generic-library/opencv
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Remove all using directives for STL namespace and members

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Made all STL usages explicit to be able automatically find all usages of
particular class or function.
This commit is contained in:
Andrey Kamaev
2013-02-24 20:14:01 +04:00
parent f783f34e0b
commit 2a6fb2867e
310 changed files with 5744 additions and 5964 deletions
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27
modules/gpu/src/cascadeclassifier.cpp
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@@ -46,15 +46,14 @@
using namespace cv;
using namespace cv::gpu;
using namespace std;
#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER)
cv::gpu::CascadeClassifier_GPU::CascadeClassifier_GPU() { throw_nogpu(); }
cv::gpu::CascadeClassifier_GPU::CascadeClassifier_GPU(const string&) { throw_nogpu(); }
cv::gpu::CascadeClassifier_GPU::CascadeClassifier_GPU(const std::string&) { throw_nogpu(); }
cv::gpu::CascadeClassifier_GPU::~CascadeClassifier_GPU() { throw_nogpu(); }
bool cv::gpu::CascadeClassifier_GPU::empty() const { throw_nogpu(); return true; }
bool cv::gpu::CascadeClassifier_GPU::load(const string&) { throw_nogpu(); return true; }
bool cv::gpu::CascadeClassifier_GPU::load(const std::string&) { throw_nogpu(); return true; }
Size cv::gpu::CascadeClassifier_GPU::getClassifierSize() const { throw_nogpu(); return Size();}
void cv::gpu::CascadeClassifier_GPU::release() { throw_nogpu(); }
int cv::gpu::CascadeClassifier_GPU::detectMultiScale( const GpuMat&, GpuMat&, double, int, Size) {throw_nogpu(); return -1;}
@@ -72,7 +71,7 @@ public:
bool findLargestObject, bool visualizeInPlace, cv::Size ncvMinSize, cv::Size maxObjectSize) = 0;
virtual cv::Size getClassifierCvSize() const = 0;
virtual bool read(const string& classifierAsXml) = 0;
virtual bool read(const std::string& classifierAsXml) = 0;
};
struct cv::gpu::CascadeClassifier_GPU::HaarCascade : cv::gpu::CascadeClassifier_GPU::CascadeClassifierImpl
@@ -83,7 +82,7 @@ public:
ncvSetDebugOutputHandler(NCVDebugOutputHandler);
}
bool read(const string& filename)
bool read(const std::string& filename)
{
ncvSafeCall( load(filename) );
return true;
@@ -172,7 +171,7 @@ public:
private:
static void NCVDebugOutputHandler(const std::string &msg) { CV_Error(CV_GpuApiCallError, msg.c_str()); }
NCVStatus load(const string& classifierFile)
NCVStatus load(const std::string& classifierFile)
{
int devId = cv::gpu::getDevice();
ncvAssertCUDAReturn(cudaGetDeviceProperties(&devProp, devId), NCV_CUDA_ERROR);
@@ -459,7 +458,7 @@ public:
virtual cv::Size getClassifierCvSize() const { return NxM; }
bool read(const string& classifierAsXml)
bool read(const std::string& classifierAsXml)
{
FileStorage fs(classifierAsXml, FileStorage::READ);
return fs.isOpened() ? read(fs.getFirstTopLevelNode()) : false;
@@ -513,10 +512,10 @@ private:
const char *GPU_CC_FEATURES = "features";
const char *GPU_CC_RECT = "rect";
std::string stageTypeStr = (string)root[GPU_CC_STAGE_TYPE];
std::string stageTypeStr = (std::string)root[GPU_CC_STAGE_TYPE];
CV_Assert(stageTypeStr == GPU_CC_BOOST);
string featureTypeStr = (string)root[GPU_CC_FEATURE_TYPE];
std::string featureTypeStr = (std::string)root[GPU_CC_FEATURE_TYPE];
CV_Assert(featureTypeStr == GPU_CC_LBP);
NxM.width = (int)root[GPU_CC_WIDTH];
@@ -663,7 +662,7 @@ private:
cv::gpu::CascadeClassifier_GPU::CascadeClassifier_GPU()
: findLargestObject(false), visualizeInPlace(false), impl(0) {}
cv::gpu::CascadeClassifier_GPU::CascadeClassifier_GPU(const string& filename)
cv::gpu::CascadeClassifier_GPU::CascadeClassifier_GPU(const std::string& filename)
: findLargestObject(false), visualizeInPlace(false), impl(0) { load(filename); }
cv::gpu::CascadeClassifier_GPU::~CascadeClassifier_GPU() { release(); }
@@ -689,7 +688,7 @@ int cv::gpu::CascadeClassifier_GPU::detectMultiScale(const GpuMat& image, GpuMat
return impl->process(image, objectsBuf, (float)scaleFactor, minNeighbors, findLargestObject, visualizeInPlace, minSize, maxObjectSize);
}
bool cv::gpu::CascadeClassifier_GPU::load(const string& filename)
bool cv::gpu::CascadeClassifier_GPU::load(const std::string& filename)
{
release();
@@ -711,7 +710,7 @@ bool cv::gpu::CascadeClassifier_GPU::load(const string& filename)
}
const char *GPU_CC_LBP = "LBP";
string featureTypeStr = (string)fs.getFirstTopLevelNode()["featureType"];
std::string featureTypeStr = (std::string)fs.getFirstTopLevelNode()["featureType"];
if (featureTypeStr == GPU_CC_LBP)
impl = new LbpCascade();
else
@@ -743,12 +742,12 @@ struct RectConvert
void groupRectangles(std::vector<NcvRect32u> &hypotheses, int groupThreshold, double eps, std::vector<Ncv32u> *weights)
{
vector<Rect> rects(hypotheses.size());
std::vector<Rect> rects(hypotheses.size());
std::transform(hypotheses.begin(), hypotheses.end(), rects.begin(), RectConvert());
if (weights)
{
vector<int> weights_int;
std::vector<int> weights_int;
weights_int.assign(weights->begin(), weights->end());
cv::groupRectangles(rects, weights_int, groupThreshold, eps);
}