(seg_in[i1]);
Minfoall_out.push_back(Minfoall_in[i1]);
}
}
}
else
{
printf("Warning: all matches are redundant and are thus removed! This step of match cleaning is short circuited. (Normally this should not happen...)\n");
}
// Guoshen Yu, 2010.09.22, Windows version
delete [] flag_unique;
}
/* Remove the ALL multiple-to-one matches */
void clean_match2(matchingslist &seg_in, matchingslist &seg_out, vector< vector <float> > &Minfoall_in, vector< vector <float> > &Minfoall_out)
{
int i1, i2;
float x1_in, x2_in, y1_in, y2_in, x1_out, x2_out, y1_out, y2_out;
// Guoshen Yu, 2010.09.22, Windows version
// int flag_unique[seg_in.size()];
int tmp_size = seg_in.size();
int *flag_unique = new int[tmp_size];
int sum_flag=0;
float d1, d2;
int Th1 = 1;
int Th2 = 4;
for ( i1 = 0; i1 < (int) seg_in.size(); i1++ )
{
flag_unique[i1] = 1;
}
/* Set the flag of redundant matches to 0. */
matchingslist::iterator ptr_in = seg_in.begin();
for ( i1 = 0; i1 < (int) seg_in.size() - 1; i1++, ptr_in++ )
{
x1_in = ptr_in->first.x;
y1_in = ptr_in->first.y;
x2_in = ptr_in->second.x;
y2_in = ptr_in->second.y;
matchingslist::iterator ptr_out = ptr_in+1;
for ( i2 = i1 + 1; i2 < (int) seg_in.size(); i2++, ptr_out++ )
{
x1_out = ptr_out->first.x;
y1_out = ptr_out->first.y;
x2_out = ptr_out->second.x;
y2_out = ptr_out->second.y;
d1 = (x1_in - x1_out)*(x1_in - x1_out) + (y1_in - y1_out)*(y1_in - y1_out);
d2 = (x2_in - x2_out)*(x2_in - x2_out) + (y2_in - y2_out)*(y2_in - y2_out);
/* If redundant, set flags of both elements to 0.*/
if ( ( d1 > Th2) && ( d2 <= Th1) )
{
flag_unique[i1] = 0;
flag_unique[i2] = 0;
}
}
}
for ( i1 = 0; i1 < (int) seg_in.size(); i1++ )
{
sum_flag += flag_unique[i1];
}
/* Copy the matches that are not redundant */
if ( sum_flag > 0 )
{
for ( i1 = 0; i1 < (int) seg_in.size(); i1++ )
{
if ( flag_unique[i1] == 1 )
{
seg_out.push_back(seg_in[i1]);
Minfoall_out.push_back(Minfoall_in[i1]);
}
}
}
else
{
printf("Warning: all matches are redundant and are thus removed! This step of match cleaning is short circuited. (Normally this should not happen...)\n");
}
// Guoshen Yu, 2010.09.22, Windows version
delete [] flag_unique;
}
// Normalize the coordinates of the matched points by compensating the simulate affine transformations
void compensate_affine_coor(matching &matching1, int w1, int h1, int w2, int h2, float t1, float t2, float Rtheta, float t_im2_1, float t_im2_2, float Rtheta2)
{
float x_ori, y_ori;
float x_ori2, y_ori2, x_tmp, y_tmp;
float x1, y1, x2, y2;
Rtheta = Rtheta*PI/180;
if ( Rtheta <= PI/2 )
{
x_ori = 0;
y_ori = w1 * sin(Rtheta) / t1;
}
else
{
x_ori = -w1 * cos(Rtheta) / t2;
y_ori = ( w1 * sin(Rtheta) + h1 * sin(Rtheta-PI/2) ) / t1;
}
Rtheta2 = Rtheta2*PI/180;
if ( Rtheta2 <= PI/2 )
{
x_ori2 = 0;
y_ori2 = w2 * sin(Rtheta2) / t_im2_1;
}
else
{
x_ori2 = -w2 * cos(Rtheta2) / t_im2_2;
y_ori2 = ( w2 * sin(Rtheta2) + h2 * sin(Rtheta2-PI/2) ) / t_im2_1;
}
float sin_Rtheta = sin(Rtheta);
float cos_Rtheta = cos(Rtheta);
float sin_Rtheta2 = sin(Rtheta2);
float cos_Rtheta2 = cos(Rtheta2);
x1 = matching1.first.x;
y1 = matching1.first.y;
x2 = matching1.second.x;
y2 = matching1.second.y;
/* project the coordinates of im1 to original image before tilt-rotation transform */
/* Get the coordinates with respect to the 'origin' of the original image before transform */
x1 = x1 - x_ori;
y1 = y1 - y_ori;
/* Invert tilt */
x1 = x1 * t2;
y1 = y1 * t1;
/* Invert rotation (Note that the y direction (vertical) is inverse to the usual concention. Hence Rtheta instead of -Rtheta to inverse the rotation.) */
x_tmp = cos_Rtheta*x1 - sin_Rtheta*y1;
y_tmp = sin_Rtheta*x1 + cos_Rtheta*y1;
x1 = x_tmp;
y1 = y_tmp;
/* Coordinate projection on image2 */
/* Get the coordinates with respect to the 'origin' of the original image before transform */
x2 = x2 - x_ori2;
y2 = y2 - y_ori2;
/* Invert tilt */
x2 = x2 * t_im2_2;
y2 = y2 * t_im2_1;
/* Invert rotation (Note that the y direction (vertical) is inverse to the usual concention. Hence Rtheta instead of -Rtheta to inverse the rotation.) */
x_tmp = cos_Rtheta2*x2 - sin_Rtheta2*y2;
y_tmp = sin_Rtheta2*x2 + cos_Rtheta2*y2;
x2 = x_tmp;
y2 = y_tmp;
matching1.first.x = x1;
matching1.first.y = y1;
matching1.second.x = x2;
matching1.second.y = y2;
}
int compute_asift_matches(int num_of_tilts1, int num_of_tilts2, int w1, int h1, int w2, int h2, int verb, vector< vector< keypointslist > >& keys1, vector< vector< keypointslist > >& keys2, matchingslist &matchings, siftPar &siftparameters)
// Match the ASIFT keypoints.
// Input:
// num_of_tilts1, num_of_tilts2: number of tilts that have been simulated on the two images. (They can be different.)
// w1, h1, w2, h2: widht/height of image1/image2.
// verb: 1/0 --> show/don not show verbose messages. (1 for debugging)
// keys1, keys2: ASIFT keypoints of image1/image2. (They should be calculated with compute_asift_keypoints.)
// matchings (output): the coordinates (col1, row1, col2, row2) of all the matching points.
//
// Output: the number of matching points.
{
float t_min, t_k, t;
int num_tilt1, num_tilt2, tt, num_rot_t2, num_rot1, rr;
int cc;
int tt2, rr2, num_rot1_2;
float t_im2;
/* It stores the coordinates of ALL matches points of ALL affine simulations */
vector< vector <float> > Minfoall;
int Tmin = 8;
float nfa_max = -2;
num_rot_t2 = 10;
t_min = 1;
t_k = sqrt(2.);
num_tilt1 = num_of_tilts1;
num_tilt2 = num_of_tilts2;
if ( ( num_tilt1 < 1 ) || ( num_tilt2 < 1 ) )
{
printf("Number of tilts num_tilt should be equal or larger than 1. \n");
exit(-1);
}
/* Initialize the vector structure for the matching points */
std::vector< vector< vector < vector < matchingslist > > > > matchings_vec(num_tilt1);
std::vector< vector< vector< vector< vector< vector <float> > > > > > Minfoall_vec(num_tilt1);
for (tt = 1; tt <= num_tilt1; tt++)
{
t = t_min * pow(t_k, tt-1);
if ( t == 1 )
{
num_rot1 = 1;
}
else
{
num_rot1 = round(num_rot_t2*t/2);
if ( num_rot1%2 == 1 )
{
num_rot1 = num_rot1 + 1;
}
num_rot1 = num_rot1 / 2;
}
matchings_vec[tt-1].resize(num_rot1);
Minfoall_vec[tt-1].resize(num_rot1);
for ( rr = 1; rr <= num_rot1; rr++ )
{
matchings_vec[tt-1][rr-1].resize(num_tilt2);
Minfoall_vec[tt-1][rr-1].resize(num_tilt2);
for (tt2 = 1; tt2 <= num_tilt2; tt2++)
{
t_im2 = t_min * pow(t_k, tt2-1);
if ( t_im2 == 1 )
{
num_rot1_2 = 1;
}
else
{
num_rot1_2 = round(num_rot_t2*t_im2/2);
if ( num_rot1_2%2 == 1 )
{
num_rot1_2 = num_rot1_2 + 1;
}
num_rot1_2 = num_rot1_2 / 2;
}
matchings_vec[tt-1][rr-1][tt2-1].resize(num_rot1_2);
Minfoall_vec[tt-1][rr-1][tt2-1].resize(num_rot1_2);
}
}
}
///*
// * setup the tilt and rotation parameters
// * for all the loops, this vector will hold
// * the following parameters:
// * tt, num_rot1, rr, tt2, num_rot1_2, rr2
// */
//vector<int> tilt_rot;
///* loop on tilts for image 1 */
//for (int tt = 1; tt <= num_tilt1; tt++)
//{
// float t = t_min * pow(t_k, tt-1);
// int num_rot1;
// /* if tilt t = 1, do not simulate rotation. */
// if ( 1 == tt )
// num_rot1 = 1;
// else
// {
// /* number of rotations to simulate */
// num_rot1 = round(num_rot_t2 * t / 2);
// if ( num_rot1%2 == 1 )
// num_rot1 = num_rot1 + 1;
// num_rot1 = num_rot1 / 2;
// }
// /* loop on rotations for image 1 */
// for (int rr = 1; rr <= num_rot1; rr++ )
// {
// /* loop on tilts for image 2 */
// for (int tt2 = 1; tt2 <= num_tilt2; tt2++)
// {
// float t_im2 = t_min * pow(t_k, tt2-1);
// int num_rot1_2;
// if ( tt2 == 1 )
// num_rot1_2 = 1;
// else
// {
// num_rot1_2 = round(num_rot_t2 * t_im2 / 2);
// if ( num_rot1_2%2 == 1 )
// num_rot1_2 = num_rot1_2 + 1;
// num_rot1_2 = num_rot1_2 / 2;
// }
// /* loop on rotations for image 2 */
// for (int rr2 = 1; rr2 <= num_rot1_2; rr2++ )
// {
// tilt_rot.push_back(tt);
// tilt_rot.push_back(num_rot1);
// tilt_rot.push_back(rr);
// tilt_rot.push_back(tt2);
// tilt_rot.push_back(num_rot1_2);
// tilt_rot.push_back(rr2);
// }
// }
// }
//}
/* Calculate the number of simulations */
#ifdef _OPENMP
omp_set_nested(1);
#endif
// loop on tilts for image 1.
#pragma omp parallel for private(tt)
for (int tt = 1; tt <= num_tilt1; tt++)
{
float t = t_min * pow(t_k, tt-1);
/* Attention: the t1, t2 do not follow the same convention as in compute_asift_keypoints */
float t1 = t;
float t2 = 1;
int num_rot1;
// If tilt t = 1, do not simulate rotation.
if ( tt == 1 )
{
num_rot1 = 1;
}
else
{
// The number of rotations to simulate under the current tilt.
num_rot1 = round(num_rot_t2*t/2);
if ( num_rot1%2 == 1 )
{
num_rot1 = num_rot1 + 1;
}
num_rot1 = num_rot1 / 2;
}
float delta_theta = PI/num_rot1;
// Loop on rotations for image 1.
#pragma omp parallel for private(rr)
for ( int rr = 1; rr <= num_rot1; rr++ )
{
float theta = delta_theta * (rr-1);
theta = theta * 180 / PI;
/* Read the keypoints of image 1 */
keypointslist keypoints1 = keys1[tt-1][rr-1];
// loop on tilts for image 2.
#pragma omp parallel for private(tt2)
for (int tt2 = 1; tt2 <= num_tilt2; tt2++)
{
float t_im2 = t_min * pow(t_k, tt2-1);
/* Attention: the t1, t2 do not follow the same convention as in asift_v1.c */
float t_im2_1 = t_im2;
float t_im2_2 = 1;
int num_rot1_2;
if ( tt2 == 1 )
{
num_rot1_2 = 1;
}
else
{
num_rot1_2 = round(num_rot_t2*t_im2/2);
if ( num_rot1_2%2 == 1 )
{
num_rot1_2 = num_rot1_2 + 1;
}
num_rot1_2 = num_rot1_2 / 2;
}
float delta_theta2 = PI/num_rot1_2;
#pragma omp parallel for private(rr2)
// Loop on rotations for image 2.
for ( int rr2 = 1; rr2 <= num_rot1_2; rr2++ )
{
float theta2 = delta_theta2 * (rr2-1);
theta2 = theta2 * 180 / PI;
/* Read the keypoints of image2. */
keypointslist keypoints2 = keys2[tt2-1][rr2-1];
// Match the keypoints of image1 and image2.
matchingslist matchings1;
compute_sift_matches(keypoints1,keypoints2,matchings1,siftparameters);
if ( verb )
{
printf("t1=%.2f, theta1=%.2f, num keys1 = %d, t2=%.2f, theta2=%.2f, num keys2 = %d, num matches=%d\n", t, theta, (int) keypoints1.size(), t_im2, theta2, (int) keypoints2.size(), (int) matchings1.size());
}
/* Store the matches */
if ( matchings1.size() > 0 )
{
matchings_vec[tt-1][rr-1][tt2-1][rr2-1] = matchingslist(matchings1.size());
Minfoall_vec[tt-1][rr-1][tt2-1][rr2-1].resize(matchings1.size());
for ( int cc = 0; cc < (int) matchings1.size(); cc++ )
{
///// In the coordinates the affine transformations have been normalized already in compute_asift_keypoints. So no need to normalize here.
// Normalize the coordinates of the matched points by compensating the simulate affine transformations
// compensate_affine_coor(matchings1[cc], w1, h1, w2, h2, t1, t2, theta, t_im2_1, t_im2_2, theta2);
matchings_vec[tt-1][rr-1][tt2-1][rr2-1][cc] = matchings1[cc];
vector<float> Minfo_1match(6);
Minfo_1match[0] = t1;
Minfo_1match[1] = t2;
Minfo_1match[2] = theta;
Minfo_1match[3] = t_im2_1;
Minfo_1match[4] = t_im2_2;
Minfo_1match[5] = theta2;
Minfoall_vec[tt-1][rr-1][tt2-1][rr2-1][cc] = Minfo_1match;
}
}
}
}
}
}
// Move the matches to a 1D vector
for (tt = 1; tt <= num_tilt1; tt++)
{
t = t_min * pow(t_k, tt-1);
if ( t == 1 )
{
num_rot1 = 1;
}
else
{
num_rot1 = round(num_rot_t2*t/2);
if ( num_rot1%2 == 1 )
{
num_rot1 = num_rot1 + 1;
}
num_rot1 = num_rot1 / 2;
}
for ( rr = 1; rr <= num_rot1; rr++ )
{
for (tt2 = 1; tt2 <= num_tilt2; tt2++)
{
t_im2 = t_min * pow(t_k, tt2-1);
if ( t_im2 == 1 )
{
num_rot1_2 = 1;
}
else
{
num_rot1_2 = round(num_rot_t2*t_im2/2);
if ( num_rot1_2%2 == 1 )
{
num_rot1_2 = num_rot1_2 + 1;
}
num_rot1_2 = num_rot1_2 / 2;
}
for ( rr2 = 1; rr2 <= num_rot1_2; rr2++ )
{
for ( cc=0; cc < (int) matchings_vec[tt-1][rr-1][tt2-1][rr2-1].size(); cc++ )
{
matchings.push_back(matchings_vec[tt-1][rr-1][tt2-1][rr2-1][cc]);
Minfoall.push_back(Minfoall_vec[tt-1][rr-1][tt2-1][rr2-1][cc]);
}
}
}
}
}
if ( verb )
{
printf("The number of matches is %d \n", (int) matchings.size());
}
if ( matchings.size() > 0 )
{
/* Remove the repetitive matches that appear in different simulations and retain only one. */
// Since tilts are simuated on both image 1 and image 2, it is normal to have repetitive matches.
matchingslist matchings_unique;
vector< vector<float> > Minfoall_unique;
unique_match1(matchings, matchings_unique, Minfoall, Minfoall_unique);
matchings = matchings_unique;
Minfoall = Minfoall_unique;
if ( verb )
{
printf("The number of unique matches is %d \n", (int) matchings.size());
}
// There often appear to be some one-to-multiple/multiple-to-one matches (one point in image 1 matches with many points in image 2/vice versa).
// This is an artifact of SIFT on interpolated images, as the interpolation tends to create some auto-similar structures (steps for example).
// These matches need to be removed.
/* Separating the removal of multiple-to-one and one-to-multiple in two steps:
- first remove multiple-to-one
- then remove one-to-multiple
This allows to avoid removing some good matches: multiple-to-one matches is much more frequent than one-to-multiple. Sometimes some of the feature points in image 1 that take part in "multiple-to-one" bad matches have also correct matches in image 2. The modified scheme avoid removing these good matches. */
// Remove to multiple-to-one matches
matchings_unique.clear();
Minfoall_unique.clear();
clean_match2(matchings, matchings_unique, Minfoall, Minfoall_unique);
matchings = matchings_unique;
Minfoall = Minfoall_unique;
// Remove to one-to-multiple matches
matchings_unique.clear();
Minfoall_unique.clear();
clean_match1(matchings, matchings_unique, Minfoall, Minfoall_unique);
matchings = matchings_unique;
Minfoall = Minfoall_unique;
if ( verb )
{
printf("The number of final matches is %d \n", (int) matchings.size());
}
// If enough matches to do epipolar filtering
if ( (int) matchings.size() >= Tmin )
{
//////// Use ORSA to filter out the incorrect matches.
// store the coordinates of the matching points
vector<Match> match_coor;
for ( cc = 0; cc < (int) matchings.size(); cc++ )
{
Match match1_coor;
match1_coor.x1 = matchings[cc].first.x;
match1_coor.y1 = matchings[cc].first.y;
match1_coor.x2 = matchings[cc].second.x;
match1_coor.y2 = matchings[cc].second.y;
match_coor.push_back(match1_coor);
}
std::vector<float> index;
// Guoshen Yu, 2010.09.23
// index.clear();
int t_value_orsa=10000;
int verb_value_orsa=0;
int n_flag_value_orsa=0;
int mode_value_orsa=2;
int stop_value_orsa=0;
// epipolar filtering with the Moisan-Stival ORSA algorithm.
// float nfa = orsa(w1, h1, match_coor, index, t_value_orsa, verb_value_orsa, n_flag_value_orsa, mode_value_orsa, stop_value_orsa);
float nfa = orsa((w1+w2)/2, (h1+h2)/2, match_coor, index, t_value_orsa, verb_value_orsa, n_flag_value_orsa, mode_value_orsa, stop_value_orsa);
// if the matching is significant, register the good matches
if ( nfa < nfa_max )
{
// extract meaningful matches
matchings_unique.clear();
Minfoall_unique.clear();
for ( cc = 0; cc < (int) index.size(); cc++ )
{
matchings_unique.push_back(matchings[(int)index[cc]]);
Minfoall_unique.push_back(Minfoall[(int)index[cc]]);
}
matchings = matchings_unique;
Minfoall = Minfoall_unique;
// cout << "The two images match! " << matchings.size() << " matchings are identified. log(nfa)=" << nfa << "." << endl;
}
else
{
matchings.clear();
Minfoall.clear();
// cout << "The two images do not match. The matching is not significant: log(nfa)=" << nfa << "." << endl;
}
}
else
{
matchings.clear();
Minfoall.clear();
// cout << "The two images do not match. Not enough matches to do epipolar filtering." << endl;
}
}
else
{
// cout << "The two images do not match.\n" << endl;
}
return matchings.size();
}

51
asift_matching/src/compute_asift_matches.h Executable file
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// Copyright (c) 2008-2011, Guoshen Yu <yu@cmap.polytechnique.fr>
// Copyright (c) 2008-2011, Jean-Michel Morel <morel@cmla.ens-cachan.fr>
//
// WARNING:
// This file implements an algorithm possibly linked to the patent
//
// Jean-Michel Morel and Guoshen Yu, Method and device for the invariant
// affine recognition recognition of shapes (WO/2009/150361), patent pending.
//
// This file is made available for the exclusive aim of serving as
// scientific tool to verify of the soundness and
// completeness of the algorithm description. Compilation,
// execution and redistribution of this file may violate exclusive
// patents rights in certain countries.
// The situation being different for every country and changing
// over time, it is your responsibility to determine which patent
// rights restrictions apply to you before you compile, use,
// modify, or redistribute this file. A patent lawyer is qualified
// to make this determination.
// If and only if they don't conflict with any patent terms, you
// can benefit from the following license terms attached to this
// file.
//
// This program is provided for scientific and educational only:
// you can use and/or modify it for these purposes, but you are
// not allowed to redistribute this work or derivative works in
// source or executable form. A license must be obtained from the
// patent right holders for any other use.
//
//
//*------------------------ compute_asift_matches-- -------------------------*/
// Match the ASIFT keypoints.
//
// Please report bugs and/or send comments to Guoshen Yu yu@cmap.polytechnique.fr
//
// Reference: J.M. Morel and G.Yu, ASIFT: A New Framework for Fully Affine Invariant Image
// Comparison, SIAM Journal on Imaging Sciences, vol. 2, issue 2, pp. 438-469, 2009.
// Reference: ASIFT online demo (You can try ASIFT with your own images online.)
// http://www.ipol.im/pub/algo/my_affine_sift/
/*---------------------------------------------------------------------------*/
#include "library.h"
#include "demo_lib_sift.h"
#include "frot.h"
#include "fproj.h"
#include <vector>
using namespace std;
int compute_asift_matches(int num_of_tilts1, int num_of_tilts2, int w1, int h1, int w2, int h2, int verb, vector< vector< keypointslist > >& keys1, vector< vector< keypointslist > >& keys2, matchingslist &matchings, siftPar &siftparameters);

