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Merge branch 'master' of code.opencv.org:opencv

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This commit is contained in:
Vladislav Vinogradov
2012-08-07 16:09:04 +04:00
parent 76d46e20de ce9b97c78c
commit cd58b7e154
70 changed files with 1251 additions and 1342 deletions
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2
modules/core/doc/basic_structures.rst
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@@ -2446,6 +2446,6 @@ The above methods are usually enough for users. If you want to make your own alg
* Make a class and specify ``Algorithm`` as its base class.
* The algorithm parameters should be the class members. See ``Algorithm::get()`` for the list of possible types of the parameters.
* Add public virtual method ``AlgorithmInfo* info() const;`` to your class.
* Add constructor function, ``AlgorithmInfo`` instance and implement the ``info()`` method. The simplest way is to take http://code.opencv.org/svn/opencv/trunk/opencv/modules/ml/src/ml_init.cpp as the reference and modify it according to the list of your parameters.
* Add constructor function, ``AlgorithmInfo`` instance and implement the ``info()`` method. The simplest way is to take http://code.opencv.org/projects/opencv/repository/revisions/master/entry/modules/ml/src/ml_init.cpp as the reference and modify it according to the list of your parameters.
* Add some public function (e.g. ``initModule_<mymodule>()``) that calls info() of your algorithm and put it into the same source file as ``info()`` implementation. This is to force C++ linker to include this object file into the target application. See ``Algorithm::create()`` for details.

4
modules/gpu/doc/introduction.rst
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@@ -42,7 +42,7 @@ You can always determine at runtime whether the OpenCV GPU-built binaries (or PT
Utilizing Multiple GPUs
-----------------------
In the current version, each of the OpenCV GPU algorithms can use only a single GPU. So, to utilize multiple GPUs, you have to manually distribute the work between GPUs.
In the current version, each of the OpenCV GPU algorithms can use only a single GPU. So, to utilize multiple GPUs, you have to manually distribute the work between GPUs.
Switching active devie can be done using :ocv:func:`gpu::setDevice()` function. For more details please read Cuda C Programing Guide.
While developing algorithms for multiple GPUs, note a data passing overhead. For primitive functions and small images, it can be significant, which may eliminate all the advantages of having multiple GPUs. But for high-level algorithms, consider using multi-GPU acceleration. For example, the Stereo Block Matching algorithm has been successfully parallelized using the following algorithm:
@@ -59,5 +59,5 @@ While developing algorithms for multiple GPUs, note a data passing overhead. For
With this algorithm, a dual GPU gave a 180
%
performance increase comparing to the single Fermi GPU. For a source code example, see
http://code.opencv.org/svn/opencv/trunk/opencv/samples/gpu/.
http://code.opencv.org/projects/opencv/repository/revisions/master/entry/samples/gpu/.

2
modules/highgui/doc/reading_and_writing_images_and_video.rst
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@@ -294,7 +294,7 @@ The methods/functions grab the next frame from video file or camera and return t
The primary use of the function is in multi-camera environments, especially when the cameras do not have hardware synchronization. That is, you call ``VideoCapture::grab()`` for each camera and after that call the slower method ``VideoCapture::retrieve()`` to decode and get frame from each camera. This way the overhead on demosaicing or motion jpeg decompression etc. is eliminated and the retrieved frames from different cameras will be closer in time.
Also, when a connected camera is multi-head (for example, a stereo camera or a Kinect device), the correct way of retrieving data from it is to call `VideoCapture::grab` first and then call :ocv:func:`VideoCapture::retrieve` one or more times with different values of the ``channel`` parameter. See http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/kinect_maps.cpp
Also, when a connected camera is multi-head (for example, a stereo camera or a Kinect device), the correct way of retrieving data from it is to call `VideoCapture::grab` first and then call :ocv:func:`VideoCapture::retrieve` one or more times with different values of the ``channel`` parameter. See http://code.opencv.org/projects/opencv/repository/revisions/master/entry/samples/cpp/kinect_maps.cpp
VideoCapture::retrieve

2
modules/highgui/doc/user_interface.rst
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@@ -203,7 +203,7 @@ Sets mouse handler for the specified window
:param winname: Window name
:param onMouse: Mouse callback. See OpenCV samples, such as http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/ffilldemo.cpp, on how to specify and use the callback.
:param onMouse: Mouse callback. See OpenCV samples, such as http://code.opencv.org/projects/opencv/repository/revisions/master/entry/samples/cpp/ffilldemo.cpp, on how to specify and use the callback.
:param userdata: The optional parameter passed to the callback.

