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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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2
apps/createsamples/utility.cpp
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@@ -1372,7 +1372,7 @@ int icvGetTraininDataFromVec( Mat& img, CvVecFile& userdata )
size_t elements_read = fread( &tmp, sizeof( tmp ), 1, userdata.input );
CV_Assert(elements_read == 1);
elements_read = fread( vector, sizeof( short ), userdata.vecsize, userdata.input );
elements_read = fread(vector.data(), sizeof(short), userdata.vecsize, userdata.input);
CV_Assert(elements_read == (size_t)userdata.vecsize);
if( feof( userdata.input ) || userdata.last++ >= userdata.count )

4
apps/traincascade/HOGfeatures.cpp
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@@ -165,7 +165,7 @@ void CvHOGEvaluator::integralHistogram(const Mat &img, vector<Mat> &histogram, M
Mat qangle(gradSize, CV_8U);
AutoBuffer<int> mapbuf(gradSize.width + gradSize.height + 4);
int* xmap = (int*)mapbuf + 1;
int* xmap = mapbuf.data() + 1;
int* ymap = xmap + gradSize.width + 2;
const int borderType = (int)BORDER_REPLICATE;
@@ -177,7 +177,7 @@ void CvHOGEvaluator::integralHistogram(const Mat &img, vector<Mat> &histogram, M
int width = gradSize.width;
AutoBuffer<float> _dbuf(width*4);
float* dbuf = _dbuf;
float* dbuf = _dbuf.data();
Mat Dx(1, width, CV_32F, dbuf);
Mat Dy(1, width, CV_32F, dbuf + width);
Mat Mag(1, width, CV_32F, dbuf + width*2);

12
apps/traincascade/boost.cpp
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@@ -383,7 +383,7 @@ CvDTreeNode* CvCascadeBoostTrainData::subsample_data( const CvMat* _subsample_id
int ci = get_var_type(vi);
CV_Assert( ci < 0 );
int *src_idx_buf = (int*)(uchar*)inn_buf;
int *src_idx_buf = (int*)inn_buf.data();
float *src_val_buf = (float*)(src_idx_buf + sample_count);
int* sample_indices_buf = (int*)(src_val_buf + sample_count);
const int* src_idx = 0;
@@ -423,7 +423,7 @@ CvDTreeNode* CvCascadeBoostTrainData::subsample_data( const CvMat* _subsample_id
}
// subsample cv_lables
const int* src_lbls = get_cv_labels(data_root, (int*)(uchar*)inn_buf);
const int* src_lbls = get_cv_labels(data_root, (int*)inn_buf.data());
if (is_buf_16u)
{
unsigned short* udst = (unsigned short*)(buf->data.s + root->buf_idx*get_length_subbuf() +
@@ -440,7 +440,7 @@ CvDTreeNode* CvCascadeBoostTrainData::subsample_data( const CvMat* _subsample_id
}
// subsample sample_indices
const int* sample_idx_src = get_sample_indices(data_root, (int*)(uchar*)inn_buf);
const int* sample_idx_src = get_sample_indices(data_root, (int*)inn_buf.data());
if (is_buf_16u)
{
unsigned short* sample_idx_dst = (unsigned short*)(buf->data.s + root->buf_idx*get_length_subbuf() +
@@ -815,7 +815,7 @@ struct FeatureIdxOnlyPrecalc : ParallelLoopBody
void operator()( const Range& range ) const
{
cv::AutoBuffer<float> valCache(sample_count);
float* valCachePtr = (float*)valCache;
float* valCachePtr = valCache.data();
for ( int fi = range.start; fi < range.end; fi++)
{
for( int si = 0; si < sample_count; si++ )
@@ -1084,7 +1084,7 @@ void CvCascadeBoostTree::split_node_data( CvDTreeNode* node )
CvMat* buf = data->buf;
size_t length_buf_row = data->get_length_subbuf();
cv::AutoBuffer<uchar> inn_buf(n*(3*sizeof(int)+sizeof(float)));
int* tempBuf = (int*)(uchar*)inn_buf;
int* tempBuf = (int*)inn_buf.data();
bool splitInputData;
complete_node_dir(node);
@@ -1398,7 +1398,7 @@ void CvCascadeBoost::update_weights( CvBoostTree* tree )
int inn_buf_size = ((params.boost_type == LOGIT) || (params.boost_type == GENTLE) ? n*sizeof(int) : 0) +
( !tree ? n*sizeof(int) : 0 );
cv::AutoBuffer<uchar> inn_buf(inn_buf_size);
uchar* cur_inn_buf_pos = (uchar*)inn_buf;
uchar* cur_inn_buf_pos = inn_buf.data();
if ( (params.boost_type == LOGIT) || (params.boost_type == GENTLE) )
{
step = CV_IS_MAT_CONT(data->responses_copy->type) ?