1222
asift_matching/src/demo_lib_sift.cpp Executable file
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298
asift_matching/src/demo_lib_sift.h Executable file
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// Authors: Unknown. Please, if you are the author of this file, or if you
// know who are the authors of this file, let us know, so we can give the
// adequate credits and/or get the adequate authorizations.
// WARNING:
// This file implements an algorithm possibly linked to the patent
//
// David Lowe "Method and apparatus for identifying scale invariant
// features in an image and use of same for locating an object in an
// image", U.S. Patent 6,711,293.
//
// This file is made available for the exclusive aim of serving as
// scientific tool to verify of the soundness and
// completeness of the algorithm description. Compilation,
// execution and redistribution of this file may violate exclusive
// patents rights in certain countries.
// The situation being different for every country and changing
// over time, it is your responsibility to determine which patent
// rights restrictions apply to you before you compile, use,
// modify, or redistribute this file. A patent lawyer is qualified
// to make this determination.
// If and only if they don't conflict with any patent terms, you
// can benefit from the following license terms attached to this
// file.
//
// This program is provided for scientific and educational only:
// you can use and/or modify it for these purposes, but you are
// not allowed to redistribute this work or derivative works in
// source or executable form. A license must be obtained from the
// patent right holders for any other use.
#ifndef _CLIBSIFT_H_
#define _CLIBSIFT_H_
///////////// Description
/// For each octave:
/// - Divide in par.Scales scales
/// - Convolve and compute differences of convolved scales
/// - Look for a 3x3 multiscale extrema and contraste enough and with no predominant direction (no 1d edge)
/// For each extrema
/// - Compute orientation histogram in neighborhood.
/// - Generate a keypoint for each mode with this orientation
/// For each keypoint
/// - Create vector
///////////// Possible differences with MW
/// Gaussian convolution
#include <stdlib.h>
#include <assert.h>
#include "numerics1.h"
#include "library.h"
#include "filter.h"
#include "domain.h"
#include "splines.h"
#include "flimage.h"
#include <vector>
// BASIC STRUCTURES:
// Keypoints:
#define OriSize 8
#define IndexSize 4
#define VecLength IndexSize * IndexSize * OriSize
/* Keypoint structure:
position: x,y
scale: s
orientation: angle
descriptor: array of gradient orientation histograms in a neighbors */
struct keypoint {
float x,y,
scale,
angle;
float vec[VecLength];
};
/* Keypoint structure:
position: x,y
scale: s
orientation: angle
descriptor: array of gradient orientation histograms in a neighbors */
struct keypoint_char {
float x,y,
scale,
angle;
unsigned char vec[VecLength];
};
/* Keypoint structure:
position: x,y
scale: s
orientation: angle
descriptor: array of gradient orientation histograms in a neighbors */
struct keypoint_short {
float x,y,
scale,
angle;
unsigned short vec[VecLength];
};
/* Keypoint structure:
position: x,y
scale: s
orientation: angle
descriptor: array of gradient orientation histograms in a neighbors */
struct keypoint_int {
float x,y,
scale,
angle;
unsigned int vec[VecLength];
};
/* List of keypoints: just use the standard class vector: */
typedef std::vector<keypoint> keypointslist;
/* List of keypoints: just use the standard class vector: */
typedef std::vector<keypoint_char> keypointslist_char;
typedef std::vector<keypoint_short> keypointslist_short;
typedef std::vector<keypoint_int> keypointslist_int;
/* Matching: just use the standard class pair: */
typedef std::pair<keypoint,keypoint> matching;
/* List of matchings: just use the standard class vector: */
typedef std::vector<matching> matchingslist;
struct siftPar
{
int OctaveMax;
int DoubleImSize;
int order;
/* InitSigma gives the amount of smoothing applied to the image at the
first level of each octave. In effect, this determines the sampling
needed in the image domain relative to amount of smoothing. Good
values determined experimentally are in the range 1.2 to 1.8.
*/
float InitSigma /*= 1.6*/;
/* Peaks in the DOG function must be at least BorderDist samples away
from the image border, at whatever sampling is used for that scale.
Keypoints close to the border (BorderDist < about 15) will have part
of the descriptor landing outside the image, which is approximated by
having the closest image pixel replicated. However, to perform as much
matching as possible close to the edge, use BorderDist of 4.
*/
int BorderDist /*= 5*/;
/* Scales gives the number of discrete smoothing levels within each octave.
For example, Scales = 2 implies dividing octave into 2 intervals, so
smoothing for each scale sample is sqrt(2) more than previous level.
Value of 2 works well, but higher values find somewhat more keypoints.
*/
int Scales /*= 3*/;
/// Decreasing PeakThresh allows more non contrasted keypoints
/* Magnitude of difference-of-Gaussian value at a keypoint must be above
this threshold. This avoids considering points with very low contrast
that are dominated by noise. It is divided by Scales because more
closely spaced scale samples produce smaller DOG values. A value of
0.08 considers only the most stable keypoints, but applications may
wish to use lower values such as 0.02 to find keypoints from low-contast
regions.
*/
//#define PeakThreshInit 255*0.04
//#define PeakThresh PeakThreshInit / Scales
float PeakThresh /*255.0 * 0.04 / 3.0*/;
/// Decreasing EdgeThresh allows more edge points
/* This threshold eliminates responses at edges. A value of 0.08 means
that the ratio of the largest to smallest eigenvalues (principle
curvatures) is below 10. A value of 0.14 means ratio is less than 5.
A value of 0.0 does not eliminate any responses.
Threshold at first octave is different.
*/
float EdgeThresh /*0.06*/;
float EdgeThresh1 /*0.08*/;
/* OriBins gives the number of bins in the histogram (36 gives 10
degree spacing of bins).
*/
int OriBins /*36*/;
/* Size of Gaussian used to select orientations as multiple of scale
of smaller Gaussian in DOG function used to find keypoint.
Best values: 1.0 for UseHistogramOri = FALSE; 1.5 for TRUE.
*/
float OriSigma /*1.5*/;
/// Look for local (3-neighborhood) maximum with valuer larger or equal than OriHistThresh * maxval
/// Setting one returns a single peak
/* All local peaks in the orientation histogram are used to generate
keypoints, as long as the local peak is within OriHistThresh of
the maximum peak. A value of 1.0 only selects a single orientation
at each location.
*/
float OriHistThresh /*0.8*/;
/// Feature vector is normalized to has euclidean norm 1.
/// This threshold avoid the excessive concentration of information on single peaks
/* Index values are thresholded at this value so that regions with
high gradients do not need to match precisely in magnitude.
Best value should be determined experimentally. Value of 1.0
has no effect. Value of 0.2 is significantly better.
*/
float MaxIndexVal /*0.2*/;
/* This constant specifies how large a region is covered by each index
vector bin. It gives the spacing of index samples in terms of
pixels at this scale (which is then multiplied by the scale of a
keypoint). It should be set experimentally to as small a value as
possible to keep features local (good values are in range 3 to 5).
*/
int MagFactor /*3*/;
/* Width of Gaussian weighting window for index vector values. It is
given relative to half-width of index, so value of 1.0 means that
weight has fallen to about half near corners of index patch. A
value of 1.0 works slightly better than large values (which are
equivalent to not using weighting). Value of 0.5 is considerably
worse.
*/
float IndexSigma /*1.0*/;
/* If this is TRUE, then treat gradients with opposite signs as being
the same. In theory, this could create more illumination invariance,
but generally harms performance in practice.
*/
int IgnoreGradSign /*0*/;
float MatchRatio /*0.6*/;
/*
In order to constrain the research zone for matches.
Useful for example when looking only at epipolar lines
*/
float MatchXradius /*= 1000000.0f*/;
float MatchYradius /*= 1000000.0f*/;
int noncorrectlylocalized;
};
//////////////////////////////////////////////////////////
/// SIFT
//////////////////////////////////////////////////////////
void default_sift_parameters(siftPar &par);
void compute_sift_keypoints(float *input, keypointslist& keypoints,int width, int height, siftPar &par);
// MATCHING DETECTION FUNCTION:
void compute_sift_matches( keypointslist& keys1, keypointslist& keys2, matchingslist& matchings, siftPar &par);
#endif // _LIBSIFT_H_

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// Authors: Unknown. Please, if you are the author of this file, or if you
// know who are the authors of this file, let us know, so we can give the
// adequate credits and/or get the adequate authorizations.
#include "domain.h"
#define DEBUG 0
void apply_zoom(float *input, float *out, float zoom, int order, int width, int height)
{
int nwidth = (int)( zoom * (float) width);
int nheight = (int)( zoom * (float) height);
float *coeffs;
float *ref;
float cx[12],cy[12],ak[13];
// Guoshen Yu, 2010.09.22, Windows versions
vector<float> input_vec, coeffs_vec, ref_vec;
input_vec = vector<float>(width*height);
coeffs_vec = vector<float>(width*height);
ref_vec = vector<float>(width*height);
for (int i = 0; i < width*height; i++)
input_vec[i] = input[i];
if (order!=0 && order!=1 && order!=-3 &&
order!=3 && order!=5 && order!=7 && order!=9 && order!=11)
{
printf("unrecognized interpolation order.\n");
exit(-1);
}
if (order>=3) {
coeffs = new float[width*height];
// Guoshen Yu, 2010.09.21, Windows version
//finvspline(input,order,coeffs,width,height);
finvspline(input_vec,order,coeffs_vec,width,height);
for (int i = 0; i < width*height; i++)
coeffs[i] = coeffs_vec[i];
ref = coeffs;
if (order>3) init_splinen(ak,order);
} else
{
coeffs = NULL;
ref = input;
}
int xi,yi;
float xp,yp;
float res;
int n1,n2;
float bg = 0.0f;
float p=-0.5;
for(int i=0; i < nwidth; i++)
for(int j=0; j < nheight; j++)
{
xp = (float) i / zoom;
yp = (float) j / zoom;
if (order == 0) {
xi = (int)floor((double)xp);
yi = (int)floor((double)yp);
if (xi<0 || xi>=width || yi<0 || yi>=height)
res = bg;
else res = input[yi*width+xi];
} else {
if (xp<0. || xp>=(float)width || yp<0. || yp>=(float)height) res=bg;
else {
xp -= 0.5; yp -= 0.5;
int xi = (int)floor((double)xp);
int yi = (int)floor((double)yp);
float ux = xp-(float)xi;
float uy = yp-(float)yi;
switch (order)
{
case 1: /* first order interpolation (bilinear) */
n2 = 1;
cx[0]=ux; cx[1]=1.-ux;
cy[0]=uy; cy[1]=1.-uy;
break;
case -3: /* third order interpolation (bicubic Keys' function) */
n2 = 2;
keys(cx,ux,p);
keys(cy,uy,p);
break;
case 3: /* spline of order 3 */
n2 = 2;
spline3(cx,ux);
spline3(cy,uy);
break;
default: /* spline of order >3 */
n2 = (1+order)/2;
splinen(cx,ux,ak,order);
splinen(cy,uy,ak,order);
break;
}
res = 0.; n1 = 1-n2;
if (xi+n1>=0 && xi+n2<width && yi+n1>=0 && yi+n2<height) {
int adr = yi*width+xi;
for (int dy=n1;dy<=n2;dy++)
for (int dx=n1;dx<=n2;dx++)
res += cy[n2-dy]*cx[n2-dx]*ref[adr+width*dy+dx];
} else
// Guoshen Yu, 2010.09.21, Windows
for (int i = 0; i < width*height; i++)
ref_vec[i] = ref[i];
for (int dy=n1;dy<=n2;dy++)
for (int dx=n1;dx<=n2;dx++)
// Guoshen Yu, 2010.09.21, Windows
// res += cy[n2-dy]*cx[n2-dx]*v(ref,xi+dx,yi+dy,bg,width,height);
res += cy[n2-dy]*cx[n2-dx]*v(ref_vec,xi+dx,yi+dy,bg,width,height);
}
}
out[j*nwidth+i] = res;
}
}

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// Authors: Unknown. Please, if you are the author of this file, or if you
// know who are the authors of this file, let us know, so we can give the
// adequate credits and/or get the adequate authorizations.
#ifndef _DOMAIN_H_
#define _DOMAIN_H_
#include "numerics1.h"
#include "library.h"
#include "splines.h"
/// Compute homography from n points using svd
void compute_planar_homography_n_points(float *x0, float *y0, float *x1, float *y1, int n, float **H);
/// Compute homography using svd + Ransac
void compute_ransac_planar_homography_n_points(float *x0, float *y0, float *x1, float *y1, int n, int niter, float tolerance, float **H);
/// Compute homography by using Lionel Code
void compute_moisan_planar_homography_n_points(float *x0, float *y0, float *x1, float *y1, int n, int niter, float &epsilon, float **H, int &counter, int recursivity);
/// Apply planar homography to image
void compute_planar_homography_bounding_box(int width, int height, float **H, float *x0, float *y0, int *nwidth, int *nheight);
void apply_planar_homography(float *input, int width, int height, float **H, float bg, int order, float *out, float x0, float y0, int nwidth, int nheight);
/// Apply zoom of factor z
void apply_zoom(float *input, float *out, float zoom, int order, int width, int height);
/// Apply general transformation
void apply_general_transformation(float *input, float *transformx, float* transformy, float *out, float bg, int order, int width, int height, int nwidth,int nheight);
#endif

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// Authors: Unknown. Please, if you are the author of this file, or if you
// know who are the authors of this file, let us know, so we can give the
// adequate credits and/or get the adequate authorizations.
#include "filter.h"
/////////////////////////////////////////////////////////////// Build Gaussian filters
float * directional_gauss_filter(float xsigma, float ysigma, float angle, int *kwidth, int *kheight)
{
int ksize = (int)(2.0 * 2.0 * MAX(xsigma, ysigma) + 1.0);
float *kernel = new float[ksize*ksize];
float xsigma2 = xsigma*xsigma;
float ysigma2 = ysigma*ysigma;
int l2 = ksize/2;
for(int y = -l2; y <= l2; y++)
for(int x = -l2; x <= l2; x++)
{
float a = (float) angle * PI / 180.0f;
float sina = sin(a);
float cosa = cos(a);
float ax = (float) x * cosa + (float) y * sina;
float ay = -(float) x * sina + (float) y * cosa;
kernel[(y+l2) * ksize + x + l2] = exp(-(ax*ax)/(2.0f*xsigma2) - (ay*ay)/(2.0f*ysigma2) );
}
float sum=0.0;
for(int i=0; i < ksize*ksize; i++) sum += kernel[i];
for(int i=0; i < ksize*ksize; i++) kernel[i] /= sum;
*kwidth = ksize;
*kheight = ksize;
return kernel;
}
/* Convolution with a kernel */
/* No padding applied to the image */
void convol(float *u,float *v,int width,int height,float *kernel,int kwidth,int kheight)
{
// float S;
// int K2,L2,m,n,kmin,kmax,lmin,lmax,l,k;
int K2 = kwidth / 2;
int L2 = kheight / 2;
for(int y=0 ; y < height; y++)
for (int x=0 ; x < width; x++) {
float S = 0.0;
// kmax = MIN(kwidth-1,n+K2);
// kmin = MAX(0,1+n+K2-width);
// lmax = MIN(kheight-1,m+L2);
// lmin = MAX(0,1+m+L2-height);
for (int l = -L2; l <= L2; l++)
for (int k = -K2 ; k<= K2; k++)
{
int px=x+k;
int py=y+l;
if (px>=0 && px < width && py>=0 && py<height)
S += u[width*py + px] * kernel[kwidth*(l+L2) + k+K2];
}
v[y*width+x] = (float) S;
}
}
void median(float *u,float *v, float radius, int niter, int width,int height)
{
int iradius = (int)(radius+1.0);
int rsize=(2*iradius+1)*(2*iradius+1);
float * vector = new float[rsize];
float * index = new float[rsize];
for(int n=0; n< niter;n++){
for(int x=0;x<width;x++)
for(int y=0;y<height;y++){
int count=0;
for(int i=-iradius;i<=iradius;i++)
for(int j=-iradius;j<=iradius;j++)
if ((float) (i*i + j*j) <= iradius*iradius){
int x0=x+i;
int y0=y+j;
if (x0>=0 && y0>=0 && x0 < width && y0 < height) {
vector[count] = u[y0*width+x0];
index[count] = count;
count++;
}
}
quick_sort(vector,index,count);
v[y*width+x] = vector[count/2];
}
copy(v,u,width*height);
}
delete[] vector;
delete[] index;
}
void remove_outliers(float *igray,float *ogray,int width, int height)
{
int bloc=1;
int bsize = (2*bloc+1)*(2*bloc+1)-1;
for(int x=bloc;x<width-bloc;x++)
for(int y=bloc;y<height-bloc;y++) {
int l = y*width+x;
int countmax=0;
int countmin=0;
float valueg0 = igray[l];
// float distmin = MAXFLOAT;
float distmin = FLT_MAX; // Guoshen Yu
float green = igray[l];
for(int i=-bloc;i<=bloc;i++)
for(int j=-bloc;j<=bloc;j++)
if ((i!=0 || j!=0)){
int l0 = (y+j)*width+x+i;
int valueg = (int) igray[l0];
if (valueg0>valueg) countmax++;
if (valueg0<valueg) countmin++;
float dist = fabsf(valueg - valueg0);
if (dist < distmin) {distmin=dist;green=valueg;}
}
if (countmin == bsize || countmax == bsize ) ogray[l]=green;
else ogray[l] = igray[l];
}
}
/* Convolution with a separable kernel */
/* boundary condition: 0=zero, 1=symmetry */
void separable_convolution(float *u, float *v, int width, int height,float * xkernel, int xsize,float *ykernel,int ysize,int boundary)
{
int width2 = 2*width;
int height2 = 2*height;
float *tmp = (float *) malloc(width*height*sizeof(float));
/* convolution along x axis */
float sum = 0.0;
int org = xsize / 2;
for (int y=height;y--;)
for (int x=width;x--;) {
sum = 0.0;
for (int i=xsize;i--;) {
int s = x-i+org;
switch(boundary) {
case 0:
if (s>=0 && s<width) sum += xkernel[i]*u[y*width+s];
break;
case 1:
while (s<0) s+=width2;
while (s>=width2) s-=width2;
if (s>=width) s = width2-1-s;
sum += xkernel[i]*u[y*width+s];
break;
}
}
tmp[y*width+x] = sum;
}
/* convolution along y axis */
org = ysize / 2;
for (int y=height;y--;)
for (int x=width;x--;) {
sum=0.0;
for (int i=ysize;i--;) {
int s = y-i+org;
switch(boundary) {
case 0:
if (s>=0 && s<height) sum += ykernel[i]*tmp[s*width+x];
break;
case 1:
while (s<0) s+=height2;
while (s>=height2) s-=height2;
if (s>=height) s = height2-1-s;
sum += ykernel[i]*tmp[s*width+x];
break;
}
}
v[y*width+x] = sum;
}
free(tmp);
}
void gaussian_convolution(float *u, float *v, int width, int height, float sigma)
{
int ksize;
float * kernel;
ksize = (int)(2.0 * 4.0 * sigma + 1.0);
kernel = gauss(1,sigma,&ksize);
int boundary = 1;
copy(u,v,width*height);
horizontal_convolution(v, v, width, height, kernel, ksize, boundary);
vertical_convolution(v, v, width, height, kernel, ksize, boundary);
delete[] kernel; /*memcheck*/
}
void gaussian_convolution(float *u, float *v, int width, int height, float sigma, int ksize)
{
float * kernel;
kernel = gauss(1,sigma,&ksize);
int boundary = 1;
copy(u,v,width*height);
horizontal_convolution(v, v, width, height, kernel, ksize, boundary);
vertical_convolution(v, v, width, height, kernel, ksize, boundary);
}
void fast_separable_convolution(float *u, float *v, int width, int height,float * xkernel, int xsize,float *ykernel,int ysize,int boundary)
{
copy(u,v,width*height);
horizontal_convolution(v, v, width, height, xkernel, xsize, boundary);
vertical_convolution(v, v, width, height, ykernel, ysize, boundary);
}
/* Loop unrolling simply sums 5 multiplications
at a time to allow the compiler to schedule
operations better and avoid loop overhead.
*/
void buffer_convolution(float *buffer,float *kernel,int size,int ksize)
{
for (int i = 0; i < size; i++) {
float sum = 0.0;
float *bp = &buffer[i];
float *kp = &kernel[0];
/* Loop unrolling: do 5 multiplications at a time. */
// int k=0;
for(int k = 0; k < ksize; k++)
sum += *bp++ * *kp++;
// for(;k + 4 < ksize; bp += 5, kp += 5, k += 5)
// sum += bp[0] * kp[0] + bp[1] * kp[1] + bp[2] * kp[2] +
// bp[3] * kp[3] + bp[4] * kp[4];
/* Do multiplications at a time on remaining items. */
// for(; k < ksize; bp++ , kp++, k++) sum += *bp * (*kp);
buffer[i] = sum;
}
}
/* Convolve image with the 1-D kernel vector along image rows. This
is designed to be as efficient as possible.
*/
void horizontal_convolution(float *u, float *v, int width, int height, float *kernel, int ksize, int boundary)
{
int halfsize = ksize / 2;
int buffersize = width + ksize;
float *buffer = new float[buffersize];
for (int r = 0; r < height; r++) {
/// symmetry
int l = r*width;
if (boundary == 1)
for (int i = 0; i < halfsize; i++)
buffer[i] = u[l + halfsize - 1 - i ];
else
for (int i = 0; i < halfsize; i++)
buffer[i] = 0.0;
for (int i = 0; i < width; i++)
buffer[halfsize + i] = u[l + i];
if (boundary == 1)
for (int i = 0; i < halfsize; i++)
buffer[i + width + halfsize] = u[l + width - 1 - i];
else
for (int i = 0; i < halfsize; i++)
buffer[i + width + halfsize] = 0.0;
buffer_convolution(buffer, kernel, width, ksize);
for (int c = 0; c < width; c++)
v[r*width+c] = buffer[c];
}
delete[] buffer; /*memcheck*/
}
void vertical_convolution(float *u, float *v, int width, int height, float *kernel,int ksize, int boundary)
{
int halfsize = ksize / 2;
int buffersize = height + ksize;
float *buffer = new float[buffersize];
for (int c = 0; c < width; c++) {
if (boundary == 1)
for (int i = 0; i < halfsize; i++)
buffer[i] = u[(halfsize-i-1)*width + c];
else
for (int i = 0; i < halfsize; i++)
buffer[i] = 0.0f;
for (int i = 0; i < height; i++)
buffer[halfsize + i] = u[i*width + c];
if (boundary == 1)
for (int i = 0; i < halfsize; i++)
buffer[halfsize + height + i] = u[(height - i - 1)*width+c];
else
for (int i = 0; i < halfsize; i++)
buffer[halfsize + height + i] = 0.0f;
buffer_convolution(buffer, kernel, height, ksize);
for (int r = 0; r < height; r++)
v[r*width+c] = buffer[r];
}
delete[] buffer; /*memcheck*/
}
void heat(float *input, float *out, float step, int niter, float sigma, int width, int height)
{
int i,j,n,ksize,size,im,i1,j1,jm;
float *kernel = NULL, *laplacian = NULL, *convolved = NULL;
size = width*height;
if (sigma > 0.0) kernel = gauss(0,sigma,&ksize);
laplacian = (float *) malloc(size*sizeof(float));
convolved = (float *) malloc(size*sizeof(float));
for(n=0; n < niter; n++)
{
if (sigma > 0.0)
{
separable_convolution(input,convolved,width,height, kernel, ksize,kernel,ksize,1);
for(i=0; i< size; i++) laplacian[i] = convolved[i] - input[i];
} else
{
for (i=0; i < width;i++)
for (j=0; j< height ;j++)
{
if (j==0) jm=1; else jm=j-1;
if (j==height-1) j1=height-2; else j1=j+1;
if (i==0) im=1; else im=i-1;
if (i==width-1) i1=width-2; else i1=i+1;
laplacian[j*width + i] = - 4.0 * input[width*j+i] + input[width*j+im]+ input[width*j+i1]+input[width*jm + i] + input[width*j1 + i];
}
}
for(i=0; i < size; i++) out[i] = input[i] + step * laplacian[i];
copy(out,input,size);
}
free(laplacian);
free(convolved);
if (kernel) free(kernel);
}

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// Authors: Unknown. Please, if you are the author of this file, or if you
// know who are the authors of this file, let us know, so we can give the
// adequate credits and/or get the adequate authorizations.
#ifndef _FILTER_H_
#define _FILTER_H_
#include "library.h"
float * directional_gauss_filter(float xsigma, float ysigma, float angle, int *kwidth, int *kheight);
void median(float *u,float *v, float radius, int niter, int width,int height);
void remove_outliers(float *igray,float *ogray,int width, int height);
/// Convolution with a separable kernel, boundary condition: 0=zero, 1=symmetry
void separable_convolution(float *u, float *v, int width, int height, float *xkernel, int xsize, float *ykernel, int ysize,int boundary);
void buffer_convolution(float *buffer,float *kernel,int size,int ksize);
void horizontal_convolution(float *u, float *v, int width, int height, float *kernel, int ksize, int boundary);
void vertical_convolution(float *u, float *v, int width, int height, float *kernel,int ksize, int boundary);
void fast_separable_convolution(float *u, float *v, int width, int height,float * xkernel, int xsize,float *ykernel,int ysize,int boundary);
/// Can be called with u=v
void gaussian_convolution(float *u, float *v, int width, int height, float sigma);
void gaussian_convolution(float *u, float *v, int width, int height, float sigma, int ksize);
void convol(float *u, float *v, int width, int height, float *kernel, int kwidth, int kheight); /// Convolution with a kernel, No padding applied to the image
void heat(float *u, float *v, float step, int niter, float sigma, int width, int height);
#endif // _FILTER_H_

