Merged the trunk r8517:8524
This commit is contained in:
Andrey Kamaev
parent
9ad470ba5c
commit
daad7900e2
@ -11,8 +11,44 @@ if(BUILD_DOCS AND HAVE_SPHINX)
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project(opencv_docs)
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file(GLOB_RECURSE OPENCV_FILES_REF ../modules/*.rst)
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file(GLOB_RECURSE OPENCV_FILES_REF_PICT ../modules/*.png ../modules/*.jpg)
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set(OPENCV2_BASE_MODULES core imgproc highgui video calib3d features2d objdetect ml flann gpu photo stitching nonfree contrib legacy)
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# build lists of modules to be documented
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set(OPENCV2_MODULES "")
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set(OPENCV_MODULES "")
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foreach(mod ${OPENCV_MODULES_BUILD} ${OPENCV_MODULES_DISABLED_USER} ${OPENCV_MODULES_DISABLED_AUTO} ${OPENCV_MODULES_DISABLED_FORCE})
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string(REGEX REPLACE "^opencv_" "" mod "${mod}")
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if("${OPENCV_MODULE_opencv_${mod}_LOCATION}" STREQUAL "${OpenCV_SOURCE_DIR}/modules/${mod}")
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list(APPEND OPENCV2_MODULES ${mod})
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else()
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list(APPEND OPENCV_MODULES ${mod})
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endif()
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endforeach()
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list(REMOVE_ITEM OPENCV2_MODULES ${OPENCV2_BASE_MODULES})
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ocv_list_sort(OPENCV2_MODULES)
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ocv_list_sort(OPENCV_MODULES)
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# build lists of documentation files and generate table of contents for reference manual
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set(OPENCV_FILES_REF "")
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set(OPENCV_FILES_REF_PICT "")
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set(OPENCV_REFMAN_TOC "")
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foreach(mod ${OPENCV2_BASE_MODULES} ${OPENCV2_MODULES} ${OPENCV_MODULES})
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file(GLOB_RECURSE _OPENCV_FILES_REF "${OPENCV_MODULE_opencv_${mod}_LOCATION}/doc/*.rst")
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file(GLOB_RECURSE _OPENCV_FILES_REF_PICT "${OPENCV_MODULE_opencv_${mod}_LOCATION}/doc/*.png" "${OPENCV_MODULE_opencv_${mod}_LOCATION}/doc/*.jpg")
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list(APPEND OPENCV_FILES_REF ${_OPENCV_FILES_REF})
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list(APPEND OPENCV_FILES_REF_PICT ${_OPENCV_FILES_REF_PICT})
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set(toc_file "${OPENCV_MODULE_opencv_${mod}_LOCATION}/doc/${mod}.rst")
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if(EXISTS "${toc_file}")
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file(RELATIVE_PATH toc_file "${OpenCV_SOURCE_DIR}/modules" "${toc_file}")
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set(OPENCV_REFMAN_TOC "${OPENCV_REFMAN_TOC} ${toc_file}\r\n")
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endif()
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endforeach()
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configure_file("${OpenCV_SOURCE_DIR}/modules/refman.rst.in" "${OpenCV_SOURCE_DIR}/modules/refman.rst" IMMEDIATE @ONLY)
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file(GLOB_RECURSE OPENCV_FILES_UG user_guide/*.rst)
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file(GLOB_RECURSE OPENCV_FILES_TUT tutorials/*.rst)
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file(GLOB_RECURSE OPENCV_FILES_TUT_PICT tutorials/*.png tutorials/*.jpg)
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@ -54,6 +90,6 @@ if(BUILD_DOCS AND HAVE_SPHINX)
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set_target_properties(html_docs PROPERTIES FOLDER "documentation")
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endif()
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endif()
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install(FILES ${FILES_DOC} DESTINATION "${OPENCV_DOC_INSTALL_PATH}" COMPONENT main)
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install(FILES ${FILES_DOC_VS} DESTINATION "${OPENCV_DOC_INSTALL_PATH}/vidsurv" COMPONENT main)
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@ -16,8 +16,8 @@ are met:
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notice, this list of conditions and the following disclaimer in the
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documentation and/or other materials provided with the distribution.
