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Merged the trunk r8589:8653 - all changes related to build warnings

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This commit is contained in:
Andrey Kamaev
2012-06-15 13:04:17 +00:00
parent 73c152abc4
commit bd0e0b5800
438 changed files with 20374 additions and 19674 deletions
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291
apps/haartraining/cvhaartraining.cpp
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@@ -108,7 +108,7 @@ CvBackgroundData* cvbgdata = NULL;
/*
* get sum image offsets for <rect> corner points
* get sum image offsets for <rect> corner points
* step - row step (measured in image pixels!) of sum image
*/
#define CV_SUM_OFFSETS( p0, p1, p2, p3, rect, step ) \
@@ -122,7 +122,7 @@ CvBackgroundData* cvbgdata = NULL;
(p3) = (rect).x + (rect).width + (step) * ((rect).y + (rect).height);
/*
* get tilted image offsets for <rect> corner points
* get tilted image offsets for <rect> corner points
* step - row step (measured in image pixels!) of tilted image
*/
#define CV_TILTED_OFFSETS( p0, p1, p2, p3, rect, step ) \
@@ -154,7 +154,7 @@ CvIntHaarFeatures* icvCreateIntHaarFeatures( CvSize winsize,
{
CvIntHaarFeatures* features = NULL;
CvTHaarFeature haarFeature;
CvMemStorage* storage = NULL;
CvSeq* seq = NULL;
CvSeqWriter writer;
@@ -169,10 +169,11 @@ CvIntHaarFeatures* icvCreateIntHaarFeatures( CvSize winsize,
int dx = 0;
int dy = 0;
#if 0
float factor = 1.0F;
factor = ((float) winsize.width) * winsize.height / (24 * 24);
#if 0
s0 = (int) (s0 * factor);
s1 = (int) (s1 * factor);
s2 = (int) (s2 * factor);
@@ -252,7 +253,7 @@ CvIntHaarFeatures* icvCreateIntHaarFeatures( CvSize winsize,
CV_WRITE_SEQ_ELEM( haarFeature, writer );
}
}
// haar_y4
if ( (x+dx <= winsize.width ) && (y+dy*4 <= winsize.height) ) {
if (dx*4*dy < s0) continue;
@@ -277,7 +278,7 @@ CvIntHaarFeatures* icvCreateIntHaarFeatures( CvSize winsize,
}
}
if (mode != 0 /*BASIC*/) {
if (mode != 0 /*BASIC*/) {
// point
if ( (x+dx*3 <= winsize.width) && (y+dy*3 <= winsize.height) ) {
if (dx*9*dy < s0) continue;
@@ -289,12 +290,12 @@ CvIntHaarFeatures* icvCreateIntHaarFeatures( CvSize winsize,
}
}
}
if (mode == 2 /*ALL*/) {
if (mode == 2 /*ALL*/) {
// tilted haar_x2 (x, y, w, h, b, weight)
if ( (x+2*dx <= winsize.width) && (y+2*dx+dy <= winsize.height) && (x-dy>= 0) ) {
if (dx*2*dy < s1) continue;
if (!symmetric || (x <= (winsize.width / 2) )) {
haarFeature = cvHaarFeature( "tilted_haar_x2",
x, y, dx*2, dy, -1,
@@ -302,11 +303,11 @@ CvIntHaarFeatures* icvCreateIntHaarFeatures( CvSize winsize,
CV_WRITE_SEQ_ELEM( haarFeature, writer );
}
}
// tilted haar_y2 (x, y, w, h, b, weight)
if ( (x+dx <= winsize.width) && (y+dx+2*dy <= winsize.height) && (x-2*dy>= 0) ) {
if (dx*2*dy < s1) continue;
if (!symmetric || (x <= (winsize.width / 2) )) {
haarFeature = cvHaarFeature( "tilted_haar_y2",
x, y, dx, 2*dy, -1,
@@ -314,11 +315,11 @@ CvIntHaarFeatures* icvCreateIntHaarFeatures( CvSize winsize,
CV_WRITE_SEQ_ELEM( haarFeature, writer );
}
}
// tilted haar_x3 (x, y, w, h, b, weight)
if ( (x+3*dx <= winsize.width) && (y+3*dx+dy <= winsize.height) && (x-dy>= 0) ) {
if (dx*3*dy < s2) continue;
if (!symmetric || (x <= (winsize.width / 2) )) {
haarFeature = cvHaarFeature( "tilted_haar_x3",
x, y, dx*3, dy, -1,
@@ -326,11 +327,11 @@ CvIntHaarFeatures* icvCreateIntHaarFeatures( CvSize winsize,
CV_WRITE_SEQ_ELEM( haarFeature, writer );
}
}
// tilted haar_y3 (x, y, w, h, b, weight)
if ( (x+dx <= winsize.width) && (y+dx+3*dy <= winsize.height) && (x-3*dy>= 0) ) {
if (dx*3*dy < s2) continue;
if (!symmetric || (x <= (winsize.width / 2) )) {
haarFeature = cvHaarFeature( "tilted_haar_y3",
x, y, dx, 3*dy, -1,
