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some more bugfixed in 2.3 propagated to trunk

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This commit is contained in:
Vadim Pisarevsky 2011-07-07 11:43:21 +00:00
parent 58b5256e05
commit 5649f35757
8 changed files with 33 additions and 239 deletions
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14
doc/CMakeLists.txt
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@ -26,14 +26,14 @@ add_custom_target(docs
${CMAKE_CURRENT_SOURCE_DIR}/.. .
COMMAND ${CMAKE_COMMAND} -E copy_directory
${CMAKE_CURRENT_SOURCE_DIR}/pics ${CMAKE_CURRENT_BINARY_DIR}/doc/opencv1/pics
COMMAND ${CMAKE_COMMAND} -E copy
COMMAND ${CMAKE_COMMAND} -E copy_if_different
${CMAKE_CURRENT_SOURCE_DIR}/mymath.sty ${CMAKE_CURRENT_BINARY_DIR}
COMMAND ${PDFLATEX_COMPILER} opencv2refman
COMMAND ${PDFLATEX_COMPILER} opencv2refman
COMMAND ${PDFLATEX_COMPILER} opencv_user
COMMAND ${PDFLATEX_COMPILER} opencv_user
COMMAND ${PDFLATEX_COMPILER} opencv_tutorials
COMMAND ${PDFLATEX_COMPILER} opencv_tutorials
COMMAND ${PDFLATEX_COMPILER} opencv2refman.tex
COMMAND ${PDFLATEX_COMPILER} opencv2refman.tex
COMMAND ${PDFLATEX_COMPILER} opencv_user.tex
COMMAND ${PDFLATEX_COMPILER} opencv_user.tex
COMMAND ${PDFLATEX_COMPILER} opencv_tutorials.tex
COMMAND ${PDFLATEX_COMPILER} opencv_tutorials.tex
DEPENDS ${OPENCV_DOC_DEPS}
WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}
COMMENT "Generating the PDF Manuals")

2
modules/core/doc/old_basic_structures.rst
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@ -1655,7 +1655,7 @@ Initializes a random number generator state.
The function initializes a random number generator and returns the state. The pointer to the state can be then passed to the :ocv:cfunc:`RandInt`, :ocv:cfunc:`RandReal` and :ocv:cfunc:`RandArr` functions. In the current implementation a multiply-with-carry generator is used.
.. sealso:: the C++ class :ocv:class:`RNG` replaced ``CvRNG``.
.. seealso:: the C++ class :ocv:class:`RNG` replaced ``CvRNG``.
RandArr

2
modules/gpu/CMakeLists.txt
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@ -218,7 +218,7 @@ if(BUILD_TESTS AND EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/test)
# Additional target properties
set_target_properties(${the_test_target} PROPERTIES
DEBUG_POSTFIX "${OPENCV_DEBUG_POSTFIX}"
RUNTIME_OUTPUT_DIRECTORY ${EXECUTABLE_OUTPUT_PATH}
RUNTIME_OUTPUT_DIRECTORY "${EXECUTABLE_OUTPUT_PATH}"
)
if(ENABLE_SOLUTION_FOLDERS)

9
modules/imgproc/doc/miscellaneous_transformations.rst
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@ -498,8 +498,7 @@ Fills a connected component with the given color.
:param mask: (For the second function only) Operation mask that should be a single-channel 8-bit image, 2 pixels wider and 2 pixels taller. The function uses and updates the mask, so you take responsibility of initializing the ``mask`` content. Flood-filling cannot go across non-zero pixels in the mask. For example, an edge detector output can be used as a mask to stop filling at edges. It is possible to use the same mask in multiple calls to the function to make sure the filled area does not overlap.
.. note::
Since the mask is larger than the filled image, a pixel :math:`(x, y)` in ``image`` corresponds to the pixel :math:`(x+1, y+1)` in the ``mask`` .
.. note:: Since the mask is larger than the filled image, a pixel :math:`(x, y)` in ``image`` corresponds to the pixel :math:`(x+1, y+1)` in the ``mask`` .
:param seed: Starting point.
@ -782,11 +781,7 @@ should be set to 0's. In the function output, each pixel in
markers is set to a value of the "seed" components or to -1 at
boundaries between the regions.
.. note:: Every two neighbor connected
components are not necessarily separated by a watershed boundary (-1's pixels); for
example, when such tangent components exist in the initial
marker image. Visual demonstration and usage example of the function
can be found in the OpenCV samples directory (see the ``watershed.cpp`` demo).
.. note:: Every two neighbor connected components are not necessarily separated by a watershed boundary (-1's pixels); for example, when such tangent components exist in the initial marker image. Visual demonstration and usage example of the function can be found in the OpenCV samples directory (see the ``watershed.cpp`` demo).
.. seealso:: :ocv:func:`findContours`