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// Authors: Unknown. Please, if you are the author of this file, or if you
// know who are the authors of this file, let us know, so we can give the
// adequate credits and/or get the adequate authorizations.
#include "flimage.h"
//////////////////////////////////////////////// Class flimage
//// Construction
flimage::flimage() : width(0), height(0), p(0)
{
}
flimage::flimage(int w, int h) : width(w), height(h), p(new float[w*h])
{
for (int j=width*height-1; j>=0 ; j--) p[j] = 0.0;
}
flimage::flimage(int w, int h, float v) : width(w), height(h), p(new float[w*h])
{
for (int j=width*height-1; j>=0 ; j--) p[j] = v;
}
flimage::flimage(int w, int h, float* v) : width(w), height(h), p(new float[w*h])
{
for (int j=width*height-1; j>=0 ; j--) p[j] = v[j];
}
void flimage::create(int w, int h)
{
erase();
width = w; height = h;
p = new float[w*h];
for (int j=width*height-1; j>=0 ; j--) p[j] = 0.0;
}
void flimage::create(int w, int h, float* v)
{
erase();
width = w; height = h; p = new float[w*h];
for (int j=width*height-1; j>=0 ; j--) p[j] = v[j];
}
flimage::flimage(const flimage& im) : width(im.width), height(im.height), p(new float[im.width*im.height])
{
for (int j=width*height-1; j>=0 ; j--) p[j] = im.p[j];
}
flimage& flimage::operator= (const flimage& im)
{
if (&im == this) {
return *this;
}
if (width != im.width || height != im.height)
{
erase();
width = im.width; height=im.height; p = new float[width*height];
}
for (int j=width*height-1; j>=0 ; j--) p[j] = im.p[j];
return *this;
}
//// Destruction
void flimage::erase()
{
width = height = 0;
if (p) delete[] p;
p=0;
}
flimage::~flimage()
{
erase();
}

53
asift_matching/src/flimage.h Executable file
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// Authors: Unknown. Please, if you are the author of this file, or if you
// know who are the authors of this file, let us know, so we can give the
// adequate credits and/or get the adequate authorizations.
#ifndef _FLIMAGE_H_
#define _FLIMAGE_H_
#include <iostream>
#include <string>
class flimage {
private:
int width, height; // image size
float* p; // array of color levels: level of pixel (x,y) is p[y*width+x]
public:
//// Construction
flimage();
flimage(int w, int h);
flimage(int w, int h, float v);
flimage(int w, int h, float* v);
flimage(const flimage& im);
flimage& operator= (const flimage& im);
void create(int w, int h);
void create(int w, int h, float *v);
//// Destruction
void erase();
~flimage();
//// Get Basic Data
int nwidth() const {return width;} // image size
int nheight() const {return height;}
/// Access values
float* getPlane() {return p;} // return the adress of the array of values
float operator()(int x, int y) const {return p[ y*width + x ];} // acces to the (x,y) value
float& operator()(int x, int y) {return p[ y*width + x ];} // by value (for const images) and by reference
};
#endif

186
asift_matching/src/fproj.cpp Executable file
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// Copyright (c) 2007 Lionel Moisan <Lionel.Moisan@parisdescartes.fr>
#include <stdio.h>
#include <math.h>
#include "splines.h"
#include "fproj.h"
/*------------------------ MAIN MODULE ---------------------------------*/
//void fproj(float *in, float *out, int nx, int ny, int *sx, int *sy, float *bg, int *o, float *p, char *i, float X1, float Y1, float X2, float Y2, float X3, float Y3, float *x4, float *y4)
void fproj(vector<float>& in, vector<float>& out, int nx, int ny, int *sx, int *sy, float *bg, int *o, float *p, char *i, float X1, float Y1, float X2, float Y2, float X3, float Y3, float *x4, float *y4)
/* Fimage in,out;
int *sx,*sy,*o;
char *i;
float *bg,*p,X1,Y1,X2,Y2,X3,Y3,*x4,*y4; */
{
/* int n1,n2,nx,ny,x,y,xi,yi,adr,dx,dy;*/
int n1,n2,x,y,xi,yi,adr,dx,dy;
float res,xx,yy,xp,yp,ux,uy,a,b,d,fx,fy,x12,x13,y12,y13;
float cx[12],cy[12],ak[13];
/* Fimage ref,coeffs; */
// float *ref, *coeffs;
vector<float> ref, coeffs;
/* CHECK ORDER */
if (*o!=0 && *o!=1 && *o!=-3 &&
*o!=3 && *o!=5 && *o!=7 && *o!=9 && *o!=11)
/* mwerror(FATAL,1,"unrecognized interpolation order.\n"); */
{
printf("unrecognized interpolation order.\n");
exit(-1);
}
/* ALLOCATE NEW IMAGE */
/* nx = in->ncol; ny = in->nrow; */
/* out = mw_change_fimage(out,*sy,*sx);
if (!out) mwerror(FATAL,1,"not enough memory\n"); */
if (*o>=3) {
/* coeffs = mw_new_fimage();
finvspline(in,*o,coeffs); */
// coeffs = new float[nx*ny];
coeffs = vector<float>(nx*ny);
finvspline(in,*o,coeffs,nx,ny);
ref = coeffs;
if (*o>3) init_splinen(ak,*o);
} else {
// coeffs = NULL;
ref = in;
}
/* COMPUTE NEW BASIS */
if (i) {
x12 = (X2-X1)/(float)nx;
y12 = (Y2-Y1)/(float)nx;
x13 = (X3-X1)/(float)ny;
y13 = (Y3-Y1)/(float)ny;
} else {
x12 = (X2-X1)/(float)(*sx);
y12 = (Y2-Y1)/(float)(*sx);
x13 = (X3-X1)/(float)(*sy);
y13 = (Y3-Y1)/(float)(*sy);
}
if (y4) {
xx=((*x4-X1)*(Y3-Y1)-(*y4-Y1)*(X3-X1))/((X2-X1)*(Y3-Y1)-(Y2-Y1)*(X3-X1));
yy=((*x4-X1)*(Y2-Y1)-(*y4-Y1)*(X2-X1))/((X3-X1)*(Y2-Y1)-(Y3-Y1)*(X2-X1));
a = (yy-1.0)/(1.0-xx-yy);
b = (xx-1.0)/(1.0-xx-yy);
}
else
{
a=b=0.0;
}
/********** MAIN LOOP **********/
for (x=0;x<*sx;x++)
for (y=0;y<*sy;y++) {
/* COMPUTE LOCATION IN INPUT IMAGE */
if (i) {
xx = 0.5+(((float)x-X1)*y13-((float)y-Y1)*x13)/(x12*y13-y12*x13);
yy = 0.5-(((float)x-X1)*y12-((float)y-Y1)*x12)/(x12*y13-y12*x13);
d = 1.0-(a/(a+1.0))*xx/(float)nx-(b/(b+1.0))*yy/(float)ny;
xp = xx/((a+1.0)*d);
yp = yy/((b+1.0)*d);
} else {
fx = (float)x + 0.5;
fy = (float)y + 0.5;
d = a*fx/(float)(*sx)+b*fy/(float)(*sy)+1.0;
xx = (a+1.0)*fx/d;
yy = (b+1.0)*fy/d;
xp = X1 + xx*x12 + yy*x13;
yp = Y1 + xx*y12 + yy*y13;
}
/* INTERPOLATION */
if (*o==0) {
/* zero order interpolation (pixel replication) */
xi = (int)floor((double)xp);
yi = (int)floor((double)yp);
/* if (xi<0 || xi>=in->ncol || yi<0 || yi>=in->nrow)*/
if (xi<0 || xi>=nx || yi<0 || yi>=ny)
res = *bg;
else
/* res = in->gray[yi*in->ncol+xi]; */
res = in[yi*nx+xi];
} else {
/* higher order interpolations */
if (xp<0. || xp>(float)nx || yp<0. || yp>(float)ny) res=*bg;
else {
xp -= 0.5; yp -= 0.5;
xi = (int)floor((double)xp);
yi = (int)floor((double)yp);
ux = xp-(float)xi;
uy = yp-(float)yi;
switch (*o)
{
case 1: /* first order interpolation (bilinear) */
n2 = 1;
cx[0]=ux; cx[1]=1.-ux;
cy[0]=uy; cy[1]=1.-uy;
break;
case -3: /* third order interpolation (bicubic Keys' function) */
n2 = 2;
keys(cx,ux,*p);
keys(cy,uy,*p);
break;
case 3: /* spline of order 3 */
n2 = 2;
spline3(cx,ux);
spline3(cy,uy);
break;
default: /* spline of order >3 */
n2 = (1+*o)/2;
splinen(cx,ux,ak,*o);
splinen(cy,uy,ak,*o);
break;
}
res = 0.; n1 = 1-n2;
/* this test saves computation time */
if (xi+n1>=0 && xi+n2<nx && yi+n1>=0 && yi+n2<ny) {
adr = yi*nx+xi;
for (dy=n1;dy<=n2;dy++)
for (dx=n1;dx<=n2;dx++)
/* res += cy[n2-dy]*cx[n2-dx]*ref->gray[adr+nx*dy+dx];*/
res += cy[n2-dy]*cx[n2-dx]*ref[adr+nx*dy+dx];
} else
for (dy=n1;dy<=n2;dy++)
for (dx=n1;dx<=n2;dx++)
/* res += cy[n2-dy]*cx[n2-dx]*v(ref,xi+dx,yi+dy,*bg); */
res += cy[n2-dy]*cx[n2-dx]*v(ref,xi+dx,yi+dy,*bg,nx,ny);
}
}
/* out->gray[y*(*sx)+x] = res; */
out[y*(*sx)+x] = res;
}
//if (coeffs)
/* mw_delete_fimage(coeffs); */
// delete[] coeffs;
}

9
asift_matching/src/fproj.h Executable file
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// Copyright (c) 2007 Lionel Moisan <Lionel.Moisan@parisdescartes.fr>
#include "library.h"
#include <vector>
using namespace std;
//void fproj(float *in, float *out, int nx, int ny, int *sx, int *sy, float *bg, int *o, float *p, char *i, float X1, float Y1, float X2, float Y2, float X3, float Y3, float *x4, float *y4);
void fproj(vector<float>& in, vector<float>& out, int nx, int ny, int *sx, int *sy, float *bg, int *o, float *p, char *i, float X1, float Y1, float X2, float Y2, float X3, float Y3, float *x4, float *y4);

116
asift_matching/src/frot.cpp Executable file
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// Copyright (c) 2007 Lionel Moisan <Lionel.Moisan@parisdescartes.fr>
#include "frot.h"
#ifndef M_PI
#define M_PI 3.14159265358979323846
#endif
void bound(int x, int y, float ca, float sa, int *xmin, int *xmax, int *ymin, int *ymax);
/* NB : calling this module with out=in is nonsense */
/* void frot(in,out,a,b,k_flag)
Fimage in,out;
float *a,*b;
char *k_flag; */
void frot(vector<float>& in, vector<float>& out, int nx, int ny, int *nx_out, int *ny_out, float *a, float *b, char *k_flag)
//void frot(float *in, float *out, int nx, int ny, int *nx_out, int *ny_out, float *a, float *b, char *k_flag)
{
/* int nx,ny,x,y,x1,y1,adr; */
int x,y,x1,y1,adr;
float ca,sa,xp,yp,a11,a12,a21,a22,ux,uy,xtrans,ytrans;
int tx1,ty1,tx2,ty2,xmin,xmax,ymin,ymax,sx,sy;
/* nx = in->ncol;
ny = in->nrow; */
ca = (float)cos((double)(*a)*M_PI/180.0);
sa = (float)sin((double)(*a)*M_PI/180.0);
/********** Compute new image location **********/
if (k_flag) {
/* crop image and fix center */
xmin = ymin = 0;
xmax = nx-1;
ymax = ny-1;
xtrans = 0.5*( (float)(nx-1)*(1.0-ca)+(float)(ny-1)*sa );
ytrans = 0.5*( (float)(ny-1)*(1.0-ca)-(float)(nx-1)*sa );
} else {
/* extend image size to include the whole input image */
xmin = xmax = ymin = ymax = 0;
bound(nx-1,0,ca,sa,&xmin,&xmax,&ymin,&ymax);
bound(0,ny-1,ca,sa,&xmin,&xmax,&ymin,&ymax);
bound(nx-1,ny-1,ca,sa,&xmin,&xmax,&ymin,&ymax);
xtrans = ytrans = 0.0;
}
sx = xmax-xmin+1;
sy = ymax-ymin+1;
/* out = mw_change_fimage(out,sy,sx);
if (!out) mwerror(FATAL,1,"not enough memory\n"); */
*nx_out = sx;
*ny_out = sy;
// printf("Hello sx=%d, sy=%d\n", sx, sy);
// out = new float[sy*sx];
out = std::vector<float>(sx*sy);
/********** Rotate image **********/
for (x=xmin;x<=xmax;x++)
for (y=ymin;y<=ymax;y++) {
xp = ca*(float)x-sa*(float)y + xtrans;
yp = sa*(float)x+ca*(float)y + ytrans;
x1 = (int)floor(xp);
y1 = (int)floor(yp);
ux = xp-(float)x1;
uy = yp-(float)y1;
adr = y1*nx+x1;
tx1 = (x1>=0 && x1<nx);
tx2 = (x1+1>=0 && x1+1<nx);
ty1 = (y1>=0 && y1<ny);
ty2 = (y1+1>=0 && y1+1<ny);
/* a11 = (tx1 && ty1? in->gray[adr]:*b);
a12 = (tx1 && ty2? in->gray[adr+nx]:*b);
a21 = (tx2 && ty1? in->gray[adr+1]:*b);
a22 = (tx2 && ty2? in->gray[adr+nx+1]:*b); */
a11 = (tx1 && ty1? in[adr]:*b);
a12 = (tx1 && ty2? in[adr+nx]:*b);
a21 = (tx2 && ty1? in[adr+1]:*b);
a22 = (tx2 && ty2? in[adr+nx+1]:*b);
/* out->gray[(y-ymin)*sx+x-xmin] =
(1.0-uy)*((1.0-ux)*a11+ux*a21)+uy*((1.0-ux)*a12+ux*a22);*/
out[(y-ymin)*sx+x-xmin] =
(1.0-uy)*((1.0-ux)*a11+ux*a21)+uy*((1.0-ux)*a12+ux*a22);
}
}
void bound(int x, int y, float ca, float sa, int *xmin, int *xmax, int *ymin, int *ymax)
/* int x,y;
float ca,sa;
int *xmin,*xmax,*ymin,*ymax;*/
{
int rx,ry;
rx = (int)floor(ca*(float)x+sa*(float)y);
ry = (int)floor(-sa*(float)x+ca*(float)y);
if (rx<*xmin) *xmin=rx; if (rx>*xmax) *xmax=rx;
if (ry<*ymin) *ymin=ry; if (ry>*ymax) *ymax=ry;
}

10
asift_matching/src/frot.h Executable file
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// Copyright (c) 2007 Lionel Moisan <Lionel.Moisan@parisdescartes.fr>
#include "library.h"
#include <vector>
using namespace std;
/*void frot(float *in, float *out, int nx, int ny, int *nx_out, int *ny_out, float *a, float *b, char *k_flag)*/
//void frot(float *, float (*)[], int, int, int *, int *, float *, float *, char *);
void frot(vector<float>&, vector<float>&, int, int, int *, int *, float *, float *, char *);

