generic-library/opencv
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Merge pull request #1 from Itseez/master

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Update from original
This commit is contained in:
Eric Sommerlade 2015-02-20 17:21:38 +00:00
commit 6447c7b2f4
3126 changed files with 616653 additions and 1147673 deletions
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2
.gitignore vendored
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@ -1,10 +1,10 @@
*.autosave
*.pyc
*.user
*~
.*.swp
.DS_Store
.sw[a-z]
/modules/refman.rst
Thumbs.db
tags
tegra/

2
.tgitconfig Normal file
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[tgit]
icon = doc/opencv.ico

2
3rdparty/ffmpeg/make.bat vendored
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@ -1,2 +1,2 @@
set path=c:\dev\msys32\bin;%path% & gcc -Wall -shared -o opencv_ffmpeg.dll -O2 -x c++ -I../include -I../include/ffmpeg_ -I../../modules/highgui/src ffopencv.c -L../lib -lavformat -lavcodec -lavdevice -lswscale -lavutil -liconv -lws2_32
set path=c:\dev\msys32\bin;%path% & gcc -Wall -shared -o opencv_ffmpeg.dll -O2 -x c++ -I../include -I../include/ffmpeg_ -I../../modules/highgui/src ffopencv.c -L../lib -lavformat -lavcodec -lavdevice -lswscale -lavutil -lws2_32
set path=c:\dev\msys64\bin;%path% & gcc -m64 -Wall -shared -o opencv_ffmpeg_64.dll -O2 -x c++ -I../include -I../include/ffmpeg_ -I../../modules/highgui/src ffopencv.c -L../lib -lavformat64 -lavcodec64 -lavdevice64 -lswscale64 -lavutil64 -lws2_32

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3rdparty/ffmpeg/opencv_ffmpeg.dll vendored
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502
3rdparty/include/MultiMon.h vendored
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//=============================================================================
//
// multimon.h -- Stub module that fakes multiple monitor apis on Win32 OSes
// without them.
//
// By using this header your code will get back default values from
// GetSystemMetrics() for new metrics, and the new multimonitor APIs
// will act like only one display is present on a Win32 OS without
// multimonitor APIs.
//
// Exactly one source must include this with COMPILE_MULTIMON_STUBS defined.
//
// Copyright (c) Microsoft Corporation. All rights reserved.
//
//=============================================================================
#ifdef __cplusplus
extern "C" { // Assume C declarations for C++
#endif // __cplusplus
//
// If we are building with Win95/NT4 headers, we need to declare
// the multimonitor-related metrics and APIs ourselves.
//
#ifndef SM_CMONITORS
#define SM_XVIRTUALSCREEN 76
#define SM_YVIRTUALSCREEN 77
#define SM_CXVIRTUALSCREEN 78
#define SM_CYVIRTUALSCREEN 79
#define SM_CMONITORS 80
#define SM_SAMEDISPLAYFORMAT 81
// HMONITOR is already declared if WINVER >= 0x0500 in windef.h
// This is for components built with an older version number.
//
#if !defined(HMONITOR_DECLARED) && (WINVER < 0x0500)
DECLARE_HANDLE(HMONITOR);
#define HMONITOR_DECLARED
#endif
#define MONITOR_DEFAULTTONULL 0x00000000
#define MONITOR_DEFAULTTOPRIMARY 0x00000001
#define MONITOR_DEFAULTTONEAREST 0x00000002
#define MONITORINFOF_PRIMARY 0x00000001
typedef struct tagMONITORINFO
{
DWORD cbSize;
RECT rcMonitor;
RECT rcWork;
DWORD dwFlags;
} MONITORINFO, *LPMONITORINFO;
#ifndef CCHDEVICENAME
#define CCHDEVICENAME 32
#endif
#ifdef __cplusplus
typedef struct tagMONITORINFOEXA : public tagMONITORINFO
{
CHAR szDevice[CCHDEVICENAME];
} MONITORINFOEXA, *LPMONITORINFOEXA;
typedef struct tagMONITORINFOEXW : public tagMONITORINFO
{
WCHAR szDevice[CCHDEVICENAME];
} MONITORINFOEXW, *LPMONITORINFOEXW;
#ifdef UNICODE
typedef MONITORINFOEXW MONITORINFOEX;
typedef LPMONITORINFOEXW LPMONITORINFOEX;
#else
typedef MONITORINFOEXA MONITORINFOEX;
typedef LPMONITORINFOEXA LPMONITORINFOEX;
#endif // UNICODE
#else // ndef __cplusplus
typedef struct tagMONITORINFOEXA
{
MONITORINFO;
CHAR szDevice[CCHDEVICENAME];
} MONITORINFOEXA, *LPMONITORINFOEXA;
typedef struct tagMONITORINFOEXW
{
MONITORINFO;
WCHAR szDevice[CCHDEVICENAME];
} MONITORINFOEXW, *LPMONITORINFOEXW;
#ifdef UNICODE
typedef MONITORINFOEXW MONITORINFOEX;
typedef LPMONITORINFOEXW LPMONITORINFOEX;
#else
typedef MONITORINFOEXA MONITORINFOEX;
typedef LPMONITORINFOEXA LPMONITORINFOEX;
#endif // UNICODE
#endif
typedef BOOL (CALLBACK* MONITORENUMPROC)(HMONITOR, HDC, LPRECT, LPARAM);
#ifndef DISPLAY_DEVICE_ATTACHED_TO_DESKTOP
typedef struct _DISPLAY_DEVICEA {
DWORD cb;
CHAR DeviceName[32];
CHAR DeviceString[128];
DWORD StateFlags;
CHAR DeviceID[128];
CHAR DeviceKey[128];
} DISPLAY_DEVICEA, *PDISPLAY_DEVICEA, *LPDISPLAY_DEVICEA;
typedef struct _DISPLAY_DEVICEW {
DWORD cb;
WCHAR DeviceName[32];
WCHAR DeviceString[128];
DWORD StateFlags;
WCHAR DeviceID[128];
WCHAR DeviceKey[128];
} DISPLAY_DEVICEW, *PDISPLAY_DEVICEW, *LPDISPLAY_DEVICEW;
#ifdef UNICODE
typedef DISPLAY_DEVICEW DISPLAY_DEVICE;
typedef PDISPLAY_DEVICEW PDISPLAY_DEVICE;
typedef LPDISPLAY_DEVICEW LPDISPLAY_DEVICE;
#else
typedef DISPLAY_DEVICEA DISPLAY_DEVICE;
typedef PDISPLAY_DEVICEA PDISPLAY_DEVICE;
typedef LPDISPLAY_DEVICEA LPDISPLAY_DEVICE;
#endif // UNICODE
#define DISPLAY_DEVICE_ATTACHED_TO_DESKTOP 0x00000001
#define DISPLAY_DEVICE_MULTI_DRIVER 0x00000002
#define DISPLAY_DEVICE_PRIMARY_DEVICE 0x00000004
#define DISPLAY_DEVICE_MIRRORING_DRIVER 0x00000008
#define DISPLAY_DEVICE_VGA_COMPATIBLE 0x00000010
#endif
#endif // SM_CMONITORS
#undef GetMonitorInfo
#undef GetSystemMetrics
#undef MonitorFromWindow
#undef MonitorFromRect
#undef MonitorFromPoint
#undef EnumDisplayMonitors
#undef EnumDisplayDevices
//
// Define COMPILE_MULTIMON_STUBS to compile the stubs;
// otherwise, you get the declarations.
//
#ifdef COMPILE_MULTIMON_STUBS
//-----------------------------------------------------------------------------
//
// Implement the API stubs.
//
//-----------------------------------------------------------------------------
#ifndef _MULTIMON_USE_SECURE_CRT
#if defined(__GOT_SECURE_LIB__) && __GOT_SECURE_LIB__ >= 200402L
#define _MULTIMON_USE_SECURE_CRT 1
#else
#define _MULTIMON_USE_SECURE_CRT 0
#endif
#endif
#ifndef MULTIMON_FNS_DEFINED
int (WINAPI* g_pfnGetSystemMetrics)(int) = NULL;
HMONITOR (WINAPI* g_pfnMonitorFromWindow)(HWND, DWORD) = NULL;
HMONITOR (WINAPI* g_pfnMonitorFromRect)(LPCRECT, DWORD) = NULL;
HMONITOR (WINAPI* g_pfnMonitorFromPoint)(POINT, DWORD) = NULL;
BOOL (WINAPI* g_pfnGetMonitorInfo)(HMONITOR, LPMONITORINFO) = NULL;
BOOL (WINAPI* g_pfnEnumDisplayMonitors)(HDC, LPCRECT, MONITORENUMPROC, LPARAM) = NULL;
BOOL (WINAPI* g_pfnEnumDisplayDevices)(PVOID, DWORD, PDISPLAY_DEVICE,DWORD) = NULL;
BOOL g_fMultiMonInitDone = FALSE;
BOOL g_fMultimonPlatformNT = FALSE;
#endif
BOOL IsPlatformNT()
{
OSVERSIONINFOA osvi = {0};
osvi.dwOSVersionInfoSize = sizeof(osvi);
GetVersionExA((OSVERSIONINFOA*)&osvi);
return (VER_PLATFORM_WIN32_NT == osvi.dwPlatformId);
}
BOOL InitMultipleMonitorStubs(void)
{
HMODULE hUser32;
if (g_fMultiMonInitDone)
{
return g_pfnGetMonitorInfo != NULL;
}
g_fMultimonPlatformNT = IsPlatformNT();
hUser32 = GetModuleHandle(TEXT("USER32"));
if (hUser32 &&
(*(FARPROC*)&g_pfnGetSystemMetrics = GetProcAddress(hUser32,"GetSystemMetrics")) != NULL &&
(*(FARPROC*)&g_pfnMonitorFromWindow = GetProcAddress(hUser32,"MonitorFromWindow")) != NULL &&
(*(FARPROC*)&g_pfnMonitorFromRect = GetProcAddress(hUser32,"MonitorFromRect")) != NULL &&
(*(FARPROC*)&g_pfnMonitorFromPoint = GetProcAddress(hUser32,"MonitorFromPoint")) != NULL &&
(*(FARPROC*)&g_pfnEnumDisplayMonitors = GetProcAddress(hUser32,"EnumDisplayMonitors")) != NULL &&
#ifdef UNICODE
(*(FARPROC*)&g_pfnEnumDisplayDevices = GetProcAddress(hUser32,"EnumDisplayDevicesW")) != NULL &&
(*(FARPROC*)&g_pfnGetMonitorInfo = g_fMultimonPlatformNT ? GetProcAddress(hUser32,"GetMonitorInfoW") :
GetProcAddress(hUser32,"GetMonitorInfoA")) != NULL
#else
(*(FARPROC*)&g_pfnGetMonitorInfo = GetProcAddress(hUser32,"GetMonitorInfoA")) != NULL &&
(*(FARPROC*)&g_pfnEnumDisplayDevices = GetProcAddress(hUser32,"EnumDisplayDevicesA")) != NULL
#endif
) {
g_fMultiMonInitDone = TRUE;
return TRUE;
}
else
{
g_pfnGetSystemMetrics = NULL;
g_pfnMonitorFromWindow = NULL;
g_pfnMonitorFromRect = NULL;
g_pfnMonitorFromPoint = NULL;
g_pfnGetMonitorInfo = NULL;
g_pfnEnumDisplayMonitors = NULL;
g_pfnEnumDisplayDevices = NULL;
g_fMultiMonInitDone = TRUE;
return FALSE;
}
}
//-----------------------------------------------------------------------------
//
// fake implementations of Monitor APIs that work with the primary display
// no special parameter validation is made since these run in client code
//
//-----------------------------------------------------------------------------
int WINAPI
xGetSystemMetrics(int nIndex)
{
if (InitMultipleMonitorStubs())
return g_pfnGetSystemMetrics(nIndex);
switch (nIndex)
{
case SM_CMONITORS:
case SM_SAMEDISPLAYFORMAT:
return 1;
case SM_XVIRTUALSCREEN:
case SM_YVIRTUALSCREEN:
return 0;
case SM_CXVIRTUALSCREEN:
nIndex = SM_CXSCREEN;
break;
case SM_CYVIRTUALSCREEN:
nIndex = SM_CYSCREEN;
break;
}
return GetSystemMetrics(nIndex);
}
#define xPRIMARY_MONITOR ((HMONITOR)0x12340042)
HMONITOR WINAPI
xMonitorFromPoint(POINT ptScreenCoords, DWORD dwFlags)
{
if (InitMultipleMonitorStubs())
return g_pfnMonitorFromPoint(ptScreenCoords, dwFlags);
if ((dwFlags & (MONITOR_DEFAULTTOPRIMARY | MONITOR_DEFAULTTONEAREST)) ||
((ptScreenCoords.x >= 0) &&
(ptScreenCoords.x < GetSystemMetrics(SM_CXSCREEN)) &&
(ptScreenCoords.y >= 0) &&
(ptScreenCoords.y < GetSystemMetrics(SM_CYSCREEN))))
{
return xPRIMARY_MONITOR;
}
return NULL;
}
HMONITOR WINAPI
xMonitorFromRect(LPCRECT lprcScreenCoords, DWORD dwFlags)
{
if (InitMultipleMonitorStubs())
return g_pfnMonitorFromRect(lprcScreenCoords, dwFlags);
if ((dwFlags & (MONITOR_DEFAULTTOPRIMARY | MONITOR_DEFAULTTONEAREST)) ||
((lprcScreenCoords->right > 0) &&
(lprcScreenCoords->bottom > 0) &&
(lprcScreenCoords->left < GetSystemMetrics(SM_CXSCREEN)) &&
(lprcScreenCoords->top < GetSystemMetrics(SM_CYSCREEN))))
{
return xPRIMARY_MONITOR;
}
return NULL;
}
HMONITOR WINAPI
xMonitorFromWindow(HWND hWnd, DWORD dwFlags)
{
WINDOWPLACEMENT wp;
if (InitMultipleMonitorStubs())
return g_pfnMonitorFromWindow(hWnd, dwFlags);
if (dwFlags & (MONITOR_DEFAULTTOPRIMARY | MONITOR_DEFAULTTONEAREST))
return xPRIMARY_MONITOR;
if (IsIconic(hWnd) ?
GetWindowPlacement(hWnd, &wp) :
GetWindowRect(hWnd, &wp.rcNormalPosition)) {
return xMonitorFromRect(&wp.rcNormalPosition, dwFlags);
}
return NULL;
}
BOOL WINAPI
xGetMonitorInfo(HMONITOR hMonitor, __inout LPMONITORINFO lpMonitorInfo)
{
RECT rcWork;
if (InitMultipleMonitorStubs())
{
BOOL f = g_pfnGetMonitorInfo(hMonitor, lpMonitorInfo);
#ifdef UNICODE
if (f && !g_fMultimonPlatformNT && (lpMonitorInfo->cbSize >= sizeof(MONITORINFOEX)))
{
MultiByteToWideChar(CP_ACP, 0,
(LPSTR)((MONITORINFOEX*)lpMonitorInfo)->szDevice, -1,
((MONITORINFOEX*)lpMonitorInfo)->szDevice, (sizeof(((MONITORINFOEX*)lpMonitorInfo)->szDevice)/sizeof(TCHAR)));
}
#endif
return f;
}
if ((hMonitor == xPRIMARY_MONITOR) &&
lpMonitorInfo &&
(lpMonitorInfo->cbSize >= sizeof(MONITORINFO)) &&
SystemParametersInfoA(SPI_GETWORKAREA, 0, &rcWork, 0))
{
lpMonitorInfo->rcMonitor.left = 0;
lpMonitorInfo->rcMonitor.top = 0;
lpMonitorInfo->rcMonitor.right = GetSystemMetrics(SM_CXSCREEN);
lpMonitorInfo->rcMonitor.bottom = GetSystemMetrics(SM_CYSCREEN);
lpMonitorInfo->rcWork = rcWork;
lpMonitorInfo->dwFlags = MONITORINFOF_PRIMARY;
if (lpMonitorInfo->cbSize >= sizeof(MONITORINFOEX))
{
#ifdef UNICODE
MultiByteToWideChar(CP_ACP, 0, "DISPLAY", -1, ((MONITORINFOEX*)lpMonitorInfo)->szDevice, (sizeof(((MONITORINFOEX*)lpMonitorInfo)->szDevice)/sizeof(TCHAR)));
#else // UNICODE
#if _MULTIMON_USE_SECURE_CRT
strncpy_s(((MONITORINFOEX*)lpMonitorInfo)->szDevice, (sizeof(((MONITORINFOEX*)lpMonitorInfo)->szDevice)/sizeof(TCHAR)), TEXT("DISPLAY"), (sizeof(((MONITORINFOEX*)lpMonitorInfo)->szDevice)/sizeof(TCHAR)) - 1);
#else
lstrcpyn(((MONITORINFOEX*)lpMonitorInfo)->szDevice, TEXT("DISPLAY"), (sizeof(((MONITORINFOEX*)lpMonitorInfo)->szDevice)/sizeof(TCHAR)));
#endif // _MULTIMON_USE_SECURE_CRT
#endif // UNICODE
}
return TRUE;
}
return FALSE;
}
BOOL WINAPI
xEnumDisplayMonitors(
HDC hdcOptionalForPainting,
LPCRECT lprcEnumMonitorsThatIntersect,
MONITORENUMPROC lpfnEnumProc,
LPARAM dwData)
{
RECT rcLimit;
if (InitMultipleMonitorStubs()) {
return g_pfnEnumDisplayMonitors(
hdcOptionalForPainting,
lprcEnumMonitorsThatIntersect,
lpfnEnumProc,
dwData);
}
if (!lpfnEnumProc)
return FALSE;
rcLimit.left = 0;
rcLimit.top = 0;
rcLimit.right = GetSystemMetrics(SM_CXSCREEN);
rcLimit.bottom = GetSystemMetrics(SM_CYSCREEN);
if (hdcOptionalForPainting)
{
RECT rcClip;
POINT ptOrg;
switch (GetClipBox(hdcOptionalForPainting, &rcClip))
{
default:
if (!GetDCOrgEx(hdcOptionalForPainting, &ptOrg))
return FALSE;
OffsetRect(&rcLimit, -ptOrg.x, -ptOrg.y);
if (IntersectRect(&rcLimit, &rcLimit, &rcClip) &&
(!lprcEnumMonitorsThatIntersect ||
IntersectRect(&rcLimit, &rcLimit, lprcEnumMonitorsThatIntersect))) {
break;
}
//fall thru
case NULLREGION:
return TRUE;
case ERROR:
return FALSE;
}
} else {
if ( lprcEnumMonitorsThatIntersect &&
!IntersectRect(&rcLimit, &rcLimit, lprcEnumMonitorsThatIntersect)) {
return TRUE;
}
}
return lpfnEnumProc(
xPRIMARY_MONITOR,
hdcOptionalForPainting,
&rcLimit,
dwData);
}
BOOL WINAPI
xEnumDisplayDevices(
PVOID Unused,
DWORD iDevNum,
__inout PDISPLAY_DEVICE lpDisplayDevice,
DWORD dwFlags)
{
if (InitMultipleMonitorStubs())
return g_pfnEnumDisplayDevices(Unused, iDevNum, lpDisplayDevice, dwFlags);
if (Unused != NULL)
return FALSE;
if (iDevNum != 0)
return FALSE;
if (lpDisplayDevice == NULL || lpDisplayDevice->cb < sizeof(DISPLAY_DEVICE))
return FALSE;
#ifdef UNICODE
MultiByteToWideChar(CP_ACP, 0, "DISPLAY", -1, lpDisplayDevice->DeviceName, (sizeof(lpDisplayDevice->DeviceName)/sizeof(TCHAR)));
MultiByteToWideChar(CP_ACP, 0, "DISPLAY", -1, lpDisplayDevice->DeviceString, (sizeof(lpDisplayDevice->DeviceString)/sizeof(TCHAR)));
#else // UNICODE
#if _MULTIMON_USE_SECURE_CRT
strncpy_s((LPTSTR)lpDisplayDevice->DeviceName, (sizeof(lpDisplayDevice->DeviceName)/sizeof(TCHAR)), TEXT("DISPLAY"), (sizeof(lpDisplayDevice->DeviceName)/sizeof(TCHAR)) - 1);
strncpy_s((LPTSTR)lpDisplayDevice->DeviceString, (sizeof(lpDisplayDevice->DeviceString)/sizeof(TCHAR)), TEXT("DISPLAY"), (sizeof(lpDisplayDevice->DeviceName)/sizeof(TCHAR)) - 1);
#else
lstrcpyn((LPTSTR)lpDisplayDevice->DeviceName, TEXT("DISPLAY"), (sizeof(lpDisplayDevice->DeviceName)/sizeof(TCHAR)));
lstrcpyn((LPTSTR)lpDisplayDevice->DeviceString, TEXT("DISPLAY"), (sizeof(lpDisplayDevice->DeviceString)/sizeof(TCHAR)));
#endif // _MULTIMON_USE_SECURE_CRT
#endif // UNICODE
lpDisplayDevice->StateFlags = DISPLAY_DEVICE_ATTACHED_TO_DESKTOP | DISPLAY_DEVICE_PRIMARY_DEVICE;
return TRUE;
}
#undef xPRIMARY_MONITOR
#undef COMPILE_MULTIMON_STUBS
#else // COMPILE_MULTIMON_STUBS
extern int WINAPI xGetSystemMetrics(int);
extern HMONITOR WINAPI xMonitorFromWindow(HWND, DWORD);
extern HMONITOR WINAPI xMonitorFromRect(LPCRECT, DWORD);
extern HMONITOR WINAPI xMonitorFromPoint(POINT, DWORD);
extern BOOL WINAPI xGetMonitorInfo(HMONITOR, LPMONITORINFO);
extern BOOL WINAPI xEnumDisplayMonitors(HDC, LPCRECT, MONITORENUMPROC, LPARAM);
extern BOOL WINAPI xEnumDisplayDevices(PVOID, DWORD, PDISPLAY_DEVICE, DWORD);
#endif // COMPILE_MULTIMON_STUBS
//
// build defines that replace the regular APIs with our versions
//
#define GetSystemMetrics xGetSystemMetrics
#define MonitorFromWindow xMonitorFromWindow
#define MonitorFromRect xMonitorFromRect
#define MonitorFromPoint xMonitorFromPoint
#define GetMonitorInfo xGetMonitorInfo
#define EnumDisplayMonitors xEnumDisplayMonitors
#define EnumDisplayDevices xEnumDisplayDevices
#ifdef __cplusplus
}
#endif // __cplusplus

2
3rdparty/include/opencl/1.2/CL/cl.hpp vendored
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@ -210,7 +210,7 @@
#include <string>
#endif
#if defined(linux) || defined(__APPLE__) || defined(__MACOSX)
#if defined(__linux__) || defined(__APPLE__) || defined(__MACOSX)
#include <alloca.h>
#include <emmintrin.h>

18
3rdparty/include/opencl/1.2/CL/cl_platform.h vendored
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@ -92,7 +92,7 @@ extern "C" {
#define CL_EXT_SUFFIX__VERSION_1_1_DEPRECATED __attribute__((deprecated))
#define CL_EXT_PREFIX__VERSION_1_1_DEPRECATED
#endif
#elif _WIN32
#elif defined(_WIN32)
#ifdef CL_USE_DEPRECATED_OPENCL_1_0_APIS
#define CL_EXT_SUFFIX__VERSION_1_0_DEPRECATED
#define CL_EXT_PREFIX__VERSION_1_0_DEPRECATED
@ -332,13 +332,13 @@ typedef unsigned int cl_GLenum;
/* Define basic vector types */
#if defined( __VEC__ )
#include <altivec.h> /* may be omitted depending on compiler. AltiVec spec provides no way to detect whether the header is required. */
typedef vector unsigned char __cl_uchar16;
typedef vector signed char __cl_char16;
typedef vector unsigned short __cl_ushort8;
typedef vector signed short __cl_short8;
typedef vector unsigned int __cl_uint4;
typedef vector signed int __cl_int4;
typedef vector float __cl_float4;
typedef __vector unsigned char __cl_uchar16;
typedef __vector signed char __cl_char16;
typedef __vector unsigned short __cl_ushort8;
typedef __vector signed short __cl_short8;
typedef __vector unsigned int __cl_uint4;
typedef __vector signed int __cl_int4;
typedef __vector float __cl_float4;
#define __CL_UCHAR16__ 1
#define __CL_CHAR16__ 1
#define __CL_USHORT8__ 1
@ -454,7 +454,7 @@ typedef unsigned int cl_GLenum;
/* Define alignment keys */
#if defined( __GNUC__ )
#define CL_ALIGNED(_x) __attribute__ ((aligned(_x)))
#elif defined( _WIN32) && (_MSC_VER)
#elif defined( _WIN32) && defined(_MSC_VER)
/* Alignment keys neutered on windows because MSVC can't swallow function arguments with alignment requirements */
/* http://msdn.microsoft.com/en-us/library/373ak2y1%28VS.71%29.aspx */
/* #include <crtdefs.h> */

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3rdparty/ippicv/.gitignore vendored Normal file
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downloads/
unpack/

108
3rdparty/ippicv/downloader.cmake vendored Normal file
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#
# The script downloads ICV package
#
# On return this will define:
# OPENCV_ICV_PATH - path to unpacked downloaded package
#
function(_icv_downloader)
# Define actual ICV versions
if(APPLE)
set(OPENCV_ICV_PACKAGE_NAME "ippicv_macosx_20141027.tgz")
set(OPENCV_ICV_PACKAGE_HASH "9662fe0694a67e59491a0dcc82fa26e0")
set(OPENCV_ICV_PLATFORM "macosx")
set(OPENCV_ICV_PACKAGE_SUBDIR "/ippicv_osx")
elseif(UNIX)
if(ANDROID AND (NOT ANDROID_ABI STREQUAL x86))
return()
endif()
set(OPENCV_ICV_PACKAGE_NAME "ippicv_linux_20141027.tgz")
set(OPENCV_ICV_PACKAGE_HASH "8b449a536a2157bcad08a2b9f266828b")
set(OPENCV_ICV_PLATFORM "linux")
set(OPENCV_ICV_PACKAGE_SUBDIR "/ippicv_lnx")
elseif(WIN32 AND NOT ARM)
set(OPENCV_ICV_PACKAGE_NAME "ippicv_windows_20141027.zip")
set(OPENCV_ICV_PACKAGE_HASH "b59f865d1ba16e8c84124e19d78eec57")
set(OPENCV_ICV_PLATFORM "windows")
set(OPENCV_ICV_PACKAGE_SUBDIR "/ippicv_win")
else()
return() # Not supported
endif()
set(OPENCV_ICV_UNPACK_PATH "${CMAKE_CURRENT_LIST_DIR}/unpack")
set(OPENCV_ICV_PATH "${OPENCV_ICV_UNPACK_PATH}${OPENCV_ICV_PACKAGE_SUBDIR}")
if(DEFINED OPENCV_ICV_PACKAGE_DOWNLOADED
AND OPENCV_ICV_PACKAGE_DOWNLOADED STREQUAL OPENCV_ICV_PACKAGE_HASH
AND EXISTS ${OPENCV_ICV_PATH})
# Package has been downloaded and checked by the previous build
set(OPENCV_ICV_PATH "${OPENCV_ICV_PATH}" PARENT_SCOPE)
return()
else()
if(EXISTS ${OPENCV_ICV_UNPACK_PATH})
message(STATUS "ICV: Removing previous unpacked package: ${OPENCV_ICV_UNPACK_PATH}")
file(REMOVE_RECURSE ${OPENCV_ICV_UNPACK_PATH})
endif()
endif()
unset(OPENCV_ICV_PACKAGE_DOWNLOADED CACHE)
set(OPENCV_ICV_PACKAGE_ARCHIVE "${CMAKE_CURRENT_LIST_DIR}/downloads/${OPENCV_ICV_PLATFORM}-${OPENCV_ICV_PACKAGE_HASH}/${OPENCV_ICV_PACKAGE_NAME}")
get_filename_component(OPENCV_ICV_PACKAGE_ARCHIVE_DIR "${OPENCV_ICV_PACKAGE_ARCHIVE}" PATH)
if(EXISTS "${OPENCV_ICV_PACKAGE_ARCHIVE}")
file(MD5 "${OPENCV_ICV_PACKAGE_ARCHIVE}" archive_md5)
if(NOT archive_md5 STREQUAL OPENCV_ICV_PACKAGE_HASH)
message(WARNING "ICV: Local copy of ICV package has invalid MD5 hash: ${archive_md5} (expected: ${OPENCV_ICV_PACKAGE_HASH})")
file(REMOVE "${OPENCV_ICV_PACKAGE_ARCHIVE}")
file(REMOVE_RECURSE "${OPENCV_ICV_PACKAGE_ARCHIVE_DIR}")
endif()
endif()
if(NOT EXISTS "${OPENCV_ICV_PACKAGE_ARCHIVE}")
if(NOT DEFINED OPENCV_ICV_URL)
if(DEFINED ENV{OPENCV_ICV_URL})
set(OPENCV_ICV_URL $ENV{OPENCV_ICV_URL})
else()
set(OPENCV_ICV_URL "http://sourceforge.net/projects/opencvlibrary/files/3rdparty/ippicv")
endif()
endif()
file(MAKE_DIRECTORY ${OPENCV_ICV_PACKAGE_ARCHIVE_DIR})
message(STATUS "ICV: Downloading ${OPENCV_ICV_PACKAGE_NAME}...")
file(DOWNLOAD "${OPENCV_ICV_URL}/${OPENCV_ICV_PACKAGE_NAME}" "${OPENCV_ICV_PACKAGE_ARCHIVE}"
TIMEOUT 600 STATUS __status
EXPECTED_MD5 ${OPENCV_ICV_PACKAGE_HASH})
if(NOT __status EQUAL 0)
message(FATAL_ERROR "ICV: Failed to download ICV package: ${OPENCV_ICV_PACKAGE_NAME}. Status=${__status}")
else()
# Don't remove this code, because EXPECTED_MD5 parameter doesn't fail "file(DOWNLOAD)" step
# on wrong hash
file(MD5 "${OPENCV_ICV_PACKAGE_ARCHIVE}" archive_md5)
if(NOT archive_md5 STREQUAL OPENCV_ICV_PACKAGE_HASH)
message(FATAL_ERROR "ICV: Downloaded copy of ICV package has invalid MD5 hash: ${archive_md5} (expected: ${OPENCV_ICV_PACKAGE_HASH})")
endif()
endif()
endif()
ocv_assert(EXISTS "${OPENCV_ICV_PACKAGE_ARCHIVE}")
ocv_assert(NOT EXISTS "${OPENCV_ICV_UNPACK_PATH}")
file(MAKE_DIRECTORY ${OPENCV_ICV_UNPACK_PATH})
ocv_assert(EXISTS "${OPENCV_ICV_UNPACK_PATH}")
message(STATUS "ICV: Unpacking ${OPENCV_ICV_PACKAGE_NAME} to ${OPENCV_ICV_UNPACK_PATH}...")
execute_process(COMMAND ${CMAKE_COMMAND} -E tar xz "${OPENCV_ICV_PACKAGE_ARCHIVE}"
WORKING_DIRECTORY "${OPENCV_ICV_UNPACK_PATH}"
RESULT_VARIABLE __result)
if(NOT __result EQUAL 0)
message(FATAL_ERROR "ICV: Failed to unpack ICV package from ${OPENCV_ICV_PACKAGE_ARCHIVE} to ${OPENCV_ICV_UNPACK_PATH} with error ${__result}")
endif()
ocv_assert(EXISTS "${OPENCV_ICV_PATH}")
set(OPENCV_ICV_PACKAGE_DOWNLOADED "${OPENCV_ICV_PACKAGE_HASH}" CACHE INTERNAL "ICV package hash")
message(STATUS "ICV: Package successfully downloaded")
set(OPENCV_ICV_PATH "${OPENCV_ICV_PATH}" PARENT_SCOPE)
endfunction()
_icv_downloader()

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@ -9,7 +9,7 @@
:license: BSD, see LICENSE for more details.
"""
import re
from _compat import text_type, string_types, int_types, \
from ._compat import text_type, string_types, int_types, \
unichr, PY2
@ -227,7 +227,7 @@ class _MarkupEscapeHelper(object):
try:
from _speedups import escape, escape_silent, soft_unicode
except ImportError:
from _native import escape, escape_silent, soft_unicode
from ._native import escape, escape_silent, soft_unicode
if not PY2:
soft_str = soft_unicode

2
3rdparty/jinja2/markupsafe/_native.py vendored
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@ -8,7 +8,7 @@
:copyright: (c) 2010 by Armin Ronacher.
:license: BSD, see LICENSE for more details.
"""
from _compat import text_type
from ._compat import text_type
def escape(s):

2
3rdparty/jinja2/utils.py vendored
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@ -517,4 +517,4 @@ class Joiner(object):
# Imported here because that's where it was in the past
from markupsafe import Markup, escape, soft_unicode
from .markupsafe import Markup, escape, soft_unicode

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2
3rdparty/libjasper/CMakeLists.txt vendored
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@ -47,5 +47,5 @@ if(ENABLE_SOLUTION_FOLDERS)
endif()
if(NOT BUILD_SHARED_LIBS)
ocv_install_target(${JASPER_LIBRARY} EXPORT OpenCVModules ARCHIVE DESTINATION ${OPENCV_3P_LIB_INSTALL_PATH} COMPONENT main)
ocv_install_target(${JASPER_LIBRARY} EXPORT OpenCVModules ARCHIVE DESTINATION ${OPENCV_3P_LIB_INSTALL_PATH} COMPONENT dev)
endif()

4
3rdparty/libjpeg/CMakeLists.txt vendored
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@ -9,7 +9,7 @@ ocv_include_directories(${CMAKE_CURRENT_SOURCE_DIR})
file(GLOB lib_srcs *.c)
file(GLOB lib_hdrs *.h)
if(ANDROID OR IOS)
if(ANDROID OR IOS OR APPLE)
ocv_list_filterout(lib_srcs jmemansi.c)
else()
ocv_list_filterout(lib_srcs jmemnobs.c)
@ -46,5 +46,5 @@ if(ENABLE_SOLUTION_FOLDERS)
endif()
if(NOT BUILD_SHARED_LIBS)
ocv_install_target(${JPEG_LIBRARY} EXPORT OpenCVModules ARCHIVE DESTINATION ${OPENCV_3P_LIB_INSTALL_PATH} COMPONENT main)
ocv_install_target(${JPEG_LIBRARY} EXPORT OpenCVModules ARCHIVE DESTINATION ${OPENCV_3P_LIB_INSTALL_PATH} COMPONENT dev)
endif()

4
3rdparty/libpng/CMakeLists.txt vendored
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@ -14,7 +14,7 @@ ocv_include_directories("${CMAKE_CURRENT_SOURCE_DIR}" ${ZLIB_INCLUDE_DIRS})
file(GLOB lib_srcs *.c)
file(GLOB lib_hdrs *.h)
if(NEON)
if(NEON AND CMAKE_SIZEOF_VOID_P EQUAL 4)
list(APPEND lib_srcs arm/filter_neon.S)
add_definitions(-DPNG_ARM_NEON)
endif()
@ -55,5 +55,5 @@ if(ENABLE_SOLUTION_FOLDERS)
endif()
if(NOT BUILD_SHARED_LIBS)
ocv_install_target(${PNG_LIBRARY} EXPORT OpenCVModules ARCHIVE DESTINATION ${OPENCV_3P_LIB_INSTALL_PATH} COMPONENT main)
ocv_install_target(${PNG_LIBRARY} EXPORT OpenCVModules ARCHIVE DESTINATION ${OPENCV_3P_LIB_INSTALL_PATH} COMPONENT dev)
endif()

2
3rdparty/libtiff/CMakeLists.txt vendored
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@ -115,5 +115,5 @@ if(ENABLE_SOLUTION_FOLDERS)
endif()
if(NOT BUILD_SHARED_LIBS)
ocv_install_target(${TIFF_LIBRARY} EXPORT OpenCVModules ARCHIVE DESTINATION ${OPENCV_3P_LIB_INSTALL_PATH} COMPONENT main)
ocv_install_target(${TIFF_LIBRARY} EXPORT OpenCVModules ARCHIVE DESTINATION ${OPENCV_3P_LIB_INSTALL_PATH} COMPONENT dev)
endif()