2
modules/imgproc/doc/structural_analysis_and_shape_descriptors.rst
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@@ -202,7 +202,7 @@ Approximates a polygonal curve(s) with the specified precision.
The functions ``approxPolyDP`` approximate a curve or a polygon with another curve/polygon with less vertices so that the distance between them is less or equal to the specified precision. It uses the Douglas-Peucker algorithm
http://en.wikipedia.org/wiki/Ramer-Douglas-Peucker_algorithm
See http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/contours.cpp for the function usage model.
See http://code.opencv.org/projects/opencv/repository/revisions/master/entry/samples/cpp/contours.cpp for the function usage model.
ApproxChains

2
modules/objdetect/doc/cascade_classification.rst
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@@ -21,7 +21,7 @@ The word "cascade" in the classifier name means that the resultant classifier co
The feature used in a particular classifier is specified by its shape (1a, 2b etc.), position within the region of interest and the scale (this scale is not the same as the scale used at the detection stage, though these two scales are multiplied). For example, in the case of the third line feature (2c) the response is calculated as the difference between the sum of image pixels under the rectangle covering the whole feature (including the two white stripes and the black stripe in the middle) and the sum of the image pixels under the black stripe multiplied by 3 in order to compensate for the differences in the size of areas. The sums of pixel values over a rectangular regions are calculated rapidly using integral images (see below and the :ocv:func:`integral` description).
To see the object detector at work, have a look at the facedetect demo:
http://code.opencv.org/svn/opencv/trunk/opencv/samples/cpp/facedetect.cpp
http://code.opencv.org/projects/opencv/repository/revisions/master/entry/samples/cpp/facedetect.cpp
The following reference is for the detection part only. There is a separate application called ``opencv_traincascade`` that can train a cascade of boosted classifiers from a set of samples.

2
modules/objdetect/src/cascadedetect.hpp
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@@ -444,7 +444,7 @@ inline int predictCategoricalStump( CascadeClassifier& cascade, Ptr<FeatureEvalu
CascadeClassifier::Data::Stage* cascadeStages = &cascade.data.stages[0];
#ifdef HAVE_TEGRA_OPTIMIZATION
float tmp; // float accumulator -- float operations are quicker
float tmp = 0; // float accumulator -- float operations are quicker
#endif
for( int si = 0; si < nstages; si++ )
{

