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Fixed #1996

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This commit is contained in:
Andrey Kamaev
2012-05-31 08:02:52 +00:00
parent 1a572c8e89
commit 9399394e6c
3 changed files with 178 additions and 131 deletions
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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
{