merged

a7192a0d · Davis King · 00b2c22c · f50faa47 · a7192a0d · a7192a0d
Commit a7192a0d authored Oct 27, 2015 by Davis King
21 changed files
--- a/dlib/control/approximate_linear_models_abstract.h
+++ b/dlib/control/approximate_linear_models_abstract.h
@@ -17,7 +17,7 @@ namespace dlib
                This object defines the interface a feature extractor must implement if it
                is to be used with the process_sample and policy objects defined at the
                bottom of this file.  Moreover, it is meant to represent the core part
-                of a model use in a reinforcement learning algorithm.
+                of a model used in a reinforcement learning algorithm.
                
                In particular, this object models a Q(state,action) function where
                    Q(state,action) == dot(w, PSI(state,action))

--- a/dlib/float_details.h
+++ b/dlib/float_details.h
@@ -3,7 +3,7 @@
 #ifndef DLIB_FLOAT_DEtAILS_Hh_
 #define DLIB_FLOAT_DEtAILS_Hh_

-#include <math.h>
+#include <cmath>
 #include "algs.h"
 #include <limits> 

@@ -100,24 +100,6 @@ namespace dlib
 // ----------------------------------------------------------------------------------------
 // ----------------------------------------------------------------------------------------

-
-
-
-        double      _frexp(double      v, int* e) const { return frexp(v,e); }
-        float       _frexp(float       v, int* e) const { return frexpf(v,e); }
-
-        double      _ldexp(double      v, int e) const { return ldexp(v,e); }
-        float       _ldexp(float       v, int e) const { return ldexpf(v,e); }
-
-#ifdef __CYGWIN__
-        // frexpl and ldexpl aren't available on cygwin so just use the double version.
-        long double _frexp(long double v, int* e) const { return _frexp((double)v,e); }
-        long double _ldexp(long double v, int e) const { return _ldexp((double)v,e); }
-#else
-        long double _frexp(long double v, int* e) const { return frexpl(v,e); }
-        long double _ldexp(long double v, int e) const { return ldexpl(v,e); }
-#endif
-
        template <typename T>
        void convert_from_T (
            const T& val
@@ -138,7 +120,7 @@ namespace dlib
            else if (val < std::numeric_limits<T>::infinity())
            {
                int exp;
-                mantissa = static_cast<int64>(_frexp(val, &exp)*(((uint64)1)<<digits));
+                mantissa = static_cast<int64>(std::frexp(val, &exp)*(((uint64)1)<<digits));
                exponent = exp - digits;

                // Compact the representation a bit by shifting off any low order bytes 
@@ -162,7 +144,7 @@ namespace dlib
        ) const
        {
            if (exponent < is_inf)
-                return _ldexp((T)mantissa, exponent);
+                return std::ldexp((T)mantissa, exponent);
            else if (exponent == is_inf)
                return std::numeric_limits<T>::infinity();
            else if (exponent == is_ninf)

--- a/dlib/matrix/lapack/fortran_id.h
+++ b/dlib/matrix/lapack/fortran_id.h
@@ -20,7 +20,7 @@
 // C with Fortran is on this platform/toolset
 #if defined(__GNUC__) || defined(__ICC) || defined(__sgi) || defined(__COMO__) || defined(__KCC)
 #define DLIB_BIND_FORTRAN_LOWERCASE_UNDERSCORE
-#elif defined(__IBMCPP__) || defined(_MSC_VER)
+#elif defined(__IBMCPP__) || defined(_MSC_VER) || defined(__BORLANDC__)
 #define DLIB_BIND_FORTRAN_LOWERCASE
 #else
 #error do not know how to link with fortran for the given platform

--- a/dlib/matrix/matrix_abstract.h
+++ b/dlib/matrix/matrix_abstract.h
@@ -733,6 +733,14 @@ namespace dlib
              from being parsed into a matrix then #in.fail() == true.
    !*/

+    /*!A csv
+        This object is used to define an io manipulator for matrix expressions.  In
+        particular, you can write statements like:
+            cout << csv << yourmatrix;
+        and have it print the matrix with commas separating each element.
+    !*/
+    some_undefined_iomnaip_type csv;
+
 // ----------------------------------------------------------------------------------------

    template <typename EXP>

--- a/docs/docs/compile.xml
+++ b/docs/docs/compile.xml
@@ -12,7 +12,7 @@

    <body>
        
-        <h2>Compiling on Any Operating System Using CMake</h2>
+        <h2>Compiling C++ Example Programs on Any Operating System Using CMake</h2>
        The best way to compile a program that uses dlib is to use <a href="http://www.cmake.org">CMake</a>.  For
        example, the following commands will compile the example programs on any operating
        system:
@@ -44,11 +44,23 @@ tell CMake which one you want it to use via the -G option.
            

