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Merged the trunk r8517:8524

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pull/13383/head
Andrey Kamaev 13 years ago
parent 9ad470ba5c
commit daad7900e2
9 changed files with 293 additions and 213 deletions
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  1. 42
      doc/CMakeLists.txt
  2. 64
      modules/features2d/src/fast.cpp
  3. 197
      modules/features2d/src/orb.cpp
  4. 48
      modules/features2d/test/test_features2d.cpp
  5. 31
      modules/highgui/doc/user_interface.rst
  6. 4
      modules/imgproc/src/color.cpp
  7. 87
      modules/imgproc/src/imgwarp.cpp
  8. 24
      modules/refman.rst
  9. 9
      modules/refman.rst.in

42
doc/CMakeLists.txt
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@ -11,8 +11,44 @@ if(BUILD_DOCS AND HAVE_SPHINX)
project(opencv_docs)
file(GLOB_RECURSE OPENCV_FILES_REF ../modules/*.rst)
file(GLOB_RECURSE OPENCV_FILES_REF_PICT ../modules/*.png ../modules/*.jpg)
set(OPENCV2_BASE_MODULES core imgproc highgui video calib3d features2d objdetect ml flann gpu photo stitching nonfree contrib legacy)
# build lists of modules to be documented
set(OPENCV2_MODULES "")
set(OPENCV_MODULES "")
foreach(mod ${OPENCV_MODULES_BUILD} ${OPENCV_MODULES_DISABLED_USER} ${OPENCV_MODULES_DISABLED_AUTO} ${OPENCV_MODULES_DISABLED_FORCE})
string(REGEX REPLACE "^opencv_" "" mod "${mod}")
if("${OPENCV_MODULE_opencv_${mod}_LOCATION}" STREQUAL "${OpenCV_SOURCE_DIR}/modules/${mod}")
list(APPEND OPENCV2_MODULES ${mod})
else()
list(APPEND OPENCV_MODULES ${mod})
endif()
endforeach()
list(REMOVE_ITEM OPENCV2_MODULES ${OPENCV2_BASE_MODULES})
ocv_list_sort(OPENCV2_MODULES)
ocv_list_sort(OPENCV_MODULES)
# build lists of documentation files and generate table of contents for reference manual
set(OPENCV_FILES_REF "")
set(OPENCV_FILES_REF_PICT "")
set(OPENCV_REFMAN_TOC "")
foreach(mod ${OPENCV2_BASE_MODULES} ${OPENCV2_MODULES} ${OPENCV_MODULES})
file(GLOB_RECURSE _OPENCV_FILES_REF "${OPENCV_MODULE_opencv_${mod}_LOCATION}/doc/*.rst")
file(GLOB_RECURSE _OPENCV_FILES_REF_PICT "${OPENCV_MODULE_opencv_${mod}_LOCATION}/doc/*.png" "${OPENCV_MODULE_opencv_${mod}_LOCATION}/doc/*.jpg")
list(APPEND OPENCV_FILES_REF ${_OPENCV_FILES_REF})
list(APPEND OPENCV_FILES_REF_PICT ${_OPENCV_FILES_REF_PICT})
set(toc_file "${OPENCV_MODULE_opencv_${mod}_LOCATION}/doc/${mod}.rst")
if(EXISTS "${toc_file}")
file(RELATIVE_PATH toc_file "${OpenCV_SOURCE_DIR}/modules" "${toc_file}")
set(OPENCV_REFMAN_TOC "${OPENCV_REFMAN_TOC} ${toc_file}\r\n")
endif()
endforeach()
configure_file("${OpenCV_SOURCE_DIR}/modules/refman.rst.in" "${OpenCV_SOURCE_DIR}/modules/refman.rst" IMMEDIATE @ONLY)
file(GLOB_RECURSE OPENCV_FILES_UG user_guide/*.rst)
file(GLOB_RECURSE OPENCV_FILES_TUT tutorials/*.rst)
file(GLOB_RECURSE OPENCV_FILES_TUT_PICT tutorials/*.png tutorials/*.jpg)
@ -54,6 +90,6 @@ if(BUILD_DOCS AND HAVE_SPHINX)
set_target_properties(html_docs PROPERTIES FOLDER "documentation")
endif()
endif()
install(FILES ${FILES_DOC} DESTINATION "${OPENCV_DOC_INSTALL_PATH}" COMPONENT main)
install(FILES ${FILES_DOC_VS} DESTINATION "${OPENCV_DOC_INSTALL_PATH}/vidsurv" COMPONENT main)

64
modules/features2d/src/fast.cpp
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@ -16,8 +16,8 @@ are met:
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.
*Neither the name of the University of Cambridge nor the names of
its contributors may be used to endorse or promote products derived
*Neither the name of the University of Cambridge nor the names of
its 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
@ -35,7 +35,7 @@ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
/*
The references are:
* Machine learning for high-speed corner detection,
* Machine learning for high-speed corner detection,
E. Rosten and T. Drummond, ECCV 2006
* Faster and better: A machine learning approach to corner detection
E. Rosten, R. Porter and T. Drummond, PAMI, 2009
@ -64,7 +64,7 @@ static void makeOffsets(int pixel[], int row_stride)
pixel[13] = -3 + row_stride * 1;
pixel[14] = -2 + row_stride * 2;
pixel[15] = -1 + row_stride * 3;
}
}
static int cornerScore(const uchar* ptr, const int pixel[], int threshold)
{
@ -73,7 +73,7 @@ static int cornerScore(const uchar* ptr, const int pixel[], int threshold)
short d[N];
for( k = 0; k < N; k++ )
d[k] = (short)(v - ptr[pixel[k]]);
#if CV_SSE2
__m128i q0 = _mm_set1_epi16(-1000), q1 = _mm_set1_epi16(1000);
for( k = 0; k < 16; k += 8 )
@ -128,7 +128,7 @@ static int cornerScore(const uchar* ptr, const int pixel[], int threshold)
a0 = std::max(a0, std::min(a, (int)d[k]));
a0 = std::max(a0, std::min(a, (int)d[k+9]));
}
int b0 = -a0;
for( k = 0; k < 16; k += 2 )
{
@ -141,14 +141,14 @@ static int cornerScore(const uchar* ptr, const int pixel[], int threshold)
b = std::max(b, (int)d[k+6]);
b = std::max(b, (int)d[k+7]);
b = std::max(b, (int)d[k+8]);
b0 = std::min(b0, std::max(b, (int)d[k]));
b0 = std::min(b0, std::max(b, (int)d[k+9]));
}
threshold = -b0-1;
#endif
#if 0
// check that with the computed "threshold" the pixel is still a corner
// and that with the increased-by-1 "threshold" the pixel is not a corner anymore
@ -157,7 +157,7 @@ static int cornerScore(const uchar* ptr, const int pixel[], int threshold)
int v0 = std::min(ptr[0] + threshold + delta, 255);