22
apps/traincascade/old_ml_boost.cpp
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@@ -168,7 +168,7 @@ CvBoostTree::try_split_node( CvDTreeNode* node )
// store the responses for the corresponding training samples
double* weak_eval = ensemble->get_weak_response()->data.db;
cv::AutoBuffer<int> inn_buf(node->sample_count);
const int* labels = data->get_cv_labels( node, (int*)inn_buf );
const int* labels = data->get_cv_labels(node, inn_buf.data());
int i, count = node->sample_count;
double value = node->value;
@@ -191,7 +191,7 @@ CvBoostTree::calc_node_dir( CvDTreeNode* node )
if( data->get_var_type(vi) >= 0 ) // split on categorical var
{
cv::AutoBuffer<int> inn_buf(n);
const int* cat_labels = data->get_cat_var_data( node, vi, (int*)inn_buf );
const int* cat_labels = data->get_cat_var_data(node, vi, inn_buf.data());
const int* subset = node->split->subset;
double sum = 0, sum_abs = 0;
@@ -210,7 +210,7 @@ CvBoostTree::calc_node_dir( CvDTreeNode* node )
else // split on ordered var
{
cv::AutoBuffer<uchar> inn_buf(2*n*sizeof(int)+n*sizeof(float));
float* values_buf = (float*)(uchar*)inn_buf;
float* values_buf = (float*)inn_buf.data();
int* sorted_indices_buf = (int*)(values_buf + n);
int* sample_indices_buf = sorted_indices_buf + n;
const float* values = 0;
@@ -260,7 +260,7 @@ CvBoostTree::find_split_ord_class( CvDTreeNode* node, int vi, float init_quality
cv::AutoBuffer<uchar> inn_buf;
if( !_ext_buf )
inn_buf.allocate(n*(3*sizeof(int)+sizeof(float)));
uchar* ext_buf = _ext_buf ? _ext_buf : (uchar*)inn_buf;
uchar* ext_buf = _ext_buf ? _ext_buf : inn_buf.data();
float* values_buf = (float*)ext_buf;
int* sorted_indices_buf = (int*)(values_buf + n);
int* sample_indices_buf = sorted_indices_buf + n;
@@ -369,7 +369,7 @@ CvBoostTree::find_split_cat_class( CvDTreeNode* node, int vi, float init_quality
cv::AutoBuffer<uchar> inn_buf((2*mi+3)*sizeof(double) + mi*sizeof(double*));
if( !_ext_buf)
inn_buf.allocate( base_size + 2*n*sizeof(int) );
uchar* base_buf = (uchar*)inn_buf;
uchar* base_buf = inn_buf.data();
uchar* ext_buf = _ext_buf ? _ext_buf : base_buf + base_size;
int* cat_labels_buf = (int*)ext_buf;
@@ -490,7 +490,7 @@ CvBoostTree::find_split_ord_reg( CvDTreeNode* node, int vi, float init_quality,
cv::AutoBuffer<uchar> inn_buf;
if( !_ext_buf )
inn_buf.allocate(2*n*(sizeof(int)+sizeof(float)));
uchar* ext_buf = _ext_buf ? _ext_buf : (uchar*)inn_buf;
uchar* ext_buf = _ext_buf ? _ext_buf : inn_buf.data();
float* values_buf = (float*)ext_buf;
int* indices_buf = (int*)(values_buf + n);
@@ -559,7 +559,7 @@ CvBoostTree::find_split_cat_reg( CvDTreeNode* node, int vi, float init_quality,
cv::AutoBuffer<uchar> inn_buf(base_size);
if( !_ext_buf )
inn_buf.allocate(base_size + n*(2*sizeof(int) + sizeof(float)));
uchar* base_buf = (uchar*)inn_buf;
uchar* base_buf = inn_buf.data();
uchar* ext_buf = _ext_buf ? _ext_buf : base_buf + base_size;
int* cat_labels_buf = (int*)ext_buf;
@@ -652,7 +652,7 @@ CvBoostTree::find_surrogate_split_ord( CvDTreeNode* node, int vi, uchar* _ext_bu
cv::AutoBuffer<uchar> inn_buf;
if( !_ext_buf )
inn_buf.allocate(n*(2*sizeof(int)+sizeof(float)));
uchar* ext_buf = _ext_buf ? _ext_buf : (uchar*)inn_buf;
uchar* ext_buf = _ext_buf ? _ext_buf : inn_buf.data();
float* values_buf = (float*)ext_buf;
