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Fixed some issues found by static analysis (4th round)

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This commit is contained in:
Maksim Shabunin
2017-06-29 16:40:22 +03:00
parent 64b483c65b
commit e0393f8557
29 changed files with 168 additions and 46 deletions
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modules/objdetect/src/detection_based_tracker.cpp
+2 -1
View File
@@ -610,7 +610,8 @@ cv::DetectionBasedTracker::DetectionBasedTracker(cv::Ptr<IDetector> mainDetector
&& trackingDetector );
if (mainDetector) {
separateDetectionWork.reset(new SeparateDetectionWork(*this, mainDetector, params));
Ptr<SeparateDetectionWork> tmp(new SeparateDetectionWork(*this, mainDetector, params));
separateDetectionWork.swap(tmp);
}
weightsPositionsSmoothing.push_back(1);
modules/objdetect/src/haar.cpp
+5 -3
View File
@@ -1848,7 +1848,7 @@ icvLoadCascadeCART( const char** input_cascade, int n, CvSize orig_window_size )
sscanf( stage, "%d%n", &count, &dl );
stage += dl;
assert( count > 0 );
CV_Assert( count > 0 && count < CV_HAAR_STAGE_MAX);
cascade->stage_classifier[i].count = count;
cascade->stage_classifier[i].classifier =
(CvHaarClassifier*)cvAlloc( count*sizeof(cascade->stage_classifier[i].classifier[0]));
@@ -1862,6 +1862,7 @@ icvLoadCascadeCART( const char** input_cascade, int n, CvSize orig_window_size )
sscanf( stage, "%d%n", &classifier->count, &dl );
stage += dl;
CV_Assert( classifier->count > 0 && classifier->count< CV_HAAR_STAGE_MAX);
classifier->haar_feature = (CvHaarFeature*) cvAlloc(
classifier->count * ( sizeof( *classifier->haar_feature ) +
sizeof( *classifier->threshold ) +
@@ -1878,7 +1879,7 @@ icvLoadCascadeCART( const char** input_cascade, int n, CvSize orig_window_size )
sscanf( stage, "%d%n", &rects, &dl );
stage += dl;
assert( rects >= 2 && rects <= CV_HAAR_FEATURE_MAX );
CV_DbgAssert( rects >= 2 && rects <= CV_HAAR_FEATURE_MAX );
for( k = 0; k < rects; k++ )
{
@@ -1890,7 +1891,7 @@ icvLoadCascadeCART( const char** input_cascade, int n, CvSize orig_window_size )
stage += dl;
classifier->haar_feature[l].rect[k].r = r;
}
sscanf( stage, "%s%n", str, &dl );
sscanf( stage, "%99s%n", str, &dl );
stage += dl;
classifier->haar_feature[l].tilted = strncmp( str, "tilted", 6 ) == 0;
@@ -1926,6 +1927,7 @@ icvLoadCascadeCART( const char** input_cascade, int n, CvSize orig_window_size )
}
stage += dl;
CV_Assert(parent >= 0 && parent < i);
cascade->stage_classifier[i].parent = parent;
cascade->stage_classifier[i].next = next;
cascade->stage_classifier[i].child = -1;
modules/objdetect/src/hog.cpp
+13 -1
View File
@@ -3694,23 +3694,31 @@ void HOGDescriptor::readALTModel(String modelfile)
String eerr("version?");
String efile(__FILE__);
String efunc(__FUNCTION__);
fclose(modelfl);
throw Exception(Error::StsError, eerr, efile, efunc, __LINE__);
}
if(strcmp(version_buffer,"V6.01")) {
String eerr("version doesnot match");
String efile(__FILE__);
String efunc(__FUNCTION__);
fclose(modelfl);
throw Exception(Error::StsError, eerr, efile, efunc, __LINE__);
}
/* read version number */
int version = 0;
if (!fread (&version,sizeof(int),1,modelfl))
{ throw Exception(); }
{
fclose(modelfl);
throw Exception();
}
if (version < 200)
{
String eerr("version doesnot match");
String efile(__FILE__);
String efunc(__FUNCTION__);
fclose(modelfl);
throw Exception();
}
int kernel_type;
@@ -3729,6 +3737,7 @@ void HOGDescriptor::readALTModel(String modelfile)
nread=fread(&(coef_const),sizeof(double),1,modelfl);
int l;
nread=fread(&l,sizeof(int),1,modelfl);
CV_Assert(l >= 0 && l < 0xFFFF);
char* custom = new char[l];
nread=fread(custom,sizeof(char),l,modelfl);
delete[] custom;
@@ -3749,11 +3758,13 @@ void HOGDescriptor::readALTModel(String modelfile)
detector.clear();
if(kernel_type == 0) { /* linear kernel */
/* save linear wts also */
CV_Assert(totwords + 1 > 0 && totwords < 0xFFFF);
double *linearwt = new double[totwords+1];
int length = totwords;
nread = fread(linearwt, sizeof(double), totwords + 1, modelfl);
if(nread != static_cast<size_t>(length) + 1) {
delete [] linearwt;
fclose(modelfl);
throw Exception();
}
@@ -3764,6 +3775,7 @@ void HOGDescriptor::readALTModel(String modelfile)
setSVMDetector(detector);
delete [] linearwt;
} else {
fclose(modelfl);
throw Exception();
}
fclose(modelfl);