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refactor CUDA CascadeClassifier

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This commit is contained in:
Vladislav Vinogradov 2015-01-14 19:48:58 +03:00
parent 8257dc3c1e
commit 734212a402
5 changed files with 519 additions and 435 deletions
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97
modules/cudaobjdetect/include/opencv2/cudaobjdetect.hpp
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@ -75,7 +75,7 @@ namespace cv { namespace cuda {
- (Python) An example applying the HOG descriptor for people detection can be found at
opencv_source_code/samples/python2/peopledetect.py
*/
class CV_EXPORTS HOG : public cv::Algorithm
class CV_EXPORTS HOG : public Algorithm
{
public:
enum
@ -204,87 +204,84 @@ public:
- A Nvidea API specific cascade classifier example can be found at
opencv_source_code/samples/gpu/cascadeclassifier_nvidia_api.cpp
*/
class CV_EXPORTS CascadeClassifier_CUDA
class CV_EXPORTS CascadeClassifier : public Algorithm
{
public:
CascadeClassifier_CUDA();
/** @brief Loads the classifier from a file. Cascade type is detected automatically by constructor parameter.
@param filename Name of the file from which the classifier is loaded. Only the old haar classifier
(trained by the haar training application) and NVIDIA's nvbin are supported for HAAR and only new
type of OpenCV XML cascade supported for LBP.
*/
CascadeClassifier_CUDA(const String& filename);
~CascadeClassifier_CUDA();
/** @brief Checks whether the classifier is loaded or not.
*/
bool empty() const;
/** @brief Loads the classifier from a file. The previous content is destroyed.
@param filename Name of the file from which the classifier is loaded. Only the old haar classifier
(trained by the haar training application) and NVIDIA's nvbin are supported for HAAR and only new
type of OpenCV XML cascade supported for LBP.
static Ptr<CascadeClassifier> create(const String& filename);
/** @overload
*/
bool load(const String& filename);
/** @brief Destroys the loaded classifier.
*/
void release();
static Ptr<CascadeClassifier> create(const FileStorage& file);
//! Maximum possible object size. Objects larger than that are ignored. Used for
//! second signature and supported only for LBP cascades.
virtual void setMaxObjectSize(Size maxObjectSize) = 0;
virtual Size getMaxObjectSize() const = 0;
//! Minimum possible object size. Objects smaller than that are ignored.
virtual void setMinObjectSize(Size minSize) = 0;
virtual Size getMinObjectSize() const = 0;
//! Parameter specifying how much the image size is reduced at each image scale.
virtual void setScaleFactor(double scaleFactor) = 0;
virtual double getScaleFactor() const = 0;
//! Parameter specifying how many neighbors each candidate rectangle should have
//! to retain it.
virtual void setMinNeighbors(int minNeighbors) = 0;
virtual int getMinNeighbors() const = 0;
virtual void setFindLargestObject(bool findLargestObject) = 0;
virtual bool getFindLargestObject() = 0;
virtual void setMaxNumObjects(int maxNumObjects) = 0;
virtual int getMaxNumObjects() const = 0;
virtual Size getClassifierSize() const = 0;
/** @overload */
int detectMultiScale(const GpuMat& image, GpuMat& objectsBuf, double scaleFactor = 1.2, int minNeighbors = 4, Size minSize = Size());
/** @brief Detects objects of different sizes in the input image.
@param image Matrix of type CV_8U containing an image where objects should be detected.
@param objectsBuf Buffer to store detected objects (rectangles). If it is empty, it is allocated
with the default size. If not empty, the function searches not more than N objects, where
N = sizeof(objectsBufer's data)/sizeof(cv::Rect).
@param maxObjectSize Maximum possible object size. Objects larger than that are ignored. Used for
second signature and supported only for LBP cascades.
@param scaleFactor Parameter specifying how much the image size is reduced at each image scale.
@param minNeighbors Parameter specifying how many neighbors each candidate rectangle should have
to retain it.
@param minSize Minimum possible object size. Objects smaller than that are ignored.
@param objects Buffer to store detected objects (rectangles).
The detected objects are returned as a list of rectangles.
To get final array of detected objects use CascadeClassifier::convert method.
The function returns the number of detected objects, so you can retrieve them as in the following
example:
@code
cuda::CascadeClassifier_CUDA cascade_gpu(...);
Ptr<cuda::CascadeClassifier> cascade_gpu = cuda::CascadeClassifier::create(...);
Mat image_cpu = imread(...)
GpuMat image_gpu(image_cpu);
GpuMat objbuf;
int detections_number = cascade_gpu.detectMultiScale( image_gpu,
objbuf, 1.2, minNeighbors);
cascade_gpu->detectMultiScale(image_gpu, objbuf);
Mat obj_host;
// download only detected number of rectangles
objbuf.colRange(0, detections_number).download(obj_host);
std::vector<Rect> faces;
cascade_gpu->convert(objbuf, faces);
Rect* faces = obj_host.ptr<Rect>();
for(int i = 0; i < detections_num; ++i)
cv::rectangle(image_cpu, faces[i], Scalar(255));
imshow("Faces", image_cpu);
@endcode
@sa CascadeClassifier::detectMultiScale
*/
int detectMultiScale(const GpuMat& image, GpuMat& objectsBuf, Size maxObjectSize, Size minSize = Size(), double scaleFactor = 1.1, int minNeighbors = 4);
virtual void detectMultiScale(InputArray image,
OutputArray objects,
Stream& stream = Stream::Null()) = 0;
bool findLargestObject;
bool visualizeInPlace;
/** @brief Converts objects array from internal representation to standard vector.
Size getClassifierSize() const;
private:
struct CascadeClassifierImpl;
CascadeClassifierImpl* impl;
struct HaarCascade;
struct LbpCascade;
friend class CascadeClassifier_CUDA_LBP;
@param gpu_objects Objects array in internal representation.
@param objects Resulting array.