int* indices_buf = (int*)(values_buf + n);
int* sample_indices_buf = indices_buf + n;
@@ -733,7 +733,7 @@ CvBoostTree::find_surrogate_split_cat( CvDTreeNode* node, int vi, uchar* _ext_bu
cv::AutoBuffer<uchar> inn_buf(base_size);
if( !_ext_buf )
inn_buf.allocate(base_size + n*sizeof(int));
uchar* ext_buf = _ext_buf ? _ext_buf : (uchar*)inn_buf;
uchar* ext_buf = _ext_buf ? _ext_buf : inn_buf.data();
int* cat_labels_buf = (int*)ext_buf;
const int* cat_labels = data->get_cat_var_data(node, vi, cat_labels_buf);
@@ -797,7 +797,7 @@ CvBoostTree::calc_node_value( CvDTreeNode* node )
int i, n = node->sample_count;
const double* weights = ensemble->get_weights()->data.db;
cv::AutoBuffer<uchar> inn_buf(n*(sizeof(int) + ( data->is_classifier ? sizeof(int) : sizeof(int) + sizeof(float))));
int* labels_buf = (int*)(uchar*)inn_buf;
int* labels_buf = (int*)inn_buf.data();
const int* labels = data->get_cv_labels(node, labels_buf);
double* subtree_weights = ensemble->get_subtree_weights()->data.db;
double rcw[2] = {0,0};
@@ -1147,7 +1147,7 @@ CvBoost::update_weights( CvBoostTree* tree )
_buf_size += data->get_length_subbuf()*(sizeof(float)+sizeof(uchar));
}
inn_buf.allocate(_buf_size);
uchar* cur_buf_pos = (uchar*)inn_buf;
uchar* cur_buf_pos = inn_buf.data();
if ( (params.boost_type == LOGIT) || (params.boost_type == GENTLE) )
{

48
apps/traincascade/old_ml_tree.cpp
View File

@@ -780,7 +780,7 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx )
if( ci >= 0 || vi >= var_count )
{
int num_valid = 0;
const int* src = CvDTreeTrainData::get_cat_var_data( data_root, vi, (int*)(uchar*)inn_buf );
const int* src = CvDTreeTrainData::get_cat_var_data(data_root, vi, (int*)inn_buf.data());
if (is_buf_16u)
{
@@ -810,7 +810,7 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx )
}
else
{
int *src_idx_buf = (int*)(uchar*)inn_buf;
int *src_idx_buf = (int*)inn_buf.data();
float *src_val_buf = (float*)(src_idx_buf + sample_count);
int* sample_indices_buf = (int*)(src_val_buf + sample_count);
const int* src_idx = 0;
@@ -870,7 +870,7 @@ CvDTreeNode* CvDTreeTrainData::subsample_data( const CvMat* _subsample_idx )
}
}
// sample indices subsampling
const int* sample_idx_src = get_sample_indices(data_root, (int*)(uchar*)inn_buf);
const int* sample_idx_src = get_sample_indices(data_root, (int*)inn_buf.data());
if (is_buf_16u)
{
unsigned short* sample_idx_dst = (unsigned short*)(buf->data.s + root->buf_idx*get_length_subbuf() +
@@ -943,7 +943,7 @@ void CvDTreeTrainData::get_vectors( const CvMat* _subsample_idx,
{
float* dst = values + vi;
uchar* m = missing ? missing + vi : 0;
const int* src = get_cat_var_data(data_root, vi, (int*)(uchar*)inn_buf);
const int* src = get_cat_var_data(data_root, vi, (int*)inn_buf.data());
for( i = 0; i < count; i++, dst += var_count )
{
@@ -962,7 +962,7 @@ void CvDTreeTrainData::get_vectors( const CvMat* _subsample_idx,
float* dst = values + vi;
uchar* m = missing ? missing + vi : 0;
int count1 = data_root->get_num_valid(vi);
float *src_val_buf = (float*)(uchar*)inn_buf;
float *src_val_buf = (float*)inn_buf.data();
int* src_idx_buf = (int*)(src_val_buf + sample_count);
int* sample_indices_buf = src_idx_buf + sample_count;
const float *src_val = 0;
@@ -999,7 +999,7 @@ void CvDTreeTrainData::get_vectors( const CvMat* _subsample_idx,