10
modules/imgproc/doc/structural_analysis_and_shape_descriptors.rst
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@ -59,14 +59,12 @@ The normalized central moments
\texttt{nu} _{ji}= \frac{\texttt{mu}_{ji}}{\texttt{m}_{00}^{(i+j)/2+1}} .
.. note::
:math:`\texttt{mu}_{00}=\texttt{m}_{00}`,
:math:`\texttt{nu}_{00}=1`
:math:`\texttt{nu}_{10}=\texttt{mu}_{10}=\texttt{mu}_{01}=\texttt{mu}_{10}=0` , hence the values are not stored.
The moments of a contour are defined in the same way but computed using the Green's formula
(see
http://en.wikipedia.org/wiki/Green_theorem
). So, due to a limited raster resolution, the moments computed for a contour are slightly different from the moments computed for the same rasterized contour.
The moments of a contour are defined in the same way but computed using the Green's formula (see http://en.wikipedia.org/wiki/Green_theorem). So, due to a limited raster resolution, the moments computed for a contour are slightly different from the moments computed for the same rasterized contour.
.. seealso::
@ -146,9 +144,7 @@ Finds contours in a binary image.
The function retrieves contours from the binary image using the algorithm
[Suzuki85]_. The contours are a useful tool for shape analysis and object detection and recognition. See ``squares.c`` in the OpenCV sample directory.
.. note::
Source ``image`` is modified by this function.
.. note:: Source ``image`` is modified by this function.
drawContours

12
modules/imgproc/include/opencv2/imgproc/imgproc.hpp
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@ -455,7 +455,7 @@ CV_EXPORTS_W void preCornerDetect( InputArray src, OutputArray dst, int ksize,
int borderType=BORDER_DEFAULT );
//! adjusts the corner locations with sub-pixel accuracy to maximize the certain cornerness criteria
CV_EXPORTS void cornerSubPix( InputArray image, InputOutputArray corners,
CV_EXPORTS_W void cornerSubPix( InputArray image, InputOutputArray corners,
Size winSize, Size zeroZone,
TermCriteria criteria );
@ -807,7 +807,7 @@ enum
};
//! retrieves contours and the hierarchical information from black-n-white image.
CV_EXPORTS void findContours( InputOutputArray image, OutputArrayOfArrays contours,
CV_EXPORTS_W void findContours( InputOutputArray image, OutputArrayOfArrays contours,
OutputArray hierarchy, int mode,
int method, Point offset=Point());
@ -816,14 +816,14 @@ CV_EXPORTS void findContours( InputOutputArray image, OutputArrayOfArrays contou
int mode, int method, Point offset=Point());
//! draws contours in the image
CV_EXPORTS void drawContours( InputOutputArray image, InputArrayOfArrays contours,
CV_EXPORTS_W void drawContours( InputOutputArray image, InputArrayOfArrays contours,
int contourIdx, const Scalar& color,
int thickness=1, int lineType=8,
InputArray hierarchy=noArray(),
int maxLevel=INT_MAX, Point offset=Point() );
//! approximates contour or a curve using Douglas-Peucker algorithm
CV_EXPORTS void approxPolyDP( InputArray curve,
CV_EXPORTS_W void approxPolyDP( InputArray curve,
OutputArray approxCurve,
double epsilon, bool closed );
@ -842,7 +842,7 @@ CV_EXPORTS_W void minEnclosingCircle( InputArray points,
CV_EXPORTS_W double matchShapes( InputArray contour1, InputArray contour2,
int method, double parameter );
//! computes convex hull for a set of 2D points.
CV_EXPORTS void convexHull( InputArray points, OutputArray hull,
CV_EXPORTS_W void convexHull( InputArray points, OutputArray hull,
bool clockwise=false, bool returnPoints=true );
//! returns true iff the contour is convex. Does not support contours with self-intersection
@ -852,7 +852,7 @@ CV_EXPORTS_W bool isContourConvex( InputArray contour );
CV_EXPORTS_W RotatedRect fitEllipse( InputArray points );
//! fits line to the set of 2D points using M-estimator algorithm
CV_EXPORTS void fitLine( InputArray points, OutputArray line, int distType,
CV_EXPORTS_W void fitLine( InputArray points, OutputArray line, int distType,
double param, double reps, double aeps );
//! checks if the point is inside the contour. Optionally computes the signed distance from the point to the contour boundary
CV_EXPORTS_W double pointPolygonTest( InputArray contour, Point2f pt, bool measureDist );

2
modules/objdetect/src/cascadedetect.cpp
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@ -846,6 +846,8 @@ bool CascadeClassifier::empty() const
bool CascadeClassifier::load(const string& filename)
{
oldCascade.release();
data = Data();
featureEvaluator.release();
FileStorage fs(filename, FileStorage::READ);
if( !fs.isOpened() )

199
modules/python/src2/opencv_extra_api.hpp
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@ -205,207 +205,8 @@ CV_WRAP void calcBackProject( const vector<Mat>& images, const vector<int>& chan
}
static void addChildContour(const vector<Mat>& contours,
const Mat& hierarchy,
int i, vector<CvSeq>& seq,
vector<CvSeqBlock>& block)
{
size_t count = contours.size();
for( ; i >= 0; i = ((const Vec4i*)hierarchy.data)[i][0] )
{
const vector<Point>& ci = contours[i];
cvMakeSeqHeaderForArray(CV_SEQ_POLYGON, sizeof(CvSeq), sizeof(Point),
!ci.empty() ? (void*)&ci[0] : 0, (int)ci.size(),
&seq[i], &block[i] );
const Vec4i h_i = ((const Vec4i*)hierarchy.data)[i];
int h_next = h_i[0], h_prev = h_i[1], v_next = h_i[2], v_prev = h_i[3];
seq[i].h_next = (size_t)h_next < count ? &seq[h_next] : 0;
seq[i].h_prev = (size_t)h_prev < count ? &seq[h_prev] : 0;
seq[i].v_next = (size_t)v_next < count ? &seq[v_next] : 0;
seq[i].v_prev = (size_t)v_prev < count ? &seq[v_prev] : 0;
if( v_next >= 0 )
addChildContour(contours, hierarchy, v_next, seq, block);
}
}
//! draws contours in the image
CV_WRAP static inline void drawContours( Mat& image, const vector<Mat>& contours,
int contourIdx, const Scalar& color,
int thickness=1, int lineType=8,
const Mat& hierarchy=Mat(),
int maxLevel=INT_MAX, Point offset=Point() )
{
CvMat _image = image;
size_t i = 0, first = 0, last = contours.size();
vector<CvSeq> seq;
vector<CvSeqBlock> block;
if( !last )
return;
seq.resize(last);
block.resize(last);
for( i = first; i < last; i++ )
seq[i].first = 0;
if( contourIdx >= 0 )
{
CV_Assert( 0 <= contourIdx && contourIdx < (int)last );
first = contourIdx;
last = contourIdx + 1;
}
for( i = first; i < last; i++ )
{
const Mat& ci = contours[i];
int ci_size = ci.checkVector(2, CV_32S);
CV_Assert( ci_size >= 0 );
cvMakeSeqHeaderForArray(CV_SEQ_POLYGON, sizeof(CvSeq), sizeof(Point),
ci_size > 0 ? ci.data : 0, ci_size, &seq[i], &block[i] );
}
if( hierarchy.empty() || maxLevel == 0 )
for( i = first; i < last; i++ )
{
seq[i].h_next = i < last-1 ? &seq[i+1] : 0;
seq[i].h_prev = i > first ? &seq[i-1] : 0;
}
else
{
int hsz = hierarchy.checkVector(4, CV_32S);
size_t count = last - first;
CV_Assert((size_t)hsz == contours.size());
if( count == contours.size() )
{
for( i = first; i < last; i++ )
{
const Vec4i& h_i = ((const Vec4i*)hierarchy.data)[i];
int h_next = h_i[0], h_prev = h_i[1], v_next = h_i[2], v_prev = h_i[3];
seq[i].h_next = (size_t)h_next < count ? &seq[h_next] : 0;
seq[i].h_prev = (size_t)h_prev < count ? &seq[h_prev] : 0;
seq[i].v_next = (size_t)v_next < count ? &seq[v_next] : 0;
seq[i].v_prev = (size_t)v_prev < count ? &seq[v_prev] : 0;
}
}
else
{
int child = ((const Vec4i*)hierarchy.data)[first][2];
if( child >= 0 )
{
addChildContour(contours, hierarchy, child, seq, block);
seq[first].v_next = &seq[child];
}
}
}
cvDrawContours( &_image, &seq[first], color, color, contourIdx >= 0 ?
-maxLevel : maxLevel, thickness, lineType, offset );
}
CV_WRAP static inline void approxPolyDP( const Mat& curve,
CV_OUT Mat& approxCurve,
double epsilon, bool closed )
{
if( curve.depth() == CV_32S )
{
vector<Point> result;
approxPolyDP(curve, result, epsilon, closed);
Mat(result).copyTo(approxCurve);
}
else if( curve.depth() == CV_32F )
{
vector<Point2f> result;
approxPolyDP(curve, result, epsilon, closed);
Mat(result).copyTo(approxCurve);
}
else
CV_Error(CV_StsUnsupportedFormat, "");
}
CV_WRAP static inline void convexHull( const Mat& points, CV_OUT Mat& hull, bool returnPoints=true, bool clockwise=false )
{
if( !returnPoints )
{
vector<int> h;
convexHull(points, h, clockwise);
Mat(h).copyTo(hull);
}
else if( points.depth() == CV_32S )
{
vector<Point> h;
convexHull(points, h, clockwise);
Mat(h).copyTo(hull);
}
else if( points.depth() == CV_32F )
{
vector<Point2f> h;
convexHull(points, h, clockwise);
Mat(h).copyTo(hull);
}
}
CV_WRAP static inline void fitLine( const Mat& points, CV_OUT vector<float>& line,
int distType, double param, double reps, double aeps )
{
if(points.channels() == 2 || points.cols == 2)
{
line.resize(4);
fitLine(points, *(Vec4f*)&line[0], distType, param, reps, aeps);
}
else
{
line.resize(6);
fitLine(points, *(Vec6f*)&line[0], distType, param, reps, aeps);
}
}
CV_WRAP static inline int estimateAffine3D( const Mat& from, const Mat& to,
CV_OUT Mat& dst, CV_OUT Mat& outliers,
double param1 = 3.0, double param2 = 0.99 )
{
vector<uchar> outliers_vec;
int res = estimateAffine3D(from, to, dst, outliers_vec, param1, param2);
Mat(outliers_vec).copyTo(outliers);
return res;
}
CV_WRAP static inline void cornerSubPix( const Mat& image, Mat& corners,
Size winSize, Size zeroZone,
TermCriteria criteria )
{
int n = corners.checkVector(2, CV_32F);
CV_Assert(n >= 0);
if( n == 0 )
return;
CvMat _image = image;
cvFindCornerSubPix(&_image, (CvPoint2D32f*)corners.data, n, winSize, zeroZone, criteria);
}
/////////////////////////////// calib3d ///////////////////////////////////////////
CV_WRAP static inline void convertPointsHomogeneous( const Mat& src, CV_OUT Mat& dst )
{
int n;
if( (n = src.checkVector(2)) >= 0 )
dst.create(n, 2, src.depth());
else if( (n = src.checkVector(3)) >= 0 )
dst.create(n, 3, src.depth());
else
CV_Error(CV_StsBadSize, "");
CvMat _src = src, _dst = dst;
cvConvertPointsHomogeneous(&_src, &_dst);
}
//! finds circles' grid pattern of the specified size in the image
CV_WRAP static inline void findCirclesGridDefault( InputArray image, Size patternSize,
OutputArray centers, int flags=CALIB_CB_SYMMETRIC_GRID )