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Fixed warnings about unused return value of fscanf, scanf and system

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pull/13383/head
Andrey Kamaev 13 years ago
parent 7fd1cfc5e7
commit c712f376d5
9 changed files with 90 additions and 42 deletions
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  1. 23
      apps/haartraining/cvboost.cpp
  2. 25
      apps/haartraining/cvhaarclassifier.cpp
  3. 46
      modules/calib3d/test/test_cameracalibration.cpp
  4. 3
      modules/features2d/test/test_mser.cpp
  5. 12
      modules/legacy/src/calibfilter.cpp
  6. 11
      samples/c/mushroom.cpp
  7. 3
      samples/cpp/detector_descriptor_matcher_evaluation.cpp
  8. 6
      samples/cpp/hybridtrackingsample.cpp
  9. 3
      samples/cpp/select3dobj.cpp

23
apps/haartraining/cvboost.cpp
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@ -3390,6 +3390,7 @@ CvClassifier* cvCreateBtClassifierFromFile( const char* filename )
int num_features; int num_features;
int num_classes; int num_classes;
int type; int type;
int values_read = -1;
CV_ASSERT( filename != NULL ); CV_ASSERT( filename != NULL );
@ -3400,7 +3401,8 @@ CvClassifier* cvCreateBtClassifierFromFile( const char* filename )
CV_ERROR( CV_StsError, "Unable to open file" ); CV_ERROR( CV_StsError, "Unable to open file" );
} }
fscanf( file, "%d %d %d %d", &type, &num_classes, &num_features, &num_classifiers ); values_read = fscanf( file, "%d %d %d %d", &type, &num_classes, &num_features, &num_classifiers );
CV_Assert(values_read == 4);
CV_ASSERT( type >= (int) CV_DABCLASS && type <= (int) CV_MREG ); CV_ASSERT( type >= (int) CV_DABCLASS && type <= (int) CV_MREG );
CV_ASSERT( num_features > 0 ); CV_ASSERT( num_features > 0 );
@ -3418,7 +3420,8 @@ CvClassifier* cvCreateBtClassifierFromFile( const char* filename )
int count; int count;
CvCARTClassifier* tree; CvCARTClassifier* tree;
fscanf( file, "%d", &count ); values_read = fscanf( file, "%d", &count );
CV_Assert(values_read == 1);
data_size = sizeof( *tree ) data_size = sizeof( *tree )
+ count * ( sizeof( *(tree->compidx) ) + sizeof( *(tree->threshold) ) + + count * ( sizeof( *(tree->compidx) ) + sizeof( *(tree->threshold) ) +
@ -3439,14 +3442,16 @@ CvClassifier* cvCreateBtClassifierFromFile( const char* filename )
tree->count = count; tree->count = count;
for( j = 0; j < tree->count; j++ ) for( j = 0; j < tree->count; j++ )
{ {
fscanf( file, "%d %g %d %d", &(tree->compidx[j]), values_read = fscanf( file, "%d %g %d %d", &(tree->compidx[j]),
&(tree->threshold[j]), &(tree->threshold[j]),
&(tree->left[j]), &(tree->left[j]),
&(tree->right[j]) ); &(tree->right[j]) );
CV_Assert(values_read == 4);
} }
for( j = 0; j <= tree->count; j++ ) for( j = 0; j <= tree->count; j++ )
{ {
fscanf( file, "%g", &(tree->val[j]) ); values_read = fscanf( file, "%g", &(tree->val[j]) );
CV_Assert(values_read == 1);
} }
ptr->trees[i] = tree; ptr->trees[i] = tree;
} }
@ -3553,6 +3558,7 @@ void cvReadTrainData( const char* filename, int flags,
int m, n; int m, n;
int i, j; int i, j;
float val; float val;
int values_read = -1;
if( filename == NULL ) if( filename == NULL )
{ {
@ -3575,7 +3581,8 @@ void cvReadTrainData( const char* filename, int flags,
CV_ERROR( CV_StsError, "Unable to open file" ); CV_ERROR( CV_StsError, "Unable to open file" );
} }
fscanf( file, "%d %d", &m, &n ); values_read = fscanf( file, "%d %d", &m, &n );
CV_Assert(values_read == 2);
if( CV_IS_ROW_SAMPLE( flags ) ) if( CV_IS_ROW_SAMPLE( flags ) )
{ {
@ -3592,7 +3599,8 @@ void cvReadTrainData( const char* filename, int flags,
{ {
for( j = 0; j < n; j++ ) for( j = 0; j < n; j++ )
{ {
fscanf( file, "%f", &val ); values_read = fscanf( file, "%f", &val );
CV_Assert(values_read == 1);
if( CV_IS_ROW_SAMPLE( flags ) ) if( CV_IS_ROW_SAMPLE( flags ) )
{ {
CV_MAT_ELEM( **trainData, float, i, j ) = val; CV_MAT_ELEM( **trainData, float, i, j ) = val;
@ -3602,7 +3610,8 @@ void cvReadTrainData( const char* filename, int flags,
CV_MAT_ELEM( **trainData, float, j, i ) = val; CV_MAT_ELEM( **trainData, float, j, i ) = val;
} }
} }
fscanf( file, "%f", &val ); values_read = fscanf( file, "%f", &val );
CV_Assert(values_read == 2);
CV_MAT_ELEM( **trainClasses, float, 0, i ) = val; CV_MAT_ELEM( **trainClasses, float, 0, i ) = val;
} }