 <br/>
-<br/>
+        <h2>Compiling Dlib's Python Interface</h2>
+        <p>
+           Go to the base folder of the dlib repository and run <tt>python setup.py install</tt>.  That
+           should compile and install the dlib python API on your system. 
+        </p>
+        <p>
+           Alternatively, if you want to add more python bindings to dlib's
+           python interface then you probably want to avoid the setup.py file
+           and work directly using CMake.  In particular, dlib's python API is
+           built by the CMake project in the tools/python folder.  You build
+           this project using the usual CMake commands and when compiled it
+           outputs the dlib shared library that defines the python API for dlib.
+        </p>
 <br/>


-        <h2>Compiling Without CMake</h2>
+        <h2>Compiling C++ Examples Without CMake</h2>

        <p>
           In most cases, to use this library all you have to do is extract it somewhere, make
@@ -137,13 +149,15 @@ sudo apt-get install libx11-dev
        </p>


-        <center><h1>Preprocessor Directives</h1></center>
+         <br/>
+        <center><h1>Miscellaneous Preprocessor Directives</h1></center>

         <p>
-            There are a few preprocessor directives you can supply during the build process
-            to cause the library to build in various optional ways.  By default, the
-            library will always do something reasonable, but they are listed here in the
-            event that you need to use them.
+            In addition to the preprocessor directives mentioned above, there
+            are a few more you can supply during the build process to cause the
+            library to build in various optional ways.  By default, the library
+            will always do something reasonable, but they are listed here in
+            the event that you need to use them.
         </p>



--- a/docs/docs/linear_algebra.xml
+++ b/docs/docs/linear_algebra.xml
@@ -409,6 +409,10 @@
         <item nolink="true">
            <name>Other Utilities</name>
            <sub>
+               <item>
+                  <name>csv</name>
+                  <link>dlib/matrix/matrix_abstract.h.html#csv</link>
+               </item>
               <item>
                  <name>fft</name>
                  <link>dlib/matrix/matrix_fft_abstract.h.html#fft</link>

--- a/docs/docs/release_notes.xml
+++ b/docs/docs/release_notes.xml
@@ -11,6 +11,31 @@
 <!-- **************************************************************************************  -->

 <current>
+New Features:
+   - Added the set_ptrm() routine for assigning dlib::matrix objects to arbitrary
+     memory blocks.
+      
+Non-Backwards Compatible Changes:
+
+Bug fixes:
+   - Fixed a bug that caused cmake to not provide the correct preprocessor
+     definitions until cmake was run twice. This was causing some projects to
+     not build properly.
+
+Other:
+   - Improvements to build system:
+      - Ehsan Azarnasab contributed a setup.py so the dlib Python API can be
+        installed via the usual 'python setup.py install' command. 
+      - Séverin Lemaignan upgraded dlib's CMake scripts so they include an 
+        install target.  Now dlib can be installed system wide by executing 
+        'cmake PATH_TO_DLIB; make install'.  This also includes installing the
+        appropriate scripts for CMake's find_package(dlib) to work.
+
+</current>
+
+<!-- **************************************************************************************  -->
+
+<old name="18.17" date="Aug 15, 2015">
 New Features:
   - More clustering tools:
      - Added bottom_up_cluster() and find_clusters_using_angular_kmeans()
@@ -45,7 +70,7 @@ Bug fixes:

 Other:

-</current>
+</old>

 <!-- **************************************************************************************  -->


--- a/docs/docs/term_index.xml
+++ b/docs/docs/term_index.xml
@@ -479,6 +479,7 @@
         <term file="containers.html" name="map"                  include="dlib/map.h"/>