int v1 = std::max(ptr[0] - threshold - delta, 0);
int c0 = 0, c1 = 0;
for( int k = 0; k < N; k++ )
{
int x = ptr[pixel[k]];
@ -184,7 +184,7 @@ static int cornerScore(const uchar* ptr, const int pixel[], int threshold)
#endif
return threshold;
}
void FAST(InputArray _img, std::vector<KeyPoint>& keypoints, int threshold, bool nonmax_suppression)
{
@ -214,7 +214,7 @@ void FAST(InputArray _img, std::vector<KeyPoint>& keypoints, int threshold, bool
cpbuf[1] = cpbuf[0] + img.cols + 1;
cpbuf[2] = cpbuf[1] + img.cols + 1;
memset(buf[0], 0, img.cols*3);
for(i = 3; i < img.rows-2; i++)
{
const uchar* ptr = img.ptr<uchar>(i) + 3;
@ -222,7 +222,7 @@ void FAST(InputArray _img, std::vector<KeyPoint>& keypoints, int threshold, bool
int* cornerpos = cpbuf[(i - 3)%3];
memset(curr, 0, img.cols);
int ncorners = 0;
if( i < img.rows - 3 )
{
j = 3;
@ -233,7 +233,7 @@ void FAST(InputArray _img, std::vector<KeyPoint>& keypoints, int threshold, bool
__m128i v0 = _mm_loadu_si128((const __m128i*)ptr);
__m128i v1 = _mm_xor_si128(_mm_subs_epu8(v0, t), delta);
v0 = _mm_xor_si128(_mm_adds_epu8(v0, t), delta);
__m128i x0 = _mm_sub_epi8(_mm_loadu_si128((const __m128i*)(ptr + pixel[0])), delta);
__m128i x1 = _mm_sub_epi8(_mm_loadu_si128((const __m128i*)(ptr + pixel[4])), delta);
__m128i x2 = _mm_sub_epi8(_mm_loadu_si128((const __m128i*)(ptr + pixel[8])), delta);
@ -256,24 +256,24 @@ void FAST(InputArray _img, std::vector<KeyPoint>& keypoints, int threshold, bool
ptr -= 8;
continue;
}
__m128i c0 = _mm_setzero_si128(), c1 = c0, max0 = c0, max1 = c0;
for( k = 0; k < N; k++ )
{
__m128i x = _mm_xor_si128(_mm_loadu_si128((const __m128i*)(ptr + pixel[k])), delta);
m0 = _mm_cmpgt_epi8(x, v0);
m1 = _mm_cmpgt_epi8(v1, x);
c0 = _mm_and_si128(_mm_sub_epi8(c0, m0), m0);
c1 = _mm_and_si128(_mm_sub_epi8(c1, m1), m1);
max0 = _mm_max_epu8(max0, c0);
max1 = _mm_max_epu8(max1, c1);
}
max0 = _mm_max_epu8(max0, max1);
int m = _mm_movemask_epi8(_mm_cmpgt_epi8(max0, K16));
for( k = 0; m > 0 && k < 16; k++, m >>= 1 )
if(m & 1)
{
@ -288,26 +288,26 @@ void FAST(InputArray _img, std::vector<KeyPoint>& keypoints, int threshold, bool
int v = ptr[0];
const uchar* tab = &threshold_tab[0] - v + 255;
int d = tab[ptr[pixel[0]]] | tab[ptr[pixel[8]]];
if( d == 0 )
continue;
d &= tab[ptr[pixel[2]]] | tab[ptr[pixel[10]]];
d &= tab[ptr[pixel[4]]] | tab[ptr[pixel[12]]];
d &= tab[ptr[pixel[6]]] | tab[ptr[pixel[14]]];
if( d == 0 )
continue;
d &= tab[ptr[pixel[1]]] | tab[ptr[pixel[9]]];
d &= tab[ptr[pixel[3]]] | tab[ptr[pixel[11]]];
d &= tab[ptr[pixel[5]]] | tab[ptr[pixel[13]]];
d &= tab[ptr[pixel[7]]] | tab[ptr[pixel[15]]];
if( d & 1 )
{
int vt = v - threshold, count = 0;
for( k = 0; k < N; k++ )
{
int x = ptr[pixel[k]];
@ -325,11 +325,11 @@ void FAST(InputArray _img, std::vector<KeyPoint>& keypoints, int threshold, bool
count = 0;
}
}
if( d & 2 )
{
int vt = v + threshold, count = 0;
for( k = 0; k < N; k++ )
{
int x = ptr[pixel[k]];
@ -349,17 +349,17 @@ void FAST(InputArray _img, std::vector<KeyPoint>& keypoints, int threshold, bool
}
}
}
cornerpos[-1] = ncorners;
if( i == 3 )
continue;
const uchar* prev = buf[(i - 4 + 3)%3];
const uchar* pprev = buf[(i - 5 + 3)%3];
cornerpos = cpbuf[(i - 4 + 3)%3];
ncorners = cornerpos[-1];
for( k = 0; k < ncorners; k++ )
{
j = cornerpos[k];
@ -375,7 +375,7 @@ void FAST(InputArray _img, std::vector<KeyPoint>& keypoints, int threshold, bool
}
}
/*
* FastFeatureDetector
*/

197
modules/features2d/src/orb.cpp
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@ -53,31 +53,31 @@ static void
HarrisResponses(const Mat& img, vector<KeyPoint>& pts, int blockSize, float harris_k)
{
CV_Assert( img.type() == CV_8UC1 && blockSize*blockSize <= 2048 );
size_t ptidx, ptsize = pts.size();
const uchar* ptr00 = img.ptr<uchar>();
int step = (int)(img.step/img.elemSize1());
int r = blockSize/2;
float scale = (1 << 2) * blockSize * 255.0f;
scale = 1.0f / scale;
float scale_sq_sq = scale * scale * scale * scale;
AutoBuffer<int> ofsbuf(blockSize*blockSize);
int* ofs = ofsbuf;
for( int i = 0; i < blockSize; i++ )
for( int j = 0; j < blockSize; j++ )
ofs[i*blockSize + j] = (int)(i*step + j);
for( ptidx = 0; ptidx < ptsize; ptidx++ )
{
int x0 = cvRound(pts[ptidx].pt.x - r);
int y0 = cvRound(pts[ptidx].pt.y - r);
const uchar* ptr0 = ptr00 + y0*step + x0;
int a = 0, b = 0, c = 0;
for( int k = 0; k < blockSize*blockSize; k++ )
{
const uchar* ptr = ptr0 + ofs[k];
@ -98,13 +98,13 @@ static float IC_Angle(const Mat& image, const int half_k, Point2f pt,
const vector<int> & u_max)
{
int m_01 = 0, m_10 = 0;
const uchar* center = &image.at<uchar> (cvRound(pt.y), cvRound(pt.x));
// Treat the center line differently, v=0
for (int u = -half_k; u <= half_k; ++u)
m_10 += u * center[u];
// Go line by line in the circular patch
int step = (int)image.step1();
for (int v = 1; v <= half_k; ++v)
@ -120,7 +120,7 @@ static float IC_Angle(const Mat& image, const int half_k, Point2f pt,
}
m_01 += v * v_sum;
}
return fastAtan2((float)m_01, (float)m_10);
}
@ -134,10 +134,10 @@ static void computeOrbDescriptor(const KeyPoint& kpt,
//angle = cvFloor(angle/12)*12.f;
angle *= (float)(CV_PI/180.f);
float a = (float)cos(angle), b = (float)sin(angle);
const uchar* center = &img.at<uchar>(cvRound(kpt.pt.y), cvRound(kpt.pt.x));
int step = (int)img.step;
#if 1
#define GET_VALUE(idx) \
center[cvRound(pattern[idx].x*b + pattern[idx].y*a)*step + \