25
apps/haartraining/cvhaarclassifier.cpp
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@ -285,18 +285,20 @@ void icvLoadHaarFeature( CvTHaarFeature* feature, FILE* file )
int weight; int weight;
nrect = 0; nrect = 0;
fscanf( file, "%d", &nrect ); int values_read = fscanf( file, "%d", &nrect );
CV_Assert(values_read == 1);
assert( nrect <= CV_HAAR_FEATURE_MAX ); assert( nrect <= CV_HAAR_FEATURE_MAX );
for( j = 0; j < nrect; j++ ) for( j = 0; j < nrect; j++ )
{ {
fscanf( file, "%d %d %d %d %d %d", values_read = fscanf( file, "%d %d %d %d %d %d",
&(feature->rect[j].r.x), &(feature->rect[j].r.x),
&(feature->rect[j].r.y), &(feature->rect[j].r.y),
&(feature->rect[j].r.width), &(feature->rect[j].r.width),
&(feature->rect[j].r.height), &(feature->rect[j].r.height),
&tmp, &weight ); &tmp, &weight );
CV_Assert(values_read == 6);
feature->rect[j].weight = (float) weight; feature->rect[j].weight = (float) weight;
} }
for( j = nrect; j < CV_HAAR_FEATURE_MAX; j++ ) for( j = nrect; j < CV_HAAR_FEATURE_MAX; j++ )
@ -307,7 +309,8 @@ void icvLoadHaarFeature( CvTHaarFeature* feature, FILE* file )
feature->rect[j].r.height = 0; feature->rect[j].r.height = 0;
feature->rect[j].weight = 0.0f; feature->rect[j].weight = 0.0f;
} }
fscanf( file, "%s", &(feature->desc[0]) ); values_read = fscanf( file, "%s", &(feature->desc[0]) );
CV_Assert(values_read == 1);
feature->tilted = ( feature->desc[0] == 't' ); feature->tilted = ( feature->desc[0] == 't' );
} }
@ -342,19 +345,23 @@ CvIntHaarClassifier* icvLoadCARTHaarClassifier( FILE* file, int step )
int count; int count;
ptr = NULL; ptr = NULL;
fscanf( file, "%d", &count ); int values_read = fscanf( file, "%d", &count );
CV_Assert(values_read == 1);
if( count > 0 ) if( count > 0 )
{ {
ptr = (CvCARTHaarClassifier*) icvCreateCARTHaarClassifier( count ); ptr = (CvCARTHaarClassifier*) icvCreateCARTHaarClassifier( count );
for( i = 0; i < count; i++ ) for( i = 0; i < count; i++ )
{ {
icvLoadHaarFeature( &(ptr->feature[i]), file ); icvLoadHaarFeature( &(ptr->feature[i]), file );
fscanf( file, "%f %d %d", &(ptr->threshold[i]), &(ptr->left[i]), values_read = fscanf( file, "%f %d %d", &(ptr->threshold[i]), &(ptr->left[i]),
&(ptr->right[i]) ); &(ptr->right[i]) );
CV_Assert(values_read == 3);
} }
for( i = 0; i <= count; i++ ) for( i = 0; i <= count; i++ )
{ {
fscanf( file, "%f", &(ptr->val[i]) ); values_read = fscanf( file, "%f", &(ptr->val[i]) );
CV_Assert(values_read == 1);
} }
icvConvertToFastHaarFeature( ptr->feature, ptr->fastfeature, ptr->count, step ); icvConvertToFastHaarFeature( ptr->feature, ptr->fastfeature, ptr->count, step );
} }
@ -402,7 +409,8 @@ CvIntHaarClassifier* icvLoadCARTStageHaarClassifierF( FILE* file, int step )
float threshold; float threshold;
count = 0; count = 0;
fscanf( file, "%d", &count ); int values_read = fscanf( file, "%d", &count );
CV_Assert(values_read == 1);
if( count > 0 ) if( count > 0 )
{ {
ptr = (CvStageHaarClassifier*) icvCreateStageHaarClassifier( count, 0.0F ); ptr = (CvStageHaarClassifier*) icvCreateStageHaarClassifier( count, 0.0F );
@ -411,7 +419,8 @@ CvIntHaarClassifier* icvLoadCARTStageHaarClassifierF( FILE* file, int step )
ptr->classifier[i] = icvLoadCARTHaarClassifier( file, step ); ptr->classifier[i] = icvLoadCARTHaarClassifier( file, step );
} }
fscanf( file, "%f", &threshold ); values_read = fscanf( file, "%f", &threshold );
CV_Assert(values_read == 1);
ptr->threshold = threshold; ptr->threshold = threshold;
/* to be compatible with the previous implementation */ /* to be compatible with the previous implementation */