12
3rdparty/libtiff/tif_config.h.cmakein vendored
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@ -54,7 +54,7 @@
/* Native cpu byte order: 1 if big-endian (Motorola) or 0 if little-endian
(Intel) */
#define HOST_BIGENDIAN 0
#define HOST_BIGENDIAN @WORDS_BIGENDIAN@
/* Set the native cpu bit order (FILLORDER_LSB2MSB or FILLORDER_MSB2LSB) */
#define HOST_FILLORDER FILLORDER_LSB2MSB
@ -156,15 +156,7 @@
/* Define WORDS_BIGENDIAN to 1 if your processor stores words with the most
significant byte first (like Motorola and SPARC, unlike Intel). */
#if defined AC_APPLE_UNIVERSAL_BUILD
# if defined __BIG_ENDIAN__
# define WORDS_BIGENDIAN 1
# endif
#else
# ifndef WORDS_BIGENDIAN
/* # undef WORDS_BIGENDIAN */
# endif
#endif
#cmakedefine WORDS_BIGENDIAN 1
/* Support Deflate compression */
#define ZIP_SUPPORT 1

2
3rdparty/openexr/CMakeLists.txt vendored
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@ -64,7 +64,7 @@ if(ENABLE_SOLUTION_FOLDERS)
endif()
if(NOT BUILD_SHARED_LIBS)
ocv_install_target(IlmImf EXPORT OpenCVModules ARCHIVE DESTINATION ${OPENCV_3P_LIB_INSTALL_PATH} COMPONENT main)
ocv_install_target(IlmImf EXPORT OpenCVModules ARCHIVE DESTINATION ${OPENCV_3P_LIB_INSTALL_PATH} COMPONENT dev)
endif()
set(OPENEXR_INCLUDE_PATHS ${OPENEXR_INCLUDE_PATHS} PARENT_SCOPE)

10
3rdparty/readme.txt vendored
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@ -1,5 +1,5 @@
This folder contains libraries and headers of a few very popular still image codecs
used by highgui module.
used by imgcodecs module.
The libraries and headers are preferably to build Win32 and Win64 versions of OpenCV.
On UNIX systems all the libraries are automatically detected by configure script.
In order to use these versions of libraries instead of system ones on UNIX systems you
@ -11,7 +11,7 @@ libjpeg 8d (8.4) - The Independent JPEG Group's JPEG software.
See IGJ home page http://www.ijg.org
for details and links to the source code
HAVE_JPEG preprocessor flag must be set to make highgui use libjpeg.
HAVE_JPEG preprocessor flag must be set to make imgcodecs use libjpeg.
On UNIX systems configure script takes care of it.
------------------------------------------------------------------------------------
libpng 1.5.12 - Portable Network Graphics library.
@ -19,7 +19,7 @@ libpng 1.5.12 - Portable Network Graphics library.
See libpng home page http://www.libpng.org
for details and links to the source code
HAVE_PNG preprocessor flag must be set to make highgui use libpng.
HAVE_PNG preprocessor flag must be set to make imgcodecs use libpng.
On UNIX systems configure script takes care of it.
------------------------------------------------------------------------------------
libtiff 4.0.2 - Tag Image File Format (TIFF) Software
@ -28,7 +28,7 @@ libtiff 4.0.2 - Tag Image File Format (TIFF) Software
See libtiff home page http://www.remotesensing.org/libtiff/
for details and links to the source code
HAVE_TIFF preprocessor flag must be set to make highgui use libtiff.
HAVE_TIFF preprocessor flag must be set to make imgcodecs use libtiff.
On UNIX systems configure script takes care of it.
In this build support for ZIP (LZ77 compression) is turned on.
------------------------------------------------------------------------------------
@ -37,7 +37,7 @@ zlib 1.2.7 - General purpose LZ77 compression library
See zlib home page http://www.zlib.net
for details and links to the source code
No preprocessor definition is needed to make highgui use this library -
No preprocessor definition is needed to make imgcodecs use this library -
it is included automatically if either libpng or libtiff are used.
------------------------------------------------------------------------------------
jasper-1.900.1 - JasPer is a collection of software

6
3rdparty/tbb/CMakeLists.txt vendored
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@ -232,9 +232,9 @@ if(ENABLE_SOLUTION_FOLDERS)
endif()
ocv_install_target(tbb EXPORT OpenCVModules
RUNTIME DESTINATION ${OPENCV_BIN_INSTALL_PATH} COMPONENT main
LIBRARY DESTINATION ${OPENCV_LIB_INSTALL_PATH} COMPONENT main
ARCHIVE DESTINATION ${OPENCV_3P_LIB_INSTALL_PATH} COMPONENT main
RUNTIME DESTINATION ${OPENCV_BIN_INSTALL_PATH} COMPONENT libs
LIBRARY DESTINATION ${OPENCV_LIB_INSTALL_PATH} COMPONENT libs
ARCHIVE DESTINATION ${OPENCV_3P_LIB_INSTALL_PATH} COMPONENT dev
)
# get TBB version

4
3rdparty/zlib/CMakeLists.txt vendored
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@ -82,7 +82,7 @@ if(UNIX)
endif()
endif()
ocv_warnings_disable(CMAKE_C_FLAGS -Wattributes -Wstrict-prototypes -Wmissing-prototypes -Wmissing-declarations)
ocv_warnings_disable(CMAKE_C_FLAGS -Wshorten-64-to-32 -Wattributes -Wstrict-prototypes -Wmissing-prototypes -Wmissing-declarations)
set_target_properties(${ZLIB_LIBRARY} PROPERTIES
OUTPUT_NAME ${ZLIB_LIBRARY}
@ -95,5 +95,5 @@ if(ENABLE_SOLUTION_FOLDERS)
endif()
if(NOT BUILD_SHARED_LIBS)
ocv_install_target(${ZLIB_LIBRARY} EXPORT OpenCVModules ARCHIVE DESTINATION ${OPENCV_3P_LIB_INSTALL_PATH} COMPONENT main)
ocv_install_target(${ZLIB_LIBRARY} EXPORT OpenCVModules ARCHIVE DESTINATION ${OPENCV_3P_LIB_INSTALL_PATH} COMPONENT dev)
endif()

292
CMakeLists.txt
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@ -6,6 +6,8 @@
#
# ----------------------------------------------------------------------------
include(cmake/OpenCVMinDepVersions.cmake)
if(CMAKE_GENERATOR MATCHES Xcode AND XCODE_VERSION VERSION_GREATER 4.3)
@ -36,6 +38,11 @@ if(POLICY CMP0022)
cmake_policy(SET CMP0022 OLD)
endif()
if(POLICY CMP0026)
# silence cmake 3.0+ warnings about reading LOCATION attribute
cmake_policy(SET CMP0026 OLD)
endif()
# must go before the project command
set(CMAKE_CONFIGURATION_TYPES "Debug;Release" CACHE STRING "Configs" FORCE)
if(DEFINED CMAKE_BUILD_TYPE)
@ -116,23 +123,26 @@ endif()
OCV_OPTION(WITH_1394 "Include IEEE1394 support" ON IF (NOT ANDROID AND NOT IOS) )
OCV_OPTION(WITH_AVFOUNDATION "Use AVFoundation for Video I/O" ON IF IOS)
OCV_OPTION(WITH_CARBON "Use Carbon for UI instead of Cocoa" OFF IF APPLE )
OCV_OPTION(WITH_CUDA "Include NVidia Cuda Runtime support" ON IF (NOT ANDROID AND NOT IOS) )
OCV_OPTION(WITH_CUFFT "Include NVidia Cuda Fast Fourier Transform (FFT) library support" ON IF (NOT ANDROID AND NOT IOS) )
OCV_OPTION(WITH_CUBLAS "Include NVidia Cuda Basic Linear Algebra Subprograms (BLAS) library support" OFF IF (NOT ANDROID AND NOT IOS) )
OCV_OPTION(WITH_NVCUVID "Include NVidia Video Decoding library support" OFF IF (NOT ANDROID AND NOT IOS AND NOT APPLE) )
OCV_OPTION(WITH_VTK "Include VTK library support (and build opencv_viz module eiher)" ON IF (NOT ANDROID AND NOT IOS) )
OCV_OPTION(WITH_CUDA "Include NVidia Cuda Runtime support" ON IF (NOT IOS) )
OCV_OPTION(WITH_CUFFT "Include NVidia Cuda Fast Fourier Transform (FFT) library support" ON IF (NOT IOS) )
OCV_OPTION(WITH_CUBLAS "Include NVidia Cuda Basic Linear Algebra Subprograms (BLAS) library support" OFF IF (NOT IOS) )
OCV_OPTION(WITH_NVCUVID "Include NVidia Video Decoding library support" OFF IF (NOT IOS AND NOT APPLE) )
OCV_OPTION(WITH_EIGEN "Include Eigen2/Eigen3 support" ON)
OCV_OPTION(WITH_VFW "Include Video for Windows support" ON IF WIN32 )
OCV_OPTION(WITH_FFMPEG "Include FFMPEG support" ON IF (NOT ANDROID AND NOT IOS))
OCV_OPTION(WITH_GSTREAMER "Include Gstreamer support" ON IF (UNIX AND NOT APPLE AND NOT ANDROID) )
OCV_OPTION(WITH_GSTREAMER_1_X "Include Gstreamer 1.x support" OFF)
OCV_OPTION(WITH_GSTREAMER "Include Gstreamer support" ON IF (UNIX AND NOT ANDROID) )
OCV_OPTION(WITH_GSTREAMER_0_10 "Enable Gstreamer 0.10 support (instead of 1.x)" OFF )
OCV_OPTION(WITH_GTK "Include GTK support" ON IF (UNIX AND NOT APPLE AND NOT ANDROID) )
OCV_OPTION(WITH_IPP "Include Intel IPP support" OFF IF (MSVC OR X86 OR X86_64) )
OCV_OPTION(WITH_GTK_2_X "Use GTK version 2" OFF IF (UNIX AND NOT APPLE AND NOT ANDROID) )
OCV_OPTION(WITH_IPP "Include Intel IPP support" ON IF (X86_64 OR X86) )
OCV_OPTION(WITH_JASPER "Include JPEG2K support" ON IF (NOT IOS) )
OCV_OPTION(WITH_JPEG "Include JPEG support" ON)
OCV_OPTION(WITH_WEBP "Include WebP support" ON IF (NOT IOS) )
OCV_OPTION(WITH_OPENEXR "Include ILM support via OpenEXR" ON IF (NOT IOS) )
OCV_OPTION(WITH_OPENGL "Include OpenGL support" OFF IF (NOT ANDROID AND NOT APPLE) )
OCV_OPTION(WITH_OPENGL "Include OpenGL support" OFF IF (NOT ANDROID) )
OCV_OPTION(WITH_OPENNI "Include OpenNI support" OFF IF (NOT ANDROID AND NOT IOS) )
OCV_OPTION(WITH_OPENNI2 "Include OpenNI2 support" OFF IF (NOT ANDROID AND NOT IOS) )
OCV_OPTION(WITH_PNG "Include PNG support" ON)
OCV_OPTION(WITH_PVAPI "Include Prosilica GigE support" ON IF (NOT ANDROID AND NOT IOS) )
OCV_OPTION(WITH_GIGEAPI "Include Smartek GigE support" ON IF (NOT ANDROID AND NOT IOS) )
@ -146,15 +156,19 @@ OCV_OPTION(WITH_TIFF "Include TIFF support" ON
OCV_OPTION(WITH_UNICAP "Include Unicap support (GPL)" OFF IF (UNIX AND NOT APPLE AND NOT ANDROID) )
OCV_OPTION(WITH_V4L "Include Video 4 Linux support" ON IF (UNIX AND NOT ANDROID) )
OCV_OPTION(WITH_LIBV4L "Use libv4l for Video 4 Linux support" ON IF (UNIX AND NOT ANDROID) )
OCV_OPTION(WITH_DSHOW "Build HighGUI with DirectShow support" ON IF (WIN32 AND NOT ARM) )
OCV_OPTION(WITH_MSMF "Build HighGUI with Media Foundation support" OFF IF WIN32 )
OCV_OPTION(WITH_XIMEA "Include XIMEA cameras support" OFF IF (NOT ANDROID AND NOT APPLE) )
OCV_OPTION(WITH_DSHOW "Build VideoIO with DirectShow support" ON IF (WIN32 AND NOT ARM) )
OCV_OPTION(WITH_MSMF "Build VideoIO with Media Foundation support" OFF IF WIN32 )
OCV_OPTION(WITH_XIMEA "Include XIMEA cameras support" OFF IF (NOT ANDROID) )
OCV_OPTION(WITH_XINE "Include Xine support (GPL)" OFF IF (UNIX AND NOT APPLE AND NOT ANDROID) )
OCV_OPTION(WITH_CLP "Include Clp support (EPL)" OFF)
OCV_OPTION(WITH_OPENCL "Include OpenCL Runtime support" ON IF (NOT ANDROID AND NOT IOS) )
OCV_OPTION(WITH_OPENCLAMDFFT "Include AMD OpenCL FFT library support" ON IF (NOT ANDROID AND NOT IOS) )
OCV_OPTION(WITH_OPENCLAMDBLAS "Include AMD OpenCL BLAS library support" ON IF (NOT ANDROID AND NOT IOS) )
OCV_OPTION(WITH_OPENCL "Include OpenCL Runtime support" NOT ANDROID IF (NOT IOS) )
OCV_OPTION(WITH_OPENCL_SVM "Include OpenCL Shared Virtual Memory support" OFF ) # experimental
OCV_OPTION(WITH_OPENCLAMDFFT "Include AMD OpenCL FFT library support" ON IF (NOT ANDROID AND NOT IOS) )
OCV_OPTION(WITH_OPENCLAMDBLAS "Include AMD OpenCL BLAS library support" ON IF (NOT ANDROID AND NOT IOS) )
OCV_OPTION(WITH_DIRECTX "Include DirectX support" ON IF WIN32 )
OCV_OPTION(WITH_INTELPERC "Include Intel Perceptual Computing support" OFF IF WIN32 )
OCV_OPTION(WITH_IPP_A "Include Intel IPP_A support" OFF IF (MSVC OR X86 OR X86_64) )
OCV_OPTION(WITH_GDAL "Include GDAL Support" OFF IF (NOT ANDROID AND NOT IOS) )
# OpenCV build components
# ===================================================
@ -168,9 +182,11 @@ OCV_OPTION(BUILD_PERF_TESTS "Build performance tests"
OCV_OPTION(BUILD_TESTS "Build accuracy & regression tests" ON IF (NOT IOS) )
OCV_OPTION(BUILD_WITH_DEBUG_INFO "Include debug info into debug libs (not MSCV only)" ON )
OCV_OPTION(BUILD_WITH_STATIC_CRT "Enables use of staticaly linked CRT for staticaly linked OpenCV" ON IF MSVC )
OCV_OPTION(BUILD_WITH_DYNAMIC_IPP "Enables dynamic linking of IPP (only for standalone IPP)" OFF )
OCV_OPTION(BUILD_FAT_JAVA_LIB "Create fat java wrapper containing the whole OpenCV library" ON IF NOT BUILD_SHARED_LIBS AND CMAKE_COMPILER_IS_GNUCXX )
OCV_OPTION(BUILD_ANDROID_SERVICE "Build OpenCV Manager for Google Play" OFF IF ANDROID AND ANDROID_SOURCE_TREE )
OCV_OPTION(BUILD_ANDROID_PACKAGE "Build platform-specific package for Google Play" OFF IF ANDROID )
OCV_OPTION(BUILD_CUDA_STUBS "Build CUDA modules stubs when no CUDA SDK" OFF IF (NOT IOS) )
# 3rd party libs
OCV_OPTION(BUILD_ZLIB "Build zlib from source" WIN32 OR APPLE )
@ -188,27 +204,39 @@ OCV_OPTION(INSTALL_C_EXAMPLES "Install C examples" OFF )
OCV_OPTION(INSTALL_PYTHON_EXAMPLES "Install Python examples" OFF )
OCV_OPTION(INSTALL_ANDROID_EXAMPLES "Install Android examples" OFF IF ANDROID )
OCV_OPTION(INSTALL_TO_MANGLED_PATHS "Enables mangled install paths, that help with side by side installs." OFF IF (UNIX AND NOT ANDROID AND NOT IOS AND BUILD_SHARED_LIBS) )
OCV_OPTION(INSTALL_TESTS "Install accuracy and performance test binaries and test data" OFF)
# OpenCV build options
# ===================================================
OCV_OPTION(ENABLE_PRECOMPILED_HEADERS "Use precompiled headers" ON IF (NOT IOS) )
OCV_OPTION(ENABLE_SOLUTION_FOLDERS "Solution folder in Visual Studio or in other IDEs" (MSVC_IDE OR CMAKE_GENERATOR MATCHES Xcode) )
OCV_OPTION(ENABLE_PROFILING "Enable profiling in the GCC compiler (Add flags: -g -pg)" OFF IF CMAKE_COMPILER_IS_GNUCXX )
OCV_OPTION(ENABLE_COVERAGE "Enable coverage collection with GCov" OFF IF CMAKE_COMPILER_IS_GNUCXX )
OCV_OPTION(ENABLE_OMIT_FRAME_POINTER "Enable -fomit-frame-pointer for GCC" ON IF CMAKE_COMPILER_IS_GNUCXX AND NOT (APPLE AND CMAKE_COMPILER_IS_CLANGCXX) )
OCV_OPTION(ENABLE_POWERPC "Enable PowerPC for GCC" ON IF (CMAKE_COMPILER_IS_GNUCXX AND CMAKE_SYSTEM_PROCESSOR MATCHES powerpc.*) )
OCV_OPTION(ENABLE_FAST_MATH "Enable -ffast-math (not recommended for GCC 4.6.x)" OFF IF (CMAKE_COMPILER_IS_GNUCXX AND (X86 OR X86_64)) )
OCV_OPTION(ENABLE_SSE "Enable SSE instructions" ON IF ((MSVC OR CMAKE_COMPILER_IS_GNUCXX) AND (X86 OR X86_64)) )
OCV_OPTION(ENABLE_SSE2 "Enable SSE2 instructions" ON IF ((MSVC OR CMAKE_COMPILER_IS_GNUCXX) AND (X86 OR X86_64)) )
OCV_OPTION(ENABLE_SSE3 "Enable SSE3 instructions" ON IF ((CV_ICC OR CMAKE_COMPILER_IS_GNUCXX) AND (X86 OR X86_64)) )
OCV_OPTION(ENABLE_SSSE3 "Enable SSSE3 instructions" OFF IF (CMAKE_COMPILER_IS_GNUCXX AND (X86 OR X86_64)) )
OCV_OPTION(ENABLE_SSE41 "Enable SSE4.1 instructions" OFF IF ((CV_ICC OR CMAKE_COMPILER_IS_GNUCXX) AND (X86 OR X86_64)) )
OCV_OPTION(ENABLE_SSE42 "Enable SSE4.2 instructions" OFF IF (CMAKE_COMPILER_IS_GNUCXX AND (X86 OR X86_64)) )
OCV_OPTION(ENABLE_SSE3 "Enable SSE3 instructions" ON IF ((MSVC OR CMAKE_COMPILER_IS_GNUCXX OR CV_ICC) AND (X86 OR X86_64)) )
OCV_OPTION(ENABLE_SSSE3 "Enable SSSE3 instructions" OFF IF ((MSVC OR CMAKE_COMPILER_IS_GNUCXX) AND (X86 OR X86_64)) )
OCV_OPTION(ENABLE_SSE41 "Enable SSE4.1 instructions" OFF IF ((MSVC OR CMAKE_COMPILER_IS_GNUCXX OR CV_ICC) AND (X86 OR X86_64)) )
OCV_OPTION(ENABLE_SSE42 "Enable SSE4.2 instructions" OFF IF ((MSVC OR CMAKE_COMPILER_IS_GNUCXX) AND (X86 OR X86_64)) )
OCV_OPTION(ENABLE_POPCNT "Enable POPCNT instructions" OFF IF ((MSVC OR CMAKE_COMPILER_IS_GNUCXX) AND (X86 OR X86_64)) )
OCV_OPTION(ENABLE_AVX "Enable AVX instructions" OFF IF ((MSVC OR CMAKE_COMPILER_IS_GNUCXX) AND (X86 OR X86_64)) )
OCV_OPTION(ENABLE_NEON "Enable NEON instructions" OFF IF (CMAKE_COMPILER_IS_GNUCXX AND ARM) )
OCV_OPTION(ENABLE_AVX2 "Enable AVX2 instructions" OFF IF ((MSVC OR CMAKE_COMPILER_IS_GNUCXX) AND (X86 OR X86_64)) )
OCV_OPTION(ENABLE_FMA3 "Enable FMA3 instructions" OFF IF ((MSVC OR CMAKE_COMPILER_IS_GNUCXX) AND (X86 OR X86_64)) )
OCV_OPTION(ENABLE_NEON "Enable NEON instructions" OFF IF CMAKE_COMPILER_IS_GNUCXX AND (ARM OR IOS) )
OCV_OPTION(ENABLE_VFPV3 "Enable VFPv3-D32 instructions" OFF IF CMAKE_COMPILER_IS_GNUCXX AND (ARM OR IOS) )
OCV_OPTION(ENABLE_NOISY_WARNINGS "Show all warnings even if they are too noisy" OFF )
OCV_OPTION(OPENCV_WARNINGS_ARE_ERRORS "Treat warnings as errors" OFF )
OCV_OPTION(ENABLE_WINRT_MODE "Build with Windows Runtime support" OFF IF WIN32 )
OCV_OPTION(ENABLE_WINRT_MODE_NATIVE "Build with Windows Runtime native C++ support" OFF IF WIN32 )
OCV_OPTION(ANDROID_EXAMPLES_WITH_LIBS "Build binaries of Android examples with native libraries" OFF IF ANDROID )
OCV_OPTION(ENABLE_IMPL_COLLECTION "Collect implementation data on function call" OFF )
if(ENABLE_IMPL_COLLECTION)
add_definitions(-DCV_COLLECT_IMPL_DATA)
endif()
# ----------------------------------------------------------------------------
@ -224,6 +252,15 @@ include(cmake/OpenCVVersion.cmake)
# Save libs and executables in the same place
set(EXECUTABLE_OUTPUT_PATH "${CMAKE_BINARY_DIR}/bin" CACHE PATH "Output directory for applications" )
if (ANDROID)
if (ANDROID_ABI MATCHES "NEON")
set(ENABLE_NEON ON)
endif()
if (ANDROID_ABI MATCHES "VFPV3")
set(ENABLE_VFPV3 ON)
endif()
endif()
if(ANDROID OR WIN32)
set(OPENCV_DOC_INSTALL_PATH doc)
elseif(INSTALL_TO_MANGLED_PATHS)
@ -232,20 +269,44 @@ else()
set(OPENCV_DOC_INSTALL_PATH share/OpenCV/doc)
endif()
if(WIN32)
if(WIN32 AND CMAKE_HOST_SYSTEM_NAME MATCHES Windows)
if(DEFINED OpenCV_RUNTIME AND DEFINED OpenCV_ARCH)
set(OpenCV_INSTALL_BINARIES_PREFIX "${OpenCV_ARCH}/${OpenCV_RUNTIME}/")
else()
message(STATUS "Can't detect runtime and/or arch")
set(OpenCV_INSTALL_BINARIES_PREFIX "")
endif()
elseif(ANDROID)
set(OpenCV_INSTALL_BINARIES_PREFIX "sdk/native/")
else()
set(OpenCV_INSTALL_BINARIES_PREFIX "")
endif()
set(OPENCV_SAMPLES_BIN_INSTALL_PATH "${OpenCV_INSTALL_BINARIES_PREFIX}samples")
if(ANDROID)
set(OPENCV_SAMPLES_BIN_INSTALL_PATH "${OpenCV_INSTALL_BINARIES_PREFIX}samples/${ANDROID_NDK_ABI_NAME}")
else()
set(OPENCV_SAMPLES_BIN_INSTALL_PATH "${OpenCV_INSTALL_BINARIES_PREFIX}samples")
endif()
set(OPENCV_BIN_INSTALL_PATH "${OpenCV_INSTALL_BINARIES_PREFIX}bin")
if(ANDROID)
set(OPENCV_BIN_INSTALL_PATH "${OpenCV_INSTALL_BINARIES_PREFIX}bin/${ANDROID_NDK_ABI_NAME}")
else()
set(OPENCV_BIN_INSTALL_PATH "${OpenCV_INSTALL_BINARIES_PREFIX}bin")
endif()
if(NOT OPENCV_TEST_INSTALL_PATH)
set(OPENCV_TEST_INSTALL_PATH "${OPENCV_BIN_INSTALL_PATH}")
endif()
if(OPENCV_TEST_DATA_PATH AND NOT OPENCV_TEST_DATA_INSTALL_PATH)
if(ANDROID)
set(OPENCV_TEST_DATA_INSTALL_PATH "sdk/etc/testdata")
elseif(WIN32)
set(OPENCV_TEST_DATA_INSTALL_PATH "testdata")
else()
set(OPENCV_TEST_DATA_INSTALL_PATH "share/OpenCV/testdata")
endif()
endif()
if(ANDROID)
set(LIBRARY_OUTPUT_PATH "${OpenCV_BINARY_DIR}/lib/${ANDROID_NDK_ABI_NAME}")
@ -254,19 +315,22 @@ if(ANDROID)
set(OPENCV_3P_LIB_INSTALL_PATH sdk/native/3rdparty/libs/${ANDROID_NDK_ABI_NAME})
set(OPENCV_CONFIG_INSTALL_PATH sdk/native/jni)
set(OPENCV_INCLUDE_INSTALL_PATH sdk/native/jni/include)
set(OPENCV_SAMPLES_SRC_INSTALL_PATH samples/native)
else()
set(LIBRARY_OUTPUT_PATH "${OpenCV_BINARY_DIR}/lib")
set(3P_LIBRARY_OUTPUT_PATH "${OpenCV_BINARY_DIR}/3rdparty/lib${LIB_SUFFIX}")
if(WIN32)
if(WIN32 AND CMAKE_HOST_SYSTEM_NAME MATCHES Windows)
if(OpenCV_STATIC)
set(OPENCV_LIB_INSTALL_PATH "${OpenCV_INSTALL_BINARIES_PREFIX}staticlib${LIB_SUFFIX}")
else()
set(OPENCV_LIB_INSTALL_PATH "${OpenCV_INSTALL_BINARIES_PREFIX}lib${LIB_SUFFIX}")
endif()
set(OPENCV_3P_LIB_INSTALL_PATH "${OpenCV_INSTALL_BINARIES_PREFIX}staticlib${LIB_SUFFIX}")
set(OPENCV_SAMPLES_SRC_INSTALL_PATH samples/native)
else()
set(OPENCV_LIB_INSTALL_PATH lib${LIB_SUFFIX})
set(OPENCV_3P_LIB_INSTALL_PATH share/OpenCV/3rdparty/${OPENCV_LIB_INSTALL_PATH})
set(OPENCV_SAMPLES_SRC_INSTALL_PATH share/OpenCV/samples)
endif()
set(OPENCV_INCLUDE_INSTALL_PATH "include")
@ -299,6 +363,9 @@ if(DEFINED CMAKE_DEBUG_POSTFIX)
set(OPENCV_DEBUG_POSTFIX "${CMAKE_DEBUG_POSTFIX}")
endif()
if(INSTALL_CREATE_DISTRIB AND BUILD_SHARED_LIBS AND NOT DEFINED BUILD_opencv_world)
set(BUILD_opencv_world ON CACHE INTERNAL "")
endif()
# ----------------------------------------------------------------------------
# Path for build/platform -specific headers
@ -317,7 +384,7 @@ set(OPENCV_EXTRA_MODULES_PATH "" CACHE PATH "Where to look for additional OpenCV
find_host_package(Git QUIET)
if(GIT_FOUND)
execute_process(COMMAND "${GIT_EXECUTABLE}" describe --tags --always --dirty --match "2.[0-9].[0-9]*"
execute_process(COMMAND "${GIT_EXECUTABLE}" describe --tags --always --dirty --match "[0-9].[0-9].[0-9]*"
WORKING_DIRECTORY "${OpenCV_SOURCE_DIR}"
OUTPUT_VARIABLE OPENCV_VCSVERSION
RESULT_VARIABLE GIT_RESULT
@ -371,6 +438,8 @@ if(UNIX)
set(OPENCV_LINKER_LIBS ${OPENCV_LINKER_LIBS} dl m log)
elseif(${CMAKE_SYSTEM_NAME} MATCHES "FreeBSD|NetBSD|DragonFly")
set(OPENCV_LINKER_LIBS ${OPENCV_LINKER_LIBS} m pthread)
elseif(EMSCRIPTEN)
# no need to link to system libs with emscripten
else()
set(OPENCV_LINKER_LIBS ${OPENCV_LINKER_LIBS} dl m pthread rt)
endif()
@ -382,6 +451,19 @@ endif()
include(cmake/OpenCVPCHSupport.cmake)
include(cmake/OpenCVModule.cmake)
# ----------------------------------------------------------------------------
# Detect endianness of build platform
# ----------------------------------------------------------------------------
if(CMAKE_SYSTEM_NAME STREQUAL iOS)
# test_big_endian needs try_compile, which doesn't work for iOS
# http://public.kitware.com/Bug/view.php?id=12288
set(WORDS_BIGENDIAN 0)
else()
include(TestBigEndian)
test_big_endian(WORDS_BIGENDIAN)
endif()
# ----------------------------------------------------------------------------
# Detect 3rd-party libraries
# ----------------------------------------------------------------------------
@ -391,14 +473,26 @@ include(cmake/OpenCVFindLibsGUI.cmake)
include(cmake/OpenCVFindLibsVideo.cmake)
include(cmake/OpenCVFindLibsPerf.cmake)
# ----------------------------------------------------------------------------
# Detect other 3rd-party libraries/tools
# ----------------------------------------------------------------------------
# --- LATEX for pdf documentation ---
# --- Doxygen and PlantUML for documentation ---
unset(DOXYGEN_FOUND CACHE)
if(BUILD_DOCS)
include(cmake/OpenCVFindLATEX.cmake)
find_package(Doxygen)
if (PLANTUML_JAR)
message(STATUS "Using PlantUML path from command line: ${PLANTUML_JAR}")
elseif(DEFINED ENV{PLANTUML_JAR})
set(PLANTUML_JAR $ENV{PLANTUML_JAR})
message(STATUS "Using PLantUML path from environment: ${PLANTUML_JAR}")
else()
message(STATUS "To enable PlantUML support, set PLANTUML_JAR environment variable or pass -DPLANTUML_JAR=<filepath> option to cmake")
endif()
if (PLANTUML_JAR AND DOXYGEN_VERSION VERSION_LESS 1.8.8)
message(STATUS "You need Doxygen version 1.8.8 or later to use PlantUML")
unset(PLANTUML_JAR)
endif()
endif(BUILD_DOCS)
# --- Python Support ---
@ -427,9 +521,16 @@ if(WITH_OPENCL)
include(cmake/OpenCVDetectOpenCL.cmake)
endif()
# --- DirectX ---
if(WITH_DIRECTX)
include(cmake/OpenCVDetectDirectX.cmake)
endif()
# --- Matlab/Octave ---
include(cmake/OpenCVFindMatlab.cmake)
include(cmake/OpenCVDetectVTK.cmake)
# ----------------------------------------------------------------------------
# Add CUDA libraries (needed for apps/tools, samples)
# ----------------------------------------------------------------------------
@ -495,7 +596,6 @@ endif()
# ----------------------------------------------------------------------------
# Finalization: generate configuration-based files
# ----------------------------------------------------------------------------
ocv_track_build_dependencies()
# Generate platform-dependent and configuration-dependent headers
include(cmake/OpenCVGenHeaders.cmake)
@ -512,6 +612,45 @@ include(cmake/OpenCVGenConfig.cmake)
# Generate Info.plist for the IOS framework
include(cmake/OpenCVGenInfoPlist.cmake)
# Generate environment setup file
if(INSTALL_TESTS AND OPENCV_TEST_DATA_PATH AND UNIX)
if(ANDROID)
get_filename_component(TEST_PATH ${OPENCV_TEST_INSTALL_PATH} DIRECTORY)
configure_file("${CMAKE_CURRENT_SOURCE_DIR}/cmake/templates/opencv_run_all_tests_android.sh.in"
"${CMAKE_BINARY_DIR}/unix-install/opencv_run_all_tests.sh" @ONLY)
install(PROGRAMS "${CMAKE_BINARY_DIR}/unix-install/opencv_run_all_tests.sh"
DESTINATION ${CMAKE_INSTALL_PREFIX} COMPONENT tests)
else()
configure_file("${CMAKE_CURRENT_SOURCE_DIR}/cmake/templates/opencv_run_all_tests_unix.sh.in"
"${CMAKE_BINARY_DIR}/unix-install/opencv_run_all_tests.sh" @ONLY)
install(PROGRAMS "${CMAKE_BINARY_DIR}/unix-install/opencv_run_all_tests.sh"
DESTINATION ${OPENCV_TEST_INSTALL_PATH} COMPONENT tests)
endif()
endif()
if(NOT OPENCV_README_FILE)
if(ANDROID)
set(OPENCV_README_FILE ${CMAKE_CURRENT_SOURCE_DIR}/platforms/android/README.android)
endif()
endif()
if(NOT OPENCV_LICENSE_FILE)
set(OPENCV_LICENSE_FILE ${CMAKE_CURRENT_SOURCE_DIR}/LICENSE)
endif()
# for UNIX it does not make sense as LICENSE and readme will be part of the package automatically
if(ANDROID OR NOT UNIX)
install(FILES ${OPENCV_LICENSE_FILE}
PERMISSIONS OWNER_READ GROUP_READ WORLD_READ
DESTINATION ${CMAKE_INSTALL_PREFIX} COMPONENT libs)
if(OPENCV_README_FILE)
install(FILES ${OPENCV_README_FILE}
PERMISSIONS OWNER_READ GROUP_READ WORLD_READ
DESTINATION ${CMAKE_INSTALL_PREFIX} COMPONENT libs)
endif()
endif()
# ----------------------------------------------------------------------------
# Summary:
# ----------------------------------------------------------------------------
@ -621,7 +760,7 @@ endif()
if(WIN32)
status("")
status(" Windows RT support:" HAVE_WINRT THEN YES ELSE NO)
if (ENABLE_WINRT_MODE)
if (ENABLE_WINRT_MODE OR ENABLE_WINRT_MODE_NATIVE)
status(" Windows SDK v8.0:" ${WINDOWS_SDK_PATH})
status(" Visual Studio 2012:" ${VISUAL_STUDIO_PATH})
endif()
@ -651,14 +790,21 @@ else()
status(" Cocoa:" YES)
endif()
else()
status(" GTK+ 2.x:" HAVE_GTK THEN "YES (ver ${ALIASOF_gtk+-2.0_VERSION})" ELSE NO)
status(" GThread :" HAVE_GTHREAD THEN "YES (ver ${ALIASOF_gthread-2.0_VERSION})" ELSE NO)
if(HAVE_GTK3)
status(" GTK+ 3.x:" HAVE_GTK THEN "YES (ver ${ALIASOF_gtk+-3.0_VERSION})" ELSE NO)
elseif(HAVE_GTK)
status(" GTK+ 2.x:" HAVE_GTK THEN "YES (ver ${ALIASOF_gtk+-2.0_VERSION})" ELSE NO)
else()
status(" GTK+:" NO)
endif()
status(" GThread :" HAVE_GTHREAD THEN "YES (ver ${ALIASOF_gthread-2.0_VERSION})" ELSE NO)
status(" GtkGlExt:" HAVE_GTKGLEXT THEN "YES (ver ${ALIASOF_gtkglext-1.0_VERSION})" ELSE NO)
endif()
endif()
endif()
status(" OpenGL support:" HAVE_OPENGL THEN "YES (${OPENGL_LIBRARIES})" ELSE NO)
status(" VTK support:" HAVE_VTK THEN "YES (ver ${VTK_VERSION})" ELSE NO)
# ========================== MEDIA IO ==========================
status("")
@ -702,6 +848,12 @@ else()
status(" OpenEXR:" "NO")
endif()
if( WITH_GDAL )
status(" GDAL:" GDAL_FOUND THEN "${GDAL_LIBRARY}")
else()
status(" GDAL:" "NO")
endif()
# ========================== VIDEO IO ==========================
status("")
status(" Video I/O:")
@ -759,6 +911,11 @@ if(DEFINED WITH_OPENNI)
THEN "YES (${OPENNI_PRIME_SENSOR_MODULE})" ELSE NO)
endif(DEFINED WITH_OPENNI)
if(DEFINED WITH_OPENNI2)
status(" OpenNI2:" HAVE_OPENNI2 THEN "YES (ver ${OPENNI2_VERSION_STRING}, build ${OPENNI2_VERSION_BUILD})"
ELSE NO)
endif(DEFINED WITH_OPENNI2)
if(DEFINED WITH_PVAPI)
status(" PvAPI:" HAVE_PVAPI THEN YES ELSE NO)
endif(DEFINED WITH_PVAPI)
@ -810,17 +967,29 @@ if(DEFINED WITH_XINE)
status(" Xine:" HAVE_XINE THEN "YES (ver ${ALIASOF_libxine_VERSION})" ELSE NO)
endif(DEFINED WITH_XINE)
if(DEFINED WITH_INTELPERC)
status(" Intel PerC:" HAVE_INTELPERC THEN "YES" ELSE NO)
endif(DEFINED WITH_INTELPERC)
# ========================== Other third-party libraries ==========================
status("")
status(" Other third-party libraries:")
if(WITH_IPP AND IPP_FOUND)
status(" Use IPP:" "${IPP_LATEST_VERSION_STR} [${IPP_LATEST_VERSION_MAJOR}.${IPP_LATEST_VERSION_MINOR}.${IPP_LATEST_VERSION_BUILD}]")
if(WITH_IPP AND HAVE_IPP)
status(" Use IPP:" "${IPP_VERSION_STR} [${IPP_VERSION_MAJOR}.${IPP_VERSION_MINOR}.${IPP_VERSION_BUILD}]")
status(" at:" "${IPP_ROOT_DIR}")
if(NOT HAVE_IPP_ICV_ONLY)
status(" linked:" BUILD_WITH_DYNAMIC_IPP THEN "dynamic" ELSE "static")
endif()
else()
status(" Use IPP:" WITH_IPP AND NOT IPP_FOUND THEN "IPP not found" ELSE NO)
status(" Use IPP:" WITH_IPP AND NOT HAVE_IPP THEN "IPP not found" ELSE NO)
endif()
if(DEFINED WITH_IPP_A)
status(" Use IPP Async:" HAVE_IPP_A THEN "YES" ELSE NO)
endif(DEFINED WITH_IPP_A)
status(" Use Eigen:" HAVE_EIGEN THEN "YES (ver ${EIGEN_WORLD_VERSION}.${EIGEN_MAJOR_VERSION}.${EIGEN_MINOR_VERSION})" ELSE NO)
status(" Use TBB:" HAVE_TBB THEN "YES (ver ${TBB_VERSION_MAJOR}.${TBB_VERSION_MINOR} interface ${TBB_INTERFACE_VERSION})" ELSE NO)
status(" Use OpenMP:" HAVE_OPENMP THEN YES ELSE NO)
@ -876,18 +1045,34 @@ endif()
# ========================== python ==========================
status("")
status(" Python:")
status(" Interpreter:" PYTHONINTERP_FOUND THEN "${PYTHON_EXECUTABLE} (ver ${PYTHON_VERSION_STRING})" ELSE NO)
if(BUILD_opencv_python)
if(PYTHONLIBS_VERSION_STRING)
status(" Libraries:" HAVE_opencv_python THEN "${PYTHON_LIBRARIES} (ver ${PYTHONLIBS_VERSION_STRING})" ELSE NO)
status(" Python 2:")
status(" Interpreter:" PYTHON2INTERP_FOUND THEN "${PYTHON2_EXECUTABLE} (ver ${PYTHON2_VERSION_STRING})" ELSE NO)
if(BUILD_opencv_python2)
if(PYTHON2LIBS_VERSION_STRING)
status(" Libraries:" HAVE_opencv_python2 THEN "${PYTHON2_LIBRARIES} (ver ${PYTHON2LIBS_VERSION_STRING})" ELSE NO)
else()
status(" Libraries:" HAVE_opencv_python THEN "${PYTHON_LIBRARIES}" ELSE NO)
status(" Libraries:" HAVE_opencv_python2 THEN "${PYTHON2_LIBRARIES}" ELSE NO)
endif()
status(" numpy:" PYTHON_NUMPY_INCLUDE_DIRS THEN "${PYTHON_NUMPY_INCLUDE_DIRS} (ver ${PYTHON_NUMPY_VERSION})" ELSE "NO (Python wrappers can not be generated)")
status(" packages path:" PYTHON_EXECUTABLE THEN "${PYTHON_PACKAGES_PATH}" ELSE "-")
status(" numpy:" PYTHON2_NUMPY_INCLUDE_DIRS THEN "${PYTHON2_NUMPY_INCLUDE_DIRS} (ver ${PYTHON2_NUMPY_VERSION})" ELSE "NO (Python wrappers can not be generated)")
status(" packages path:" PYTHON2_EXECUTABLE THEN "${PYTHON2_PACKAGES_PATH}" ELSE "-")
endif()
status("")
status(" Python 3:")
status(" Interpreter:" PYTHON3INTERP_FOUND THEN "${PYTHON3_EXECUTABLE} (ver ${PYTHON3_VERSION_STRING})" ELSE NO)
if(BUILD_opencv_python3)
if(PYTHON3LIBS_VERSION_STRING)
status(" Libraries:" HAVE_opencv_python3 THEN "${PYTHON3_LIBRARIES} (ver ${PYTHON3LIBS_VERSION_STRING})" ELSE NO)
else()
status(" Libraries:" HAVE_opencv_python3 THEN "${PYTHON3_LIBRARIES}" ELSE NO)
endif()
status(" numpy:" PYTHON3_NUMPY_INCLUDE_DIRS THEN "${PYTHON3_NUMPY_INCLUDE_DIRS} (ver ${PYTHON3_NUMPY_VERSION})" ELSE "NO (Python3 wrappers can not be generated)")
status(" packages path:" PYTHON3_EXECUTABLE THEN "${PYTHON3_PACKAGES_PATH}" ELSE "-")
endif()
status("")
status(" Python (for build):" PYTHON_DEFAULT_AVAILABLE THEN "${PYTHON_DEFAULT_EXECUTABLE}" ELSE NO)
# ========================== java ==========================
status("")
status(" Java:")
@ -895,7 +1080,8 @@ status(" ant:" ANT_EXECUTABLE THEN "${ANT_EXECUTABLE} (ver ${A
if(NOT ANDROID)
status(" JNI:" JNI_INCLUDE_DIRS THEN "${JNI_INCLUDE_DIRS}" ELSE NO)
endif()
status(" Java tests:" BUILD_TESTS AND (CAN_BUILD_ANDROID_PROJECTS OR HAVE_opencv_java) THEN YES ELSE NO)
status(" Java wrappers:" HAVE_opencv_java THEN YES ELSE NO)
status(" Java tests:" BUILD_TESTS AND opencv_test_java_BINARY_DIR THEN YES ELSE NO)
# ========================= matlab =========================
status("")
@ -909,14 +1095,8 @@ endif()
if(BUILD_DOCS)
status("")
status(" Documentation:")
if(HAVE_SPHINX)
status(" Build Documentation:" PDFLATEX_COMPILER THEN YES ELSE "YES (only HTML and without math expressions)")
else()
status(" Build Documentation:" NO)
endif()
status(" Sphinx:" HAVE_SPHINX THEN "${SPHINX_BUILD} (ver ${SPHINX_VERSION})" ELSE NO)
status(" PdfLaTeX compiler:" PDFLATEX_COMPILER THEN "${PDFLATEX_COMPILER}" ELSE NO)
status(" PlantUML:" PLANTUML THEN "${PLANTUML}" ELSE NO)
status(" Doxygen:" DOXYGEN_FOUND THEN "${DOXYGEN_EXECUTABLE} (ver ${DOXYGEN_VERSION})" ELSE NO)
status(" PlantUML:" PLANTUML_JAR THEN "${PLANTUML_JAR}" ELSE NO)
endif()
# ========================== samples and tests ==========================
@ -942,3 +1122,9 @@ ocv_finalize_status()
if("${CMAKE_CURRENT_SOURCE_DIR}" STREQUAL "${CMAKE_CURRENT_BINARY_DIR}")
message(WARNING "The source directory is the same as binary directory. \"make clean\" may damage the source tree")
endif()
# ----------------------------------------------------------------------------
# CPack stuff
# ----------------------------------------------------------------------------
include(cmake/OpenCVPackaging.cmake)