@ -153,7 +153,7 @@ static void computeOrbDescriptor(const KeyPoint& kpt,
cvRound(center[iy*step + ix]*(1-x)*(1-y) + center[(iy+1)*step + ix]*(1-x)*y + \
center[iy*step + ix+1]*x*(1-y) + center[(iy+1)*step + ix+1]*x*y))
#endif
if( WTA_K == 2 )
{
for (int i = 0; i < dsize; ++i, pattern += 16)
@ -175,7 +175,7 @@ static void computeOrbDescriptor(const KeyPoint& kpt,
val |= (t0 < t1) << 6;
t0 = GET_VALUE(14); t1 = GET_VALUE(15);
val |= (t0 < t1) << 7;
desc[i] = (uchar)val;
}
}
@ -186,16 +186,16 @@ static void computeOrbDescriptor(const KeyPoint& kpt,
int t0, t1, t2, val;
t0 = GET_VALUE(0); t1 = GET_VALUE(1); t2 = GET_VALUE(2);
val = t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0);
t0 = GET_VALUE(3); t1 = GET_VALUE(4); t2 = GET_VALUE(5);
val |= (t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0)) << 2;
t0 = GET_VALUE(6); t1 = GET_VALUE(7); t2 = GET_VALUE(8);
val |= (t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0)) << 4;
t0 = GET_VALUE(9); t1 = GET_VALUE(10); t2 = GET_VALUE(11);
val |= (t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0)) << 6;
desc[i] = (uchar)val;
}
}
@ -211,7 +211,7 @@ static void computeOrbDescriptor(const KeyPoint& kpt,
if( t3 > t2 ) t2 = t3, v = 3;
k = t0 > t2 ? u : v;
val = k;
t0 = GET_VALUE(4); t1 = GET_VALUE(5);
t2 = GET_VALUE(6); t3 = GET_VALUE(7);
u = 0, v = 2;
@ -219,7 +219,7 @@ static void computeOrbDescriptor(const KeyPoint& kpt,
if( t3 > t2 ) t2 = t3, v = 3;
k = t0 > t2 ? u : v;
val |= k << 2;
t0 = GET_VALUE(8); t1 = GET_VALUE(9);
t2 = GET_VALUE(10); t3 = GET_VALUE(11);
u = 0, v = 2;
@ -227,7 +227,7 @@ static void computeOrbDescriptor(const KeyPoint& kpt,
if( t3 > t2 ) t2 = t3, v = 3;
k = t0 > t2 ? u : v;
val |= k << 4;
t0 = GET_VALUE(12); t1 = GET_VALUE(13);
t2 = GET_VALUE(14); t3 = GET_VALUE(15);
u = 0, v = 2;
@ -235,23 +235,23 @@ static void computeOrbDescriptor(const KeyPoint& kpt,
if( t3 > t2 ) t2 = t3, v = 3;
k = t0 > t2 ? u : v;
val |= k << 6;
desc[i] = (uchar)val;
}
}
else
CV_Error( CV_StsBadSize, "Wrong WTA_K. It can be only 2, 3 or 4." );
#undef GET_VALUE
}
static void initializeOrbPattern( const Point* pattern0, vector<Point>& pattern, int ntuples, int tupleSize, int poolSize )
{
RNG rng(0x12345678);
int i, k, k1;
pattern.resize(ntuples*tupleSize);
for( i = 0; i < ntuples; i++ )
{
for( k = 0; k < tupleSize; k++ )
@ -545,7 +545,7 @@ static void makeRandomPattern(int patchSize, Point* pattern, int npoints)
}
}
static inline float getScale(int level, int firstLevel, double scaleFactor)
{
return (float)std::pow(scaleFactor, (double)(level - firstLevel));
@ -570,8 +570,8 @@ int ORB::descriptorSize() const
int ORB::descriptorType() const
{
return CV_8U;
}
}
/** Compute the ORB features and descriptors on an image
* @param img the image to compute the features and descriptors on
* @param mask the mask to apply
@ -599,7 +599,7 @@ static void computeOrientation(const Mat& image, vector<KeyPoint>& keypoints,
keypoint->angle = IC_Angle(image, halfPatchSize, keypoint->pt, umax);
}
}
/** Compute the ORB keypoints on an image
* @param image_pyramid the image pyramid to compute the features and descriptors on
@ -614,11 +614,11 @@ static void computeKeyPoints(const vector<Mat>& imagePyramid,
{
int nlevels = (int)imagePyramid.size();
vector<int> nfeaturesPerLevel(nlevels);
// fill the extractors and descriptors for the corresponding scales
float factor = (float)(1.0 / scaleFactor);
float ndesiredFeaturesPerScale = nfeatures*(1 - factor)/(1 - (float)pow((double)factor, (double)nlevels));
int sumFeatures = 0;
for( int level = 0; level < nlevels-1; level++ )
{
@ -627,19 +627,19 @@ static void computeKeyPoints(const vector<Mat>& imagePyramid,
ndesiredFeaturesPerScale *= factor;
}
nfeaturesPerLevel[nlevels-1] = std::max(nfeatures - sumFeatures, 0);
// Make sure we forget about what is too close to the boundary
//edge_threshold_ = std::max(edge_threshold_, patch_size_/2 + kKernelWidth / 2 + 2);
// pre-compute the end of a row in a circular patch
int halfPatchSize = patchSize / 2;
vector<int> umax(halfPatchSize + 1);
int v, v0, vmax = cvFloor(halfPatchSize * sqrt(2.f) / 2 + 1);
int vmin = cvCeil(halfPatchSize * sqrt(2.f) / 2);
for (v = 0; v <= vmax; ++v)
umax[v] = cvRound(sqrt((double)halfPatchSize * halfPatchSize - v * v));
// Make sure we are symmetric
for (v = halfPatchSize, v0 = 0; v >= vmin; --v)
{
@ -648,37 +648,37 @@ static void computeKeyPoints(const vector<Mat>& imagePyramid,
umax[v] = v0;
++v0;
}
allKeypoints.resize(nlevels);
for (int level = 0; level < nlevels; ++level)
{
int nfeatures = nfeaturesPerLevel[level];
allKeypoints[level].reserve(nfeatures*2);
vector<KeyPoint> & keypoints = allKeypoints[level];
// Detect FAST features, 20 is a good threshold
FastFeatureDetector fd(20, true);
fd.detect(imagePyramid[level], keypoints, maskPyramid[level]);
// Remove keypoints very close to the border
KeyPointsFilter::runByImageBorder(keypoints, imagePyramid[level].size(), edgeThreshold);
if( scoreType == ORB::HARRIS_SCORE )
{
// Keep more points than necessary as FAST does not give amazing corners
KeyPointsFilter::retainBest(keypoints, 2 * nfeatures);
// Compute the Harris cornerness (better scoring than FAST)
HarrisResponses(imagePyramid[level], keypoints, 7, HARRIS_K);
}
//cull to the final desired level, using the new Harris scores or the original FAST scores.