46
modules/calib3d/test/test_cameracalibration.cpp
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@ -333,6 +333,7 @@ void CV_CameraCalibrationTest::run( int start_from )
goodTransVects = 0; goodTransVects = 0;
goodRotMatrs = 0; goodRotMatrs = 0;
int progress = 0; int progress = 0;
int values_read = -1;
sprintf( filepath, "%scameracalibration/", ts->get_data_path().c_str() ); sprintf( filepath, "%scameracalibration/", ts->get_data_path().c_str() );
sprintf( filename, "%sdatafiles.txt", filepath ); sprintf( filename, "%sdatafiles.txt", filepath );
@ -344,11 +345,13 @@ void CV_CameraCalibrationTest::run( int start_from )
goto _exit_; goto _exit_;
} }
fscanf(datafile,"%d",&numTests); values_read = fscanf(datafile,"%d",&numTests);
CV_Assert(values_read == 1);
for( currTest = start_from; currTest < numTests; currTest++ ) for( currTest = start_from; currTest < numTests; currTest++ )
{ {
fscanf(datafile,"%s",i_dat_file); values_read = fscanf(datafile,"%s",i_dat_file);
CV_Assert(values_read == 1);
sprintf(filename, "%s%s", filepath, i_dat_file); sprintf(filename, "%s%s", filepath, i_dat_file);
file = fopen(filename,"r"); file = fopen(filename,"r");
@ -366,7 +369,8 @@ void CV_CameraCalibrationTest::run( int start_from )
continue; // if there is more than one test, just skip the test continue; // if there is more than one test, just skip the test
} }
fscanf(file,"%d %d\n",&(imageSize.width),&(imageSize.height)); values_read = fscanf(file,"%d %d\n",&(imageSize.width),&(imageSize.height));
CV_Assert(values_read == 2);
if( imageSize.width <= 0 || imageSize.height <= 0 ) if( imageSize.width <= 0 || imageSize.height <= 0 )
{ {
ts->printf( cvtest::TS::LOG, "Image size in test file is incorrect\n" ); ts->printf( cvtest::TS::LOG, "Image size in test file is incorrect\n" );
@ -375,7 +379,8 @@ void CV_CameraCalibrationTest::run( int start_from )
} }
/* Read etalon size */ /* Read etalon size */
fscanf(file,"%d %d\n",&(etalonSize.width),&(etalonSize.height)); values_read = fscanf(file,"%d %d\n",&(etalonSize.width),&(etalonSize.height));
CV_Assert(values_read == 2);
if( etalonSize.width <= 0 || etalonSize.height <= 0 ) if( etalonSize.width <= 0 || etalonSize.height <= 0 )
{ {
ts->printf( cvtest::TS::LOG, "Pattern size in test file is incorrect\n" ); ts->printf( cvtest::TS::LOG, "Pattern size in test file is incorrect\n" );
@ -386,7 +391,8 @@ void CV_CameraCalibrationTest::run( int start_from )
numPoints = etalonSize.width * etalonSize.height; numPoints = etalonSize.width * etalonSize.height;
/* Read number of images */ /* Read number of images */
fscanf(file,"%d\n",&numImages); values_read = fscanf(file,"%d\n",&numImages);
CV_Assert(values_read == 1);
if( numImages <=0 ) if( numImages <=0 )
{ {
ts->printf( cvtest::TS::LOG, "Number of images in test file is incorrect\n"); ts->printf( cvtest::TS::LOG, "Number of images in test file is incorrect\n");