@@ -338,12 +339,12 @@ CvIntHaarFeatures* icvCreateIntHaarFeatures( CvSize winsize,
CV_WRITE_SEQ_ELEM( haarFeature, writer );
}
}
// tilted haar_x4 (x, y, w, h, b, weight)
if ( (x+4*dx <= winsize.width) && (y+4*dx+dy <= winsize.height) && (x-dy>= 0) ) {
if (dx*4*dy < s3) continue;
if (!symmetric || (x <= (winsize.width / 2) )) {
haarFeature = cvHaarFeature( "tilted_haar_x4",
@@ -353,11 +354,11 @@ CvIntHaarFeatures* icvCreateIntHaarFeatures( CvSize winsize,
CV_WRITE_SEQ_ELEM( haarFeature, writer );
}
}
// tilted haar_y4 (x, y, w, h, b, weight)
if ( (x+dx <= winsize.width) && (y+dx+4*dy <= winsize.height) && (x-4*dy>= 0) ) {
if (dx*4*dy < s3) continue;
if (!symmetric || (x <= (winsize.width / 2) )) {
haarFeature = cvHaarFeature( "tilted_haar_y4",
x, y, dx, 4*dy, -1,
@@ -365,10 +366,10 @@ CvIntHaarFeatures* icvCreateIntHaarFeatures( CvSize winsize,
CV_WRITE_SEQ_ELEM( haarFeature, writer );
}
}
/*
// tilted point
if ( (x+dx*3 <= winsize.width - 1) && (y+dy*3 <= winsize.height - 1) && (x-3*dy>= 0)) {
if (dx*9*dy < 36) continue;
@@ -395,10 +396,10 @@ CvIntHaarFeatures* icvCreateIntHaarFeatures( CvSize winsize,
features->winsize = winsize;
cvCvtSeqToArray( seq, (CvArr*) features->feature );
cvReleaseMemStorage( &storage );
icvConvertToFastHaarFeature( features->feature, features->fastfeature,
features->count, (winsize.width + 1) );
return features;
}
@@ -438,7 +439,7 @@ void icvConvertToFastHaarFeature( CvTHaarFeature* haarFeature,
fastHaarFeature[i].rect[j].p3,
haarFeature[i].rect[j].r, step )
}
}
else
{
@@ -469,15 +470,15 @@ static
CvHaarTrainigData* icvCreateHaarTrainingData( CvSize winsize, int maxnumsamples )
{
CvHaarTrainigData* data;
CV_FUNCNAME( "icvCreateHaarTrainingData" );
__BEGIN__;
data = NULL;
uchar* ptr = NULL;
size_t datasize = 0;
datasize = sizeof( CvHaarTrainigData ) +
/* sum and tilted */
( 2 * (winsize.width + 1) * (winsize.height + 1) * sizeof( sum_type ) +
@@ -548,7 +549,7 @@ void icvGetTrainingDataCallback( CvMat* mat, CvMat* sampleIdx, CvMat*,
int j = 0;
float val = 0.0F;
float normfactor = 0.0F;
CvHaarTrainingData* training_data;
CvIntHaarFeatures* haar_features;
@@ -639,7 +640,7 @@ void icvGetTrainingDataCallback( CvMat* mat, CvMat* sampleIdx, CvMat*,
#if 0 /*def CV_VERBOSE*/
if( first % 5000 == 0 )
{
fprintf( stderr, "%3d%%\r", (int) (100.0 * first /
fprintf( stderr, "%3d%%\r", (int) (100.0 * first /
haar_features->count) );
fflush( stderr );
}
@@ -692,7 +693,7 @@ void icvPrecalculate( CvHaarTrainingData* data, CvIntHaarFeatures* haarFeatures,
t_data = *data->valcache;
t_idx = *data->idxcache;
t_portion = MIN( portion, (numprecalculated - first) );
/* indices */
t_idx.rows = t_portion;
t_idx.data.ptr = data->idxcache->data.ptr + first * ((size_t)t_idx.step);
@@ -766,7 +767,7 @@ void icvSplitIndicesCallback( int compidx, float threshold,
{
if( cvEvalFastHaarFeature( fastfeature,
(sum_type*) (data->sum.data.ptr + i * data->sum.step),
(sum_type*) (data->tilted.data.ptr + i * data->tilted.step) )
(sum_type*) (data->tilted.data.ptr + i * data->tilted.step) )
< threshold * data->normfactor.data.fl[i] )
{
(*left)->data.fl[(*left)->cols++] = (float) i;
@@ -792,7 +793,7 @@ void icvSplitIndicesCallback( int compidx, float threshold,
index = (int) *((float*) (idxdata + i * idxstep));
if( cvEvalFastHaarFeature( fastfeature,
(sum_type*) (data->sum.data.ptr + index * data->sum.step),
(sum_type*) (data->tilted.data.ptr + index * data->tilted.step) )
(sum_type*) (data->tilted.data.ptr + index * data->tilted.step) )
< threshold * data->normfactor.data.fl[index] )
{
(*left)->data.fl[(*left)->cols++] = (float) index;
@@ -858,7 +859,7 @@ CvIntHaarClassifier* icvCreateCARTStageClassifier( CvHaarTrainingData* data,
float sum_stage = 0.0F;