KeyPointsFilter::retainBest(keypoints, nfeatures);
KeyPointsFilter::retainBest(keypoints, nfeatures);
float sf = getScale(level, firstLevel, scaleFactor);
// Set the level of the coordinates
for (vector<KeyPoint>::iterator keypoint = keypoints.begin(),
keypointEnd = keypoints.end(); keypoint != keypointEnd; ++keypoint)
@ -686,12 +686,12 @@ static void computeKeyPoints(const vector<Mat>& imagePyramid,
keypoint->octave = level;
keypoint->size = patchSize*sf;
}
computeOrientation(imagePyramid[level], keypoints, halfPatchSize, umax);
}
}
}
/** Compute the ORB decriptors
* @param image the image to compute the features and descriptors on
* @param integral_image the integral image of the image (can be empty, but the computation will be slower)
@ -706,12 +706,12 @@ static void computeDescriptors(const Mat& image, vector<KeyPoint>& keypoints, Ma
CV_Assert(image.type() == CV_8UC1);
//create the descriptor mat, keypoints.size() rows, BYTES cols
descriptors = Mat::zeros((int)keypoints.size(), dsize, CV_8UC1);
for (size_t i = 0; i < keypoints.size(); i++)
computeOrbDescriptor(keypoints[i], image, &pattern[0], descriptors.ptr((int)i), dsize, WTA_K);
}
/** Compute the ORB features and descriptors on an image
* @param img the image to compute the features and descriptors on
* @param mask the mask to apply
@ -725,21 +725,21 @@ void ORB::operator()( InputArray _image, InputArray _mask, vector<KeyPoint>& _ke
{
bool do_keypoints = !useProvidedKeypoints;
bool do_descriptors = _descriptors.needed();
if( (!do_keypoints && !do_descriptors) || _image.empty() )
return;
//ROI handling
const int HARRIS_BLOCK_SIZE = 9;
int halfPatchSize = patchSize / 2;
int border = std::max(edgeThreshold, std::max(halfPatchSize, HARRIS_BLOCK_SIZE/2))+1;
Mat image = _image.getMat(), mask = _mask.getMat();
if( image.type() != CV_8UC1 )
cvtColor(_image, image, CV_BGR2GRAY);
int nlevels = this->nlevels;
if( !do_keypoints )
{
// if we have pre-computed keypoints, they may use more levels than it is set in parameters
@ -756,7 +756,7 @@ void ORB::operator()( InputArray _image, InputArray _mask, vector<KeyPoint>& _ke
nlevels = std::max(nlevels, std::max(_keypoints[i].octave, 0));
nlevels++;
}
// Pre-compute the scale pyramids
vector<Mat> imagePyramid(nlevels), maskPyramid(nlevels);
for (int level = 0; level < nlevels; ++level)
@ -766,49 +766,48 @@ void ORB::operator()( InputArray _image, InputArray _mask, vector<KeyPoint>& _ke
Size wholeSize(sz.width + border*2, sz.height + border*2);
Mat temp(wholeSize, image.type()), masktemp;
imagePyramid[level] = temp(Rect(border, border, sz.width, sz.height));
if( !mask.empty() )
{
masktemp = Mat(wholeSize, mask.type());
maskPyramid[level] = masktemp(Rect(border, border, sz.width, sz.height));
}
// Compute the resized image
if( level != firstLevel )
{
if( level < firstLevel )
{
resize(image, imagePyramid[level], sz, scale, scale, INTER_LINEAR);
resize(image, imagePyramid[level], sz, 0, 0, INTER_LINEAR);
if (!mask.empty())
resize(mask, maskPyramid[level], sz, scale, scale, INTER_LINEAR);
copyMakeBorder(imagePyramid[level], temp, border, border, border, border,
BORDER_REFLECT_101+BORDER_ISOLATED);
resize(mask, maskPyramid[level], sz, 0, 0, INTER_LINEAR);
}
else
{
resize(imagePyramid[level-1], imagePyramid[level], sz,
1./scaleFactor, 1./scaleFactor, INTER_LINEAR);
resize(imagePyramid[level-1], imagePyramid[level], sz, 0, 0, INTER_LINEAR);
if (!mask.empty())
resize(maskPyramid[level-1], maskPyramid[level], sz,
1./scaleFactor, 1./scaleFactor, INTER_LINEAR);
copyMakeBorder(imagePyramid[level], temp, border, border, border, border,
BORDER_REFLECT_101+BORDER_ISOLATED);
{
resize(maskPyramid[level-1], maskPyramid[level], sz, 0, 0, INTER_LINEAR);
threshold(maskPyramid[level], maskPyramid[level], 254, 0, THRESH_TOZERO);
}
}
copyMakeBorder(imagePyramid[level], temp, border, border, border, border,
BORDER_REFLECT_101+BORDER_ISOLATED);
if (!mask.empty())
copyMakeBorder(maskPyramid[level], masktemp, border, border, border, border,
BORDER_CONSTANT+BORDER_ISOLATED);
}
else
{
copyMakeBorder(image, temp, border, border, border, border,
BORDER_REFLECT_101);
image.copyTo(imagePyramid[level]);
if( !mask.empty() )
mask.copyTo(maskPyramid[level]);
copyMakeBorder(mask, masktemp, border, border, border, border,
BORDER_CONSTANT+BORDER_ISOLATED);
}
if( !mask.empty() )
copyMakeBorder(maskPyramid[level], masktemp, border, border, border, border,
BORDER_CONSTANT+BORDER_ISOLATED);
}
// Pre-compute the keypoints (we keep the best over all scales, so this has to be done beforehand
vector < vector<KeyPoint> > allKeypoints;
if( do_keypoints )
@ -817,19 +816,19 @@ void ORB::operator()( InputArray _image, InputArray _mask, vector<KeyPoint>& _ke
computeKeyPoints(imagePyramid, maskPyramid, allKeypoints,
nfeatures, firstLevel, scaleFactor,
edgeThreshold, patchSize, scoreType);
// make sure we have the right number of keypoints keypoints
/*vector<KeyPoint> temp;
for (int level = 0; level < n_levels; ++level)
{