20
doc/license.txt → LICENSE
View File

@ -1,16 +1,11 @@
IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
By downloading, copying, installing or using the software you agree to this license.
If you do not agree to this license, do not download, install,
copy or use the software.
By downloading, copying, installing or using the software you agree to this license.
If you do not agree to this license, do not download, install,
copy or use the software.
License Agreement
For Open Source Computer Vision Library
Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
Copyright (C) 2008-2011, Willow Garage Inc., all rights reserved.
Third party copyrights are property of their respective owners.
(3-clause BSD License)
Redistribution and use in source and binary forms, with or without modification,
are permitted provided that the following conditions are met:
@ -22,13 +17,14 @@ are permitted provided that the following conditions are met:
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
* The name of the copyright holders may not be used to endorse or promote products
derived from this software without specific prior written permission.
* Neither the names of the copyright holders nor the names of the contributors
may be used to endorse or promote products derived from this software
without specific prior written permission.
This software is provided by the copyright holders and contributors "as is" and
any express or implied warranties, including, but not limited to, the implied
warranties of merchantability and fitness for a particular purpose are disclaimed.
In no event shall the Intel Corporation or contributors be liable for any direct,
In no event shall copyright holders or contributors be liable for any direct,
indirect, incidental, special, exemplary, or consequential damages
(including, but not limited to, procurement of substitute goods or services;
loss of use, data, or profits; or business interruption) however caused

5
README.md
View File

@ -1,5 +1,7 @@
### OpenCV: Open Source Computer Vision Library
[![Gittip](http://img.shields.io/gittip/OpenCV.png)](https://www.gittip.com/OpenCV/)
#### Resources
* Homepage: <http://opencv.org>
@ -18,6 +20,3 @@ Summary of guidelines:
* Include tests and documentation;
* Clean up "oops" commits before submitting;
* Follow the coding style guide.
[![Donate OpenCV project](http://opencv.org/wp-content/uploads/2013/07/gittip1.png)](https://www.gittip.com/OpenCV/)
[![Donate OpenCV project](http://opencv.org/wp-content/uploads/2013/07/paypal-donate-button.png)](https://www.paypal.com/cgi-bin/webscr?item_name=Donation+to+OpenCV&cmd=_donations&business=accountant%40opencv.org)

3
apps/CMakeLists.txt
View File

@ -1,6 +1,5 @@
add_definitions(-D__OPENCV_BUILD=1)
link_libraries(${OPENCV_LINKER_LIBS})
add_subdirectory(haartraining)
add_subdirectory(traincascade)
add_subdirectory(sft)
add_subdirectory(createsamples)

39
apps/createsamples/CMakeLists.txt Normal file
View File

@ -0,0 +1,39 @@
set(OPENCV_CREATESAMPLES_DEPS opencv_core opencv_imgproc opencv_objdetect opencv_imgcodecs opencv_highgui opencv_calib3d opencv_features2d opencv_videoio)
ocv_check_dependencies(${OPENCV_CREATESAMPLES_DEPS})
if(NOT OCV_DEPENDENCIES_FOUND)
return()
endif()
project(createsamples)
set(the_target opencv_createsamples)
ocv_target_include_directories(${the_target} PRIVATE "${CMAKE_CURRENT_SOURCE_DIR}" "${OpenCV_SOURCE_DIR}/include/opencv")
ocv_target_include_modules(${the_target} ${OPENCV_CREATESAMPLES_DEPS})
file(GLOB SRCS *.cpp)
file(GLOB HDRS *.h*)
set(createsamples_files ${SRCS} ${HDRS})
ocv_add_executable(${the_target} ${createsamples_files})
ocv_target_link_libraries(${the_target} ${OPENCV_CREATESAMPLES_DEPS})
set_target_properties(${the_target} PROPERTIES
DEBUG_POSTFIX "${OPENCV_DEBUG_POSTFIX}"
ARCHIVE_OUTPUT_DIRECTORY ${LIBRARY_OUTPUT_PATH}
RUNTIME_OUTPUT_DIRECTORY ${EXECUTABLE_OUTPUT_PATH}
INSTALL_NAME_DIR lib
OUTPUT_NAME "opencv_createsamples")
if(ENABLE_SOLUTION_FOLDERS)
set_target_properties(${the_target} PROPERTIES FOLDER "applications")
endif()
if(INSTALL_CREATE_DISTRIB)
if(BUILD_SHARED_LIBS)
install(TARGETS ${the_target} RUNTIME DESTINATION ${OPENCV_BIN_INSTALL_PATH} CONFIGURATIONS Release COMPONENT dev)
endif()
else()
install(TARGETS ${the_target} OPTIONAL RUNTIME DESTINATION ${OPENCV_BIN_INSTALL_PATH} COMPONENT dev)
endif()

2
apps/haartraining/createsamples.cpp → apps/createsamples/createsamples.cpp
View File

@ -53,7 +53,7 @@
using namespace std;
#include "cvhaartraining.h"
#include "utility.hpp"
int main( int argc, char* argv[] )
{

818
apps/haartraining/cvsamples.cpp → apps/createsamples/utility.cpp
View File

@ -39,23 +39,117 @@
//
//M*/
/*
* cvsamples.cpp
*
* support functions for training and test samples creation.
*/
#include <cstring>
#include <ctime>
#include "cvhaartraining.h"
#include "_cvhaartraining.h"
#include <sys/stat.h>
#include <sys/types.h>
#ifdef _WIN32
#include <direct.h>
#endif /* _WIN32 */
/* if ipl.h file is included then iplWarpPerspectiveQ function
is used for image transformation during samples creation;
otherwise internal cvWarpPerspective function is used */
#include "utility.hpp"
#include "opencv2/core.hpp"
#include "opencv2/core/core_c.h"
#include "opencv2/imgcodecs/imgcodecs_c.h"
#include "opencv2/imgproc/imgproc_c.h"
#include "opencv2/highgui/highgui_c.h"
#include "opencv2/calib3d/calib3d_c.h"
//#include <ipl.h>
#ifndef PATH_MAX
#define PATH_MAX 512
#endif /* PATH_MAX */
#include "cv.h"
#include "highgui.h"
#define __BEGIN__ __CV_BEGIN__
#define __END__ __CV_END__
#define EXIT __CV_EXIT__
static int icvMkDir( const char* filename )
{
char path[PATH_MAX];
char* p;
int pos;
#ifdef _WIN32
struct _stat st;
#else /* _WIN32 */
struct stat st;
mode_t mode;
mode = 0755;
#endif /* _WIN32 */
strcpy( path, filename );
p = path;
for( ; ; )
{
pos = (int)strcspn( p, "/\\" );
if( pos == (int) strlen( p ) ) break;
if( pos != 0 )
{
p[pos] = '\0';
#ifdef _WIN32
if( p[pos-1] != ':' )
{
if( _stat( path, &st ) != 0 )
{
if( _mkdir( path ) != 0 ) return 0;
}
}
#else /* _WIN32 */
if( stat( path, &st ) != 0 )
{
if( mkdir( path, mode ) != 0 ) return 0;
}
#endif /* _WIN32 */
}
p[pos] = '/';
p += pos + 1;
}
return 1;
}
static void icvWriteVecHeader( FILE* file, int count, int width, int height )
{
int vecsize;
short tmp;
/* number of samples */
fwrite( &count, sizeof( count ), 1, file );
/* vector size */
vecsize = width * height;
fwrite( &vecsize, sizeof( vecsize ), 1, file );
/* min/max values */
tmp = 0;
fwrite( &tmp, sizeof( tmp ), 1, file );
fwrite( &tmp, sizeof( tmp ), 1, file );
}
static void icvWriteVecSample( FILE* file, CvArr* sample )
{
CvMat* mat, stub;
int r, c;
short tmp;
uchar chartmp;
mat = cvGetMat( sample, &stub );
chartmp = 0;
fwrite( &chartmp, sizeof( chartmp ), 1, file );
for( r = 0; r < mat->rows; r++ )
{
for( c = 0; c < mat->cols; c++ )
{
tmp = (short) (CV_MAT_ELEM( *mat, uchar, r, c ));
fwrite( &tmp, sizeof( tmp ), 1, file );
}
}
}
/* Calculates coefficients of perspective transformation
* which maps <quad> into rectangle ((0,0), (w,0), (w,h), (h,0)):
@ -83,8 +177,7 @@
* cij - coeffs[i][j], coeffs[2][2] = 1
* (ui, vi) - rectangle vertices
*/
static void cvGetPerspectiveTransform( CvSize src_size, double quad[4][2],
double coeffs[3][3] )
static void cvGetPerspectiveTransform( CvSize src_size, double quad[4][2], double coeffs[3][3] )
{
//CV_FUNCNAME( "cvWarpPerspective" );
@ -460,7 +553,58 @@ void icvRandomQuad( int width, int height, double quad[4][2],
}
int icvStartSampleDistortion( const char* imgfilename, int bgcolor, int bgthreshold,
typedef struct CvSampleDistortionData
{
IplImage* src;
IplImage* erode;
IplImage* dilate;
IplImage* mask;
IplImage* img;
IplImage* maskimg;
int dx;
int dy;
int bgcolor;
} CvSampleDistortionData;
#if defined CV_OPENMP && (defined _MSC_VER || defined CV_ICC)
#define CV_OPENMP 1
#else
#undef CV_OPENMP
#endif
typedef struct CvBackgroundData
{
int count;
char** filename;
int last;
int round;
CvSize winsize;
} CvBackgroundData;
typedef struct CvBackgroundReader
{
CvMat src;
CvMat img;
CvPoint offset;
float scale;
float scalefactor;
float stepfactor;
CvPoint point;
} CvBackgroundReader;
/*
* Background reader
* Created in each thread
*/
CvBackgroundReader* cvbgreader = NULL;
#if defined CV_OPENMP
#pragma omp threadprivate(cvbgreader)
#endif
CvBackgroundData* cvbgdata = NULL;
static int icvStartSampleDistortion( const char* imgfilename, int bgcolor, int bgthreshold,
CvSampleDistortionData* data )
{
memset( data, 0, sizeof( *data ) );
@ -546,6 +690,7 @@ int icvStartSampleDistortion( const char* imgfilename, int bgcolor, int bgthresh
return 0;
}
static
void icvPlaceDistortedSample( CvArr* background,
int inverse, int maxintensitydev,
double maxxangle, double maxyangle, double maxzangle,
@ -658,6 +803,7 @@ void icvPlaceDistortedSample( CvArr* background,
cvReleaseImage( &maskimg );
}
static
void icvEndSampleDistortion( CvSampleDistortionData* data )
{
if( data->src )
@ -686,40 +832,585 @@ void icvEndSampleDistortion( CvSampleDistortionData* data )
}
}
void icvWriteVecHeader( FILE* file, int count, int width, int height )
static
CvBackgroundData* icvCreateBackgroundData( const char* filename, CvSize winsize )
{
int vecsize;
short tmp;
CvBackgroundData* data = NULL;
/* number of samples */
fwrite( &count, sizeof( count ), 1, file );
/* vector size */
vecsize = width * height;
fwrite( &vecsize, sizeof( vecsize ), 1, file );
/* min/max values */
tmp = 0;
fwrite( &tmp, sizeof( tmp ), 1, file );
fwrite( &tmp, sizeof( tmp ), 1, file );
const char* dir = NULL;
char full[PATH_MAX];
char* imgfilename = NULL;
size_t datasize = 0;
int count = 0;
FILE* input = NULL;
char* tmp = NULL;
int len = 0;
assert( filename != NULL );
dir = strrchr( filename, '\\' );
if( dir == NULL )
{
dir = strrchr( filename, '/' );
}
if( dir == NULL )
{
imgfilename = &(full[0]);
}
else
{
strncpy( &(full[0]), filename, (dir - filename + 1) );
imgfilename = &(full[(dir - filename + 1)]);
}
input = fopen( filename, "r" );
if( input != NULL )
{
count = 0;
datasize = 0;
/* count */
while( !feof( input ) )
{
*imgfilename = '\0';
if( !fgets( imgfilename, PATH_MAX - (int)(imgfilename - full) - 1, input ))
break;
len = (int)strlen( imgfilename );
for( ; len > 0 && isspace(imgfilename[len-1]); len-- )
imgfilename[len-1] = '\0';
if( len > 0 )
{
if( (*imgfilename) == '#' ) continue; /* comment */
count++;
datasize += sizeof( char ) * (strlen( &(full[0]) ) + 1);
}
}
if( count > 0 )
{
//rewind( input );
fseek( input, 0, SEEK_SET );
datasize += sizeof( *data ) + sizeof( char* ) * count;
data = (CvBackgroundData*) cvAlloc( datasize );
memset( (void*) data, 0, datasize );
data->count = count;
data->filename = (char**) (data + 1);
data->last = 0;
data->round = 0;
data->winsize = winsize;
tmp = (char*) (data->filename + data->count);
count = 0;
while( !feof( input ) )
{
*imgfilename = '\0';
if( !fgets( imgfilename, PATH_MAX - (int)(imgfilename - full) - 1, input ))
break;
len = (int)strlen( imgfilename );
if( len > 0 && imgfilename[len-1] == '\n' )
imgfilename[len-1] = 0, len--;
if( len > 0 )
{
if( (*imgfilename) == '#' ) continue; /* comment */
data->filename[count++] = tmp;
strcpy( tmp, &(full[0]) );
tmp += strlen( &(full[0]) ) + 1;
}
}
}
fclose( input );
}
return data;
}
void icvWriteVecSample( FILE* file, CvArr* sample )
static
void icvReleaseBackgroundData( CvBackgroundData** data )
{
CvMat* mat, stub;
int r, c;
short tmp;
uchar chartmp;
assert( data != NULL && (*data) != NULL );
mat = cvGetMat( sample, &stub );
chartmp = 0;
fwrite( &chartmp, sizeof( chartmp ), 1, file );
for( r = 0; r < mat->rows; r++ )
cvFree( data );
}
static
CvBackgroundReader* icvCreateBackgroundReader()
{
CvBackgroundReader* reader = NULL;
reader = (CvBackgroundReader*) cvAlloc( sizeof( *reader ) );
memset( (void*) reader, 0, sizeof( *reader ) );
reader->src = cvMat( 0, 0, CV_8UC1, NULL );
reader->img = cvMat( 0, 0, CV_8UC1, NULL );
reader->offset = cvPoint( 0, 0 );
reader->scale = 1.0F;
reader->scalefactor = 1.4142135623730950488016887242097F;
reader->stepfactor = 0.5F;
reader->point = reader->offset;
return reader;
}
static
void icvReleaseBackgroundReader( CvBackgroundReader** reader )
{
assert( reader != NULL && (*reader) != NULL );
if( (*reader)->src.data.ptr != NULL )
{
for( c = 0; c < mat->cols; c++ )
cvFree( &((*reader)->src.data.ptr) );
}
if( (*reader)->img.data.ptr != NULL )
{
cvFree( &((*reader)->img.data.ptr) );
}
cvFree( reader );
}
static
void icvGetNextFromBackgroundData( CvBackgroundData* data,
CvBackgroundReader* reader )
{
IplImage* img = NULL;
size_t datasize = 0;
int round = 0;
int i = 0;
CvPoint offset = cvPoint(0,0);
assert( data != NULL && reader != NULL );
if( reader->src.data.ptr != NULL )
{
cvFree( &(reader->src.data.ptr) );
reader->src.data.ptr = NULL;
}
if( reader->img.data.ptr != NULL )
{
cvFree( &(reader->img.data.ptr) );
reader->img.data.ptr = NULL;
}
#ifdef CV_OPENMP
#pragma omp critical(c_background_data)
#endif /* CV_OPENMP */
{
for( i = 0; i < data->count; i++ )
{
tmp = (short) (CV_MAT_ELEM( *mat, uchar, r, c ));
fwrite( &tmp, sizeof( tmp ), 1, file );
round = data->round;
#ifdef CV_VERBOSE
printf( "Open background image: %s\n", data->filename[data->last] );
#endif /* CV_VERBOSE */
data->last = rand() % data->count;
data->last %= data->count;
img = cvLoadImage( data->filename[data->last], 0 );
if( !img )
continue;
data->round += data->last / data->count;
data->round = data->round % (data->winsize.width * data->winsize.height);
offset.x = round % data->winsize.width;
offset.y = round / data->winsize.width;
offset.x = MIN( offset.x, img->width - data->winsize.width );
offset.y = MIN( offset.y, img->height - data->winsize.height );
if( img != NULL && img->depth == IPL_DEPTH_8U && img->nChannels == 1 &&
offset.x >= 0 && offset.y >= 0 )
{
break;
}
if( img != NULL )
cvReleaseImage( &img );
img = NULL;
}
}
if( img == NULL )
{
/* no appropriate image */
#ifdef CV_VERBOSE
printf( "Invalid background description file.\n" );
#endif /* CV_VERBOSE */
assert( 0 );
exit( 1 );
}
datasize = sizeof( uchar ) * img->width * img->height;
reader->src = cvMat( img->height, img->width, CV_8UC1, (void*) cvAlloc( datasize ) );
cvCopy( img, &reader->src, NULL );
cvReleaseImage( &img );
img = NULL;
//reader->offset.x = round % data->winsize.width;
//reader->offset.y = round / data->winsize.width;
reader->offset = offset;
reader->point = reader->offset;
reader->scale = MAX(
((float) data->winsize.width + reader->point.x) / ((float) reader->src.cols),
((float) data->winsize.height + reader->point.y) / ((float) reader->src.rows) );
reader->img = cvMat( (int) (reader->scale * reader->src.rows + 0.5F),
(int) (reader->scale * reader->src.cols + 0.5F),
CV_8UC1, (void*) cvAlloc( datasize ) );
cvResize( &(reader->src), &(reader->img) );
}
/*
* icvGetBackgroundImage
*
* Get an image from background
* <img> must be allocated and have size, previously passed to icvInitBackgroundReaders
*
* Usage example:
* icvInitBackgroundReaders( "bg.txt", cvSize( 24, 24 ) );
* ...
* #pragma omp parallel
* {
* ...
* icvGetBackgourndImage( cvbgdata, cvbgreader, img );
* ...
* }
* ...
* icvDestroyBackgroundReaders();
*/
static
void icvGetBackgroundImage( CvBackgroundData* data,
CvBackgroundReader* reader,
CvMat* img )
{
CvMat mat;
assert( data != NULL && reader != NULL && img != NULL );
assert( CV_MAT_TYPE( img->type ) == CV_8UC1 );
assert( img->cols == data->winsize.width );
assert( img->rows == data->winsize.height );
if( reader->img.data.ptr == NULL )
{
icvGetNextFromBackgroundData( data, reader );
}
mat = cvMat( data->winsize.height, data->winsize.width, CV_8UC1 );
cvSetData( &mat, (void*) (reader->img.data.ptr + reader->point.y * reader->img.step
+ reader->point.x * sizeof( uchar )), reader->img.step );
cvCopy( &mat, img, 0 );
if( (int) ( reader->point.x + (1.0F + reader->stepfactor ) * data->winsize.width )
< reader->img.cols )
{
reader->point.x += (int) (reader->stepfactor * data->winsize.width);
}
else
{
reader->point.x = reader->offset.x;
if( (int) ( reader->point.y + (1.0F + reader->stepfactor ) * data->winsize.height )
< reader->img.rows )
{
reader->point.y += (int) (reader->stepfactor * data->winsize.height);
}
else
{
reader->point.y = reader->offset.y;
reader->scale *= reader->scalefactor;
if( reader->scale <= 1.0F )
{
reader->img = cvMat( (int) (reader->scale * reader->src.rows),
(int) (reader->scale * reader->src.cols),
CV_8UC1, (void*) (reader->img.data.ptr) );
cvResize( &(reader->src), &(reader->img) );
}
else
{
icvGetNextFromBackgroundData( data, reader );
}
}
}
}
/*
* icvInitBackgroundReaders
*
* Initialize background reading process.
* <cvbgreader> and <cvbgdata> are initialized.
* Must be called before any usage of background
*
* filename - name of background description file
* winsize - size of images will be obtained from background
*
* return 1 on success, 0 otherwise.
*/
static int icvInitBackgroundReaders( const char* filename, CvSize winsize )
{
if( cvbgdata == NULL && filename != NULL )
{
cvbgdata = icvCreateBackgroundData( filename, winsize );
}
if( cvbgdata )
{
#ifdef CV_OPENMP
#pragma omp parallel
#endif /* CV_OPENMP */
{
#ifdef CV_OPENMP
#pragma omp critical(c_create_bg_data)
#endif /* CV_OPENMP */
{
if( cvbgreader == NULL )
{
cvbgreader = icvCreateBackgroundReader();
}
}
}
}
return (cvbgdata != NULL);
}
/*
* icvDestroyBackgroundReaders
*
* Finish backgournd reading process
*/
static
void icvDestroyBackgroundReaders()
{
/* release background reader in each thread */
#ifdef CV_OPENMP
#pragma omp parallel
#endif /* CV_OPENMP */
{
#ifdef CV_OPENMP
#pragma omp critical(c_release_bg_data)
#endif /* CV_OPENMP */
{
if( cvbgreader != NULL )
{
icvReleaseBackgroundReader( &cvbgreader );
cvbgreader = NULL;
}
}
}
if( cvbgdata != NULL )
{
icvReleaseBackgroundData( &cvbgdata );
cvbgdata = NULL;
}
}
void cvCreateTrainingSamples( const char* filename,
const char* imgfilename, int bgcolor, int bgthreshold,
const char* bgfilename, int count,
int invert, int maxintensitydev,
double maxxangle, double maxyangle, double maxzangle,
int showsamples,
int winwidth, int winheight )
{
CvSampleDistortionData data;
assert( filename != NULL );
assert( imgfilename != NULL );
if( !icvMkDir( filename ) )
{
fprintf( stderr, "Unable to create output file: %s\n", filename );
return;
}
if( icvStartSampleDistortion( imgfilename, bgcolor, bgthreshold, &data ) )
{
FILE* output = NULL;
output = fopen( filename, "wb" );
if( output != NULL )
{
int hasbg;
int i;
CvMat sample;
int inverse;
hasbg = 0;
hasbg = (bgfilename != NULL && icvInitBackgroundReaders( bgfilename,
cvSize( winwidth,winheight ) ) );
sample = cvMat( winheight, winwidth, CV_8UC1, cvAlloc( sizeof( uchar ) *
winheight * winwidth ) );
icvWriteVecHeader( output, count, sample.cols, sample.rows );
if( showsamples )
{
cvNamedWindow( "Sample", CV_WINDOW_AUTOSIZE );
}
inverse = invert;
for( i = 0; i < count; i++ )
{
if( hasbg )
{
icvGetBackgroundImage( cvbgdata, cvbgreader, &sample );
}
else
{
cvSet( &sample, cvScalar( bgcolor ) );
}
if( invert == CV_RANDOM_INVERT )
{
inverse = (rand() > (RAND_MAX/2));
}
icvPlaceDistortedSample( &sample, inverse, maxintensitydev,
maxxangle, maxyangle, maxzangle,
0 /* nonzero means placing image without cut offs */,
0.0 /* nozero adds random shifting */,
0.0 /* nozero adds random scaling */,
&data );
if( showsamples )
{
cvShowImage( "Sample", &sample );
if( cvWaitKey( 0 ) == 27 )
{
showsamples = 0;
}
}
icvWriteVecSample( output, &sample );
#ifdef CV_VERBOSE
if( i % 500 == 0 )
{
printf( "\r%3d%%", 100 * i / count );
}
#endif /* CV_VERBOSE */
}
icvDestroyBackgroundReaders();
cvFree( &(sample.data.ptr) );
fclose( output );
} /* if( output != NULL ) */
icvEndSampleDistortion( &data );
}
#ifdef CV_VERBOSE
printf( "\r \r" );
#endif /* CV_VERBOSE */
}
#define CV_INFO_FILENAME "info.dat"
void cvCreateTestSamples( const char* infoname,
const char* imgfilename, int bgcolor, int bgthreshold,
const char* bgfilename, int count,
int invert, int maxintensitydev,
double maxxangle, double maxyangle, double maxzangle,
int showsamples,
int winwidth, int winheight )
{
CvSampleDistortionData data;
assert( infoname != NULL );
assert( imgfilename != NULL );
assert( bgfilename != NULL );
if( !icvMkDir( infoname ) )
{
#if CV_VERBOSE
fprintf( stderr, "Unable to create directory hierarchy: %s\n", infoname );
#endif /* CV_VERBOSE */
return;
}
if( icvStartSampleDistortion( imgfilename, bgcolor, bgthreshold, &data ) )
{
char fullname[PATH_MAX];
char* filename;
CvMat win;
FILE* info;
if( icvInitBackgroundReaders( bgfilename, cvSize( 10, 10 ) ) )
{
int i;
int x, y, width, height;
float scale;
float maxscale;
int inverse;
if( showsamples )
{
cvNamedWindow( "Image", CV_WINDOW_AUTOSIZE );
}
info = fopen( infoname, "w" );
strcpy( fullname, infoname );
filename = strrchr( fullname, '\\' );
if( filename == NULL )
{
filename = strrchr( fullname, '/' );
}
if( filename == NULL )
{
filename = fullname;
}
else
{
filename++;
}
count = MIN( count, cvbgdata->count );
inverse = invert;
for( i = 0; i < count; i++ )
{
icvGetNextFromBackgroundData( cvbgdata, cvbgreader );
maxscale = MIN( 0.7F * cvbgreader->src.cols / winwidth,
0.7F * cvbgreader->src.rows / winheight );
if( maxscale < 1.0F ) continue;
scale = (maxscale - 1.0F) * rand() / RAND_MAX + 1.0F;
width = (int) (scale * winwidth);
height = (int) (scale * winheight);
x = (int) ((0.1+0.8 * rand()/RAND_MAX) * (cvbgreader->src.cols - width));
y = (int) ((0.1+0.8 * rand()/RAND_MAX) * (cvbgreader->src.rows - height));
cvGetSubArr( &cvbgreader->src, &win, cvRect( x, y ,width, height ) );
if( invert == CV_RANDOM_INVERT )
{
inverse = (rand() > (RAND_MAX/2));
}
icvPlaceDistortedSample( &win, inverse, maxintensitydev,
maxxangle, maxyangle, maxzangle,
1, 0.0, 0.0, &data );
sprintf( filename, "%04d_%04d_%04d_%04d_%04d.jpg",
(i + 1), x, y, width, height );
if( info )
{
fprintf( info, "%s %d %d %d %d %d\n",
filename, 1, x, y, width, height );
}
cvSaveImage( fullname, &cvbgreader->src );
if( showsamples )
{
cvShowImage( "Image", &cvbgreader->src );
if( cvWaitKey( 0 ) == 27 )
{
showsamples = 0;
}
}
}
if( info ) fclose( info );
icvDestroyBackgroundReaders();
}
icvEndSampleDistortion( &data );
}
}
@ -867,7 +1558,47 @@ int cvCreateTrainingSamplesFromInfo( const char* infoname, const char* vecfilena
return total;
}
typedef struct CvVecFile
{
FILE* input;
int count;
int vecsize;
int last;
short* vector;
} CvVecFile;
static
int icvGetTraininDataFromVec( CvMat* img, void* userdata )
{
uchar tmp = 0;
int r = 0;
int c = 0;
assert( img->rows * img->cols == ((CvVecFile*) userdata)->vecsize );
size_t elements_read = fread( &tmp, sizeof( tmp ), 1, ((CvVecFile*) userdata)->input );
CV_Assert(elements_read == 1);
elements_read = fread( ((CvVecFile*) userdata)->vector, sizeof( short ),
((CvVecFile*) userdata)->vecsize, ((CvVecFile*) userdata)->input );
CV_Assert(elements_read == (size_t)((CvVecFile*) userdata)->vecsize);
if( feof( ((CvVecFile*) userdata)->input ) ||
(((CvVecFile*) userdata)->last)++ >= ((CvVecFile*) userdata)->count )
{
return 0;
}
for( r = 0; r < img->rows; r++ )
{
for( c = 0; c < img->cols; c++ )
{
CV_MAT_ELEM( *img, uchar, r, c ) =
(uchar) ( ((CvVecFile*) userdata)->vector[r * img->cols + c] );
}
}
return 1;
}
void cvShowVecSamples( const char* filename, int winwidth, int winheight,
double scale )
{
@ -936,7 +1667,7 @@ void cvShowVecSamples( const char* filename, int winwidth, int winheight,
cvNamedWindow( "Sample", CV_WINDOW_AUTOSIZE );
for( i = 0; i < file.count; i++ )
{
icvGetHaarTraininDataFromVecCallback( sample, &file );
icvGetTraininDataFromVec( sample, &file );
if( scale != 1.0 ) cvResize( sample, scaled_sample, CV_INTER_LINEAR);
cvShowImage( "Sample", scaled_sample );
if( cvWaitKey( 0 ) == 27 ) break;
@ -947,7 +1678,4 @@ void cvShowVecSamples( const char* filename, int winwidth, int winheight,
}
fclose( file.input );
}
}
/* End of file. */
}

