mirror of
https://github.com/opencv/opencv_contrib.git
synced 2025-10-22 16:08:41 +08:00
avoid Ptr<> == NULL
checks
This commit is contained in:
@@ -562,7 +562,7 @@ bool RetinaImpl::ocl_run(InputArray inputMatToConvert)
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void RetinaImpl::run(InputArray inputMatToConvert)
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{
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CV_OCL_RUN((_ocl_retina != 0 && inputMatToConvert.isUMat()), ocl_run(inputMatToConvert));
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CV_OCL_RUN((_ocl_retina && inputMatToConvert.isUMat()), ocl_run(inputMatToConvert));
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_wasOCLRunCalled = false;
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// first convert input image to the compatible format : std::valarray<float>
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@@ -830,7 +830,7 @@ bool RetinaImpl::_convertCvMat2ValarrayBuffer(InputArray inputMat, std::valarray
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void RetinaImpl::clearBuffers()
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{
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#ifdef HAVE_OPENCL
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if (_ocl_retina != 0)
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if (_ocl_retina)
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_ocl_retina->clearBuffers();
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#endif
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@@ -1055,8 +1055,8 @@ bool Odometry::compute(Ptr<OdometryFrame>& srcFrame, Ptr<OdometryFrame>& dstFram
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Size Odometry::prepareFrameCache(Ptr<OdometryFrame> &frame, int /*cacheType*/) const
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{
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if(frame == 0)
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CV_Error(Error::StsBadArg, "Null frame pointer.\n");
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if (!frame)
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CV_Error(Error::StsBadArg, "Null frame pointer.");
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return Size();
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}
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@@ -665,12 +665,12 @@ void ERFilterNM::er_merge(ERStat *parent, ERStat *child)
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child->level = child->level*thresholdDelta;
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// before saving calculate P(child|character) and filter if possible
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if (classifier != NULL)
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if (classifier)
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{
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child->probability = classifier->eval(*child);
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}
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if ( (((classifier!=NULL)?(child->probability >= minProbability):true)||(nonMaxSuppression)) &&
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if ( (((classifier)?(child->probability >= minProbability):true)||(nonMaxSuppression)) &&
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((child->area >= (minArea*region_mask.rows*region_mask.cols)) &&
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(child->area <= (maxArea*region_mask.rows*region_mask.cols)) &&
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(child->rect.width > 2) && (child->rect.height > 2)) )
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@@ -858,12 +858,12 @@ ERStat* ERFilterNM::er_tree_filter ( InputArray image, ERStat * stat, ERStat *pa
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// calculate P(child|character) and filter if possible
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if ( (classifier != NULL) && (stat->parent != NULL) )
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if (classifier && (stat->parent != NULL))
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{
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stat->probability = classifier->eval(*stat);
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}
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if ( ( ((classifier != NULL)?(stat->probability >= minProbability):true) &&
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if ( ( ((classifier)?(stat->probability >= minProbability):true) &&
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((stat->area >= minArea*region_mask.rows*region_mask.cols) &&
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(stat->area <= maxArea*region_mask.rows*region_mask.cols)) ) ||
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(stat->parent == NULL) )
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@@ -93,7 +93,7 @@ int main( int argc, char** argv ){
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//instantiates the specific Tracker
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Ptr<Tracker> tracker = createTrackerByName(tracker_algorithm);
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if( tracker == NULL )
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if (!tracker)
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{
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cout << "***Error in the instantiation of the tracker...***\n";
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return -1;
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@@ -145,7 +145,7 @@ int main(int argc, char *argv[])
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//Create Tracker
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Ptr<Tracker> tracker = createTrackerByName(tracker_algorithm);
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if (tracker == NULL)
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if (!tracker)
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{
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cout << "***Error in the instantiation of the tracker...***\n";
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getchar();
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@@ -47,7 +47,7 @@ namespace cv
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bool MultiTracker_Alt::addTarget(InputArray image, const Rect2d& boundingBox, Ptr<Tracker> tracker_algorithm)
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{
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Ptr<Tracker> tracker = tracker_algorithm;
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if (tracker == NULL)
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if (!tracker)
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return false;
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if (!tracker->init(image, boundingBox))
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@@ -70,7 +70,7 @@ bool Tracker::init( InputArray image, const Rect2d& boundingBox )
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bool initTracker = initImpl( image.getMat(), boundingBox );
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//check if the model component is initialized
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if( model == 0 )
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if (!model)
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{
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CV_Error( -1, "The model is not initialized" );
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}
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@@ -101,7 +101,7 @@ bool TrackerFeatureSet::addTrackerFeature( String trackerFeatureType )
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}
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Ptr<TrackerFeature> feature = TrackerFeature::create( trackerFeatureType );
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if( feature == 0 )
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if (!feature)
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{
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return false;
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}
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@@ -61,7 +61,7 @@ TrackerModel::~TrackerModel()
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bool TrackerModel::setTrackerStateEstimator( Ptr<TrackerStateEstimator> trackerStateEstimator )
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{
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if( stateEstimator != 0 )
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if (stateEstimator.get())
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{
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return false;
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}
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@@ -109,12 +109,12 @@ void TrackerModel::modelUpdate()
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bool TrackerModel::runStateEstimator()
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{
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if( stateEstimator == 0 )
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if (!stateEstimator)
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{
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CV_Error( -1, "Tracker state estimator is not setted" );
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}
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Ptr<TrackerTargetState> targetState = stateEstimator->estimate( confidenceMaps );
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if( targetState == 0 )
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if (!targetState)
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return false;
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setLastTargetState( targetState );
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@@ -96,7 +96,7 @@ bool TrackerSampler::addTrackerSamplerAlgorithm( String trackerSamplerAlgorithmT
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}
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Ptr<TrackerSamplerAlgorithm> sampler = TrackerSamplerAlgorithm::create( trackerSamplerAlgorithmType );
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if( sampler == 0 )
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if (!sampler)
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{
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return false;
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}
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@@ -113,7 +113,7 @@ bool TrackerSampler::addTrackerSamplerAlgorithm( Ptr<TrackerSamplerAlgorithm>& s
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return false;
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}
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if( sampler == 0 )
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if (!sampler)
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{
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return false;
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}
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@@ -262,14 +262,14 @@ TEST(Features2d_BruteForceDescriptorMatcher_knnMatch, regression)
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const int k = 3;
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Ptr<DescriptorExtractor> ext = SURF::create();
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ASSERT_TRUE(ext != NULL);
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ASSERT_TRUE(ext);
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Ptr<FeatureDetector> det = SURF::create();
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//"%YAML:1.0\nhessianThreshold: 8000.\noctaves: 3\noctaveLayers: 4\nupright: 0\n"
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ASSERT_TRUE(det != NULL);
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ASSERT_TRUE(det);
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Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("BruteForce");
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ASSERT_TRUE(matcher != NULL);
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ASSERT_TRUE(matcher);
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Mat imgT(256, 256, CV_8U, Scalar(255));
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line(imgT, Point(20, sz/2), Point(sz-21, sz/2), Scalar(100), 2);
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