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*Neither the name of the University of Cambridge nor the names of
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its contributors may be used to endorse or promote products derived
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*Neither the name of the University of Cambridge nor the names of
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its contributors may be used to endorse or promote products derived
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from this software without specific prior written permission.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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@ -35,7 +35,7 @@ SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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/*
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The references are:
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* Machine learning for high-speed corner detection,
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* Machine learning for high-speed corner detection,
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E. Rosten and T. Drummond, ECCV 2006
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* Faster and better: A machine learning approach to corner detection
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E. Rosten, R. Porter and T. Drummond, PAMI, 2009
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@ -64,7 +64,7 @@ static void makeOffsets(int pixel[], int row_stride)
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pixel[13] = -3 + row_stride * 1;
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pixel[14] = -2 + row_stride * 2;
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pixel[15] = -1 + row_stride * 3;
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}
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}
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static int cornerScore(const uchar* ptr, const int pixel[], int threshold)
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{
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@ -73,7 +73,7 @@ static int cornerScore(const uchar* ptr, const int pixel[], int threshold)
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short d[N];
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for( k = 0; k < N; k++ )
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d[k] = (short)(v - ptr[pixel[k]]);
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#if CV_SSE2
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__m128i q0 = _mm_set1_epi16(-1000), q1 = _mm_set1_epi16(1000);
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for( k = 0; k < 16; k += 8 )
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@ -128,7 +128,7 @@ static int cornerScore(const uchar* ptr, const int pixel[], int threshold)
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a0 = std::max(a0, std::min(a, (int)d[k]));
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a0 = std::max(a0, std::min(a, (int)d[k+9]));
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}
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int b0 = -a0;
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for( k = 0; k < 16; k += 2 )
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{
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@ -141,14 +141,14 @@ static int cornerScore(const uchar* ptr, const int pixel[], int threshold)
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b = std::max(b, (int)d[k+6]);
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b = std::max(b, (int)d[k+7]);
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b = std::max(b, (int)d[k+8]);
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b0 = std::min(b0, std::max(b, (int)d[k]));
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b0 = std::min(b0, std::max(b, (int)d[k+9]));
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}
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threshold = -b0-1;
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#endif
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#if 0
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// check that with the computed "threshold" the pixel is still a corner
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// and that with the increased-by-1 "threshold" the pixel is not a corner anymore
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@ -157,7 +157,7 @@ static int cornerScore(const uchar* ptr, const int pixel[], int threshold)
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int v0 = std::min(ptr[0] + threshold + delta, 255);
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int v1 = std::max(ptr[0] - threshold - delta, 0);
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int c0 = 0, c1 = 0;
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for( int k = 0; k < N; k++ )
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{
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int x = ptr[pixel[k]];
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@ -184,7 +184,7 @@ static int cornerScore(const uchar* ptr, const int pixel[], int threshold)
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#endif
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return threshold;
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}
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void FAST(InputArray _img, std::vector<KeyPoint>& keypoints, int threshold, bool nonmax_suppression)
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{
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@ -214,7 +214,7 @@ void FAST(InputArray _img, std::vector<KeyPoint>& keypoints, int threshold, bool
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cpbuf[1] = cpbuf[0] + img.cols + 1;
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cpbuf[2] = cpbuf[1] + img.cols + 1;
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memset(buf[0], 0, img.cols*3);
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for(i = 3; i < img.rows-2; i++)
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{
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const uchar* ptr = img.ptr<uchar>(i) + 3;
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@ -222,7 +222,7 @@ void FAST(InputArray _img, std::vector<KeyPoint>& keypoints, int threshold, bool
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int* cornerpos = cpbuf[(i - 3)%3];
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memset(curr, 0, img.cols);
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int ncorners = 0;
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if( i < img.rows - 3 )
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{
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j = 3;
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@ -233,7 +233,7 @@ void FAST(InputArray _img, std::vector<KeyPoint>& keypoints, int threshold, bool
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__m128i v0 = _mm_loadu_si128((const __m128i*)ptr);
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__m128i v1 = _mm_xor_si128(_mm_subs_epu8(v0, t), delta);
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v0 = _mm_xor_si128(_mm_adds_epu8(v0, t), delta);
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__m128i x0 = _mm_sub_epi8(_mm_loadu_si128((const __m128i*)(ptr + pixel[0])), delta);
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__m128i x1 = _mm_sub_epi8(_mm_loadu_si128((const __m128i*)(ptr + pixel[4])), delta);
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__m128i x2 = _mm_sub_epi8(_mm_loadu_si128((const __m128i*)(ptr + pixel[8])), delta);
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@ -256,24 +256,24 @@ void FAST(InputArray _img, std::vector<KeyPoint>& keypoints, int threshold, bool