@ -427,7 +433,8 @@ void CV_CameraCalibrationTest::run( int start_from )
for( currPoint = 0; currPoint < numPoints; currPoint++ ) for( currPoint = 0; currPoint < numPoints; currPoint++ )
{ {
double x,y,z; double x,y,z;
fscanf(file,"%lf %lf %lf\n",&x,&y,&z); values_read = fscanf(file,"%lf %lf %lf\n",&x,&y,&z);
CV_Assert(values_read == 3);
(objectPoints+i)->x = x; (objectPoints+i)->x = x;
(objectPoints+i)->y = y; (objectPoints+i)->y = y;
@ -443,7 +450,8 @@ void CV_CameraCalibrationTest::run( int start_from )
for( currPoint = 0; currPoint < numPoints; currPoint++ ) for( currPoint = 0; currPoint < numPoints; currPoint++ )
{ {
double x,y; double x,y;
fscanf(file,"%lf %lf\n",&x,&y); values_read = fscanf(file,"%lf %lf\n",&x,&y);
CV_Assert(values_read == 2);
(imagePoints+i)->x = x; (imagePoints+i)->x = x;
(imagePoints+i)->y = y; (imagePoints+i)->y = y;
@ -455,32 +463,40 @@ void CV_CameraCalibrationTest::run( int start_from )
/* Focal lengths */ /* Focal lengths */
double goodFcx,goodFcy; double goodFcx,goodFcy;
fscanf(file,"%lf %lf",&goodFcx,&goodFcy); values_read = fscanf(file,"%lf %lf",&goodFcx,&goodFcy);
CV_Assert(values_read == 2);
/* Principal points */ /* Principal points */
double goodCx,goodCy; double goodCx,goodCy;
fscanf(file,"%lf %lf",&goodCx,&goodCy); values_read = fscanf(file,"%lf %lf",&goodCx,&goodCy);
CV_Assert(values_read == 2);
/* Read distortion */ /* Read distortion */
fscanf(file,"%lf",goodDistortion+0); values_read = fscanf(file,"%lf",goodDistortion+0); CV_Assert(values_read == 1);
fscanf(file,"%lf",goodDistortion+1); values_read = fscanf(file,"%lf",goodDistortion+1); CV_Assert(values_read == 1);
fscanf(file,"%lf",goodDistortion+2); values_read = fscanf(file,"%lf",goodDistortion+2); CV_Assert(values_read == 1);
fscanf(file,"%lf",goodDistortion+3); values_read = fscanf(file,"%lf",goodDistortion+3); CV_Assert(values_read == 1);
/* Read good Rot matrixes */ /* Read good Rot matrixes */
for( currImage = 0; currImage < numImages; currImage++ ) for( currImage = 0; currImage < numImages; currImage++ )
{ {
for( i = 0; i < 3; i++ ) for( i = 0; i < 3; i++ )
for( j = 0; j < 3; j++ ) for( j = 0; j < 3; j++ )
fscanf(file, "%lf", goodRotMatrs + currImage * 9 + j * 3 + i); {
values_read = fscanf(file, "%lf", goodRotMatrs + currImage * 9 + j * 3 + i);
CV_Assert(values_read == 1);
}
} }
/* Read good Trans vectors */ /* Read good Trans vectors */
for( currImage = 0; currImage < numImages; currImage++ ) for( currImage = 0; currImage < numImages; currImage++ )
{ {
for( i = 0; i < 3; i++ ) for( i = 0; i < 3; i++ )
fscanf(file, "%lf", goodTransVects + currImage * 3 + i); {
values_read = fscanf(file, "%lf", goodTransVects + currImage * 3 + i);
CV_Assert(values_read == 1);
}
} }
calibFlags = 0 calibFlags = 0