+         <term file="dlib/matrix/matrix_abstract.h.html" name="csv"       include="dlib/matrix.h"/>
         <term file="dlib/matrix/matrix_data_layout_abstract.h.html" name="row_major_layout"       include="dlib/matrix.h"/>
         <term file="dlib/matrix/matrix_data_layout_abstract.h.html" name="column_major_layout"    include="dlib/matrix.h"/>


--- a/docs/makerel
+++ b/docs/makerel
@@ -69,7 +69,6 @@ tar -cf $SOURCE_TAR $RELDIR  || report_failure
 #find $RELDIR -name "*.cpp" -or -name "*.h" -or -name "*.txt" -or -name "*.html" -or -name "*.py" | xargs flip -m
 find $RELDIR -name "*.cpp" -or -name "*.h" -or -name "*.txt" -or -name "*.html" -or -name "*.py" | xargs unix2dos &> /dev/null 

-scp davis@home.dlib.net:dlib_win_python/* $RELDIR/python_examples || report_failure
 zip -r9 $SOURCE_ZIP $RELDIR > /dev/null || report_failure
 tar -C ../docs -cf $WEBPAGE web || report_failure
 bzip2 $SOURCE_TAR || report_failure

--- a/python_examples/compile_dlib_python_module.bat
+++ b/python_examples/compile_dlib_python_module.bat
-mkdir build
-cd build
-cmake ../../tools/python
-cmake --build . --config Release --target install
-cd ..
--- a/python_examples/correlation_tracker.py
+++ b/python_examples/correlation_tracker.py
@@ -14,18 +14,27 @@
 # and someone is waving the camera around.  The task is to track the position of
 # the juice box as the camera moves around.
 #
-# COMPILING THE DLIB PYTHON INTERFACE
-#   Dlib comes with a compiled python interface for python 2.7 on MS Windows. If
-#   you are using another python version or operating system then you need to
-#   compile the dlib python interface before you can use this file.  To do this,
-#   run compile_dlib_python_module.bat.  This should work on any operating
-#   system so long as you have CMake and boost-python installed.
-#   On Ubuntu, this can be done easily by running the command:
+#
+# COMPILING/INSTALLING THE DLIB PYTHON INTERFACE
+#   You can install dlib using the command:
+#       pip install dlib
+#
+#   Alternatively, if you want to compile dlib yourself then go into the dlib
+#   root folder and run:
+#       python setup.py install
+#   or
+#       python setup.py install --yes USE_AVX_INSTRUCTIONS
+#   if you have a CPU that supports AVX instructions, since this makes some
+#   things run faster.  
+#
+#   Compiling dlib should work on any operating system so long as you have
+#   CMake and boost-python installed.  On Ubuntu, this can be done easily by
+#   running the command:
 #       sudo apt-get install libboost-python-dev cmake
 #
 #   Also note that this example requires scikit-image which can be installed
 #   via the command:
-#       pip install -U scikit-image
+#       pip install scikit-image
 #   Or downloaded from http://scikit-image.org/download.html. 

 import os

--- a/python_examples/face_detector.py
+++ b/python_examples/face_detector.py
@@ -20,18 +20,26 @@
 #   program.  
 #
 #
-# COMPILING THE DLIB PYTHON INTERFACE
-#   Dlib comes with a compiled python interface for python 2.7 on MS Windows. If
-#   you are using another python version or operating system then you need to
-#   compile the dlib python interface before you can use this file.  To do this,
-#   run compile_dlib_python_module.bat.  This should work on any operating
-#   system so long as you have CMake and boost-python installed.
-#   On Ubuntu, this can be done easily by running the command:
+# COMPILING/INSTALLING THE DLIB PYTHON INTERFACE
+#   You can install dlib using the command:
+#       pip install dlib
+#
+#   Alternatively, if you want to compile dlib yourself then go into the dlib
+#   root folder and run:
+#       python setup.py install
+#   or
+#       python setup.py install --yes USE_AVX_INSTRUCTIONS
+#   if you have a CPU that supports AVX instructions, since this makes some
+#   things run faster.  
+#
+#   Compiling dlib should work on any operating system so long as you have
+#   CMake and boost-python installed.  On Ubuntu, this can be done easily by
+#   running the command:
 #       sudo apt-get install libboost-python-dev cmake
 #
 #   Also note that this example requires scikit-image which can be installed
 #   via the command:
-#       pip install -U scikit-image
+#       pip install scikit-image
 #   Or downloaded from http://scikit-image.org/download.html. 

