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merged all the latest changes from 2.4 to trunk

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This commit is contained in:
Vadim Pisarevsky
2012-04-13 21:50:59 +00:00
parent 020f9a6047
commit 2fd1e2ea57
416 changed files with 12852 additions and 6070 deletions
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90
modules/core/src/matrix.cpp
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@@ -262,10 +262,9 @@ void Mat::deallocate()
}
Mat::Mat(const Mat& m, const Range& rowRange, const Range& colRange)
: flags(0), dims(0), rows(0), cols(0), data(0), refcount(0),
datastart(0), dataend(0), datalimit(0), allocator(0), size(&rows)
Mat::Mat(const Mat& m, const Range& rowRange, const Range& colRange) : size(&rows)
{
initEmpty();
CV_Assert( m.dims >= 2 );
if( m.dims > 2 )
{
@@ -336,21 +335,19 @@ Mat::Mat(const Mat& m, const Rect& roi)
}
Mat::Mat(int _dims, const int* _sizes, int _type, void* _data, const size_t* _steps)
: flags(MAGIC_VAL|CV_MAT_TYPE(_type)), dims(0),
rows(0), cols(0), data((uchar*)_data), refcount(0),
datastart((uchar*)_data), dataend((uchar*)_data), datalimit((uchar*)_data),
allocator(0), size(&rows)
Mat::Mat(int _dims, const int* _sizes, int _type, void* _data, const size_t* _steps) : size(&rows)
{
initEmpty();
flags |= CV_MAT_TYPE(_type);
data = datastart = (uchar*)_data;
setSize(*this, _dims, _sizes, _steps, true);
finalizeHdr(*this);
}
Mat::Mat(const Mat& m, const Range* ranges)
: flags(m.flags), dims(0), rows(0), cols(0), data(0), refcount(0),
datastart(0), dataend(0), datalimit(0), allocator(0), size(&rows)
Mat::Mat(const Mat& m, const Range* ranges) : size(&rows)
{
initEmpty();
int i, d = m.dims;
CV_Assert(ranges);
@@ -374,12 +371,13 @@ Mat::Mat(const Mat& m, const Range* ranges)
}
Mat::Mat(const CvMatND* m, bool copyData)
: flags(MAGIC_VAL|CV_MAT_TYPE(m->type)), dims(0), rows(0), cols(0),
data((uchar*)m->data.ptr), refcount(0),
datastart((uchar*)m->data.ptr), allocator(0),
size(&rows)
Mat::Mat(const CvMatND* m, bool copyData) : size(&rows)
{
initEmpty();
if( !m )
return;
data = datastart = m->data.ptr;
flags |= CV_MAT_TYPE(m->type);
int _sizes[CV_MAX_DIM];
size_t _steps[CV_MAX_DIM];
@@ -434,12 +432,45 @@ Mat Mat::diag(int d) const
return m;
}
Mat::Mat(const IplImage* img, bool copyData)
: flags(MAGIC_VAL), dims(2), rows(0), cols(0),
data(0), refcount(0), datastart(0), dataend(0), allocator(0), size(&rows)
Mat::Mat(const CvMat* m, bool copyData) : size(&rows)
{
initEmpty();
if( !m )
return;
if( !copyData )
{
flags = MAGIC_VAL + (m->type & (CV_MAT_TYPE_MASK|CV_MAT_CONT_FLAG));
dims = 2;
rows = m->rows;
cols = m->cols;
data = datastart = m->data.ptr;
size_t esz = CV_ELEM_SIZE(m->type), minstep = cols*esz, _step = m->step;
if( _step == 0 )
_step = minstep;
datalimit = datastart + _step*rows;
dataend = datalimit - _step + minstep;
step[0] = _step; step[1] = esz;
}
else
{
data = datastart = dataend = 0;
Mat(m->rows, m->cols, m->type, m->data.ptr, m->step).copyTo(*this);
}
}
Mat::Mat(const IplImage* img, bool copyData) : size(&rows)
{
initEmpty();
if( !img )
return;
dims = 2;
CV_DbgAssert(CV_IS_IMAGE(img) && img->imageData != 0);
int depth = IPL2CV_DEPTH(img->depth);
@@ -2428,8 +2459,9 @@ double cv::kmeans( InputArray _data, int K,
{
const int SPP_TRIALS = 3;
Mat data = _data.getMat();
int N = data.rows > 1 ? data.rows : data.cols;
int dims = (data.rows > 1 ? data.cols : 1)*data.channels();
bool isrow = data.rows == 1 && data.channels() > 1;
int N = !isrow ? data.rows : data.cols;
int dims = (!isrow ? data.cols : 1)*data.channels();
int type = data.depth();
attempts = std::max(attempts, 1);
@@ -2458,7 +2490,7 @@ double cv::kmeans( InputArray _data, int K,
}
int* labels = _labels.ptr<int>();
Mat centers(K, dims, type), old_centers(K, dims, type);
Mat centers(K, dims, type), old_centers(K, dims, type), temp(1, dims, type);
vector<int> counters(K);
vector<Vec2f> _box(dims);
Vec2f* box = &_box[0];
@@ -2502,7 +2534,7 @@ double cv::kmeans( InputArray _data, int K,
for( a = 0; a < attempts; a++ )
{
double max_center_shift = DBL_MAX;
for( iter = 0; iter < criteria.maxCount && max_center_shift > criteria.epsilon; iter++ )
for( iter = 0;; )
{
swap(centers, old_centers);
@@ -2579,13 +2611,17 @@ double cv::kmeans( InputArray _data, int K,
int farthest_i = -1;
float* new_center = centers.ptr<float>(k);
float* old_center = centers.ptr<float>(max_k);
float* _old_center = temp.ptr<float>(); // normalized
float scale = 1.f/counters[max_k];
for( j = 0; j < dims; j++ )
_old_center[j] = old_center[j]*scale;
for( i = 0; i < N; i++ )
{
if( labels[i] != max_k )
continue;
sample = data.ptr<float>(i);
double dist = normL2Sqr_(sample, old_center, dims);
double dist = normL2Sqr_(sample, _old_center, dims);
if( max_dist <= dist )
{
@@ -2596,6 +2632,7 @@ double cv::kmeans( InputArray _data, int K,
counters[max_k]--;
counters[k]++;
labels[farthest_i] = k;
sample = data.ptr<float>(farthest_i);
for( j = 0; j < dims; j++ )
@@ -2627,6 +2664,9 @@ double cv::kmeans( InputArray _data, int K,
}
}
}
if( ++iter == MAX(criteria.maxCount, 2) || max_center_shift <= criteria.epsilon )
break;
// assign labels
compactness = 0;