float threshold = 0.0F;
float falsealarm = 0.0F;
//CvMat* sampleIdx = NULL;
CvMat* trimmedIdx;
//float* idxdata = NULL;
@@ -871,7 +872,7 @@ CvIntHaarClassifier* icvCreateCARTStageClassifier( CvHaarTrainingData* data,
int idx;
int numsamples;
int numtrimmed;
CvCARTHaarClassifier* classifier;
CvSeq* seq = NULL;
CvMemStorage* storage = NULL;
@@ -885,7 +886,7 @@ CvIntHaarClassifier* icvCreateCARTStageClassifier( CvHaarTrainingData* data,
printf( "| N |%%SMP|F| ST.THR | HR | FA | EXP. ERR|\n" );
printf( "+----+----+-+---------+---------+---------+---------+\n" );
#endif /* CV_VERBOSE */
n = haarFeatures->count;
m = data->sum.rows;
numsamples = (sampleIdx) ? MAX( sampleIdx->rows, sampleIdx->cols ) : m;
@@ -909,7 +910,7 @@ CvIntHaarClassifier* icvCreateCARTStageClassifier( CvHaarTrainingData* data,
trainParams.userdata = &userdata;
eval = cvMat( 1, m, CV_32FC1, cvAlloc( sizeof( float ) * m ) );
storage = cvCreateMemStorage();
seq = cvCreateSeq( 0, sizeof( *seq ), sizeof( classifier ), storage );
@@ -919,7 +920,7 @@ CvIntHaarClassifier* icvCreateCARTStageClassifier( CvHaarTrainingData* data,
num_splits = 0;
sumalpha = 0.0F;
do
{
{
#ifdef CV_VERBOSE
int v_wt = 0;
@@ -947,12 +948,12 @@ CvIntHaarClassifier* icvCreateCARTStageClassifier( CvHaarTrainingData* data,
num_splits += classifier->count;
cart->release( (CvClassifier**) &cart );
if( symmetric && (seq->total % 2) )
{
float normfactor = 0.0F;
CvStumpClassifier* stump;
/* flip haar features */
for( i = 0; i < classifier->count; i++ )
{
@@ -961,9 +962,9 @@ CvIntHaarClassifier* icvCreateCARTStageClassifier( CvHaarTrainingData* data,
for( j = 0; j < CV_HAAR_FEATURE_MAX &&
classifier->feature[i].rect[j].weight != 0.0F; j++ )
{
classifier->feature[i].rect[j].r.x = data->winsize.width -
classifier->feature[i].rect[j].r.x = data->winsize.width -
classifier->feature[i].rect[j].r.x -
classifier->feature[i].rect[j].r.width;
classifier->feature[i].rect[j].r.width;
}
}
else
@@ -975,7 +976,7 @@ CvIntHaarClassifier* icvCreateCARTStageClassifier( CvHaarTrainingData* data,
for( j = 0; j < CV_HAAR_FEATURE_MAX &&
classifier->feature[i].rect[j].weight != 0.0F; j++ )
{
classifier->feature[i].rect[j].r.x = data->winsize.width -
classifier->feature[i].rect[j].r.x = data->winsize.width -
classifier->feature[i].rect[j].r.x;
CV_SWAP( classifier->feature[i].rect[j].r.width,
classifier->feature[i].rect[j].r.height, tmp );
@@ -1010,7 +1011,7 @@ CvIntHaarClassifier* icvCreateCARTStageClassifier( CvHaarTrainingData* data,
weakTrainVals, 0, 0, 0, trimmedIdx,
&(data->weights),
trainParams.stumpTrainParams );
classifier->threshold[i] = stump->threshold;
if( classifier->left[i] <= 0 )
{
@@ -1021,8 +1022,8 @@ CvIntHaarClassifier* icvCreateCARTStageClassifier( CvHaarTrainingData* data,
classifier->val[-classifier->right[i]] = stump->right;
}
stump->release( (CvClassifier**) &stump );
stump->release( (CvClassifier**) &stump );
}
stumpTrainParams.getTrainData = icvGetTrainingDataCallback;
@@ -1040,7 +1041,7 @@ CvIntHaarClassifier* icvCreateCARTStageClassifier( CvHaarTrainingData* data,
cvReleaseMat( &trimmedIdx );
trimmedIdx = NULL;
}
for( i = 0; i < numsamples; i++ )
{
idx = icvGetIdxAt( sampleIdx, i );
@@ -1054,10 +1055,10 @@ CvIntHaarClassifier* icvCreateCARTStageClassifier( CvHaarTrainingData* data,
alpha = cvBoostNextWeakClassifier( &eval, &data->cls, weakTrainVals,
&data->weights, trainer );
sumalpha += alpha;
for( i = 0; i <= classifier->count; i++ )
{
if( boosttype == CV_RABCLASS )
if( boosttype == CV_RABCLASS )
{
classifier->val[i] = cvLogRatio( classifier->val[i] );
}
@@ -1077,7 +1078,7 @@ CvIntHaarClassifier* icvCreateCARTStageClassifier( CvHaarTrainingData* data,
for( j = 0; j < seq->total; j++ )
{