3
modules/features2d/test/test_mser.cpp
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@ -75,7 +75,8 @@ int CV_MserTest::LoadBoxes(const char* path, vector<CvBox2D>& boxes)
while (!feof(f)) while (!feof(f))
{ {
CvBox2D box; CvBox2D box;
fscanf(f,"%f,%f,%f,%f,%f\n",&box.angle,&box.center.x,&box.center.y,&box.size.width,&box.size.height); int values_read = fscanf(f,"%f,%f,%f,%f,%f\n",&box.angle,&box.center.x,&box.center.y,&box.size.width,&box.size.height);
CV_Assert(values_read == 5);
boxes.push_back(box); boxes.push_back(box);
} }
fclose(f); fclose(f);

12
modules/legacy/src/calibfilter.cpp
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@ -734,7 +734,8 @@ bool CvCalibFilter::LoadCameraParams( const char* filename )
{ {
for( j = 0; j < (int)(sizeof(cameraParams[i])/sizeof(float)); j++ ) for( j = 0; j < (int)(sizeof(cameraParams[i])/sizeof(float)); j++ )
{ {
fscanf( f, "%f", &((float*)(cameraParams + i))[j] ); int values_read = fscanf( f, "%f", &((float*)(cameraParams + i))[j] );
CV_Assert(values_read == 1);
} }
} }
@ -746,8 +747,10 @@ bool CvCalibFilter::LoadCameraParams( const char* filename )
{ {
for( j = 0; j < 4; j++ ) for( j = 0; j < 4; j++ )
{ {
fscanf(f, "%f ", &(stereo.quad[i][j].x) ); int values_read = fscanf(f, "%f ", &(stereo.quad[i][j].x) );
fscanf(f, "%f ", &(stereo.quad[i][j].y) ); CV_Assert(values_read == 1);
values_read = fscanf(f, "%f ", &(stereo.quad[i][j].y) );
CV_Assert(values_read == 1);
} }
} }
@ -756,7 +759,8 @@ bool CvCalibFilter::LoadCameraParams( const char* filename )
{ {
for( j = 0; j < 9; j++ ) for( j = 0; j < 9; j++ )
{ {
fscanf(f, "%lf ", &(stereo.coeffs[i][j/3][j%3]) ); int values_read = fscanf(f, "%lf ", &(stereo.coeffs[i][j/3][j%3]) );
CV_Assert(values_read == 1);
} }
} }

