From 734212a4023ca82a7b0ae6f7fabca53e08de177f Mon Sep 17 00:00:00 2001 From: Vladislav Vinogradov Date: Wed, 14 Jan 2015 19:48:58 +0300 Subject: [PATCH] refactor CUDA CascadeClassifier --- .../include/opencv2/cudaobjdetect.hpp | 99 ++- modules/cudaobjdetect/perf/perf_objdetect.cpp | 26 +- modules/cudaobjdetect/src/cascadeclassifier.cpp | 690 ++++++++++++--------- modules/cudaobjdetect/test/test_objdetect.cpp | 33 +- samples/gpu/cascadeclassifier.cpp | 60 +- 5 files changed, 496 insertions(+), 412 deletions(-) diff --git a/modules/cudaobjdetect/include/opencv2/cudaobjdetect.hpp b/modules/cudaobjdetect/include/opencv2/cudaobjdetect.hpp index 2887bf4..31a6a94 100644 --- a/modules/cudaobjdetect/include/opencv2/cudaobjdetect.hpp +++ b/modules/cudaobjdetect/include/opencv2/cudaobjdetect.hpp @@ -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(); + static Ptr create(const String& filename); + /** @overload + */ + static Ptr create(const FileStorage& file); - /** @brief Checks whether the classifier is loaded or not. - */ - bool empty() const; - /** @brief Loads the classifier from a file. The previous content is destroyed. + //! 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; - @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. - */ - bool load(const String& filename); - /** @brief Destroys the loaded classifier. - */ - void release(); + //! 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. - - The detected objects are returned as a list of rectangles. - - The function returns the number of detected objects, so you can retrieve them as in the following - example: + @param objects Buffer to store detected objects (rectangles). + + To get final array of detected objects use CascadeClassifier::convert method. + @code - cuda::CascadeClassifier_CUDA cascade_gpu(...); + Ptr 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 faces; + cascade_gpu->convert(objbuf, faces); - Rect* faces = obj_host.ptr(); 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& objects) = 0; }; //! @} diff --git a/modules/cudaobjdetect/perf/perf_objdetect.cpp b/modules/cudaobjdetect/perf/perf_objdetect.cpp index 8aeabc4..8b31124 100644 --- a/modules/cudaobjdetect/perf/perf_objdetect.cpp +++ b/modules/cudaobjdetect/perf/perf_objdetect.cpp @@ -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 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 gpu_rects; + d_cascade->convert(objects_buffer, gpu_rects); - std::vector gpu_rects(detections_num); - cv::Mat gpu_rects_mat(1, detections_num, cv::DataType::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 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 gpu_rects; + d_cascade->convert(objects_buffer, gpu_rects); - std::vector gpu_rects(detections_num); - cv::Mat gpu_rects_mat(1, detections_num, cv::DataType::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); } diff --git a/modules/cudaobjdetect/src/cascadeclassifier.cpp b/modules/cudaobjdetect/src/cascadeclassifier.cpp index c4e9870..10443aa 100644 --- a/modules/cudaobjdetect/src/cascadeclassifier.cpp +++ b/modules/cudaobjdetect/src/cascadeclassifier.cpp @@ -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 cv::cuda::CascadeClassifier::create(const String&) { throw_no_cuda(); return Ptr(); } +Ptr cv::cuda::CascadeClassifier::create(const FileStorage&) { throw_no_cuda(); return Ptr(); } #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 -{ -public: - HaarCascade() - { - throw_no_cuda(); - } + virtual void setMinNeighbors(int minNeighbors) { minNeighbors_ = minNeighbors; } + virtual int getMinNeighbors() const { return minNeighbors_; } - unsigned int process(const GpuMat&, GpuMat&, float, int, bool, bool, cv::Size, cv::Size) - { - throw_no_cuda(); - return 0; - } + virtual void setFindLargestObject(bool findLargestObject) { findLargestObject_ = findLargestObject; } + virtual bool getFindLargestObject() { return findLargestObject_; } - cv::Size getClassifierCvSize() const - { - throw_no_cuda(); - return cv::Size(); - } + 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_; + }; - bool