classifier = *((CvCARTHaarClassifier**) cvGetSeqElem( seq, j ));
eval.data.fl[numpos] += classifier->eval(
eval.data.fl[numpos] += classifier->eval(
(CvIntHaarClassifier*) classifier,
(sum_type*) (data->sum.data.ptr + idx * data->sum.step),
(sum_type*) (data->tilted.data.ptr + idx * data->tilted.step),
@@ -1163,7 +1164,7 @@ CvIntHaarClassifier* icvCreateCARTStageClassifier( CvHaarTrainingData* data,
fflush( stdout );
}
#endif /* CV_VERBOSE */
} while( falsealarm > maxfalsealarm && (!maxsplits || (num_splits < maxsplits) ) );
cvBoostEndTraining( &trainer );
@@ -1177,12 +1178,12 @@ CvIntHaarClassifier* icvCreateCARTStageClassifier( CvHaarTrainingData* data,
threshold );
cvCvtSeqToArray( seq, (CvArr*) stage->classifier );
}
/* CLEANUP */
cvReleaseMemStorage( &storage );
cvReleaseMat( &weakTrainVals );
cvFree( &(eval.data.ptr) );
return (CvIntHaarClassifier*) stage;
}
@@ -1192,7 +1193,7 @@ CvBackgroundData* icvCreateBackgroundData( const char* filename, CvSize winsize
{
CvBackgroundData* data = NULL;
const char* dir = NULL;
const char* dir = NULL;
char full[PATH_MAX];
char* imgfilename = NULL;
size_t datasize = 0;
@@ -1202,7 +1203,7 @@ CvBackgroundData* icvCreateBackgroundData( const char* filename, CvSize winsize
int len = 0;
assert( filename != NULL );
dir = strrchr( filename, '\\' );
if( dir == NULL )
{
@@ -1223,7 +1224,7 @@ CvBackgroundData* icvCreateBackgroundData( const char* filename, CvSize winsize
{
count = 0;
datasize = 0;
/* count */
while( !feof( input ) )
{
@@ -1257,11 +1258,11 @@ CvBackgroundData* icvCreateBackgroundData( const char* filename, CvSize winsize
while( !feof( input ) )
{
*imgfilename = '\0';
if( !fgets( imgfilename, PATH_MAX - (int)(imgfilename - full) - 1, input ))
if( !fgets( imgfilename, PATH_MAX - (int)(imgfilename - full) - 1, input ))
break;
len = (int)strlen( imgfilename );
if( len > 0 && imgfilename[len-1] == '\n' )
imgfilename[len-1] = 0, len--;
if( len > 0 && imgfilename[len-1] == '\n' )
imgfilename[len-1] = 0, len--;
if( len > 0 )
{
if( (*imgfilename) == '#' ) continue; /* comment */
@@ -1351,14 +1352,14 @@ void icvGetNextFromBackgroundData( CvBackgroundData* data,
{
round = data->round;
//#ifdef CV_VERBOSE
//#ifdef CV_VERBOSE
// printf( "Open background image: %s\n", data->filename[data->last] );
//#endif /* CV_VERBOSE */
data->last = rand() % data->count;
data->last %= data->count;
img = cvLoadImage( data->filename[data->last], 0 );
if( !img )
if( !img )
continue;
data->round += data->last / data->count;
data->round = data->round % (data->winsize.width * data->winsize.height);
@@ -1368,7 +1369,7 @@ void icvGetNextFromBackgroundData( CvBackgroundData* data,
offset.x = MIN( offset.x, img->width - data->winsize.width );
offset.y = MIN( offset.y, img->height - data->winsize.height );
if( img != NULL && img->depth == IPL_DEPTH_8U && img->nChannels == 1 &&
offset.x >= 0 && offset.y >= 0 )
{
@@ -1403,7 +1404,7 @@ void icvGetNextFromBackgroundData( CvBackgroundData* data,
reader->scale = MAX(
((float) data->winsize.width + reader->point.x) / ((float) reader->src.cols),
((float) data->winsize.height + reader->point.y) / ((float) reader->src.rows) );
reader->img = cvMat( (int) (reader->scale * reader->src.rows + 0.5F),
(int) (reader->scale * reader->src.cols + 0.5F),
CV_8UC1, (void*) cvAlloc( datasize ) );
@@ -1576,11 +1577,11 @@ void icvGetAuxImages( CvMat* img, CvMat* sum, CvMat* tilted,
sum_type valsum = 0;
sqsum_type valsqsum = 0;
double area = 0.0;
cvIntegral( img, sum, sqsum, tilted );
normrect = cvRect( 1, 1, img->cols - 2, img->rows - 2 );
CV_SUM_OFFSETS( p0, p1, p2, p3, normrect, img->cols + 1 )
area = normrect.width * normrect.height;
valsum = ((sum_type*) (sum->data.ptr))[p0] - ((sum_type*) (sum->data.ptr))[p1]