76
apps/haartraining/cvhaartraining.h → apps/createsamples/utility.hpp
View File

@ -39,14 +39,10 @@
//
//M*/
/*
* cvhaartraining.h
*
* haar training functions
*/
#ifndef __CREATESAMPLES_UTILITY_HPP__
#define __CREATESAMPLES_UTILITY_HPP__
#ifndef _CVHAARTRAINING_H_
#define _CVHAARTRAINING_H_
#define CV_VERBOSE 1
/*
* cvCreateTrainingSamples
@ -125,68 +121,4 @@ int cvCreateTrainingSamplesFromInfo( const char* infoname, const char* vecfilena
*/
void cvShowVecSamples( const char* filename, int winwidth, int winheight, double scale );
/*
* cvCreateCascadeClassifier
*
* Create cascade classifier
* dirname - directory name in which cascade classifier will be created.
* It must exist and contain subdirectories 0, 1, 2, ... (nstages-1).
* vecfilename - name of .vec file with object's images
* bgfilename - name of background description file
* bg_vecfile - true if bgfilename represents a vec file with discrete negatives
* npos - number of positive samples used in training of each stage
* nneg - number of negative samples used in training of each stage
* nstages - number of stages
* numprecalculated - number of features being precalculated. Each precalculated feature
* requires (number_of_samples*(sizeof( float ) + sizeof( short ))) bytes of memory
* numsplits - number of binary splits in each weak classifier
* 1 - stumps, 2 and more - trees.
* minhitrate - desired min hit rate of each stage
* maxfalsealarm - desired max false alarm of each stage
* weightfraction - weight trimming parameter
* mode - 0 - BASIC = Viola
* 1 - CORE = All upright
* 2 - ALL = All features
* symmetric - if not 0 vertical symmetry is assumed
* equalweights - if not 0 initial weights of all samples will be equal
* winwidth - sample width
* winheight - sample height
* boosttype - type of applied boosting algorithm
* 0 - Discrete AdaBoost
* 1 - Real AdaBoost
* 2 - LogitBoost
* 3 - Gentle AdaBoost
* stumperror - type of used error if Discrete AdaBoost algorithm is applied
* 0 - misclassification error
* 1 - gini error
* 2 - entropy error
*/
void cvCreateCascadeClassifier( const char* dirname,
const char* vecfilename,
const char* bgfilename,
int npos, int nneg, int nstages,
int numprecalculated,
int numsplits,
float minhitrate = 0.995F, float maxfalsealarm = 0.5F,
float weightfraction = 0.95F,
int mode = 0, int symmetric = 1,
int equalweights = 1,
int winwidth = 24, int winheight = 24,
int boosttype = 3, int stumperror = 0 );
void cvCreateTreeCascadeClassifier( const char* dirname,
const char* vecfilename,
const char* bgfilename,
int npos, int nneg, int nstages,
int numprecalculated,
int numsplits,
float minhitrate, float maxfalsealarm,
float weightfraction,
int mode, int symmetric,
int equalweights,
int winwidth, int winheight,
int boosttype, int stumperror,
int maxtreesplits, int minpos, bool bg_vecfile = false );
#endif /* _CVHAARTRAINING_H_ */
#endif //__CREATESAMPLES_UTILITY_HPP__

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SET(OPENCV_HAARTRAINING_DEPS opencv_core opencv_imgproc opencv_photo opencv_ml opencv_highgui opencv_objdetect opencv_calib3d opencv_video opencv_features2d opencv_flann opencv_legacy)
ocv_check_dependencies(${OPENCV_HAARTRAINING_DEPS})
if(NOT OCV_DEPENDENCIES_FOUND)
return()
endif()
project(haartraining)
ocv_include_directories("${CMAKE_CURRENT_SOURCE_DIR}" "${OpenCV_SOURCE_DIR}/include/opencv")
ocv_include_modules(${OPENCV_HAARTRAINING_DEPS})
if(WIN32)
link_directories(${CMAKE_CURRENT_BINARY_DIR})
endif()
link_libraries(${OPENCV_HAARTRAINING_DEPS} opencv_haartraining_engine)
# -----------------------------------------------------------
# Library
# -----------------------------------------------------------
set(cvhaartraining_lib_src
_cvcommon.h
cvclassifier.h
_cvhaartraining.h
cvhaartraining.h
cvboost.cpp
cvcommon.cpp
cvhaarclassifier.cpp
cvhaartraining.cpp
cvsamples.cpp
)
add_library(opencv_haartraining_engine STATIC ${cvhaartraining_lib_src})
set_target_properties(opencv_haartraining_engine PROPERTIES
DEBUG_POSTFIX "${OPENCV_DEBUG_POSTFIX}"
ARCHIVE_OUTPUT_DIRECTORY ${LIBRARY_OUTPUT_PATH}
RUNTIME_OUTPUT_DIRECTORY ${EXECUTABLE_OUTPUT_PATH}
INSTALL_NAME_DIR lib
)
# -----------------------------------------------------------
# haartraining
# -----------------------------------------------------------
add_executable(opencv_haartraining cvhaartraining.h haartraining.cpp)
set_target_properties(opencv_haartraining PROPERTIES
DEBUG_POSTFIX "${OPENCV_DEBUG_POSTFIX}"
OUTPUT_NAME "opencv_haartraining")
# -----------------------------------------------------------
# createsamples
# -----------------------------------------------------------
add_executable(opencv_createsamples cvhaartraining.h createsamples.cpp)
set_target_properties(opencv_createsamples PROPERTIES
DEBUG_POSTFIX "${OPENCV_DEBUG_POSTFIX}"
OUTPUT_NAME "opencv_createsamples")
# -----------------------------------------------------------
# performance
# -----------------------------------------------------------
add_executable(opencv_performance performance.cpp)
set_target_properties(opencv_performance PROPERTIES
DEBUG_POSTFIX "${OPENCV_DEBUG_POSTFIX}"
OUTPUT_NAME "opencv_performance")
# -----------------------------------------------------------
# Install part
# -----------------------------------------------------------
if(INSTALL_CREATE_DISTRIB)
if(BUILD_SHARED_LIBS)
install(TARGETS opencv_haartraining RUNTIME DESTINATION ${OPENCV_BIN_INSTALL_PATH} CONFIGURATIONS Release COMPONENT main)
install(TARGETS opencv_createsamples RUNTIME DESTINATION ${OPENCV_BIN_INSTALL_PATH} CONFIGURATIONS Release COMPONENT main)
install(TARGETS opencv_performance RUNTIME DESTINATION ${OPENCV_BIN_INSTALL_PATH} CONFIGURATIONS Release COMPONENT main)
endif()
else()
install(TARGETS opencv_haartraining RUNTIME DESTINATION ${OPENCV_BIN_INSTALL_PATH} COMPONENT main)
install(TARGETS opencv_createsamples RUNTIME DESTINATION ${OPENCV_BIN_INSTALL_PATH} COMPONENT main)
install(TARGETS opencv_performance RUNTIME DESTINATION ${OPENCV_BIN_INSTALL_PATH} COMPONENT main)
endif()
if(ENABLE_SOLUTION_FOLDERS)
set_target_properties(opencv_performance PROPERTIES FOLDER "applications")
set_target_properties(opencv_createsamples PROPERTIES FOLDER "applications")
set_target_properties(opencv_haartraining PROPERTIES FOLDER "applications")
set_target_properties(opencv_haartraining_engine PROPERTIES FOLDER "applications")
endif()

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __CVCOMMON_H_
#define __CVCOMMON_H_
#include "opencv2/core.hpp"
#include "cxcore.h"
#include "cv.h"
#include "cxmisc.h"
#define __BEGIN__ __CV_BEGIN__
#define __END__ __CV_END__
#define EXIT __CV_EXIT__
#ifndef PATH_MAX
#define PATH_MAX 512
#endif /* PATH_MAX */
int icvMkDir( const char* filename );
/* returns index at specified position from index matrix of any type.
if matrix is NULL, then specified position is returned */
CV_INLINE
int icvGetIdxAt( CvMat* idx, int pos );
CV_INLINE
int icvGetIdxAt( CvMat* idx, int pos )
{
if( idx == NULL )
{
return pos;
}
else
{
CvScalar sc;
int type;
type = CV_MAT_TYPE( idx->type );
cvRawDataToScalar( idx->data.ptr + pos *
( (idx->rows == 1) ? CV_ELEM_SIZE( type ) : idx->step ), type, &sc );
return (int) sc.val[0];
}
}
/* debug functions */
#define CV_DEBUG_SAVE( ptr ) icvSave( ptr, __FILE__, __LINE__ );
void icvSave( const CvArr* ptr, const char* filename, int line );
#endif /* __CVCOMMON_H_ */

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
/*
* _cvhaartraining.h
*
* training of cascade of boosted classifiers based on haar features
*/
#ifndef __CVHAARTRAINING_H_
#define __CVHAARTRAINING_H_
#include "_cvcommon.h"
#include "cvclassifier.h"
#include <cstring>
#include <cstdio>
/* parameters for tree cascade classifier training */
/* max number of clusters */
#define CV_MAX_CLUSTERS 3
/* term criteria for K-Means */
#define CV_TERM_CRITERIA() cvTermCriteria( CV_TERMCRIT_EPS, 1000, 1E-5 )
/* print statistic info */
#define CV_VERBOSE 1
#define CV_STAGE_CART_FILE_NAME "AdaBoostCARTHaarClassifier.txt"
#define CV_HAAR_FEATURE_MAX 3
#define CV_HAAR_FEATURE_DESC_MAX 20
typedef int sum_type;
typedef double sqsum_type;
typedef short idx_type;
#define CV_SUM_MAT_TYPE CV_32SC1
#define CV_SQSUM_MAT_TYPE CV_64FC1
#define CV_IDX_MAT_TYPE CV_16SC1
#define CV_STUMP_TRAIN_PORTION 100
#define CV_THRESHOLD_EPS (0.00001F)
typedef struct CvTHaarFeature
{
char desc[CV_HAAR_FEATURE_DESC_MAX];
int tilted;
struct
{
CvRect r;
float weight;
} rect[CV_HAAR_FEATURE_MAX];
} CvTHaarFeature;
typedef struct CvFastHaarFeature
{
int tilted;
struct
{
int p0, p1, p2, p3;
float weight;
} rect[CV_HAAR_FEATURE_MAX];
} CvFastHaarFeature;
typedef struct CvIntHaarFeatures
{
CvSize winsize;
int count;
CvTHaarFeature* feature;
CvFastHaarFeature* fastfeature;
} CvIntHaarFeatures;
CV_INLINE CvTHaarFeature cvHaarFeature( const char* desc,
int x0, int y0, int w0, int h0, float wt0,
int x1, int y1, int w1, int h1, float wt1,
int x2 CV_DEFAULT( 0 ), int y2 CV_DEFAULT( 0 ),
int w2 CV_DEFAULT( 0 ), int h2 CV_DEFAULT( 0 ),
float wt2 CV_DEFAULT( 0.0F ) );
CV_INLINE CvTHaarFeature cvHaarFeature( const char* desc,
int x0, int y0, int w0, int h0, float wt0,
int x1, int y1, int w1, int h1, float wt1,
int x2, int y2, int w2, int h2, float wt2 )
{
CvTHaarFeature hf;
assert( CV_HAAR_FEATURE_MAX >= 3 );
assert( strlen( desc ) < CV_HAAR_FEATURE_DESC_MAX );
strcpy( &(hf.desc[0]), desc );
hf.tilted = ( hf.desc[0] == 't' );
hf.rect[0].r.x = x0;
hf.rect[0].r.y = y0;
hf.rect[0].r.width = w0;
hf.rect[0].r.height = h0;
hf.rect[0].weight = wt0;
hf.rect[1].r.x = x1;
hf.rect[1].r.y = y1;
hf.rect[1].r.width = w1;
hf.rect[1].r.height = h1;
hf.rect[1].weight = wt1;
hf.rect[2].r.x = x2;
hf.rect[2].r.y = y2;
hf.rect[2].r.width = w2;
hf.rect[2].r.height = h2;
hf.rect[2].weight = wt2;
return hf;
}
/* Prepared for training samples */
typedef struct CvHaarTrainingData
{
CvSize winsize; /* training image size */
int maxnum; /* maximum number of samples */
CvMat sum; /* sum images (each row represents image) */
CvMat tilted; /* tilted sum images (each row represents image) */
CvMat normfactor; /* normalization factor */
CvMat cls; /* classes. 1.0 - object, 0.0 - background */
CvMat weights; /* weights */
CvMat* valcache; /* precalculated feature values (CV_32FC1) */
CvMat* idxcache; /* presorted indices (CV_IDX_MAT_TYPE) */
} CvHaarTrainigData;
/* Passed to callback functions */
typedef struct CvUserdata
{
CvHaarTrainingData* trainingData;
CvIntHaarFeatures* haarFeatures;
} CvUserdata;
CV_INLINE
CvUserdata cvUserdata( CvHaarTrainingData* trainingData,
CvIntHaarFeatures* haarFeatures );
CV_INLINE
CvUserdata cvUserdata( CvHaarTrainingData* trainingData,
CvIntHaarFeatures* haarFeatures )
{
CvUserdata userdata;
userdata.trainingData = trainingData;
userdata.haarFeatures = haarFeatures;
return userdata;
}
#define CV_INT_HAAR_CLASSIFIER_FIELDS() \
float (*eval)( CvIntHaarClassifier*, sum_type*, sum_type*, float ); \
void (*save)( CvIntHaarClassifier*, FILE* file ); \
void (*release)( CvIntHaarClassifier** );
/* internal weak classifier*/
typedef struct CvIntHaarClassifier
{
CV_INT_HAAR_CLASSIFIER_FIELDS()
} CvIntHaarClassifier;
/*
* CART classifier
*/
typedef struct CvCARTHaarClassifier
{
CV_INT_HAAR_CLASSIFIER_FIELDS()
int count;
int* compidx;
CvTHaarFeature* feature;
CvFastHaarFeature* fastfeature;
float* threshold;
int* left;
int* right;
float* val;
} CvCARTHaarClassifier;
/* internal stage classifier */
typedef struct CvStageHaarClassifier
{
CV_INT_HAAR_CLASSIFIER_FIELDS()
int count;
float threshold;
CvIntHaarClassifier** classifier;
} CvStageHaarClassifier;
/* internal cascade classifier */
typedef struct CvCascadeHaarClassifier
{
CV_INT_HAAR_CLASSIFIER_FIELDS()
int count;
CvIntHaarClassifier** classifier;
} CvCascadeHaarClassifier;
/* internal tree cascade classifier node */
typedef struct CvTreeCascadeNode
{
CvStageHaarClassifier* stage;
struct CvTreeCascadeNode* next;
struct CvTreeCascadeNode* child;
struct CvTreeCascadeNode* parent;
struct CvTreeCascadeNode* next_same_level;
struct CvTreeCascadeNode* child_eval;
int idx;
int leaf;
} CvTreeCascadeNode;
/* internal tree cascade classifier */
typedef struct CvTreeCascadeClassifier
{
CV_INT_HAAR_CLASSIFIER_FIELDS()
CvTreeCascadeNode* root; /* root of the tree */
CvTreeCascadeNode* root_eval; /* root node for the filtering */
int next_idx;
} CvTreeCascadeClassifier;
CV_INLINE float cvEvalFastHaarFeature( const CvFastHaarFeature* feature,
const sum_type* sum, const sum_type* tilted )
{
const sum_type* img = feature->tilted ? tilted : sum;
float ret = feature->rect[0].weight*
(img[feature->rect[0].p0] - img[feature->rect[0].p1] -
img[feature->rect[0].p2] + img[feature->rect[0].p3]) +
feature->rect[1].weight*
(img[feature->rect[1].p0] - img[feature->rect[1].p1] -
img[feature->rect[1].p2] + img[feature->rect[1].p3]);
if( feature->rect[2].weight != 0.0f )
ret += feature->rect[2].weight *
( img[feature->rect[2].p0] - img[feature->rect[2].p1] -
img[feature->rect[2].p2] + img[feature->rect[2].p3] );
return ret;
}
typedef struct CvSampleDistortionData
{
IplImage* src;
IplImage* erode;
IplImage* dilate;
IplImage* mask;
IplImage* img;
IplImage* maskimg;
int dx;
int dy;
int bgcolor;
} CvSampleDistortionData;
/*
* icvConvertToFastHaarFeature
*
* Convert to fast representation of haar features
*
* haarFeature - input array
* fastHaarFeature - output array
* size - size of arrays
* step - row step for the integral image
*/
void icvConvertToFastHaarFeature( CvTHaarFeature* haarFeature,
CvFastHaarFeature* fastHaarFeature,
int size, int step );
void icvWriteVecHeader( FILE* file, int count, int width, int height );
void icvWriteVecSample( FILE* file, CvArr* sample );
void icvPlaceDistortedSample( CvArr* background,
int inverse, int maxintensitydev,
double maxxangle, double maxyangle, double maxzangle,
int inscribe, double maxshiftf, double maxscalef,
CvSampleDistortionData* data );
void icvEndSampleDistortion( CvSampleDistortionData* data );
int icvStartSampleDistortion( const char* imgfilename, int bgcolor, int bgthreshold,
CvSampleDistortionData* data );
typedef int (*CvGetHaarTrainingDataCallback)( CvMat* img, void* userdata );
typedef struct CvVecFile
{
FILE* input;
int count;
int vecsize;
int last;
short* vector;
} CvVecFile;
int icvGetHaarTraininDataFromVecCallback( CvMat* img, void* userdata );
/*
* icvGetHaarTrainingDataFromVec
*
* Fill <data> with samples from .vec file, passed <cascade>
int icvGetHaarTrainingDataFromVec( CvHaarTrainingData* data, int first, int count,
CvIntHaarClassifier* cascade,
const char* filename,
int* consumed );
*/
CvIntHaarClassifier* icvCreateCARTHaarClassifier( int count );
void icvReleaseHaarClassifier( CvIntHaarClassifier** classifier );
void icvInitCARTHaarClassifier( CvCARTHaarClassifier* carthaar, CvCARTClassifier* cart,
CvIntHaarFeatures* intHaarFeatures );
float icvEvalCARTHaarClassifier( CvIntHaarClassifier* classifier,
sum_type* sum, sum_type* tilted, float normfactor );
CvIntHaarClassifier* icvCreateStageHaarClassifier( int count, float threshold );
void icvReleaseStageHaarClassifier( CvIntHaarClassifier** classifier );
float icvEvalStageHaarClassifier( CvIntHaarClassifier* classifier,
sum_type* sum, sum_type* tilted, float normfactor );
CvIntHaarClassifier* icvCreateCascadeHaarClassifier( int count );
void icvReleaseCascadeHaarClassifier( CvIntHaarClassifier** classifier );
float icvEvalCascadeHaarClassifier( CvIntHaarClassifier* classifier,
sum_type* sum, sum_type* tilted, float normfactor );
void icvSaveHaarFeature( CvTHaarFeature* feature, FILE* file );
void icvLoadHaarFeature( CvTHaarFeature* feature, FILE* file );
void icvSaveCARTHaarClassifier( CvIntHaarClassifier* classifier, FILE* file );
CvIntHaarClassifier* icvLoadCARTHaarClassifier( FILE* file, int step );
void icvSaveStageHaarClassifier( CvIntHaarClassifier* classifier, FILE* file );
CvIntHaarClassifier* icvLoadCARTStageHaarClassifier( const char* filename, int step );
/* tree cascade classifier */
float icvEvalTreeCascadeClassifier( CvIntHaarClassifier* classifier,
sum_type* sum, sum_type* tilted, float normfactor );
void icvSetLeafNode( CvTreeCascadeClassifier* tree, CvTreeCascadeNode* leaf );
float icvEvalTreeCascadeClassifierFilter( CvIntHaarClassifier* classifier, sum_type* sum,
sum_type* tilted, float normfactor );
CvTreeCascadeNode* icvCreateTreeCascadeNode();
void icvReleaseTreeCascadeNodes( CvTreeCascadeNode** node );
void icvReleaseTreeCascadeClassifier( CvIntHaarClassifier** classifier );
/* Prints out current tree structure to <stdout> */
void icvPrintTreeCascade( CvTreeCascadeNode* root );
/* Loads tree cascade classifier */
CvIntHaarClassifier* icvLoadTreeCascadeClassifier( const char* filename, int step,
int* splits );
/* Finds leaves belonging to maximal level and connects them via leaf->next_same_level */
CvTreeCascadeNode* icvFindDeepestLeaves( CvTreeCascadeClassifier* tree );
#endif /* __CVHAARTRAINING_H_ */

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
/*
* File cvclassifier.h
*
* Classifier types
*/
#ifndef _CVCLASSIFIER_H_
#define _CVCLASSIFIER_H_
#include <cmath>
#include "cxcore.h"
#define CV_BOOST_API
/* Convert matrix to vector */
#define CV_MAT2VEC( mat, vdata, vstep, num ) \
assert( (mat).rows == 1 || (mat).cols == 1 ); \
(vdata) = ((mat).data.ptr); \
if( (mat).rows == 1 ) \
{ \
(vstep) = CV_ELEM_SIZE( (mat).type ); \
(num) = (mat).cols; \
} \
else \
{ \
(vstep) = (mat).step; \
(num) = (mat).rows; \
}
/* Set up <sample> matrix header to be <num> sample of <trainData> samples matrix */
#define CV_GET_SAMPLE( trainData, tdflags, num, sample ) \
if( CV_IS_ROW_SAMPLE( tdflags ) ) \
{ \
cvInitMatHeader( &(sample), 1, (trainData).cols, \
CV_MAT_TYPE( (trainData).type ), \
((trainData).data.ptr + (num) * (trainData).step), \
(trainData).step ); \
} \
else \
{ \
cvInitMatHeader( &(sample), (trainData).rows, 1, \
CV_MAT_TYPE( (trainData).type ), \
((trainData).data.ptr + (num) * CV_ELEM_SIZE( (trainData).type )), \
(trainData).step ); \
}
#define CV_GET_SAMPLE_STEP( trainData, tdflags, sstep ) \
(sstep) = ( ( CV_IS_ROW_SAMPLE( tdflags ) ) \
? (trainData).step : CV_ELEM_SIZE( (trainData).type ) );
#define CV_LOGRATIO_THRESHOLD 0.00001F
/* log( val / (1 - val ) ) */
CV_INLINE float cvLogRatio( float val );
CV_INLINE float cvLogRatio( float val )
{
float tval;
tval = MAX(CV_LOGRATIO_THRESHOLD, MIN( 1.0F - CV_LOGRATIO_THRESHOLD, (val) ));
return logf( tval / (1.0F - tval) );
}
/* flags values for classifier consturctor flags parameter */
/* each trainData matrix column is a sample */
#define CV_COL_SAMPLE 0
/* each trainData matrix row is a sample */
#define CV_ROW_SAMPLE 1
#ifndef CV_IS_ROW_SAMPLE
# define CV_IS_ROW_SAMPLE( flags ) ( ( flags ) & CV_ROW_SAMPLE )
#endif
/* Classifier supports tune function */
#define CV_TUNABLE (1 << 1)
#define CV_IS_TUNABLE( flags ) ( (flags) & CV_TUNABLE )
/* classifier fields common to all classifiers */
#define CV_CLASSIFIER_FIELDS() \
int flags; \
float(*eval)( struct CvClassifier*, CvMat* ); \
void (*tune)( struct CvClassifier*, CvMat*, int flags, CvMat*, CvMat*, CvMat*, \
CvMat*, CvMat* ); \
int (*save)( struct CvClassifier*, const char* file_name ); \
void (*release)( struct CvClassifier** );
typedef struct CvClassifier
{
CV_CLASSIFIER_FIELDS()
} CvClassifier;
#define CV_CLASSIFIER_TRAIN_PARAM_FIELDS()
typedef struct CvClassifierTrainParams
{
CV_CLASSIFIER_TRAIN_PARAM_FIELDS()
} CvClassifierTrainParams;
/*
Common classifier constructor:
CvClassifier* cvCreateMyClassifier( CvMat* trainData,
int flags,
CvMat* trainClasses,
CvMat* typeMask,
CvMat* missedMeasurementsMask CV_DEFAULT(0),
CvCompIdx* compIdx CV_DEFAULT(0),
CvMat* sampleIdx CV_DEFAULT(0),
CvMat* weights CV_DEFAULT(0),
CvClassifierTrainParams* trainParams CV_DEFAULT(0)
)
*/
typedef CvClassifier* (*CvClassifierConstructor)( CvMat*, int, CvMat*, CvMat*, CvMat*,
CvMat*, CvMat*, CvMat*,
CvClassifierTrainParams* );
typedef enum CvStumpType
{
CV_CLASSIFICATION = 0,
CV_CLASSIFICATION_CLASS = 1,
CV_REGRESSION = 2
} CvStumpType;
typedef enum CvStumpError
{
CV_MISCLASSIFICATION = 0,
CV_GINI = 1,
CV_ENTROPY = 2,
CV_SQUARE = 3
} CvStumpError;
typedef struct CvStumpTrainParams
{
CV_CLASSIFIER_TRAIN_PARAM_FIELDS()
CvStumpType type;
CvStumpError error;
} CvStumpTrainParams;
typedef struct CvMTStumpTrainParams
{
CV_CLASSIFIER_TRAIN_PARAM_FIELDS()
CvStumpType type;
CvStumpError error;
int portion; /* number of components calculated in each thread */
int numcomp; /* total number of components */
/* callback which fills <mat> with components [first, first+num[ */
void (*getTrainData)( CvMat* mat, CvMat* sampleIdx, CvMat* compIdx,
int first, int num, void* userdata );
CvMat* sortedIdx; /* presorted samples indices */
void* userdata; /* passed to callback */
} CvMTStumpTrainParams;
typedef struct CvStumpClassifier
{
CV_CLASSIFIER_FIELDS()
int compidx;
float lerror; /* impurity of the right node */
float rerror; /* impurity of the left node */
float threshold;
float left;
float right;
} CvStumpClassifier;
typedef struct CvCARTTrainParams
{
CV_CLASSIFIER_TRAIN_PARAM_FIELDS()
/* desired number of internal nodes */
int count;
CvClassifierTrainParams* stumpTrainParams;
CvClassifierConstructor stumpConstructor;
/*
* Split sample indices <idx>
* on the "left" indices <left> and "right" indices <right>
* according to samples components <compidx> values and <threshold>.
*
* NOTE: Matrices <left> and <right> must be allocated using cvCreateMat function
* since they are freed using cvReleaseMat function
*
* If it is NULL then the default implementation which evaluates training
* samples from <trainData> passed to classifier constructor is used
*/
void (*splitIdx)( int compidx, float threshold,
CvMat* idx, CvMat** left, CvMat** right,
void* userdata );
void* userdata;
} CvCARTTrainParams;
typedef struct CvCARTClassifier
{
CV_CLASSIFIER_FIELDS()
/* number of internal nodes */
int count;
/* internal nodes (each array of <count> elements) */
int* compidx;
float* threshold;
int* left;
int* right;
/* leaves (array of <count>+1 elements) */
float* val;
} CvCARTClassifier;
CV_BOOST_API
void cvGetSortedIndices( CvMat* val, CvMat* idx, int sortcols CV_DEFAULT( 0 ) );
CV_BOOST_API
void cvReleaseStumpClassifier( CvClassifier** classifier );
CV_BOOST_API
float cvEvalStumpClassifier( CvClassifier* classifier, CvMat* sample );
CV_BOOST_API
CvClassifier* cvCreateStumpClassifier( CvMat* trainData,
int flags,
CvMat* trainClasses,
CvMat* typeMask,
CvMat* missedMeasurementsMask CV_DEFAULT(0),
CvMat* compIdx CV_DEFAULT(0),
CvMat* sampleIdx CV_DEFAULT(0),
CvMat* weights CV_DEFAULT(0),
CvClassifierTrainParams* trainParams CV_DEFAULT(0) );
/*
* cvCreateMTStumpClassifier
*
* Multithreaded stump classifier constructor
* Includes huge train data support through callback function
*/
CV_BOOST_API
CvClassifier* cvCreateMTStumpClassifier( CvMat* trainData,
int flags,
CvMat* trainClasses,
CvMat* typeMask,
CvMat* missedMeasurementsMask,
CvMat* compIdx,
CvMat* sampleIdx,
CvMat* weights,
CvClassifierTrainParams* trainParams );
/*
* cvCreateCARTClassifier
*
* CART classifier constructor
*/
CV_BOOST_API
CvClassifier* cvCreateCARTClassifier( CvMat* trainData,
int flags,
CvMat* trainClasses,
CvMat* typeMask,
CvMat* missedMeasurementsMask,
CvMat* compIdx,
CvMat* sampleIdx,
CvMat* weights,
CvClassifierTrainParams* trainParams );
CV_BOOST_API
void cvReleaseCARTClassifier( CvClassifier** classifier );
CV_BOOST_API
float cvEvalCARTClassifier( CvClassifier* classifier, CvMat* sample );
/****************************************************************************************\
* Boosting *
\****************************************************************************************/
/*
* CvBoostType
*
* The CvBoostType enumeration specifies the boosting type.
*
* Remarks
* Four different boosting variants for 2 class classification problems are supported:
* Discrete AdaBoost, Real AdaBoost, LogitBoost and Gentle AdaBoost.
* The L2 (2 class classification problems) and LK (K class classification problems)
* algorithms are close to LogitBoost but more numerically stable than last one.
* For regression three different loss functions are supported:
* Least square, least absolute deviation and huber loss.
*/
typedef enum CvBoostType
{
CV_DABCLASS = 0, /* 2 class Discrete AdaBoost */
CV_RABCLASS = 1, /* 2 class Real AdaBoost */
CV_LBCLASS = 2, /* 2 class LogitBoost */
CV_GABCLASS = 3, /* 2 class Gentle AdaBoost */
CV_L2CLASS = 4, /* classification (2 class problem) */
CV_LKCLASS = 5, /* classification (K class problem) */
CV_LSREG = 6, /* least squares regression */
CV_LADREG = 7, /* least absolute deviation regression */
CV_MREG = 8, /* M-regression (Huber loss) */
} CvBoostType;
/****************************************************************************************\
* Iterative training functions *
\****************************************************************************************/
/*
* CvBoostTrainer
*
* The CvBoostTrainer structure represents internal boosting trainer.
*/
typedef struct CvBoostTrainer CvBoostTrainer;
/*
* cvBoostStartTraining
*
* The cvBoostStartTraining function starts training process and calculates
* response values and weights for the first weak classifier training.
*
* Parameters
* trainClasses
* Vector of classes of training samples classes. Each element must be 0 or 1 and
* of type CV_32FC1.
* weakTrainVals
* Vector of response values for the first trained weak classifier.
* Must be of type CV_32FC1.
* weights
* Weight vector of training samples for the first trained weak classifier.
* Must be of type CV_32FC1.
* type
* Boosting type. CV_DABCLASS, CV_RABCLASS, CV_LBCLASS, CV_GABCLASS
* types are supported.
*
* Return Values
* The return value is a pointer to internal trainer structure which is used
* to perform next training iterations.
*
* Remarks
* weakTrainVals and weights must be allocated before calling the function
* and of the same size as trainingClasses. Usually weights should be initialized
* with 1.0 value.
* The function calculates response values and weights for the first weak
* classifier training and stores them into weakTrainVals and weights
* respectively.
* Note, the training of the weak classifier using weakTrainVals, weight,
* trainingData is outside of this function.
*/
CV_BOOST_API
CvBoostTrainer* cvBoostStartTraining( CvMat* trainClasses,
CvMat* weakTrainVals,
CvMat* weights,
CvMat* sampleIdx,
CvBoostType type );
/*
* cvBoostNextWeakClassifier
*
* The cvBoostNextWeakClassifier function performs next training
* iteration and caluclates response values and weights for the next weak
* classifier training.
*
* Parameters
* weakEvalVals
* Vector of values obtained by evaluation of each sample with
* the last trained weak classifier (iteration i). Must be of CV_32FC1 type.
* trainClasses
* Vector of classes of training samples. Each element must be 0 or 1,
* and of type CV_32FC1.
* weakTrainVals
* Vector of response values for the next weak classifier training
* (iteration i+1). Must be of type CV_32FC1.
* weights
* Weight vector of training samples for the next weak classifier training
* (iteration i+1). Must be of type CV_32FC1.
* trainer
* A pointer to internal trainer returned by the cvBoostStartTraining
* function call.
*
* Return Values
* The return value is the coefficient for the last trained weak classifier.
*
* Remarks
* weakTrainVals and weights must be exactly the same vectors as used in
* the cvBoostStartTraining function call and should not be modified.
* The function calculates response values and weights for the next weak
* classifier training and stores them into weakTrainVals and weights
* respectively.
* Note, the training of the weak classifier of iteration i+1 using
* weakTrainVals, weight, trainingData is outside of this function.
*/
CV_BOOST_API
float cvBoostNextWeakClassifier( CvMat* weakEvalVals,
CvMat* trainClasses,
CvMat* weakTrainVals,
CvMat* weights,
CvBoostTrainer* trainer );
/*
* cvBoostEndTraining
*
* The cvBoostEndTraining function finishes training process and releases
* internally allocated memory.
*
* Parameters
* trainer
* A pointer to a pointer to internal trainer returned by the cvBoostStartTraining
* function call.
*/
CV_BOOST_API
void cvBoostEndTraining( CvBoostTrainer** trainer );
/****************************************************************************************\
* Boosted tree models *
\****************************************************************************************/
/*
* CvBtClassifier
*
* The CvBtClassifier structure represents boosted tree model.
*
* Members
* flags
* Flags. If CV_IS_TUNABLE( flags ) != 0 then the model supports tuning.
* eval
* Evaluation function. Returns sample predicted class (0, 1, etc.)
* for classification or predicted value for regression.
* tune
* Tune function. If the model supports tuning then tune call performs
* one more boosting iteration if passed to the function flags parameter
* is CV_TUNABLE otherwise releases internally allocated for tuning memory
* and makes the model untunable.
* NOTE: Since tuning uses the pointers to parameters,
* passed to the cvCreateBtClassifier function, they should not be modified
* or released between tune calls.
* save
* This function stores the model into given file.
* release
* This function releases the model.
* type
* Boosted tree model type.
* numclasses
* Number of classes for CV_LKCLASS type or 1 for all other types.
* numiter
* Number of iterations. Number of weak classifiers is equal to number
* of iterations for all types except CV_LKCLASS. For CV_LKCLASS type
* number of weak classifiers is (numiter * numclasses).
* numfeatures
* Number of features in sample.
* trees
* Stores weak classifiers when the model does not support tuning.
* seq
* Stores weak classifiers when the model supports tuning.
* trainer
* Pointer to internal tuning parameters if the model supports tuning.
*/
typedef struct CvBtClassifier
{
CV_CLASSIFIER_FIELDS()
CvBoostType type;
int numclasses;
int numiter;
int numfeatures;
union
{
CvCARTClassifier** trees;
CvSeq* seq;
};
void* trainer;
} CvBtClassifier;
/*
* CvBtClassifierTrainParams
*
* The CvBtClassifierTrainParams structure stores training parameters for
* boosted tree model.
*
* Members
* type
* Boosted tree model type.
* numiter
* Desired number of iterations.
* param
* Parameter Model Type Parameter Meaning
* param[0] Any Shrinkage factor
* param[1] CV_MREG alpha. (1-alpha) determines "break-down" point of
* the training procedure, i.e. the fraction of samples
* that can be arbitrary modified without serious
* degrading the quality of the result.
* CV_DABCLASS, Weight trimming factor.
* CV_RABCLASS,
* CV_LBCLASS,
* CV_GABCLASS,
* CV_L2CLASS,
* CV_LKCLASS
* numsplits
* Desired number of splits in each tree.
*/
typedef struct CvBtClassifierTrainParams
{
CV_CLASSIFIER_TRAIN_PARAM_FIELDS()
CvBoostType type;
int numiter;
float param[2];
int numsplits;
} CvBtClassifierTrainParams;
/*
* cvCreateBtClassifier
*
* The cvCreateBtClassifier function creates boosted tree model.
*
* Parameters
* trainData
* Matrix of feature values. Must have CV_32FC1 type.
* flags
* Determines how samples are stored in trainData.
* One of CV_ROW_SAMPLE or CV_COL_SAMPLE.
* Optionally may be combined with CV_TUNABLE to make tunable model.
* trainClasses
* Vector of responses for regression or classes (0, 1, 2, etc.) for classification.
* typeMask,
* missedMeasurementsMask,
* compIdx
* Not supported. Must be NULL.
* sampleIdx
* Indices of samples used in training. If NULL then all samples are used.
* For CV_DABCLASS, CV_RABCLASS, CV_LBCLASS and CV_GABCLASS must be NULL.
* weights
* Not supported. Must be NULL.
* trainParams
* A pointer to CvBtClassifierTrainParams structure. Training parameters.
* See CvBtClassifierTrainParams description for details.
*
* Return Values
* The return value is a pointer to created boosted tree model of type CvBtClassifier.
*
* Remarks
* The function performs trainParams->numiter training iterations.
* If CV_TUNABLE flag is specified then created model supports tuning.
* In this case additional training iterations may be performed by
* tune function call.
*/
CV_BOOST_API
CvClassifier* cvCreateBtClassifier( CvMat* trainData,
int flags,
CvMat* trainClasses,
CvMat* typeMask,
CvMat* missedMeasurementsMask,
CvMat* compIdx,
CvMat* sampleIdx,
CvMat* weights,
CvClassifierTrainParams* trainParams );
/*
* cvCreateBtClassifierFromFile
*
* The cvCreateBtClassifierFromFile function restores previously saved
* boosted tree model from file.
*
* Parameters
* filename
* The name of the file with boosted tree model.
*
* Remarks
* The restored model does not support tuning.
*/
CV_BOOST_API
CvClassifier* cvCreateBtClassifierFromFile( const char* filename );
/****************************************************************************************\
* Utility functions *
\****************************************************************************************/
/*
* cvTrimWeights
*
* The cvTrimWeights function performs weight trimming.
*
* Parameters
* weights
* Weights vector.
* idx
* Indices vector of weights that should be considered.
* If it is NULL then all weights are used.
* factor
* Weight trimming factor. Must be in [0, 1] range.
*
* Return Values
* The return value is a vector of indices. If all samples should be used then
* it is equal to idx. In other case the cvReleaseMat function should be called
* to release it.
*
* Remarks
*/
CV_BOOST_API
CvMat* cvTrimWeights( CvMat* weights, CvMat* idx, float factor );
/*
* cvReadTrainData
*
* The cvReadTrainData function reads feature values and responses from file.
*
* Parameters
* filename
* The name of the file to be read.
* flags
* One of CV_ROW_SAMPLE or CV_COL_SAMPLE. Determines how feature values
* will be stored.
* trainData
* A pointer to a pointer to created matrix with feature values.
* cvReleaseMat function should be used to destroy created matrix.
* trainClasses
* A pointer to a pointer to created matrix with response values.
* cvReleaseMat function should be used to destroy created matrix.
*
* Remarks
* File format:
* ============================================
* m n
* value_1_1 value_1_2 ... value_1_n response_1
* value_2_1 value_2_2 ... value_2_n response_2
* ...
* value_m_1 value_m_2 ... value_m_n response_m
* ============================================
* m
* Number of samples
* n
* Number of features in each sample
* value_i_j
* Value of j-th feature of i-th sample
* response_i
* Response value of i-th sample
* For classification problems responses represent classes (0, 1, etc.)
* All values and classes are integer or real numbers.
*/
CV_BOOST_API
void cvReadTrainData( const char* filename,
int flags,
CvMat** trainData,
CvMat** trainClasses );
/*
* cvWriteTrainData
*
* The cvWriteTrainData function stores feature values and responses into file.
*
* Parameters
* filename
* The name of the file.
* flags
* One of CV_ROW_SAMPLE or CV_COL_SAMPLE. Determines how feature values
* are stored.
* trainData
* Feature values matrix.
* trainClasses
* Response values vector.
* sampleIdx
* Vector of idicies of the samples that should be stored. If it is NULL
* then all samples will be stored.
*
* Remarks
* See the cvReadTrainData function for file format description.
*/
CV_BOOST_API
void cvWriteTrainData( const char* filename,
int flags,
CvMat* trainData,
CvMat* trainClasses,
CvMat* sampleIdx );
/*
* cvRandShuffle
*
* The cvRandShuffle function perfroms random shuffling of given vector.
*
* Parameters
* vector
* Vector that should be shuffled.
* Must have CV_8UC1, CV_16SC1, CV_32SC1 or CV_32FC1 type.
*/
CV_BOOST_API
void cvRandShuffleVec( CvMat* vector );
#endif /* _CVCLASSIFIER_H_ */