*/
virtual void convert(OutputArray gpu_objects,
std::vector<Rect>& objects) = 0;
};
//! @}

26
modules/cudaobjdetect/perf/perf_objdetect.cpp
View File

@ -107,18 +107,17 @@ PERF_TEST_P(ImageAndCascade, ObjDetect_HaarClassifier,
if (PERF_RUN_CUDA())
{
cv::cuda::CascadeClassifier_CUDA d_cascade;
ASSERT_TRUE(d_cascade.load(perf::TestBase::getDataPath(GetParam().second)));
cv::Ptr<cv::cuda::CascadeClassifier> d_cascade =
cv::cuda::CascadeClassifier::create(perf::TestBase::getDataPath(GetParam().second));
const cv::cuda::GpuMat d_img(img);
cv::cuda::GpuMat objects_buffer;
int detections_num = 0;
TEST_CYCLE() detections_num = d_cascade.detectMultiScale(d_img, objects_buffer);
TEST_CYCLE() d_cascade->detectMultiScale(d_img, objects_buffer);
std::vector<cv::Rect> gpu_rects;
d_cascade->convert(objects_buffer, gpu_rects);
std::vector<cv::Rect> gpu_rects(detections_num);
cv::Mat gpu_rects_mat(1, detections_num, cv::DataType<cv::Rect>::type, &gpu_rects[0]);
objects_buffer.colRange(0, detections_num).download(gpu_rects_mat);
cv::groupRectangles(gpu_rects, 3, 0.2);
SANITY_CHECK(gpu_rects);
}
@ -146,18 +145,17 @@ PERF_TEST_P(ImageAndCascade, ObjDetect_LBPClassifier,
if (PERF_RUN_CUDA())
{
cv::cuda::CascadeClassifier_CUDA d_cascade;
ASSERT_TRUE(d_cascade.load(perf::TestBase::getDataPath(GetParam().second)));
cv::Ptr<cv::cuda::CascadeClassifier> d_cascade =
cv::cuda::CascadeClassifier::create(perf::TestBase::getDataPath(GetParam().second));
const cv::cuda::GpuMat d_img(img);
cv::cuda::GpuMat objects_buffer;
int detections_num = 0;
TEST_CYCLE() detections_num = d_cascade.detectMultiScale(d_img, objects_buffer);
TEST_CYCLE() d_cascade->detectMultiScale(d_img, objects_buffer);
std::vector<cv::Rect> gpu_rects;
d_cascade->convert(objects_buffer, gpu_rects);
std::vector<cv::Rect> gpu_rects(detections_num);
cv::Mat gpu_rects_mat(1, detections_num, cv::DataType<cv::Rect>::type, &gpu_rects[0]);
objects_buffer.colRange(0, detections_num).download(gpu_rects_mat);
cv::groupRectangles(gpu_rects, 3, 0.2);
SANITY_CHECK(gpu_rects);
}

738
modules/cudaobjdetect/src/cascadeclassifier.cpp
View File

@ -48,160 +48,185 @@ using namespace cv::cuda;
#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER)
cv::cuda::CascadeClassifier_CUDA::CascadeClassifier_CUDA() { throw_no_cuda(); }
cv::cuda::CascadeClassifier_CUDA::CascadeClassifier_CUDA(const String&) { throw_no_cuda(); }
cv::cuda::CascadeClassifier_CUDA::~CascadeClassifier_CUDA() { throw_no_cuda(); }
bool cv::cuda::CascadeClassifier_CUDA::empty() const { throw_no_cuda(); return true; }
bool cv::cuda::CascadeClassifier_CUDA::load(const String&) { throw_no_cuda(); return true; }
Size cv::cuda::CascadeClassifier_CUDA::getClassifierSize() const { throw_no_cuda(); return Size();}
void cv::cuda::CascadeClassifier_CUDA::release() { throw_no_cuda(); }
int cv::cuda::CascadeClassifier_CUDA::detectMultiScale( const GpuMat&, GpuMat&, double, int, Size) {throw_no_cuda(); return -1;}
int cv::cuda::CascadeClassifier_CUDA::detectMultiScale( const GpuMat&, GpuMat&, Size, Size, double, int) {throw_no_cuda(); return -1;}
Ptr<cuda::CascadeClassifier> cv::cuda::CascadeClassifier::create(const String&) { throw_no_cuda(); return Ptr<cuda::CascadeClassifier>(); }
Ptr<cuda::CascadeClassifier> cv::cuda::CascadeClassifier::create(const FileStorage&) { throw_no_cuda(); return Ptr<cuda::CascadeClassifier>(); }
#else
struct cv::cuda::CascadeClassifier_CUDA::CascadeClassifierImpl
//
// CascadeClassifierBase
//
namespace
{
public:
CascadeClassifierImpl(){}
virtual ~CascadeClassifierImpl(){}
class CascadeClassifierBase : public cuda::CascadeClassifier
{
public:
CascadeClassifierBase();
virtual unsigned int process(const GpuMat& src, GpuMat& objects, float scaleStep, int minNeighbors,
bool findLargestObject, bool visualizeInPlace, cv::Size ncvMinSize, cv::Size maxObjectSize) = 0;
virtual void setMaxObjectSize(Size maxObjectSize) { maxObjectSize_ = maxObjectSize; }
virtual Size getMaxObjectSize() const { return maxObjectSize_; }
virtual cv::Size getClassifierCvSize() const = 0;
virtual bool read(const String& classifierAsXml) = 0;
};
virtual void setMinObjectSize(Size minSize) { minObjectSize_ = minSize; }
virtual Size getMinObjectSize() const { return minObjectSize_; }
#ifndef HAVE_OPENCV_CUDALEGACY
virtual void setScaleFactor(double scaleFactor) { scaleFactor_ = scaleFactor; }
virtual double getScaleFactor() const { return scaleFactor_; }
struct cv::cuda::CascadeClassifier_CUDA::HaarCascade : cv::cuda::CascadeClassifier_CUDA::CascadeClassifierImpl
virtual void setMinNeighbors(int minNeighbors) { minNeighbors_ = minNeighbors; }