- ((sum_type*) (sum->data.ptr))[p2] + ((sum_type*) (sum->data.ptr))[p3];
@@ -1621,28 +1622,28 @@ int icvGetHaarTrainingData( CvHaarTrainingData* data, int first, int count,
int i = 0;
ccounter_t getcount = 0;
ccounter_t thread_getcount = 0;
ccounter_t consumed_count;
ccounter_t consumed_count;
ccounter_t thread_consumed_count;
/* private variables */
CvMat img;
CvMat sum;
CvMat tilted;
CvMat sqsum;
sum_type* sumdata;
sum_type* tilteddata;
float* normfactor;
/* end private variables */
assert( data != NULL );
assert( first + count <= data->maxnum );
assert( cascade != NULL );
assert( callback != NULL );
// if( !cvbgdata ) return 0; this check needs to be done in the callback for BG
CCOUNTER_SET_ZERO(getcount);
CCOUNTER_SET_ZERO(thread_getcount);
CCOUNTER_SET_ZERO(consumed_count);
@@ -1691,14 +1692,14 @@ int icvGetHaarTrainingData( CvHaarTrainingData* data, int first, int count,
normfactor = data->normfactor.data.fl + i;
sum.data.ptr = (uchar*) sumdata;
tilted.data.ptr = (uchar*) tilteddata;
icvGetAuxImages( &img, &sum, &tilted, &sqsum, normfactor );
icvGetAuxImages( &img, &sum, &tilted, &sqsum, normfactor );
if( cascade->eval( cascade, sumdata, tilteddata, *normfactor ) != 0.0F )
{
CCOUNTER_INC(thread_getcount);
break;
}
}
#ifdef CV_VERBOSE
if( (i - first) % 500 == 0 )
{
@@ -1720,7 +1721,7 @@ int icvGetHaarTrainingData( CvHaarTrainingData* data, int first, int count,
CCOUNTER_ADD(consumed_count, thread_consumed_count);
}
} /* omp parallel */
if( consumed != NULL )
{
*consumed = (int)consumed_count;
@@ -1731,7 +1732,7 @@ int icvGetHaarTrainingData( CvHaarTrainingData* data, int first, int count,
/* *acceptance_ratio = ((double) count) / consumed_count; */
*acceptance_ratio = CCOUNTER_DIV(count, consumed_count);
}
return static_cast<int>(getcount);
}
@@ -1791,7 +1792,7 @@ int icvGetHaarTrainingData( CvHaarTrainingData* data, int first, int count,
// CV_SQSUM_MAT_TYPE,
// cvAlloc( sizeof( sqsum_type ) * (data->winsize.height + 1)
// * (data->winsize.width + 1) ) );
//
//
// #ifdef CV_OPENMP
// #pragma omp for schedule(static, 1)
// #endif /* CV_OPENMP */
@@ -1800,7 +1801,7 @@ int icvGetHaarTrainingData( CvHaarTrainingData* data, int first, int count,
// for( ; ; )
// {
// icvGetBackgroundImage( cvbgdata, cvbgreader, &img );
//
//
// CCOUNTER_INC(thread_consumed_count);
//
// sumdata = (sum_type*) (data->sum.data.ptr + i * data->sum.step);
@@ -1808,7 +1809,7 @@ int icvGetHaarTrainingData( CvHaarTrainingData* data, int first, int count,
// normfactor = data->normfactor.data.fl + i;
// sum.data.ptr = (uchar*) sumdata;
// tilted.data.ptr = (uchar*) tilteddata;
// icvGetAuxImages( &img, &sum, &tilted, &sqsum, normfactor );
// icvGetAuxImages( &img, &sum, &tilted, &sqsum, normfactor );
// if( cascade->eval( cascade, sumdata, tilteddata, *normfactor ) != 0.0F )
// {
// break;
@@ -1822,7 +1823,7 @@ int icvGetHaarTrainingData( CvHaarTrainingData* data, int first, int count,
// fflush( stderr );
// }
//#endif /* CV_VERBOSE */
//
//
// }
//
// cvFree( &(img.data.ptr) );
@@ -1842,7 +1843,7 @@ int icvGetHaarTrainingData( CvHaarTrainingData* data, int first, int count,
// /* *acceptance_ratio = ((double) count) / consumed_count; */
// *acceptance_ratio = CCOUNTER_DIV(count, consumed_count);
// }
//
//
// return count;
//}
@@ -1853,24 +1854,24 @@ int icvGetHaarTraininDataFromVecCallback( CvMat* img, void* userdata )
int c = 0;
assert( img->rows * img->cols == ((CvVecFile*) userdata)->vecsize );
size_t elements_read = fread( &tmp, sizeof( tmp ), 1, ((CvVecFile*) userdata)->input );
CV_Assert(elements_read == 1);
elements_read = fread( ((CvVecFile*) userdata)->vector, sizeof( short ),
((CvVecFile*) userdata)->vecsize, ((CvVecFile*) userdata)->input );