125
apps/haartraining/cvcommon.cpp
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@ -1,125 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "_cvcommon.h"
#include <cstring>
#include <ctime>
#include <sys/stat.h>
#include <sys/types.h>
#ifdef _WIN32
#include <direct.h>
#endif /* _WIN32 */
int icvMkDir( const char* filename )
{
char path[PATH_MAX];
char* p;
int pos;
#ifdef _WIN32
struct _stat st;
#else /* _WIN32 */
struct stat st;
mode_t mode;
mode = 0755;
#endif /* _WIN32 */
strcpy( path, filename );
p = path;
for( ; ; )
{
pos = (int)strcspn( p, "/\\" );
if( pos == (int) strlen( p ) ) break;
if( pos != 0 )
{
p[pos] = '\0';
#ifdef _WIN32
if( p[pos-1] != ':' )
{
if( _stat( path, &st ) != 0 )
{
if( _mkdir( path ) != 0 ) return 0;
}
}
#else /* _WIN32 */
if( stat( path, &st ) != 0 )
{
if( mkdir( path, mode ) != 0 ) return 0;
}
#endif /* _WIN32 */
}
p[pos] = '/';
p += pos + 1;
}
return 1;
}
#if 0
/* debug functions */
void icvSave( const CvArr* ptr, const char* filename, int line )
{
CvFileStorage* fs;
char buf[PATH_MAX];
const char* name;
name = strrchr( filename, '\\' );
if( !name ) name = strrchr( filename, '/' );
if( !name ) name = filename;
else name++; /* skip '/' or '\\' */
sprintf( buf, "%s-%d-%d", name, line, time( NULL ) );
fs = cvOpenFileStorage( buf, NULL, CV_STORAGE_WRITE_TEXT );
if( !fs ) return;
cvWrite( fs, "debug", ptr );
cvReleaseFileStorage( &fs );
}
#endif // #if 0
/* End of file. */

835
apps/haartraining/cvhaarclassifier.cpp
View File

@ -1,835 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
/*
* cvhaarclassifier.cpp
*
* haar classifiers (stump, CART, stage, cascade)
*/
#include "_cvhaartraining.h"
CvIntHaarClassifier* icvCreateCARTHaarClassifier( int count )
{
CvCARTHaarClassifier* cart;
size_t datasize;
datasize = sizeof( *cart ) +
( sizeof( int ) +
sizeof( CvTHaarFeature ) + sizeof( CvFastHaarFeature ) +
sizeof( float ) + sizeof( int ) + sizeof( int ) ) * count +
sizeof( float ) * (count + 1);
cart = (CvCARTHaarClassifier*) cvAlloc( datasize );
memset( cart, 0, datasize );
cart->feature = (CvTHaarFeature*) (cart + 1);
cart->fastfeature = (CvFastHaarFeature*) (cart->feature + count);
cart->threshold = (float*) (cart->fastfeature + count);
cart->left = (int*) (cart->threshold + count);
cart->right = (int*) (cart->left + count);
cart->val = (float*) (cart->right + count);
cart->compidx = (int*) (cart->val + count + 1 );
cart->count = count;
cart->eval = icvEvalCARTHaarClassifier;
cart->save = icvSaveCARTHaarClassifier;
cart->release = icvReleaseHaarClassifier;
return (CvIntHaarClassifier*) cart;
}
void icvReleaseHaarClassifier( CvIntHaarClassifier** classifier )
{
cvFree( classifier );
*classifier = NULL;
}
void icvInitCARTHaarClassifier( CvCARTHaarClassifier* carthaar, CvCARTClassifier* cart,
CvIntHaarFeatures* intHaarFeatures )
{
int i;
for( i = 0; i < cart->count; i++ )
{
carthaar->feature[i] = intHaarFeatures->feature[cart->compidx[i]];
carthaar->fastfeature[i] = intHaarFeatures->fastfeature[cart->compidx[i]];
carthaar->threshold[i] = cart->threshold[i];
carthaar->left[i] = cart->left[i];
carthaar->right[i] = cart->right[i];
carthaar->val[i] = cart->val[i];
carthaar->compidx[i] = cart->compidx[i];
}
carthaar->count = cart->count;
carthaar->val[cart->count] = cart->val[cart->count];
}
float icvEvalCARTHaarClassifier( CvIntHaarClassifier* classifier,
sum_type* sum, sum_type* tilted, float normfactor )
{
int idx = 0;
do
{
if( cvEvalFastHaarFeature(
((CvCARTHaarClassifier*) classifier)->fastfeature + idx, sum, tilted )
< (((CvCARTHaarClassifier*) classifier)->threshold[idx] * normfactor) )
{
idx = ((CvCARTHaarClassifier*) classifier)->left[idx];
}
else
{
idx = ((CvCARTHaarClassifier*) classifier)->right[idx];
}
} while( idx > 0 );
return ((CvCARTHaarClassifier*) classifier)->val[-idx];
}
CvIntHaarClassifier* icvCreateStageHaarClassifier( int count, float threshold )
{
CvStageHaarClassifier* stage;
size_t datasize;
datasize = sizeof( *stage ) + sizeof( CvIntHaarClassifier* ) * count;
stage = (CvStageHaarClassifier*) cvAlloc( datasize );
memset( stage, 0, datasize );
stage->count = count;
stage->threshold = threshold;
stage->classifier = (CvIntHaarClassifier**) (stage + 1);
stage->eval = icvEvalStageHaarClassifier;
stage->save = icvSaveStageHaarClassifier;
stage->release = icvReleaseStageHaarClassifier;
return (CvIntHaarClassifier*) stage;
}
void icvReleaseStageHaarClassifier( CvIntHaarClassifier** classifier )
{
int i;
for( i = 0; i < ((CvStageHaarClassifier*) *classifier)->count; i++ )
{
if( ((CvStageHaarClassifier*) *classifier)->classifier[i] != NULL )
{
((CvStageHaarClassifier*) *classifier)->classifier[i]->release(
&(((CvStageHaarClassifier*) *classifier)->classifier[i]) );
}
}
cvFree( classifier );
*classifier = NULL;
}
float icvEvalStageHaarClassifier( CvIntHaarClassifier* classifier,
sum_type* sum, sum_type* tilted, float normfactor )
{
int i;
float stage_sum;
stage_sum = 0.0F;
for( i = 0; i < ((CvStageHaarClassifier*) classifier)->count; i++ )
{
stage_sum +=
((CvStageHaarClassifier*) classifier)->classifier[i]->eval(
((CvStageHaarClassifier*) classifier)->classifier[i],
sum, tilted, normfactor );
}
return stage_sum;
}
CvIntHaarClassifier* icvCreateCascadeHaarClassifier( int count )
{
CvCascadeHaarClassifier* ptr;
size_t datasize;
datasize = sizeof( *ptr ) + sizeof( CvIntHaarClassifier* ) * count;
ptr = (CvCascadeHaarClassifier*) cvAlloc( datasize );
memset( ptr, 0, datasize );
ptr->count = count;
ptr->classifier = (CvIntHaarClassifier**) (ptr + 1);
ptr->eval = icvEvalCascadeHaarClassifier;
ptr->save = NULL;
ptr->release = icvReleaseCascadeHaarClassifier;
return (CvIntHaarClassifier*) ptr;
}
void icvReleaseCascadeHaarClassifier( CvIntHaarClassifier** classifier )
{
int i;
for( i = 0; i < ((CvCascadeHaarClassifier*) *classifier)->count; i++ )
{
if( ((CvCascadeHaarClassifier*) *classifier)->classifier[i] != NULL )
{
((CvCascadeHaarClassifier*) *classifier)->classifier[i]->release(
&(((CvCascadeHaarClassifier*) *classifier)->classifier[i]) );
}
}
cvFree( classifier );
*classifier = NULL;
}
float icvEvalCascadeHaarClassifier( CvIntHaarClassifier* classifier,
sum_type* sum, sum_type* tilted, float normfactor )
{
int i;
for( i = 0; i < ((CvCascadeHaarClassifier*) classifier)->count; i++ )
{
if( ((CvCascadeHaarClassifier*) classifier)->classifier[i]->eval(
((CvCascadeHaarClassifier*) classifier)->classifier[i],
sum, tilted, normfactor )
< ( ((CvStageHaarClassifier*)
((CvCascadeHaarClassifier*) classifier)->classifier[i])->threshold
- CV_THRESHOLD_EPS) )
{
return 0.0;
}
}
return 1.0;
}
void icvSaveHaarFeature( CvTHaarFeature* feature, FILE* file )
{
fprintf( file, "%d\n", ( ( feature->rect[2].weight == 0.0F ) ? 2 : 3) );
fprintf( file, "%d %d %d %d %d %d\n",
feature->rect[0].r.x,
feature->rect[0].r.y,
feature->rect[0].r.width,
feature->rect[0].r.height,
0,
(int) (feature->rect[0].weight) );
fprintf( file, "%d %d %d %d %d %d\n",
feature->rect[1].r.x,
feature->rect[1].r.y,
feature->rect[1].r.width,
feature->rect[1].r.height,
0,
(int) (feature->rect[1].weight) );
if( feature->rect[2].weight != 0.0F )
{
fprintf( file, "%d %d %d %d %d %d\n",
feature->rect[2].r.x,
feature->rect[2].r.y,
feature->rect[2].r.width,
feature->rect[2].r.height,
0,
(int) (feature->rect[2].weight) );
}
fprintf( file, "%s\n", &(feature->desc[0]) );
}
void icvLoadHaarFeature( CvTHaarFeature* feature, FILE* file )
{
int nrect;
int j;
int tmp;
int weight;
nrect = 0;
int values_read = fscanf( file, "%d", &nrect );
CV_Assert(values_read == 1);
assert( nrect <= CV_HAAR_FEATURE_MAX );
for( j = 0; j < nrect; j++ )
{
values_read = fscanf( file, "%d %d %d %d %d %d",
&(feature->rect[j].r.x),
&(feature->rect[j].r.y),
&(feature->rect[j].r.width),
&(feature->rect[j].r.height),
&tmp, &weight );
CV_Assert(values_read == 6);
feature->rect[j].weight = (float) weight;
}
for( j = nrect; j < CV_HAAR_FEATURE_MAX; j++ )
{
feature->rect[j].r.x = 0;
feature->rect[j].r.y = 0;
feature->rect[j].r.width = 0;
feature->rect[j].r.height = 0;
feature->rect[j].weight = 0.0f;
}
values_read = fscanf( file, "%s", &(feature->desc[0]) );
CV_Assert(values_read == 1);
feature->tilted = ( feature->desc[0] == 't' );
}
void icvSaveCARTHaarClassifier( CvIntHaarClassifier* classifier, FILE* file )
{
int i;
int count;
count = ((CvCARTHaarClassifier*) classifier)->count;
fprintf( file, "%d\n", count );
for( i = 0; i < count; i++ )
{
icvSaveHaarFeature( &(((CvCARTHaarClassifier*) classifier)->feature[i]), file );
fprintf( file, "%e %d %d\n",
((CvCARTHaarClassifier*) classifier)->threshold[i],
((CvCARTHaarClassifier*) classifier)->left[i],
((CvCARTHaarClassifier*) classifier)->right[i] );
}
for( i = 0; i <= count; i++ )
{
fprintf( file, "%e ", ((CvCARTHaarClassifier*) classifier)->val[i] );
}
fprintf( file, "\n" );
}
CvIntHaarClassifier* icvLoadCARTHaarClassifier( FILE* file, int step )
{
CvCARTHaarClassifier* ptr;
int i;
int count;
ptr = NULL;
int values_read = fscanf( file, "%d", &count );
CV_Assert(values_read == 1);
if( count > 0 )
{
ptr = (CvCARTHaarClassifier*) icvCreateCARTHaarClassifier( count );
for( i = 0; i < count; i++ )
{
icvLoadHaarFeature( &(ptr->feature[i]), file );
values_read = fscanf( file, "%f %d %d", &(ptr->threshold[i]), &(ptr->left[i]),
&(ptr->right[i]) );
CV_Assert(values_read == 3);
}
for( i = 0; i <= count; i++ )
{
values_read = fscanf( file, "%f", &(ptr->val[i]) );
CV_Assert(values_read == 1);
}
icvConvertToFastHaarFeature( ptr->feature, ptr->fastfeature, ptr->count, step );
}
return (CvIntHaarClassifier*) ptr;
}
void icvSaveStageHaarClassifier( CvIntHaarClassifier* classifier, FILE* file )
{
int count;
int i;
float threshold;
count = ((CvStageHaarClassifier*) classifier)->count;
fprintf( file, "%d\n", count );
for( i = 0; i < count; i++ )
{
((CvStageHaarClassifier*) classifier)->classifier[i]->save(
((CvStageHaarClassifier*) classifier)->classifier[i], file );
}
threshold = ((CvStageHaarClassifier*) classifier)->threshold;
/* to be compatible with the previous implementation */
/* threshold = 2.0F * ((CvStageHaarClassifier*) classifier)->threshold - count; */
fprintf( file, "%e\n", threshold );
}
static CvIntHaarClassifier* icvLoadCARTStageHaarClassifierF( FILE* file, int step )
{
CvStageHaarClassifier* ptr = NULL;
//CV_FUNCNAME( "icvLoadCARTStageHaarClassifierF" );
__BEGIN__;
if( file != NULL )
{
int count;
int i;
float threshold;
count = 0;
int values_read = fscanf( file, "%d", &count );
CV_Assert(values_read == 1);
if( count > 0 )
{
ptr = (CvStageHaarClassifier*) icvCreateStageHaarClassifier( count, 0.0F );
for( i = 0; i < count; i++ )
{
ptr->classifier[i] = icvLoadCARTHaarClassifier( file, step );
}
values_read = fscanf( file, "%f", &threshold );
CV_Assert(values_read == 1);
ptr->threshold = threshold;
/* to be compatible with the previous implementation */
/* ptr->threshold = 0.5F * (threshold + count); */
}
if( feof( file ) )
{
ptr->release( (CvIntHaarClassifier**) &ptr );
ptr = NULL;
}
}
__END__;
return (CvIntHaarClassifier*) ptr;
}
CvIntHaarClassifier* icvLoadCARTStageHaarClassifier( const char* filename, int step )
{
CvIntHaarClassifier* ptr = NULL;
CV_FUNCNAME( "icvLoadCARTStageHaarClassifier" );
__BEGIN__;
FILE* file;
file = fopen( filename, "r" );
if( file )
{
CV_CALL( ptr = icvLoadCARTStageHaarClassifierF( file, step ) );
fclose( file );
}
__END__;
return ptr;
}
/* tree cascade classifier */
/* evaluates a tree cascade classifier */
float icvEvalTreeCascadeClassifier( CvIntHaarClassifier* classifier,
sum_type* sum, sum_type* tilted, float normfactor )
{
CvTreeCascadeNode* ptr;
ptr = ((CvTreeCascadeClassifier*) classifier)->root;
while( ptr )
{
if( ptr->stage->eval( (CvIntHaarClassifier*) ptr->stage,
sum, tilted, normfactor )
>= ptr->stage->threshold - CV_THRESHOLD_EPS )
{
ptr = ptr->child;
}
else
{
while( ptr && ptr->next == NULL ) ptr = ptr->parent;
if( ptr == NULL ) return 0.0F;
ptr = ptr->next;
}
}
return 1.0F;
}
/* sets path int the tree form the root to the leaf node */
void icvSetLeafNode( CvTreeCascadeClassifier* tcc, CvTreeCascadeNode* leaf )
{
CV_FUNCNAME( "icvSetLeafNode" );
__BEGIN__;
CvTreeCascadeNode* ptr;
ptr = NULL;
while( leaf )
{
leaf->child_eval = ptr;
ptr = leaf;
leaf = leaf->parent;
}
leaf = tcc->root;
while( leaf && leaf != ptr ) leaf = leaf->next;
if( leaf != ptr )
CV_ERROR( CV_StsError, "Invalid tcc or leaf node." );
tcc->root_eval = ptr;
__END__;
}
/* evaluates a tree cascade classifier. used in filtering */
float icvEvalTreeCascadeClassifierFilter( CvIntHaarClassifier* classifier, sum_type* sum,
sum_type* tilted, float normfactor )
{
CvTreeCascadeNode* ptr;
//CvTreeCascadeClassifier* tree;
//tree = (CvTreeCascadeClassifier*) classifier;
ptr = ((CvTreeCascadeClassifier*) classifier)->root_eval;
while( ptr )
{
if( ptr->stage->eval( (CvIntHaarClassifier*) ptr->stage,
sum, tilted, normfactor )
< ptr->stage->threshold - CV_THRESHOLD_EPS )
{
return 0.0F;
}
ptr = ptr->child_eval;
}
return 1.0F;
}
/* creates tree cascade node */
CvTreeCascadeNode* icvCreateTreeCascadeNode()
{
CvTreeCascadeNode* ptr = NULL;
CV_FUNCNAME( "icvCreateTreeCascadeNode" );
__BEGIN__;
size_t data_size;
data_size = sizeof( *ptr );
CV_CALL( ptr = (CvTreeCascadeNode*) cvAlloc( data_size ) );
memset( ptr, 0, data_size );
__END__;
return ptr;
}
/* releases all tree cascade nodes accessible via links */
void icvReleaseTreeCascadeNodes( CvTreeCascadeNode** node )
{
//CV_FUNCNAME( "icvReleaseTreeCascadeNodes" );
__BEGIN__;
if( node && *node )
{
CvTreeCascadeNode* ptr;
CvTreeCascadeNode* ptr_;
ptr = *node;
while( ptr )
{
while( ptr->child ) ptr = ptr->child;
if( ptr->stage ) ptr->stage->release( (CvIntHaarClassifier**) &ptr->stage );
ptr_ = ptr;
while( ptr && ptr->next == NULL ) ptr = ptr->parent;
if( ptr ) ptr = ptr->next;
cvFree( &ptr_ );
}
}
__END__;
}
/* releases tree cascade classifier */
void icvReleaseTreeCascadeClassifier( CvIntHaarClassifier** classifier )
{
if( classifier && *classifier )
{
icvReleaseTreeCascadeNodes( &((CvTreeCascadeClassifier*) *classifier)->root );
cvFree( classifier );
*classifier = NULL;
}
}
void icvPrintTreeCascade( CvTreeCascadeNode* root )
{
//CV_FUNCNAME( "icvPrintTreeCascade" );
__BEGIN__;
CvTreeCascadeNode* node;
CvTreeCascadeNode* n;
char buf0[256];
char buf[256];
int level;
int i;
int max_level;
node = root;
level = max_level = 0;
while( node )
{
while( node->child ) { node = node->child; level++; }
if( level > max_level ) { max_level = level; }
while( node && !node->next ) { node = node->parent; level--; }
if( node ) node = node->next;
}
printf( "\nTree Classifier\n" );
printf( "Stage\n" );
for( i = 0; i <= max_level; i++ ) printf( "+---" );
printf( "+\n" );
for( i = 0; i <= max_level; i++ ) printf( "|%3d", i );
printf( "|\n" );
for( i = 0; i <= max_level; i++ ) printf( "+---" );
printf( "+\n\n" );
node = root;
buf[0] = 0;
while( node )
{
sprintf( buf + strlen( buf ), "%3d", node->idx );
while( node->child )
{
node = node->child;
sprintf( buf + strlen( buf ),
((node->idx < 10) ? "---%d" : ((node->idx < 100) ? "--%d" : "-%d")),
node->idx );
}
printf( " %s\n", buf );
while( node && !node->next ) { node = node->parent; }
if( node )
{
node = node->next;
n = node->parent;
buf[0] = 0;
while( n )
{
if( n->next )
sprintf( buf0, " | %s", buf );
else
sprintf( buf0, " %s", buf );
strcpy( buf, buf0 );
n = n->parent;
}
printf( " %s |\n", buf );
}
}
printf( "\n" );
fflush( stdout );
__END__;
}
CvIntHaarClassifier* icvLoadTreeCascadeClassifier( const char* filename, int step,
int* splits )
{
CvTreeCascadeClassifier* ptr = NULL;
CvTreeCascadeNode** nodes = NULL;
CV_FUNCNAME( "icvLoadTreeCascadeClassifier" );
__BEGIN__;
size_t data_size;
CvStageHaarClassifier* stage;
char stage_name[PATH_MAX];
char* suffix;
int i, num;
FILE* f;
int result, parent=0, next=0;
int stub;
if( !splits ) splits = &stub;
*splits = 0;
data_size = sizeof( *ptr );
CV_CALL( ptr = (CvTreeCascadeClassifier*) cvAlloc( data_size ) );
memset( ptr, 0, data_size );
ptr->eval = icvEvalTreeCascadeClassifier;
ptr->release = icvReleaseTreeCascadeClassifier;
sprintf( stage_name, "%s/", filename );
suffix = stage_name + strlen( stage_name );
for( i = 0; ; i++ )
{
sprintf( suffix, "%d/%s", i, CV_STAGE_CART_FILE_NAME );
f = fopen( stage_name, "r" );
if( !f ) break;
fclose( f );
}
num = i;
if( num < 1 ) EXIT;
data_size = sizeof( *nodes ) * num;
CV_CALL( nodes = (CvTreeCascadeNode**) cvAlloc( data_size ) );
for( i = 0; i < num; i++ )
{
sprintf( suffix, "%d/%s", i, CV_STAGE_CART_FILE_NAME );
f = fopen( stage_name, "r" );
CV_CALL( stage = (CvStageHaarClassifier*)
icvLoadCARTStageHaarClassifierF( f, step ) );
result = ( f && stage ) ? fscanf( f, "%d%d", &parent, &next ) : 0;
if( f ) fclose( f );
if( result != 2 )
{
num = i;
break;
}
printf( "Stage %d loaded\n", i );
if( parent >= i || (next != -1 && next != i + 1) )
CV_ERROR( CV_StsError, "Invalid tree links" );
CV_CALL( nodes[i] = icvCreateTreeCascadeNode() );
nodes[i]->stage = stage;
nodes[i]->idx = i;
nodes[i]->parent = (parent != -1 ) ? nodes[parent] : NULL;
nodes[i]->next = ( next != -1 ) ? nodes[i] : NULL;
nodes[i]->child = NULL;
}
for( i = 0; i < num; i++ )
{
if( nodes[i]->next )
{
(*splits)++;
nodes[i]->next = nodes[i+1];
}
if( nodes[i]->parent && nodes[i]->parent->child == NULL )
{
nodes[i]->parent->child = nodes[i];
}
}
ptr->root = nodes[0];
ptr->next_idx = num;
__END__;
cvFree( &nodes );
return (CvIntHaarClassifier*) ptr;
}
CvTreeCascadeNode* icvFindDeepestLeaves( CvTreeCascadeClassifier* tcc )
{
CvTreeCascadeNode* leaves;
//CV_FUNCNAME( "icvFindDeepestLeaves" );
__BEGIN__;
int level, cur_level;
CvTreeCascadeNode* ptr;
CvTreeCascadeNode* last;
leaves = last = NULL;
ptr = tcc->root;
level = -1;
cur_level = 0;
/* find leaves with maximal level */
while( ptr )
{
if( ptr->child ) { ptr = ptr->child; cur_level++; }
else
{
if( cur_level == level )
{
last->next_same_level = ptr;
ptr->next_same_level = NULL;
last = ptr;
}
if( cur_level > level )
{
level = cur_level;
leaves = last = ptr;
ptr->next_same_level = NULL;
}
while( ptr && ptr->next == NULL ) { ptr = ptr->parent; cur_level--; }
if( ptr ) ptr = ptr->next;
}
}
__END__;
return leaves;
}
/* End of file. */

3060
apps/haartraining/cvhaartraining.cpp
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284
apps/haartraining/haartraining.cpp
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@ -1,284 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
/*
* haartraining.cpp
*
* Train cascade classifier
*/
#include <cstdio>
#include <cstring>
#include <cstdlib>
using namespace std;
#include "cvhaartraining.h"
int main( int argc, char* argv[] )
{
int i = 0;
char* nullname = (char*)"(NULL)";
char* vecname = NULL;
char* dirname = NULL;
char* bgname = NULL;
bool bg_vecfile = false;
int npos = 2000;
int nneg = 2000;
int nstages = 14;
int mem = 200;
int nsplits = 1;
float minhitrate = 0.995F;
float maxfalsealarm = 0.5F;
float weightfraction = 0.95F;
int mode = 0;
int symmetric = 1;
int equalweights = 0;
int width = 24;
int height = 24;
const char* boosttypes[] = { "DAB", "RAB", "LB", "GAB" };
int boosttype = 3;
const char* stumperrors[] = { "misclass", "gini", "entropy" };
int stumperror = 0;
int maxtreesplits = 0;
int minpos = 500;
if( argc == 1 )
{
printf( "Usage: %s\n -data <dir_name>\n"
" -vec <vec_file_name>\n"
" -bg <background_file_name>\n"
" [-bg-vecfile]\n"
" [-npos <number_of_positive_samples = %d>]\n"
" [-nneg <number_of_negative_samples = %d>]\n"
" [-nstages <number_of_stages = %d>]\n"
" [-nsplits <number_of_splits = %d>]\n"
" [-mem <memory_in_MB = %d>]\n"
" [-sym (default)] [-nonsym]\n"
" [-minhitrate <min_hit_rate = %f>]\n"
" [-maxfalsealarm <max_false_alarm_rate = %f>]\n"
" [-weighttrimming <weight_trimming = %f>]\n"
" [-eqw]\n"
" [-mode <BASIC (default) | CORE | ALL>]\n"
" [-w <sample_width = %d>]\n"
" [-h <sample_height = %d>]\n"
" [-bt <DAB | RAB | LB | GAB (default)>]\n"
" [-err <misclass (default) | gini | entropy>]\n"
" [-maxtreesplits <max_number_of_splits_in_tree_cascade = %d>]\n"
" [-minpos <min_number_of_positive_samples_per_cluster = %d>]\n",
argv[0], npos, nneg, nstages, nsplits, mem,
minhitrate, maxfalsealarm, weightfraction, width, height,
maxtreesplits, minpos );
return 0;
}
for( i = 1; i < argc; i++ )
{
if( !strcmp( argv[i], "-data" ) )
{
dirname = argv[++i];
}
else if( !strcmp( argv[i], "-vec" ) )
{
vecname = argv[++i];
}
else if( !strcmp( argv[i], "-bg" ) )
{
bgname = argv[++i];
}
else if( !strcmp( argv[i], "-bg-vecfile" ) )
{
bg_vecfile = true;
}
else if( !strcmp( argv[i], "-npos" ) )
{
npos = atoi( argv[++i] );
}
else if( !strcmp( argv[i], "-nneg" ) )
{
nneg = atoi( argv[++i] );
}
else if( !strcmp( argv[i], "-nstages" ) )
{
nstages = atoi( argv[++i] );
}
else if( !strcmp( argv[i], "-nsplits" ) )
{
nsplits = atoi( argv[++i] );
}
else if( !strcmp( argv[i], "-mem" ) )
{
mem = atoi( argv[++i] );
}
else if( !strcmp( argv[i], "-sym" ) )
{
symmetric = 1;
}
else if( !strcmp( argv[i], "-nonsym" ) )
{
symmetric = 0;
}
else if( !strcmp( argv[i], "-minhitrate" ) )
{
minhitrate = (float) atof( argv[++i] );
}
else if( !strcmp( argv[i], "-maxfalsealarm" ) )
{
maxfalsealarm = (float) atof( argv[++i] );
}
else if( !strcmp( argv[i], "-weighttrimming" ) )
{
weightfraction = (float) atof( argv[++i] );
}
else if( !strcmp( argv[i], "-eqw" ) )
{
equalweights = 1;
}
else if( !strcmp( argv[i], "-mode" ) )
{
char* tmp = argv[++i];
if( !strcmp( tmp, "CORE" ) )
{
mode = 1;
}
else if( !strcmp( tmp, "ALL" ) )
{
mode = 2;
}
else
{
mode = 0;
}
}
else if( !strcmp( argv[i], "-w" ) )
{
width = atoi( argv[++i] );
}
else if( !strcmp( argv[i], "-h" ) )
{
height = atoi( argv[++i] );
}
else if( !strcmp( argv[i], "-bt" ) )
{
i++;
if( !strcmp( argv[i], boosttypes[0] ) )
{
boosttype = 0;
}
else if( !strcmp( argv[i], boosttypes[1] ) )
{
boosttype = 1;
}
else if( !strcmp( argv[i], boosttypes[2] ) )
{
boosttype = 2;
}
else
{
boosttype = 3;
}
}
else if( !strcmp( argv[i], "-err" ) )
{
i++;
if( !strcmp( argv[i], stumperrors[0] ) )
{
stumperror = 0;
}
else if( !strcmp( argv[i], stumperrors[1] ) )
{
stumperror = 1;
}
else
{
stumperror = 2;
}
}
else if( !strcmp( argv[i], "-maxtreesplits" ) )
{
maxtreesplits = atoi( argv[++i] );
}
else if( !strcmp( argv[i], "-minpos" ) )
{
minpos = atoi( argv[++i] );
}
}
printf( "Data dir name: %s\n", ((dirname == NULL) ? nullname : dirname ) );
printf( "Vec file name: %s\n", ((vecname == NULL) ? nullname : vecname ) );
printf( "BG file name: %s, is a vecfile: %s\n", ((bgname == NULL) ? nullname : bgname ), bg_vecfile ? "yes" : "no" );
printf( "Num pos: %d\n", npos );
printf( "Num neg: %d\n", nneg );
printf( "Num stages: %d\n", nstages );
printf( "Num splits: %d (%s as weak classifier)\n", nsplits,
(nsplits == 1) ? "stump" : "tree" );
printf( "Mem: %d MB\n", mem );
printf( "Symmetric: %s\n", (symmetric) ? "TRUE" : "FALSE" );
printf( "Min hit rate: %f\n", minhitrate );
printf( "Max false alarm rate: %f\n", maxfalsealarm );
printf( "Weight trimming: %f\n", weightfraction );
printf( "Equal weights: %s\n", (equalweights) ? "TRUE" : "FALSE" );
printf( "Mode: %s\n", ( (mode == 0) ? "BASIC" : ( (mode == 1) ? "CORE" : "ALL") ) );
printf( "Width: %d\n", width );
printf( "Height: %d\n", height );
//printf( "Max num of precalculated features: %d\n", numprecalculated );
printf( "Applied boosting algorithm: %s\n", boosttypes[boosttype] );
printf( "Error (valid only for Discrete and Real AdaBoost): %s\n",
stumperrors[stumperror] );
printf( "Max number of splits in tree cascade: %d\n", maxtreesplits );
printf( "Min number of positive samples per cluster: %d\n", minpos );
cvCreateTreeCascadeClassifier( dirname, vecname, bgname,
npos, nneg, nstages, mem,
nsplits,
minhitrate, maxfalsealarm, weightfraction,
mode, symmetric,
equalweights, width, height,
boosttype, stumperror,
maxtreesplits, minpos, bg_vecfile );
return 0;
}