 import sys

--- a/python_examples/face_landmark_detection.py
+++ b/python_examples/face_landmark_detection.py
@@ -20,18 +20,26 @@
 #   You can get the shape_predictor_68_face_landmarks.dat file from:
 #   http://sourceforge.net/projects/dclib/files/dlib/v18.10/shape_predictor_68_face_landmarks.dat.bz2
 #
-# COMPILING THE DLIB PYTHON INTERFACE
-#   Dlib comes with a compiled python interface for python 2.7 on MS Windows. If
-#   you are using another python version or operating system then you need to
-#   compile the dlib python interface before you can use this file.  To do this,
-#   run compile_dlib_python_module.bat.  This should work on any operating
-#   system so long as you have CMake and boost-python installed.
-#   On Ubuntu, this can be done easily by running the command:
+# COMPILING/INSTALLING THE DLIB PYTHON INTERFACE
+#   You can install dlib using the command:
+#       pip install dlib
+#
+#   Alternatively, if you want to compile dlib yourself then go into the dlib
+#   root folder and run:
+#       python setup.py install
+#   or
+#       python setup.py install --yes USE_AVX_INSTRUCTIONS
+#   if you have a CPU that supports AVX instructions, since this makes some
+#   things run faster.  
+#
+#   Compiling dlib should work on any operating system so long as you have
+#   CMake and boost-python installed.  On Ubuntu, this can be done easily by
+#   running the command:
 #       sudo apt-get install libboost-python-dev cmake
 #
 #   Also note that this example requires scikit-image which can be installed
 #   via the command:
-#       pip install -U scikit-image
+#       pip install scikit-image
 #   Or downloaded from http://scikit-image.org/download.html. 

 import sys

--- a/python_examples/find_candidate_object_locations.py
+++ b/python_examples/find_candidate_object_locations.py
@@ -13,9 +13,27 @@
 # a few thousand rectangles it is much faster than scanning all possible
 # rectangles inside an image.
 #
+#
+# COMPILING/INSTALLING THE DLIB PYTHON INTERFACE
+#   You can install dlib using the command:
+#       pip install dlib
+#
+#   Alternatively, if you want to compile dlib yourself then go into the dlib
+#   root folder and run:
+#       python setup.py install
+#   or
+#       python setup.py install --yes USE_AVX_INSTRUCTIONS
+#   if you have a CPU that supports AVX instructions, since this makes some
+#   things run faster.  
+#
+#   Compiling dlib should work on any operating system so long as you have
+#   CMake and boost-python installed.  On Ubuntu, this can be done easily by
+#   running the command:
+#       sudo apt-get install libboost-python-dev cmake
+#
 #   Also note that this example requires scikit-image which can be installed
 #   via the command:
-#       pip install -U scikit-image
+#       pip install scikit-image
 #   Or downloaded from http://scikit-image.org/download.html. 



--- a/python_examples/max_cost_assignment.py
+++ b/python_examples/max_cost_assignment.py
@@ -5,14 +5,24 @@
 # problem solver.  It is an implementation of the famous Hungarian algorithm
 # and is quite fast, operating in O(N^3) time.
 #
-# COMPILING THE DLIB PYTHON INTERFACE
-#   Dlib comes with a compiled python interface for python 2.7 on MS Windows. If
-#   you are using another python version or operating system then you need to
-#   compile the dlib python interface before you can use this file.  To do this,
-#   run compile_dlib_python_module.bat.  This should work on any operating
-#   system so long as you have CMake and boost-python installed.
-#   On Ubuntu, this can be done easily by running the command:
+# COMPILING/INSTALLING THE DLIB PYTHON INTERFACE
+#   You can install dlib using the command:
+#       pip install dlib
+#
+#   Alternatively, if you want to compile dlib yourself then go into the dlib
+#   root folder and run:
+#       python setup.py install
+#   or
+#       python setup.py install --yes USE_AVX_INSTRUCTIONS
+#   if you have a CPU that supports AVX instructions, since this makes some
+#   things run faster.  
+#
+#   Compiling dlib should work on any operating system so long as you have
+#   CMake and boost-python installed.  On Ubuntu, this can be done easily by
+#   running the command:
 #       sudo apt-get install libboost-python-dev cmake
+#
+
 import dlib