virtual int getMinNeighbors() const { return minNeighbors_; }
virtual void setFindLargestObject(bool findLargestObject) { findLargestObject_ = findLargestObject; }
virtual bool getFindLargestObject() { return findLargestObject_; }
virtual void setMaxNumObjects(int maxNumObjects) { maxNumObjects_ = maxNumObjects; }
virtual int getMaxNumObjects() const { return maxNumObjects_; }
protected:
Size maxObjectSize_;
Size minObjectSize_;
double scaleFactor_;
int minNeighbors_;
bool findLargestObject_;
int maxNumObjects_;
};
CascadeClassifierBase::CascadeClassifierBase() :
maxObjectSize_(),
minObjectSize_(),
scaleFactor_(1.2),
minNeighbors_(4),
findLargestObject_(false),
maxNumObjects_(100)
{
}
}
//
// HaarCascade
//
#ifdef HAVE_OPENCV_CUDALEGACY
namespace
{
public:
HaarCascade()
class HaarCascade_Impl : public CascadeClassifierBase
{
throw_no_cuda();
public:
explicit HaarCascade_Impl(const String& filename);
virtual Size getClassifierSize() const;
virtual void detectMultiScale(InputArray image,
OutputArray objects,
Stream& stream);
virtual void convert(OutputArray gpu_objects,
std::vector<Rect>& objects);
private:
NCVStatus load(const String& classifierFile);
NCVStatus calculateMemReqsAndAllocate(const Size& frameSize);
NCVStatus process(const GpuMat& src, GpuMat& objects, cv::Size ncvMinSize, /*out*/ unsigned int& numDetections);
Size lastAllocatedFrameSize;
Ptr<NCVMemStackAllocator> gpuAllocator;
Ptr<NCVMemStackAllocator> cpuAllocator;
cudaDeviceProp devProp;
NCVStatus ncvStat;
Ptr<NCVMemNativeAllocator> gpuCascadeAllocator;
Ptr<NCVMemNativeAllocator> cpuCascadeAllocator;
Ptr<NCVVectorAlloc<HaarStage64> > h_haarStages;
Ptr<NCVVectorAlloc<HaarClassifierNode128> > h_haarNodes;
Ptr<NCVVectorAlloc<HaarFeature64> > h_haarFeatures;
HaarClassifierCascadeDescriptor haar;
Ptr<NCVVectorAlloc<HaarStage64> > d_haarStages;
Ptr<NCVVectorAlloc<HaarClassifierNode128> > d_haarNodes;
Ptr<NCVVectorAlloc<HaarFeature64> > d_haarFeatures;
};
static void NCVDebugOutputHandler(const String &msg)
{
CV_Error(Error::GpuApiCallError, msg.c_str());
}
unsigned int process(const GpuMat&, GpuMat&, float, int, bool, bool, cv::Size, cv::Size)
{
throw_no_cuda();
return 0;
}
cv::Size getClassifierCvSize() const
{
throw_no_cuda();
return cv::Size();
}
bool read(const String&)
{
throw_no_cuda();
return false;
}
};
#else
struct cv::cuda::CascadeClassifier_CUDA::HaarCascade : cv::cuda::CascadeClassifier_CUDA::CascadeClassifierImpl
{
public:
HaarCascade() : lastAllocatedFrameSize(-1, -1)
HaarCascade_Impl::HaarCascade_Impl(const String& filename) :
lastAllocatedFrameSize(-1, -1)
{
ncvSetDebugOutputHandler(NCVDebugOutputHandler);
}
bool read(const String& filename)
{
ncvSafeCall( load(filename) );
return true;
}
NCVStatus process(const GpuMat& src, GpuMat& objects, float scaleStep, int minNeighbors,
bool findLargestObject, bool visualizeInPlace, cv::Size ncvMinSize,
/*out*/unsigned int& numDetections)
Size HaarCascade_Impl::getClassifierSize() const
{
calculateMemReqsAndAllocate(src.size());
NCVMemPtr src_beg;
src_beg.ptr = (void*)src.ptr<Ncv8u>();
src_beg.memtype = NCVMemoryTypeDevice;
NCVMemSegment src_seg;
src_seg.begin = src_beg;
src_seg.size = src.step * src.rows;
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);
NCVMemPtr objects_beg;
objects_beg.ptr = (void*)objects.ptr<NcvRect32u>();
objects_beg.memtype = NCVMemoryTypeDevice;
NCVMemSegment objects_seg;
objects_seg.begin = objects_beg;
objects_seg.size = objects.step * objects.rows;
NCVVectorReuse<NcvRect32u> d_rects(objects_seg, objects.cols);
ncvAssertReturn(d_rects.isMemReused(), NCV_ALLOCATOR_BAD_REUSE);
NcvSize32u roi;
roi.width = d_src.width();
roi.height = d_src.height();
NcvSize32u winMinSize(ncvMinSize.width, ncvMinSize.height);
Ncv32u flags = 0;
flags |= findLargestObject? NCVPipeObjDet_FindLargestObject : 0;
flags |= visualizeInPlace ? NCVPipeObjDet_VisualizeInPlace : 0;
ncvStat = ncvDetectObjectsMultiScale_device(
d_src, roi, d_rects, numDetections, haar, *h_haarStages,
*d_haarStages, *d_haarNodes, *d_haarFeatures,
winMinSize,
minNeighbors,
scaleStep, 1,
flags,
*gpuAllocator, *cpuAllocator, devProp, 0);
ncvAssertReturnNcvStat(ncvStat);
ncvAssertCUDAReturn(cudaStreamSynchronize(0), NCV_CUDA_ERROR);
return NCV_SUCCESS;
return Size(haar.ClassifierSize.width, haar.ClassifierSize.height);
}
unsigned int process(const GpuMat& image, GpuMat& objectsBuf, float scaleFactor, int minNeighbors,
bool findLargestObject, bool visualizeInPlace, cv::Size minSize, cv::Size /*maxObjectSize*/)
void HaarCascade_Impl::detectMultiScale(InputArray _image,
OutputArray _objects,
Stream& stream)
{
CV_Assert( scaleFactor > 1 && image.depth() == CV_8U);