CV_Assert(elements_read == (size_t)((CvVecFile*) userdata)->vecsize);
if( feof( ((CvVecFile*) userdata)->input ) ||
if( feof( ((CvVecFile*) userdata)->input ) ||
(((CvVecFile*) userdata)->last)++ >= ((CvVecFile*) userdata)->count )
{
return 0;
}
for( r = 0; r < img->rows; r++ )
{
for( c = 0; c < img->cols; c++ )
{
CV_MAT_ELEM( *img, uchar, r, c ) =
CV_MAT_ELEM( *img, uchar, r, c ) =
(uchar) ( ((CvVecFile*) userdata)->vector[r * img->cols + c] );
}
}
@@ -1878,14 +1879,14 @@ int icvGetHaarTraininDataFromVecCallback( CvMat* img, void* userdata )
return 1;
}
int icvGetHaarTrainingDataFromBGCallback ( CvMat* img, void* /*userdata*/ )
static int icvGetHaarTrainingDataFromBGCallback ( CvMat* img, void* /*userdata*/ )
{
if (! cvbgdata)
return 0;
if (! cvbgreader)
return 0;
// just in case icvGetBackgroundImage is not thread-safe ...
#ifdef CV_OPENMP
#pragma omp critical (get_background_image_callback)
@@ -1893,7 +1894,7 @@ int icvGetHaarTrainingDataFromBGCallback ( CvMat* img, void* /*userdata*/ )
{
icvGetBackgroundImage( cvbgdata, cvbgreader, img );
}
return 1;
}
@@ -1902,7 +1903,7 @@ int icvGetHaarTrainingDataFromBGCallback ( CvMat* img, void* /*userdata*/ )
* Get training data from .vec file
*/
static
int icvGetHaarTrainingDataFromVec( CvHaarTrainingData* data, int first, int count,
int icvGetHaarTrainingDataFromVec( CvHaarTrainingData* data, int first, int count,
CvIntHaarClassifier* cascade,
const char* filename,
int* consumed )
@@ -1914,8 +1915,8 @@ int icvGetHaarTrainingDataFromVec( CvHaarTrainingData* data, int first, int coun
__BEGIN__;
CvVecFile file;
short tmp = 0;
short tmp = 0;
file.input = NULL;
if( filename ) file.input = fopen( filename, "rb" );
@@ -1967,8 +1968,8 @@ int icvGetHaarTrainingDataFromBG( CvHaarTrainingData* data, int first, int count
if (filename)
{
CvVecFile file;
short tmp = 0;
short tmp = 0;
file.input = NULL;
if( filename ) file.input = fopen( filename, "rb" );
@@ -2009,7 +2010,7 @@ int icvGetHaarTrainingDataFromBG( CvHaarTrainingData* data, int first, int count
void cvCreateCascadeClassifier( const char* dirname,
const char* vecfilename,
const char* bgfilename,
const char* bgfilename,
int npos, int nneg, int nstages,
int numprecalculated,
int numsplits,
@@ -2048,7 +2049,7 @@ void cvCreateCascadeClassifier( const char* dirname,
cascade = (CvCascadeHaarClassifier*) icvCreateCascadeHaarClassifier( nstages );
cascade->count = 0;
if( icvInitBackgroundReaders( bgfilename, winsize ) )
{
data = icvCreateHaarTrainingData( winsize, npos + nneg );
@@ -2061,7 +2062,7 @@ void cvCreateCascadeClassifier( const char* dirname,
for( i = 0; i < nstages; i++, cascade->count++ )
{
sprintf( stagename, "%s%d/%s", dirname, i, CV_STAGE_CART_FILE_NAME );
cascade->classifier[i] =
cascade->classifier[i] =
icvLoadCARTStageHaarClassifier( stagename, winsize.width + 1 );
if( !icvMkDir( stagename ) )
@@ -2129,7 +2130,7 @@ void cvCreateCascadeClassifier( const char* dirname,
data->sum.rows = data->tilted.rows = poscount + negcount;
data->normfactor.cols = data->weights.cols = data->cls.cols =
poscount + negcount;
posweight = (equalweights) ? 1.0F / (poscount + negcount) : (0.5F / poscount);
negweight = (equalweights) ? 1.0F / (poscount + negcount) : (0.5F / negcount);
for( j = 0; j < poscount; j++ )
@@ -2169,7 +2170,7 @@ void cvCreateCascadeClassifier( const char* dirname,
file = fopen( stagename, "w" );
if( file != NULL )
{
cascade->classifier[i]->save(
cascade->classifier[i]->save(
(CvIntHaarClassifier*) cascade->classifier[i], file );
fclose( file );
}
@@ -2190,15 +2191,15 @@ void cvCreateCascadeClassifier( const char* dirname,
{
char xml_path[1024];
int len = (int)strlen(dirname);
CvHaarClassifierCascade* cascade = 0;
