Commit abb211d0 authored by Alexander Alekhin's avatar Alexander Alekhin

avoid `Ptr<> == NULL` checks

parent 5c362968
......@@ -562,7 +562,7 @@ bool RetinaImpl::ocl_run(InputArray inputMatToConvert)
void RetinaImpl::run(InputArray inputMatToConvert)
{
CV_OCL_RUN((_ocl_retina != 0 && inputMatToConvert.isUMat()), ocl_run(inputMatToConvert));
CV_OCL_RUN((_ocl_retina && inputMatToConvert.isUMat()), ocl_run(inputMatToConvert));
_wasOCLRunCalled = false;
// first convert input image to the compatible format : std::valarray<float>
......@@ -830,7 +830,7 @@ bool RetinaImpl::_convertCvMat2ValarrayBuffer(InputArray inputMat, std::valarray
void RetinaImpl::clearBuffers()
{
#ifdef HAVE_OPENCL
if (_ocl_retina != 0)
if (_ocl_retina)
_ocl_retina->clearBuffers();
#endif
......
......@@ -1055,8 +1055,8 @@ bool Odometry::compute(Ptr<OdometryFrame>& srcFrame, Ptr<OdometryFrame>& dstFram
Size Odometry::prepareFrameCache(Ptr<OdometryFrame> &frame, int /*cacheType*/) const
{
if(frame == 0)
CV_Error(Error::StsBadArg, "Null frame pointer.\n");
if (!frame)
CV_Error(Error::StsBadArg, "Null frame pointer.");
return Size();
}
......
......@@ -665,12 +665,12 @@ void ERFilterNM::er_merge(ERStat *parent, ERStat *child)
child->level = child->level*thresholdDelta;
// before saving calculate P(child|character) and filter if possible
if (classifier != NULL)
if (classifier)
{
child->probability = classifier->eval(*child);
}
if ( (((classifier!=NULL)?(child->probability >= minProbability):true)||(nonMaxSuppression)) &&
if ( (((classifier)?(child->probability >= minProbability):true)||(nonMaxSuppression)) &&
((child->area >= (minArea*region_mask.rows*region_mask.cols)) &&
(child->area <= (maxArea*region_mask.rows*region_mask.cols)) &&
(child->rect.width > 2) && (child->rect.height > 2)) )
......@@ -858,12 +858,12 @@ ERStat* ERFilterNM::er_tree_filter ( InputArray image, ERStat * stat, ERStat *pa
// calculate P(child|character) and filter if possible
if ( (classifier != NULL) && (stat->parent != NULL) )
if (classifier && (stat->parent != NULL))
{
stat->probability = classifier->eval(*stat);
}
if ( ( ((classifier != NULL)?(stat->probability >= minProbability):true) &&
if ( ( ((classifier)?(stat->probability >= minProbability):true) &&
((stat->area >= minArea*region_mask.rows*region_mask.cols) &&
(stat->area <= maxArea*region_mask.rows*region_mask.cols)) ) ||
(stat->parent == NULL) )
......
......@@ -93,7 +93,7 @@ int main( int argc, char** argv ){
//instantiates the specific Tracker
Ptr<Tracker> tracker = createTrackerByName(tracker_algorithm);
if( tracker == NULL )
if (!tracker)
{
cout << "***Error in the instantiation of the tracker...***\n";
return -1;
......
......@@ -145,7 +145,7 @@ int main(int argc, char *argv[])
//Create Tracker
Ptr<Tracker> tracker = createTrackerByName(tracker_algorithm);
if (tracker == NULL)
if (!tracker)
{
cout << "***Error in the instantiation of the tracker...***\n";
getchar();
......
......@@ -47,7 +47,7 @@ namespace cv
bool MultiTracker_Alt::addTarget(InputArray image, const Rect2d& boundingBox, Ptr<Tracker> tracker_algorithm)
{
Ptr<Tracker> tracker = tracker_algorithm;
if (tracker == NULL)
if (!tracker)
return false;
if (!tracker->init(image, boundingBox))
......
......@@ -70,7 +70,7 @@ bool Tracker::init( InputArray image, const Rect2d& boundingBox )
bool initTracker = initImpl( image.getMat(), boundingBox );
//check if the model component is initialized
if( model == 0 )
if (!model)
{
CV_Error( -1, "The model is not initialized" );
}
......
......@@ -101,7 +101,7 @@ bool TrackerFeatureSet::addTrackerFeature( String trackerFeatureType )
}
Ptr<TrackerFeature> feature = TrackerFeature::create( trackerFeatureType );
if( feature == 0 )
if (!feature)
{
return false;
}
......
......@@ -61,7 +61,7 @@ TrackerModel::~TrackerModel()
bool TrackerModel::setTrackerStateEstimator( Ptr<TrackerStateEstimator> trackerStateEstimator )
{
if( stateEstimator != 0 )
if (stateEstimator.get())
{
return false;
}
......@@ -109,12 +109,12 @@ void TrackerModel::modelUpdate()
bool TrackerModel::runStateEstimator()
{
if( stateEstimator == 0 )
if (!stateEstimator)
{
CV_Error( -1, "Tracker state estimator is not setted" );
}
Ptr<TrackerTargetState> targetState = stateEstimator->estimate( confidenceMaps );
if( targetState == 0 )
if (!targetState)
return false;
setLastTargetState( targetState );
......
......@@ -96,7 +96,7 @@ bool TrackerSampler::addTrackerSamplerAlgorithm( String trackerSamplerAlgorithmT
}
Ptr<TrackerSamplerAlgorithm> sampler = TrackerSamplerAlgorithm::create( trackerSamplerAlgorithmType );
if( sampler == 0 )
if (!sampler)
{
return false;
}
......@@ -113,7 +113,7 @@ bool TrackerSampler::addTrackerSamplerAlgorithm( Ptr<TrackerSamplerAlgorithm>& s
return false;
}
if( sampler == 0 )
if (!sampler)
{
return false;
}
......
......@@ -262,14 +262,14 @@ TEST(Features2d_BruteForceDescriptorMatcher_knnMatch, regression)
const int k = 3;
Ptr<DescriptorExtractor> ext = SURF::create();
ASSERT_TRUE(ext != NULL);
ASSERT_TRUE(ext);
Ptr<FeatureDetector> det = SURF::create();
//"%YAML:1.0\nhessianThreshold: 8000.\noctaves: 3\noctaveLayers: 4\nupright: 0\n"
ASSERT_TRUE(det != NULL);
ASSERT_TRUE(det);
Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("BruteForce");
ASSERT_TRUE(matcher != NULL);
ASSERT_TRUE(matcher);
Mat imgT(256, 256, CV_8U, Scalar(255));
line(imgT, Point(20, sz/2), Point(sz-21, sz/2), Scalar(100), 2);
......
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