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// Authors: Unknown. Please, if you are the author of this file, or if you
// know who are the authors of this file, let us know, so we can give the
// adequate credits and/or get the adequate authorizations.
#include "homography.h"
#include "numerics.h"
#include <algorithm>
#include <math.h> /* For sqrt */
#include <string.h>
static const float minEigenValue = 1e-3f; // For regular matrix
namespace libNumerics {
/// Constructor. Field `b' used only for error computation.
ComputeH::ComputeH(Type type)
: _type(type), n( size(type) ), b(0)
{
clear();
}
// Destructor
ComputeH::~ComputeH()
{}
// Dimension of matrix w.r.t. type
int ComputeH::size(Type type)
{
switch(type) {
case Translation:
return 2;
case Zoom:
return 3;
case Rotation: // In fact 3, but nonlinear system
case GeneralZoom:
case Similarity:
return 4;
case Affine:
return 6;
case Projective:
return 8;
}
return 8;
}
// Return less general motion
ComputeH::Type ComputeH::restrict(Type t)
{
switch(t) {
case Translation:
return Translation; // Should return identity
case Rotation:
case Zoom:
return Translation;
case Similarity:
return Zoom; // Rotation also correct. Arbitrary choice.
case GeneralZoom:
return Zoom;
case Affine:
return Similarity;
case Projective:
return Affine;
}
return Affine;
}
// Reinitialize
void ComputeH::clear()
{
memset(Ann, 0, n*n*sizeof(double));
memset(Bn, 0, n*sizeof(double));
b = 0;
}
// Add two corresponding points
void ComputeH::add(float x, float y, float X, float Y, float w)
{
if(_type <= Similarity) { // Separate for readability
add_4parameters(x, y, X, Y, w);
return;
}
double x2 = x*x, y2 = y*y, xy = x*y;
double xX = x*X, yX = y*X, xY = x*Y, yY = y*Y;
double *A = Ann, *B = Bn;
*A++ += w* x2; // Equation 1
*A++ += w* xy;
A += 2;
*A++ += w* x;
A++;
if(_type == Projective) {
*A++ -= w* x*xX;
*A++ -= w* x*yX;
}
*B++ += w* x*X;
A++; // Equation 2
*A++ += w* y2;
A += 2;
*A++ += w* y;
A++;
if(_type == Projective) {
*A++ -= w* y*xX;
*A++ -= w* y*yX;
}
*B++ += w* y*X;
A +=2; // Equation 3
*A++ += w* x2;
*A++ += w* xy;
A++;
*A++ += w* x;
if(_type == Projective) {
*A++ -= w* x*xY;
*A++ -= w* x*yY;
}
*B++ += w* x*Y;
A +=3; // Equation 4
*A++ += w* y2;
A++;
*A++ += w* y;
if(_type == Projective) {
*A++ -= w* y*xY;
*A++ -= w* y*yY;
}
*B++ += w* y*Y;
A+= 4; // Equation 5
*A++ += w;
A++;
if(_type == Projective) {
*A++ -= w* xX;
*A++ -= w* yX;
}
*B++ += w* X;
A += 5; // Equation 6
*A++ += w;
*B++ += w* Y;
if(_type == Projective) {
*A++ -= w* xY;
*A++ -= w* yY;
A += 6; // Equation 7
*A++ += w* (xX*xX + xY*xY);
*A++ += w* (xX*yX + xY*yY);
*B++ -= w* (xX*X + xY*Y);
A+= 7; // Equation 8
*A++ += w* (yX*yX + yY*yY);
*B++ -= w* (yX*X + yY*Y);
}
b += w* (X*X + Y*Y);
}
// Add two corresponding points, type involving at most 4 parameters
void ComputeH::add_4parameters(float x, float y, float X, float Y, float w)
{
double *A = Ann, *B = Bn;
if(_type == Translation) {
A[0] += w;
A[3] += w;
B[0] += w* (X - x);
B[1] += w* (Y - y);
b += w* ((X-x)*(X-x) + (Y-y)*(Y-y));
return;
}
b += w* (X*X + Y*Y);
if(_type == GeneralZoom) {
A[0] += w* x*x;
A[2] += w* x;
B[0] += w* x*X;
A[5] += w* y*y;
A[7] += w* y;
B[1] += w* y*Y;
A[10]+= w;
B[2] += w* X;
A[15]+= w;
B[3] += w* Y;
return;
}
*A++ += w* (x*x + y*y); // Equation 1
if(_type != Zoom) // Similarity or Rotation
A++;
*A++ += w* x;
*A++ += w* y;
*B++ += w* (x*X + y*Y);
if(_type != Zoom) { // Similarity or Rotation
A++; // Equation 2
*A++ += w* (x*x + y*y);
*A++ += w* y;
*A++ -= w* x;
*B++ += w* (y*X - x*Y);
A++; // Prepare for next line
}
A++; // Equation 3
*A++ += w;
A++;
*B++ += w* X;
A += n-1; // Equation 4
*A++ += w;
*B++ += w* Y;
}
// Add corresponding lines of equation ux + by + x = 0
void ComputeH::add(float x, float y, float z, float X, float Y, float Z,
float w)
{
float s = 1.0f / (float)sqrt(x*x + y*y);
x *= s;
y *= s;
z *= s;
s = 1.0f / (float)sqrt(X*X + Y*Y);
X *= s;
Y *= s;
Z *= s;
if(_type <= Similarity) { // Separate for readability
add_4parameters(x, y, z, X, Y, Z, w);
return;
}
double x2 = x*x, y2 = y*y, z2 = z*z, xy = x*y, xz = x*z, yz = y*z;
double X2 = X*X, Y2 = Y*Y, Z2 = Z*Z, XY = X*Y, XZ = X*Z, YZ = Y*Z;
double *A = Ann, *B = Bn;
*A++ += w* (y2+z2) * X2; // Equation 1
*A++ -= w* xy * X2;
*A++ += w* (y2+z2) * XY;
*A++ -= w* xy * XY;
*A++ -= w* xz * X2;
*A++ -= w* xz * XY;
if(_type == Projective) {
*A++ += w* (y2+z2) * XZ;
*A++ -= w* xy * XZ;
}
*B++ += w* xz * XZ;
A++; // Equation 2
*A++ += w* (x2+z2) * X2;
*A++ -= w* xy * XY;
*A++ += w* (x2+z2) * XY;
*A++ -= w* yz * X2;
*A++ -= w* yz * XY;
if(_type == Projective) {
*A++ -= w* xy * XZ;
*A++ += w* (x2+z2) * XZ;
}
*B++ -= w* yz * XZ;
A += 2; // Equation 3
*A++ += w* (y2+z2) * Y2;
*A++ -= w* xy * Y2;
*A++ -= w* xz * XY;
*A++ -= w* xz * Y2;
if(_type == Projective) {
*A++ += w* (y2+z2) * YZ;
*A++ -= w* xy * YZ;
}
*B++ += w* xz * YZ;
A += 3; // Equation 4
*A++ += w* (x2+z2) * Y2;
*A++ -= w* yz * XY;
*A++ -= w* yz * Y2;
if(_type == Projective) {
*A++ -= w* xy * YZ;
*A++ += w* (x2+z2) * YZ;
}
*B++ += w* yz * YZ;
A += 4; // Equation 5
*A++ += w* X2; // *(x2+y2=1)
*A++ += w* XY; // *(x2+y2=1)
if(_type == Projective) {
*A++ -= w* xz * XZ;
*A++ -= w* yz * XZ;
}
*B++ -= w* XZ; // *(x2+y2=1)
A += 5; // Equation 6
*A++ += w* Y2; // *(x2+y2=1)
*B++ -= w* YZ; // *(x2+y2=1)
if(_type == Projective) {
*A++ -= w* xz * YZ;
*A++ -= w* yz * YZ;
A += 6; // Equation 7
*A++ += w* (y2+z2) * Y2;
*A++ -= w* xy * Z2;
*B++ += w* xz * Z2;
A += 7; // Equation 8
*A++ += w* (x2+z2) * Z2;
*B++ += w* yz * Z2;
}
b += w* Z2; // *(x2+y2=1)
}
// Add two corresponding lines, type involving at most 4 parameters
void ComputeH::add_4parameters(float x, float y, float z,
float X, float Y, float Z, float w)
{
double x2 = x*x, y2 = y*y, z2 = z*z, xy = x*y, xz = x*z, yz = y*z;
double X2 = X*X, Y2 = Y*Y, Z2 = Z*Z, XY = X*Y, XZ = X*Z, YZ = Y*Z;
double *A = Ann, *B = Bn;
if(_type == Translation) {
*A++ += w* X2; // *(x2+y2=1)
*A++ += w* XY; // *(x2+y2=1)
*B++ += w* (yz*XY + xz*X2 - XZ/* *(x2+y2=1) */);
A++;
*A++ += w* Y2; // *(x2+y2=1)
*B++ += w* (xz*XY + yz*Y2 - YZ/* *(x2+y2=1) */);
b += w* (z2 + Z2 + y2*X2 + x2*Z2 - 2*(xz*XZ + yz*YZ + xy*XZ));
return;
}
b += w* Z2; // *(x2+y2=1)
if(_type == GeneralZoom) {
*A++ += w* (y2+z2) * X2;
*A++ -= w* xy * XY;
*A++ -= w* xz * X2;
*A++ -= w* xz * XY;
*B++ += w* xz * XZ;
A++;
*A++ += w* (x2+z2) * Y2;
*A++ -= w* yz * XY;
*A++ -= w* yz * Y2;
*B++ += w* yz * YZ;
A += 2;
*A++ += w* X2; // *(x2+y2=1)
*A++ += w* XY; // *(x2+y2=1)
*B++ -= w* XZ; // *(x2+y2=1)
A += 3;
*A++ += w* Y2; // *(x2+y2=1)
*B++ -= w* YZ; // *(x2+y2=1)
return;
}
if(_type == Zoom) {
*A++ += w* (z2/* *(X2+Y2=1)*/ + y2*X2 + x2*Y2 - 2*xy*XY);
*A++ -= w* (yz*XY + xz*X2);
*A++ -= w* (yz*Y2 + xz*XY);
*B++ += w* (yz*YZ + xz*X2);
} else { // Similarity or Rotation
*A++ += w* (1 /* =x2+y2*/+ 2*(z2 - xy)) * X2;
*A++ += w* (x2 - y2) * XY;
*A++ -= w* (xz + yz) * X2;
*A++ -= w* (xz + yz) * XY;
*B++ += w* (xz + yz) * XZ;
A++;
*A++ += w* (1 /* =x2+y2*/+ 2*(z2 + xy)) * Y2;
*A++ += w* (xz - yz) * XY;
*A++ += w* (xz - yz) * Y2;
*B++ += w* (yz - xz) * YZ;
A++; // Prepare for next line
}
A++;
*A++ += w* X2; // *(x2+y2=1)
*A++ += w* XY; // *(x2+y2=1)
*B++ -= w* XZ; // *(x2+y2=1)
A += n-1;
*A++ += w* Y2; // *(x2+y2=1)
*B++ -= w* YZ; // *(x2+y2=1)
}
// Wrap vector of unknowns `v' into structure `map'
void ComputeH::wrap(Homography& h, const vector<double>& v) const
{
int i = 0;
h.mat()(0,0) = (_type==Translation)? 1.0f: v(i++);
h.mat()(0,1) = (_type==Translation || _type==Zoom || _type==GeneralZoom) ?
0: v(i++);
if(n >= 6) {
h.mat()(1,0) = v(i++);
h.mat()(1,1) = v(i++);
} else {
h.mat()(1,0) = -h.mat()(0,1);
h.mat()(1,1) = (_type==GeneralZoom)? v(i++): h.mat()(0,0);
}
h.mat()(0,2) = v(i++);
h.mat()(1,2) = v(i++);
if(_type == Projective) {
h.mat()(2,0) = v(i++);
h.mat()(2,1) = v(i++);
} else
h.mat()(2,0) = h.mat()(2,1) = 0;
h.mat()(2,2) = 1.0;
}
/// Unwrap parameters in \a h into vector of unknowns \a v.
void ComputeH::unwrap(const Homography& h, vector<double>& v) const
{
int i = 0;
if(_type != Translation) {
v(i++) = h.mat()(0,0);
if(_type != Zoom) {
if(_type != GeneralZoom) {
v(i++) = h.mat()(0,1); // Rotation or Similarity or...
if(n >= 6) // Affine or Projective
v(i++) = h.mat()(1,0);
}
if(_type==GeneralZoom || _type==Affine || _type==Projective)
v(i++) = h.mat()(1,1);
}
}
v(i++) = h.mat()(0,2);
v(i++) = h.mat()(1,2);
if(_type == Projective) {
v(i++) = h.mat()(2,0);
v(i++) = h.mat()(2,1);
}
}
// Sum of weights (=#correspondences)
float ComputeH::weight() const
{
// Diagonal coefficient affecting translation
int i = (_type == Projective) ? 6 : n;
return static_cast<float>(Ann[(i-1)*(n+1)]); // Element (i-1,i-1)
}
// Return quadratic error when mapping with `motion'
float ComputeH::q_error(const Homography& map) const
{
vector<double> v(n);
unwrap(map, v);
return q_error(v);
}
// Idem, with arguments in a vector
float ComputeH::q_error(const vector<double>& v) const
{
double e = b;
// Diagonal terms
const double* A = Ann + n*n-1;
for(int i = n-1; i >= 0; i--, A -= n+1)
e += *A * v(i) * v(i);
// Cross terms
A = Ann + (n-1)*n; // Last row
for(int i = n-1; i >= 0; i--, A -= n) {
double vi = v(i);
e -= 2.0 * Bn[i] * vi;
for(int j = n-1; j > i; j--)
e += 2.0 * A[j] * vi * v(j);
}
return static_cast<float>(e);
}
// LSE for rotation: solve linear system under quadratic constraint
bool ComputeH::compute_rotation(vector<double>& B) const
{
if(Ann[15] <= 0) // No point added or absurd value
return false;
B(0) = Ann[15] * Bn[0] - Ann[2] * Bn[2] - Ann[3] * Bn[3];
B(1) = Ann[15] * Bn[1] - Ann[3] * Bn[2] + Ann[2] * Bn[3];
double root = sqrt(B(0)*B(0) + B(1)*B(1));
if(root < minEigenValue)
return false;
// Test first solution
double lambda1 = (Ann[2]*Ann[2] + Ann[3]*Ann[3] + root) / Ann[15];
B(0) /= root;
B(1) /= root;
B(2) = (-Ann[2]*Bn[0] - Ann[3]*Bn[1] + lambda1 * Bn[2]) / root;
B(3) = (-Ann[3]*Bn[0] + Ann[2]*Bn[1] + lambda1 * Bn[3]) / root;
float v1 = q_error(B);
// Test second solution
vector<double> C(4);
double lambda2 = (Ann[2]*Ann[2] + Ann[3]*Ann[3] - root) / Ann[15];
C(0) = -B(0);
C(1) = -B(1);
C(2) = -(-Ann[2]*Bn[0] - Ann[3]*Bn[1] + lambda2 * Bn[2]) / root;
C(3) = -(-Ann[3]*Bn[0] + Ann[2]*Bn[1] + lambda2 * Bn[3]) / root;
if(v1 > q_error(C)) // Keep second solution
B = C;
return true;
}
// Return LSE motion and the sum of weights
float ComputeH::compute(Homography& map) const
{
vector<double> B(n);
B.read(Bn);
if(_type == Rotation) {
if(! compute_rotation(B))
return 0;
} else {
matrix<double> A(n,n);
A.read(Ann);
Normalization left, right;
if(_type == Projective && !normalize(left, A, B, right))
return 0;
A.symUpper();
vector<double> oldB(B);
if(! solveLU(A, B))
return 0;
if(_type == Projective && ! de_normalize(left, B, right))
return 0;
}
wrap(map, B);
return weight();
}
// Normalize independently original and final points so that the new
// origin is their centroid and their mean square distance (to it) is 2
bool ComputeH::normalize(Normalization& left,
matrix<double>& A, vector<double>& B,
Normalization& right) const
{
double w = A(5,5); // Total weight
if(w < minEigenValue)
return false;
double invW = 1.0 / w;
// Find normalizations (zoom-translation)
right.s = (A(0,0) + A(1,1)) - (A(0,4)*A(0,4) + A(1,4)*A(1,4))*invW;
if(right.s < minEigenValue)
return false;
right.s = sqrt(2.0*w / right.s);
right.x = - invW * right.s * A(0,4);
right.y = - invW * right.s * A(1,4);
left.s = b - (B(4)*B(4) + B(5)*B(5))*invW;
if(left.s < minEigenValue)
return false;
left.s = sqrt(2.0*w / left.s);
left.x = - invW * left.s * B(4);
left.y = - invW * left.s * B(5);
double norm = left.x*left.x + left.y*left.y;
double s2 = right.s*right.s, sS = right.s*left.s, S2 = left.s*left.s;
// Normalization of vector B
double b0 = B(0), b1 = B(1), b2 = B(2), b3 = B(3);
B(0) = sS * B(0) - w*right.x*left.x;
B(1) = sS * B(1) - w*right.y*left.x;
B(2) = sS * B(2) - w*right.x*left.y;
B(3) = sS * B(3) - w*right.y*left.y;
B(4) = B(5) = 0;
B(6) = sS*(left.s*B(6) - 2*(left.x*b0 + left.y*b2)) +
w*right.x*(norm - 2.0);
B(7) = sS*(left.s*B(7) - 2*(left.x*b1 + left.y*b3)) +
w*right.y*(norm - 2.0);
// Normalization of matrix A
double a0 = A(0,0), a1 = A(0,1), a6 = A(0,6), a7 = A(0,7), a9 = A(1,1);
double a15 = A(1,7), a22 = A(2,6), a23 = A(2,7), a31 = A(3,7);
A(0,0) = s2 * A(0,0) - w*right.x*right.x;
A(0,1) = s2 * A(0,1) - w*right.x*right.y;
A(0,4) = 0;
A(0,6) = right.s*(sS*A(0,6) - right.s*left.x*a0 - left.s*right.x*b0) +
w*right.x*left.x*right.x - right.x * B(0);
A(0,7) = right.s*(sS*A(0,7) - right.s*left.x*a1 - left.s*right.x*b1) +
w*right.x*left.x*right.y - right.y * B(0);
A(1,1) = s2 * A(1,1) - w*right.y*right.y;
A(1,4) = 0;
A(1,6) = A(0,7);
A(1,7) = right.s*(sS*A(1,7) - right.s*left.x*a9 - left.s*right.y*b1) +
w*right.y*left.x*right.y - right.y * B(1);
A(2,2) = A(0,0);
A(2,3) = A(0,1);
A(2,5) = 0;
A(2,6) = right.s*(sS*A(2,6) - right.s*left.y*a0 - left.s*right.x*b2) +
w*right.x*left.y*right.x - right.x * B(2);
A(2,7) = right.s*(sS*A(2,7) - right.s*left.y*a1 - left.s*right.x*b3) +
w*right.x*left.y*right.y - right.y * B(2);
A(3,3) = A(1,1);
A(3,5) = 0;
A(3,6) = A(2,7);
A(3,7) = right.s*(sS*A(3,7) - right.s*left.y*a9 - left.s*right.y*b3) +
w*right.y*left.y*right.y - right.y * B(3);
A(4,6) = -B(0);
A(4,7) = -B(1);
A(5,6) = -B(2);
A(5,7) = -B(3);
A(6,6) = s2*(S2*A(6,6) - 2*left.s*(left.x*a6+left.y*a22) + a0*norm) -
2*right.x*(B(6) + w*right.x);
A(6,7) = s2*(S2*A(6,7) - 2*left.s*(left.x*a7+left.y*a23) + a1*norm) -
right.x*B(7) - right.y*B(6) - 2*w*right.x*right.y;
A(7,7) = s2*(S2*A(7,7) - 2*left.s*(left.x*a15+left.y*a31) + a9*norm) -
2*right.y*(B(7) + w*right.y);
return true;
}
// `l' (left) and 'r' (right) representing zoom-translation normalizations,
// and `B' the parameters of a projective motion,
// compute l^-1 B r
bool ComputeH::de_normalize(const Normalization& l,
vector<double>& B,
const Normalization& r)
{
// B := B r
B(4) += r.x * B(0) + r.y * B(1); // Line 1
B(0) *= r.s;
B(1) *= r.s;
B(5) += r.x * B(2) + r.y * B(3); // Line 2
B(2) *= r.s;
B(3) *= r.s;
double f = r.x * B(6) + r.y * B(7) + 1.0; // Line 3
if(-minEigenValue < f && f < minEigenValue)
return false; // Origin of right normalization on line at infinity
B(6) *= r.s;
B(7) *= r.s;
// B := l^-1 B
double s = 1.0 / (l.s * f);
B(0) = (B(0) - l.x*B(6)) * s; // Line 1
B(1) = (B(1) - l.x*B(7)) * s;
B(4) = (B(4) - l.x* f ) * s;
B(2) = (B(2) - l.y*B(6)) * s; // Line 2
B(3) = (B(3) - l.y*B(7)) * s;
B(5) = (B(5) - l.y* f ) * s;
B(6) /= f; // Line 3
B(7) /= f;
return true;
}
} // libNumerics

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// Authors: Unknown. Please, if you are the author of this file, or if you
// know who are the authors of this file, let us know, so we can give the
// adequate credits and/or get the adequate authorizations.
#include "homography.h"
namespace libNumerics {
/// Constructor.
Homography::Homography()
: m_H( matrix<double>::eye(3) )
{}
/// Put to identity.
void Homography::setId()
{
m_H = matrix<double>::eye(3);
}
/// Set to translation.
void Homography::setTrans(double dx, double dy)
{
setId();
m_H(0,2) = dx;
m_H(1,2) = dy;
}
/// Set to zoom.
void Homography::setZoom(double zx, double zy)
{
setId();
m_H(0,0) = zx;
m_H(1,1) = zy;
}
/// Apply homography.
void Homography::operator()(double& x, double& y) const
{
vector<double> m(3);
m(0) = x;
m(1) = y;
m(2) = 1.0f;
m = m_H * m;
double z_1 = 1.0 / m(2);
x = m(0) * z_1;
y = m(1) * z_1;
}
/// Compose homographies.
Homography Homography::operator*(const Homography& rhs) const
{
Homography h;
h.m_H = m_H * rhs.m_H;
h.normalize();
return h;
}
/// Inverse homography.
Homography Homography::inverse() const
{
Homography h;
h.m_H = m_H.inv();
h.normalize();
return h;
}
/// Put coef(2,2) to 1.
void Homography::normalize()
{
m_H /= m_H(2,2);
}
} // libNumerics

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#ifndef HOMOGRAPHY_H
#define HOMOGRAPHY_H
#include "matrix.h"
namespace libNumerics {
/// 2-D homography transform.
class Homography {
public:
Homography();
void setId();
void setTrans(double dx, double dy);
void setZoom(double zx, double zy);
matrix<double>& mat() { return m_H; }
const matrix<double>& mat() const { return m_H; }
void operator()(double& x, double& y) const;
Homography operator*(const Homography& rhs) const;
Homography inverse() const;
private:
matrix<double> m_H;
void normalize();
};
/// Homography (and more restricted transforms) estimation.
class ComputeH {
public:
enum Type { Translation, // (2 parameters)
Rotation, // Rotation/Translation (3 parameters)
Zoom, // Zoom/Translation (3 parameters)
GeneralZoom, // Non uniform zoom/Translation (4 parameters)
Similarity, // Zoom/Rotation/Translation (4 parameters)
Affine, // (6 parameters)
Projective // (8 parameters)
};
static Type restrict(Type t); // Return less general motion
public:
ComputeH(Type type);
~ComputeH();
Type type() const { return _type; }
void clear();
/// Add corresponding points (x1,y1) and (x2,y2)
void add(float x1, float y1, float x2, float y2, float w = 1.0f);
/// Add corresponding lines of equation u x + v y + w = 0
void add(float a1, float b1, float c1,
float a2, float b2, float c2, float w = 1.0f);
float weight() const; ///< Sum of weights (=#correspondences)
float q_error(const Homography& map) const; ///< Quadratic error
float compute(Homography& map) const; ///< LSE motion, return support weight
private:
Type _type;
int n; ///< Dimension of matrix = # unknown parameters
double Ann[64], Bn[8], b; // Min (X 1) (A B) (X 1)^T is X^T = Ann^-1 Bn
static int size(Type type);
void add_4parameters(float x1, float y1, float x2, float y2, float w);
void add_4parameters(float a1, float b1, float c1,
float a2, float b2, float c2, float w);
void wrap(Homography& map, const vector<double>& v) const;
void unwrap(const Homography& map, vector<double>& v) const;
float q_error(const vector<double>& v) const; // Quadratic error
bool compute_rotation(vector<double>& B) const;
/// For Projective, data normalization is required
class Normalization { public: double x, y, s; };
bool normalize(Normalization& left,
matrix<double>& A, vector<double>& B,
Normalization& right) const;
static bool de_normalize(const Normalization& left,
vector<double>& B,
const Normalization& right);
};
} // libNumerics
#endif

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// Authors: Unknown. Please, if you are the author of this file, or if you
// know who are the authors of this file, let us know, so we can give the
// adequate credits and/or get the adequate authorizations.
#ifdef MATRIX_H // Do nothing if not included from matrix.h
#define INDEX(i,j) ((i) * m_cols + (j))
namespace libNumerics {
/// Constructor for \a m*\a n matrix.
/// \param m number of rows.
/// \param n number of columns.
template <typename T>
matrix<T>::matrix(int m, int n)
{
alloc(m, n);
}
/// Copy constructor.
template <typename T>
matrix<T>::matrix(const matrix<T>& m)
{
alloc(m.m_rows, m.m_cols);
for(int i = nElements()-1; i >= 0; i--)
p[i] = m.p[i];
}
/// Destructor.
template <typename T>
matrix<T>::~matrix()
{
free();
}
/// Assignment operator.
template <typename T>
matrix<T>& matrix<T>::operator=(const matrix<T>& m)
{
if(&m == this) return *this;
if(m.nElements() != nElements()){
free();
alloc(m.m_rows, m.m_cols);
} else {
m_rows = m.m_rows;
m_cols = m.m_cols;
}
for(int i = nElements()-1; i >= 0; i--)
p[i] = m.p[i];
return *this;
}
/// Access the coefficient on the \a i-th row, \a j-th column.
template <typename T>
inline T matrix<T>::operator() (int i, int j) const
{
assert(i >= 0 && i < m_rows && j >= 0 && j < m_cols);
return p[INDEX(i,j)];
}
/// Access the coefficient on the \a i-th row, \a j-th column.
template <typename T>
inline T& matrix<T>::operator() (int i, int j)
{
assert(i >= 0 && i < m_rows && j >= 0 && j < m_cols);
return p[INDEX(i,j)];
}
template <typename T>
inline T matrix<T>::operator() (int i) const
{
assert(i >= 0 && i < nElements());
return p[i];
}
template <typename T>
inline T& matrix<T>::operator() (int i)
{
assert(i >= 0 && i < nElements());
return p[i];
}
/// Set matrix at constant value.
///
/// Assign all coefficients to the value \a a.
template <typename T>
inline void matrix<T>::operator=(T a)
{
for(int i = nElements()-1; i >= 0; i--)
p[i] = a;
}
/// Multiply a matrix by scalar.
/// \param a a scalar.
template <typename T>
matrix<T> matrix<T>::operator*(T a) const
{
matrix<T> prod(m_rows, m_cols);
for(int i = nElements()-1; i >= 0; i--)
prod.p[i] = a * p[i];
return prod;
}
/// Multiply a matrix by scalar.
/// \param a a scalar.
template <typename T>
void matrix<T>::operator*=(T a)
{
for(int i = nElements()-1; i >= 0; i--)
p[i] *= a;
}
/// Divide a matrix by scalar.
/// \param a a scalar.
template <typename T>
matrix<T> matrix<T>::operator/(T a) const
{
return (*this) * ((T)1/a);
}
/// Divide a matrix by scalar.
/// \param a a scalar.
template <typename T>
void matrix<T>::operator/=(T a)
{
*this *= (T)1 / a;
}
/// Matrix sum.
template <typename T>
matrix<T> matrix<T>::operator+(const matrix<T>& m) const
{
assert(m.m_rows == m_rows && m.m_cols == m_cols);
matrix<T> sum(m_rows,m_cols);
for(int i = nElements()-1; i >= 0; i--)
sum.p[i] = p[i] + m.p[i];
return sum;
}
/// Matrix sum.
template <typename T>
void matrix<T>::operator+=(const matrix<T>& m)
{
assert(m.m_rows == m_rows && m.m_cols == m_cols);
for(int i = nElements()-1; i >= 0; i--)
p[i] += m.p[i];
}
/// Matrix subtraction.
template <typename T>
matrix<T> matrix<T>::operator-(const matrix<T>& m) const
{
assert(m.m_rows == m_rows && m.m_cols == m_cols);
matrix<T> sub(m_rows,m_cols);
for(int i = nElements()-1; i >= 0; i--)
sub.p[i] = p[i] - m.p[i];
return sub;
}
/// Matrix subtraction.
template <typename T>
void matrix<T>::operator-=(const matrix<T>& m)
{
assert(m.m_rows == m_rows && m.m_cols == m_cols);
for(int i = nElements()-1; i >= 0; i--)
p[i] -= m.p[i];
}
template <typename T>
matrix<T> matrix<T>::operator-() const
{
matrix<T> opp(m_rows, m_cols);
for(int i = nElements()-1; i >= 0; i--)
opp.p[i] = -p[i];
return opp;
}
/// Matrix multiplication.
template <typename T>
matrix<T> matrix<T>::operator*(const matrix<T>& m) const
{
assert(m_cols == m.m_rows);
matrix<T> prod(m_rows, m.m_cols);
T* out = prod.p;
for(int i = 0; i < prod.m_rows; i++) {
const T* left = p + i*m_cols;
for(int j = 0; j < prod.m_cols; j++, out++) {
const T* right = m.p + j;
*out = 0;
for(int k = 0; k < m_cols; k++) {
*out += left[k] * *right;
right += m.m_cols;
}
}
}
return prod;
}
/// Matrix-vector multiplication.
template <typename T>
vector<T> matrix<T>::operator*(const vector<T>& m) const
{
assert(m_cols == m.m_rows);
vector<T> prod(m_rows);
T* out = prod.p;
for(int i = 0; i < prod.m_rows; i++, out++) {
const T* left = p + i*m_cols;
const T* right = m.p;
*out = 0;
for(int k = 0; k < m_cols; k++)
*out += left[k] * right[k];
}
return prod;
}
/// Tranposed of matrix.
template <typename T>
matrix<T> matrix<T>::t() const
{
matrix<T> t(ncol(), nrow());
T* out = t.p;
for(int i = 0; i < t.nrow(); i++) {
const T* in = p + i;
for(int j = 0; j < t.ncol(); j++) {
*out++ = *in;
in += ncol();
}
}
return t;
}
/// Symmetrize upper part of matrix.
template <typename T>
void matrix<T>::symUpper()
{
assert(m_rows == m_cols);
for(int i = 1; i < m_rows; i++) {
const T* in = p + i;
T* out = p + m_cols*i;
for(int j = 0; j < i; j++) {
*out++ = *in;
in += m_cols;
}
}
}
/// Symmetrize lower part of matrix.
template <typename T>
void matrix<T>::symLower()
{
assert(m_rows == m_cols);
for(int i = 1; i < m_rows; i++) {
const T* in = p + m_cols*i;
T* out = p + i;
for(int j = 0; j < i; j++) {
*out = *in++;
out += m_cols;
}
}
}
template <typename T>
vector<T> matrix<T>::diag() const
{
assert(m_rows == m_cols);
vector<T> t(m_rows);
for(int i = 0; i < m_rows; i++)
t.p[i] = p[i*(m_cols+1)];
return t;
}
/// Matrix made of zeros.
template <typename T>
matrix<T> matrix<T>::zeros(int m, int n)
{
matrix<T> M(m,n);
for(int i = M.nElements()-1; i >= 0; i--)
M.p[i] = (T)0;
return M;
}
/// Matrix made of ones.
template <typename T>
matrix<T> matrix<T>::ones(int m, int n)
{
matrix<T> M(m,n);
for(int i = M.nElements()-1; i >= 0; i--)
M.p[i] = (T)1;
return M;
}
/// Identity matrix.
template <typename T>
matrix<T> matrix<T>::eye(int n)
{
matrix<T> M(n,n);
for(int i = M.nElements()-1; i >= 0; i--)
M.p[i] = (T)0;
for(int i = n-1; i >= 0; i--)
M.p[i*(n+1)] = (T)1;
return M;
}
/// Extract the submatrix [i0,i1]x[j0,j1].
/// \param i0 first row
/// \param i1 last row
/// \param j0 first column
/// \param j1 last column
template <typename T>
matrix<T> matrix<T>::copy(int i0, int i1, int j0, int j1) const
{
assert(0 <= i0 && i0 <= i1 && i1 <= m_rows &&
0 <= j0 && j0 <= j1 && j1 <= m_cols);
matrix<T> sub(i1-i0+1,j1-j0+1);
T* out = sub.p;
for(int i = i0; i <= i1; i++) {
const T* in = p + INDEX(i, j0);
for(int j = j0; j <= j1; j++)
*out++ = *in++;
}
return sub;
}
/// Extract the columns of index in [j0,j1].
/// \param j0 first column
/// \param j1 last column
template <typename T>
matrix<T> matrix<T>::copyCols(int j0, int j1) const
{
return copy(0, lastRow(), j0, j1);
}
/// Extract the rows of index in [i0,i1].
/// \param i0 first row
/// \param i1 last row
template <typename T>
matrix<T> matrix<T>::copyRows(int i0, int i1) const
{
return copy(i0, i1, 0, lastCol());
}
/// Paste a matrix in another one, at position (\a i0,\a j0)
/// \param i0 first row where to paste in
/// \param j0 first column where to paste in
/// \param matrix to paste
template <typename T>
void matrix<T>::paste(int i0, int j0, const matrix<T>& m)
{
assert(i0 >= 0 && i0+m.m_rows <= m_rows &&
j0 >= 0 && j0+m.m_cols <= m_cols);
const T* in = m.p;
for(int i = 0; i < m.m_rows; i++) {
T* out = p + INDEX(i0+i, j0);
for(int j = 0; j < m.m_cols; j++)
*out++ = *in++;
}
}
/// Concatenate matrices.
template <typename T>
matrix<T> cat(const matrix<T>& m1, const matrix<T>& m2)
{
assert(m1.m_rows == m2.m_rows);
matrix<T> m(m1.m_rows, m1.m_cols+m2.m_cols);
m.paste(0, 0, m1);
m.paste(0, m1.m_cols, m2);
return m;
}
/// Copy column number \a j.
template <typename T>
vector<T> matrix<T>::col(int j) const
{
assert(j >= 0 && j < m_cols);
vector<T> c(m_rows);
const T* in = p + j;
for(int i = 0; i < m_rows; i++) {
c(i) = *in;
in += m_cols;
}
return c;
}
/// Copy row number \a i.
template <typename T>
inline matrix<T> matrix<T>::row(int i) const
{
return copy(i, i, 0, lastCol());
}
template <class T>
void swap(matrix<T>& A, matrix<T>& B)
{
int i=A.m_rows;
A.m_rows = B.m_rows;
B.m_rows = i;
i = A.m_cols;
A.m_cols = B.m_cols;
B.m_cols = i;
T* p = A.p;
A.p = B.p;
B.p = p;
}
template <typename T>
void matrix<T>::swapRows(int i0, int i1)
{
assert(0 <= i0 && i0 < m_rows &&
0 <= i1 && i1 < m_rows);
T* row0 = p + i0*m_cols;
T* row1 = p + i1*m_cols;
for(int j = m_cols-1; j >= 0; j--) {
T tmp = *row0; *row0++ = *row1; *row1++ = tmp;
}
}
template <typename T>
void matrix<T>::swapCols(int j0, int j1)
{
assert(0 <= j0 && j0 < m_cols &&
0 <= j1 && j1 < m_cols);
T* col0 = p + j0;
T* col1 = p + j1;
for(int i = m_rows-1; i >= 0; i--) {
T tmp = *col0; *col0 = *col1; *col1 = tmp;
col0 += m_cols;
col1 += m_cols;
}
}
/// Copy the array values in a matrix, row by row.
/// \param m number of rows
/// \param n number of columns
/// \param v an array of scalar of size m*n
template <typename T> template <typename U>
void matrix<T>::read(const U* v)
{
for(int i = nElements()-1; i >= 0; i--)
p[i] = (T)v[i];
}
/// Read the coefficients from \a m.
template <typename T>
inline void matrix<T>::read(const matrix<T>& m)
{
assert(m.nElements() == nElements());
read(m.p);
}
/// Copy the matrix coefficients in an array.
///
/// The matrix is scanned row by row.
template <typename T>
void matrix<T>::write(T* vect) const
{
for(int i = nElements()-1; i >= 0; i--)
vect[i] = p[i];
}
template <typename T>
void matrix<T>::alloc(int m, int n)
{
assert(m > 0 && n > 0);
m_rows = m;
m_cols = n;
p = new T[m*n];
}
template <typename T>
inline void matrix<T>::free()
{
delete [] p;
p = NULL;
}
template <typename T>
inline int matrix<T>::nElements() const
{
return m_rows*m_cols;
}
/// Submatrix without row \a i0 and col \a j0.
template <typename T>
matrix<T>& matrix<T>::sub(matrix<T>& s, int i0, int j0) const
{
const T* in = p;
T* out = s.p;
for(int i = 0; i < i0; i++) {
for(int j = 0; j < j0; j++)
*out++ = *in++;
++in; // Skip col j0
for(int j = j0+1; j < m_cols; j++)
*out++ = *in++;
}
in += m_cols; // Skip row i0
for(int i = i0+1; i < m_rows; i++) {
for(int j = 0; j < j0; j++)
*out++ = *in++;
++in; // Skip col j0
for(int j = j0+1; j < m_cols; j++)
*out++ = *in++;
}
return s;
}
/// Trace.
template <typename T>
T matrix<T>::tr() const
{
assert(m_rows == m_cols);
T res = (T)0;
for(int i = 0; i < m_rows; i++)
res += p[i*(m_cols+1)];
return res;
}
/// Determinant. Slow, use only for small matrices.
template <typename T>
T matrix<T>::det() const
{
assert(m_rows == m_cols);
if(m_rows == 1)
return p[0];
if(m_rows == 2)
return (p[0]*p[3]-p[1]*p[2]);
T res = (T)0;
T sign = (T)1;
matrix<T> s(m_rows-1, m_cols-1);
for(int j = 0; j < m_cols; j++) {
res += sign*p[j]*sub(s,0,j).det();
sign = -sign;
}
return res;
}
/// Inverse. Slow, use only for small matrices.
template <typename T>
matrix<T> matrix<T>::inv() const
{
assert(m_rows == m_cols);
matrix<T> res(m_rows, m_cols);
if(m_rows == 1)
res.p[0] = (T)1/p[0];
else {
T d = (T)1 / det();
T signi = (T)1;
T* out = res.p;
matrix<T> s(m_rows-1, m_cols-1);
for(int i = 0; i < m_rows; i++) {
T signj = signi;
for(int j = 0; j < m_cols; j++) {
*out++ = signj*d*sub(s,j,i).det();
signj = -signj;
}
signi = -signi;
}
}
return res;
}
} // namespace libNumerics
#undef INDEX
#endif // MATRIX_H