vector<KeyPoint>& keypoints = all_keypoints[level];
temp.insert(temp.end(), keypoints.begin(), keypoints.end());
keypoints.clear();
}
KeyPoint::retainBest(temp, n_features_);
for (vector<KeyPoint>::iterator keypoint = temp.begin(),
keypoint_end = temp.end(); keypoint != keypoint_end; ++keypoint)
all_keypoints[keypoint->octave].push_back(*keypoint);*/
@ -838,19 +837,19 @@ void ORB::operator()( InputArray _image, InputArray _mask, vector<KeyPoint>& _ke
{
// Remove keypoints very close to the border
KeyPointsFilter::runByImageBorder(_keypoints, image.size(), edgeThreshold);
// Cluster the input keypoints depending on the level they were computed at
allKeypoints.resize(nlevels);
for (vector<KeyPoint>::iterator keypoint = _keypoints.begin(),
keypointEnd = _keypoints.end(); keypoint != keypointEnd; ++keypoint)
allKeypoints[keypoint->octave].push_back(*keypoint);
// Make sure we rescale the coordinates
for (int level = 0; level < nlevels; ++level)
{
if (level == firstLevel)
continue;
vector<KeyPoint> & keypoints = allKeypoints[level];
float scale = 1/getScale(level, firstLevel, scaleFactor);
for (vector<KeyPoint>::iterator keypoint = keypoints.begin(),
@ -858,10 +857,10 @@ void ORB::operator()( InputArray _image, InputArray _mask, vector<KeyPoint>& _ke
keypoint->pt *= scale;
}
}
Mat descriptors;
vector<Point> pattern;
if( do_descriptors )
{
int nkeypoints = 0;
@ -874,19 +873,19 @@ void ORB::operator()( InputArray _image, InputArray _mask, vector<KeyPoint>& _ke
_descriptors.create(nkeypoints, descriptorSize(), CV_8U);
descriptors = _descriptors.getMat();
}
const int npoints = 512;
Point patternbuf[npoints];
const Point* pattern0 = (const Point*)bit_pattern_31_;
if( patchSize != 31 )
{
pattern0 = patternbuf;
makeRandomPattern(patchSize, patternbuf, npoints);
}
CV_Assert( WTA_K == 2 || WTA_K == 3 || WTA_K == 4 );
if( WTA_K == 2 )
std::copy(pattern0, pattern0 + npoints, std::back_inserter(pattern));
else
@ -895,7 +894,7 @@ void ORB::operator()( InputArray _image, InputArray _mask, vector<KeyPoint>& _ke
initializeOrbPattern(pattern0, pattern, ntuples, WTA_K, npoints);
}
}
_keypoints.clear();
int offset = 0;
for (int level = 0; level < nlevels; ++level)
@ -903,15 +902,15 @@ void ORB::operator()( InputArray _image, InputArray _mask, vector<KeyPoint>& _ke
// Get the features and compute their orientation
vector<KeyPoint>& keypoints = allKeypoints[level];
int nkeypoints = (int)keypoints.size();
// Compute the descriptors
if (do_descriptors)
{
Mat desc;
if (!descriptors.empty())
if (!descriptors.empty())
{
desc = descriptors.rowRange(offset, offset + nkeypoints);
}
}
offset += nkeypoints;
// preprocess the resized image
@ -920,7 +919,7 @@ void ORB::operator()( InputArray _image, InputArray _mask, vector<KeyPoint>& _ke
GaussianBlur(workingMat, workingMat, Size(7, 7), 2, 2, BORDER_REFLECT_101);
computeDescriptors(workingMat, keypoints, desc, pattern, descriptorSize(), WTA_K);
}
// Copy to the output data
if (level != firstLevel)
{
@ -933,11 +932,11 @@ void ORB::operator()( InputArray _image, InputArray _mask, vector<KeyPoint>& _ke
_keypoints.insert(_keypoints.end(), keypoints.begin(), keypoints.end());
}
}
void ORB::detectImpl( const Mat& image, vector<KeyPoint>& keypoints, const Mat& mask) const
{
(*this)(image, mask, keypoints, noArray(), false);
}
}
void ORB::computeImpl( const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors) const
{

48
modules/features2d/test/test_features2d.cpp
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@ -1091,3 +1091,51 @@ TEST( Features2d_DescriptorMatcher_FlannBased, regression )
CV_DescriptorMatcherTest test( "descriptor-matcher-flann-based", new FlannBasedMatcher, 0.04f );
test.safe_run();
}
TEST(Features2D_ORB, _1996)
{
cv::Ptr<cv::FeatureDetector> fd = cv::FeatureDetector::create("ORB");
cv::Ptr<cv::DescriptorExtractor> de = cv::DescriptorExtractor::create("ORB");
Mat image = cv::imread(string(cvtest::TS::ptr()->get_data_path()) + "shared/lena.jpg");
ASSERT_FALSE(image.empty());
Mat roi(image.size(), CV_8UC1, Scalar(0));
Point poly[] = {Point(100, 20), Point(300, 50), Point(400, 200), Point(10, 500)};
fillConvexPoly(roi, poly, int(sizeof(poly) / sizeof(poly[0])), Scalar(255));
std::vector<cv::KeyPoint> keypoints;
fd->detect(image, keypoints, roi);
cv::Mat descriptors;
de->compute(image, keypoints, descriptors);
//image.setTo(Scalar(255,255,255), roi);
int roiViolations = 0;
for(std::vector<cv::KeyPoint>::const_iterator kp = keypoints.begin(); kp != keypoints.end(); ++kp)
{
int x = cvRound(kp->pt.x);
int y = cvRound(kp->pt.y);
ASSERT_LE(0, x);
ASSERT_LE(0, y);
ASSERT_GT(image.cols, x);
ASSERT_GT(image.rows, y);
// if (!roi.at<uchar>(y,x))
// {
// roiViolations++;
// circle(image, kp->pt, 3, Scalar(0,0,255));
// }
}
// if(roiViolations)
// {
// imshow("img", image);
// waitKey();
// }
ASSERT_EQ(0, roiViolations);
}

31
modules/highgui/doc/user_interface.rst
Unescape View File

@ -10,6 +10,7 @@ Creates a trackbar and attaches it to the specified window.