const GpuMat image = _image.getGpuMat();
const int defaultObjSearchNum = 100;
if (objectsBuf.empty())
CV_Assert( image.depth() == CV_8U);
CV_Assert( scaleFactor_ > 1 );
CV_Assert( !stream );
Size ncvMinSize = getClassifierSize();
if (ncvMinSize.width < minObjectSize_.width && ncvMinSize.height < minObjectSize_.height)
{
objectsBuf.create(1, defaultObjSearchNum, DataType<Rect>::type);
ncvMinSize.width = minObjectSize_.width;
ncvMinSize.height = minObjectSize_.height;
}
cv::Size ncvMinSize = this->getClassifierCvSize();
if (ncvMinSize.width < minSize.width && ncvMinSize.height < minSize.height)
{
ncvMinSize.width = minSize.width;
ncvMinSize.height = minSize.height;
}
BufferPool pool(stream);
GpuMat objectsBuf = pool.getBuffer(1, maxNumObjects_, DataType<Rect>::type);
unsigned int numDetections;
ncvSafeCall(this->process(image, objectsBuf, (float)scaleFactor, minNeighbors, findLargestObject, visualizeInPlace, ncvMinSize, numDetections));
ncvSafeCall( process(image, objectsBuf, ncvMinSize, numDetections) );
return numDetections;
if (numDetections > 0)
{
objectsBuf.colRange(0, numDetections).copyTo(_objects);
}
else
{
_objects.release();
}
}
cv::Size getClassifierCvSize() const { return cv::Size(haar.ClassifierSize.width, haar.ClassifierSize.height); }
void HaarCascade_Impl::convert(OutputArray _gpu_objects, std::vector<Rect>& objects)
{
if (_gpu_objects.empty())
{
objects.clear();
return;
}
private:
static void NCVDebugOutputHandler(const String &msg) { CV_Error(cv::Error::GpuApiCallError, msg.c_str()); }
Mat gpu_objects;
if (_gpu_objects.kind() == _InputArray::CUDA_GPU_MAT)
{
_gpu_objects.getGpuMat().download(gpu_objects);
}
else
{
gpu_objects = _gpu_objects.getMat();
}
NCVStatus load(const String& classifierFile)
CV_Assert( gpu_objects.rows == 1 );
CV_Assert( gpu_objects.type() == DataType<Rect>::type );
Rect* ptr = gpu_objects.ptr<Rect>();
objects.assign(ptr, ptr + gpu_objects.cols);
}
NCVStatus HaarCascade_Impl::load(const String& classifierFile)
{
int devId = cv::cuda::getDevice();
ncvAssertCUDAReturn(cudaGetDeviceProperties(&devProp, devId), NCV_CUDA_ERROR);
@ -246,7 +271,7 @@ private:
return NCV_SUCCESS;
}
NCVStatus calculateMemReqsAndAllocate(const Size& frameSize)
NCVStatus HaarCascade_Impl::calculateMemReqsAndAllocate(const Size& frameSize)
{
if (lastAllocatedFrameSize == frameSize)
{
@ -289,88 +314,62 @@ private:
return NCV_SUCCESS;
}
cudaDeviceProp devProp;
NCVStatus ncvStat;
NCVStatus HaarCascade_Impl::process(const GpuMat& src, GpuMat& objects, cv::Size ncvMinSize, /*out*/ unsigned int& numDetections)
{
calculateMemReqsAndAllocate(src.size());
Ptr<NCVMemNativeAllocator> gpuCascadeAllocator;
Ptr<NCVMemNativeAllocator> cpuCascadeAllocator;
NCVMemPtr src_beg;
src_beg.ptr = (void*)src.ptr<Ncv8u>();
src_beg.memtype = NCVMemoryTypeDevice;
Ptr<NCVVectorAlloc<HaarStage64> > h_haarStages;
Ptr<NCVVectorAlloc<HaarClassifierNode128> > h_haarNodes;
Ptr<NCVVectorAlloc<HaarFeature64> > h_haarFeatures;
NCVMemSegment src_seg;
src_seg.begin = src_beg;
src_seg.size = src.step * src.rows;
HaarClassifierCascadeDescriptor haar;
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);
Ptr<NCVVectorAlloc<HaarStage64> > d_haarStages;
Ptr<NCVVectorAlloc<HaarClassifierNode128> > d_haarNodes;
Ptr<NCVVectorAlloc<HaarFeature64> > d_haarFeatures;
CV_Assert(objects.rows == 1);
Size lastAllocatedFrameSize;
NCVMemPtr objects_beg;
objects_beg.ptr = (void*)objects.ptr<NcvRect32u>();
objects_beg.memtype = NCVMemoryTypeDevice;
Ptr<NCVMemStackAllocator> gpuAllocator;
Ptr<NCVMemStackAllocator> cpuAllocator;
NCVMemSegment objects_seg;
objects_seg.begin = objects_beg;
objects_seg.size = objects.step * objects.rows;
NCVVectorReuse<NcvRect32u> d_rects(objects_seg, objects.cols);
ncvAssertReturn(d_rects.isMemReused(), NCV_ALLOCATOR_BAD_REUSE);
virtual ~HaarCascade(){}
};
NcvSize32u roi;
roi.width = d_src.width();
roi.height = d_src.height();
NcvSize32u winMinSize(ncvMinSize.width, ncvMinSize.height);
Ncv32u flags = 0;
flags |= findLargestObject_ ? NCVPipeObjDet_FindLargestObject : 0;
ncvStat = ncvDetectObjectsMultiScale_device(
d_src, roi, d_rects, numDetections, haar, *h_haarStages,
*d_haarStages, *d_haarNodes, *d_haarFeatures,
winMinSize,
minNeighbors_,
scaleFactor_, 1,
flags,
*gpuAllocator, *cpuAllocator, devProp, 0);
ncvAssertReturnNcvStat(ncvStat);
ncvAssertCUDAReturn(cudaStreamSynchronize(0), NCV_CUDA_ERROR);
return NCV_SUCCESS;
}
}
#endif
cv::Size operator -(const cv::Size& a, const cv::Size& b)
{
return cv::Size(a.width - b.width, a.height - b.height);
}
cv::Size operator +(const cv::Size& a, const int& i)
{
return cv::Size(a.width + i, a.height + i);