377
apps/haartraining/performance.cpp
View File

@ -1,377 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
/*
* performance.cpp
*
* Measure performance of classifier
*/
#include "opencv2/core.hpp"
#include "cv.h"
#include "highgui.h"
#include <cstdio>
#include <cmath>
#include <ctime>
#ifdef _WIN32
/* use clock() function insted of time() */
#define time( arg ) (((double) clock()) / CLOCKS_PER_SEC)
#endif /* _WIN32 */
#ifndef PATH_MAX
#define PATH_MAX 512
#endif /* PATH_MAX */
typedef struct HidCascade
{
int size;
int count;
} HidCascade;
typedef struct ObjectPos
{
float x;
float y;
float width;
int found; /* for reference */
int neghbors;
} ObjectPos;
int main( int argc, char* argv[] )
{
int i, j;
char* classifierdir = NULL;
//char* samplesdir = NULL;
int saveDetected = 1;
double scale_factor = 1.2;
float maxSizeDiff = 1.5F;
float maxPosDiff = 0.3F;
/* number of stages. if <=0 all stages are used */
int nos = -1, nos0;
int width = 24;
int height = 24;
int rocsize;
FILE* info;
char* infoname;
char fullname[PATH_MAX];
char detfilename[PATH_MAX];
char* filename;
char detname[] = "det-";
CvHaarClassifierCascade* cascade;
CvMemStorage* storage;
CvSeq* objects;
double totaltime;
infoname = (char*)"";
rocsize = 40;
if( argc == 1 )
{
printf( "Usage: %s\n -data <classifier_directory_name>\n"
" -info <collection_file_name>\n"
" [-maxSizeDiff <max_size_difference = %f>]\n"
" [-maxPosDiff <max_position_difference = %f>]\n"
" [-sf <scale_factor = %f>]\n"
" [-ni]\n"
" [-nos <number_of_stages = %d>]\n"
" [-rs <roc_size = %d>]\n"
" [-w <sample_width = %d>]\n"
" [-h <sample_height = %d>]\n",
argv[0], maxSizeDiff, maxPosDiff, scale_factor, nos, rocsize,
width, height );
return 0;
}
for( i = 1; i < argc; i++ )
{
if( !strcmp( argv[i], "-data" ) )
{
classifierdir = argv[++i];
}
else if( !strcmp( argv[i], "-info" ) )
{
infoname = argv[++i];
}
else if( !strcmp( argv[i], "-maxSizeDiff" ) )
{
maxSizeDiff = (float) atof( argv[++i] );
}
else if( !strcmp( argv[i], "-maxPosDiff" ) )
{
maxPosDiff = (float) atof( argv[++i] );
}
else if( !strcmp( argv[i], "-sf" ) )
{
scale_factor = atof( argv[++i] );
}
else if( !strcmp( argv[i], "-ni" ) )
{
saveDetected = 0;
}
else if( !strcmp( argv[i], "-nos" ) )
{
nos = atoi( argv[++i] );
}
else if( !strcmp( argv[i], "-rs" ) )
{
rocsize = atoi( argv[++i] );
}
else if( !strcmp( argv[i], "-w" ) )
{
width = atoi( argv[++i] );
}
else if( !strcmp( argv[i], "-h" ) )
{
height = atoi( argv[++i] );
}
}
cascade = cvLoadHaarClassifierCascade( classifierdir, cvSize( width, height ) );
if( cascade == NULL )
{
printf( "Unable to load classifier from %s\n", classifierdir );
return 1;
}
int* numclassifiers = new int[cascade->count];
numclassifiers[0] = cascade->stage_classifier[0].count;
for( i = 1; i < cascade->count; i++ )
{
numclassifiers[i] = numclassifiers[i-1] + cascade->stage_classifier[i].count;
}
storage = cvCreateMemStorage();
nos0 = cascade->count;
if( nos <= 0 )
nos = nos0;
strcpy( fullname, infoname );
filename = strrchr( fullname, '\\' );
if( filename == NULL )
{
filename = strrchr( fullname, '/' );
}
if( filename == NULL )
{
filename = fullname;
}
else
{
filename++;
}
info = fopen( infoname, "r" );
totaltime = 0.0;
if( info != NULL )
{
int x, y;
IplImage* img;
int hits, missed, falseAlarms;
int totalHits, totalMissed, totalFalseAlarms;
int found;
float distance;
int refcount;
ObjectPos* ref;
int detcount;
ObjectPos* det;
int error=0;
int* pos;
int* neg;
pos = (int*) cvAlloc( rocsize * sizeof( *pos ) );
neg = (int*) cvAlloc( rocsize * sizeof( *neg ) );
for( i = 0; i < rocsize; i++ ) { pos[i] = neg[i] = 0; }
printf( "+================================+======+======+======+\n" );
printf( "| File Name | Hits |Missed| False|\n" );
printf( "+================================+======+======+======+\n" );
totalHits = totalMissed = totalFalseAlarms = 0;
while( !feof( info ) )
{
if( fscanf( info, "%s %d", filename, &refcount ) != 2 || refcount <= 0 ) break;
img = cvLoadImage( fullname );
if( !img ) continue;
ref = (ObjectPos*) cvAlloc( refcount * sizeof( *ref ) );
for( i = 0; i < refcount; i++ )
{
int w, h;
error = (fscanf( info, "%d %d %d %d", &x, &y, &w, &h ) != 4);
if( error ) break;
ref[i].x = 0.5F * w + x;
ref[i].y = 0.5F * h + y;
ref[i].width = sqrtf( 0.5F * (w * w + h * h) );
ref[i].found = 0;
ref[i].neghbors = 0;
}
if( !error )
{
cvClearMemStorage( storage );
cascade->count = nos;
totaltime -= time( 0 );
objects = cvHaarDetectObjects( img, cascade, storage, scale_factor, 1 );
totaltime += time( 0 );
cascade->count = nos0;
detcount = ( objects ? objects->total : 0);
det = (detcount > 0) ?
( (ObjectPos*)cvAlloc( detcount * sizeof( *det )) ) : NULL;
hits = missed = falseAlarms = 0;
for( i = 0; i < detcount; i++ )
{
CvAvgComp r = *((CvAvgComp*) cvGetSeqElem( objects, i ));
det[i].x = 0.5F * r.rect.width + r.rect.x;
det[i].y = 0.5F * r.rect.height + r.rect.y;
det[i].width = sqrtf( 0.5F * (r.rect.width * r.rect.width +
r.rect.height * r.rect.height) );
det[i].neghbors = r.neighbors;
if( saveDetected )
{
cvRectangle( img, cvPoint( r.rect.x, r.rect.y ),
cvPoint( r.rect.x + r.rect.width, r.rect.y + r.rect.height ),
CV_RGB( 255, 0, 0 ), 3 );
}
found = 0;
for( j = 0; j < refcount; j++ )
{
distance = sqrtf( (det[i].x - ref[j].x) * (det[i].x - ref[j].x) +
(det[i].y - ref[j].y) * (det[i].y - ref[j].y) );
if( (distance < ref[j].width * maxPosDiff) &&
(det[i].width > ref[j].width / maxSizeDiff) &&
(det[i].width < ref[j].width * maxSizeDiff) )
{
ref[j].found = 1;
ref[j].neghbors = MAX( ref[j].neghbors, det[i].neghbors );
found = 1;
}
}
if( !found )
{
falseAlarms++;
neg[MIN(det[i].neghbors, rocsize - 1)]++;
}
}
for( j = 0; j < refcount; j++ )
{
if( ref[j].found )
{
hits++;
pos[MIN(ref[j].neghbors, rocsize - 1)]++;
}
else
{
missed++;
}
}
totalHits += hits;
totalMissed += missed;
totalFalseAlarms += falseAlarms;
printf( "|%32.32s|%6d|%6d|%6d|\n", filename, hits, missed, falseAlarms );
printf( "+--------------------------------+------+------+------+\n" );
fflush( stdout );
if( saveDetected )
{
strcpy( detfilename, detname );
strcat( detfilename, filename );
strcpy( filename, detfilename );
cvvSaveImage( fullname, img );
}
if( det ) { cvFree( &det ); det = NULL; }
} /* if( !error ) */
cvReleaseImage( &img );
cvFree( &ref );
}
fclose( info );
printf( "|%32.32s|%6d|%6d|%6d|\n", "Total",
totalHits, totalMissed, totalFalseAlarms );
printf( "+================================+======+======+======+\n" );
printf( "Number of stages: %d\n", nos );
printf( "Number of weak classifiers: %d\n", numclassifiers[nos - 1] );
printf( "Total time: %f\n", totaltime );
/* print ROC to stdout */
for( i = rocsize - 1; i > 0; i-- )
{
pos[i-1] += pos[i];
neg[i-1] += neg[i];
}
fprintf( stderr, "%d\n", nos );
for( i = 0; i < rocsize; i++ )
{
fprintf( stderr, "\t%d\t%d\t%f\t%f\n", pos[i], neg[i],
((float)pos[i]) / (totalHits + totalMissed),
((float)neg[i]) / (totalHits + totalMissed) );
}
cvFree( &pos );
cvFree( &neg );
}
delete[] numclassifiers;
cvReleaseHaarClassifierCascade( &cascade );
cvReleaseMemStorage( &storage );
return 0;
}

33
apps/sft/CMakeLists.txt
View File

@ -1,33 +0,0 @@
set(name sft)
set(the_target opencv_${name})
set(OPENCV_${the_target}_DEPS opencv_core opencv_softcascade opencv_highgui opencv_imgproc opencv_ml)
ocv_check_dependencies(${OPENCV_${the_target}_DEPS})
if(NOT OCV_DEPENDENCIES_FOUND)
return()
endif()
project(${the_target})
ocv_include_directories("${CMAKE_CURRENT_SOURCE_DIR}/include" "${OpenCV_SOURCE_DIR}/include/opencv")
ocv_include_modules(${OPENCV_${the_target}_DEPS})
file(GLOB ${the_target}_SOURCES ${CMAKE_CURRENT_SOURCE_DIR}/*.cpp)
add_executable(${the_target} ${${the_target}_SOURCES})
target_link_libraries(${the_target} ${OPENCV_${the_target}_DEPS})
set_target_properties(${the_target} PROPERTIES
DEBUG_POSTFIX "${OPENCV_DEBUG_POSTFIX}"
ARCHIVE_OUTPUT_DIRECTORY ${LIBRARY_OUTPUT_PATH}
RUNTIME_OUTPUT_DIRECTORY ${EXECUTABLE_OUTPUT_PATH}
INSTALL_NAME_DIR lib
OUTPUT_NAME "opencv_trainsoftcascade")
if(ENABLE_SOLUTION_FOLDERS)
set_target_properties(${the_target} PROPERTIES FOLDER "applications")
endif()
install(TARGETS ${the_target} RUNTIME DESTINATION bin COMPONENT main)

162
apps/sft/config.cpp
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@ -1,162 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2008-2012, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include <sft/config.hpp>
#include <iomanip>
sft::Config::Config(): seed(0) {}
void sft::Config::write(cv::FileStorage& fs) const
{
fs << "{"
<< "trainPath" << trainPath
<< "testPath" << testPath
<< "modelWinSize" << modelWinSize
<< "offset" << offset
<< "octaves" << octaves
<< "positives" << positives
<< "negatives" << negatives
<< "btpNegatives" << btpNegatives
<< "shrinkage" << shrinkage
<< "treeDepth" << treeDepth
<< "weaks" << weaks
<< "poolSize" << poolSize
<< "cascadeName" << cascadeName
<< "outXmlPath" << outXmlPath
<< "seed" << seed
<< "featureType" << featureType
<< "}";
}
void sft::Config::read(const cv::FileNode& node)
{
trainPath = (string)node["trainPath"];
testPath = (string)node["testPath"];
cv::FileNodeIterator nIt = node["modelWinSize"].end();
modelWinSize = cv::Size((int)*(--nIt), (int)*(--nIt));
nIt = node["offset"].end();
offset = cv::Point2i((int)*(--nIt), (int)*(--nIt));
node["octaves"] >> octaves;
positives = (int)node["positives"];
negatives = (int)node["negatives"];
btpNegatives = (int)node["btpNegatives"];
shrinkage = (int)node["shrinkage"];
treeDepth = (int)node["treeDepth"];
weaks = (int)node["weaks"];
poolSize = (int)node["poolSize"];
cascadeName = (std::string)node["cascadeName"];
outXmlPath = (std::string)node["outXmlPath"];
seed = (int)node["seed"];
featureType = (std::string)node["featureType"];
}
void sft::write(cv::FileStorage& fs, const string&, const Config& x)
{
x.write(fs);
}
void sft::read(const cv::FileNode& node, Config& x, const Config& default_value)
{
x = default_value;
if(!node.empty())
x.read(node);
}
namespace {
struct Out
{
Out(std::ostream& _out): out(_out) {}
template<typename T>
void operator ()(const T a) const {out << a << " ";}
std::ostream& out;
private:
Out& operator=(Out const& other);
};
}
std::ostream& sft::operator<<(std::ostream& out, const Config& m)
{
out << std::setw(14) << std::left << "trainPath" << m.trainPath << std::endl
<< std::setw(14) << std::left << "testPath" << m.testPath << std::endl
<< std::setw(14) << std::left << "modelWinSize" << m.modelWinSize << std::endl
<< std::setw(14) << std::left << "offset" << m.offset << std::endl
<< std::setw(14) << std::left << "octaves";
Out o(out);
for_each(m.octaves.begin(), m.octaves.end(), o);
out << std::endl
<< std::setw(14) << std::left << "positives" << m.positives << std::endl
<< std::setw(14) << std::left << "negatives" << m.negatives << std::endl
<< std::setw(14) << std::left << "btpNegatives" << m.btpNegatives << std::endl
<< std::setw(14) << std::left << "shrinkage" << m.shrinkage << std::endl
<< std::setw(14) << std::left << "treeDepth" << m.treeDepth << std::endl
<< std::setw(14) << std::left << "weaks" << m.weaks << std::endl
<< std::setw(14) << std::left << "poolSize" << m.poolSize << std::endl
<< std::setw(14) << std::left << "cascadeName" << m.cascadeName << std::endl
<< std::setw(14) << std::left << "outXmlPath" << m.outXmlPath << std::endl
<< std::setw(14) << std::left << "seed" << m.seed << std::endl
<< std::setw(14) << std::left << "featureType" << m.featureType << std::endl;
return out;
}

77
apps/sft/dataset.cpp
View File

@ -1,77 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2008-2012, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include <sft/dataset.hpp>
#include <opencv2/highgui.hpp>
#include <iostream>
#include <queue>
// in the default case data folders should be aligned as following:
// 1. positives: <train or test path>/octave_<octave number>/pos/*.png
// 2. negatives: <train or test path>/octave_<octave number>/neg/*.png
sft::ScaledDataset::ScaledDataset(const string& path, const int oct)
{
dprintf("%s\n", "get dataset file names...");
dprintf("%s\n", "Positives globing...");
cv::glob(path + "/pos/octave_" + cv::format("%d", oct) + "/*.png", pos);
dprintf("%s\n", "Negatives globing...");
cv::glob(path + "/neg/octave_" + cv::format("%d", oct) + "/*.png", neg);
// Check: files not empty
CV_Assert(pos.size() != size_t(0));
CV_Assert(neg.size() != size_t(0));
}
cv::Mat sft::ScaledDataset::get(SampleType type, int idx) const
{
const std::string& src = (type == POSITIVE)? pos[idx]: neg[idx];
return cv::imread(src);
}
int sft::ScaledDataset::available(SampleType type) const
{
return (int)((type == POSITIVE)? pos.size():neg.size());
}
sft::ScaledDataset::~ScaledDataset(){}

138
apps/sft/include/sft/config.hpp
View File

@ -1,138 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2008-2012, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __SFT_CONFIG_HPP__
#define __SFT_CONFIG_HPP__
#include <sft/common.hpp>
#include <ostream>
namespace sft {
struct Config
{
Config();
void write(cv::FileStorage& fs) const;
void read(const cv::FileNode& node);
// Scaled and shrunk model size.
cv::Size model(ivector::const_iterator it) const
{
float octave = powf(2.f, (float)(*it));
return cv::Size( cvRound(modelWinSize.width * octave) / shrinkage,
cvRound(modelWinSize.height * octave) / shrinkage );
}
// Scaled but, not shrunk bounding box for object in sample image.
cv::Rect bbox(ivector::const_iterator it) const
{
float octave = powf(2.f, (float)(*it));
return cv::Rect( cvRound(offset.x * octave), cvRound(offset.y * octave),
cvRound(modelWinSize.width * octave), cvRound(modelWinSize.height * octave));
}
string resPath(ivector::const_iterator it) const
{
return cv::format("%s%d.xml",cascadeName.c_str(), *it);
}
// Paths to a rescaled data
string trainPath;
string testPath;
// Original model size.
cv::Size modelWinSize;
// example offset into positive image
cv::Point2i offset;
// List of octaves for which have to be trained cascades (a list of powers of two)
ivector octaves;
// Maximum number of positives that should be used during training
int positives;
// Initial number of negatives used during training.
int negatives;
// Number of weak negatives to add each bootstrapping step.
int btpNegatives;
// Inverse of scale for feature resizing
int shrinkage;
// Depth on weak classifier's decision tree
int treeDepth;
// Weak classifiers number in resulted cascade
int weaks;
// Feature random pool size
int poolSize;
// file name to store cascade
string cascadeName;
// path to resulting cascade
string outXmlPath;
// seed for random generation
int seed;
// channel feature type
string featureType;
// // bounding rectangle for actual example into example window
// cv::Rect exampleWindow;
};
// required for cv::FileStorage serialization
void write(cv::FileStorage& fs, const string&, const Config& x);
void read(const cv::FileNode& node, Config& x, const Config& default_value);
std::ostream& operator<<(std::ostream& out, const Config& m);
}
#endif

67
apps/sft/include/sft/dataset.hpp
View File

@ -1,67 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2008-2012, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __SFT_OCTAVE_HPP__
#define __SFT_OCTAVE_HPP__
#include <sft/common.hpp>
namespace sft
{
using cv::softcascade::Dataset;
class ScaledDataset : public Dataset
{
public:
ScaledDataset(const sft::string& path, const int octave);
virtual cv::Mat get(SampleType type, int idx) const;
virtual int available(SampleType type) const;
virtual ~ScaledDataset();
private:
svector pos;
svector neg;
};
}
#endif

168
apps/sft/sft.cpp
View File

@ -1,168 +0,0 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// License Agreement
// For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2008-2012, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of the copyright holders may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
// Training application for Soft Cascades.
#include <sft/common.hpp>
#include <iostream>
#include <sft/dataset.hpp>
#include <sft/config.hpp>
#include <opencv2/core/core_c.h>
int main(int argc, char** argv)
{
using namespace sft;
const string keys =
"{help h usage ? | | print this message }"
"{config c | | path to configuration xml }"
;
cv::CommandLineParser parser(argc, argv, keys);
parser.about("Soft cascade training application.");
if (parser.has("help"))
{
parser.printMessage();
return 0;
}
if (!parser.check())
{
parser.printErrors();
return 1;
}
string configPath = parser.get<string>("config");
if (configPath.empty())
{
std::cout << "Configuration file is missing or empty. Could not start training." << std::endl;
return 0;
}
std::cout << "Read configuration from file " << configPath << std::endl;
cv::FileStorage fs(configPath, cv::FileStorage::READ);
if(!fs.isOpened())
{
std::cout << "Configuration file " << configPath << " can't be opened." << std::endl;
return 1;
}
// 1. load config
sft::Config cfg;
fs["config"] >> cfg;
std::cout << std::endl << "Training will be executed for configuration:" << std::endl << cfg << std::endl;
// 2. check and open output file
cv::FileStorage fso(cfg.outXmlPath, cv::FileStorage::WRITE);
if(!fso.isOpened())
{
std::cout << "Training stopped. Output classifier Xml file " << cfg.outXmlPath << " can't be opened." << std::endl;
return 1;
}
fso << cfg.cascadeName
<< "{"
<< "stageType" << "BOOST"
<< "featureType" << cfg.featureType
<< "octavesNum" << (int)cfg.octaves.size()
<< "width" << cfg.modelWinSize.width
<< "height" << cfg.modelWinSize.height
<< "shrinkage" << cfg.shrinkage
<< "octaves" << "[";
// 3. Train all octaves
for (ivector::const_iterator it = cfg.octaves.begin(); it != cfg.octaves.end(); ++it)
{
// a. create random feature pool
int nfeatures = cfg.poolSize;
cv::Size model = cfg.model(it);
std::cout << "Model " << model << std::endl;
int nchannels = (cfg.featureType == "HOG6MagLuv") ? 10: 8;
std::cout << "number of feature channels is " << nchannels << std::endl;
cv::Ptr<cv::FeaturePool> pool = cv::FeaturePool::create(model, nfeatures, nchannels);
nfeatures = pool->size();
int npositives = cfg.positives;
int nnegatives = cfg.negatives;
int shrinkage = cfg.shrinkage;
cv::Rect boundingBox = cfg.bbox(it);
std::cout << "Object bounding box" << boundingBox << std::endl;
typedef cv::Octave Octave;
cv::Ptr<cv::ChannelFeatureBuilder> builder = cv::ChannelFeatureBuilder::create(cfg.featureType);
std::cout << "Channel builder " << builder->info()->name() << std::endl;
cv::Ptr<Octave> boost = Octave::create(boundingBox, npositives, nnegatives, *it, shrinkage, builder);
std::string path = cfg.trainPath;
sft::ScaledDataset dataset(path, *it);
if (boost->train(&dataset, pool, cfg.weaks, cfg.treeDepth))
{
CvFileStorage* fout = cvOpenFileStorage(cfg.resPath(it).c_str(), 0, CV_STORAGE_WRITE);
boost->write(fout, cfg.cascadeName);
cvReleaseFileStorage( &fout);
cv::Mat thresholds;
boost->setRejectThresholds(thresholds);
boost->write(fso, pool, thresholds);
cv::FileStorage tfs(("thresholds." + cfg.resPath(it)).c_str(), cv::FileStorage::WRITE);
tfs << "thresholds" << thresholds;
std::cout << "Octave " << *it << " was successfully trained..." << std::endl;
}
}
fso << "]" << "}";
fso.release();
std::cout << "Training complete..." << std::endl;
return 0;
}

31
apps/traincascade/CMakeLists.txt
View File

@ -1,4 +1,4 @@
set(OPENCV_TRAINCASCADE_DEPS opencv_core opencv_ml opencv_imgproc opencv_photo opencv_objdetect opencv_highgui opencv_calib3d opencv_video opencv_features2d opencv_flann opencv_legacy)
set(OPENCV_TRAINCASCADE_DEPS opencv_core opencv_imgproc opencv_objdetect opencv_imgcodecs opencv_highgui opencv_calib3d opencv_features2d)
ocv_check_dependencies(${OPENCV_TRAINCASCADE_DEPS})
if(NOT OCV_DEPENDENCIES_FOUND)
@ -6,21 +6,18 @@ if(NOT OCV_DEPENDENCIES_FOUND)
endif()
project(traincascade)
ocv_include_directories("${CMAKE_CURRENT_SOURCE_DIR}" "${OpenCV_SOURCE_DIR}/include/opencv")
ocv_include_modules(${OPENCV_TRAINCASCADE_DEPS})
set(traincascade_files traincascade.cpp
cascadeclassifier.cpp cascadeclassifier.h
boost.cpp boost.h features.cpp traincascade_features.h
haarfeatures.cpp haarfeatures.h
lbpfeatures.cpp lbpfeatures.h
HOGfeatures.cpp HOGfeatures.h
imagestorage.cpp imagestorage.h)
set(the_target opencv_traincascade)
add_executable(${the_target} ${traincascade_files})
target_link_libraries(${the_target} ${OPENCV_TRAINCASCADE_DEPS} opencv_haartraining_engine)
ocv_target_include_directories(${the_target} PRIVATE "${CMAKE_CURRENT_SOURCE_DIR}" "${OpenCV_SOURCE_DIR}/include/opencv")
ocv_target_include_modules(${the_target} ${OPENCV_TRAINCASCADE_DEPS})
file(GLOB SRCS *.cpp)
file(GLOB HDRS *.h*)
set(traincascade_files ${SRCS} ${HDRS})
ocv_add_executable(${the_target} ${traincascade_files})
ocv_target_link_libraries(${the_target} ${OPENCV_TRAINCASCADE_DEPS})
set_target_properties(${the_target} PROPERTIES
DEBUG_POSTFIX "${OPENCV_DEBUG_POSTFIX}"
@ -35,8 +32,8 @@ endif()
if(INSTALL_CREATE_DISTRIB)
if(BUILD_SHARED_LIBS)
install(TARGETS ${the_target} RUNTIME DESTINATION ${OPENCV_BIN_INSTALL_PATH} CONFIGURATIONS Release COMPONENT main)
install(TARGETS ${the_target} RUNTIME DESTINATION ${OPENCV_BIN_INSTALL_PATH} CONFIGURATIONS Release COMPONENT dev)
endif()
else()
install(TARGETS ${the_target} RUNTIME DESTINATION ${OPENCV_BIN_INSTALL_PATH} COMPONENT main)
install(TARGETS ${the_target} OPTIONAL RUNTIME DESTINATION ${OPENCV_BIN_INSTALL_PATH} COMPONENT dev)
endif()

16
apps/traincascade/HOGfeatures.cpp
View File

@ -187,11 +187,11 @@ void CvHOGEvaluator::integralHistogram(const Mat &img, vector<Mat> &histogram, M
for( y = 0; y < gradSize.height; y++ )
{
const uchar* currPtr = img.data + img.step*ymap[y];
const uchar* prevPtr = img.data + img.step*ymap[y-1];
const uchar* nextPtr = img.data + img.step*ymap[y+1];
float* gradPtr = (float*)grad.ptr(y);
uchar* qanglePtr = (uchar*)qangle.ptr(y);
const uchar* currPtr = img.ptr(ymap[y]);
const uchar* prevPtr = img.ptr(ymap[y-1]);
const uchar* nextPtr = img.ptr(ymap[y+1]);
float* gradPtr = grad.ptr<float>(y);
uchar* qanglePtr = qangle.ptr(y);
for( x = 0; x < width; x++ )
{
@ -226,9 +226,9 @@ void CvHOGEvaluator::integralHistogram(const Mat &img, vector<Mat> &histogram, M
int magStep = (int)( grad.step / sizeof(float) );
for( binIdx = 0; binIdx < nbins; binIdx++ )
{
histBuf = (float*)histogram[binIdx].data;
magBuf = (const float*)grad.data;
binsBuf = (const uchar*)qangle.data;
histBuf = histogram[binIdx].ptr<float>();
magBuf = grad.ptr<float>();
binsBuf = qangle.ptr();
memset( histBuf, 0, histSize.width * sizeof(histBuf[0]) );
histBuf += histStep + 1;

15
apps/traincascade/boost.cpp
View File

@ -14,6 +14,19 @@ using cv::FileNodeIterator;
using cv::ParallelLoopBody;
using cv::Size;
using cv::Mat;
using cv::Point;
using cv::FileStorage;
using cv::Rect;
using cv::Ptr;
using cv::FileNode;
using cv::Mat_;
using cv::Range;
using cv::FileNodeIterator;
using cv::ParallelLoopBody;
#include "boost.h"
#include "cascadeclassifier.h"
#include <queue>
@ -890,7 +903,7 @@ struct FeatureValAndIdxPrecalc : ParallelLoopBody
*(idst + fi*sample_count + si) = si;
}
if ( is_buf_16u )
std::sort(idst + fi*sample_count, idst + (fi + 1)*sample_count, LessThanIdx<float, unsigned short>(valCache->ptr<float>(fi)) );
std::sort(udst + fi*sample_count, udst + (fi + 1)*sample_count, LessThanIdx<float, unsigned short>(valCache->ptr<float>(fi)) );
else
std::sort(idst + fi*sample_count, idst + (fi + 1)*sample_count, LessThanIdx<float, int>(valCache->ptr<float>(fi)) );
}

2
apps/traincascade/boost.h
View File

@ -2,7 +2,7 @@
#define _OPENCV_BOOST_H_
#include "traincascade_features.h"
#include "ml.h"
#include "old_ml.hpp"
struct CvCascadeBoostParams : CvBoostParams
{

12
apps/traincascade/cascadeclassifier.cpp
View File

@ -137,6 +137,9 @@ bool CvCascadeClassifier::train( const string _cascadeDirName,
const CvCascadeBoostParams& _stageParams,
bool baseFormatSave )
{
// Start recording clock ticks for training time output
const clock_t begin_time = clock();
if( _cascadeDirName.empty() || _posFilename.empty() || _negFilename.empty() )
CV_Error( CV_StsBadArg, "_cascadeDirName or _bgfileName or _vecFileName is NULL" );
@ -247,6 +250,14 @@ bool CvCascadeClassifier::train( const string _cascadeDirName,
fs << FileStorage::getDefaultObjectName(stageFilename) << "{";
tempStage->write( fs, Mat() );
fs << "}";
// Output training time up till now
float seconds = float( clock () - begin_time ) / CLOCKS_PER_SEC;
int days = int(seconds) / 60 / 60 / 24;
int hours = (int(seconds) / 60 / 60) % 24;
int minutes = (int(seconds) / 60) % 60;
int seconds_left = int(seconds) % 60;
cout << "Training until now has taken " << days << " days " << hours << " hours " << minutes << " minutes " << seconds_left <<" seconds." << endl;
}
if(stageClassifiers.size() == 0)
@ -310,6 +321,7 @@ int CvCascadeClassifier::fillPassedSamples( int first, int count, bool isPositiv
if( predict( i ) == 1.0F )
{
getcount++;
printf("%s current samples: %d\r", isPositive ? "POS":"NEG", getcount);
break;
}
}

2
apps/traincascade/cascadeclassifier.h
View File

@ -7,8 +7,6 @@
#include "lbpfeatures.h"
#include "HOGfeatures.h" //new
#include "boost.h"
#include "cv.h"
#include "cxcore.h"
#define CC_CASCADE_FILENAME "cascade.xml"
#define CC_PARAMS_FILENAME "params.xml"

4
apps/traincascade/features.cpp
View File

@ -13,9 +13,9 @@ float calcNormFactor( const Mat& sum, const Mat& sqSum )
size_t p0, p1, p2, p3;
CV_SUM_OFFSETS( p0, p1, p2, p3, normrect, sum.step1() )
double area = normrect.width * normrect.height;
const int *sp = (const int*)sum.data;
const int *sp = sum.ptr<int>();
int valSum = sp[p0] - sp[p1] - sp[p2] + sp[p3];
const double *sqp = (const double *)sqSum.data;
const double *sqp = sqSum.ptr<double>();
double valSqSum = sqp[p0] - sqp[p1] - sqp[p2] + sqp[p3];
return (float) sqrt( (double) (area * valSqSum - (double)valSum * valSum) );
}

7
apps/traincascade/imagestorage.cpp
View File

@ -1,6 +1,7 @@
#include "opencv2/core.hpp"
#include "opencv2/core/core_c.h"
#include "opencv2/imgproc.hpp"
#include "opencv2/highgui.hpp"
#include "opencv2/imgcodecs.hpp"
#include "imagestorage.h"
#include <stdio.h>
@ -70,7 +71,7 @@ bool CvCascadeImageReader::NegReader::nextImg()
_offset.x = std::min( (int)round % winSize.width, src.cols - winSize.width );
_offset.y = std::min( (int)round / winSize.width, src.rows - winSize.height );
if( !src.empty() && src.type() == CV_8UC1
&& offset.x >= 0 && offset.y >= 0 )
&& _offset.x >= 0 && _offset.y >= 0 )
break;
}
@ -97,7 +98,7 @@ bool CvCascadeImageReader::NegReader::get( Mat& _img )
return false;
Mat mat( winSize.height, winSize.width, CV_8UC1,
(void*)(img.data + point.y * img.step + point.x * img.elemSize()), img.step );
(void*)(img.ptr(point.y) + point.x * img.elemSize()), img.step );
mat.copyTo(_img);
if( (int)( point.x + (1.0F + stepFactor ) * winSize.width ) < img.cols )

3
apps/traincascade/imagestorage.h
View File

@ -1,9 +1,6 @@
#ifndef _OPENCV_IMAGESTORAGE_H_
#define _OPENCV_IMAGESTORAGE_H_
#include "highgui.h"
class CvCascadeImageReader
{

2068
apps/traincascade/old_ml.hpp Normal file
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File diff suppressed because it is too large Load Diff

2162
apps/traincascade/old_ml_boost.cpp Normal file
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File diff suppressed because it is too large Load Diff