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// Authors: Unknown. Please, if you are the author of this file, or if you
// know who are the authors of this file, let us know, so we can give the
// adequate credits and/or get the adequate authorizations.
#ifndef MATRIX_H
#define MATRIX_H
#include <iostream>
#include <cassert>
namespace libNumerics {
// Forward declaration, definition below
template <typename T> class vector;
template <typename T> class matrix;
template <typename T> matrix<T> cat(const matrix<T>&, const matrix<T>&);
template <typename T> void swap(matrix<T>&, matrix<T>&);
/// Matrix class
template <typename T>
class matrix
{
public:
static matrix<T> zeros(int m) { return zeros(m,m); }
static matrix<T> zeros(int m, int n);
static matrix<T> ones(int m) { return ones(m,m); }
static matrix<T> ones(int m, int n);
static matrix<T> eye(int n); ///< Identity matrix.
public:
matrix(int m, int n);
matrix(const matrix<T>& m);
virtual ~matrix();
matrix<T>& operator=(const matrix<T>& m);
int nrow() const { return m_rows; } ///< The number of rows.
int ncol() const { return m_cols; } ///< The number of columns.
T operator() (int i, int j) const;
T& operator() (int i, int j);
T operator() (int i) const;
T& operator() (int i);
void operator=(T a);
matrix<T> operator*(T a) const;
matrix<T> operator/(T a) const;
void operator*=(T a);
void operator/=(T a);
/// Product by scalar.
friend matrix<T> operator*(T a, const matrix<T>& m)
{ return m * a; }
matrix<T> operator+(const matrix<T>& m) const;
matrix<T> operator-(const matrix<T>& m) const;
matrix<T> operator-() const; ///< Matrix opposite.
matrix<T> operator*(const matrix<T>& m) const;
vector<T> operator*(const vector<T>& m) const;
void operator+=(const matrix<T>& m);
void operator-=(const matrix<T>& m);
matrix<T> t() const; ///< Transpose.
vector<T> diag() const; ///< Diagonal of matrix.
T tr() const;
T det() const;
matrix<T> inv() const;
void symUpper();
void symLower();
matrix<T> copy(int i0, int i1, int j0, int j1) const;
matrix<T> copyCols(int j0, int j1) const;
matrix<T> copyRows(int i0, int i1) const;
void paste(int i0, int j0, const matrix<T>& block);
friend matrix<T> cat<T>(const matrix<T>& left, const matrix<T>& right);
vector<T> col(int j) const; ///< Copy column.
matrix<T> row(int i) const; ///< Copy row.
int lastCol() const {return m_cols-1;} ///< Index of last column.
int lastRow() const {return m_rows-1;} ///< Index of last row.
friend void swap<T>(matrix<T>&, matrix<T>&);
void swapRows(int i0, int i1);
void swapCols(int j0, int j1);
template <typename U>
void read(const U* v);
void read(const matrix<T>& v);
void write(T* vect) const;
protected:
int m_rows; ///< Number of rows.
int m_cols; ///< Number of columns.
T* p; ///< 1-D array of coefficients.
void alloc(int m, int n); ///< Allocate the array value.
void free(); ///< Free the array value.
int nElements() const; ///< Number of elements in the matrix.
matrix<T>& sub(matrix<T>& s, int i, int j) const;
}; // class matrix
/// Column vector class (template)
template <typename T>
class vector : public matrix<T>
{
public:
explicit vector(int m);
vector(T x);
vector(T x, T y);
vector(T x, T y, T z);
vector(const vector<T>& v);
virtual ~vector() {}
using matrix<T>::operator=;
vector<T>& operator=(const vector<T>& v);
// void operator=(T a);
vector<T> operator*(T a) const;
vector<T> operator/(T a) const;
/// Product of a vector by a scalar.
friend vector<T> operator*(T a, const vector<T>& v)
{ return v * a; }
vector<T> operator+(const vector<T>& v) const;
vector<T> operator-(const vector<T>& v) const;
vector<T> operator-() const; ///< Vector opposite.
matrix<T> operator*(const matrix<T>& m) const;
matrix<T> diag() const;
T qnorm() const;
vector<T> copy(int i0, int i1) const;
void paste(int i0, const vector<T>& v);
};
} // namespace libNumerics
/// Output matrix coefficients.
template <typename T>
inline std::ostream& operator<<(std::ostream& out,
const libNumerics::matrix<T>& m)
{
for(int i = 0; i < m.nrow(); ++i) {
out << ((i==0)? "[": ";");
for (int j = 0; j < m.ncol(); ++j)
out << " " << m(i,j);
}
out << " ]";
return out;
}
/// Input matrix. Need to know the dimensions in advance...
template <class T>
inline std::istream& operator>>(std::istream& in,
libNumerics::matrix<T>& m)
{
char c;
for(int i=0; i < m.nrow(); ++i) {
in >> c;
for(int j=0; j < m.ncol(); ++j)
in >> m(i,j);
}
in >> c;
return in;
}
template <typename T>
T dot(const libNumerics::vector<T>& u, const libNumerics::vector<T>& v);
template <typename T>
libNumerics::vector<T> cross(const libNumerics::vector<T>& u,
const libNumerics::vector<T>& v);
// Need to see definitions for templates...
#include "matrix.cpp"
#include "vector.cpp"
#endif

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// Authors: Unknown. Please, if you are the author of this file, or if you
// know who are the authors of this file, let us know, so we can give the
// adequate credits and/or get the adequate authorizations.
#include "numerics.h"
#include <cmath>
#include <vector>
#include <limits>
#include <algorithm>
namespace libNumerics {
const flnum MinLM::DEFAULT_RELATIVE_TOL = 1E-3;
const flnum MinLM::DEFAULT_LAMBDA_INIT = 1E-3;
const flnum MinLM::DEFAULT_LAMBDA_FACT = 10.0;
const flnum MinLM::EPSILON_KERNEL = 1E-9;
inline flnum ABS(flnum x)
{ return (x >= 0)? x: -x; }
/// Resolution by LU decomposition with pivot.
bool solveLU(const matrix<flnum>& A, const vector<flnum>& B, vector<flnum>& X)
{
X = B;
return solveLU(A, X);
}
/// Replace X by A^{-1}X, by LU solver.
bool solveLU(matrix<flnum> A, vector<flnum>& X)
{
assert(A.nrow() == A.ncol());
int n = A.nrow();
vector<flnum> rowscale(n); // Implicit scaling of each row
std::vector<int> permut(n,0); // Permutation of rows
// Get the implicit scaling information of each row
for(int i=0; i< n; i++) {
flnum max = 0.0;
for(int j=0; j< n; j++) {
flnum tmp = ABS(A(i,j));
if (tmp> max)
max = tmp;
}
if(max == 0.0)
return false;
rowscale(i) = 1.0/max;
}
// Perform the decomposition
for(int k=0; k < n; k++) {
// Search for largest pivot element
flnum max = rowscale(k)*ABS(A(k,k));
int imax = k;
for(int i=k+1; i < n; i++) {
flnum tmp = rowscale(i)*ABS(A(i,k));
if(tmp > max) {
max = tmp;
imax = i;
}
}
if(max == 0.0)
return false;
// Interchange rows if needed
if(k != imax) {
A.swapRows(k, imax);
rowscale(imax) = rowscale(k); // Scale of row k no longer needed
}
permut[k] = imax; // permut(k) was not initialized before
flnum Akk = 1/A(k,k);
for(int i=k+1; i < n; i++) {
flnum tmp = A(i,k) *= Akk; // Divide by pivot
for (int j=k+1;j < n; j++) // Reduce the row
A(i,j) -= tmp*A(k,j);
}
}
// Forward substitution
for (int k=0; k < n; k++) {
flnum sum = X(permut[k]);
X(permut[k]) = X(k);
for(int j = 0; j < k; j++)
sum -= A(k,j)*X(j);
X(k) = sum;
}
// Backward substitution
for(int k=n-1; k >= 0; k--) {
flnum sum = X(k);
for(int j=k+1; j < n; j++)
sum -= A(k,j)*X(j);
X(k) = sum/A(k,k);
}
return true;
}
/// Decompose A into U diag(W) V^T with U(m,n) and V(n,n) having orthonormal
/// vectors.
SVD::SVD(const matrix<flnum>& A)
: m_U(A), m_V(A.ncol(),A.ncol()), m_W(A.ncol())
{
compute();
sort();
}
/// SVD computation. Initial matrix stored in m_U as input.
void SVD::compute()
{
const flnum EPSILON = std::numeric_limits<flnum>::epsilon();
const int SVD_MAX_ITS = 30;
int rows = m_U.nrow();
int cols = m_U.ncol();
flnum g, scale, anorm;
vector<flnum> RV1(cols);
// Householder reduction to bidiagonal form:
anorm = g = scale = 0.0;
for (int i=0; i< cols; i++) {
int l = i + 1;
RV1(i) = scale*g;
g = scale = 0.0;
if(i< rows) {
for (int k=i; k< rows; k++)
scale += ABS(m_U(k,i));
if (scale != 0.0) {
flnum invScale=1.0/scale, s=0.0;
for (int k=i; k< rows; k++) {
m_U(k,i) *= invScale;
s += m_U(k,i) * m_U(k,i);
}
flnum f = m_U(i,i);
g = - withSignOf(std::sqrt(s),f);
flnum h = 1.0 / (f*g - s);
m_U(i,i) = f - g;
for (int j=l; j< cols; j++) {
s = 0.0;
for (int k=i; k< rows; k++)
s += m_U(k,i) * m_U(k,j);
f = s * h;
for (int k=i; k< rows; k++)
m_U(k,j) += f * m_U(k,i);
}
for (int k=i; k< rows; k++)
m_U(k,i) *= scale;
}
}
m_W(i) = scale * g;
g = scale = 0.0;
if ( i< rows && i< cols-1 ) {
for (int k=l; k< cols; k++)
scale += ABS(m_U(i,k));
if (scale != 0.0) {
flnum invScale=1.0/scale, s=0.0;
for (int k=l; k< cols; k++) {
m_U(i,k) *= invScale;
s += m_U(i,k) * m_U(i,k);
}
flnum f = m_U(i,l);
g = - withSignOf(std::sqrt(s),f);
flnum h = 1.0 / (f*g - s);
m_U(i,l) = f - g;
for (int k=l; k< cols; k++)
RV1(k) = m_U(i,k) * h;
for (int j=l; j< rows; j++) {
s = 0.0;
for (int k=l; k< cols; k++)
s += m_U(j,k) * m_U(i,k);
for (int k=l; k< cols; k++)
m_U(j,k) += s * RV1(k);
}
for (int k=l; k< cols; k++)
m_U(i,k) *= scale;
}
}
anorm = std::max(anorm, ABS(m_W(i)) + ABS(RV1(i)) );
}
// Accumulation of right-hand transformations:
m_V(cols-1,cols-1) = 1.0;
for (int i= cols-2; i>=0; i--) {
m_V(i,i) = 1.0;
int l = i+1;
g = RV1(l);
if (g != 0.0) {
flnum invgUil = 1.0 / (m_U(i,l)*g);
for (int j=l; j< cols; j++)
m_V(j,i) = m_U(i,j) * invgUil;
for (int j=l; j< cols; j++){
flnum s = 0.0;
for (int k=l; k< cols; k++)
s += m_U(i,k) * m_V(k,j);
for (int k=l; k< cols; k++)
m_V(k,j) += s * m_V(k,i);
}
}
for (int j=l; j< cols; j++)
m_V(i,j) = m_V(j,i) = 0.0;
}
// Accumulation of left-hand transformations:
for (int i=std::min(rows,cols)-1; i>=0; i--) {
int l = i+1;
g = m_W(i);
for (int j=l; j< cols; j++)
m_U(i,j) = 0.0;
if (g != 0.0) {
g = 1.0 / g;
flnum invUii = 1.0 / m_U(i,i);
for (int j=l; j< cols; j++) {
flnum s = 0.0;
for (int k=l; k< rows; k++)
s += m_U(k,i) * m_U(k,j);
flnum f = (s * invUii) * g;
for (int k=i; k< rows; k++)
m_U(k,j) += f * m_U(k,i);
}
for (int j=i; j< rows; j++)
m_U(j,i) *= g;
} else
for (int j=i; j< rows; j++)
m_U(j,i) = 0.0;
m_U(i,i) = m_U(i,i) + 1.0;
}
// Diagonalization of the bidiagonal form:
for (int k=cols-1; k>=0; k--) { // Loop over singular values
for (int its=1; its<=SVD_MAX_ITS; its++) {
bool flag = false;
int l = k;
int nm = k-1;
while(l>0 && ABS(RV1(l)) > EPSILON*anorm) { // Test for splitting
if(ABS(m_W(nm)) <= EPSILON*anorm) {
flag = true;
break;
}
l--;
nm--;
}
if (flag) { // Cancellation of RV1(l), if l > 0
flnum c=0.0, s=1.0;
for (int i=l; i< k+1; i++) {
flnum f = s * RV1(i);
RV1(i) = c * RV1(i);
if (ABS(f)<=EPSILON*anorm)
break;
g = m_W(i);
flnum h = SVD::hypot(f,g);
m_W(i) = h;
h = 1.0 / h;
c = g * h;
s = - f * h;
for (int j=0; j< rows; j++)
rotate(m_U(j,nm),m_U(j,i), c,s);
}
}
flnum z = m_W(k);
if (l==k) { // Convergence of the singular value
if (z< 0.0) { // Singular value is made nonnegative
m_W(k) = -z;
for (int j=0; j< cols; j++)
m_V(j,k) = - m_V(j,k);
}
break;
}
// Exception if convergence to the singular value not reached:
if(its==SVD_MAX_ITS) throw SvdConvergenceError();
flnum x = m_W(l); // Get QR shift value from bottom 2x2 minor
nm = k-1;
flnum y = m_W(nm);
g = RV1(nm);
flnum h = RV1(k);
flnum f = ( (y-z)*(y+z) + (g-h)*(g+h) ) / ( 2.0*h*y );
g = SVD::hypot(f,1.0);
f = ( (x-z)*(x+z) + h*(y/(f+withSignOf(g,f)) - h) ) / x;
// Next QR transformation (through Givens reflections)
flnum c=1.0, s=1.0;
for (int j=l; j<=nm; j++) {
int i = j+1;
g = RV1(i);
y = m_W(i);
h = s * g;
g = c * g;
z = SVD::hypot(f,h);
RV1(j) = z;
z = 1.0 / z;
c = f * z;
s = h * z;
f = x*c + g*s;
g = g*c - x*s;
h = y * s;
y *= c;
for(int jj=0; jj < cols; jj++)
rotate(m_V(jj,j),m_V(jj,i), c,s);
z = SVD::hypot(f,h);
m_W(j) = z;
if (z!=0.0) { // Rotation can be arbitrary if z = 0.0
z = 1.0 / z;
c = f * z;
s = h * z;
}
f = c*g + s*y;
x = c*y - s*g;
for(int jj=0; jj < rows; jj++)
rotate(m_U(jj,j),m_U(jj,i), c,s);
}
RV1(l) = 0.0;
RV1(k) = f;
m_W(k) = x;
}
}
}
/// Recompose from SVD. This should be the initial matrix.
matrix<flnum> SVD::compose() const
{
return m_U * m_W.diag() * m_V.t();
}
flnum SVD::withSignOf(flnum a, flnum b)
{ return b >= 0 ? (a >= 0 ? a : -a) : (a >= 0 ? -a : a); }
/// Replace hypot of math.h by robust numeric implementation.
flnum SVD::hypot(flnum a, flnum b)
{
a = ABS(a);
b = ABS(b);
if(a > b) {
b /= a;
return a*std::sqrt(1.0 + b*b);
} else if(b) {
a /= b;
return b*std::sqrt(1.0 + a*a);
}
return 0.0;
}
/// Utility function used while computing SVD.
void SVD::rotate(flnum& a, flnum& b, flnum c, flnum s)
{
flnum d = a;
a = +d*c +b*s;
b = -d*s +b*c;
}
class SVDElement {
public:
SVDElement(const vector<flnum>& W, int i)
: m_val(W(i)), m_i(i) {}
bool operator<(const SVDElement& e) const
{ return (m_val>e.m_val); }
flnum m_val;
int m_i;
};
/// Sort SVD by decreasing order of singular value.
void SVD::sort()
{
std::vector<SVDElement> vec;
for(int i=0; i < m_U.ncol(); i++)
vec.push_back( SVDElement(m_W, i) );
std::sort(vec.begin(), vec.end());
// Apply permutation
for(int i=m_U.ncol()-1; i >=0; i--)
if(vec[i].m_i != i) { // Find cycle of i
const vector<flnum> colU = m_U.col(i);
const vector<flnum> colV = m_V.col(i);
const flnum w = m_W(i);
int j = i;
while(vec[j].m_i != i) {
m_U.paste(0,j, m_U.col(vec[j].m_i));
m_V.paste(0,j, m_V.col(vec[j].m_i));
m_W(j) = m_W(vec[j].m_i);
std::swap(j,vec[j].m_i);
}
vec[j].m_i = j;
m_U.paste(0,j, colU);
m_V.paste(0,j, colV);
m_W(j) = w;
}
}
/// Constructor.
MinLM::MinLM()
: iterations(0), relativeTol(DEFAULT_RELATIVE_TOL),
lambdaInit(DEFAULT_LAMBDA_INIT), lambdaFact(DEFAULT_LAMBDA_FACT)
{}
/// In equation JtJ X = B, remove columns of J close to 0, so that JtJ can be
/// invertible
void MinLM::compress(matrix<flnum>& JtJ, vector<flnum>& B)
{
flnum max=0;
for(int i=0; i < JtJ.nrow(); i++)
if(JtJ(i,i) > max)
max = JtJ(i,i);
max *= EPSILON_KERNEL;
m_nullCols.clear();
for(int i=0; i < JtJ.nrow(); i++)
if(JtJ(i,i) <= max)
m_nullCols.push_back(i);
if( m_nullCols.empty() )
return;
int n=(int)m_nullCols.size();
matrix<flnum> JtJ2(JtJ.nrow()-m_nullCols.size(),
JtJ.ncol()-m_nullCols.size());
vector<flnum> B2(B.nrow()-(int)m_nullCols.size());
for(int i=0,i2=0; i < JtJ.nrow(); i++) {
if(i-i2 < n && m_nullCols[i-i2]==i)
continue;
for(int j=0,j2=0; j < JtJ.ncol(); j++) {
if(j-j2 < n && m_nullCols[j-j2]==j)
continue;
JtJ2(i2,j2) = JtJ(i,j);
j2++;
}
B2(i2) = B(i);
i2++;
}
swap(JtJ,JtJ2);
swap(B,B2);
}
/// Insert 0 in rows of B that were removed by \c compress()
void MinLM::uncompress(vector<flnum>& B)
{
if(m_nullCols.empty())
return;
int n=(int)m_nullCols.size();
vector<flnum> B2(B.nrow()+(int)m_nullCols.size());
for(int i=0,i2=0; i2 < B2.nrow(); i2++)
if(i2-i < n && m_nullCols[i2-i]==i2)
B2(i2)=0;
else
B2(i2) = B(i++);
swap(B,B2);
}
/// Perform minimization.
/// \a targetRMSE is the root mean square error aimed at.
/// Return the reached RMSE. Since the class does not know the dimension, the
/// real RMSE should be this value multiplied by sqrt(dim). For example, for 2-D
/// points this would be sqrt(2) times the returned value.
flnum MinLM::minimize(vector<flnum>& P, const vector<flnum>& yData,
flnum targetRMSE, int maxIters)
{
flnum errorMax = targetRMSE*targetRMSE*yData.nrow();
vector<flnum> yModel( yData.nrow() );
modelData(P, yModel);
vector<flnum> E( yData-yModel );
flnum error = E.qnorm();
matrix<flnum> J( yData.nrow(), P.nrow() );
modelJacobian(P, J);
matrix<flnum> Jt = J.t();
matrix<flnum> JtJ = Jt*J;
vector<flnum> B = Jt*E;
compress(JtJ, B);
flnum lambda = lambdaInit;
for(iterations=0; iterations < maxIters && error > errorMax; iterations++) {
matrix<flnum> H(JtJ);
for(int i = 0; i < H.nrow(); i++)
H(i,i) *= 1+lambda;
vector<flnum> dP( P.nrow() );
solveLU(H, B, dP);
uncompress(dP);
vector<flnum> tryP = P + dP;
modelData(tryP, yModel);
E = yData - yModel;
flnum tryError = E.qnorm();
if(ABS(tryError-error) <= relativeTol*error)
break;
if(tryError > error)
lambda *= lambdaFact;
else {
lambda /= lambdaFact;
error = tryError;
P = tryP;
modelJacobian(P, J);
Jt = J.t();
JtJ = Jt*J;
B = Jt*E;
compress(JtJ, B);
}
}
return sqrt(error/yData.nrow());
}
} // namespace libNumerics