}
cv::Size operator *(const cv::Size& a, const float& f)
{
return cv::Size(cvRound(a.width * f), cvRound(a.height * f));
}
cv::Size operator /(const cv::Size& a, const float& f)
{
return cv::Size(cvRound(a.width / f), cvRound(a.height / f));
}
bool operator <=(const cv::Size& a, const cv::Size& b)
{
return a.width <= b.width && a.height <= b.width;
}
struct PyrLavel
{
PyrLavel(int _order, float _scale, cv::Size frame, cv::Size window, cv::Size minObjectSize)
{
do
{
order = _order;
scale = pow(_scale, order);
sFrame = frame / scale;
workArea = sFrame - window + 1;
sWindow = window * scale;
_order++;
} while (sWindow <= minObjectSize);
}
bool isFeasible(cv::Size maxObj)
{
return workArea.width > 0 && workArea.height > 0 && sWindow <= maxObj;
}
PyrLavel next(float factor, cv::Size frame, cv::Size window, cv::Size minObjectSize)
{
return PyrLavel(order + 1, factor, frame, window, minObjectSize);
}
int order;
float scale;
cv::Size sFrame;
cv::Size workArea;
cv::Size sWindow;
};
//
// LbpCascade
//
namespace cv { namespace cuda { namespace device
{
@ -394,42 +393,154 @@ namespace cv { namespace cuda { namespace device
unsigned int* classified,
PtrStepSzi integral);
void connectedConmonents(PtrStepSz<int4> candidates, int ncandidates, PtrStepSz<int4> objects,int groupThreshold, float grouping_eps, unsigned int* nclasses);
void connectedConmonents(PtrStepSz<int4> candidates,
int ncandidates,
PtrStepSz<int4> objects,
int groupThreshold,
float grouping_eps,
unsigned int* nclasses);
}
}}}
struct cv::cuda::CascadeClassifier_CUDA::LbpCascade : cv::cuda::CascadeClassifier_CUDA::CascadeClassifierImpl
namespace
{
public:
struct Stage
cv::Size operator -(const cv::Size& a, const cv::Size& b)
{
int first;
int ntrees;
float threshold;
return cv::Size(a.width - b.width, a.height - b.height);
}
cv::Size operator +(const cv::Size& a, const int& i)
{
return cv::Size(a.width + i, a.height + i);
}
cv::Size operator *(const cv::Size& a, const float& f)
{
return cv::Size(cvRound(a.width * f), cvRound(a.height * f));
}
cv::Size operator /(const cv::Size& a, const float& f)
{
return cv::Size(cvRound(a.width / f), cvRound(a.height / f));
}
bool operator <=(const cv::Size& a, const cv::Size& b)
{
return a.width <= b.width && a.height <= b.width;
}
struct PyrLavel
{
PyrLavel(int _order, float _scale, cv::Size frame, cv::Size window, cv::Size minObjectSize)
{
do
{
order = _order;
scale = pow(_scale, order);
sFrame = frame / scale;
workArea = sFrame - window + 1;
sWindow = window * scale;
_order++;
} while (sWindow <= minObjectSize);
}
bool isFeasible(cv::Size maxObj)
{
return workArea.width > 0 && workArea.height > 0 && sWindow <= maxObj;
}
PyrLavel next(float factor, cv::Size frame, cv::Size window, cv::Size minObjectSize)
{
return PyrLavel(order + 1, factor, frame, window, minObjectSize);
}
int order;
float scale;
cv::Size sFrame;
cv::Size workArea;
cv::Size sWindow;
};
LbpCascade(){}
virtual ~LbpCascade(){}
virtual unsigned int process(const GpuMat& image, GpuMat& objects, float scaleFactor, int groupThreshold, bool /*findLargestObject*/,
bool /*visualizeInPlace*/, cv::Size minObjectSize, cv::Size maxObjectSize)
class LbpCascade_Impl : public CascadeClassifierBase
{
CV_Assert(scaleFactor > 1 && image.depth() == CV_8U);
public:
explicit LbpCascade_Impl(const FileStorage& file);
virtual Size getClassifierSize() const { return NxM; }
virtual void detectMultiScale(InputArray image,
OutputArray objects,
Stream& stream);
virtual void convert(OutputArray gpu_objects,
std::vector<Rect>& objects);
private:
bool load(const FileNode &root);
void allocateBuffers(cv::Size frame);
private:
struct Stage
{
int first;
int ntrees;
float threshold;
};
enum stage { BOOST = 0 };
enum feature { LBP = 1, HAAR = 2 };
static const stage stageType = BOOST;
static const feature featureType = LBP;
cv::Size NxM;
bool isStumps;
int ncategories;
int subsetSize;
int nodeStep;
// gpu representation of classifier
GpuMat stage_mat;
GpuMat trees_mat;
GpuMat nodes_mat;
GpuMat leaves_mat;
GpuMat subsets_mat;
GpuMat features_mat;
GpuMat integral;
GpuMat integralBuffer;
GpuMat resuzeBuffer;
GpuMat candidates;
static const int integralFactor = 4;
};
LbpCascade_Impl::LbpCascade_Impl(const FileStorage& file)
{
load(file.getFirstTopLevelNode());
}
void LbpCascade_Impl::detectMultiScale(InputArray _image,
OutputArray _objects,
Stream& stream)
{
const GpuMat image = _image.getGpuMat();
CV_Assert( image.depth() == CV_8U);
CV_Assert( scaleFactor_ > 1 );
CV_Assert( !stream );
// const int defaultObjSearchNum = 100;
const float grouping_eps = 0.2f;
if( !objects.empty() && objects.depth() == CV_32S)