792
apps/traincascade/old_ml_data.cpp Normal file
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@ -0,0 +1,792 @@
/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#include "old_ml_precomp.hpp"
#include <ctype.h>
#define MISS_VAL FLT_MAX
#define CV_VAR_MISS 0
CvTrainTestSplit::CvTrainTestSplit()
{
train_sample_part_mode = CV_COUNT;
train_sample_part.count = -1;
mix = false;
}
CvTrainTestSplit::CvTrainTestSplit( int _train_sample_count, bool _mix )
{
train_sample_part_mode = CV_COUNT;
train_sample_part.count = _train_sample_count;
mix = _mix;
}
CvTrainTestSplit::CvTrainTestSplit( float _train_sample_portion, bool _mix )
{
train_sample_part_mode = CV_PORTION;
train_sample_part.portion = _train_sample_portion;
mix = _mix;
}
////////////////
CvMLData::CvMLData()
{
values = missing = var_types = var_idx_mask = response_out = var_idx_out = var_types_out = 0;
train_sample_idx = test_sample_idx = 0;
header_lines_number = 0;
sample_idx = 0;
response_idx = -1;
train_sample_count = -1;
delimiter = ',';
miss_ch = '?';
//flt_separator = '.';
rng = &cv::theRNG();
}
CvMLData::~CvMLData()
{
clear();
}
void CvMLData::free_train_test_idx()
{
cvReleaseMat( &train_sample_idx );
cvReleaseMat( &test_sample_idx );
sample_idx = 0;
}
void CvMLData::clear()
{
class_map.clear();
cvReleaseMat( &values );
cvReleaseMat( &missing );
cvReleaseMat( &var_types );
cvReleaseMat( &var_idx_mask );
cvReleaseMat( &response_out );
cvReleaseMat( &var_idx_out );
cvReleaseMat( &var_types_out );
free_train_test_idx();
total_class_count = 0;
response_idx = -1;
train_sample_count = -1;
}
void CvMLData::set_header_lines_number( int idx )
{
header_lines_number = std::max(0, idx);
}
int CvMLData::get_header_lines_number() const
{
return header_lines_number;
}
static char *fgets_chomp(char *str, int n, FILE *stream)
{
char *head = fgets(str, n, stream);
if( head )
{
for(char *tail = head + strlen(head) - 1; tail >= head; --tail)
{
if( *tail != '\r' && *tail != '\n' )
break;
*tail = '\0';
}
}
return head;
}
int CvMLData::read_csv(const char* filename)
{
const int M = 1000000;
const char str_delimiter[3] = { ' ', delimiter, '\0' };
FILE* file = 0;
CvMemStorage* storage;
CvSeq* seq;
char *ptr;
float* el_ptr;
CvSeqReader reader;
int cols_count = 0;
uchar *var_types_ptr = 0;
clear();
file = fopen( filename, "rt" );
if( !file )
return -1;
std::vector<char> _buf(M);
char* buf = &_buf[0];
// skip header lines
for( int i = 0; i < header_lines_number; i++ )
{
if( fgets( buf, M, file ) == 0 )
{
fclose(file);
return -1;
}
}
// read the first data line and determine the number of variables
if( !fgets_chomp( buf, M, file ))
{
fclose(file);
return -1;
}
ptr = buf;
while( *ptr == ' ' )
ptr++;
for( ; *ptr != '\0'; )
{
if(*ptr == delimiter || *ptr == ' ')
{
cols_count++;
ptr++;
while( *ptr == ' ' ) ptr++;
}
else
ptr++;
}
cols_count++;
if ( cols_count == 0)
{
fclose(file);
return -1;
}
// create temporary memory storage to store the whole database
el_ptr = new float[cols_count];
storage = cvCreateMemStorage();
seq = cvCreateSeq( 0, sizeof(*seq), cols_count*sizeof(float), storage );
var_types = cvCreateMat( 1, cols_count, CV_8U );
cvZero( var_types );
var_types_ptr = var_types->data.ptr;
for(;;)
{
char *token = NULL;
int type;
token = strtok(buf, str_delimiter);
if (!token)
break;
for (int i = 0; i < cols_count-1; i++)
{
str_to_flt_elem( token, el_ptr[i], type);
var_types_ptr[i] |= type;
token = strtok(NULL, str_delimiter);
if (!token)
{
fclose(file);
delete [] el_ptr;
return -1;
}
}
str_to_flt_elem( token, el_ptr[cols_count-1], type);
var_types_ptr[cols_count-1] |= type;
cvSeqPush( seq, el_ptr );
if( !fgets_chomp( buf, M, file ) )
break;
}
fclose(file);
values = cvCreateMat( seq->total, cols_count, CV_32FC1 );
missing = cvCreateMat( seq->total, cols_count, CV_8U );
var_idx_mask = cvCreateMat( 1, values->cols, CV_8UC1 );
cvSet( var_idx_mask, cvRealScalar(1) );
train_sample_count = seq->total;
cvStartReadSeq( seq, &reader );
for(int i = 0; i < seq->total; i++ )
{
const float* sdata = (float*)reader.ptr;
float* ddata = values->data.fl + cols_count*i;
uchar* dm = missing->data.ptr + cols_count*i;
for( int j = 0; j < cols_count; j++ )
{
ddata[j] = sdata[j];
dm[j] = ( fabs( MISS_VAL - sdata[j] ) <= FLT_EPSILON );
}
CV_NEXT_SEQ_ELEM( seq->elem_size, reader );
}
if ( cvNorm( missing, 0, CV_L1 ) <= FLT_EPSILON )
cvReleaseMat( &missing );
cvReleaseMemStorage( &storage );
delete []el_ptr;
return 0;
}
const CvMat* CvMLData::get_values() const
{
return values;
}
const CvMat* CvMLData::get_missing() const
{
CV_FUNCNAME( "CvMLData::get_missing" );
__BEGIN__;
if ( !values )
CV_ERROR( CV_StsInternal, "data is empty" );
__END__;
return missing;
}
const std::map<cv::String, int>& CvMLData::get_class_labels_map() const
{
return class_map;
}
void CvMLData::str_to_flt_elem( const char* token, float& flt_elem, int& type)
{
char* stopstring = NULL;
flt_elem = (float)strtod( token, &stopstring );
assert( stopstring );
type = CV_VAR_ORDERED;
if ( *stopstring == miss_ch && strlen(stopstring) == 1 ) // missed value
{
flt_elem = MISS_VAL;
type = CV_VAR_MISS;
}
else
{
if ( (*stopstring != 0) && (*stopstring != '\n') && (strcmp(stopstring, "\r\n") != 0) ) // class label
{
int idx = class_map[token];
if ( idx == 0)
{
total_class_count++;
idx = total_class_count;
class_map[token] = idx;
}
flt_elem = (float)idx;
type = CV_VAR_CATEGORICAL;
}
}
}
void CvMLData::set_delimiter(char ch)
{
CV_FUNCNAME( "CvMLData::set_delimited" );
__BEGIN__;
if (ch == miss_ch /*|| ch == flt_separator*/)
CV_ERROR(CV_StsBadArg, "delimited, miss_character and flt_separator must be different");
delimiter = ch;
__END__;
}
char CvMLData::get_delimiter() const
{
return delimiter;
}
void CvMLData::set_miss_ch(char ch)
{
CV_FUNCNAME( "CvMLData::set_miss_ch" );
__BEGIN__;
if (ch == delimiter/* || ch == flt_separator*/)
CV_ERROR(CV_StsBadArg, "delimited, miss_character and flt_separator must be different");
miss_ch = ch;
__END__;
}
char CvMLData::get_miss_ch() const
{
return miss_ch;
}
void CvMLData::set_response_idx( int idx )
{
CV_FUNCNAME( "CvMLData::set_response_idx" );
__BEGIN__;
if ( !values )
CV_ERROR( CV_StsInternal, "data is empty" );
if ( idx >= values->cols)
CV_ERROR( CV_StsBadArg, "idx value is not correct" );
if ( response_idx >= 0 )
chahge_var_idx( response_idx, true );
if ( idx >= 0 )
chahge_var_idx( idx, false );
response_idx = idx;
__END__;
}
int CvMLData::get_response_idx() const
{
CV_FUNCNAME( "CvMLData::get_response_idx" );
__BEGIN__;
if ( !values )
CV_ERROR( CV_StsInternal, "data is empty" );
__END__;
return response_idx;
}
void CvMLData::change_var_type( int var_idx, int type )
{
CV_FUNCNAME( "CvMLData::change_var_type" );
__BEGIN__;
int var_count = 0;
if ( !values )
CV_ERROR( CV_StsInternal, "data is empty" );
var_count = values->cols;
if ( var_idx < 0 || var_idx >= var_count)
CV_ERROR( CV_StsBadArg, "var_idx is not correct" );
if ( type != CV_VAR_ORDERED && type != CV_VAR_CATEGORICAL)
CV_ERROR( CV_StsBadArg, "type is not correct" );
assert( var_types );
if ( var_types->data.ptr[var_idx] == CV_VAR_CATEGORICAL && type == CV_VAR_ORDERED)
CV_ERROR( CV_StsBadArg, "it`s impossible to assign CV_VAR_ORDERED type to categorical variable" );
var_types->data.ptr[var_idx] = (uchar)type;
__END__;
return;
}
void CvMLData::set_var_types( const char* str )
{
CV_FUNCNAME( "CvMLData::set_var_types" );
__BEGIN__;
const char* ord = 0, *cat = 0;
int var_count = 0, set_var_type_count = 0;
if ( !values )
CV_ERROR( CV_StsInternal, "data is empty" );
var_count = values->cols;
assert( var_types );
ord = strstr( str, "ord" );
cat = strstr( str, "cat" );
if ( !ord && !cat )
CV_ERROR( CV_StsBadArg, "types string is not correct" );
if ( !ord && strlen(cat) == 3 ) // str == "cat"
{
cvSet( var_types, cvScalarAll(CV_VAR_CATEGORICAL) );
return;
}
if ( !cat && strlen(ord) == 3 ) // str == "ord"
{
cvSet( var_types, cvScalarAll(CV_VAR_ORDERED) );
return;
}
if ( ord ) // parse ord str
{
char* stopstring = NULL;
if ( ord[3] != '[')
CV_ERROR( CV_StsBadArg, "types string is not correct" );
ord += 4; // pass "ord["
do
{
int b1 = (int)strtod( ord, &stopstring );
if ( *stopstring == 0 || (*stopstring != ',' && *stopstring != ']' && *stopstring != '-') )
CV_ERROR( CV_StsBadArg, "types string is not correct" );
ord = stopstring + 1;
if ( (stopstring[0] == ',') || (stopstring[0] == ']'))
{
if ( var_types->data.ptr[b1] == CV_VAR_CATEGORICAL)
CV_ERROR( CV_StsBadArg, "it`s impossible to assign CV_VAR_ORDERED type to categorical variable" );
var_types->data.ptr[b1] = CV_VAR_ORDERED;
set_var_type_count++;
}
else
{
if ( stopstring[0] == '-')
{
int b2 = (int)strtod( ord, &stopstring);
if ( (*stopstring == 0) || (*stopstring != ',' && *stopstring != ']') )
CV_ERROR( CV_StsBadArg, "types string is not correct" );
ord = stopstring + 1;
for (int i = b1; i <= b2; i++)
{
if ( var_types->data.ptr[i] == CV_VAR_CATEGORICAL)
CV_ERROR( CV_StsBadArg, "it`s impossible to assign CV_VAR_ORDERED type to categorical variable" );
var_types->data.ptr[i] = CV_VAR_ORDERED;
}
set_var_type_count += b2 - b1 + 1;
}
else
CV_ERROR( CV_StsBadArg, "types string is not correct" );
}
}
while (*stopstring != ']');
if ( stopstring[1] != '\0' && stopstring[1] != ',')
CV_ERROR( CV_StsBadArg, "types string is not correct" );
}
if ( cat ) // parse cat str
{
char* stopstring = NULL;
if ( cat[3] != '[')
CV_ERROR( CV_StsBadArg, "types string is not correct" );
cat += 4; // pass "cat["
do
{
int b1 = (int)strtod( cat, &stopstring );
if ( *stopstring == 0 || (*stopstring != ',' && *stopstring != ']' && *stopstring != '-') )
CV_ERROR( CV_StsBadArg, "types string is not correct" );
cat = stopstring + 1;
if ( (stopstring[0] == ',') || (stopstring[0] == ']'))
{
var_types->data.ptr[b1] = CV_VAR_CATEGORICAL;
set_var_type_count++;
}
else
{
if ( stopstring[0] == '-')
{
int b2 = (int)strtod( cat, &stopstring);
if ( (*stopstring == 0) || (*stopstring != ',' && *stopstring != ']') )
CV_ERROR( CV_StsBadArg, "types string is not correct" );
cat = stopstring + 1;
for (int i = b1; i <= b2; i++)
var_types->data.ptr[i] = CV_VAR_CATEGORICAL;
set_var_type_count += b2 - b1 + 1;
}
else
CV_ERROR( CV_StsBadArg, "types string is not correct" );
}
}
while (*stopstring != ']');
if ( stopstring[1] != '\0' && stopstring[1] != ',')
CV_ERROR( CV_StsBadArg, "types string is not correct" );
}
if (set_var_type_count != var_count)
CV_ERROR( CV_StsBadArg, "types string is not correct" );
__END__;
}
const CvMat* CvMLData::get_var_types()
{
CV_FUNCNAME( "CvMLData::get_var_types" );
__BEGIN__;
uchar *var_types_out_ptr = 0;
int avcount, vt_size;
if ( !values )
CV_ERROR( CV_StsInternal, "data is empty" );
assert( var_idx_mask );
avcount = cvFloor( cvNorm( var_idx_mask, 0, CV_L1 ) );
vt_size = avcount + (response_idx >= 0);
if ( avcount == values->cols || (avcount == values->cols-1 && response_idx == values->cols-1) )
return var_types;
if ( !var_types_out || ( var_types_out && var_types_out->cols != vt_size ) )
{
cvReleaseMat( &var_types_out );
var_types_out = cvCreateMat( 1, vt_size, CV_8UC1 );
}
var_types_out_ptr = var_types_out->data.ptr;
for( int i = 0; i < var_types->cols; i++)
{
if (i == response_idx || !var_idx_mask->data.ptr[i]) continue;
*var_types_out_ptr = var_types->data.ptr[i];
var_types_out_ptr++;
}
if ( response_idx >= 0 )
*var_types_out_ptr = var_types->data.ptr[response_idx];
__END__;
return var_types_out;
}
int CvMLData::get_var_type( int var_idx ) const
{
return var_types->data.ptr[var_idx];
}
const CvMat* CvMLData::get_responses()
{
CV_FUNCNAME( "CvMLData::get_responses_ptr" );
__BEGIN__;
int var_count = 0;
if ( !values )
CV_ERROR( CV_StsInternal, "data is empty" );
var_count = values->cols;
if ( response_idx < 0 || response_idx >= var_count )
return 0;
if ( !response_out )
response_out = cvCreateMatHeader( values->rows, 1, CV_32FC1 );
else
cvInitMatHeader( response_out, values->rows, 1, CV_32FC1);
cvGetCol( values, response_out, response_idx );
__END__;
return response_out;
}
void CvMLData::set_train_test_split( const CvTrainTestSplit * spl)
{
CV_FUNCNAME( "CvMLData::set_division" );
__BEGIN__;
int sample_count = 0;
if ( !values )
CV_ERROR( CV_StsInternal, "data is empty" );
sample_count = values->rows;
float train_sample_portion;
if (spl->train_sample_part_mode == CV_COUNT)
{
train_sample_count = spl->train_sample_part.count;
if (train_sample_count > sample_count)
CV_ERROR( CV_StsBadArg, "train samples count is not correct" );
train_sample_count = train_sample_count<=0 ? sample_count : train_sample_count;
}
else // dtype.train_sample_part_mode == CV_PORTION
{
train_sample_portion = spl->train_sample_part.portion;
if ( train_sample_portion > 1)
CV_ERROR( CV_StsBadArg, "train samples count is not correct" );
train_sample_portion = train_sample_portion <= FLT_EPSILON ||
1 - train_sample_portion <= FLT_EPSILON ? 1 : train_sample_portion;
train_sample_count = std::max(1, cvFloor( train_sample_portion * sample_count ));
}
if ( train_sample_count == sample_count )
{
free_train_test_idx();
return;
}
if ( train_sample_idx && train_sample_idx->cols != train_sample_count )
free_train_test_idx();
if ( !sample_idx)
{
int test_sample_count = sample_count- train_sample_count;
sample_idx = (int*)cvAlloc( sample_count * sizeof(sample_idx[0]) );
for (int i = 0; i < sample_count; i++ )
sample_idx[i] = i;
train_sample_idx = cvCreateMatHeader( 1, train_sample_count, CV_32SC1 );
*train_sample_idx = cvMat( 1, train_sample_count, CV_32SC1, &sample_idx[0] );
CV_Assert(test_sample_count > 0);
test_sample_idx = cvCreateMatHeader( 1, test_sample_count, CV_32SC1 );
*test_sample_idx = cvMat( 1, test_sample_count, CV_32SC1, &sample_idx[train_sample_count] );
}
mix = spl->mix;
if ( mix )
mix_train_and_test_idx();
__END__;
}
const CvMat* CvMLData::get_train_sample_idx() const
{
CV_FUNCNAME( "CvMLData::get_train_sample_idx" );
__BEGIN__;
if ( !values )
CV_ERROR( CV_StsInternal, "data is empty" );
__END__;
return train_sample_idx;
}
const CvMat* CvMLData::get_test_sample_idx() const
{
CV_FUNCNAME( "CvMLData::get_test_sample_idx" );
__BEGIN__;
if ( !values )
CV_ERROR( CV_StsInternal, "data is empty" );
__END__;
return test_sample_idx;
}
void CvMLData::mix_train_and_test_idx()
{
CV_FUNCNAME( "CvMLData::mix_train_and_test_idx" );
__BEGIN__;
if ( !values )
CV_ERROR( CV_StsInternal, "data is empty" );
__END__;
if ( !sample_idx)
return;
if ( train_sample_count > 0 && train_sample_count < values->rows )
{
int n = values->rows;
for (int i = 0; i < n; i++)
{
int a = (*rng)(n);
int b = (*rng)(n);
int t;
CV_SWAP( sample_idx[a], sample_idx[b], t );
}
}
}
const CvMat* CvMLData::get_var_idx()
{
CV_FUNCNAME( "CvMLData::get_var_idx" );
__BEGIN__;
int avcount = 0;
if ( !values )
CV_ERROR( CV_StsInternal, "data is empty" );
assert( var_idx_mask );
avcount = cvFloor( cvNorm( var_idx_mask, 0, CV_L1 ) );
int* vidx;
if ( avcount == values->cols )
return 0;
if ( !var_idx_out || ( var_idx_out && var_idx_out->cols != avcount ) )
{
cvReleaseMat( &var_idx_out );
var_idx_out = cvCreateMat( 1, avcount, CV_32SC1);
if ( response_idx >=0 )
var_idx_mask->data.ptr[response_idx] = 0;
}
vidx = var_idx_out->data.i;
for(int i = 0; i < var_idx_mask->cols; i++)
if ( var_idx_mask->data.ptr[i] )
{
*vidx = i;
vidx++;
}
__END__;
return var_idx_out;
}
void CvMLData::chahge_var_idx( int vi, bool state )
{
change_var_idx( vi, state );
}
void CvMLData::change_var_idx( int vi, bool state )
{
CV_FUNCNAME( "CvMLData::change_var_idx" );
__BEGIN__;
int var_count = 0;
if ( !values )
CV_ERROR( CV_StsInternal, "data is empty" );
var_count = values->cols;
if ( vi < 0 || vi >= var_count)
CV_ERROR( CV_StsBadArg, "variable index is not correct" );
assert( var_idx_mask );
var_idx_mask->data.ptr[vi] = state;
__END__;
}
/* End of file. */

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/*M///////////////////////////////////////////////////////////////////////////////////////
//
// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
// By downloading, copying, installing or using the software you agree to this license.
// If you do not agree to this license, do not download, install,
// copy or use the software.
//
//
// Intel License Agreement
//
// Copyright (C) 2000, Intel Corporation, all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
// * Redistribution's of source code must retain the above copyright notice,
// this list of conditions and the following disclaimer.
//
// * Redistribution's in binary form must reproduce the above copyright notice,
// this list of conditions and the following disclaimer in the documentation
// and/or other materials provided with the distribution.
//
// * The name of Intel Corporation may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// This software is provided by the copyright holders and contributors "as is" and
// any express or implied warranties, including, but not limited to, the implied
// warranties of merchantability and fitness for a particular purpose are disclaimed.
// In no event shall the Intel Corporation or contributors be liable for any direct,
// indirect, incidental, special, exemplary, or consequential damages
// (including, but not limited to, procurement of substitute goods or services;
// loss of use, data, or profits; or business interruption) however caused
// and on any theory of liability, whether in contract, strict liability,
// or tort (including negligence or otherwise) arising in any way out of
// the use of this software, even if advised of the possibility of such damage.
//
//M*/
#ifndef __OPENCV_PRECOMP_H__
#define __OPENCV_PRECOMP_H__
#include "opencv2/core.hpp"
#include "old_ml.hpp"
#include "opencv2/core/core_c.h"
#include "opencv2/core/utility.hpp"
#include "opencv2/core/private.hpp"
#include <assert.h>
#include <float.h>
#include <limits.h>
#include <math.h>
#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include <time.h>
#define ML_IMPL CV_IMPL
#define __BEGIN__ __CV_BEGIN__
#define __END__ __CV_END__
#define EXIT __CV_EXIT__
#define CV_MAT_ELEM_FLAG( mat, type, comp, vect, tflag ) \
(( tflag == CV_ROW_SAMPLE ) \
? (CV_MAT_ELEM( mat, type, comp, vect )) \
: (CV_MAT_ELEM( mat, type, vect, comp )))
/* Convert matrix to vector */
#define ICV_MAT2VEC( mat, vdata, vstep, num ) \
if( MIN( (mat).rows, (mat).cols ) != 1 ) \
CV_ERROR( CV_StsBadArg, "" ); \
(vdata) = ((mat).data.ptr); \
if( (mat).rows == 1 ) \
{ \
(vstep) = CV_ELEM_SIZE( (mat).type ); \
(num) = (mat).cols; \
} \
else \
{ \
(vstep) = (mat).step; \
(num) = (mat).rows; \
}
/* get raw data */
#define ICV_RAWDATA( mat, flags, rdata, sstep, cstep, m, n ) \
(rdata) = (mat).data.ptr; \
if( CV_IS_ROW_SAMPLE( flags ) ) \
{ \
(sstep) = (mat).step; \
(cstep) = CV_ELEM_SIZE( (mat).type ); \
(m) = (mat).rows; \
(n) = (mat).cols; \
} \
else \
{ \
(cstep) = (mat).step; \
(sstep) = CV_ELEM_SIZE( (mat).type ); \
(n) = (mat).rows; \
(m) = (mat).cols; \
}
#define ICV_IS_MAT_OF_TYPE( mat, mat_type) \
(CV_IS_MAT( mat ) && CV_MAT_TYPE( mat->type ) == (mat_type) && \
(mat)->cols > 0 && (mat)->rows > 0)
/*
uchar* data; int sstep, cstep; - trainData->data
uchar* classes; int clstep; int ncl;- trainClasses
uchar* tmask; int tmstep; int ntm; - typeMask
uchar* missed;int msstep, mcstep; -missedMeasurements...
int mm, mn; == m,n == size,dim
uchar* sidx;int sistep; - sampleIdx
uchar* cidx;int cistep; - compIdx
int k, l; == n,m == dim,size (length of cidx, sidx)
int m, n; == size,dim
*/
#define ICV_DECLARE_TRAIN_ARGS() \
uchar* data; \
int sstep, cstep; \
uchar* classes; \
int clstep; \
int ncl; \
uchar* tmask; \
int tmstep; \
int ntm; \
uchar* missed; \
int msstep, mcstep; \
int mm, mn; \
uchar* sidx; \
int sistep; \
uchar* cidx; \
int cistep; \
int k, l; \
int m, n; \
\
data = classes = tmask = missed = sidx = cidx = NULL; \
sstep = cstep = clstep = ncl = tmstep = ntm = msstep = mcstep = mm = mn = 0; \
sistep = cistep = k = l = m = n = 0;
#define ICV_TRAIN_DATA_REQUIRED( param, flags ) \
if( !ICV_IS_MAT_OF_TYPE( (param), CV_32FC1 ) ) \
{ \
CV_ERROR( CV_StsBadArg, "Invalid " #param " parameter" ); \
} \
else \
{ \
ICV_RAWDATA( *(param), (flags), data, sstep, cstep, m, n ); \
k = n; \
l = m; \
}
#define ICV_TRAIN_CLASSES_REQUIRED( param ) \
if( !ICV_IS_MAT_OF_TYPE( (param), CV_32FC1 ) ) \
{ \
CV_ERROR( CV_StsBadArg, "Invalid " #param " parameter" ); \
} \
else \
{ \
ICV_MAT2VEC( *(param), classes, clstep, ncl ); \
if( m != ncl ) \
{ \
CV_ERROR( CV_StsBadArg, "Unmatched sizes" ); \
} \
}
#define ICV_ARG_NULL( param ) \
if( (param) != NULL ) \
{ \
CV_ERROR( CV_StsBadArg, #param " parameter must be NULL" ); \
}
#define ICV_MISSED_MEASUREMENTS_OPTIONAL( param, flags ) \
if( param ) \
{ \
if( !ICV_IS_MAT_OF_TYPE( param, CV_8UC1 ) ) \
{ \
CV_ERROR( CV_StsBadArg, "Invalid " #param " parameter" ); \
} \
else \
{ \
ICV_RAWDATA( *(param), (flags), missed, msstep, mcstep, mm, mn ); \
if( mm != m || mn != n ) \
{ \
CV_ERROR( CV_StsBadArg, "Unmatched sizes" ); \
} \
} \
}
#define ICV_COMP_IDX_OPTIONAL( param ) \
if( param ) \
{ \
if( !ICV_IS_MAT_OF_TYPE( param, CV_32SC1 ) ) \
{ \
CV_ERROR( CV_StsBadArg, "Invalid " #param " parameter" ); \
} \
else \
{ \
ICV_MAT2VEC( *(param), cidx, cistep, k ); \
if( k > n ) \
CV_ERROR( CV_StsBadArg, "Invalid " #param " parameter" ); \
} \
}
#define ICV_SAMPLE_IDX_OPTIONAL( param ) \
if( param ) \
{ \
if( !ICV_IS_MAT_OF_TYPE( param, CV_32SC1 ) ) \
{ \
CV_ERROR( CV_StsBadArg, "Invalid " #param " parameter" ); \
} \
else \
{ \
ICV_MAT2VEC( *sampleIdx, sidx, sistep, l ); \
if( l > m ) \
CV_ERROR( CV_StsBadArg, "Invalid " #param " parameter" ); \
} \
}
/****************************************************************************************/
#define ICV_CONVERT_FLOAT_ARRAY_TO_MATRICE( array, matrice ) \
{ \
CvMat a, b; \
int dims = (matrice)->cols; \
int nsamples = (matrice)->rows; \
int type = CV_MAT_TYPE((matrice)->type); \
int i, offset = dims; \
\
CV_ASSERT( type == CV_32FC1 || type == CV_64FC1 ); \
offset *= ((type == CV_32FC1) ? sizeof(float) : sizeof(double));\
\
b = cvMat( 1, dims, CV_32FC1 ); \
cvGetRow( matrice, &a, 0 ); \
for( i = 0; i < nsamples; i++, a.data.ptr += offset ) \
{ \
b.data.fl = (float*)array[i]; \
CV_CALL( cvConvert( &b, &a ) ); \
} \
}
/****************************************************************************************\
* Auxiliary functions declarations *
\****************************************************************************************/
/* Generates a set of classes centers in quantity <num_of_clusters> that are generated as
uniform random vectors in parallelepiped, where <data> is concentrated. Vectors in
<data> should have horizontal orientation. If <centers> != NULL, the function doesn't
allocate any memory and stores generated centers in <centers>, returns <centers>.
If <centers> == NULL, the function allocates memory and creates the matrice. Centers
are supposed to be oriented horizontally. */
CvMat* icvGenerateRandomClusterCenters( int seed,
const CvMat* data,
int num_of_clusters,
CvMat* centers CV_DEFAULT(0));
/* Fills the <labels> using <probs> by choosing the maximal probability. Outliers are
fixed by <oulier_tresh> and have cluster label (-1). Function also controls that there
weren't "empty" clusters by filling empty clusters with the maximal probability vector.
If probs_sums != NULL, filles it with the sums of probabilities for each sample (it is
useful for normalizing probabilities' matrice of FCM) */
void icvFindClusterLabels( const CvMat* probs, float outlier_thresh, float r,
const CvMat* labels );
typedef struct CvSparseVecElem32f
{
int idx;
float val;
}
CvSparseVecElem32f;
/* Prepare training data and related parameters */
#define CV_TRAIN_STATMODEL_DEFRAGMENT_TRAIN_DATA 1
#define CV_TRAIN_STATMODEL_SAMPLES_AS_ROWS 2
#define CV_TRAIN_STATMODEL_SAMPLES_AS_COLUMNS 4
#define CV_TRAIN_STATMODEL_CATEGORICAL_RESPONSE 8
#define CV_TRAIN_STATMODEL_ORDERED_RESPONSE 16
#define CV_TRAIN_STATMODEL_RESPONSES_ON_OUTPUT 32
#define CV_TRAIN_STATMODEL_ALWAYS_COPY_TRAIN_DATA 64
#define CV_TRAIN_STATMODEL_SPARSE_AS_SPARSE 128
int
cvPrepareTrainData( const char* /*funcname*/,
const CvMat* train_data, int tflag,
const CvMat* responses, int response_type,
const CvMat* var_idx,
const CvMat* sample_idx,
bool always_copy_data,
const float*** out_train_samples,
int* _sample_count,
int* _var_count,
int* _var_all,
CvMat** out_responses,
CvMat** out_response_map,
CvMat** out_var_idx,
CvMat** out_sample_idx=0 );
void
cvSortSamplesByClasses( const float** samples, const CvMat* classes,
int* class_ranges, const uchar** mask CV_DEFAULT(0) );
void
cvCombineResponseMaps (CvMat* _responses,
const CvMat* old_response_map,
CvMat* new_response_map,
CvMat** out_response_map);
void
cvPreparePredictData( const CvArr* sample, int dims_all, const CvMat* comp_idx,
int class_count, const CvMat* prob, float** row_sample,
int as_sparse CV_DEFAULT(0) );
/* copies clustering [or batch "predict"] results
(labels and/or centers and/or probs) back to the output arrays */
void
cvWritebackLabels( const CvMat* labels, CvMat* dst_labels,
const CvMat* centers, CvMat* dst_centers,
const CvMat* probs, CvMat* dst_probs,
const CvMat* sample_idx, int samples_all,
const CvMat* comp_idx, int dims_all );
#define cvWritebackResponses cvWritebackLabels
#define XML_FIELD_NAME "_name"
CvFileNode* icvFileNodeGetChild(CvFileNode* father, const char* name);
CvFileNode* icvFileNodeGetChildArrayElem(CvFileNode* father, const char* name,int index);
CvFileNode* icvFileNodeGetNext(CvFileNode* n, const char* name);
void cvCheckTrainData( const CvMat* train_data, int tflag,
const CvMat* missing_mask,
int* var_all, int* sample_all );
CvMat* cvPreprocessIndexArray( const CvMat* idx_arr, int data_arr_size, bool check_for_duplicates=false );
CvMat* cvPreprocessVarType( const CvMat* type_mask, const CvMat* var_idx,
int var_all, int* response_type );
CvMat* cvPreprocessOrderedResponses( const CvMat* responses,
const CvMat* sample_idx, int sample_all );
CvMat* cvPreprocessCategoricalResponses( const CvMat* responses,
const CvMat* sample_idx, int sample_all,
CvMat** out_response_map, CvMat** class_counts=0 );
const float** cvGetTrainSamples( const CvMat* train_data, int tflag,
const CvMat* var_idx, const CvMat* sample_idx,
int* _var_count, int* _sample_count,
bool always_copy_data=false );
namespace cv
{
struct DTreeBestSplitFinder
{
DTreeBestSplitFinder(){ splitSize = 0, tree = 0; node = 0; }
DTreeBestSplitFinder( CvDTree* _tree, CvDTreeNode* _node);
DTreeBestSplitFinder( const DTreeBestSplitFinder& finder, Split );
virtual ~DTreeBestSplitFinder() {}
virtual void operator()(const BlockedRange& range);
void join( DTreeBestSplitFinder& rhs );
Ptr<CvDTreeSplit> bestSplit;
Ptr<CvDTreeSplit> split;
int splitSize;
CvDTree* tree;
CvDTreeNode* node;
};
struct ForestTreeBestSplitFinder : DTreeBestSplitFinder
{
ForestTreeBestSplitFinder() : DTreeBestSplitFinder() {}
ForestTreeBestSplitFinder( CvForestTree* _tree, CvDTreeNode* _node );
ForestTreeBestSplitFinder( const ForestTreeBestSplitFinder& finder, Split );
virtual void operator()(const BlockedRange& range);
};
}
#endif /* __ML_H__ */

4151
apps/traincascade/old_ml_tree.cpp Normal file
View File

File diff suppressed because it is too large Load Diff

9
apps/traincascade/traincascade.cpp
View File

@ -1,6 +1,4 @@
#include "opencv2/core.hpp"
#include "cv.h"
#include "cascadeclassifier.h"
using namespace std;
@ -13,6 +11,7 @@ int main( int argc, char* argv[] )
int numPos = 2000;
int numNeg = 1000;
int numStages = 20;
int numThreads = getNumThreads();
int precalcValBufSize = 256,
precalcIdxBufSize = 256;
bool baseFormatSave = false;
@ -36,6 +35,7 @@ int main( int argc, char* argv[] )
cout << " [-precalcValBufSize <precalculated_vals_buffer_size_in_Mb = " << precalcValBufSize << ">]" << endl;
cout << " [-precalcIdxBufSize <precalculated_idxs_buffer_size_in_Mb = " << precalcIdxBufSize << ">]" << endl;
cout << " [-baseFormatSave]" << endl;
cout << " [-numThreads <max_number_of_threads = " << numThreads << ">]" << endl;
cascadeParams.printDefaults();
stageParams.printDefaults();
for( int fi = 0; fi < fc; fi++ )
@ -82,6 +82,10 @@ int main( int argc, char* argv[] )
{
baseFormatSave = true;
}
else if( !strcmp( argv[i], "-numThreads" ) )
{
numThreads = atoi(argv[++i]);
}
else if ( cascadeParams.scanAttr( argv[i], argv[i+1] ) ) { i++; }
else if ( stageParams.scanAttr( argv[i], argv[i+1] ) ) { i++; }
else if ( !set )
@ -98,6 +102,7 @@ int main( int argc, char* argv[] )
}
}
setNumThreads( numThreads );
classifier.train( cascadeDirName,
vecName,
bgName,

3
apps/traincascade/traincascade_features.h
View File

@ -2,9 +2,6 @@
#define _OPENCV_FEATURES_H_
#include "imagestorage.h"
#include "cxcore.h"
#include "cv.h"
#include "ml.h"
#include <stdio.h>
#define FEATURES "features"

