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opencv: Use cv::AutoBuffer<>::data()

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This commit is contained in:
Alexander Alekhin
2018-06-10 22:42:00 +00:00
committed by Alexander Alekhin
parent 135ea264ef
commit b09a4a98d4
90 changed files with 333 additions and 339 deletions
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modules/ml/src/tree.cpp
+12 -12
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@@ -417,7 +417,7 @@ int DTreesImpl::findBestSplit( const vector<int>& _sidx )
int splitidx = -1;
int vi_, nv = (int)activeVars.size();
AutoBuffer<int> buf(w->maxSubsetSize*2);
int *subset = buf, *best_subset = subset + w->maxSubsetSize;
int *subset = buf.data(), *best_subset = subset + w->maxSubsetSize;
WSplit split, best_split;
best_split.quality = 0.;
@@ -488,7 +488,7 @@ void DTreesImpl::calcValue( int nidx, const vector<int>& _sidx )
// misclassified samples with cv_labels(*)==j.
// compute the number of instances of each class
double* cls_count = buf;
double* cls_count = buf.data();
double* cv_cls_count = cls_count + m;
double max_val = -1, total_weight = 0;
@@ -592,7 +592,7 @@ void DTreesImpl::calcValue( int nidx, const vector<int>& _sidx )
}
else
{
double *cv_sum = buf, *cv_sum2 = cv_sum + cv_n;
double *cv_sum = buf.data(), *cv_sum2 = cv_sum + cv_n;
double* cv_count = (double*)(cv_sum2 + cv_n);
for( j = 0; j < cv_n; j++ )
@@ -646,7 +646,7 @@ DTreesImpl::WSplit DTreesImpl::findSplitOrdClass( int vi, const vector<int>& _si
const int* sidx = &_sidx[0];
const int* responses = &w->cat_responses[0];
const double* weights = &w->sample_weights[0];
double* lcw = (double*)(uchar*)buf;
double* lcw = (double*)buf.data();
double* rcw = lcw + m;
float* values = (float*)(rcw + m);
int* sorted_idx = (int*)(values + n);
@@ -717,7 +717,7 @@ void DTreesImpl::clusterCategories( const double* vectors, int n, int m, double*
int iters = 0, max_iters = 100;
int i, j, idx;
cv::AutoBuffer<double> buf(n + k);
double *v_weights = buf, *c_weights = buf + n;
double *v_weights = buf.data(), *c_weights = buf.data() + n;
bool modified = true;
RNG r((uint64)-1);
@@ -819,12 +819,12 @@ DTreesImpl::WSplit DTreesImpl::findSplitCatClass( int vi, const vector<int>& _si
base_size += mi;
AutoBuffer<double> buf(base_size + n);
double* lc = (double*)buf;
double* lc = buf.data();
double* rc = lc + m;
double* _cjk = rc + m*2, *cjk = _cjk;
double* c_weights = cjk + m*mi;
int* labels = (int*)(buf + base_size);
int* labels = (int*)(buf.data() + base_size);
w->data->getNormCatValues(vi, _sidx, labels);
const int* responses = &w->cat_responses[0];
const double* weights = &w->sample_weights[0];
@@ -991,7 +991,7 @@ DTreesImpl::WSplit DTreesImpl::findSplitOrdReg( int vi, const vector<int>& _sidx
AutoBuffer<uchar> buf(n*(sizeof(int) + sizeof(float)));
float* values = (float*)(uchar*)buf;
float* values = (float*)buf.data();
int* sorted_idx = (int*)(values + n);
w->data->getValues(vi, _sidx, values);
const double* responses = &w->ord_responses[0];
@@ -1053,7 +1053,7 @@ DTreesImpl::WSplit DTreesImpl::findSplitCatReg( int vi, const vector<int>& _sidx
int mi = getCatCount(vi);
AutoBuffer<double> buf(3*mi + 3 + n);
double* sum = (double*)buf + 1;
double* sum = buf.data() + 1;
double* counts = sum + mi + 1;
double** sum_ptr = (double**)(counts + mi);
int* cat_labels = (int*)(sum_ptr + mi);
@@ -1148,7 +1148,7 @@ int DTreesImpl::calcDir( int splitidx, const vector<int>& _sidx,
if( mi <= 0 ) // split on an ordered variable
{
float c = split.c;
float* values = buf;
float* values = buf.data();
w->data->getValues(vi, _sidx, values);
for( i = 0; i < n; i++ )
@@ -1169,7 +1169,7 @@ int DTreesImpl::calcDir( int splitidx, const vector<int>& _sidx,
else
{
const int* subset = &w->wsubsets[split.subsetOfs];
int* cat_labels = (int*)(float*)buf;
int* cat_labels = (int*)buf.data();
w->data->getNormCatValues(vi, _sidx, cat_labels);
for( i = 0; i < n; i++ )
@@ -1372,7 +1372,7 @@ float DTreesImpl::predictTrees( const Range& range, const Mat& sample, int flags
int i, ncats = (int)catOfs.size(), nclasses = (int)classLabels.size();
int catbufsize = ncats > 0 ? nvars : 0;
AutoBuffer<int> buf(nclasses + catbufsize + 1);
int* votes = buf;
int* votes = buf.data();
int* catbuf = votes + nclasses;
const int* cvidx = (flags & (COMPRESSED_INPUT|PREPROCESSED_INPUT)) == 0 && !varIdx.empty() ? &compVarIdx[0] : 0;
const uchar* vtype = &varType[0];