11
samples/c/mushroom.cpp
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@ -192,7 +192,9 @@ void print_variable_importance( CvDTree* dtree, const char** var_desc )
} }
printf( "Print variable importance information? (y/n) " ); printf( "Print variable importance information? (y/n) " );
scanf( "%1s", input ); int values_read = scanf( "%1s", input );
CV_Assert(values_read == 1);
if( input[0] != 'y' && input[0] != 'Y' ) if( input[0] != 'y' && input[0] != 'Y' )
return; return;
@ -230,7 +232,9 @@ void interactive_classification( CvDTree* dtree, const char** var_desc )
const CvDTreeNode* node; const CvDTreeNode* node;
printf( "Start/Proceed with interactive mushroom classification (y/n): " ); printf( "Start/Proceed with interactive mushroom classification (y/n): " );
scanf( "%1s", input ); int values_read = scanf( "%1s", input );
CV_Assert(values_read == 1);
if( input[0] != 'y' && input[0] != 'Y' ) if( input[0] != 'y' && input[0] != 'Y' )
break; break;
printf( "Enter 1-letter answers, '?' for missing/unknown value...\n" ); printf( "Enter 1-letter answers, '?' for missing/unknown value...\n" );
@ -252,7 +256,8 @@ void interactive_classification( CvDTree* dtree, const char** var_desc )
const int* map = data->cat_map->data.i + data->cat_ofs->data.i[vi]; const int* map = data->cat_map->data.i + data->cat_ofs->data.i[vi];
printf( "%s: ", var_desc[vi] ); printf( "%s: ", var_desc[vi] );
scanf( "%1s", input ); values_read = scanf( "%1s", input );
CV_Assert(values_read == 1);
if( input[0] == '?' ) if( input[0] == '?' )
{ {

3
samples/cpp/detector_descriptor_matcher_evaluation.cpp
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@ -273,7 +273,8 @@ int main(int argc, char** argv)
if( dir[dir.size()-1] != '\\' && dir[dir.size()-1] != '/' ) if( dir[dir.size()-1] != '\\' && dir[dir.size()-1] != '/' )
dir += "/"; dir += "/";
system(("mkdir " + dir).c_str()); int result = system(("mkdir " + dir).c_str());
CV_Assert(result == 0);
for( int i = 0; ddms[i*4] != 0; i++ ) for( int i = 0; ddms[i*4] != 0; i++ )
{ {

6
samples/cpp/hybridtrackingsample.cpp
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@ -82,7 +82,8 @@ int main(int argc, char** argv)
sprintf(test_file, "%s", argv[1]); sprintf(test_file, "%s", argv[1]);
f = fopen(test_file, "r"); f = fopen(test_file, "r");
char vid[20]; char vid[20];
fscanf(f, "%s\n", vid); int values_read = fscanf(f, "%s\n", vid);
CV_Assert(values_read == 1);
cout << "Benchmarking against " << vid << endl; cout << "Benchmarking against " << vid << endl;
live = 0; live = 0;
} }
@ -133,7 +134,8 @@ int main(int argc, char** argv)
} }
else else
{ {
fscanf(f, "%d %f %f %f %f\n", &i, &w[0], &w[1], &w[2], &w[3]); int values_read = fscanf(f, "%d %f %f %f %f\n", &i, &w[0], &w[1], &w[2], &w[3]);
CV_Assert(values_read == 5);
sprintf(img_file, "seqG/%04d.png", i); sprintf(img_file, "seqG/%04d.png", i);
image = imread(img_file, CV_LOAD_IMAGE_COLOR); image = imread(img_file, CV_LOAD_IMAGE_COLOR);
if (image.empty()) if (image.empty())

3
samples/cpp/select3dobj.cpp
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@ -491,7 +491,8 @@ int main(int argc, char** argv)
if( outbarename ) if( outbarename )
{ {
cmd[6 + outbarename - outprefix] = '\0'; cmd[6 + outbarename - outprefix] = '\0';
system(cmd); int result = system(cmd);
CV_Assert(result == 0);
outbarename++; outbarename++;
} }
else else

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