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ptr -= 8;
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continue;
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}
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__m128i c0 = _mm_setzero_si128(), c1 = c0, max0 = c0, max1 = c0;
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for( k = 0; k < N; k++ )
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{
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__m128i x = _mm_xor_si128(_mm_loadu_si128((const __m128i*)(ptr + pixel[k])), delta);
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m0 = _mm_cmpgt_epi8(x, v0);
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m1 = _mm_cmpgt_epi8(v1, x);
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c0 = _mm_and_si128(_mm_sub_epi8(c0, m0), m0);
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c1 = _mm_and_si128(_mm_sub_epi8(c1, m1), m1);
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max0 = _mm_max_epu8(max0, c0);
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max1 = _mm_max_epu8(max1, c1);
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}
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max0 = _mm_max_epu8(max0, max1);
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int m = _mm_movemask_epi8(_mm_cmpgt_epi8(max0, K16));
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for( k = 0; m > 0 && k < 16; k++, m >>= 1 )
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if(m & 1)
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{
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@ -288,26 +288,26 @@ void FAST(InputArray _img, std::vector<KeyPoint>& keypoints, int threshold, bool
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int v = ptr[0];
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const uchar* tab = &threshold_tab[0] - v + 255;
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int d = tab[ptr[pixel[0]]] | tab[ptr[pixel[8]]];
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if( d == 0 )
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continue;
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d &= tab[ptr[pixel[2]]] | tab[ptr[pixel[10]]];
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d &= tab[ptr[pixel[4]]] | tab[ptr[pixel[12]]];
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d &= tab[ptr[pixel[6]]] | tab[ptr[pixel[14]]];
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if( d == 0 )
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continue;
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d &= tab[ptr[pixel[1]]] | tab[ptr[pixel[9]]];
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d &= tab[ptr[pixel[3]]] | tab[ptr[pixel[11]]];
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d &= tab[ptr[pixel[5]]] | tab[ptr[pixel[13]]];
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d &= tab[ptr[pixel[7]]] | tab[ptr[pixel[15]]];
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if( d & 1 )
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{
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int vt = v - threshold, count = 0;
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for( k = 0; k < N; k++ )
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{
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int x = ptr[pixel[k]];
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@ -325,11 +325,11 @@ void FAST(InputArray _img, std::vector<KeyPoint>& keypoints, int threshold, bool
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count = 0;
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}
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}
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if( d & 2 )
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{
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int vt = v + threshold, count = 0;
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for( k = 0; k < N; k++ )
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{
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int x = ptr[pixel[k]];
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@ -349,17 +349,17 @@ void FAST(InputArray _img, std::vector<KeyPoint>& keypoints, int threshold, bool
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}
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}
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}
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cornerpos[-1] = ncorners;
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if( i == 3 )
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continue;
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const uchar* prev = buf[(i - 4 + 3)%3];
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const uchar* pprev = buf[(i - 5 + 3)%3];
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cornerpos = cpbuf[(i - 4 + 3)%3];
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ncorners = cornerpos[-1];
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for( k = 0; k < ncorners; k++ )
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{
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j = cornerpos[k];
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@ -375,7 +375,7 @@ void FAST(InputArray _img, std::vector<KeyPoint>& keypoints, int threshold, bool
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}
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}
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/*
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* FastFeatureDetector
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*/
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@ -53,31 +53,31 @@ static void
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HarrisResponses(const Mat& img, vector<KeyPoint>& pts, int blockSize, float harris_k)
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{
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CV_Assert( img.type() == CV_8UC1 && blockSize*blockSize <= 2048 );
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size_t ptidx, ptsize = pts.size();
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const uchar* ptr00 = img.ptr<uchar>();
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int step = (int)(img.step/img.elemSize1());
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int r = blockSize/2;
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float scale = (1 << 2) * blockSize * 255.0f;
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scale = 1.0f / scale;
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float scale_sq_sq = scale * scale * scale * scale;
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AutoBuffer<int> ofsbuf(blockSize*blockSize);
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int* ofs = ofsbuf;
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for( int i = 0; i < blockSize; i++ )
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for( int j = 0; j < blockSize; j++ )
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ofs[i*blockSize + j] = (int)(i*step + j);
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for( ptidx = 0; ptidx < ptsize; ptidx++ )
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{
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int x0 = cvRound(pts[ptidx].pt.x - r);
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int y0 = cvRound(pts[ptidx].pt.y - r);
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const uchar* ptr0 = ptr00 + y0*step + x0;
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int a = 0, b = 0, c = 0;
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for( int k = 0; k < blockSize*blockSize; k++ )
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{
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const uchar* ptr = ptr0 + ofs[k];