objects.reshape(4, 1);
else
objects.create(1 , image.cols >> 4, CV_32SC4);
BufferPool pool(stream);
GpuMat objects = pool.getBuffer(1, maxNumObjects_, DataType<Rect>::type);
// used for debug
// candidates.setTo(cv::Scalar::all(0));
// objects.setTo(cv::Scalar::all(0));
if (maxObjectSize == cv::Size())
maxObjectSize = image.size();
if (maxObjectSize_ == cv::Size())
maxObjectSize_ = image.size();
allocateBuffers(image.size());
@ -437,9 +548,9 @@ public:
GpuMat dclassified(1, 1, CV_32S);
cudaSafeCall( cudaMemcpy(dclassified.ptr(), &classified, sizeof(int), cudaMemcpyHostToDevice) );
PyrLavel level(0, scaleFactor, image.size(), NxM, minObjectSize);
PyrLavel level(0, scaleFactor_, image.size(), NxM, minObjectSize_);
while (level.isFeasible(maxObjectSize))
while (level.isFeasible(maxObjectSize_))
{
int acc = level.sFrame.width + 1;
float iniScale = level.scale;
@ -449,7 +560,7 @@ public:
int total = 0, prev = 0;
while (acc <= integralFactor * (image.cols + 1) && level.isFeasible(maxObjectSize))
while (acc <= integralFactor * (image.cols + 1) && level.isFeasible(maxObjectSize_))
{
// create sutable matrix headers
GpuMat src = resuzeBuffer(cv::Rect(0, 0, level.sFrame.width, level.sFrame.height));
@ -465,7 +576,7 @@ public:
total += totalWidth * (level.workArea.height / step);
// go to next pyramide level
level = level.next(scaleFactor, image.size(), NxM, minObjectSize);
level = level.next(scaleFactor_, image.size(), NxM, minObjectSize_);
area = level.workArea;
step = (1 + (level.scale <= 2.f));
@ -473,60 +584,55 @@ public:
acc += level.sFrame.width + 1;
}
device::lbp::classifyPyramid(image.cols, image.rows, NxM.width - 1, NxM.height - 1, iniScale, scaleFactor, total, stage_mat, stage_mat.cols / sizeof(Stage), nodes_mat,
device::lbp::classifyPyramid(image.cols, image.rows, NxM.width - 1, NxM.height - 1, iniScale, scaleFactor_, total, stage_mat, stage_mat.cols / sizeof(Stage), nodes_mat,
leaves_mat, subsets_mat, features_mat, subsetSize, candidates, dclassified.ptr<unsigned int>(), integral);
}
if (groupThreshold <= 0 || objects.empty())
return 0;
if (minNeighbors_ <= 0 || objects.empty())
return;
cudaSafeCall( cudaMemcpy(&classified, dclassified.ptr(), sizeof(int), cudaMemcpyDeviceToHost) );
device::lbp::connectedConmonents(candidates, classified, objects, groupThreshold, grouping_eps, dclassified.ptr<unsigned int>());
device::lbp::connectedConmonents(candidates, classified, objects, minNeighbors_, grouping_eps, dclassified.ptr<unsigned int>());
cudaSafeCall( cudaMemcpy(&classified, dclassified.ptr(), sizeof(int), cudaMemcpyDeviceToHost) );
cudaSafeCall( cudaDeviceSynchronize() );
return classified;
}
virtual cv::Size getClassifierCvSize() const { return NxM; }
bool read(const String& classifierAsXml)
{
FileStorage fs(classifierAsXml, FileStorage::READ);
return fs.isOpened() ? read(fs.getFirstTopLevelNode()) : false;
}
private:
void allocateBuffers(cv::Size frame)
{
if (frame == cv::Size())
return;
if (resuzeBuffer.empty() || frame.width > resuzeBuffer.cols || frame.height > resuzeBuffer.rows)
if (classified > 0)
{
resuzeBuffer.create(frame, CV_8UC1);
integral.create(frame.height + 1, integralFactor * (frame.width + 1), CV_32SC1);
#ifdef HAVE_OPENCV_CUDALEGACY
NcvSize32u roiSize;
roiSize.width = frame.width;
roiSize.height = frame.height;
cudaDeviceProp prop;
cudaSafeCall( cudaGetDeviceProperties(&prop, cv::cuda::getDevice()) );
Ncv32u bufSize;
ncvSafeCall( nppiStIntegralGetSize_8u32u(roiSize, &bufSize, prop) );
integralBuffer.create(1, bufSize, CV_8UC1);
#endif
candidates.create(1 , frame.width >> 1, CV_32SC4);
objects.colRange(0, classified).copyTo(_objects);
}
else
{
_objects.release();
}
}
bool read(const FileNode &root)
void LbpCascade_Impl::convert(OutputArray _gpu_objects, std::vector<Rect>& objects)
{
if (_gpu_objects.empty())
{
objects.clear();
return;
}
Mat gpu_objects;
if (_gpu_objects.kind() == _InputArray::CUDA_GPU_MAT)
{
_gpu_objects.getGpuMat().download(gpu_objects);
}
else
{
gpu_objects = _gpu_objects.getMat();
}
CV_Assert( gpu_objects.rows == 1 );
CV_Assert( gpu_objects.type() == DataType<Rect>::type );
Rect* ptr = gpu_objects.ptr<Rect>();
objects.assign(ptr, ptr + gpu_objects.cols);
}
bool LbpCascade_Impl::load(const FileNode &root)
{
const char *CUDA_CC_STAGE_TYPE = "stageType";
const char *CUDA_CC_FEATURE_TYPE = "featureType";
@ -667,92 +773,90 @@ private:
return true;
}
enum stage { BOOST = 0 };
enum feature { LBP = 1, HAAR = 2 };
static const stage stageType = BOOST;
static const feature featureType = LBP;
void LbpCascade_Impl::allocateBuffers(cv::Size frame)
{
if (frame == cv::Size())
return;
cv::Size NxM;
bool isStumps;