CvHaarClassifierCascade* cascade1 = 0;
strcpy( xml_path, dirname );
if( xml_path[len-1] == '\\' || xml_path[len-1] == '/' )
len--;
strcpy( xml_path + len, ".xml" );
cascade = cvLoadHaarClassifierCascade( dirname, cvSize(winwidth,winheight) );
if( cascade )
cvSave( xml_path, cascade );
cvReleaseHaarClassifierCascade( &cascade );
cascade1 = cvLoadHaarClassifierCascade( dirname, cvSize(winwidth,winheight) );
if( cascade1 )
cvSave( xml_path, cascade1 );
cvReleaseHaarClassifierCascade( &cascade1 );
}
}
else
@@ -2207,7 +2208,7 @@ void cvCreateCascadeClassifier( const char* dirname,
printf( "FAILED TO INITIALIZE BACKGROUND READERS\n" );
#endif /* CV_VERBOSE */
}
/* CLEAN UP */
icvDestroyBackgroundReaders();
cascade->release( (CvIntHaarClassifier**) &cascade );
@@ -2215,7 +2216,7 @@ void cvCreateCascadeClassifier( const char* dirname,
/* tree cascade classifier */
int icvNumSplits( CvStageHaarClassifier* stage )
static int icvNumSplits( CvStageHaarClassifier* stage )
{
int i;
int num;
@@ -2229,7 +2230,7 @@ int icvNumSplits( CvStageHaarClassifier* stage )
return num;
}
void icvSetNumSamples( CvHaarTrainingData* training_data, int num )
static void icvSetNumSamples( CvHaarTrainingData* training_data, int num )
{
assert( num <= training_data->maxnum );
@@ -2238,7 +2239,7 @@ void icvSetNumSamples( CvHaarTrainingData* training_data, int num )
training_data->cls.cols = training_data->weights.cols = num;
}
void icvSetWeightsAndClasses( CvHaarTrainingData* training_data,
static void icvSetWeightsAndClasses( CvHaarTrainingData* training_data,
int num1, float weight1, float cls1,
int num2, float weight2, float cls2 )
{
@@ -2258,7 +2259,7 @@ void icvSetWeightsAndClasses( CvHaarTrainingData* training_data,
}
}
CvMat* icvGetUsedValues( CvHaarTrainingData* training_data,
static CvMat* icvGetUsedValues( CvHaarTrainingData* training_data,
int start, int num,
CvIntHaarFeatures* haar_features,
CvStageHaarClassifier* stage )
@@ -2302,7 +2303,7 @@ CvMat* icvGetUsedValues( CvHaarTrainingData* training_data,
}
total = last + 1;
CV_CALL( ptr = cvCreateMat( num, total, CV_32FC1 ) );
#ifdef CV_OPENMP
#pragma omp parallel for
@@ -2351,7 +2352,7 @@ typedef struct CvSplit
void cvCreateTreeCascadeClassifier( const char* dirname,
const char* vecfilename,
const char* bgfilename,
const char* bgfilename,
int npos, int nneg, int nstages,
int numprecalculated,
int numsplits,
@@ -2425,11 +2426,11 @@ void cvCreateTreeCascadeClassifier( const char* dirname,
sprintf( stage_name, "%s/", dirname );
suffix = stage_name + strlen( stage_name );
if (! bg_vecfile)
if( !icvInitBackgroundReaders( bgfilename, winsize ) && nstages > 0 )
CV_ERROR( CV_StsError, "Unable to read negative images" );
if( nstages > 0 )
{
/* width-first search in the tree */
@@ -2438,7 +2439,7 @@ void cvCreateTreeCascadeClassifier( const char* dirname,
CvSplit* first_split;
CvSplit* last_split;
CvSplit* cur_split;
CvTreeCascadeNode* parent;
CvTreeCascadeNode* cur_node;
CvTreeCascadeNode* last_node;
@@ -2447,7 +2448,7 @@ void cvCreateTreeCascadeClassifier( const char* dirname,
parent = leaves;
leaves = NULL;
do
{
{
int best_clusters; /* best selected number of clusters */
float posweight, negweight;
double leaf_fa_rate;
@@ -2501,7 +2502,6 @@ void cvCreateTreeCascadeClassifier( const char* dirname,
{
CvTreeCascadeNode* single_cluster;
CvTreeCascadeNode* multiple_clusters;
CvSplit* cur_split;
int single_num;
icvSetNumSamples( training_data, poscount + negcount );
@@ -2536,7 +2536,7 @@ void cvCreateTreeCascadeClassifier( const char* dirname,
multiple_clusters = NULL;
printf( "Number of used features: %d\n", single_num );
if( maxtreesplits >= 0 )
{
max_clusters = MIN( max_clusters, maxtreesplits - total_splits + 1 );