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// Authors: Unknown. Please, if you are the author of this file, or if you
// know who are the authors of this file, let us know, so we can give the
// adequate credits and/or get the adequate authorizations.
#ifndef NUMERICS_H
#define NUMERICS_H
#include "matrix.h"
#include <vector>
namespace libNumerics {
class NumericsException {};
class SvdConvergenceError : public NumericsException {};
typedef double flnum;
/// Solve system AX = B.
bool solveLU(const matrix<flnum>& A, const vector<flnum>& B,
vector<flnum>& X);
bool solveLU(matrix<flnum> A, vector<flnum>& B);
/// Singular Value Decomposition
class SVD {
public:
SVD(const matrix<flnum>& A);
matrix<flnum>& U() { return m_U; }
vector<flnum>& W() { return m_W; }
matrix<flnum>& V() { return m_V; }
matrix<flnum> compose() const;
private:
matrix<flnum> m_U, m_V;
vector<flnum> m_W;
static flnum withSignOf(flnum a, flnum b);
static flnum hypot(flnum a, flnum b);
static void rotate(flnum& a, flnum& b, flnum c, flnum s);
void compute();
void sort();
};
/// Levenberg-Marquardt minimization.
class MinLM {
static const flnum DEFAULT_RELATIVE_TOL;
static const flnum DEFAULT_LAMBDA_INIT;
static const flnum DEFAULT_LAMBDA_FACT;
static const flnum EPSILON_KERNEL;
public:
MinLM();
flnum minimize(vector<flnum>& P, const vector<flnum>& ydata,
flnum targetRMSE=0.1, int maxIters=300);
virtual void modelData(const vector<flnum>& P,
vector<flnum>& ymodel) const = 0;
virtual void modelJacobian(const vector<flnum>& P,
matrix<flnum>& J) const = 0;
int iterations;
flnum relativeTol;
flnum lambdaInit;
flnum lambdaFact;
private:
std::vector<int> m_nullCols;
void compress(matrix<flnum>& JtJ, vector<flnum>& B);
void uncompress(vector<flnum>& B);
};
} // namespace libNumerics
#endif

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// Authors: Unknown. Please, if you are the author of this file, or if you
// know who are the authors of this file, let us know, so we can give the
// adequate credits and/or get the adequate authorizations.
#ifdef RODRIGUES_H
namespace libNumerics {
template <class T>
matrix<T> skew(const vector<T>& v)
{
assert(v.nrow() == 3);
matrix<T> M(3,3);
M(0,0) = M(1,1) = M(2,2) = 0;
M(1,2) = -(M(2,1)=v(0));
M(2,0) = -(M(0,2)=v(1));
M(0,1) = -(M(1,0)=v(2));
return M;
}
template <class T>
matrix<T> rotation(vector<T> w)
{
assert(w.nrow() == 3);
T n = sqrt(w.qnorm());
T c = cos(n);
matrix<T> R = c*matrix<T>::eye(3);
if(n) {
w /= n;
R += skew(sin(n)*w);
R += (1-c)*w*w.t();
}
return R;
}
template <class T>
vector<T> rotationAxis(const matrix<T>& R)
{
assert(R.nrow() == 3 && R.ncol() == 3);
vector<T> w(3);
T n = acos(0.5*(R.tr()-1));
if(n == 0)
w = 0;
else {
w(0) = R(2,1)-R(1,2);
w(1) = R(0,2)-R(2,0);
w(2) = R(1,0)-R(0,1);
w *= n/(2*sin(n));
}
return w;
}
} // libNumerics
#endif // RODRIGUES_H

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// Authors: Unknown. Please, if you are the author of this file, or if you
// know who are the authors of this file, let us know, so we can give the
// adequate credits and/or get the adequate authorizations.
#ifndef RODRIGUES_H
#define RODRIGUES_H
#include "matrix.h"
#include <math.h>
namespace libNumerics {
/// Skew-symmetric matrix of 3-vector v.
template <class T> matrix<T> skew(const vector<T>& v);
/// Rodrigues's rotation: exp(w_x).
template <class T> matrix<T> rotation(vector<T> w);
/// Inverse Rodrigues's formula: w s.t. R=exp(w_x).
template <class T> vector<T> rotationAxis(const matrix<T>& R);
} // libNumerics
#include "rodrigues.cpp"
#endif

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// Authors: Unknown. Please, if you are the author of this file, or if you
// know who are the authors of this file, let us know, so we can give the
// adequate credits and/or get the adequate authorizations.
#ifdef MATRIX_H // Do nothing if not included from matrix.h
namespace libNumerics {
/// Constructor
template <typename T>
vector<T>::vector(int m)
: matrix<T>(m, 1)
{}
/// 1-vector constructor.
template <typename T>
vector<T>::vector(T x)
: matrix<T>(1,1)
{
this->p[0] = x;
}
/// 2-vector constructor.
template <typename T>
vector<T>::vector(T x, T y)
: matrix<T>(2,1)
{
this->p[0] = x;
this->p[1] = y;
}
/// 3-vector constructor.
template <typename T>
vector<T>::vector(T x, T y, T z)
: matrix<T>(3,1)
{
this->p[0] = x;
this->p[1] = y;
this->p[2] = z;
}
/// Copy constructor
template <typename T>
vector<T>::vector(const vector<T>& v)
: matrix<T>(v)
{}
/// Assignment operator
template <typename T>
vector<T>& vector<T>::operator=(const vector<T>& v)
{
matrix<T>::operator=(v);
return *this;
}
/// Multiply a vector by scalar.
/// \param a a scalar.
template <typename T>
vector<T> vector<T>::operator*(T a) const
{
vector<T> v(this->m_rows);
for(int i = this->m_rows-1; i >= 0; i--)
v.p[i] = a*this->p[i];
return v;
}
/// Divide a vector by scalar.
/// \param a a scalar.
template <typename T>
inline vector<T> vector<T>::operator/(T a) const
{
return operator*( (T)1/a );
}
/// Addition of vectors.
template <typename T>
vector<T> vector<T>::operator+(const vector<T>& v) const
{
assert(this->m_rows == v.m_rows);
vector<T> sum(this->m_rows);
for(int i = this->m_rows-1; i >= 0; i--)
sum.p[i] = this->p[i] + v.p[i];
return sum;
}
/// Subtraction of vectors.
template <typename T>
vector<T> vector<T>::operator-(const vector<T>& v) const
{
assert(this->m_rows == v.m_rows);
vector<T> sub(this->m_rows);
for(int i = this->m_rows-1; i >= 0; i--)
sub.p[i] = this->p[i] - v.p[i];
return sub;
}
/// Opposite of vector.
template <typename T>
vector<T> vector<T>::operator-() const
{
vector<T> v(this->m_rows);
for(int i = this->m_rows-1; i >= 0; i--)
v.p[i] = -this->p[i];
return v;
}
/// Vector times matrix.
template <typename T>
matrix<T> vector<T>::operator*(const matrix<T>& m) const
{
return matrix<T>::operator*(m);
}
/// Diagonal matrix defined by its diagonal vector.
template <typename T>
matrix<T> vector<T>::diag() const
{
matrix<T> d(this->m_rows, this->m_rows);
d = (T)0;
for(int i = this->m_rows-1; i >= 0; i--)
d(i,i) = this->p[i];
return d;
}
/// Square L^2 norm of vector.
template <typename T>
T vector<T>::qnorm() const
{
T q = (T)0;
for(int i = this->m_rows-1; i >= 0; i--)
q += this->p[i]*this->p[i];
return q;
}
/// Subvector from \a i0 to \a i1.
template <typename T>
vector<T> vector<T>::copy(int i0, int i1) const
{
assert(0 <= i0 && i0 <= i1 && i1 <= this->m_rows);
vector<T> v(i1-i0+1);
for(int i=i0; i <= i1; i++)
v.p[i-i0] = this->p[i];
return v;
}
/// Paste vector \a v from row i0.
template <typename T>
void vector<T>::paste(int i0, const vector<T>& v)
{
matrix<T>::paste(i0, 0, v);
}
} // namespace libNumerics
/// Scalar product.
template <typename T>
T dot(const libNumerics::vector<T>& u, const libNumerics::vector<T>& v)
{
assert(u.nrow() == v.nrow());
T d = (T)0;
for(int i = u.nrow()-1; i >= 0; i--)
d += u(i)*v(i);
return d;
}
/// Cross product.
template <typename T>
libNumerics::vector<T> cross(const libNumerics::vector<T>& u,
const libNumerics::vector<T>& v)
{
assert(u.nrow() == 3 && v.nrow() == 3);
libNumerics::vector<T> w(3);
w(0) = u(1)*v(2) - u(2)*v(1);
w(1) = u(2)*v(0) - u(0)*v(2);
w(2) = u(0)*v(1) - u(1)*v(0);
return w;
}
#endif // MATRIX_H

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// Authors: Unknown. Please, if you are the author of this file, or if you
// know who are the authors of this file, let us know, so we can give the
// adequate credits and/or get the adequate authorizations.
#include "library.h"
void wxwarning(const char * message, const char *function,const char *file)
{
printf("warning :: %s :: %s :: %s\n", file, function, message);
}
void wxerror(const char * message, const char *function, const char *file)
{
printf("error :: %s :: %s :: %s\n", file, function, message);
exit(-1);
}
double fsqr(double a) { return a*a; }
void fill_exp_lut(float *lut, int size)
{
for(int i=0; i< size;i++) lut[i]=expf( - (float) i / LUTPRECISION);
}
/* Looks for f(dif) in the lut */
float slut(float dif,float *lut)
{
if (dif >= (float) LUTMAX) return 0.0;
int x= (int) floor(dif*LUTPRECISION);
float y1=lut[x];
float y2=lut[x+1];
return y1 + (y2-y1)*(dif*LUTPRECISION - (float) x);
}
float max(float *u,int *pos, int size)
{
float max=u[0];
if (pos) *pos=0;
for(int i=1; i<size; i++) if(u[i]>max){ max=u[i]; if (pos) *pos=i; }
return max;
}
float min(float *u,int *pos,int size)
{
float min=u[0];
if (pos) *pos=0;
for(int i=1;i<size;i++) if(u[i]<min){ min=u[i]; if (pos) *pos=i; }
return min;
}
void max_u_v(float *u,float *v,int size) { for(int i=0;i<size;i++) u[i]=MAX(u[i],v[i]);}
void max_u_k(float *u, float k, int size) { for(int i=0;i<size;i++) u[i]=MAX(u[i],k);}
void min_u_v(float *u, float *v,int size) { for(int i=0;i<size;i++) u[i]=MIN(u[i],v[i]);}
void min_u_k(float *u,float k,int size) { int i=0; for(i=0;i<size;i++) u[i]=MIN(u[i],k);}
void abs(float *u,float *v,int size){ for(int i=0;i<size ;i++) v[i] = fabsf(u[i]); }
void copy(float *u,float *v,int size) { for(int i=0; i<size ;i++) v[i]=u[i]; }
void clear(float *u,float value,int size) { for(int i=0; i<size; i++) u[i]=value; }
void combine(float *u,float a,float *v,float b, float *w, int size) { for(int i=0;i<size ;i++) w[i]= a*u[i] + b*v[i]; }
void multiple(float *u,float multiplier,int size) { for(int i=0;i<size;i++) u[i]=multiplier*u[i]; }
float scalar_product(float *u, float *v, int n)
{
float aux = 0.0f;
for(int i=0; i < n; i++)
aux += u[i] * v[i];
return aux;
}
float var(float *u,int size)
{
float *ptru=&u[0];
float mean=0.0;
float mean2 = 0.0;
for(int i=0;i<size;i++,ptru++) { mean += *ptru; mean2 += *ptru * (*ptru);}
mean/=(float) size; mean2/=(float) size;
float var = mean2- mean*mean;
return var;
}
float mean(float *u,int size)
{
float *ptru=&u[0];
float mean=0.0;
for(int i=0; i<size; i++,ptru++) mean += *ptru;
mean/=(float) size;
return mean;
}
float median(float *u,int size)
{
float *vector = new float[size];
float *index = new float[size];
float *ptru=&u[0];
for(int i=0; i<size; i++,ptru++) { vector[i] = *ptru; index[i] = (float) i;}
quick_sort(vector,index,size);
float median;
if (size%2==1) median = vector[size/2];
else median = (vector[size/2] + vector[(size/2) - 1])/2;
delete[] vector; delete[] index;
return median;
}
int normalize(float *u,int size)
{
float some = 0.0;
for(int i=0;i<size;i++) some+=u[i];
if (some != 0.0) { for(int i=0;i<size;i++) u[i]/=some; return 1;}
else return 0;
}
float nearest(float *u,float value,int *pos,int size)
{
float mindif = fabsf(value - u[0]);
float minvalue = u[0];
if (pos) *pos = 0;
for(int i=0;i<size;i++){
float dif = fabsf(value - u[i]);
if (dif < mindif) { mindif=dif; minvalue=u[i]; if (pos!=NULL) *pos=i;}
}
return minvalue;
}
void binarize(float *u, float *v,float value, int inverse, int size)
{
for(int i=0;i<size;i++){
if (u[i] >= value && !inverse) v[i]= 255.0;
else if (u[i] <= value && inverse) v[i]= 255.0;
else v[i]= 0.0;
}
}
float * gauss(int sflag,float std,int *size)
{
float *u,prec = 4.0,shift;
double v;
// int n,i,flag;
int n,i;
int flag = 1; //Guoshen Yu
if (sflag) n=*size;
else
n = 1+2*(int)ceil((double)std*sqrt(prec*2.*log(10.)));
u =new float[n];
if (n==1)
u[0]=1.0;
else{
shift = 0.5*(float)(n-1);
for (i=(n+1)/2;i--;) {
v = ((double)i - (double) shift)/(double)std;
u[i] = u[n-1-i] = (float) exp(-0.5*v*v);
}
}
// if (flag = normalize(u,n)) {
if (flag == normalize(u,n))
{
*size=n;
return u;
}
else
{
printf("ERROR: _gauss: _normalize: normalization equals zero.\n");
delete[] u; /*memcheck*/
return 0; // Guoshen Yu
}
}
/* Quicksort, values in arr are set in increasing order and brr elements are switched at the same time*/
void FSWAP(float *x,float *y)
{
float aux;
aux=*x;
*x=*y;
*y=aux;
}
void quick_sort(float *arr,float *brr,int n)
{
int M=7,NSTACK=50;
int i,ir,j,k,jstack=-1,l=0;
float a,b;
int istack[50];
ir=n-1;
for(;;){
if(ir-l<M){
for(j=l+1;j<=ir;j++){
a=arr[j];
b=brr[j];
for(i=j-1;i>=l;i--){
if (arr[i]<=a) break;
arr[i+1]=arr[i];
brr[i+1]=brr[i];
}
arr[i+1]=a;
brr[i+1]=b;
}
if (jstack<0) break;
ir=istack[jstack--];
l=istack[jstack--];
} else {
k=(l+ir) >> 1;
FSWAP(&arr[k],&arr[l+1]);
FSWAP(&brr[k],&brr[l+1]);
if (arr[l]>arr[ir]){
FSWAP(&arr[l],&arr[ir]);
FSWAP(&brr[l],&brr[ir]);
}
if (arr[l+1]>arr[ir]){
FSWAP(&arr[l+1],&arr[ir]);
FSWAP(&brr[l+1],&brr[ir]);
}
if (arr[l]>arr[l+1]){
FSWAP(&arr[l],&arr[l+1]);
FSWAP(&brr[l],&brr[l+1]);
}
i=l+1;
j=ir;
a=arr[l+1];
b=brr[l+1];
for(;;){
do i++; while (arr[i]<a);
do j--; while (arr[j]>a);
if (j<i) break;
FSWAP(&arr[i],&arr[j]);
FSWAP(&brr[i],&brr[j]);
}
arr[l+1]=arr[j];
arr[j]=a;
brr[l+1]=brr[j];
brr[j]=b;
jstack+=2;
if (jstack>=NSTACK) { printf("Stack too small\n"); exit(-1);}
if (ir-i+1>=j-l){
istack[jstack]=ir;
istack[jstack-1]=i;
ir=j-1;
} else {
istack[jstack]=j-1;
istack[jstack-1]=l;
l=i;
}
}
}
}
/// histogram of values. 'n' (number of bins) or 's' (step) must be selected in flag while the other value is filled
float * histo(float* input, float *iminim, float *imaxim, int *n, float *s, int size, char flag)
{
if (flag != 's' && flag != 'n') { printf("Warning (histo): Please select s or n as flag\n"); return NULL; }
float minim;
if (iminim) minim = *iminim;
else minim = min(input, NULL, size);
float maxim;
if (imaxim) maxim = *imaxim;
else maxim = max(input, NULL, size);
int num;
float step;
if (flag == 'n')
{
num = *n;
step = (maxim-minim)/ (float)num;
*s = step;
} else
{
step = *s;
num = (int)(0.5+(maxim-minim)/step);
*n = num;
}
float *histo = new float[num];
clear(histo,0.0,num);
for(int i=0; i < size; i++)
{
int cell = (int) floorf((input[i]-minim) / step);
if (cell < 0) cell = 0;
if (cell >= num) cell = num - 1;
histo[cell]++;
}
return histo;
}
///////////////////////////////////////////////////////////////////////////////////////////////////// Used and checked image functions
void compute_gradient_orientation(float* igray,float *grad, float *ori, int width, int height)
{
float xgrad, ygrad;
int rows, cols, r, c;
rows = height;
cols = width;
for (r = 0; r < rows; r++)
for (c = 0; c < cols; c++) {
if (c == 0)
xgrad = 2.0 * (igray[r*cols+c+1] - igray[r*cols+c]);
else if (c == cols-1)
xgrad = 2.0 * (igray[r*cols+c] - igray[r*cols+c-1]);
else
xgrad = igray[r*cols+c+1] - igray[r*cols+c-1];
if (r == 0)
ygrad = 2.0 * (igray[r*cols+c] - igray[(r+1)*cols+c]);
else if (r == rows-1)
ygrad = 2.0 * (igray[(r-1)*cols+c] - igray[r*cols+c]);
else
ygrad = igray[(r-1)*cols+c] - igray[(r+1)*cols+c];
if (grad) grad[r*cols+c] = (float)sqrt((double)(xgrad * xgrad + ygrad * ygrad));
if (ori) ori[r*cols+c] = (float)atan2 (-(double)ygrad,(double)xgrad);
}
}
void sample(float *igray,float *ogray, float factor, int width, int height)
{
int swidth = (int)((float) width / factor);
int sheight = (int)((float) height / factor);
for(int j=0; j < sheight; j++)
for(int i=0; i < swidth; i++)
ogray[j*swidth + i] = igray[(int)((float) j * factor) * width + (int) ((float) i*factor)];
}
void sample_aglomeration(float *igray,float *ogray, float factor, int width, int height)
{
int swidth = (int)((float) width / factor);
int sheight = (int)((float) height / factor);
int ssize = swidth * sheight;
clear(ogray,0.0,swidth*sheight);
for(int j=0; j < height; j++)
for(int i=0; i < width; i++){
int index = (int)((float) j / factor) * swidth + (int) ((float) i / factor);
if (index < ssize) ogray[index] += igray[j*width+i];
}
factor *= factor;
for(int i = 0; i < swidth*sheight; i++)
ogray[i] /= factor;
}
/*
void extract(float *igray,float *ogray, int ax, int ay,int cwidth, int cheight,int width, int height)
{
for(int j=0; j < cheight; j++)
for(int i=0; i < cwidth; i++)
ogray[j*cwidth + i] = igray[(ay+j)*width + ax+i];
}
*/
void gray(float *red, float *green,float *blue, float *out, int width, int height)
{
for(int i=width*height-1; i>0; i--) out[i] = (red[i] + green[i] + blue[i]) /3.0;
}
/*
float l2_distance(float *u0,float *u1,int i0,int j0,int i1,int j1,int radius,int width,int height)
{
int wsize=(2*radius+1)*(2*radius+1);
float dist=0.0;
for (int s=-radius; s<= radius; s++){
int l = (j0+s)*width + (i0-radius);
float *ptr0 = &u0[l];
l = (j1+s)*width + (i1-radius);
float *ptr1 = &u1[l];
for(int r=-radius;r<=radius;r++,ptr0++,ptr1++){ float dif = (*ptr0 - *ptr1); dist += (dif*dif); }
}
dist/=(float) wsize;
return dist;
}
float l2_distance_non_normalized(float *u0,float *u1,int i0,int j0,int i1,int j1,int radius,int width,int height)
{
float dist=0.0;
for (int s=-radius; s<= radius; s++){
int l = (j0+s)*width + (i0-radius);
float *ptr0 = &u0[l];
l = (j1+s)*width + (i1-radius);
float *ptr1 = &u1[l];
for(int r=-radius;r<=radius;r++,ptr0++,ptr1++){ float dif = (*ptr0 - *ptr1); dist += (dif*dif); }
}
return dist;
}
float l2_distance_nsq(float *u0,float *u1,int i0,int j0,int i1,int j1,int xradius, int yradius,int width,int height)
{
int wsize=(2*xradius+1)*(2*yradius+1);
float dist=0.0;
for (int s=-yradius; s <= yradius; s++){
int l = (j0+s)*width + (i0-xradius);
float *ptr0 = &u0[l];
l = (j1+s)*width + (i1-xradius);
float *ptr1 = &u1[l];
for(int r=-xradius;r<=xradius;r++,ptr0++,ptr1++)
{
float dif = (*ptr0 - *ptr1); dist += (dif*dif);
}
}
dist/=(float) wsize;
return dist;
}
float weighted_l2_distance(float *u0,float *u1,int i0,int j0,int i1,int j1,int width,int height,float *kernel,int radius)
{
float *ptrk=&kernel[0];
float dist=0.0;
for (int s=-radius; s<= radius; s++){
int l = (j0+s)*width + (i0-radius);
float *ptr0 = &u0[l];
l = (j1+s)*width + (i1-radius);
float *ptr1 = &u1[l];
for(int r=-radius;r<=radius;r++,ptr0++,ptr1++,ptrk++){ float dif = (*ptr0 - *ptr1); dist += *ptrk*(dif*dif); }
}
return dist;
}
float weighted_l2_distance_nsq(float *u0,float *u1,int i0,int j0,int i1,int j1,int width,int height,float *kernel,int xradius, int yradius)
{
float *ptrk=&kernel[0];
float dist=0.0;
for (int s=-yradius; s<= yradius; s++){
int l = (j0+s)*width + (i0-xradius);
float *ptr0 = &u0[l];
l = (j1+s)*width + (i1-xradius);
float *ptr1 = &u1[l];
for(int r=-xradius;r<=xradius;r++,ptr0++,ptr1++,ptrk++){ float dif = (*ptr0 - *ptr1); dist += *ptrk*(dif*dif); }
}
return dist;
}
*/
/// RGV to YUV conversion
void rgb2yuv(float *r,float *g,float *b,float *y,float *u,float *v,int width,int height)
{
int size=height*width;
for(int i=0;i<size;i++){
y[i] = COEFF_YR * r[i] + COEFF_YG * g[i] + COEFF_YB * b[i];
u[i] = r[i] - y[i];
v[i] = b[i] - y[i];
}
}
void rgb2yuv(float *r,float *g,float *b,float *y,float *u,float *v,float yR, float yG, float yB, int width,int height)
{
int size=height*width;
for(int i=0;i<size;i++){
y[i] = yR * r[i] + yG * g[i] + yB * b[i];
u[i] = r[i] - y[i];
v[i] = b[i] - y[i];
}
}
/// YUV to RGB conversion
void yuv2rgb(float *r,float *g,float *b,float *y,float *u,float *v,int width,int height)
{
int size=height*width;
for(int i=0;i<size;i++){
g[i] = (y[i] - COEFF_YR * (u[i] + y[i]) - COEFF_YB * (v[i] + y[i])) / COEFF_YG;
r[i] = u[i] + y[i];
b[i] = v[i] + y[i];
}
}
void yuv2rgb(float *r,float *g,float *b,float *y,float *u,float *v,float yR, float yG, float yB, int width,int height)
{
int size=height*width;
for(int i=0;i<size;i++){
g[i] = (y[i] - yR * (u[i] + y[i]) - yB * (v[i] + y[i])) / yG;
r[i] = u[i] + y[i];
b[i] = v[i] + y[i];
}
}
void draw_line(float *igray, int a0, int b0, int a1, int b1, float value, int width, int height)
{
int bdx,bdy;
int sx,sy,dx,dy,x,y,z,l;
bdx = width;
bdy = height;
if (a0 < 0) a0=0;
else if (a0>=bdx) a0=bdx-1;
if (a1<0) a1=0;
else if (a1>=bdx) a1=bdx-1;
if (b0<0) b0=0;
else if (b0>=bdy) b0=bdy-1;
if (b1<0) b1=0;
else if (b1>=bdy) b1=bdy-1;
if (a0<a1) { sx = 1; dx = a1-a0; } else { sx = -1; dx = a0-a1; }
if (b0<b1) { sy = 1; dy = b1-b0; } else { sy = -1; dy = b0-b1; }
x=0; y=0;
if (dx>=dy)
{
z = (-dx) / 2;
while (abs(x) <= dx)
{
l = (y+b0)*bdx+x+a0;
igray[l] = value;
x+=sx;
z+=dy;
if (z>0) { y+=sy; z-=dx; }
}
}
else
{
z = (-dy) / 2;
while (abs(y) <= dy) {
l = (y+b0)*bdx+x+a0;
igray[l] = value;
y+=sy;
z+=dx;
if (z>0) { x+=sx; z-=dy; }
}
}
}
void draw_square(float *igray, int a0, int b0, int w0, int h0, float value, int width, int height)
{
draw_line(igray,a0,b0,a0+w0,b0,value,width,height);
draw_line(igray,a0,b0,a0,b0+h0,value,width,height);
draw_line(igray,a0+w0,b0,a0+w0,b0+h0,value,width,height);
draw_line(igray,a0,b0+h0,a0+w0,b0+h0,value,width,height);
}
///////////////////////////////////////////////////////////////
///////////////////////////////////////////////////////////////
///////////////////////////////////////////////////////////////
/*
*/
/*
*/
/*
void _sign(float *u,float *v, int size)
{
int i=0;
for(i=0;i<size;i++){
if (u[i]>0) v[i] = 1.0;
else if (u[i]<0) v[i]=-1.0;
else v[i]=0.0;
}
}
void _multiple(float *u,float multiplier,int size)
{
int i=0;
float *ptru;
ptru=&u[0];
for(i=0;i<size;i++,ptru++) *ptru=multiplier*(*ptru);
}
void _product(float *u,float *v,int size)
{
int i;
float *ptru,*ptrv;
ptru=&u[0];
ptrv=&v[0];
for(i=0;i<size;i++,ptru++,ptrv++) *ptru= *ptru*(*ptrv);
}
int _is_increasing(float *u,float tolerance, int size)
{
int i=1;
while (i < size && u[i] > (u[i-1] - tolerance))
{
i++;
}
if (i==size) return 1;
else return 0;
}
void _offset(float *u,float offset,int size)
{
int i=0;
float *ptru;
ptru=&u[0];
for(i=0;i<size;i++,ptru++) *ptru=*ptru + offset;
}
void _threshold(float *u, float *v,float valuem,float valueM, int size)
{
int i;
for(i=0;i<size;i++){
if (u[i] >= valueM) v[i]= valueM;
else if (u[i] <= valuem) v[i]= valuem;
else v[i] = u[i];
}
}
void _absdif(float *u, float *v,int size)
{
int i=0;
for(i=0;i<size;i++) u[i] = (float) fabsf( u[i] - v[i]);
}
float * _diag_gauss(int sflag,float std,int *size) //Create a 1d gauss kernel of standard deviation std (megawave2)
{
float *u,prec = 4.0,shift;
double v;
int n,i,flag;
if (sflag) n=*size;
else
n = 1+2*(int)ceil((double)std*sqrt(prec*2.*log(10.)));
u =(float *) malloc(n*sizeof(float));
if (n==1)
u[0]=1.0;
else{
shift = 0.5*(float)(n-1);
for (i=(n+1)/2;i--;) {
v = ((double)i - (double) shift)/(double)std;
u[i] = u[n-1-i] = (float) exp(-2.0*0.5*v*v); // 2.0 because distances are in the diagonal
}
}
if (flag = _normalize(u,n)) {
*size=n;
return u;
} else {
printf("ERROR: mdSigGaussKernel: mdSigNormalize: normalization equals zero.\n Try to reduce std.\n");
}
}
void _quant(float *u,float *v,float lambda,int size)
{
int i,n;
float a=lambda/2;
for(i=0;i<size;i++){
n = (int) floorf(u[i] / a);
if (n%2==0)
v[i] = (float) n * a;
else
v[i] = (float) (n+1) * a;
}
}
void _projectquant(float *u,float *v,float lambda,int size)
{
int i;
float a=lambda/2;
for(i=0;i<size;i++){
if (v[i] < u[i] - a) v[i]=u[i] - a;
else if (v[i] > u[i] + a) v[i]=u[i] + a;
}
}
*/