 # Let's imagine you need to assign N people to N jobs.  Additionally, each

--- a/python_examples/sequence_segmenter.py
+++ b/python_examples/sequence_segmenter.py
@@ -10,14 +10,23 @@
 # will be to learn to identify person names.  Once we have our segmentation
 # model we can use it to find names in new sentences, as we will show.
 #
-# COMPILING THE DLIB PYTHON INTERFACE
-#   Dlib comes with a compiled python interface for python 2.7 on MS Windows. If
-#   you are using another python version or operating system then you need to
-#   compile the dlib python interface before you can use this file.  To do this,
-#   run compile_dlib_python_module.bat.  This should work on any operating
-#   system so long as you have CMake and boost-python installed.
-#   On Ubuntu, this can be done easily by running the command:
+# COMPILING/INSTALLING THE DLIB PYTHON INTERFACE
+#   You can install dlib using the command:
+#       pip install dlib
+#
+#   Alternatively, if you want to compile dlib yourself then go into the dlib
+#   root folder and run:
+#       python setup.py install
+#   or
+#       python setup.py install --yes USE_AVX_INSTRUCTIONS
+#   if you have a CPU that supports AVX instructions, since this makes some
+#   things run faster.  
+#
+#   Compiling dlib should work on any operating system so long as you have
+#   CMake and boost-python installed.  On Ubuntu, this can be done easily by
+#   running the command:
 #       sudo apt-get install libboost-python-dev cmake
+#
 import sys
 import dlib


--- a/python_examples/svm_rank.py
+++ b/python_examples/svm_rank.py
@@ -13,14 +13,25 @@
 # that you use this score to order the objects so that the most relevant objects
 # come to the top of the ranked list.
 #
-# COMPILING THE DLIB PYTHON INTERFACE
-#   Dlib comes with a compiled python interface for python 2.7 on MS Windows. If
-#   you are using another python version or operating system then you need to
-#   compile the dlib python interface before you can use this file.  To do this,
-#   run compile_dlib_python_module.bat.  This should work on any operating
-#   system so long as you have CMake and boost-python installed.
-#   On Ubuntu, this can be done easily by running the command:
+#
+# COMPILING/INSTALLING THE DLIB PYTHON INTERFACE
+#   You can install dlib using the command:
+#       pip install dlib
+#
+#   Alternatively, if you want to compile dlib yourself then go into the dlib
+#   root folder and run:
+#       python setup.py install
+#   or
+#       python setup.py install --yes USE_AVX_INSTRUCTIONS
+#   if you have a CPU that supports AVX instructions, since this makes some
+#   things run faster.  
+#
+#   Compiling dlib should work on any operating system so long as you have
+#   CMake and boost-python installed.  On Ubuntu, this can be done easily by
+#   running the command:
 #       sudo apt-get install libboost-python-dev cmake
+#
+
 import dlib



--- a/python_examples/svm_struct.py
+++ b/python_examples/svm_struct.py
@@ -13,14 +13,25 @@
 # interface.  So consider using the C++ interface instead if you find that
 # running it in python is slow.
 #
-# COMPILING THE DLIB PYTHON INTERFACE
-#   Dlib comes with a compiled python interface for python 2.7 on MS Windows. If
-#   you are using another python version or operating system then you need to
-#   compile the dlib python interface before you can use this file.  To do this,
-#   run compile_dlib_python_module.bat.  This should work on any operating
-#   system so long as you have CMake and boost-python installed.
-#   On Ubuntu, this can be done easily by running the command:
+#
+# COMPILING/INSTALLING THE DLIB PYTHON INTERFACE
+#   You can install dlib using the command:
+#       pip install dlib
+#
+#   Alternatively, if you want to compile dlib yourself then go into the dlib
+#   root folder and run:
+#       python setup.py install
+#   or
+#       python setup.py install --yes USE_AVX_INSTRUCTIONS
+#   if you have a CPU that supports AVX instructions, since this makes some
+#   things run faster.  
+#
+#   Compiling dlib should work on any operating system so long as you have
+#   CMake and boost-python installed.  On Ubuntu, this can be done easily by
+#   running the command:
 #       sudo apt-get install libboost-python-dev cmake
+#
+
 import dlib



