From 9399394e6cc95daf912bc8f22870d495c542df65 Mon Sep 17 00:00:00 2001 From: Andrey Kamaev Date: Thu, 31 May 2012 08:02:52 +0000 Subject: [PATCH] Fixed #1996 --- modules/features2d/src/fast.cpp | 64 ++++----- modules/features2d/src/orb.cpp | 197 ++++++++++++++-------------- modules/features2d/test/test_features2d.cpp | 48 +++++++ 3 files changed, 178 insertions(+), 131 deletions(-) diff --git a/modules/features2d/src/fast.cpp b/modules/features2d/src/fast.cpp index 2bc25dd..9495d35 100644 --- a/modules/features2d/src/fast.cpp +++ b/modules/features2d/src/fast.cpp @@ -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& keypoints, int threshold, bool nonmax_suppression) { @@ -214,7 +214,7 @@ void FAST(InputArray _img, std::vector& 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(i) + 3; @@ -222,7 +222,7 @@ void FAST(InputArray _img, std::vector& 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& 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& 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& 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& 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& 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& keypoints, int threshold, bool } } - + /* * FastFeatureDetector */ diff --git a/modules/features2d/src/orb.cpp b/modules/features2d/src/orb.cpp index ed838d3..32b4849 100644 --- a/modules/features2d/src/orb.cpp +++ b/modules/features2d/src/orb.cpp @@ -53,31 +53,31 @@ static void HarrisResponses(const Mat& img, vector& 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(); 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 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 & u_max) { int m_01 = 0, m_10 = 0; - + const uchar* center = &image.at (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(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& 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& 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& imagePyramid, { int nlevels = (int)imagePyramid.size(); vector 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& 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 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& 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 & 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::iterator keypoint = keypoints.begin(), keypointEnd = keypoints.end(); keypoint != keypointEnd; ++keypoint) @@ -686,12 +686,12 @@ static void computeKeyPoints(const vector& 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& 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& _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& _ke nlevels = std::max(nlevels, std::max(_keypoints[i].octave, 0)); nlevels++; } - + // Pre-compute the scale pyramids vector imagePyramid(nlevels), maskPyramid(nlevels); for (int level = 0; level < nlevels; ++level) @@ -766,49 +766,48 @@ void ORB::operator()( InputArray _image, InputArray _mask, vector& _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 > allKeypoints; if( do_keypoints ) @@ -817,19 +816,19 @@ void ORB::operator()( InputArray _image, InputArray _mask, vector& _ke computeKeyPoints(imagePyramid, maskPyramid, allKeypoints, nfeatures, firstLevel, scaleFactor, edgeThreshold, patchSize, scoreType); - + // make sure we have the right number of keypoints keypoints /*vector temp; - + for (int level = 0; level < n_levels; ++level) { vector& keypoints = all_keypoints[level]; temp.insert(temp.end(), keypoints.begin(), keypoints.end()); keypoints.clear(); } - + KeyPoint::retainBest(temp, n_features_); - + for (vector::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& _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::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 & keypoints = allKeypoints[level]; float scale = 1/getScale(level, firstLevel, scaleFactor); for (vector::iterator keypoint = keypoints.begin(), @@ -858,10 +857,10 @@ void ORB::operator()( InputArray _image, InputArray _mask, vector& _ke keypoint->pt *= scale; } } - + Mat descriptors; vector pattern; - + if( do_descriptors ) { int nkeypoints = 0; @@ -874,19 +873,19 @@ void ORB::operator()( InputArray _image, InputArray _mask, vector& _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& _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& _ke // Get the features and compute their orientation vector& 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& _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& _ke _keypoints.insert(_keypoints.end(), keypoints.begin(), keypoints.end()); } } - + void ORB::detectImpl( const Mat& image, vector& keypoints, const Mat& mask) const { (*this)(image, mask, keypoints, noArray(), false); -} +} void ORB::computeImpl( const Mat& image, vector& keypoints, Mat& descriptors) const { diff --git a/modules/features2d/test/test_features2d.cpp b/modules/features2d/test/test_features2d.cpp index 713c0c4..272acca 100644 --- a/modules/features2d/test/test_features2d.cpp +++ b/modules/features2d/test/test_features2d.cpp @@ -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 fd = cv::FeatureDetector::create("ORB"); + cv::Ptr 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 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::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(y,x)) + // { + // roiViolations++; + // circle(image, kp->pt, 3, Scalar(0,0,255)); + // } + } + + // if(roiViolations) + // { + // imshow("img", image); + // waitKey(); + // } + + ASSERT_EQ(0, roiViolations); +} \ No newline at end of file -- 2.7.4