383
cmake/FindCUDA.cmake
View File

@ -31,10 +31,8 @@
# The following variables affect the behavior of the macros in the
# script (in alphebetical order). Note that any of these flags can be
# changed multiple times in the same directory before calling
# CUDA_ADD_EXECUTABLE, CUDA_ADD_LIBRARY, CUDA_COMPILE, CUDA_COMPILE_PTX
# or CUDA_WRAP_SRCS.
#
# ::
# CUDA_ADD_EXECUTABLE, CUDA_ADD_LIBRARY, CUDA_COMPILE, CUDA_COMPILE_PTX,
# CUDA_COMPILE_FATBIN, CUDA_COMPILE_CUBIN or CUDA_WRAP_SRCS::
#
# CUDA_64_BIT_DEVICE_CODE (Default matches host bit size)
# -- Set to ON to compile for 64 bit device code, OFF for 32 bit device code.
@ -43,19 +41,11 @@
# nvcc in the generated source. If you compile to PTX and then load the
# file yourself, you can mix bit sizes between device and host.
#
#
#
# ::
#
# CUDA_ATTACH_VS_BUILD_RULE_TO_CUDA_FILE (Default ON)
# -- Set to ON if you want the custom build rule to be attached to the source
# file in Visual Studio. Turn OFF if you add the same cuda file to multiple
# targets.
#
#
#
# ::
#
# This allows the user to build the target from the CUDA file; however, bad
# things can happen if the CUDA source file is added to multiple targets.
# When performing parallel builds it is possible for the custom build
@ -68,44 +58,24 @@
# this script could detect the reuse of source files across multiple targets
# and turn the option off for the user, but no good solution could be found.
#
#
#
# ::
#
# CUDA_BUILD_CUBIN (Default OFF)
# -- Set to ON to enable and extra compilation pass with the -cubin option in
# Device mode. The output is parsed and register, shared memory usage is
# printed during build.
#
#
#
# ::
#
# CUDA_BUILD_EMULATION (Default OFF for device mode)
# -- Set to ON for Emulation mode. -D_DEVICEEMU is defined for CUDA C files
# when CUDA_BUILD_EMULATION is TRUE.
#
#
#
# ::
#
# CUDA_GENERATED_OUTPUT_DIR (Default CMAKE_CURRENT_BINARY_DIR)
# -- Set to the path you wish to have the generated files placed. If it is
# blank output files will be placed in CMAKE_CURRENT_BINARY_DIR.
# Intermediate files will always be placed in
# CMAKE_CURRENT_BINARY_DIR/CMakeFiles.
#
#
#
# ::
#
# CUDA_HOST_COMPILATION_CPP (Default ON)
# -- Set to OFF for C compilation of host code.
#
#
#
# ::
#
# CUDA_HOST_COMPILER (Default CMAKE_C_COMPILER, $(VCInstallDir)/bin for VS)
# -- Set the host compiler to be used by nvcc. Ignored if -ccbin or
# --compiler-bindir is already present in the CUDA_NVCC_FLAGS or
@ -113,19 +83,11 @@
# $(VCInstallDir)/bin is a special value that expands out to the path when
# the command is run from withing VS.
#
#
#
# ::
#
# CUDA_NVCC_FLAGS
# CUDA_NVCC_FLAGS_<CONFIG>
# -- Additional NVCC command line arguments. NOTE: multiple arguments must be
# semi-colon delimited (e.g. --compiler-options;-Wall)
#
#
#
# ::
#
# CUDA_PROPAGATE_HOST_FLAGS (Default ON)
# -- Set to ON to propagate CMAKE_{C,CXX}_FLAGS and their configuration
# dependent counterparts (e.g. CMAKE_C_FLAGS_DEBUG) automatically to the
@ -137,10 +99,6 @@
# CUDA_ADD_LIBRARY, CUDA_ADD_EXECUTABLE, or CUDA_WRAP_SRCS. Flags used for
# shared library compilation are not affected by this flag.
#
#
#
# ::
#
# CUDA_SEPARABLE_COMPILATION (Default OFF)
# -- If set this will enable separable compilation for all CUDA runtime object
# files. If used outside of CUDA_ADD_EXECUTABLE and CUDA_ADD_LIBRARY
@ -148,38 +106,22 @@
# CUDA_COMPUTE_SEPARABLE_COMPILATION_OBJECT_FILE_NAME and
# CUDA_LINK_SEPARABLE_COMPILATION_OBJECTS should be called.
#
#
#
# ::
#
# CUDA_VERBOSE_BUILD (Default OFF)
# -- Set to ON to see all the commands used when building the CUDA file. When
# using a Makefile generator the value defaults to VERBOSE (run make
# VERBOSE=1 to see output), although setting CUDA_VERBOSE_BUILD to ON will
# always print the output.
#
#
#
# The script creates the following macros (in alphebetical order):
#
# ::
# The script creates the following macros (in alphebetical order)::
#
# CUDA_ADD_CUFFT_TO_TARGET( cuda_target )
# -- Adds the cufft library to the target (can be any target). Handles whether
# you are in emulation mode or not.
#
#
#
# ::
#
# CUDA_ADD_CUBLAS_TO_TARGET( cuda_target )
# -- Adds the cublas library to the target (can be any target). Handles
# whether you are in emulation mode or not.
#
#
#
# ::
#
# CUDA_ADD_EXECUTABLE( cuda_target file0 file1 ...
# [WIN32] [MACOSX_BUNDLE] [EXCLUDE_FROM_ALL] [OPTIONS ...] )
# -- Creates an executable "cuda_target" which is made up of the files
@ -193,42 +135,28 @@
# nvcc. Such flags should be modified before calling CUDA_ADD_EXECUTABLE,
# CUDA_ADD_LIBRARY or CUDA_WRAP_SRCS.
#
#
#
# ::
#
# CUDA_ADD_LIBRARY( cuda_target file0 file1 ...
# [STATIC | SHARED | MODULE] [EXCLUDE_FROM_ALL] [OPTIONS ...] )
# -- Same as CUDA_ADD_EXECUTABLE except that a library is created.
#
#
#
# ::
#
# CUDA_BUILD_CLEAN_TARGET()
# -- Creates a convience target that deletes all the dependency files
# generated. You should make clean after running this target to ensure the
# dependency files get regenerated.
#
#
#
# ::
#
# CUDA_COMPILE( generated_files file0 file1 ... [STATIC | SHARED | MODULE]
# [OPTIONS ...] )
# -- Returns a list of generated files from the input source files to be used
# with ADD_LIBRARY or ADD_EXECUTABLE.
#
#
#
# ::
#
# CUDA_COMPILE_PTX( generated_files file0 file1 ... [OPTIONS ...] )
# -- Returns a list of PTX files generated from the input source files.
#
# CUDA_COMPILE_FATBIN( generated_files file0 file1 ... [OPTIONS ...] )
# -- Returns a list of FATBIN files generated from the input source files.
#
#
# ::
# CUDA_COMPILE_CUBIN( generated_files file0 file1 ... [OPTIONS ...] )
# -- Returns a list of CUBIN files generated from the input source files.
#
# CUDA_COMPUTE_SEPARABLE_COMPILATION_OBJECT_FILE_NAME( output_file_var
# cuda_target
@ -242,10 +170,6 @@
# automatically for CUDA_ADD_LIBRARY and CUDA_ADD_EXECUTABLE. Note that
# this is a function and not a macro.
#
#
#
# ::
#
# CUDA_INCLUDE_DIRECTORIES( path0 path1 ... )
# -- Sets the directories that should be passed to nvcc
# (e.g. nvcc -Ipath0 -Ipath1 ... ). These paths usually contain other .cu
@ -253,17 +177,9 @@
#
#
#
#
#
# ::
#
# CUDA_LINK_SEPARABLE_COMPILATION_OBJECTS( output_file_var cuda_target
# nvcc_flags object_files)
#
#
#
# ::
#
# -- Generates the link object required by separable compilation from the given
# object files. This is called automatically for CUDA_ADD_EXECUTABLE and
# CUDA_ADD_LIBRARY, but can be called manually when using CUDA_WRAP_SRCS
@ -273,91 +189,51 @@
# specified by CUDA_64_BIT_DEVICE_CODE. Note that this is a function
# instead of a macro.
#
#
#
# ::
#
# CUDA_WRAP_SRCS ( cuda_target format generated_files file0 file1 ...
# [STATIC | SHARED | MODULE] [OPTIONS ...] )
# -- This is where all the magic happens. CUDA_ADD_EXECUTABLE,
# CUDA_ADD_LIBRARY, CUDA_COMPILE, and CUDA_COMPILE_PTX all call this
# function under the hood.
#
#
#
# ::
#
# Given the list of files (file0 file1 ... fileN) this macro generates
# custom commands that generate either PTX or linkable objects (use "PTX" or
# "OBJ" for the format argument to switch). Files that don't end with .cu
# or have the HEADER_FILE_ONLY property are ignored.
#
#
#
# ::
#
# The arguments passed in after OPTIONS are extra command line options to
# give to nvcc. You can also specify per configuration options by
# specifying the name of the configuration followed by the options. General
# options must preceed configuration specific options. Not all
# configurations need to be specified, only the ones provided will be used.
#
#
#
# ::
#
# OPTIONS -DFLAG=2 "-DFLAG_OTHER=space in flag"
# DEBUG -g
# RELEASE --use_fast_math
# RELWITHDEBINFO --use_fast_math;-g
# MINSIZEREL --use_fast_math
#
#
#
# ::
#
# For certain configurations (namely VS generating object files with
# CUDA_ATTACH_VS_BUILD_RULE_TO_CUDA_FILE set to ON), no generated file will
# be produced for the given cuda file. This is because when you add the
# cuda file to Visual Studio it knows that this file produces an object file
# and will link in the resulting object file automatically.
#
#
#
# ::
#
# This script will also generate a separate cmake script that is used at
# build time to invoke nvcc. This is for several reasons.
#
#
#
# ::
#
# 1. nvcc can return negative numbers as return values which confuses
# Visual Studio into thinking that the command succeeded. The script now
# checks the error codes and produces errors when there was a problem.
#
#
#
# ::
#
# 2. nvcc has been known to not delete incomplete results when it
# encounters problems. This confuses build systems into thinking the
# target was generated when in fact an unusable file exists. The script
# now deletes the output files if there was an error.
#
#
#
# ::
#
# 3. By putting all the options that affect the build into a file and then
# make the build rule dependent on the file, the output files will be
# regenerated when the options change.
#
#
#
# ::
#
# This script also looks at optional arguments STATIC, SHARED, or MODULE to
# determine when to target the object compilation for a shared library.
# BUILD_SHARED_LIBS is ignored in CUDA_WRAP_SRCS, but it is respected in
@ -366,27 +242,17 @@
# <target_name>_EXPORTS is defined when a shared library compilation is
# detected.
#
#
#
# ::
#
# Flags passed into add_definitions with -D or /D are passed along to nvcc.
#
#
#
# The script defines the following variables:
#
# ::
# The script defines the following variables::
#
# CUDA_VERSION_MAJOR -- The major version of cuda as reported by nvcc.
# CUDA_VERSION_MINOR -- The minor version.
# CUDA_VERSION
# CUDA_VERSION_STRING -- CUDA_VERSION_MAJOR.CUDA_VERSION_MINOR
#
#
#
# ::
#
# CUDA_TOOLKIT_ROOT_DIR -- Path to the CUDA Toolkit (defined if not set).
# CUDA_SDK_ROOT_DIR -- Path to the CUDA SDK. Use this to find files in the
# SDK. This script will not directly support finding
@ -412,13 +278,13 @@
# Only available for CUDA version 3.2+.
# CUDA_cusparse_LIBRARY -- CUDA Sparse Matrix library.
# Only available for CUDA version 3.2+.
# CUDA_npp_LIBRARY -- NVIDIA Performance Primitives library.
# CUDA_npp_LIBRARY -- NVIDIA Performance Primitives lib.
# Only available for CUDA version 4.0+.
# CUDA_nppc_LIBRARY -- NVIDIA Performance Primitives library (core).
# CUDA_nppc_LIBRARY -- NVIDIA Performance Primitives lib (core).
# Only available for CUDA version 5.5+.
# CUDA_nppi_LIBRARY -- NVIDIA Performance Primitives library (image processing).
# CUDA_nppi_LIBRARY -- NVIDIA Performance Primitives lib (image processing).
# Only available for CUDA version 5.5+.
# CUDA_npps_LIBRARY -- NVIDIA Performance Primitives library (signal processing).
# CUDA_npps_LIBRARY -- NVIDIA Performance Primitives lib (signal processing).
# Only available for CUDA version 5.5+.
# CUDA_nvcuvenc_LIBRARY -- CUDA Video Encoder library.
# Only available for CUDA version 3.2+.
@ -427,32 +293,15 @@
# Only available for CUDA version 3.2+.
# Windows only.
#
#
#
#
#
# ::
#
# James Bigler, NVIDIA Corp (nvidia.com - jbigler)
# Abe Stephens, SCI Institute -- http://www.sci.utah.edu/~abe/FindCuda.html
#
#
#
# ::
#
# Copyright (c) 2008 - 2009 NVIDIA Corporation. All rights reserved.
#
#
#
# ::
#
# Copyright (c) 2007-2009
# Scientific Computing and Imaging Institute, University of Utah
#
#
#
# ::
#
# This code is licensed under the MIT License. See the FindCUDA.cmake script
# for the text of the license.
@ -481,11 +330,6 @@
# FindCUDA.cmake
# We need to have at least this version to support the VERSION_LESS argument to 'if' (2.6.2) and unset (2.6.3)
cmake_policy(PUSH)
cmake_minimum_required(VERSION 2.6.3)
cmake_policy(POP)
# This macro helps us find the location of helper files we will need the full path to
macro(CUDA_FIND_HELPER_FILE _name _extension)
set(_full_name "${_name}.${_extension}")
@ -608,7 +452,17 @@ set(CUDA_NVCC_FLAGS "" CACHE STRING "Semi-colon delimit multiple arguments.")
if(CMAKE_GENERATOR MATCHES "Visual Studio")
set(CUDA_HOST_COMPILER "$(VCInstallDir)bin" CACHE FILEPATH "Host side compiler used by NVCC")
else()
set(CUDA_HOST_COMPILER "${CMAKE_C_COMPILER}" CACHE FILEPATH "Host side compiler used by NVCC")
# Using cc which is symlink to clang may let NVCC think it is GCC and issue
# unhandled -dumpspecs option to clang. Also in case neither
# CMAKE_C_COMPILER is defined (project does not use C language) nor
# CUDA_HOST_COMPILER is specified manually we should skip -ccbin and let
# nvcc use its own default C compiler.
if(DEFINED CMAKE_C_COMPILER AND NOT DEFINED CUDA_HOST_COMPILER)
get_filename_component(c_compiler_realpath "${CMAKE_C_COMPILER}" REALPATH)
else()
set(c_compiler_realpath "")
endif()
set(CUDA_HOST_COMPILER "${c_compiler_realpath}" CACHE FILEPATH "Host side compiler used by NVCC")
endif()
# Propagate the host flags to the host compiler via -Xcompiler
@ -675,14 +529,16 @@ endmacro()
# Check to see if the CUDA_TOOLKIT_ROOT_DIR and CUDA_SDK_ROOT_DIR have changed,
# if they have then clear the cache variables, so that will be detected again.
if(NOT "${CUDA_TOOLKIT_ROOT_DIR}" STREQUAL "${CUDA_TOOLKIT_ROOT_DIR_INTERNAL}")
if(DEFINED CUDA_TOOLKIT_ROOT_DIR_INTERNAL AND (NOT "${CUDA_TOOLKIT_ROOT_DIR}" STREQUAL "${CUDA_TOOLKIT_ROOT_DIR_INTERNAL}"))
unset(CUDA_TARGET_TRIPLET CACHE)
unset(CUDA_TOOLKIT_TARGET_DIR CACHE)
unset(CUDA_NVCC_EXECUTABLE CACHE)
unset(CUDA_VERSION CACHE)
cuda_unset_include_and_libraries()
endif()
if(NOT "${CUDA_TOOLKIT_TARGET_DIR}" STREQUAL "${CUDA_TOOLKIT_TARGET_DIR_INTERNAL}")
if(DEFINED CUDA_TARGET_TRIPLET_INTERNAL AND (NOT "${CUDA_TARGET_TRIPLET}" STREQUAL "${CUDA_TARGET_TRIPLET_INTERNAL}") OR
(DEFINED CUDA_TOOLKIT_TARGET_DIR AND DEFINED CUDA_TOOLKIT_TARGET_DIR_INTERNAL AND NOT "${CUDA_TOOLKIT_TARGET_DIR}" STREQUAL "${CUDA_TOOLKIT_TARGET_DIR_INTERNAL}"))
cuda_unset_include_and_libraries()
endif()
@ -758,23 +614,46 @@ endif()
# Always set this convenience variable
set(CUDA_VERSION_STRING "${CUDA_VERSION}")
# Support for arm cross compilation with CUDA 5.5
if(CUDA_VERSION VERSION_GREATER "5.0" AND CMAKE_CROSSCOMPILING AND ${CMAKE_SYSTEM_PROCESSOR} MATCHES "arm" AND EXISTS "${CUDA_TOOLKIT_ROOT_DIR}/targets/armv7-linux-gnueabihf")
set(CUDA_TOOLKIT_TARGET_DIR "${CUDA_TOOLKIT_ROOT_DIR}/targets/armv7-linux-gnueabihf" CACHE PATH "Toolkit target location.")
else()
set(CUDA_TOOLKIT_TARGET_DIR "${CUDA_TOOLKIT_ROOT_DIR}" CACHE PATH "Toolkit target location.")
endif()
mark_as_advanced(CUDA_TOOLKIT_TARGET_DIR)
# Target CPU architecture
if(CUDA_VERSION VERSION_GREATER "5.0" AND CMAKE_CROSSCOMPILING AND ${CMAKE_SYSTEM_PROCESSOR} MATCHES "arm")
if(DEFINED CUDA_TARGET_CPU_ARCH)
set(_cuda_target_cpu_arch_initial "${CUDA_TARGET_CPU_ARCH}")
elseif(CUDA_VERSION VERSION_GREATER "5.0" AND CMAKE_CROSSCOMPILING AND CMAKE_SYSTEM_PROCESSOR MATCHES "^(arm|ARM)")
set(_cuda_target_cpu_arch_initial "ARM")
else()
set(_cuda_target_cpu_arch_initial "")
endif()
set(CUDA_TARGET_CPU_ARCH ${_cuda_target_cpu_arch_initial} CACHE STRING "Specify the name of the class of CPU architecture for which the input files must be compiled.")
set(CUDA_TARGET_CPU_ARCH "${_cuda_target_cpu_arch_initial}" CACHE STRING "Specify the name of the class of CPU architecture for which the input files must be compiled.")
mark_as_advanced(CUDA_TARGET_CPU_ARCH)
# Target OS variant
if(DEFINED CUDA_TARGET_OS_VARIANT)
set(_cuda_target_os_variant_initial "${CUDA_TARGET_OS_VARIANT}")
else()
set(_cuda_target_os_variant_initial "")
endif()
set(CUDA_TARGET_OS_VARIANT "${_cuda_target_os_variant_initial}" CACHE STRING "Specify the name of the class of OS for which the input files must be compiled.")
mark_as_advanced(CUDA_TARGET_OS_VARIANT)
# Target triplet
if(DEFINED CUDA_TARGET_TRIPLET)
set(_cuda_target_triplet_initial "${CUDA_TARGET_TRIPLET}")
elseif(CUDA_VERSION VERSION_GREATER "5.0" AND CMAKE_CROSSCOMPILING AND "${CUDA_TARGET_CPU_ARCH}" STREQUAL "ARM")
if("${CUDA_TARGET_OS_VARIANT}" STREQUAL "Android" AND EXISTS "${CUDA_TOOLKIT_ROOT_DIR}/targets/armv7-linux-androideabi")
set(_cuda_target_triplet_initial "armv7-linux-androideabi")
elseif(EXISTS "${CUDA_TOOLKIT_ROOT_DIR}/targets/armv7-linux-gnueabihf")
set(_cuda_target_triplet_initial "armv7-linux-gnueabihf")
endif()
endif()
set(CUDA_TARGET_TRIPLET "${_cuda_target_triplet_initial}" CACHE STRING "Specify the target triplet for which the input files must be compiled.")
file(GLOB __cuda_available_target_tiplets RELATIVE "${CUDA_TOOLKIT_ROOT_DIR}/targets" "${CUDA_TOOLKIT_ROOT_DIR}/targets/*" )
set_property(CACHE CUDA_TARGET_TRIPLET PROPERTY STRINGS ${__cuda_available_target_tiplets})
mark_as_advanced(CUDA_TARGET_TRIPLET)
# Target directory
if(NOT DEFINED CUDA_TOOLKIT_TARGET_DIR AND CUDA_TARGET_TRIPLET AND EXISTS "${CUDA_TOOLKIT_ROOT_DIR}/targets/${CUDA_TARGET_TRIPLET}")
set(CUDA_TOOLKIT_TARGET_DIR "${CUDA_TOOLKIT_ROOT_DIR}/targets/${CUDA_TARGET_TRIPLET}")
endif()
# CUDA_TOOLKIT_INCLUDE
find_path(CUDA_TOOLKIT_INCLUDE
device_functions.h # Header included in toolkit
@ -798,10 +677,16 @@ macro(cuda_find_library_local_first_with_path_ext _var _names _doc _path_ext )
# and old paths.
set(_cuda_64bit_lib_dir "${_path_ext}lib/x64" "${_path_ext}lib64" "${_path_ext}libx64" )
endif()
if(CUDA_VERSION VERSION_GREATER "6.0")
set(_cuda_static_lib_names "")
foreach(name ${_names})
list(APPEND _cuda_static_lib_names "${name}_static")
endforeach()
endif()
# CUDA 3.2+ on Windows moved the library directories, so we need to new
# (lib/Win32) and the old path (lib).
find_library(${_var}
NAMES ${_names}
NAMES ${_names} ${_cuda_static_lib_names}
PATHS "${CUDA_TOOLKIT_TARGET_DIR}" "${CUDA_TOOLKIT_ROOT_DIR}"
ENV CUDA_PATH
ENV CUDA_LIB_PATH
@ -811,7 +696,7 @@ macro(cuda_find_library_local_first_with_path_ext _var _names _doc _path_ext )
)
# Search default search paths, after we search our own set of paths.
find_library(${_var}
NAMES ${_names}
NAMES ${_names} ${_cuda_static_lib_names}
PATHS "/usr/lib/nvidia-current"
DOC ${_doc}
)
@ -849,18 +734,6 @@ if(CUDA_BUILD_EMULATION AND CUDA_CUDARTEMU_LIBRARY)
else()
set(CUDA_LIBRARIES ${CUDA_CUDART_LIBRARY})
endif()
if(APPLE)
# We need to add the path to cudart to the linker using rpath, since the
# library name for the cuda libraries is prepended with @rpath.
if(CUDA_BUILD_EMULATION AND CUDA_CUDARTEMU_LIBRARY)
get_filename_component(_cuda_path_to_cudart "${CUDA_CUDARTEMU_LIBRARY}" PATH)
else()
get_filename_component(_cuda_path_to_cudart "${CUDA_CUDART_LIBRARY}" PATH)
endif()
if(_cuda_path_to_cudart)
list(APPEND CUDA_LIBRARIES -Wl,-rpath "-Wl,${_cuda_path_to_cudart}")
endif()
endif()
# 1.1 toolkit on linux doesn't appear to have a separate library on
# some platforms.
@ -993,6 +866,8 @@ set(CUDA_FOUND TRUE)
set(CUDA_TOOLKIT_ROOT_DIR_INTERNAL "${CUDA_TOOLKIT_ROOT_DIR}" CACHE INTERNAL
"This is the value of the last time CUDA_TOOLKIT_ROOT_DIR was set successfully." FORCE)
set(CUDA_TARGET_TRIPLET_INTERNAL "${CUDA_TARGET_TRIPLET}" CACHE INTERNAL
"This is the value of the last time CUDA_TARGET_TRIPLET was set successfully." FORCE)
set(CUDA_TOOLKIT_TARGET_DIR_INTERNAL "${CUDA_TOOLKIT_TARGET_DIR}" CACHE INTERNAL
"This is the value of the last time CUDA_TOOLKIT_TARGET_DIR was set successfully." FORCE)
set(CUDA_SDK_ROOT_DIR_INTERNAL "${CUDA_SDK_ROOT_DIR}" CACHE INTERNAL
@ -1040,15 +915,15 @@ macro(CUDA_GET_SOURCES_AND_OPTIONS _sources _cmake_options _options)
set( ${_options} )
set( _found_options FALSE )
foreach(arg ${ARGN})
if(arg STREQUAL "OPTIONS")
if("x${arg}" STREQUAL "xOPTIONS")
set( _found_options TRUE )
elseif(
arg STREQUAL "WIN32" OR
arg STREQUAL "MACOSX_BUNDLE" OR
arg STREQUAL "EXCLUDE_FROM_ALL" OR
arg STREQUAL "STATIC" OR
arg STREQUAL "SHARED" OR
arg STREQUAL "MODULE"
"x${arg}" STREQUAL "xWIN32" OR
"x${arg}" STREQUAL "xMACOSX_BUNDLE" OR
"x${arg}" STREQUAL "xEXCLUDE_FROM_ALL" OR
"x${arg}" STREQUAL "xSTATIC" OR
"x${arg}" STREQUAL "xSHARED" OR
"x${arg}" STREQUAL "xMODULE"
)
list(APPEND ${_cmake_options} ${arg})
else()
@ -1144,7 +1019,7 @@ function(CUDA_COMPUTE_BUILD_PATH path build_path)
endif()
endif()
# This recipie is from cmLocalGenerator::CreateSafeUniqueObjectFileName in the
# This recipe is from cmLocalGenerator::CreateSafeUniqueObjectFileName in the
# CMake source.
# Remove leading /
@ -1173,7 +1048,7 @@ endfunction()
# a .cpp or .ptx file.
# INPUT:
# cuda_target - Target name
# format - PTX or OBJ
# format - PTX, CUBIN, FATBIN or OBJ
# FILE1 .. FILEN - The remaining arguments are the sources to be wrapped.
# OPTIONS - Extra options to NVCC
# OUTPUT:
@ -1223,6 +1098,10 @@ macro(CUDA_WRAP_SRCS cuda_target format generated_files)
set(nvcc_flags ${nvcc_flags} "--target-cpu-architecture=${CUDA_TARGET_CPU_ARCH}")
endif()
if(CUDA_TARGET_OS_VARIANT AND CUDA_VERSION VERSION_LESS "7.0")
set(nvcc_flags ${nvcc_flags} "-target-os-variant=${CUDA_TARGET_OS_VARIANT}")
endif()
# This needs to be passed in at this stage, because VS needs to fill out the
# value of VCInstallDir from within VS. Note that CCBIN is only used if
# -ccbin or --compiler-bindir isn't used and CUDA_HOST_COMPILER matches
@ -1351,7 +1230,7 @@ macro(CUDA_WRAP_SRCS cuda_target format generated_files)
foreach(file ${ARGN})
# Ignore any file marked as a HEADER_FILE_ONLY
get_source_file_property(_is_header ${file} HEADER_FILE_ONLY)
if(${file} MATCHES ".*\\.cu$" AND NOT _is_header)
if(${file} MATCHES "\\.cu$" AND NOT _is_header)
# Allow per source file overrides of the format.
get_source_file_property(_cuda_source_format ${file} CUDA_SOURCE_PROPERTY_FORMAT)
@ -1359,16 +1238,22 @@ macro(CUDA_WRAP_SRCS cuda_target format generated_files)
set(_cuda_source_format ${format})
endif()
if( ${_cuda_source_format} MATCHES "PTX" )
set( compile_to_ptx ON )
elseif( ${_cuda_source_format} MATCHES "OBJ")
set( compile_to_ptx OFF )
if( ${_cuda_source_format} MATCHES "OBJ")
set( cuda_compile_to_external_module OFF )
else()
message( FATAL_ERROR "Invalid format flag passed to CUDA_WRAP_SRCS for file '${file}': '${_cuda_source_format}'. Use OBJ or PTX.")
set( cuda_compile_to_external_module ON )
if( ${_cuda_source_format} MATCHES "PTX" )
set( cuda_compile_to_external_module_type "ptx" )
elseif( ${_cuda_source_format} MATCHES "CUBIN")
set( cuda_compile_to_external_module_type "cubin" )
elseif( ${_cuda_source_format} MATCHES "FATBIN")
set( cuda_compile_to_external_module_type "fatbin" )
else()
message( FATAL_ERROR "Invalid format flag passed to CUDA_WRAP_SRCS for file '${file}': '${_cuda_source_format}'. Use OBJ, PTX, CUBIN or FATBIN.")
endif()
endif()
if(compile_to_ptx)
if(cuda_compile_to_external_module)
# Don't use any of the host compilation flags for PTX targets.
set(CUDA_HOST_FLAGS)
set(CUDA_NVCC_FLAGS_CONFIG)
@ -1383,7 +1268,7 @@ macro(CUDA_WRAP_SRCS cuda_target format generated_files)
if(CUDA_GENERATED_OUTPUT_DIR)
set(cuda_compile_output_dir "${CUDA_GENERATED_OUTPUT_DIR}")
else()
if ( compile_to_ptx )
if ( cuda_compile_to_external_module )
set(cuda_compile_output_dir "${CMAKE_CURRENT_BINARY_DIR}")
else()
set(cuda_compile_output_dir "${cuda_compile_intermediate_directory}")
@ -1393,10 +1278,10 @@ macro(CUDA_WRAP_SRCS cuda_target format generated_files)
# Add a custom target to generate a c or ptx file. ######################
get_filename_component( basename ${file} NAME )
if( compile_to_ptx )
if( cuda_compile_to_external_module )
set(generated_file_path "${cuda_compile_output_dir}")
set(generated_file_basename "${cuda_target}_generated_${basename}.ptx")
set(format_flag "-ptx")
set(generated_file_basename "${cuda_target}_generated_${basename}.${cuda_compile_to_external_module_type}")
set(format_flag "-${cuda_compile_to_external_module_type}")
file(MAKE_DIRECTORY "${cuda_compile_output_dir}")
else()
set(generated_file_path "${cuda_compile_output_dir}/${CMAKE_CFG_INTDIR}")
@ -1419,7 +1304,7 @@ macro(CUDA_WRAP_SRCS cuda_target format generated_files)
set(custom_target_script "${cuda_compile_intermediate_directory}/${generated_file_basename}.cmake")
# Setup properties for obj files:
if( NOT compile_to_ptx )
if( NOT cuda_compile_to_external_module )
set_source_files_properties("${generated_file}"
PROPERTIES
EXTERNAL_OBJECT true # This is an object file not to be compiled, but only be linked.
@ -1434,7 +1319,7 @@ macro(CUDA_WRAP_SRCS cuda_target format generated_files)
set(source_file "${CMAKE_CURRENT_SOURCE_DIR}/${file}")
endif()
if( NOT compile_to_ptx AND CUDA_SEPARABLE_COMPILATION)
if( NOT cuda_compile_to_external_module AND CUDA_SEPARABLE_COMPILATION)
list(APPEND ${cuda_target}_SEPARABLE_COMPILATION_OBJECTS "${generated_file}")
endif()
@ -1451,7 +1336,7 @@ macro(CUDA_WRAP_SRCS cuda_target format generated_files)
# Build the NVCC made dependency file ###################################
set(build_cubin OFF)
if ( NOT CUDA_BUILD_EMULATION AND CUDA_BUILD_CUBIN )
if ( NOT compile_to_ptx )
if ( NOT cuda_compile_to_external_module )
set ( build_cubin ON )
endif()
endif()
@ -1478,8 +1363,8 @@ macro(CUDA_WRAP_SRCS cuda_target format generated_files)
# Create up the comment string
file(RELATIVE_PATH generated_file_relative_path "${CMAKE_BINARY_DIR}" "${generated_file}")
if(compile_to_ptx)
set(cuda_build_comment_string "Building NVCC ptx file ${generated_file_relative_path}")
if(cuda_compile_to_external_module)
set(cuda_build_comment_string "Building NVCC ${cuda_compile_to_external_module_type} file ${generated_file_relative_path}")
else()
set(cuda_build_comment_string "Building NVCC (${cuda_build_type}) object ${generated_file_relative_path}")
endif()
@ -1572,18 +1457,27 @@ function(CUDA_LINK_SEPARABLE_COMPILATION_OBJECTS output_file cuda_target options
# If -ccbin, --compiler-bindir has been specified, don't do anything. Otherwise add it here.
list( FIND nvcc_flags "-ccbin" ccbin_found0 )
list( FIND nvcc_flags "--compiler-bindir" ccbin_found1 )
if( ccbin_found0 LESS 0 AND ccbin_found1 LESS 0 )
if( ccbin_found0 LESS 0 AND ccbin_found1 LESS 0 AND CUDA_HOST_COMPILER )
list(APPEND nvcc_flags -ccbin "\"${CUDA_HOST_COMPILER}\"")
endif()
# Create a list of flags specified by CUDA_NVCC_FLAGS_${CONFIG}
set(config_specific_flags)
set(flags)
foreach(config ${CUDA_configuration_types})
string(TOUPPER ${config} config_upper)
# Add config specific flags
foreach(f ${CUDA_NVCC_FLAGS_${config_upper}})
list(APPEND config_specific_flags $<$<CONFIG:${config}>:${f}>)
endforeach()
set(important_host_flags)
_cuda_get_important_host_flags(important_host_flags ${CMAKE_${CUDA_C_OR_CXX}_FLAGS_${config_upper}})
foreach(f ${important_host_flags})
list(APPEND flags $<$<CONFIG:${config}>:-Xcompiler> $<$<CONFIG:${config}>:${f}>)
endforeach()
endforeach()
# Add our general CUDA_NVCC_FLAGS with the configuration specifig flags
set(nvcc_flags ${CUDA_NVCC_FLAGS} ${config_specific_flags} ${nvcc_flags})
file(RELATIVE_PATH output_file_relative_path "${CMAKE_BINARY_DIR}" "${output_file}")
# Some generators don't handle the multiple levels of custom command
@ -1709,21 +1603,29 @@ endmacro()
###############################################################################
###############################################################################
# CUDA COMPILE
# (Internal) helper for manually added cuda source files with specific targets
###############################################################################
###############################################################################
macro(CUDA_COMPILE generated_files)
macro(cuda_compile_base cuda_target format generated_files)
# Separate the sources from the options
CUDA_GET_SOURCES_AND_OPTIONS(_sources _cmake_options _options ${ARGN})
# Create custom commands and targets for each file.
CUDA_WRAP_SRCS( cuda_compile OBJ _generated_files ${_sources} ${_cmake_options}
CUDA_WRAP_SRCS( ${cuda_target} ${format} _generated_files ${_sources} ${_cmake_options}
OPTIONS ${_options} )
set( ${generated_files} ${_generated_files})
endmacro()
###############################################################################
###############################################################################
# CUDA COMPILE
###############################################################################
###############################################################################
macro(CUDA_COMPILE generated_files)
cuda_compile_base(cuda_compile OBJ ${generated_files} ${ARGN})
endmacro()
###############################################################################
###############################################################################
@ -1731,17 +1633,28 @@ endmacro()
###############################################################################
###############################################################################
macro(CUDA_COMPILE_PTX generated_files)
# Separate the sources from the options
CUDA_GET_SOURCES_AND_OPTIONS(_sources _cmake_options _options ${ARGN})
# Create custom commands and targets for each file.
CUDA_WRAP_SRCS( cuda_compile_ptx PTX _generated_files ${_sources} ${_cmake_options}
OPTIONS ${_options} )
set( ${generated_files} ${_generated_files})
cuda_compile_base(cuda_compile_ptx PTX ${generated_files} ${ARGN})
endmacro()
###############################################################################
###############################################################################
# CUDA COMPILE FATBIN
###############################################################################
###############################################################################
macro(CUDA_COMPILE_FATBIN generated_files)
cuda_compile_base(cuda_compile_fatbin FATBIN ${generated_files} ${ARGN})
endmacro()
###############################################################################
###############################################################################
# CUDA COMPILE CUBIN
###############################################################################
###############################################################################
macro(CUDA_COMPILE_CUBIN generated_files)
cuda_compile_base(cuda_compile_cubin CUBIN ${generated_files} ${ARGN})
endmacro()
###############################################################################
###############################################################################
# CUDA ADD CUFFT TO TARGET

5
cmake/FindCUDA/make2cmake.cmake
View File

@ -37,12 +37,11 @@
file(READ ${input_file} depend_text)
if (${depend_text} MATCHES ".+")
if (NOT "${depend_text}" STREQUAL "")
# message("FOUND DEPENDS")
# Remember, four backslashes is escaped to one backslash in the string.
string(REGEX REPLACE "\\\\ " " " depend_text ${depend_text})
string(REPLACE "\\ " " " depend_text ${depend_text})
# This works for the nvcc -M generated dependency files.
string(REGEX REPLACE "^.* : " "" depend_text ${depend_text})

15
cmake/FindCUDA/parse_cubin.cmake
View File

@ -37,11 +37,10 @@
file(READ ${input_file} file_text)
if (${file_text} MATCHES ".+")
if (NOT "${file_text}" STREQUAL "")
# Remember, four backslashes is escaped to one backslash in the string.
string(REGEX REPLACE ";" "\\\\;" file_text ${file_text})
string(REGEX REPLACE "\ncode" ";code" file_text ${file_text})
string(REPLACE ";" "\\;" file_text ${file_text})
string(REPLACE "\ncode" ";code" file_text ${file_text})
list(LENGTH file_text len)
@ -57,7 +56,7 @@ if (${file_text} MATCHES ".+")
# Extract kernel names.
if (${entry} MATCHES "[^g]name = ([^ ]+)")
string(REGEX REPLACE ".* = ([^ ]+)" "\\1" entry ${entry})
set(entry "${CMAKE_MATCH_1}")
# Check to see if the kernel name starts with "_"
set(skip FALSE)
@ -76,19 +75,19 @@ if (${file_text} MATCHES ".+")
# Registers
if (${entry} MATCHES "reg([ ]+)=([ ]+)([^ ]+)")
string(REGEX REPLACE ".*([ ]+)=([ ]+)([^ ]+)" "\\3" entry ${entry})
set(entry "${CMAKE_MATCH_3}")
message("Registers: ${entry}")
endif()
# Local memory
if (${entry} MATCHES "lmem([ ]+)=([ ]+)([^ ]+)")
string(REGEX REPLACE ".*([ ]+)=([ ]+)([^ ]+)" "\\3" entry ${entry})
set(entry "${CMAKE_MATCH_3}")
message("Local: ${entry}")
endif()
# Shared memory
if (${entry} MATCHES "smem([ ]+)=([ ]+)([^ ]+)")
string(REGEX REPLACE ".*([ ]+)=([ ]+)([^ ]+)" "\\3" entry ${entry})
set(entry "${CMAKE_MATCH_3}")
message("Shared: ${entry}")
endif()

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