read(const String&) + CascadeClassifierBase::CascadeClassifierBase() : + maxObjectSize_(), + minObjectSize_(), + scaleFactor_(1.2), + minNeighbors_(4), + findLargestObject_(false), + maxNumObjects_(100) { - throw_no_cuda(); - return false; } -}; +} -#else +// +// HaarCascade +// + +#ifdef HAVE_OPENCV_CUDALEGACY -struct cv::cuda::CascadeClassifier_CUDA::HaarCascade : cv::cuda::CascadeClassifier_CUDA::CascadeClassifierImpl +namespace { -public: - HaarCascade() : lastAllocatedFrameSize(-1, -1) + class HaarCascade_Impl : public CascadeClassifierBase { - ncvSetDebugOutputHandler(NCVDebugOutputHandler); - } + public: + explicit HaarCascade_Impl(const String& filename); - bool read(const String& filename) - { - ncvSafeCall( load(filename) ); - return true; - } + virtual Size getClassifierSize() const; - NCVStatus process(const GpuMat& src, GpuMat& objects, float scaleStep, int minNeighbors, - bool findLargestObject, bool visualizeInPlace, cv::Size ncvMinSize, - /*out*/unsigned int& numDetections) - { - calculateMemReqsAndAllocate(src.size()); + virtual void detectMultiScale(InputArray image, + OutputArray objects, + Stream& stream); - NCVMemPtr src_beg; - src_beg.ptr = (void*)src.ptr(); - src_beg.memtype = NCVMemoryTypeDevice; + virtual void convert(OutputArray gpu_objects, + std::vector& objects); - NCVMemSegment src_seg; - src_seg.begin = src_beg; - src_seg.size = src.step * src.rows; + 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); - NCVMatrixReuse d_src(src_seg, static_cast(devProp.textureAlignment), src.cols, src.rows, static_cast(src.step), true); - ncvAssertReturn(d_src.isMemReused(), NCV_ALLOCATOR_BAD_REUSE); + Size lastAllocatedFrameSize; - CV_Assert(objects.rows == 1); + Ptr gpuAllocator; + Ptr cpuAllocator; - NCVMemPtr objects_beg; - objects_beg.ptr = (void*)objects.ptr(); - objects_beg.memtype = NCVMemoryTypeDevice; + cudaDeviceProp devProp; + NCVStatus ncvStat; - NCVMemSegment objects_seg; - objects_seg.begin = objects_beg; - objects_seg.size = objects.step * objects.rows; - NCVVectorReuse d_rects(objects_seg, objects.cols); - ncvAssertReturn(d_rects.isMemReused(), NCV_ALLOCATOR_BAD_REUSE); + Ptr gpuCascadeAllocator; + Ptr cpuCascadeAllocator; - NcvSize32u roi; - roi.width = d_src.width(); - roi.height = d_src.height(); + Ptr > h_haarStages; + Ptr > h_haarNodes; + Ptr > h_haarFeatures; - NcvSize32u winMinSize(ncvMinSize.width, ncvMinSize.height); + HaarClassifierCascadeDescriptor haar; - Ncv32u flags = 0; - flags |= findLargestObject? NCVPipeObjDet_FindLargestObject : 0; - flags |= visualizeInPlace ? NCVPipeObjDet_VisualizeInPlace : 0; + Ptr > d_haarStages; + Ptr > d_haarNodes; + Ptr > d_haarFeatures; + }; - 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); + static void NCVDebugOutputHandler(const String &msg) + { + CV_Error(Error::GpuApiCallError, msg.c_str()); + } - return NCV_SUCCESS; + HaarCascade_Impl::HaarCascade_Impl(const String& filename) : + lastAllocatedFrameSize(-1, -1) + { + ncvSetDebugOutputHandler(NCVDebugOutputHandler); + ncvSafeCall( load(filename) ); } - unsigned int process(const GpuMat& image, GpuMat& objectsBuf, float scaleFactor, int minNeighbors, - bool findLargestObject, bool visualizeInPlace, cv::Size minSize, cv::Size /*maxObjectSize*/) + Size HaarCascade_Impl::getClassifierSize() const { - CV_Assert( scaleFactor > 1 && image.depth() == CV_8U); + return Size(haar.ClassifierSize.width, haar.ClassifierSize.height); + } - const int defaultObjSearchNum = 100; - if (objectsBuf.empty()) - { - objectsBuf.create(1, defaultObjSearchNum, DataType::type); - } + void HaarCascade_Impl::detectMultiScale(InputArray _image, + OutputArray _objects, + Stream& stream) + { + const GpuMat image = _image.getGpuMat(); - cv::Size ncvMinSize = this->getClassifierCvSize(); + CV_Assert( image.depth() == CV_8U); + CV_Assert( scaleFactor_ > 1 ); + CV_Assert( !stream ); - if (ncvMinSize.width < minSize.width && ncvMinSize.height < minSize.height) + Size ncvMinSize = getClassifierSize(); + if (ncvMinSize.width < minObjectSize_.width && ncvMinSize.height < minObjectSize_.height) { - ncvMinSize.width = minSize.width; - ncvMinSize.height = minSize.height; + ncvMinSize.width = minObjectSize_.width; + ncvMinSize.height = minObjectSize_.height; } + BufferPool pool(stream); + GpuMat objectsBuf = pool.getBuffer(1, maxNumObjects_, DataType::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& 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::type ); -private: - static void NCVDebugOutputHandler(const String &msg) { CV_Error(cv::Error::GpuApiCallError, msg.c_str()); } + Rect* ptr = gpu_objects.ptr(); + objects.assign(ptr, ptr + gpu_objects.cols); + } - NCVStatus load(const String& classifierFile) + 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; - - Ptr gpuCascadeAllocator; - Ptr cpuCascadeAllocator; - - Ptr > h_haarStages; - Ptr > h_haarNodes; - Ptr > h_haarFeatures; - - HaarClassifierCascadeDescriptor haar; - - Ptr > d_haarStages; - Ptr > d_haarNodes; - Ptr > d_haarFeatures; + NCVStatus HaarCascade_Impl::process(const GpuMat& src, GpuMat& objects, cv::Size ncvMinSize, /*out*/ unsigned int& numDetections) + { + calculateMemReqsAndAllocate(src.size()); - Size lastAllocatedFrameSize; + NCVMemPtr src_beg; + src_beg.ptr = (void*)src.ptr(); + src_beg.memtype = NCVMemoryTypeDevice; - Ptr gpuAllocator; - Ptr cpuAllocator; + NCVMemSegment src_seg; + src_seg.begin = src_beg; + src_seg.size = src.step * src.rows; - virtual ~HaarCascade(){} -}; + NCVMatrixReuse d_src(src_seg, static_cast(devProp.textureAlignment), src.cols, src.rows, static_cast(src.step), true); + ncvAssertReturn(d_src.isMemReused(), NCV_ALLOCATOR_BAD_REUSE); -#endif + CV_Assert(objects.rows == 1); -cv::Size operator -(const cv::Size& a, const cv::Size& b) -{ - return cv::Size(a.width - b.width, a.height - b.height); -} + NCVMemPtr objects_beg; + objects_beg.ptr = (void*)objects.ptr(); + objects_beg.memtype = NCVMemoryTypeDevice; -cv::Size operator +(const cv::Size& a, const int& i) -{ - return cv::Size(a.width + i, a.height + i); -} + NCVMemSegment objects_seg; + objects_seg.begin = objects_beg; + objects_seg.size = objects.step * objects.rows; + NCVVectorReuse d_rects(objects_seg, objects.cols); + ncvAssertReturn(d_rects.isMemReused(), NCV_ALLOCATOR_BAD_REUSE); -cv::Size operator *(const cv::Size& a, const float& f) -{ - return cv::Size(cvRound(a.width * f), cvRound(a.height * f)); -} + NcvSize32u roi; + roi.width = d_src.width(); + roi.height = d_src.height(); -cv::Size operator /(const cv::Size& a, const float& f) -{ - return cv::Size(cvRound(a.width / f), cvRound(a.height / f)); -} + NcvSize32u winMinSize(ncvMinSize.width, ncvMinSize.height); -bool operator <=(const cv::Size& a, const cv::Size& b) -{ - return a.width <= b.width && a.height <= b.width; -} + Ncv32u flags = 0; + flags |= findLargestObject_ ? NCVPipeObjDet_FindLargestObject : 0; -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); - } + 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); - bool isFeasible(cv::Size maxObj) - { - return workArea.width > 0 && workArea.height > 0 && sWindow <= maxObj; + return NCV_SUCCESS; } +} - PyrLavel next(float factor, cv::Size frame, cv::Size window, cv::Size minObjectSize) - { - return PyrLavel(order + 1, factor, frame, window, minObjectSize); - } +#endif - 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 candidates, int ncandidates, PtrStepSz objects,int groupThreshold, float grouping_eps, unsigned int* nclasses); + void connectedConmonents(PtrStepSz candidates, + int ncandidates, + PtrStepSz 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) + { + 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 { - int first; - int ntrees; - float threshold; + 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; + }; + + class LbpCascade_Impl : public CascadeClassifierBase + { + 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& 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(){} - virtual ~LbpCascade(){} + LbpCascade_Impl::LbpCascade_Impl(const FileStorage& file) + { + load(file.getFirstTopLevelNode()); + } - virtual