--- a/python_examples/train_object_detector.py
+++ b/python_examples/train_object_detector.py
@@ -7,18 +7,27 @@
 #   window object detector first published by Dalal and Triggs in 2005 in the
 #   paper Histograms of Oriented Gradients for Human Detection.
 #
-# COMPILING THE DLIB PYTHON INTERFACE
-#   Dlib comes with a compiled python interface for python 2.7 on MS Windows. If
-#   you are using another python version or operating system then you need to
-#   compile the dlib python interface before you can use this file.  To do this,
-#   run compile_dlib_python_module.bat.  This should work on any operating
-#   system so long as you have CMake and boost-python installed.
-#   On Ubuntu, this can be done easily by running the command:
+#
+# COMPILING/INSTALLING THE DLIB PYTHON INTERFACE
+#   You can install dlib using the command:
+#       pip install dlib
+#
+#   Alternatively, if you want to compile dlib yourself then go into the dlib
+#   root folder and run:
+#       python setup.py install
+#   or
+#       python setup.py install --yes USE_AVX_INSTRUCTIONS
+#   if you have a CPU that supports AVX instructions, since this makes some
+#   things run faster.  
+#
+#   Compiling dlib should work on any operating system so long as you have
+#   CMake and boost-python installed.  On Ubuntu, this can be done easily by
+#   running the command:
 #       sudo apt-get install libboost-python-dev cmake
 #
 #   Also note that this example requires scikit-image which can be installed
 #   via the command:
-#       pip install -U scikit-image
+#       pip install scikit-image
 #   Or downloaded from http://scikit-image.org/download.html. 

 import os

--- a/python_examples/train_shape_predictor.py
+++ b/python_examples/train_shape_predictor.py
@@ -16,18 +16,26 @@
 #   prediction tasks.  But here we demonstrate it only on a simple face
 #   landmarking task.
 #
-# COMPILING THE DLIB PYTHON INTERFACE
-#   Dlib comes with a compiled python interface for python 2.7 on MS Windows. If
-#   you are using another python version or operating system then you need to
-#   compile the dlib python interface before you can use this file.  To do this,
-#   run compile_dlib_python_module.bat.  This should work on any operating
-#   system so long as you have CMake and boost-python installed.
-#   On Ubuntu, this can be done easily by running the command:
+# COMPILING/INSTALLING THE DLIB PYTHON INTERFACE
+#   You can install dlib using the command:
+#       pip install dlib
+#
+#   Alternatively, if you want to compile dlib yourself then go into the dlib
+#   root folder and run:
+#       python setup.py install
+#   or
+#       python setup.py install --yes USE_AVX_INSTRUCTIONS
+#   if you have a CPU that supports AVX instructions, since this makes some
+#   things run faster.  
+#
+#   Compiling dlib should work on any operating system so long as you have
+#   CMake and boost-python installed.  On Ubuntu, this can be done easily by
+#   running the command:
 #       sudo apt-get install libboost-python-dev cmake
 #
 #   Also note that this example requires scikit-image which can be installed
 #   via the command:
-#       pip install -U scikit-image
+#       pip install scikit-image
 #   Or downloaded from http://scikit-image.org/download.html. 

 import os

--- a/setup.py
+++ b/setup.py
@@ -2,6 +2,10 @@
 Copyright (C) 2015  Ehsan Azar (dashesy@linux.com)
 License: Boost Software License   See LICENSE.txt for the full license.

+This file basically just uses CMake to compile the dlib python bindings project
+located in the tools/python folder and then puts the outputs into standard
+python packages.
+
 To build the dlib:
    python setup.py build
 To build and install: