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fixed and improving formatting in opencv2refman.pdf. added support for n-channel mask in Mat::copyTo() and n-channel images in cv::compare(). fixed 2 compile warnings in opencv_python.

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This commit is contained in:
Vadim Pisarevsky 2011-07-24 10:34:14 +00:00
parent df78bc04d6
commit d8417af086
9 changed files with 109 additions and 47 deletions
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1
doc/CMakeLists.txt
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@ -29,6 +29,7 @@ add_custom_target(docs
${CMAKE_CURRENT_SOURCE_DIR}/pics ${CMAKE_CURRENT_BINARY_DIR}/doc/opencv1/pics
COMMAND ${CMAKE_COMMAND} -E copy_if_different
${CMAKE_CURRENT_SOURCE_DIR}/mymath.sty ${CMAKE_CURRENT_BINARY_DIR}
COMMAND ${PYTHON_EXECUTABLE} "${CMAKE_CURRENT_SOURCE_DIR}/patch_refman_latex.py" opencv2refman.tex
COMMAND ${PDFLATEX_COMPILER} opencv2refman.tex
COMMAND ${PDFLATEX_COMPILER} opencv2refman.tex
COMMAND ${PDFLATEX_COMPILER} opencv_user.tex

1
doc/conf.py
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@ -236,6 +236,7 @@ preamble ="""
\usepackage[scaled=0.85]{beramono}
\usepackage{mymath}\usepackage{amssymb}\usepackage{amsmath}\usepackage{bbm}\setcounter{secnumdepth}{1}
\usepackage{colortbl}
\usepackage{enumitem}
"""
latex_elements = {'preamble': preamble}

22
doc/patch_refman_latex.py Normal file
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@ -0,0 +1,22 @@
import sys
f=open(sys.argv[1], "rt")
ll = list(f.readlines())
f.close()
f=open(sys.argv[1], "wt")
singleparam = False
for l in ll:
l = l.replace("\\code{~const}}{}", "}{\\code{~const}}")
if l.startswith("\\item[{Parameters}] \\leavevmode"):
if not l.startswith("\\item[{Parameters}] \\leavevmode\\begin{itemize}"):
singleparam = True
l = "\\item[{Parameters}] \\leavevmode\\begin{itemize}[label=]\n"
if singleparam:
l += "\\item {}\n"
elif singleparam and l.startswith("\\end{description}\\end{quote}"):
l = "\\end{itemize}\n" + l
singleparam = False
f.write(l)
f.close()

92
modules/core/doc/basic_structures.rst
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@ -654,50 +654,86 @@ Matrix Expressions
------------------
This is a list of implemented matrix operations that can be combined in arbitrary complex expressions
(here
*A*,*B*
stand for matrices ( ``Mat`` ),
*s*
for a scalar ( ``Scalar`` ),
:math:`\alpha` for a real-valued scalar ( ``double`` )):
(here ``A``, ``B`` stand for matrices ( ``Mat`` ), ``s`` for a scalar ( ``Scalar`` ),
``alpha`` for a real-valued scalar ( ``double`` )):
*
Addition, subtraction, negation:
:math:`A \pm B,\;A \pm s,\;s \pm A,\;-A` *
scaling:
:math:`A*\alpha`, :math:`A*\alpha` *
per-element multiplication and division:
:math:`A.mul(B), A/B, \alpha/A` *
matrix multiplication:
:math:`A*B` *
transposition:
:math:`A.t() \sim A^t` *
matrix inversion and pseudo-inversion, solving linear systems and least-squares problems:
``A+B, A-B, A+s, A-s, s+A, s-A, -A``
:math:`A.inv([method]) \sim A^{-1}, A.inv([method])*B \sim X:\,AX=B`
*
Scaling:
``A*alpha``
*
Per-element multiplication and division:
``A.mul(B), A/B, alpha/A``
*
Matrix multiplication:
``A*B``
*
Transposition:
``A.t()`` (means ``A``\ :sup:`T`)
*
Matrix inversion and pseudo-inversion, solving linear systems and least-squares problems:
``A.inv([method])`` (~ ``A``\ :sup:`-1`) ``, A.inv([method])*B`` (~ ``X: AX=B``)
*
Comparison:
:math:`A\gtreqqless B,\;A \ne B,\;A \gtreqqless \alpha,\;A \ne \alpha`. The result of comparison is an 8-bit single channel mask whose elements are set to 255 (if the particular element or pair of elements satisfy the condition) or 0.
``A cmpop B, A cmpop alpha, alpha cmpop A``, where ``cmpop`` is one of ``: >, >=, ==, !=, <=, <``. The result of comparison is an 8-bit single channel mask whose elements are set to 255 (if the particular element or pair of elements satisfy the condition) or 0.
*
Bitwise logical operations: ``A & B, A & s, A | B, A | s, A textasciicircum B, A textasciicircum s, ~ A`` *
element-wise minimum and maximum:
:math:`min(A, B), min(A, \alpha), max(A, B), max(A, \alpha)` *
element-wise absolute value:
:math:`abs(A)` *
cross-product, dot-product:
:math:`A.cross(B), A.dot(B)` *
any function of matrix or matrices and scalars that returns a matrix or a scalar, such as ``norm``, ``mean``, ``sum``, ``countNonZero``, ``trace``, ``determinant``, ``repeat``, and others.
Bitwise logical operations: ``A logicop B, A logicop s, s logicop A, ~A``, where ``logicop`` is one of ``: &, |, ^``.
*
Matrix initializers ( ``eye(), zeros(), ones()`` ), matrix comma-separated initializers, matrix constructors and operators that extract sub-matrices (see :ocv:class:`Mat` description).
Element-wise minimum and maximum:
``min(A, B), min(A, alpha), max(A, B), max(A, alpha)``
*
Element-wise absolute value:
``abs(A)``
*
Cross-product, dot-product:
``A.cross(B)``
``A.dot(B)``
*
Any function of matrix or matrices and scalars that returns a matrix or a scalar, such as ``norm``, ``mean``, ``sum``, ``countNonZero``, ``trace``, ``determinant``, ``repeat``, and others.
*
Matrix initializers ( ``Mat::eye(), Mat::zeros(), Mat::ones()`` ), matrix comma-separated initializers, matrix constructors and operators that extract sub-matrices (see :ocv:class:`Mat` description).
*
``Mat_<destination_type>()`` constructors to cast the result to the proper type.
.. note:: Comma-separated initializers and probably some other operations may require additional explicit ``Mat()`` or ``Mat_<T>()`` constuctor calls to resolve a possible ambiguity.
Here are examples of matrix expressions:
::
// compute pseudo-inverse of A, equivalent to A.inv(DECOMP_SVD)
SVD svd(A);
Mat pinvA = svd.vt.t()*Mat::diag(1./svd.w)*svd.u.t();
// compute the new vector of parameters in the Levenberg-Marquardt algorithm
x -= (A.t()*A + lambda*Mat::eye(A.cols,A.cols,A.type())).inv(DECOMP_CHOLESKY)*(A.t()*err);
// sharpen image using "unsharp mask" algorithm
Mat blurred; double sigma = 1, threshold = 5, amount = 1;
GaussianBlur(img, blurred, Size(), sigma, sigma);
Mat lowConstrastMask = abs(img - blurred) < threshold;
Mat sharpened = img*(1+amount) + blurred*(-amount);
img.copyTo(sharpened, lowContrastMask);
..
Below is the formal description of the ``Mat`` methods.
Mat::Mat
@ -1488,7 +1524,7 @@ Mat::elemSize
-----------------
Returns the matrix element size in bytes.
.. ocv:function:: size_t Mat::elemSize(void) const
.. ocv:function:: size_t Mat::elemSize() const
The method returns the matrix element size in bytes. For example, if the matrix type is ``CV_16SC3`` , the method returns ``3*sizeof(short)`` or 6.

14
modules/core/src/arithm.cpp
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@ -2093,8 +2093,8 @@ void cv::compare(InputArray _src1, InputArray _src2, OutputArray _dst, int op)
if( kind1 == kind2 && src1.dims <= 2 && src2.dims <= 2 && src1.size() == src2.size() && src1.type() == src2.type() )
{
CV_Assert(src1.channels() == 1);
_dst.create(src1.size(), CV_8UC1);
int cn = src1.channels();
_dst.create(src1.size(), CV_8UC(cn));
Mat dst = _dst.getMat();
Size sz = getContinuousSize(src1, src2, dst, src1.channels());
cmpTab[src1.depth()](src1.data, src1.step, src2.data, src2.step, dst.data, dst.step, sz, &op);
@ -2120,15 +2120,15 @@ void cv::compare(InputArray _src1, InputArray _src2, OutputArray _dst, int op)
haveScalar = true;
}
int cn = src1.channels(), depth1 = src1.depth(), depth2 = src2.depth();
if( cn != 1 )
CV_Error( CV_StsUnsupportedFormat, "compare() can only process single-channel arrays" );
_dst.create(src1.dims, src1.size, CV_8UC(cn));
src1 = src1.reshape(1); src2 = src2.reshape(1);
Mat dst = _dst.getMat().reshape(1);
size_t esz = src1.elemSize();
size_t blocksize0 = (size_t)(BLOCK_SIZE + esz-1)/esz;
_dst.create(src1.dims, src1.size, CV_8U);
Mat dst = _dst.getMat();
BinaryFunc func = cmpTab[depth1];
if( !haveScalar )

10
modules/core/src/copy.cpp
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@ -207,9 +207,11 @@ void Mat::copyTo( OutputArray _dst, InputArray _mask ) const
return;
}
CV_Assert( mask.type() == CV_8U );
int cn = channels(), mcn = mask.channels();
CV_Assert( mask.depth() == CV_8U && (mcn == 1 || mcn == cn) );
bool colorMask = mcn > 1;
size_t esz = elemSize();
size_t esz = colorMask ? elemSize1() : elemSize();
BinaryFunc copymask = getCopyMaskFunc(esz);
uchar* data0 = _dst.getMat().data;
@ -221,7 +223,7 @@ void Mat::copyTo( OutputArray _dst, InputArray _mask ) const
if( dims <= 2 )
{
Size sz = getContinuousSize(*this, dst, mask);
Size sz = getContinuousSize(*this, dst, mask, mcn);
copymask(data, step, mask.data, mask.step, dst.data, dst.step, sz, &esz);
return;
}
@ -229,7 +231,7 @@ void Mat::copyTo( OutputArray _dst, InputArray _mask ) const
const Mat* arrays[] = { this, &dst, &mask, 0 };
uchar* ptrs[3];
NAryMatIterator it(arrays, ptrs);
Size sz((int)it.size, 1);
Size sz((int)(it.size*mcn), 1);
for( size_t i = 0; i < it.nplanes; i++, ++it )
copymask(ptrs[0], 0, ptrs[2], 0, ptrs[1], 0, sz, &esz);

2
modules/imgproc/doc/filtering.rst
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@ -1299,7 +1299,7 @@ The function performs the downsampling step of the Gaussian pyramid construction
.. math::
\frac{1}{16} \begin{bmatrix} 1 & 4 & 6 & 4 & 1 \\ 4 & 16 & 24 & 16 & 4 \\ 6 & 24 & 36 & 24 & 6 \\ 4 & 16 & 24 & 16 & 4 \\ 1 & 4 & 6 & 4 & 1 \end{bmatrix}
\frac{1}{256} \begin{bmatrix} 1 & 4 & 6 & 4 & 1 \\ 4 & 16 & 24 & 16 & 4 \\ 6 & 24 & 36 & 24 & 6 \\ 4 & 16 & 24 & 16 & 4 \\ 1 & 4 & 6 & 4 & 1 \end{bmatrix}
Then, it downsamples the image by rejecting even rows and columns.

4
modules/python/src2/cv2.cpp
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@ -746,8 +746,8 @@ static inline PyObject* pyopencv_from(const CvDTreeNode* node)
static bool pyopencv_to(PyObject *o, cv::flann::IndexParams& p, const char *name="<unknown>")
{
bool ok = false;
PyObject* keys = PyMapping_Keys(o);
PyObject* values = PyMapping_Values(o);
PyObject* keys = PyObject_CallMethod(o,(char*)"keys",0);
PyObject* values = PyObject_CallMethod(o,(char*)"values",0);
if( keys && values )
{