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@ -98,13 +98,13 @@ static float IC_Angle(const Mat& image, const int half_k, Point2f pt,
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const vector<int> & u_max)
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{
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int m_01 = 0, m_10 = 0;
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const uchar* center = &image.at<uchar> (cvRound(pt.y), cvRound(pt.x));
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// Treat the center line differently, v=0
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for (int u = -half_k; u <= half_k; ++u)
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m_10 += u * center[u];
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// Go line by line in the circular patch
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int step = (int)image.step1();
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for (int v = 1; v <= half_k; ++v)
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@ -120,7 +120,7 @@ static float IC_Angle(const Mat& image, const int half_k, Point2f pt,
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}
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m_01 += v * v_sum;
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}
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return fastAtan2((float)m_01, (float)m_10);
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}
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@ -134,10 +134,10 @@ static void computeOrbDescriptor(const KeyPoint& kpt,
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//angle = cvFloor(angle/12)*12.f;
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angle *= (float)(CV_PI/180.f);
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float a = (float)cos(angle), b = (float)sin(angle);
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const uchar* center = &img.at<uchar>(cvRound(kpt.pt.y), cvRound(kpt.pt.x));
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int step = (int)img.step;
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#if 1
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#define GET_VALUE(idx) \
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center[cvRound(pattern[idx].x*b + pattern[idx].y*a)*step + \
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@ -153,7 +153,7 @@ static void computeOrbDescriptor(const KeyPoint& kpt,
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cvRound(center[iy*step + ix]*(1-x)*(1-y) + center[(iy+1)*step + ix]*(1-x)*y + \
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center[iy*step + ix+1]*x*(1-y) + center[(iy+1)*step + ix+1]*x*y))
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#endif
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if( WTA_K == 2 )
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{
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for (int i = 0; i < dsize; ++i, pattern += 16)
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@ -175,7 +175,7 @@ static void computeOrbDescriptor(const KeyPoint& kpt,
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val |= (t0 < t1) << 6;
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t0 = GET_VALUE(14); t1 = GET_VALUE(15);
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val |= (t0 < t1) << 7;
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desc[i] = (uchar)val;
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}
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}
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@ -186,16 +186,16 @@ static void computeOrbDescriptor(const KeyPoint& kpt,
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int t0, t1, t2, val;
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t0 = GET_VALUE(0); t1 = GET_VALUE(1); t2 = GET_VALUE(2);
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val = t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0);
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t0 = GET_VALUE(3); t1 = GET_VALUE(4); t2 = GET_VALUE(5);
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val |= (t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0)) << 2;
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t0 = GET_VALUE(6); t1 = GET_VALUE(7); t2 = GET_VALUE(8);
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val |= (t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0)) << 4;
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t0 = GET_VALUE(9); t1 = GET_VALUE(10); t2 = GET_VALUE(11);
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val |= (t2 > t1 ? (t2 > t0 ? 2 : 0) : (t1 > t0)) << 6;
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desc[i] = (uchar)val;
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}
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}
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@ -211,7 +211,7 @@ static void computeOrbDescriptor(const KeyPoint& kpt,
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if( t3 > t2 ) t2 = t3, v = 3;
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k = t0 > t2 ? u : v;
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val = k;
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t0 = GET_VALUE(4); t1 = GET_VALUE(5);
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t2 = GET_VALUE(6); t3 = GET_VALUE(7);
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u = 0, v = 2;
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@ -219,7 +219,7 @@ static void computeOrbDescriptor(const KeyPoint& kpt,
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if( t3 > t2 ) t2 = t3, v = 3;
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k = t0 > t2 ? u : v;
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val |= k << 2;
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t0 = GET_VALUE(8); t1 = GET_VALUE(9);
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t2 = GET_VALUE(10); t3 = GET_VALUE(11);
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u = 0, v = 2;
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@ -227,7 +227,7 @@ static void computeOrbDescriptor(const KeyPoint& kpt,
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if( t3 > t2 ) t2 = t3, v = 3;
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k = t0 > t2 ? u : v;
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val |= k << 4;
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t0 = GET_VALUE(12); t1 = GET_VALUE(13);
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t2 = GET_VALUE(14); t3 = GET_VALUE(15);
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u = 0, v = 2;
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@ -235,23 +235,23 @@ static void computeOrbDescriptor(const KeyPoint& kpt,
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if( t3 > t2 ) t2 = t3, v = 3;
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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
|
||||
{
|
||||
|
||||
@ -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);
|
||||
}
|
||||
@ -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".
|
||||
|
||||
@ -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
|
||||
|
||||
@ -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)
|
||||
{
|
||||
|
||||
@ -1,24 +0,0 @@
|
||||
############################
|
||||
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
|
||||
|
||||
9
modules/refman.rst.in
Normal file
9
modules/refman.rst.in
Normal file
@ -0,0 +1,9 @@
|
||||
############################
|
||||
OpenCV API Reference
|
||||
############################
|
||||
|
||||
.. toctree::
|
||||
:maxdepth: 2
|
||||
|
||||
core/doc/intro.rst
|
||||
@OPENCV_REFMAN_TOC@
|
||||
Loading…
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Reference in New Issue
Block a user