{
if( is_classifier )
{
const int* src = get_class_labels(data_root, (int*)(uchar*)inn_buf);
const int* src = get_class_labels(data_root, (int*)inn_buf.data());
for( i = 0; i < count; i++ )
{
int idx = sidx ? sidx[i] : i;
@@ -1010,7 +1010,7 @@ void CvDTreeTrainData::get_vectors( const CvMat* _subsample_idx,
}
else
{
float* val_buf = (float*)(uchar*)inn_buf;
float* val_buf = (float*)inn_buf.data();
int* sample_idx_buf = (int*)(val_buf + sample_count);
const float* _values = get_ord_responses(data_root, val_buf, sample_idx_buf);
for( i = 0; i < count; i++ )
@@ -1780,7 +1780,7 @@ double CvDTree::calc_node_dir( CvDTreeNode* node )
if( data->get_var_type(vi) >= 0 ) // split on categorical var
{
cv::AutoBuffer<int> inn_buf(n*(!data->have_priors ? 1 : 2));
int* labels_buf = (int*)inn_buf;
int* labels_buf = inn_buf.data();
const int* labels = data->get_cat_var_data( node, vi, labels_buf );
const int* subset = node->split->subset;
if( !data->have_priors )
@@ -1824,7 +1824,7 @@ double CvDTree::calc_node_dir( CvDTreeNode* node )
int split_point = node->split->ord.split_point;
int n1 = node->get_num_valid(vi);
cv::AutoBuffer<uchar> inn_buf(n*(sizeof(int)*(data->have_priors ? 3 : 2) + sizeof(float)));
float* val_buf = (float*)(uchar*)inn_buf;
float* val_buf = (float*)inn_buf.data();
int* sorted_buf = (int*)(val_buf + n);
int* sample_idx_buf = sorted_buf + n;
const float* val = 0;
@@ -1929,16 +1929,16 @@ void DTreeBestSplitFinder::operator()(const BlockedRange& range)
if( data->is_classifier )
{
if( ci >= 0 )
res = tree->find_split_cat_class( node, vi, bestSplit->quality, split, (uchar*)inn_buf );
res = tree->find_split_cat_class( node, vi, bestSplit->quality, split, inn_buf.data() );
else
res = tree->find_split_ord_class( node, vi, bestSplit->quality, split, (uchar*)inn_buf );
res = tree->find_split_ord_class( node, vi, bestSplit->quality, split, inn_buf.data() );
}
else
{
if( ci >= 0 )
res = tree->find_split_cat_reg( node, vi, bestSplit->quality, split, (uchar*)inn_buf );
res = tree->find_split_cat_reg( node, vi, bestSplit->quality, split, inn_buf.data() );
else
res = tree->find_split_ord_reg( node, vi, bestSplit->quality, split, (uchar*)inn_buf );
res = tree->find_split_ord_reg( node, vi, bestSplit->quality, split, inn_buf.data() );
}
if( res && bestSplit->quality < split->quality )
@@ -1982,7 +1982,7 @@ CvDTreeSplit* CvDTree::find_split_ord_class( CvDTreeNode* node, int vi,
cv::AutoBuffer<uchar> inn_buf(base_size);
if( !_ext_buf )
inn_buf.allocate(base_size + n*(3*sizeof(int)+sizeof(float)));
uchar* base_buf = (uchar*)inn_buf;
uchar* base_buf = inn_buf.data();
uchar* ext_buf = _ext_buf ? _ext_buf : base_buf + base_size;
float* values_buf = (float*)ext_buf;
int* sorted_indices_buf = (int*)(values_buf + n);
@@ -2096,7 +2096,7 @@ void CvDTree::cluster_categories( const int* vectors, int n, int m,
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 = data->rng;
@@ -2201,7 +2201,7 @@ CvDTreeSplit* CvDTree::find_split_cat_class( CvDTreeNode* node, int vi, float in
cv::AutoBuffer<uchar> inn_buf(base_size);
if( !_ext_buf )
inn_buf.allocate(base_size + 2*n*sizeof(int));
uchar* base_buf = (uchar*)inn_buf;
uchar* base_buf = inn_buf.data();
uchar* ext_buf = _ext_buf ? _ext_buf : base_buf + base_size;