int ncategories;
int subsetSize;
int nodeStep;
if (resuzeBuffer.empty() || frame.width > resuzeBuffer.cols || frame.height > resuzeBuffer.rows)
{
resuzeBuffer.create(frame, CV_8UC1);
// gpu representation of classifier
GpuMat stage_mat;
GpuMat trees_mat;
GpuMat nodes_mat;
GpuMat leaves_mat;
GpuMat subsets_mat;
GpuMat features_mat;
integral.create(frame.height + 1, integralFactor * (frame.width + 1), CV_32SC1);
GpuMat integral;
GpuMat integralBuffer;
GpuMat resuzeBuffer;
#ifdef HAVE_OPENCV_CUDALEGACY
NcvSize32u roiSize;
roiSize.width = frame.width;
roiSize.height = frame.height;
GpuMat candidates;
static const int integralFactor = 4;
};
cudaDeviceProp prop;
cudaSafeCall( cudaGetDeviceProperties(&prop, cv::cuda::getDevice()) );
cv::cuda::CascadeClassifier_CUDA::CascadeClassifier_CUDA()
: findLargestObject(false), visualizeInPlace(false), impl(0) {}
Ncv32u bufSize;
ncvSafeCall( nppiStIntegralGetSize_8u32u(roiSize, &bufSize, prop) );
integralBuffer.create(1, bufSize, CV_8UC1);
#endif
cv::cuda::CascadeClassifier_CUDA::CascadeClassifier_CUDA(const String& filename)
: findLargestObject(false), visualizeInPlace(false), impl(0) { load(filename); }
candidates.create(1 , frame.width >> 1, CV_32SC4);
}
}
cv::cuda::CascadeClassifier_CUDA::~CascadeClassifier_CUDA() { release(); }
void cv::cuda::CascadeClassifier_CUDA::release() { if (impl) { delete impl; impl = 0; } }
bool cv::cuda::CascadeClassifier_CUDA::empty() const { return impl == 0; }
Size cv::cuda::CascadeClassifier_CUDA::getClassifierSize() const
{
return this->empty() ? Size() : impl->getClassifierCvSize();
}
int cv::cuda::CascadeClassifier_CUDA::detectMultiScale( const GpuMat& image, GpuMat& objectsBuf, double scaleFactor, int minNeighbors, Size minSize)
{
CV_Assert( !this->empty());
return impl->process(image, objectsBuf, (float)scaleFactor, minNeighbors, findLargestObject, visualizeInPlace, minSize, cv::Size());
}
//
// create
//
int cv::cuda::CascadeClassifier_CUDA::detectMultiScale(const GpuMat& image, GpuMat& objectsBuf, Size maxObjectSize, Size minSize, double scaleFactor, int minNeighbors)
Ptr<cuda::CascadeClassifier> cv::cuda::CascadeClassifier::create(const String& filename)
{
CV_Assert( !this->empty());
return impl->process(image, objectsBuf, (float)scaleFactor, minNeighbors, findLargestObject, visualizeInPlace, minSize, maxObjectSize);
}
bool cv::cuda::CascadeClassifier_CUDA::load(const String& filename)
{
release();
String fext = filename.substr(filename.find_last_of(".") + 1);
fext = fext.toLowerCase();
if (fext == "nvbin")
{
impl = new HaarCascade();
return impl->read(filename);
#ifndef HAVE_OPENCV_CUDALEGACY
CV_Error(Error::StsUnsupportedFormat, "OpenCV CUDA objdetect was built without HaarCascade");
return Ptr<cuda::CascadeClassifier>();
#else
return makePtr<HaarCascade_Impl>(filename);
#endif
}
FileStorage fs(filename, FileStorage::READ);
if (!fs.isOpened())
{
impl = new HaarCascade();
return impl->read(filename);
#ifndef HAVE_OPENCV_CUDALEGACY
CV_Error(Error::StsUnsupportedFormat, "OpenCV CUDA objdetect was built without HaarCascade");
return Ptr<cuda::CascadeClassifier>();
#else
return makePtr<HaarCascade_Impl>(filename);
#endif
}
const char *CUDA_CC_LBP = "LBP";
String featureTypeStr = (String)fs.getFirstTopLevelNode()["featureType"];
if (featureTypeStr == CUDA_CC_LBP)
impl = new LbpCascade();
{
return makePtr<LbpCascade_Impl>(fs);
}
else
impl = new HaarCascade();
{
#ifndef HAVE_OPENCV_CUDALEGACY
CV_Error(Error::StsUnsupportedFormat, "OpenCV CUDA objdetect was built without HaarCascade");
return Ptr<cuda::CascadeClassifier>();
#else
return makePtr<HaarCascade_Impl>(filename);
#endif
}
impl->read(filename);
return !this->empty();
CV_Error(Error::StsUnsupportedFormat, "Unsupported format for CUDA CascadeClassifier");
return Ptr<cuda::CascadeClassifier>();
}
Ptr<cuda::CascadeClassifier> cv::cuda::CascadeClassifier::create(const FileStorage& file)
{
return makePtr<LbpCascade_Impl>(file);
}
#endif

33
modules/cudaobjdetect/test/test_objdetect.cpp
View File

@ -287,9 +287,15 @@ PARAM_TEST_CASE(LBP_Read_classifier, cv::cuda::DeviceInfo, int)
CUDA_TEST_P(LBP_Read_classifier, Accuracy)
{
cv::cuda::CascadeClassifier_CUDA classifier;
std::string classifierXmlPath = std::string(cvtest::TS::ptr()->get_data_path()) + "lbpcascade/lbpcascade_frontalface.xml";
ASSERT_TRUE(classifier.load(classifierXmlPath));
cv::Ptr<cv::cuda::CascadeClassifier> d_cascade;
ASSERT_NO_THROW(
d_cascade = cv::cuda::CascadeClassifier::create(classifierXmlPath);
);
ASSERT_FALSE(d_cascade.empty());
}
INSTANTIATE_TEST_CASE_P(CUDA_ObjDetect, LBP_Read_classifier,
@ -329,29 +335,28 @@ CUDA_TEST_P(LBP_classify, Accuracy)