202
asift_matching/src/library.h Executable file
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// Authors: Unknown. Please, if you are the author of this file, or if you
// know who are the authors of this file, let us know, so we can give the
// adequate credits and/or get the adequate authorizations.
#ifndef _LIBRARY_H_
#define _LIBRARY_H_
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <time.h>
// #include <unistd.h>
#include <float.h>
#define MAX(i,j) ( (i)<(j) ? (j):(i) )
#define MIN(i,j) ( (i)<(j) ? (i):(j) )
#define LUTMAX 30
#define LUTPRECISION 1000.0
#define TINY 1.0e-10
//#define MAXFLOAT 10000000.0
#define IRAC8 0.35355339 /* 1/sqrt(8) */
#define IRAC2P2 0.29289322 /* 1/(sqrt(2)+2) */
#define IRAC2 0.70710678 /* 1/sqrt(2) */
#define RAC8P4 6.8284271 /* sqrt(8)+4 */
#define RADIANS_TO_DEGREES (180.0/M_PI)
#define PI 3.14159
#define COEFF_YR 0.299
#define COEFF_YG 0.587
#define COEFF_YB 0.114
///////////////////////////////////////////////////////////////// Errors
void wxwarning(const char * message,const char *function, const char *file);
void wxerror(const char * message, const char *function, const char *file);
///////////////////////////////////////////////////////////////////////////////////////////////////// Used and checked mathematical functions
double fsqr(double a);
void fill_exp_lut(float *lut,int size); /* Fills exp(x) for x great or equal than zero*/
float slut(float dif,float *lut); /* We look for f(dif) in the lut*/
///////////////////////////////////////////////////////////////////////////////////////////////////// Used and checked mxsignal functions
float max(float *u,int *pos, int size); /// Max(u), pos contains the index of the maximum
float min(float *u,int *pos, int size); /// Min(u), pos contains the index of the minimum
void max_u_v(float *u,float *v,int size);
void max_u_k(float *u,float k,int size);
void min_u_v(float *u,float *v,int size);
void min_u_k(float *u,float k,int size);
void abs(float *u,float *v,int size); /// v = abs(u)
void copy(float *u,float *v,int size); /// v = u
void clear(float *u, float value ,int size); /// u = k
void combine(float *u,float a,float *v,float b, float *w, int size); /// w = a*u + b*v
void multiple(float *u,float multiplier,int size); /// u = K * u
float scalar_product(float *u, float *v, int n);
// float lpdist(float *u,float *v,float *mask,int pow,int size);
// float lpnorm(float *u,int fpow,int size);
float mean(float *u,int size);
float var(float *u,int size);
float median(float *u,int size);
float nearest(float *u,float value,int *pos,int size); /// Returns the nearest value in the vector u and its position if selected
void binarize(float *u, float *v,float value, int inverse, int size); /// v = 255 if u > value 0 else
int normalize(float *u,int size); /// u = u / sum_i u(i). Returns 0 if mxsignal sum equals zero
float * gauss(int sflag,float std,int *size); /// Create a 1d gauss kernel of standard deviation std
// void addnoise(float *u,float *v,float std,long int randinit, int size);
// void addnoise_var_afine(float *u,float *v,float a,float b,long int randinit, int size);
void quick_sort(float *arr,float *brr,int n); /// Quicksort
/// histogram of values. 'n' (number of bins) or 's' (step) must be selected in flag while the other value is filled
float * histo ( float* input, float *iminim, float *imaxim, int *n, float *s, int size, char flag );
///////////////////////////////////////////////////////////////////////////////////////////////////// Used and checked image functions
void compute_gradient_orientation(float* igray,float *grad, float *ori, int width, int height);
// void extract ( float *igray,float *ogray, int ax, int ay,int cwidth, int cweight,int width, int height );
void sample ( float *igray,float *ogray, float factor ,int width, int height);
void sample_aglomeration(float *igray,float *ogray, float factor, int width, int height);
void gray ( float *red, float *green,float *blue, float *out, int width, int height );
/*
float l2_distance ( float * u0,float *u1,int i0,int j0,int i1,int j1,int radius,int width,int height);
float l2_distance_non_normalized(float *u0,float *u1,int i0,int j0,int i1,int j1,int radius,int width,int height);
float weighted_l2_distance ( float *u0,float *u1,int i0,int j0,int i1,int j1,int width,int height,float * kernel,int radius );
float l2_distance_nsq(float *u0,float *u1,int i0,int j0,int i1,int j1,int xradius, int yradius,int width,int height);
float weighted_l2_distance_nsq(float *u0,float *u1,int i0,int j0,int i1,int j1,int width,int height,float *kernel,int xradius, int yradius);
*/
void rgb2yuv ( float *r,float *g,float *b,float *y,float *u,float *v,int width,int height );
void yuv2rgb ( float *r,float *g,float *b,float *y,float *u,float *v,int width,int height );
void rgb2yuv(float *r,float *g,float *b,float *y,float *u,float *v,float yR, float yG, float yB, int width,int height);
void yuv2rgb(float *r,float *g,float *b,float *y,float *u,float *v,float yR, float yG, float yB, int width,int height);
void draw_line(float *igray, int a0, int b0, int a1, int b1, float value, int width, int height);
// void draw_circle(float *igray, int pi,int pj,float radius, float value, int width, int height);
void draw_square(float *igray, int a0, int b0, int w0, int h0, float value, int width, int height);
#endif // _LIBRARY_H_
/////////////////////////////////////// Not often used and not checked
//
//
/*
*/
//void md_fsig_absdif(float *u,float *v,int size); /// v = abs(v-u)
//void md_fsig_sign(float *u,float *v, int size); //// ¿¿¿¿¿ ?????
//int md_fsig_is_increasing(float *u,float tolerance, int size); ///// ¿¿¿¿¿ ?????
//void md_fsig_multiple(float *u,float multiplier,int size); // u = K * u
//void md_fsig_product(float *u,float *v,int size); // u = u * v
//void md_fsig_offset(float *u,float offset, int size); // u = u - K
//
//
//void md_fsig_threshold(float *u, float *v,float valuem,float valueM, int size); // threshold into (m,M)
//--- Conversion ---
//void md_fsig_float2char(float min, float max, float *u, float *v, int size); // Linear Conversion between (min,max) and (0,255)
//void md_fsig_addnoise(float *u,float *v,float std,long int randinit, int size); // Add gaussian noise of standard deviation sigma
// v quantified mxmximage u with interval length lambda
// if u \in ( (n - 1/2) l, (n + 1/2 ) l ) - > v = n l
// n= 0 -> (-1/2 * l, 1/2 * l) //
//void md_fsig_quant(float *u, float *v, float lambda, int size);
// v is projected to the space of quantizations of length lambda that gives u
//void md_fsig_projectquant(float *u,float *v,float lambda, int size);

185
asift_matching/src/numerics1.cpp Executable file
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// Authors: Unknown. Please, if you are the author of this file, or if you
// know who are the authors of this file, let us know, so we can give the
// adequate credits and/or get the adequate authorizations.
#include "numerics1.h"
float **allocate_float_matrix(int nrows, int ncols)
{
float ** matrix;
matrix = new float*[nrows];
for(int i=0; i < nrows; i++) matrix[i] = new float[ncols];
return matrix;
}
void desallocate_float_matrix(float **matrix, int nrows, int ncols)
{
if (matrix == NULL) return;
for(int i=0; i < nrows; i++) { delete[] matrix[i]; matrix[i] = 0;}
matrix = 0;
ncols = ncols; // to remove the warning unused parameter ¡®ncols¡¯
}
// **********************************************
// LU based algorithms
// **********************************************
// Solves Ax=b by using lu decomposition
int lusolve(float **a, float *x, float *b, int n)
{
float d;
int *indx = new int[n];
if (ludcmp(a,n,indx,&d))
{
for(int i=0; i < n; i++) x[i] = b[i];
lubksb(a,n,indx,x);
delete[] indx; /*memcheck*/
return 1;
} else
{
printf("lusolve::lu decomposition failed\n");
delete[] indx; /*memcheck*/
return 0;
}
delete[] indx; // Guoshen Yu
}
int ludcmp(float **a, int n, int *indx, float *d)
{
int i,imax=0,j,k,aux;
float big,dum,sum,temp;
float *vv;
vv=(float *) malloc(n*sizeof(float));
*d=1.0;
for(i=0;i<n;i++) indx[i]=i;
/**** Look for the largest value of every line and store 1/this value****/
for(i=0;i<n;i++){
big=0.0;
for(j=0;j<n;j++)
if ( (temp=fabs(a[i][j]))>big ) big=temp;
if (big==0.0) { return 0; printf("LU Decomposition failed\n");}
vv[i]=1.0/big;
}
for(j=0;j<n;j++){
for(i=0;i<j;i++){
sum=a[i][j];
for(k=0;k<i;k++) sum-= a[i][k]*a[k][j];
a[i][j]=sum;
}
big=0.0;
for(i=j;i<n;i++){
sum=a[i][j];
for(k=0;k<j;k++)
sum -=a[i][k]*a[k][j];
a[i][j]=sum;
if ( (dum=vv[i]*fabs(sum))>=big){
big=dum;
imax=i;
}
}
if (j != imax){
for(k=0;k<n;k++){
dum=a[imax][k];
a[imax][k]=a[j][k];
a[j][k]=dum;
}
*d=-(*d);
vv[imax]=vv[j];
aux=indx[j];
indx[j]=indx[imax];
indx[imax]=aux;
}
if (a[j][j]==0.0) a[j][j]=NRTINY;
if (j!=n-1 ){
dum=1.0 / a[j][j];
for(i=j+1;i<n;i++) a[i][j]*=dum;
}
}
free(vv);
return 1;
}
/* Solves the set of n linear equations Ax=b. Here a[0..n-1][0..n-1] as input, not as the matrix A but rather as its LU decomposition,*/
/* determined by the routine ludcmp. indx[0..n-1] is input as the permutation vector returned by ludcmp. b[0..n-1] is input as the */
/* right hand side vector and returns with the solution vector x. */
void lubksb(float **a, int n, int *indx, float *b)
{
int i,ii=0,j;
float sum,*aux;
aux= (float *) malloc(n*sizeof(float));
for(i=0;i<n;i++)
aux[i]=b[indx[i]];
for(i=0;i<n;i++){
sum=aux[i];
if (ii)
for(j=ii-1;j<i;j++) sum-=a[i][j]*aux[j];
else if (sum) ii=i+1;
aux[i]=sum;
}
for(i=n-1;i>=0;i--){
sum=aux[i];
for(j=i+1;j<n;j++) sum-=a[i][j]*aux[j];
aux[i]=sum/a[i][i];
b[i]=sum/a[i][i];
}
free(aux);
}

57
asift_matching/src/numerics1.h Executable file
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@ -0,0 +1,57 @@
// Authors: Unknown. Please, if you are the author of this file, or if you
// know who are the authors of this file, let us know, so we can give the
// adequate credits and/or get the adequate authorizations.
#ifndef _NUMERICS_H_
#define _NUMERICS_H_
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <fstream>
#include <iostream>
#include "library.h"
#define NRMAX(i,j) ( (i)<(j) ? (j):(i) )
#define NRMIN(i,j) ( (i)<(j) ? (i):(j) )
#define NRTINY 1.0e-10
// **********************************************
// float ** basic functions
// **********************************************
float ** allocate_float_matrix(int nrows, int ncols);
void desallocate_float_matrix(float **matrix, int nrows, int ncols);
// **********************************************
// LU based algorithms
// **********************************************
// Solves Ax=b by using lu decomposition
// a matrix a[1..n][1..n] is replaced by the LU decompositions of a rowwise permutation of itself
// b[1..n] and x[1..n]
int lusolve(float **a, float *x, float *b, int n);
/*-- LU decomposition */
/* Given a matrix a[1..n][1..n] this routine replacess it by the LU decompositions of a rowwise permutation of itself. */
/* a and n are input, a is output, arranged as in equation (2.3.14) above; indx[1..n] in an output vector that records */
/* the row permutation effected by the partial pivoting; d is output as +-1 depending on whether the number of row */
/* interchanges was even or odd respectively. */
int ludcmp(float **a, int n, int *indx, float *d); /* LU decomposition */
/* Solves the set of n linear equations Ax=b. Here a[0..n-1][0..n-1] as input, not as the matrix A but rather as its LU decomposition,*/
/* determined by the routine ludcmp. indx[0..n-1] is input as the permutation vector returned by ludcmp. b[0..n-1] is input as the */
/* right hand side vector and returns with the solution vector x. */
void lubksb(float **a, int n, int *indx, float *b); /* LU linear solution */
#endif