.. ocv:function:: int createTrackbar( const string& trackbarname, const string& winname, int* value, int count, TrackbarCallback onChange=0, void* userdata=0)
.. ocv:cfunction:: int cvCreateTrackbar( const char* trackbar_name, const char* window_name, int* value, int count, CvTrackbarCallback on_change=NULL )
.. ocv:pyoldfunction:: cv.CreateTrackbar(trackbarName, windowName, value, count, onChange) -> None
:param trackbarname: Name of the created trackbar.
@ -41,6 +42,7 @@ Returns the trackbar position.
.. ocv:pyfunction:: cv2.getTrackbarPos(trackbarname, winname) -> retval
.. ocv:cfunction:: int cvGetTrackbarPos( const char* trackbar_name, const char* window_name )
.. ocv:pyoldfunction:: cv.GetTrackbarPos(trackbarName, windowName) -> retval
:param trackbarname: Name of the trackbar.
@ -62,6 +64,7 @@ Displays an image in the specified window.
.. ocv:pyfunction:: cv2.imshow(winname, mat) -> None
.. ocv:cfunction:: void cvShowImage( const char* name, const CvArr* image )
.. ocv:pyoldfunction:: cv.ShowImage(name, image) -> None
:param winname: Name of the window.
@ -86,6 +89,7 @@ Creates a window.
.. ocv:pyfunction:: cv2.namedWindow(winname[, flags]) -> None
.. ocv:cfunction:: int cvNamedWindow( const char* name, int flags=CV_WINDOW_AUTOSIZE )
.. ocv:pyoldfunction:: cv.NamedWindow(name, flags=CV_WINDOW_AUTOSIZE)-> None
:param name: Name of the window in the window caption that may be used as a window identifier.
@ -120,6 +124,7 @@ Destroys a window.
.. ocv:pyfunction:: cv2.destroyWindow(winname) -> None
.. ocv:cfunction:: void cvDestroyWindow( const char* name )
.. ocv:pyoldfunction:: cv.DestroyWindow(name)-> None
:param winname: Name of the window to be destroyed.
@ -136,6 +141,7 @@ Destroys all of the HighGUI windows.
.. ocv:pyfunction:: cv2.destroyAllWindows() -> None
.. ocv:cfunction:: void cvDestroyAllWindows()
.. ocv:pyoldfunction:: cv.DestroyAllWindows()-> None
The function ``destroyAllWindows`` destroys all of the opened HighGUI windows.
@ -145,10 +151,15 @@ MoveWindow
----------
Moves window to the specified position
.. ocv:function:: void moveWindow( const string& winname, int x, int y )
.. ocv:pyfunction:: cv2.moveWindow(winname, x, y) -> None
.. ocv:cfunction:: void cvMoveWindow( const char* name, int x, int y )
.. ocv:pyoldfunction:: cv.MoveWindow(name, x, y)-> None
:param name: Window name
:param winname: Window name
:param x: The new x-coordinate of the window
@ -159,10 +170,15 @@ ResizeWindow
------------
Resizes window to the specified size
.. ocv:function:: void resizeWindow( const string& winname, int width, int height )
.. ocv:pyfunction:: cv2.resizeWindow(winname, width, height) -> None
.. ocv:cfunction:: void cvResizeWindow( const char* name, int width, int height )
.. ocv:pyoldfunction:: cv.ResizeWindow(name, width, height)-> None
:param name: Window name
:param winname: Window name
:param width: The new window width
@ -179,14 +195,17 @@ SetMouseCallback
----------------
Sets mouse handler for the specified window
.. ocv:function:: void setMouseCallback( const string& winname, MouseCallback onMouse, void* userdata=0 )
.. ocv:cfunction:: void cvSetMouseCallback( const char* window_name, CvMouseCallback on_mouse, void* param=NULL )
.. ocv:pyoldfunction:: cv.SetMouseCallback(windowName, onMouse, param=None) -> None
:param window_name: Window name
:param winname: Window name
:param on_mouse: 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/svn/opencv/trunk/opencv/samples/cpp/ffilldemo.cpp, on how to specify and use the callback.
:param param: The optional parameter passed to the callback.
:param userdata: The optional parameter passed to the callback.
setTrackbarPos
@ -198,6 +217,7 @@ Sets the trackbar position.
.. ocv:pyfunction:: cv2.setTrackbarPos(trackbarname, winname, pos) -> None
.. ocv:cfunction:: void cvSetTrackbarPos( const char* trackbar_name, const char* window_name, int pos )
.. ocv:pyoldfunction:: cv.SetTrackbarPos(trackbarName, windowName, pos)-> None
:param trackbarname: Name of the trackbar.
@ -221,6 +241,7 @@ Waits for a pressed key.
.. ocv:pyfunction:: cv2.waitKey([delay]) -> retval
.. ocv:cfunction:: int cvWaitKey( int delay=0 )
.. ocv:pyoldfunction:: cv.WaitKey(delay=0)-> int
:param delay: Delay in milliseconds. 0 is the special value that means "forever".

