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fixed some warnings under win64

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This commit is contained in:
Vladislav Vinogradov
2011-08-08 11:28:14 +00:00
parent 767ac9aa10
commit 42ced17c2c
23 changed files with 191 additions and 171 deletions
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modules/gpu/src/cascadeclassifier.cpp
+14 -14
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@@ -87,7 +87,7 @@ struct cv::gpu::CascadeClassifier_GPU::CascadeClassifierImpl
src_seg.begin = src_beg;
src_seg.size = src.step * src.rows;
NCVMatrixReuse<Ncv8u> d_src(src_seg, devProp.textureAlignment, src.cols, src.rows, src.step, true);
NCVMatrixReuse<Ncv8u> d_src(src_seg, static_cast<int>(devProp.textureAlignment), src.cols, src.rows, static_cast<int>(src.step), true);
ncvAssertReturn(d_src.isMemReused(), NCV_ALLOCATOR_BAD_REUSE);
CV_Assert(objects.rows == 1);
@@ -141,8 +141,8 @@ private:
ncvAssertCUDAReturn(cudaGetDeviceProperties(&devProp, devId), NCV_CUDA_ERROR);
// Load the classifier from file (assuming its size is about 1 mb) using a simple allocator
gpuCascadeAllocator = new NCVMemNativeAllocator(NCVMemoryTypeDevice, devProp.textureAlignment);
cpuCascadeAllocator = new NCVMemNativeAllocator(NCVMemoryTypeHostPinned, devProp.textureAlignment);
gpuCascadeAllocator = new NCVMemNativeAllocator(NCVMemoryTypeDevice, static_cast<int>(devProp.textureAlignment));
cpuCascadeAllocator = new NCVMemNativeAllocator(NCVMemoryTypeHostPinned, static_cast<int>(devProp.textureAlignment));
ncvAssertPrintReturn(gpuCascadeAllocator->isInitialized(), "Error creating cascade GPU allocator", NCV_CUDA_ERROR);
ncvAssertPrintReturn(cpuCascadeAllocator->isInitialized(), "Error creating cascade CPU allocator", NCV_CUDA_ERROR);
@@ -189,8 +189,8 @@ private:
}
// Calculate memory requirements and create real allocators
NCVMemStackAllocator gpuCounter(devProp.textureAlignment);
NCVMemStackAllocator cpuCounter(devProp.textureAlignment);
NCVMemStackAllocator gpuCounter(static_cast<int>(devProp.textureAlignment));
NCVMemStackAllocator cpuCounter(static_cast<int>(devProp.textureAlignment));
ncvAssertPrintReturn(gpuCounter.isInitialized(), "Error creating GPU memory counter", NCV_CUDA_ERROR);
ncvAssertPrintReturn(cpuCounter.isInitialized(), "Error creating CPU memory counter", NCV_CUDA_ERROR);
@@ -214,8 +214,8 @@ private:
ncvAssertReturnNcvStat(ncvStat);
ncvAssertCUDAReturn(cudaStreamSynchronize(0), NCV_CUDA_ERROR);
gpuAllocator = new NCVMemStackAllocator(NCVMemoryTypeDevice, gpuCounter.maxSize(), devProp.textureAlignment);
cpuAllocator = new NCVMemStackAllocator(NCVMemoryTypeHostPinned, cpuCounter.maxSize(), devProp.textureAlignment);
gpuAllocator = new NCVMemStackAllocator(NCVMemoryTypeDevice, gpuCounter.maxSize(), static_cast<int>(devProp.textureAlignment));
cpuAllocator = new NCVMemStackAllocator(NCVMemoryTypeHostPinned, cpuCounter.maxSize(), static_cast<int>(devProp.textureAlignment));
ncvAssertPrintReturn(gpuAllocator->isInitialized(), "Error creating GPU memory allocator", NCV_CUDA_ERROR);
ncvAssertPrintReturn(cpuAllocator->isInitialized(), "Error creating CPU memory allocator", NCV_CUDA_ERROR);
@@ -372,7 +372,7 @@ NCVStatus loadFromXML(const std::string &filename,
for(int s = 0; s < stagesCound; ++s) // by stages
{
HaarStage64 curStage;
curStage.setStartClassifierRootNodeOffset(haarClassifierNodes.size());
curStage.setStartClassifierRootNodeOffset(static_cast<Ncv32u>(haarClassifierNodes.size()));
curStage.setStageThreshold(oldCascade->stage_classifier[s].threshold);
@@ -452,7 +452,7 @@ NCVStatus loadFromXML(const std::string &filename,
HaarFeatureDescriptor32 tmpFeatureDesc;
ncvStat = tmpFeatureDesc.create(haar.bNeedsTiltedII, bIsLeftNodeLeaf, bIsRightNodeLeaf,
featureId, haarFeatures.size() - featureId);
featureId, static_cast<Ncv32u>(haarFeatures.size()) - featureId);
ncvAssertReturn(NCV_SUCCESS == ncvStat, ncvStat);
curNode.setFeatureDesc(tmpFeatureDesc);
@@ -478,13 +478,13 @@ NCVStatus loadFromXML(const std::string &filename,
}
//fill in cascade stats
haar.NumStages = haarStages.size();
haar.NumClassifierRootNodes = haarClassifierNodes.size();
haar.NumClassifierTotalNodes = haar.NumClassifierRootNodes + h_TmpClassifierNotRootNodes.size();
haar.NumFeatures = haarFeatures.size();
haar.NumStages = static_cast<Ncv32u>(haarStages.size());
haar.NumClassifierRootNodes = static_cast<Ncv32u>(haarClassifierNodes.size());
haar.NumClassifierTotalNodes = static_cast<Ncv32u>(haar.NumClassifierRootNodes + h_TmpClassifierNotRootNodes.size());
haar.NumFeatures = static_cast<Ncv32u>(haarFeatures.size());
//merge root and leaf nodes in one classifiers array
Ncv32u offsetRoot = haarClassifierNodes.size();
Ncv32u offsetRoot = static_cast<Ncv32u>(haarClassifierNodes.size());
for (Ncv32u i=0; i<haarClassifierNodes.size(); i++)
{
HaarFeatureDescriptor32 featureDesc = haarClassifierNodes[i].getFeatureDesc();