599
asift_matching/src/orsa.cpp Executable file
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//
// C++ Implementation: stereomatch
//
// Description: eliminate the false matches with epipolar geometry constraint.
// See http://www.math-info.univ-paris5.fr/~moisan/epipolar/
//
// Copyright (c) 2007 Lionel Moisan <Lionel.Moisan@parisdescartes.fr>
// Changelog : 2011 Use Eigen SVD <Pierre Moulon>
//
// Copyright: See COPYING file that comes with this distribution
//
//
#include <assert.h>
#include <stdio.h>
#include <stdlib.h>
#include <math.h>
#include <time.h>
#include "orsa.h"
// #include <third_party/Eigen/Cholesky>
// #include <third_party/Eigen/Core>
// #include <third_party/Eigen/Eigenvalues>
// #include <third_party/Eigen/LU>
// #include <third_party/Eigen/QR>
// #include <third_party/Eigen/SVD>
#include <Eigen/Cholesky>
#include <Eigen/Core>
#include <Eigen/Eigenvalues>
#include <Eigen/LU>
#include <Eigen/QR>
#include <Eigen/SVD>
#ifndef M_PI
#define M_PI 3.14159265358979323846
#endif
/*-------------------- GENERAL PURPOSE ROUTINES --------------------*/
/* routines for vectors and matrices */
float *vector(int nl, int nh)
{
float *v;
v=(float *)malloc((unsigned) (nh-nl+1)*sizeof(float));
if (!v) {
// mwerror(FATAL,1,"allocation failure in vector()");
fprintf(stderr, "allocation failure in vector()\n");
exit(EXIT_FAILURE); /* indicate failure.*/
}
return v-nl;
}
float **matrix(int nrl, int nrh, int ncl, int nch)
{
int i;
float **m;
m=(float **) malloc((unsigned) (nrh-nrl+1)*sizeof(float*));
if (!m) {
// mwerror(FATAL,1,"allocation failure 1 in matrix()");
fprintf(stderr, "allocation failure 1 in matrix()\n");
exit(EXIT_FAILURE); /* indicate failure.*/
}
m -= nrl;
for(i=nrl;i<=nrh;i++) {
m[i]=(float *) malloc((unsigned) (nch-ncl+1)*sizeof(float));
if (!m[i]) {
// mwerror(FATAL,1,"allocation failure 2 in matrix()");
fprintf(stderr, "allocation failure 2 in matrix()\n");
exit(EXIT_FAILURE); /* indicate failure.*/
}
m[i] -= ncl;
}
return m;
}
void free_vector(float *v, int nl, int nh)
{
free((char*) (v+nl));
nh = nh; // to remove the warning "unused parameter ‘nh’"
}
void free_matrix(float **m, int nrl, int nrh, int ncl, int nch)
{
int i;
for(i=nrh;i>=nrl;i--) free((char*) (m[i]+ncl));
free((char*) (m+nrl));
nch = nch; // to remove the warning "unused parameter ‘nh’"
}
/* Compute the real roots of a third order polynomial */
/* returns 1 or 3, the number of roots found */
int FindCubicRoots(float coeff[4], float x[3])
{
float a1 = coeff[2] / coeff[3];
float a2 = coeff[1] / coeff[3];
float a3 = coeff[0] / coeff[3];
double Q = (a1 * a1 - 3 * a2) / 9;
double R = (2 * a1 * a1 * a1 - 9 * a1 * a2 + 27 * a3) / 54;
double Qcubed = Q * Q * Q;
double d = Qcubed - R * R;
/* Three real roots */
if (d >= 0) {
double theta = acos(R / sqrt(Qcubed));
double sqrtQ = sqrt(Q);
x[0] = -2 * sqrtQ * cos( theta / 3) - a1 / 3;
x[1] = -2 * sqrtQ * cos((theta + 2 * M_PI) / 3) - a1 / 3;
x[2] = -2 * sqrtQ * cos((theta + 4 * M_PI) / 3) - a1 / 3;
return (3);
}
/* One real root */
else {
double e = pow(sqrt(-d) + fabs(R), 1. / 3.);
if (R > 0)
e = -e;
x[0] = (e + Q / e) - a1 / 3.;
return (1);
}
}
/* logarithm (base 10) of binomial coefficient */
float logcombi(int k, int n)
{
double r;
int i;
if (k>=n || k<=0) return(0.);
if (n-k<k) k=n-k;
r = 0.;
for (i=1;i<=k;i++)
r += log10((double)(n-i+1))-log10((double)i);
return((float)r);
}
/* tabulate logcombi(.,n) */
float *makelogcombi_n(int n)
{
float *l;
int k;
l = (float *)malloc((n+1)*sizeof(float));
for (k=0;k<=n;k++) l[k]=logcombi(k,n);
return(l);
}
/* tabulate logcombi(k,.) */
float *makelogcombi_k(int k, int nmax)
{
float *l;
int n;
l = (float *)malloc((nmax+1)*sizeof(float));
for (n=0;n<=nmax;n++) l[n]=logcombi(k,n);
return(l);
}
/* get a (sorted) random 7-uple of 0..n-1 */
void random_p7(int *k, int n)
{
int i,j,j0,r;
for (i=0;i<7;i++) {
r = (rand()>>3)%(n-i);
for (j=0;j<i && r>=k[j];j++) r++;
j0 = j;
for (j=i;j>j0;j--) k[j]=k[j-1];
k[j0]=r;
}
}
/*-------------------- END OF GENERAL PURPOSE ROUTINES --------------------*/
/* float comparison for qsort() */
//According to http://www.cplusplus.com/reference/clibrary/cstdlib/qsort/,
//we should have: void qsort ( void * base, size_t num, size_t size, int ( * comparator ) ( const void *, const void * ) ); that means, for "qsort", the "comparator" has two constant void* type input parameters
// static int compf(void *i, void *j)
int compf(const void *i, const void *j)
{
float a,b;
a = *((float *)i);
b = *((float *)j);
return(a<b?-1:(a>b?1:0));
}
/* find the increasing sequence of squared distances to epipolar lines */
/* e[n*2] = distances, e[n*2+1] = indexes (to cast into an int) */
//void matcherrorn(float **F, Flist p1, Flist p2, float *e)
void matcherrorn(float **F, const std::vector<float>& p1, const std::vector<float>& p2, float *e)
{
int i;
double x,y,a,b,c,d; // Guoshen Yu, double precision is needed. When the two images are identical, the error under float precision is 0 => log(error)=-inf.
int pt_size = (p1.size())/2;
for (i = 0; i < pt_size; i++) {
x = (double) p1[i*2];
y = (double) p1[i*2+1];
a = (double) F[1][1]*x+(double) F[1][2]*y+(double) F[1][3];
b = (double) F[2][1]*x+(double) F[2][2]*y+(double) F[2][3];
c = (double) F[3][1]*x+(double) F[3][2]*y+(double) F[3][3];
d = (a*(double) p2[i*2]+b*(double) p2[i*2+1]+c);
e[i*2] = (d*d)/(a*a+b*b);
e[i*2+1] = (float)i;
}
qsort(e, pt_size, 2*sizeof(float), compf);
}
/*---------- compute the epipolar geometry associated to 7 pairs ----------*/
/* */
/* INPUT: the points are (m1[k[i]*2],m1[k[i]*2+1]), m2... 0<i<7 */
/* */
/* OUTPUT: */
/* return the number of roots found, stored in z[] */
/* the epipolar matrices are F1[i][j]+z[k]*F2[i][j], 1<=i,j<=3, 0<=k<m */
// int epipolar(float *m1, float *m2, int *k, float *z, float **F1, float **F2)
int epipolar(std::vector<float>& m1, std::vector<float>& m2, int *k, float *z, float **F1, float **F2)
{
float a[4];
int i,j,i2,i3;
typedef Eigen::MatrixXf Mat;
Mat c(7,9);
/* build 9xn matrix from point matches */
for (i=0;i<7;i++) {
c(i,0) = m1[k[i]*2 ]*m2[k[i]*2 ];
c(i,1) = m1[k[i]*2+1]*m2[k[i]*2 ];
c(i,2) = m2[k[i]*2 ];
c(i,3) = m1[k[i]*2 ]*m2[k[i]*2+1];
c(i,4) = m1[k[i]*2+1]*m2[k[i]*2+1];
c(i,5) = m2[k[i]*2+1];
c(i,6) = m1[k[i]*2 ];
c(i,7) = m1[k[i]*2+1];
c(i,8) = 1.;
}
// SVD
Eigen::JacobiSVD<Mat> svd(c, Eigen::ComputeFullV);
// look for the two smallest eigenvalue of c'c
typedef Eigen::Matrix<float, 9, 1> Vec9;
Vec9 F1Vec = svd.matrixV().col(c.cols()-1);
Vec9 F2Vec = svd.matrixV().col(c.cols()-2);
/* build basis of solutions */
int cpt = 0;
for (i=1;i<=3;i++)
for (j=1;j<=3;j++)
{
F1[i][j] = F1Vec(cpt);
F2[i][j] = F2Vec(cpt);
cpt++;
}
/* build cubic polynomial P(x)=det(F1+xF2) */
a[0] = a[1] = a[2] = a[3] = 0.;
for (i=1;i<=3;i++) {
i2 = i%3+1;
i3 = i2%3+1;
a[0] += F1[i][1]*F1[i2][2]*F1[i3][3];
a[1] +=
F2[i][1]*F1[i2][2]*F1[i3][3]+
F1[i][1]*F2[i2][2]*F1[i3][3]+
F1[i][1]*F1[i2][2]*F2[i3][3];
a[2] +=
F1[i][1]*F2[i2][2]*F2[i3][3]+
F2[i][1]*F1[i2][2]*F2[i3][3]+
F2[i][1]*F2[i2][2]*F1[i3][3];
a[3] += F2[i][1]*F2[i2][2]*F2[i3][3];
}
for (i=1;i<=3;i++) {
i2 = (i+1)%3+1;
i3 = (i2+1)%3+1;
a[0] -= F1[i][1]*F1[i2][2]*F1[i3][3];
a[1] -=
F2[i][1]*F1[i2][2]*F1[i3][3]+
F1[i][1]*F2[i2][2]*F1[i3][3]+
F1[i][1]*F1[i2][2]*F2[i3][3];
a[2] -=
F1[i][1]*F2[i2][2]*F2[i3][3]+
F2[i][1]*F1[i2][2]*F2[i3][3]+
F2[i][1]*F2[i2][2]*F1[i3][3];
a[3] -= F2[i][1]*F2[i2][2]*F2[i3][3];
}
return(FindCubicRoots(a,z));
}
void divide_match(const std::vector<Match>& matches, std::vector<float>& p1, std::vector<float>& p2)
{
float x1, y1, x2, y2;
p1.clear();
p2.clear();
p1.reserve(2 * matches.size());
p2.reserve(2 * matches.size());
std::vector<Match>::const_iterator it=matches.begin();
for(; it != matches.end(); ++it) {
x1 = (*it).x1; y1 = (*it).y1;
x2 = (*it).x2; y2 = (*it).y2;
p1.push_back(x1); p1.push_back(y1);
p2.push_back(x2); p2.push_back(y2);
}
}
// float stereomatch(int img_x, int img_y, int size_pt, float* p1, float* p2, float** f, float* index, int* t, int* verb, int* n_flag, int* mode, int* stop)
// float stereomatch(const wxImage& u1, std::vector<float>& p1, std::vector<float>& p2, std::vector<SmallVector<float,3> >& f, std::vector<float>& index, int* t, int* verb, int* n_flag, int* mode, int* stop)
//int main(int argc, char** argv)
float orsa(int width, int height, std::vector<Match>& match, std::vector<float>& index, int t_value, int verb_value, int n_flag_value, int mode_value, int stop_value)
{
// int width = 0, height = 0;
// int t_value, verb_value, n_flag_value, mode_value, stop_value;
int *t, *verb, *n_flag, *mode, *stop;
t = (int*)malloc(sizeof(int)); // maximum number of ransac trials
verb = (int*)malloc(sizeof(int)); //verbose
n_flag = (int*)malloc(sizeof(int)); // in order NOT to reinitialize the random seed
mode = (int*)malloc(sizeof(int)); // mode: 0=deterministic 1=ransac 2=optimized ransac (ORSA) 3=automatic
stop = (int*)malloc(sizeof(int)); // stop as soon as the first meaningful correspondence is found
if(width <=0 || height <= 0) {
std::cerr << "Wrong dimensions of image" << std::endl;
return 1;
}
std::vector<float> p1(2*match.size()), p2(2*match.size()), p1_backup(2*match.size()), p2_backup(2*match.size());
divide_match(match, p1, p2);
p1_backup = p1;
p2_backup = p2;
libNumerics::matrix<libNumerics::flnum> f(3, 3);
f = 0;
index = std::vector<float>(match.size());
// Guoshen Yu, 2010.09.23
// index.clear();
if(t_value <= 0) {
std::cerr << "t should be greater than 0" << std::endl;
return 1;
}
*t = t_value;
if(verb_value == 0) {
free(verb);
verb = NULL;
}
else
*verb = verb_value;
if(verb_value != 1 && verb_value != 0) {
std::cerr << "verb can only be 0 or 1" << std::endl;
return 1;
}
if(n_flag_value == 0) {
free(n_flag);
n_flag = NULL;
}
else
*n_flag = n_flag_value;
if(n_flag_value != 1 && n_flag_value != 0) {
std::cerr << "n_flag can only be 0 or 1" << std::endl;
return 1;
}
if(mode_value != 0 && mode_value != 1 && mode_value != 2 && mode_value != 3) {
std::cerr << "mode can only be 0 or 1 or 2 or 3" << std::endl;
return 1;
}
*mode = mode_value;
if(stop_value == 0) {
free(stop);
stop = NULL;
}
else
*stop = stop_value;
if(stop_value != 1 && stop_value != 0) {
std::cerr << "stop can only be 0 or 1" << std::endl;
return 1;
}
int i,j,i0,k[8],idk[8],*id,m,n,l,minicur=0,miniall=0,delete_index,nid;
int niter,maxniter,better,cont,optimization;
float **F1,**F2,**F,nx,ny,z[3],minepscur,minepsall,nfa;
float norm,minlogalphacur,minlogalphaall,logalpha,logalpha0;
float *e,*logcn,*logc7,loge0;
/* initialize random seed if necessary */
// if (!n_flag) srand48( (long int) time (NULL) + (long int) getpid() );
// if (!n_flag) srand( (long int) time (NULL) + (long int) getpid() );
// Guoshen Yu, 2010.09.21: remove getpid which does not exist under Windows
if (!n_flag) srand( (long int) time (NULL) );
/* check sizes */
if (p1.size() != p2.size() || p1.size() < 14) {
fprintf(stderr, "Inconsistent sizes.\n");
exit(EXIT_FAILURE); /* indicate failure.*/
}
n = p1.size()/2;
/* tabulate logcombi */
loge0 = (float)log10(3.*(double)(n-7));
logcn = makelogcombi_n(n);
logc7 = makelogcombi_k(7,n);
/* choose mode */
if (*mode==3) {
if (logcn[7]<=(float)log10((double)(*t)))
*mode=0;
else *mode=2;
}
if (verb)
switch(*mode) {
case 0:
// i = (int)(0.5+pow(10.,logc7[n]));
// Guoshen Yu, 2010.09.22, Windows version
i = (int)(0.5+pow(10., (double)(logc7[n])));
printf("I will use deterministic mode (systematic search).\n");
printf("I have to test %d bases\n",i);
break;
case 1:
printf("I will use pure stochastic mode with no optimization.\n");
break;
case 2:
printf("I will use optimized stochastic mode (ORSA).\n");
}
/* normalize coordinates */
nx = (float)width;
ny = (float)height;
norm = 1./(float)sqrt((double)(nx*ny));
logalpha0 = (float)(log10(2.)+0.5*log10((double)((nx*nx+ny*ny)*norm*norm)));
for (i=0;i<n;i++) {
p1[i*2 ] = (p1[i*2 ]-0.5*nx)*norm;
p1[i*2+1] = (p1[i*2+1]-0.5*ny)*norm;
p2[i*2 ] = (p2[i*2 ]-0.5*nx)*norm;
p2[i*2+1] = (p2[i*2+1]-0.5*ny)*norm;
}
/* allocate and initialize memory */
F = matrix(1,3,1,3);
F1 = matrix(1,3,1,3);
F2 = matrix(1,3,1,3);
//delete_index = (index?0:1);
delete_index = 0;
e = (float *)malloc(2*n*sizeof(float));
id = (int *)malloc(n*sizeof(int));
for (i=0;i<n;i++) id[i]=i;
nid = n;
maxniter = (*mode==0?*t:*t-(*t)/10);
minlogalphaall = minepsall = 10000.;
niter = optimization = 0;
i0=0; k[0]=-1; k[7]=n;
/********** MAIN LOOP **********/
do {
niter++;
/* build next list of points */
if (*mode) random_p7(k,nid);
else {
k[i0]++;
for (i=i0+1;i<=6;i++) k[i]=k[i-1]+1;
}
for (i=0;i<7;i++) idk[i]=id[k[i]];
/* find epipolar transform */
m = epipolar(p1,p2,idk,z,F1,F2);
/* loop on roots */
for (;m--;) {
for (i=1;i<=3;i++)
for (j=1;j<=3;j++)
F[i][j] = F1[i][j]+z[m]*F2[i][j];
/* sort errors */
matcherrorn(F,p1,p2,e);
/* find most meaningful subset */
minepscur = minlogalphacur = 10000.;
for (i=7;i<n;i++) {
logalpha = logalpha0+0.5*(float)log10((double)e[i*2]);
nfa = loge0+logalpha*(float)(i-6)+logcn[i+1]+logc7[i+1];
if (nfa<minepscur) {
minepscur = nfa;
minicur = i;
minlogalphacur = logalpha;
}
}
if (minepscur<minepsall) {
/* store best result so far */
better = 1;
minepsall = minepscur;
minlogalphaall = minlogalphacur;
miniall = minicur;
// if (f)
for (l=1;l<=3;l++)
for (j=1;j<=3;j++)
f(l-1, j-1) = F[l][j];
// Guoshen Yu, 2010.09.22
// for (i=0;i<=minicur;i++)
for (i=0;i<minicur;i++)
{
index[i] = e[i*2+1];
}
} else better=0;
if (*mode==2 && ((better && minepsall<0.) ||
(niter==maxniter && !optimization))) {
if (!optimization) maxniter = niter + (*t)/10;
optimization = 1;
/* final optimization */
if (verb) {
printf(" nfa=%f size=%d (niter=%d)\n",minepsall,miniall+1,niter);
printf("optimization...\n");
}
nid = miniall+1;
// Guoshen Yu, 2010.09.22
// for (j=0;j<=miniall;j++)
for (j=0;j<miniall;j++)
id[j] = (int)(index[j]);
}
}
/* prepare next list of points */
if (*mode==0)
for(i0=6;i0>=0 && k[i0]==k[i0+1]-1;i0--){};
if (stop && minepsall<0.) cont=0;
else if (*mode==0) cont=(i0>=0?1:0);
else cont=(niter<maxniter?1:0);
} while (cont);
//erase "index", only get the index of the meaningful matchings
index.erase(index.begin()+miniall+1, index.end());
if (verb)
printf("best matching found: %d points log(alpha)=%f (%d iterations)\n",
miniall+1,minlogalphaall,niter);
/* free memory */
free(id);
free(e);
// if (delete_index) mw_delete_flist(index);
free_matrix(F2,1,3,1,3);
free_matrix(F1,1,3,1,3);
free_matrix(F,1,3,1,3);
free(logc7);
free(logcn);
if(t) free(t);
if(verb) free(verb);
if(n_flag) free(n_flag);
if(mode) free(mode);
if(stop) free(stop);
// return 0;
return(minepsall);
}

77
asift_matching/src/orsa.h Executable file
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//
// C++ Implementation: stereomatch
//
// Description: eliminate the false matches with epipolar geometry constraint.
// See http://www.math-info.univ-paris5.fr/~moisan/epipolar/
//
// Copyright (c) 2007 Lionel Moisan <Lionel.Moisan@parisdescartes.fr>
// Changelog : 2011 Use Eigen SVD <Pierre Moulon>
//
// Copyright: See COPYING file that comes with this distribution
//
//
#ifndef STEREOMATCH_H
#define STEREOMATCH_H
#include <vector>
#include "libNumerics/numerics.h"
#include "libMatch/match.h"
#include <sstream>
#include <iostream>
#include <fstream>
#include <cmath>
#include <cstdio>
#include <cmath>
#include <cstdlib>
/*-------------------- GENERAL PURPOSE ROUTINES --------------------*/
/* routines for vectors and matrices */
//float *vector(int nl, int nh);
float **matrix(int nrl, int nrh, int ncl, int nch);
void free_vector(float *v, int nl, int nh);
void free_matrix(float **m, int nrl, int nrh, int ncl, int nch);
/* Singular Value Decomposition routine */
void svdcmp(float **a, int m, int n, float *w, float **v);
/* Compute the real roots of a third order polynomial */
/* returns 1 or 3, the number of roots found */
int FindCubicRoots(float coeff[4], float x[3]);
/* logarithm (base 10) of binomial coefficient */
float logcombi(int k, int n);
/* tabulate logcombi(.,n) */
float *makelogcombi_n(int n);
/* tabulate logcombi(k,.) */
float *makelogcombi_k(int k, int nmax);
/* get a (sorted) random 7-uple of 0..n-1 */
void random_p7(int *k, int n);
/*-------------------- END OF GENERAL PURPOSE ROUTINES --------------------*/
/* float comparison for qsort() */
//According to http://www.cplusplus.com/reference/clibrary/cstdlib/qsort/,
//we should have: void qsort ( void * base, size_t num, size_t size, int ( * comparator ) ( const void *, const void * ) ); that means, for "qsort", the "comparator" has two constant void* type input parameters
int compf(const void *i, const void *j);
void matcherrorn(float **F, const std::vector<float>& p1, const std::vector<float>& p2, float *e);
int epipolar(std::vector<float>& m1, std::vector<float>& m2, int *k, float *z, float **F1, float **F2);
float orsa(int width, int height, std::vector<Match>& match, std::vector<float>& index, int t_value, int verb_value, int n_flag_value, int mode_value, int stop_value);
#endif

217
asift_matching/src/splines.cpp Executable file
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// Authors: Unknown. Please, if you are the author of this file, or if you
// know who are the authors of this file, let us know, so we can give the
// adequate credits and/or get the adequate authorizations.
#include "splines.h"
double initcausal(double *c,int n,double z)
{
double zk,z2k,iz,sum;
int k;
zk = z; iz = 1./z;
z2k = pow(z,(double)n-1.);
sum = c[0] + z2k * c[n-1];
z2k = z2k*z2k*iz;
for (k=1;k<=n-2;k++) {
sum += (zk+z2k)*c[k];
zk *= z;
z2k *= iz;
}
return (sum/(1.-zk*zk));
}
double initanticausal(double *c,int n,double z)
{
return((z/(z*z-1.))*(z*c[n-2]+c[n-1]));
}
void invspline1D(double *c,int size,double *z,int npoles)
{
double lambda;
int n,k;
/* normalization */
for (k=npoles,lambda=1.;k--;) lambda *= (1.-z[k])*(1.-1./z[k]);
for (n=size;n--;) c[n] *= lambda;
/*----- Loop on poles -----*/
for (k=0;k<npoles;k++) {
/* forward recursion */
c[0] = initcausal(c,size,z[k]);
for (n=1;n<size;n++)
c[n] += z[k]*c[n-1];
/* backwards recursion */
c[size-1] = initanticausal(c,size,z[k]);
for (n=size-1;n--;)
c[n] = z[k]*(c[n+1]-c[n]);
}
}
/*------------------------------ MAIN MODULE ------------------------------*/
// void finvspline(float *in,int order,float *out, int width, int height)
// Guoshen Yu, 2010.09.21, Windows version
void finvspline(vector<float> &in,int order,vector<float>& out, int width, int height)
// void finvspline(float *in,int order,float *out, int width, int height)
{
double *c,*d,z[5];
int npoles,nx,ny,x,y;
ny = height; nx = width;
/* initialize poles of associated z-filter */
switch (order)
{
case 2: z[0]=-0.17157288; /* sqrt(8)-3 */
break;
case 3: z[0]=-0.26794919; /* sqrt(3)-2 */
break;
case 4: z[0]=-0.361341; z[1]=-0.0137254;
break;
case 5: z[0]=-0.430575; z[1]=-0.0430963;
break;
case 6: z[0]=-0.488295; z[1]=-0.0816793; z[2]=-0.00141415;
break;
case 7: z[0]=-0.53528; z[1]=-0.122555; z[2]=-0.00914869;
break;
case 8: z[0]=-0.574687; z[1]=-0.163035; z[2]=-0.0236323; z[3]=-0.000153821;
break;
case 9: z[0]=-0.607997; z[1]=-0.201751; z[2]=-0.0432226; z[3]=-0.00212131;
break;
case 10: z[0]=-0.636551; z[1]=-0.238183; z[2]=-0.065727; z[3]=-0.00752819;
z[4]=-0.0000169828;
break;
case 11: z[0]=-0.661266; z[1]=-0.27218; z[2]=-0.0897596; z[3]=-0.0166696;
z[4]=-0.000510558;
break;
default:
printf("finvspline: order should be in 2..11.\n");
exit(-1);
}
npoles = order/2;
/* initialize double array containing image */
c = (double *)malloc(nx*ny*sizeof(double));
d = (double *)malloc(nx*ny*sizeof(double));
for (x=nx*ny;x--;)
c[x] = (double)in[x];
/* apply filter on lines */
for (y=0;y<ny;y++)
invspline1D(c+y*nx,nx,z,npoles);
/* transpose */
for (x=0;x<nx;x++)
for (y=0;y<ny;y++)
d[x*ny+y] = c[y*nx+x];
/* apply filter on columns */
for (x=0;x<nx;x++)
invspline1D(d+x*ny,ny,z,npoles);
/* transpose directy into image */
for (x=0;x<nx;x++)
for (y=0;y<ny;y++)
out[y*nx+x] = (float)(d[x*ny+y]);
/* free array */
free(d);
free(c);
}
/* extract image value (even outside image domain) */
//float v(float *in,int x,int y,float bg, int width, int height)
// Guoshen Yu, 2010.09.21, Windows version
float v(vector<float>& in,int x,int y,float bg, int width, int height)
// float v(float *in, int x,int y,float bg, int width, int height)
{
if (x<0 || x>=width || y<0 || y>=height)
return(bg); else return(in[y*width+x]);
}
/* c[] = values of interpolation function at ...,t-2,t-1,t,t+1,... */
/* coefficients for cubic interpolant (Keys' function) */
void keys(float *c,float t,float a)
{
float t2,at;
t2 = t*t;
at = a*t;
c[0] = a*t2*(1.0-t);
c[1] = (2.0*a+3.0 - (a+2.0)*t)*t2 - at;
c[2] = ((a+2.0)*t - a-3.0)*t2 + 1.0;
c[3] = a*(t-2.0)*t2 + at;
}
/* coefficients for cubic spline */
void spline3(float *c,float t)
{
float tmp;
tmp = 1.-t;
c[0] = 0.1666666666*t*t*t;
c[1] = 0.6666666666-0.5*tmp*tmp*(1.+t);
c[2] = 0.6666666666-0.5*t*t*(2.-t);
c[3] = 0.1666666666*tmp*tmp*tmp;
}
/* pre-computation for spline of order >3 */
void init_splinen(float *a,int n)
{
int k;
a[0] = 1.;
for (k=2;k<=n;k++) a[0]/=(float)k;
for (k=1;k<=n+1;k++)
a[k] = - a[k-1] *(float)(n+2-k)/(float)k;
}
/* fast integral power function */
float ipow(float x,int n)
{
float res;
for (res=1.;n;n>>=1) {
if (n&1) res*=x;
x*=x;
}
return(res);
}
/* coefficients for spline of order >3 */
void splinen(float *c,float t,float *a,int n)
{
int i,k;
float xn;
memset((void *)c,0,(n+1)*sizeof(float));
for (k=0;k<=n+1;k++) {
xn = ipow(t+(float)k,n);
for (i=k;i<=n;i++)
c[i] += a[i-k]*xn;
}
}

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asift_matching/src/splines.h Executable file
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// Authors: Unknown. Please, if you are the author of this file, or if you
// know who are the authors of this file, let us know, so we can give the
// adequate credits and/or get the adequate authorizations.
#ifndef _SPLINES_H_
#define _SPLINES_H_
#include "numerics1.h"
#include "library.h"
#include "string.h"
#include <vector>
using namespace std;
//float v(float *in,int x,int y,float bg, int width, int height);
// Guoshen Yu, 2010.09.21, Windows version
float v(vector<float>& in,int x,int y,float bg, int width, int height);
//float v(float *in, int x,int y,float bg, int width, int height);
void keys(float *c,float t,float a);
void spline3(float *c,float t);
void init_splinen(float *a,int n);
void splinen(float *c,float t,float *a,int n);
//void finvspline(float *in,int order,float *out, int width, int height);
// Guoshen Yu, 2010.09.22, Windows versions
void finvspline(vector<float>& in,int order,vector<float>& out, int width, int height);
// void finvspline(float *in,int order,float *out, int width, int height);
#endif