4
modules/imgproc/src/color.cpp
Unescape View File

@ -3353,6 +3353,10 @@ void cv::cvtColor( InputArray _src, OutputArray _dst, int code, int dcn )
if( code == CV_BGR2HSV || code == CV_RGB2HSV ||
code == CV_BGR2HSV_FULL || code == CV_RGB2HSV_FULL )
{
#ifdef HAVE_TEGRA_OPTIMIZATION
if(tegra::cvtRGB2HSV(src, dst, bidx, hrange))
break;
#endif
if( depth == CV_8U )
CvtColorLoop(src, dst, RGB2HSV_b(scn, bidx, hrange));
else

87
modules/imgproc/src/imgwarp.cpp
Unescape View File

@ -47,7 +47,7 @@
// */
#include "precomp.hpp"
namespace cv
{
@ -349,7 +349,7 @@ struct VResizeLinearVec_32s8u
{
if( !checkHardwareSupport(CV_CPU_SSE2) )
return 0;
const int** src = (const int**)_src;
const short* beta = (const short*)_beta;
const int *S0 = src[0], *S1 = src[1];
@ -432,7 +432,7 @@ template<int shiftval> struct VResizeLinearVec_32f16
{
if( !checkHardwareSupport(CV_CPU_SSE2) )
return 0;
const float** src = (const float**)_src;
const float* beta = (const float*)_beta;
const float *S0 = src[0], *S1 = src[1];
@ -522,7 +522,7 @@ template<int shiftval> struct VResizeLinearVec_32f16
};
typedef VResizeLinearVec_32f16<SHRT_MIN> VResizeLinearVec_32f16u;
typedef VResizeLinearVec_32f16<0> VResizeLinearVec_32f16s;
typedef VResizeLinearVec_32f16<0> VResizeLinearVec_32f16s;
struct VResizeLinearVec_32f
{
@ -530,7 +530,7 @@ struct VResizeLinearVec_32f
{
if( !checkHardwareSupport(CV_CPU_SSE) )
return 0;
const float** src = (const float**)_src;
const float* beta = (const float*)_beta;
const float *S0 = src[0], *S1 = src[1];
@ -581,7 +581,7 @@ struct VResizeCubicVec_32s8u
{
if( !checkHardwareSupport(CV_CPU_SSE2) )
return 0;
const int** src = (const int**)_src;
const short* beta = (const short*)_beta;
const int *S0 = src[0], *S1 = src[1], *S2 = src[2], *S3 = src[3];
@ -676,7 +676,7 @@ template<int shiftval> struct VResizeCubicVec_32f16
{
if( !checkHardwareSupport(CV_CPU_SSE2) )
return 0;
const float** src = (const float**)_src;
const float* beta = (const float*)_beta;
const float *S0 = src[0], *S1 = src[1], *S2 = src[2], *S3 = src[3];
@ -729,7 +729,7 @@ template<int shiftval> struct VResizeCubicVec_32f16
};
typedef VResizeCubicVec_32f16<SHRT_MIN> VResizeCubicVec_32f16u;
typedef VResizeCubicVec_32f16<0> VResizeCubicVec_32f16s;
typedef VResizeCubicVec_32f16<0> VResizeCubicVec_32f16s;
struct VResizeCubicVec_32f
{
@ -737,7 +737,7 @@ struct VResizeCubicVec_32f
{
if( !checkHardwareSupport(CV_CPU_SSE) )
return 0;
const float** src = (const float**)_src;
const float* beta = (const float*)_beta;
const float *S0 = src[0], *S1 = src[1], *S2 = src[2], *S3 = src[3];
@ -795,17 +795,17 @@ typedef HResizeNoVec HResizeLinearVec_8u32s;
typedef HResizeNoVec HResizeLinearVec_16u32f;
typedef HResizeNoVec HResizeLinearVec_16s32f;
typedef HResizeNoVec HResizeLinearVec_32f;
typedef VResizeNoVec VResizeLinearVec_32s8u;
typedef VResizeNoVec VResizeLinearVec_32f16u;
typedef VResizeNoVec VResizeLinearVec_32f16s;
typedef VResizeNoVec VResizeLinearVec_32f;
typedef VResizeNoVec VResizeCubicVec_32s8u;
typedef VResizeNoVec VResizeCubicVec_32f16u;
typedef VResizeNoVec VResizeCubicVec_32f16s;
typedef VResizeNoVec VResizeCubicVec_32f;
#endif
@ -869,7 +869,7 @@ struct VResizeLinear
typedef T value_type;
typedef WT buf_type;
typedef AT alpha_type;
void operator()(const WT** src, T* dst, const AT* beta, int width ) const
{
WT b0 = beta[0], b1 = beta[1];
@ -1035,7 +1035,7 @@ struct VResizeLanczos4
CastOp castOp;
VecOp vecOp;
int k, x = vecOp((const uchar**)src, (uchar*)dst, (const uchar*)beta, width);
#if CV_ENABLE_UNROLLED
#if CV_ENABLE_UNROLLED
for( ; x <= width - 4; x += 4 )
{
WT b = beta[0];
@ -1144,7 +1144,7 @@ static void resizeAreaFast_( const Mat& src, Mat& dst, const int* ofs, const int
int dy, dx, k = 0;
int area = scale_x*scale_y;
float scale = 1.f/(scale_x*scale_y);
int dwidth1 = (ssize.width/scale_x)*cn;
int dwidth1 = (ssize.width/scale_x)*cn;
dsize.width *= cn;
ssize.width *= cn;
@ -1158,7 +1158,7 @@ static void resizeAreaFast_( const Mat& src, Mat& dst, const int* ofs, const int
D[dx] = 0;
continue;
}
for( dx = 0; dx < w; dx++ )
{
const T* S = (const T*)(src.data + src.step*sy0) + xofs[dx];
@ -1173,14 +1173,14 @@ static void resizeAreaFast_( const Mat& src, Mat& dst, const int* ofs, const int
D[dx] = saturate_cast<T>(sum*scale);
}
for( ; dx < dsize.width; dx++ )
{
WT sum = 0;
int count = 0, sx0 = xofs[dx];
if( sx0 >= ssize.width )
D[dx] = 0;
for( int sy = 0; sy < scale_y; sy++ )
{
if( sy0 + sy >= ssize.height )
@ -1194,7 +1194,7 @@ static void resizeAreaFast_( const Mat& src, Mat& dst, const int* ofs, const int
count++;
}
}
D[dx] = saturate_cast<T>((float)sum/count);
}
}
@ -1318,7 +1318,7 @@ typedef void (*ResizeAreaFunc)( const Mat& src, Mat& dst,
const DecimateAlpha* xofs, int xofs_count );
}
//////////////////////////////////////////////////////////////////////////////////////////
void cv::resize( InputArray _src, OutputArray _dst, Size dsize,
@ -1428,7 +1428,7 @@ void cv::resize( InputArray _src, OutputArray _dst, Size dsize,
Mat src = _src.getMat();
Size ssize = src.size();
CV_Assert( ssize.area() > 0 );
CV_Assert( !(dsize == Size()) || (inv_scale_x > 0 && inv_scale_y > 0) );
if( dsize == Size() )
@ -2310,7 +2310,7 @@ static void remapLanczos4( const Mat& _src, Mat& _dst, const Mat& _xy,
int dx, dy;
CastOp castOp;
int borderType1 = borderType != BORDER_TRANSPARENT ? borderType : BORDER_REFLECT_101;