57
modules/ts/misc/run.py
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@@ -56,6 +56,7 @@ parse_patterns = (
{'name': "tests_dir", 'default': None, 'pattern': re.compile("^EXECUTABLE_OUTPUT_PATH:PATH=(.+)$")},
{'name': "build_type", 'default': "Release", 'pattern': re.compile("^CMAKE_BUILD_TYPE:STRING=(.*)$")},
{'name': "svnversion_path", 'default': None, 'pattern': re.compile("^SVNVERSION_PATH:FILEPATH=(.*)$")},
{'name': "git_executable", 'default': None, 'pattern': re.compile("^GIT_EXECUTABLE:FILEPATH=(.*)$")},
{'name': "cxx_flags", 'default': "", 'pattern': re.compile("^CMAKE_CXX_FLAGS:STRING=(.*)$")},
{'name': "cxx_flags_debug", 'default': "", 'pattern': re.compile("^CMAKE_CXX_FLAGS_DEBUG:STRING=(.*)$")},
{'name': "cxx_flags_release", 'default': "", 'pattern': re.compile("^CMAKE_CXX_FLAGS_RELEASE:STRING=(.*)$")},
@@ -303,13 +304,15 @@ class RunInfo(object):
# detect target arch
if self.targetos == "android":
if "armeabi-v7a" in self.android_abi:
self.targetarch = "ARMv7a"
self.targetarch = "armv7a"
elif "armeabi-v6" in self.android_abi:
self.targetarch = "ARMv6"
self.targetarch = "armv6"
elif "armeabi" in self.android_abi:
self.targetarch = "ARMv5te"
self.targetarch = "armv5te"
elif "x86" in self.android_abi:
self.targetarch = "x86"
elif "mips" in self.android_abi:
self.targetarch = "mips"
else:
self.targetarch = "ARM"
elif self.is_x64 and hostmachine in ["AMD64", "x86_64"]:
@@ -327,19 +330,38 @@ class RunInfo(object):
self.hardware = None
self.getSvnVersion(self.cmake_home, "cmake_home_svn")
self.cmake_home_vcver = self.getVCVersion(self.cmake_home)
if self.opencv_home == self.cmake_home:
self.opencv_home_svn = self.cmake_home_svn
self.opencv_home_vcver = self.cmake_home_vcver
else:
self.getSvnVersion(self.opencv_home, "opencv_home_svn")
self.opencv_home_vcver = self.getVCVersion(self.opencv_home)
self.tests = self.getAvailableTestApps()
def getSvnVersion(self, path, name):
def getVCVersion(self, root_path):
if os.path.isdir(os.path.join(root_path, ".svn")):
return self.getSvnVersion(root_path)
elif os.path.isdir(os.path.join(root_path, ".git")):
return self.getGitHash(root_path)
return None
def getGitHash(self, path):
if not path or not self.git_executable:
return None
try:
output = Popen([self.git_executable, "rev-parse", "--short", "HEAD"], stdout=PIPE, stderr=PIPE, cwd = path).communicate()
if not output[1]:
return output[0].strip()
else:
return None
except OSError:
return None
def getSvnVersion(self, path):
if not path:
val = None
elif not self.svnversion_path and hostos == 'nt':
val = self.tryGetSvnVersionWithTortoise(path, name)
val = self.tryGetSvnVersionWithTortoise(path)
else:
svnversion = self.svnversion_path
if not svnversion:
@@ -354,9 +376,9 @@ class RunInfo(object):
val = None
if val:
val = val.replace(" ", "_")
setattr(self, name, val)
return val
def tryGetSvnVersionWithTortoise(self, path, name):
def tryGetSvnVersionWithTortoise(self, path):
try:
wcrev = "SubWCRev.exe"
dir = tempfile.mkdtemp()
@@ -408,13 +430,13 @@ class RunInfo(object):
if app.startswith(self.nameprefix):
app = app[len(self.nameprefix):]
if self.cmake_home_svn:
if self.cmake_home_svn == self.opencv_home_svn:
rev = self.cmake_home_svn
elif self.opencv_home_svn:
rev = self.cmake_home_svn + "-" + self.opencv_home_svn
if self.cmake_home_vcver:
if self.cmake_home_vcver == self.opencv_home_vcver:
rev = self.cmake_home_vcver
elif self.opencv_home_vcver:
rev = self.cmake_home_vcver + "-" + self.opencv_home_vcver
else:
rev = self.cmake_home_svn
rev = self.cmake_home_vcver
else:
rev = None
if rev:
@@ -486,7 +508,6 @@ class RunInfo(object):
else:
prev_option = prev_option + " " + opt
options.append(tmpfile[1])
print options
output = Popen(options, stdout=PIPE, stderr=PIPE).communicate()
compiler_output = output[1]
os.remove(tmpfile[1])
@@ -508,7 +529,7 @@ class RunInfo(object):
hw = "CUDA_"
else:
hw = ""
tstamp = timestamp.strftime("%Y-%m-%d--%H-%M-%S")
tstamp = timestamp.strftime("%Y%m%d-%H%M%S")
return "%s_%s_%s_%s%s%s.xml" % (app, self.targetos, self.targetarch, hw, rev, tstamp)
def getTest(self, name):