@@ -2594,7 +2594,7 @@ void cvCreateTreeCascadeClassifier( const char* dirname,
printf( "Clusters are too small. Clustering aborted.\n" );
break;
}
cur_num = 0;
cur_node = last_node = NULL;
for( cluster = 0; (cluster < k) && (cur_num < best_num); cluster++ )
@@ -2674,18 +2674,19 @@ void cvCreateTreeCascadeClassifier( const char* dirname,
} /* try different number of clusters */
cvReleaseMat( &vals );
CV_CALL( cur_split = (CvSplit*) cvAlloc( sizeof( *cur_split ) ) );
CV_ZERO_OBJ( cur_split );
if( last_split ) last_split->next = cur_split;
else first_split = cur_split;
last_split = cur_split;
CvSplit* curSplit;
CV_CALL( curSplit = (CvSplit*) cvAlloc( sizeof( *curSplit ) ) );
CV_ZERO_OBJ( curSplit );
cur_split->single_cluster = single_cluster;
cur_split->multiple_clusters = multiple_clusters;
cur_split->num_clusters = best_clusters;
cur_split->parent = parent;
cur_split->single_multiple_ratio = (float) single_num / best_num;
if( last_split ) last_split->next = curSplit;
else first_split = curSplit;
last_split = curSplit;
curSplit->single_cluster = single_cluster;
curSplit->multiple_clusters = multiple_clusters;
curSplit->num_clusters = best_clusters;
curSplit->parent = parent;
curSplit->single_multiple_ratio = (float) single_num / best_num;
}
if( parent ) parent = parent->next_same_level;
@@ -2734,7 +2735,7 @@ void cvCreateTreeCascadeClassifier( const char* dirname,
? last_split->multiple_clusters : last_split->single_cluster;
parent = last_split->parent;
if( parent ) parent->child = cur_node;
/* connect leaves via next_same_level and save them */
for( ; cur_node; cur_node = cur_node->next )
{
@@ -2768,14 +2769,14 @@ void cvCreateTreeCascadeClassifier( const char* dirname,
printf( "\nParent node: %s\n", buf );
printf( "Chosen number of splits: %d\n\n", (last_split->multiple_clusters)
? (last_split->num_clusters - 1) : 0 );
cur_split = last_split;
last_split = last_split->next;
cvFree( &cur_split );
} /* for each split point */
printf( "Total number of splits: %d\n", total_splits );
if( !(tcc->root) ) tcc->root = leaves;
CV_CALL( icvPrintTreeCascade( tcc->root ) );
@@ -2903,7 +2904,7 @@ void cvCreateTrainingSamples( const char* filename,
inverse = (rand() > (RAND_MAX/2));
}
icvPlaceDistortedSample( &sample, inverse, maxintensitydev,
maxxangle, maxyangle, maxzangle,
maxxangle, maxyangle, maxzangle,
0 /* nonzero means placing image without cut offs */,
0.0 /* nozero adds random shifting */,
0.0 /* nozero adds random scaling */,
@@ -2931,13 +2932,13 @@ void cvCreateTrainingSamples( const char* filename,
cvFree( &(sample.data.ptr) );
fclose( output );
} /* if( output != NULL ) */
icvEndSampleDistortion( &data );
}
#ifdef CV_VERBOSE
printf( "\r \r" );
#endif /* CV_VERBOSE */
#endif /* CV_VERBOSE */
}
@@ -2986,7 +2987,7 @@ void cvCreateTestSamples( const char* infoname,
{
cvNamedWindow( "Image", CV_WINDOW_AUTOSIZE );
}
info = fopen( infoname, "w" );
strcpy( fullname, infoname );
filename = strrchr( fullname, '\\' );
@@ -3008,7 +3009,7 @@ void cvCreateTestSamples( const char* infoname,
for( i = 0; i < count; i++ )
{
icvGetNextFromBackgroundData( cvbgdata, cvbgreader );
maxscale = MIN( 0.7F * cvbgreader->src.cols / winwidth,
0.7F * cvbgreader->src.rows / winheight );
if( maxscale < 1.0F ) continue;
@@ -3025,14 +3026,14 @@ void cvCreateTestSamples( const char* infoname,
inverse = (rand() > (RAND_MAX/2));
}
icvPlaceDistortedSample( &win, inverse, maxintensitydev,
maxxangle, maxyangle, maxzangle,
maxxangle, maxyangle, maxzangle,
1, 0.0, 0.0, &data );
sprintf( filename, "%04d_%04d_%04d_%04d_%04d.jpg",
(i + 1), x, y, width, height );
if( info )
if( info )
{
fprintf( info, "%s %d %d %d %d %d\n",
filename, 1, x, y, width, height );