for (; it != rects.end(); ++it)
cv::rectangle(markedImage, *it, cv::Scalar(255, 0, 0));
cv::cuda::CascadeClassifier_CUDA gpuClassifier;
ASSERT_TRUE(gpuClassifier.load(classifierXmlPath));
cv::Ptr<cv::cuda::CascadeClassifier> gpuClassifier =
cv::cuda::CascadeClassifier::create(classifierXmlPath);
cv::cuda::GpuMat gpu_rects;
cv::cuda::GpuMat tested(grey);
int count = gpuClassifier.detectMultiScale(tested, gpu_rects);
cv::cuda::GpuMat gpu_rects_buf;
gpuClassifier->detectMultiScale(tested, gpu_rects_buf);
std::vector<cv::Rect> gpu_rects;
gpuClassifier->convert(gpu_rects_buf, gpu_rects);
#if defined (LOG_CASCADE_STATISTIC)
cv::Mat downloaded(gpu_rects);
const cv::Rect* faces = downloaded.ptr<cv::Rect>();
for (int i = 0; i < count; i++)
for (size_t i = 0; i < gpu_rects.size(); i++)
{
cv::Rect r = faces[i];
cv::Rect r = gpu_rects[i];
std::cout << r.x << " " << r.y << " " << r.width << " " << r.height << std::endl;
cv::rectangle(markedImage, r , CV_RGB(255, 0, 0));
}
#endif
#if defined (LOG_CASCADE_STATISTIC)
cv::imshow("Res", markedImage); cv::waitKey();
cv::imshow("Res", markedImage);
cv::waitKey();
#endif
(void)count;
}
INSTANTIATE_TEST_CASE_P(CUDA_ObjDetect, LBP_classify,

60
samples/gpu/cascadeclassifier.cpp
View File

@ -173,13 +173,9 @@ int main(int argc, const char *argv[])
}
}
CascadeClassifier_CUDA cascade_gpu;
if (!cascade_gpu.load(cascadeName))
{
return cerr << "ERROR: Could not load cascade classifier \"" << cascadeName << "\"" << endl, help(), -1;
}
Ptr<cuda::CascadeClassifier> cascade_gpu = cuda::CascadeClassifier::create(cascadeName);
CascadeClassifier cascade_cpu;
cv::CascadeClassifier cascade_cpu;
if (!cascade_cpu.load(cascadeName))
{
return cerr << "ERROR: Could not load cascade classifier \"" << cascadeName << "\"" << endl, help(), -1;
@ -206,8 +202,8 @@ int main(int argc, const char *argv[])
namedWindow("result", 1);
Mat frame, frame_cpu, gray_cpu, resized_cpu, faces_downloaded, frameDisp;
vector<Rect> facesBuf_cpu;
Mat frame, frame_cpu, gray_cpu, resized_cpu, frameDisp;
vector<Rect> faces;
GpuMat frame_gpu, gray_gpu, resized_gpu, facesBuf_gpu;
@ -218,7 +214,6 @@ int main(int argc, const char *argv[])
bool filterRects = true;
bool helpScreen = false;
int detections_num;
for (;;)
{
if (isInputCamera || isInputVideo)
@ -241,40 +236,26 @@ int main(int argc, const char *argv[])
if (useGPU)
{
//cascade_gpu.visualizeInPlace = true;
cascade_gpu.findLargestObject = findLargestObject;
cascade_gpu->setFindLargestObject(findLargestObject);
cascade_gpu->setScaleFactor(1.2);
cascade_gpu->setMinNeighbors((filterRects || findLargestObject) ? 4 : 0);
detections_num = cascade_gpu.detectMultiScale(resized_gpu, facesBuf_gpu, 1.2,
(filterRects || findLargestObject) ? 4 : 0);
facesBuf_gpu.colRange(0, detections_num).download(faces_downloaded);
cascade_gpu->detectMultiScale(resized_gpu, facesBuf_gpu);
cascade_gpu->convert(facesBuf_gpu, faces);
}
else
{
Size minSize = cascade_gpu.getClassifierSize();
cascade_cpu.detectMultiScale(resized_cpu, facesBuf_cpu, 1.2,
Size minSize = cascade_gpu->getClassifierSize();
cascade_cpu.detectMultiScale(resized_cpu, faces, 1.2,
(filterRects || findLargestObject) ? 4 : 0,
(findLargestObject ? CASCADE_FIND_BIGGEST_OBJECT : 0)
| CASCADE_SCALE_IMAGE,
minSize);
detections_num = (int)facesBuf_cpu.size();
}
if (!useGPU && detections_num)
for (size_t i = 0; i < faces.size(); ++i)
{
for (int i = 0; i < detections_num; ++i)
{
rectangle(resized_cpu, facesBuf_cpu[i], Scalar(255));
}
}
if (useGPU)
{
resized_gpu.download(resized_cpu);
for (int i = 0; i < detections_num; ++i)
{
rectangle(resized_cpu, faces_downloaded.ptr<cv::Rect>()[i], Scalar(255));
}
rectangle(resized_cpu, faces[i], Scalar(255));
}
tm.stop();
@ -283,16 +264,15 @@ int main(int argc, const char *argv[])
//print detections to console
cout << setfill(' ') << setprecision(2);
cout << setw(6) << fixed << fps << " FPS, " << detections_num << " det";
if ((filterRects || findLargestObject) && detections_num > 0)
cout << setw(6) << fixed << fps << " FPS, " << faces.size() << " det";
if ((filterRects || findLargestObject) && !faces.empty())
{
Rect *faceRects = useGPU ? faces_downloaded.ptr<Rect>() : &facesBuf_cpu[0];
for (int i = 0; i < min(detections_num, 2); ++i)
for (size_t i = 0; i < faces.size(); ++i)
{
cout << ", [" << setw(4) << faceRects[i].x
<< ", " << setw(4) << faceRects[i].y
<< ", " << setw(4) << faceRects[i].width
<< ", " << setw(4) << faceRects[i].height << "]";
cout << ", [" << setw(4) << faces[i].x
<< ", " << setw(4) << faces[i].y
<< ", " << setw(4) << faces[i].width
<< ", " << setw(4) << faces[i].height << "]";
}
}
cout << endl;