unsigned width1 = std::max(ssize.width-7, 0), height1 = std::max(ssize.height-7, 0);
if( _dst.isContinuous() && _xy.isContinuous() && _fxy.isContinuous() )
@ -2410,7 +2410,7 @@ typedef void (*RemapFunc)(const Mat& _src, Mat& _dst, const Mat& _xy,
int borderType, const Scalar& _borderValue);
}
void cv::remap( InputArray _src, OutputArray _dst,
InputArray _map1, InputArray _map2,
int interpolation, int borderType, const Scalar& borderValue )
@ -2449,9 +2449,9 @@ void cv::remap( InputArray _src, OutputArray _dst,
};
Mat src = _src.getMat(), map1 = _map1.getMat(), map2 = _map2.getMat();
CV_Assert( (!map2.data || map2.size() == map1.size()));
_dst.create( map1.size(), src.type() );
Mat dst = _dst.getMat();
if( dst.data == src.data )
@ -2703,7 +2703,7 @@ void cv::convertMaps( InputArray _map1, InputArray _map2,
CV_Assert( dstm1type == CV_16SC2 || dstm1type == CV_32FC1 || dstm1type == CV_32FC2 );
_dstmap1.create( size, dstm1type );
dstmap1 = _dstmap1.getMat();
if( !nninterpolate && dstm1type != CV_32FC2 )
{
_dstmap2.create( size, dstm1type == CV_16SC2 ? CV_16UC1 : CV_32FC1 );
@ -2855,7 +2855,7 @@ void cv::warpAffine( InputArray _src, OutputArray _dst,
if (borderType == BORDER_REPLICATE)
{
if( tegra::warpAffine(src, dst, M, interpolation, borderType, borderValue) )
return;
return;
}
else
{
@ -2979,7 +2979,7 @@ void cv::warpPerspective( InputArray _src, OutputArray _dst, InputArray _M0,
Mat src = _src.getMat(), M0 = _M0.getMat();
_dst.create( dsize.area() == 0 ? src.size() : dsize, src.type() );
Mat dst = _dst.getMat();
CV_Assert( src.cols > 0 && src.rows > 0 );
if( dst.data == src.data )
src = src.clone();
@ -2999,19 +2999,6 @@ void cv::warpPerspective( InputArray _src, OutputArray _dst, InputArray _M0,
invert(matM, matM);
#ifdef HAVE_TEGRA_OPTIMIZATION
// if (borderType == BORDER_REPLICATE)
// {
// if( tegra::warpPerspective(src, dst, M, interpolation, borderType, borderValue) )
// return;
// }
// else
// {
// double warp_mat[9];
// Mat warp_m(3, 3, CV_64F, warp_mat);
// M0.convertTo(warp_m, warp_m.type());
// if( tegra::warpPerspective(src, dst, warp_mat, interpolation, borderType, borderValue) )
// return;
// }
if( tegra::warpPerspective(src, dst, M, interpolation, borderType, borderValue) )
return;
#endif
@ -3048,7 +3035,7 @@ void cv::warpPerspective( InputArray _src, OutputArray _dst, InputArray _M0,
double fY = std::max((double)INT_MIN, std::min((double)INT_MAX, (Y0 + M[3]*x1)*W));
int X = saturate_cast<int>(fX);
int Y = saturate_cast<int>(fY);
xy[x1*2] = saturate_cast<short>(X);
xy[x1*2+1] = saturate_cast<short>(Y);
}
@ -3063,7 +3050,7 @@ void cv::warpPerspective( InputArray _src, OutputArray _dst, InputArray _M0,
double fY = std::max((double)INT_MIN, std::min((double)INT_MAX, (Y0 + M[3]*x1)*W));
int X = saturate_cast<int>(fX);
int Y = saturate_cast<int>(fY);
xy[x1*2] = saturate_cast<short>(X >> INTER_BITS);
xy[x1*2+1] = saturate_cast<short>(Y >> INTER_BITS);
alpha[x1] = (short)((Y & (INTER_TAB_SIZE-1))*INTER_TAB_SIZE +
@ -3195,26 +3182,26 @@ cv::Mat cv::getAffineTransform( const Point2f src[], const Point2f dst[] )
solve( A, B, X );
return M;
}
void cv::invertAffineTransform(InputArray _matM, OutputArray __iM)
{
Mat matM = _matM.getMat();
CV_Assert(matM.rows == 2 && matM.cols == 3);
__iM.create(2, 3, matM.type());
Mat _iM = __iM.getMat();
if( matM.type() == CV_32F )
{
const float* M = (const float*)matM.data;
float* iM = (float*)_iM.data;
int step = (int)(matM.step/sizeof(M[0])), istep = (int)(_iM.step/sizeof(iM[0]));
double D = M[0]*M[step+1] - M[1]*M[step];
D = D != 0 ? 1./D : 0;
double A11 = M[step+1]*D, A22 = M[0]*D, A12 = -M[1]*D, A21 = -M[step]*D;
double b1 = -A11*M[2] - A12*M[step+2];
double b2 = -A21*M[2] - A22*M[step+2];
iM[0] = (float)A11; iM[1] = (float)A12; iM[2] = (float)b1;
iM[istep] = (float)A21; iM[istep+1] = (float)A22; iM[istep+2] = (float)b2;
}
@ -3223,19 +3210,19 @@ void cv::invertAffineTransform(InputArray _matM, OutputArray __iM)
const double* M = (const double*)matM.data;
double* iM = (double*)_iM.data;
int step = (int)(matM.step/sizeof(M[0])), istep = (int)(_iM.step/sizeof(iM[0]));
double D = M[0]*M[step+1] - M[1]*M[step];
D = D != 0 ? 1./D : 0;
double A11 = M[step+1]*D, A22 = M[0]*D, A12 = -M[1]*D, A21 = -M[step]*D;
double b1 = -A11*M[2] - A12*M[step+2];
double b2 = -A21*M[2] - A22*M[step+2];
iM[0] = A11; iM[1] = A12; iM[2] = b1;
iM[istep] = A21; iM[istep+1] = A22; iM[istep+2] = b2;
}
else
CV_Error( CV_StsUnsupportedFormat, "" );
}
}
cv::Mat cv::getPerspectiveTransform(InputArray _src, InputArray _dst)
{

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modules/refman.rst
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############################
OpenCV API Reference
############################
.. toctree::
:maxdepth: 2
core/doc/intro.rst
core/doc/core.rst
imgproc/doc/imgproc.rst
highgui/doc/highgui.rst
video/doc/video.rst
calib3d/doc/calib3d.rst
features2d/doc/features2d.rst
objdetect/doc/objdetect.rst
ml/doc/ml.rst
flann/doc/flann.rst
gpu/doc/gpu.rst
photo/doc/photo.rst
stitching/doc/stitching.rst
nonfree/doc/nonfree.rst
contrib/doc/contrib.rst
legacy/doc/legacy.rst

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############################
OpenCV API Reference
############################
.. toctree::
:maxdepth: 2
core/doc/intro.rst
@OPENCV_REFMAN_TOC@
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