int* lc = (int*)base_buf;
@@ -2383,7 +2383,7 @@ CvDTreeSplit* CvDTree::find_split_ord_reg( CvDTreeNode* node, int vi, float init
cv::AutoBuffer<uchar> inn_buf;
if( !_ext_buf )
inn_buf.allocate(2*n*(sizeof(int) + sizeof(float)));
uchar* ext_buf = _ext_buf ? _ext_buf : (uchar*)inn_buf;
uchar* ext_buf = _ext_buf ? _ext_buf : inn_buf.data();
float* values_buf = (float*)ext_buf;
int* sorted_indices_buf = (int*)(values_buf + n);
int* sample_indices_buf = sorted_indices_buf + n;
@@ -2443,7 +2443,7 @@ CvDTreeSplit* CvDTree::find_split_cat_reg( CvDTreeNode* node, int vi, float init
cv::AutoBuffer<uchar> inn_buf(base_size);
if( !_ext_buf )
inn_buf.allocate(base_size + n*(2*sizeof(int) + sizeof(float)));
uchar* base_buf = (uchar*)inn_buf;
uchar* base_buf = inn_buf.data();
uchar* ext_buf = _ext_buf ? _ext_buf : base_buf + base_size;
int* labels_buf = (int*)ext_buf;
const int* labels = data->get_cat_var_data(node, vi, labels_buf);
@@ -2534,7 +2534,7 @@ CvDTreeSplit* CvDTree::find_surrogate_split_ord( CvDTreeNode* node, int vi, ucha
cv::AutoBuffer<uchar> inn_buf;
if( !_ext_buf )
inn_buf.allocate( n*(sizeof(int)*(data->have_priors ? 3 : 2) + sizeof(float)) );
uchar* ext_buf = _ext_buf ? _ext_buf : (uchar*)inn_buf;
uchar* ext_buf = _ext_buf ? _ext_buf : inn_buf.data();
float* values_buf = (float*)ext_buf;
int* sorted_indices_buf = (int*)(values_buf + n);
int* sample_indices_buf = sorted_indices_buf + n;
@@ -2658,7 +2658,7 @@ CvDTreeSplit* CvDTree::find_surrogate_split_cat( CvDTreeNode* node, int vi, ucha
cv::AutoBuffer<uchar> inn_buf(base_size);
if( !_ext_buf )
inn_buf.allocate(base_size + n*(sizeof(int) + (data->have_priors ? sizeof(int) : 0)));
uchar* base_buf = (uchar*)inn_buf;
uchar* base_buf = inn_buf.data();
uchar* ext_buf = _ext_buf ? _ext_buf : base_buf + base_size;
int* labels_buf = (int*)ext_buf;
@@ -2758,7 +2758,7 @@ void CvDTree::calc_node_value( CvDTreeNode* node )
int base_size = data->is_classifier ? m*cv_n*sizeof(int) : 2*cv_n*sizeof(double)+cv_n*sizeof(int);
int ext_size = n*(sizeof(int) + (data->is_classifier ? sizeof(int) : sizeof(int)+sizeof(float)));
cv::AutoBuffer<uchar> inn_buf(base_size + ext_size);
uchar* base_buf = (uchar*)inn_buf;
uchar* base_buf = inn_buf.data();
uchar* ext_buf = base_buf + base_size;
int* cv_labels_buf = (int*)ext_buf;
@@ -2961,7 +2961,7 @@ void CvDTree::complete_node_dir( CvDTreeNode* node )
if( data->get_var_type(vi) >= 0 ) // split on categorical var
{
int* labels_buf = (int*)(uchar*)inn_buf;
int* labels_buf = (int*)inn_buf.data();
const int* labels = data->get_cat_var_data(node, vi, labels_buf);
const int* subset = split->subset;
@@ -2980,7 +2980,7 @@ void CvDTree::complete_node_dir( CvDTreeNode* node )
}
else // split on ordered var
{
float* values_buf = (float*)(uchar*)inn_buf;
float* values_buf = (float*)inn_buf.data();
int* sorted_indices_buf = (int*)(values_buf + n);
int* sample_indices_buf = sorted_indices_buf + n;
const float* values = 0;
@@ -3042,7 +3042,7 @@ void CvDTree::split_node_data( CvDTreeNode* node )
CvMat* buf = data->buf;
size_t length_buf_row = data->get_length_subbuf();
cv::AutoBuffer<uchar> inn_buf(n*(3*sizeof(int) + sizeof(float)));
int* temp_buf = (int*)(uchar*)inn_buf;
int* temp_buf = (int*)inn_buf.data();
complete_node_dir(node);