unsigned int process(const GpuMat& image, GpuMat& objects, float scaleFactor, int groupThreshold, bool /*findLargestObject*/, - bool /*visualizeInPlace*/, cv::Size minObjectSize, cv::Size maxObjectSize) + void LbpCascade_Impl::detectMultiScale(InputArray _image, + OutputArray _objects, + Stream& stream) { - CV_Assert(scaleFactor > 1 && image.depth() == CV_8U); + 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::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(), 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()); + device::lbp::connectedConmonents(candidates, classified, objects, minNeighbors_, grouping_eps, dclassified.ptr()); 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; + if (classified > 0) + { + objects.colRange(0, classified).copyTo(_objects); + } + else + { + _objects.release(); + } } -private: - - void allocateBuffers(cv::Size frame) + void LbpCascade_Impl::convert(OutputArray _gpu_objects, std::vector& objects) { - if (frame == cv::Size()) + if (_gpu_objects.empty()) + { + objects.clear(); return; + } - if (resuzeBuffer.empty() || frame.width > resuzeBuffer.cols || frame.height > resuzeBuffer.rows) + Mat gpu_objects; + if (_gpu_objects.kind() == _InputArray::CUDA_GPU_MAT) { - 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()) ); + _gpu_objects.getGpuMat().download(gpu_objects); + } + else + { + gpu_objects = _gpu_objects.getMat(); + } - Ncv32u bufSize; - ncvSafeCall( nppiStIntegralGetSize_8u32u(roiSize, &bufSize, prop) ); - integralBuffer.create(1, bufSize, CV_8UC1); -#endif + CV_Assert( gpu_objects.rows == 1 ); + CV_Assert( gpu_objects.type() == DataType::type ); - candidates.create(1 , frame.width >> 1, CV_32SC4); - } + Rect* ptr = gpu_objects.ptr(); + objects.assign(ptr, ptr + gpu_objects.cols); } - bool read(const FileNode &root) + 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; - - 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; -}; + void LbpCascade_Impl::allocateBuffers(cv::Size frame) + { + if (frame == cv::Size()) + return; -cv::cuda::CascadeClassifier_CUDA::CascadeClassifier_CUDA() -: findLargestObject(false), visualizeInPlace(false), impl(0) {} + if (resuzeBuffer.empty() || frame.width > resuzeBuffer.cols || frame.height > resuzeBuffer.rows) + { + resuzeBuffer.create(frame, CV_8UC1); -cv::cuda::CascadeClassifier_CUDA::CascadeClassifier_CUDA(const String& filename) -: findLargestObject(false), visualizeInPlace(false), impl(0) { load(filename); } + integral.create(frame.height + 1, integralFactor * (frame.width + 1), CV_32SC1); -cv::cuda::CascadeClassifier_CUDA::~CascadeClassifier_CUDA() { release(); } + #ifdef HAVE_OPENCV_CUDALEGACY + NcvSize32u roiSize; + roiSize.width = frame.width; + roiSize.height = frame.height; -void cv::cuda::CascadeClassifier_CUDA::release() { if (impl) { delete impl; impl = 0; } } + cudaDeviceProp prop; + cudaSafeCall( cudaGetDeviceProperties(&prop, cv::cuda::getDevice()) ); -bool cv::cuda::CascadeClassifier_CUDA::empty() const { return impl == 0; } + Ncv32u bufSize; + ncvSafeCall( nppiStIntegralGetSize_8u32u(roiSize, &bufSize, prop) ); + integralBuffer.create(1, bufSize, CV_8UC1); + #endif -Size cv::cuda::CascadeClassifier_CUDA::getClassifierSize() const -{ - return this->empty() ? Size() : impl->getClassifierCvSize(); -} + candidates.create(1 , frame.width >> 1, CV_32SC4); + } + } -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()); } -int cv::cuda::CascadeClassifier_CUDA::detectMultiScale(const GpuMat& image, GpuMat& objectsBuf, Size maxObjectSize, Size minSize, double scaleFactor, int minNeighbors) -{ - CV_Assert( !this->empty()); - return impl->process(image, objectsBuf, (float)scaleFactor, minNeighbors, findLargestObject, visualizeInPlace, minSize, maxObjectSize); -} +// +// create +// -bool cv::cuda::CascadeClassifier_CUDA::load(const String& filename) +Ptr cv::cuda::CascadeClassifier::create(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(); + #else + return makePtr(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(); + #else + return makePtr(filename); + #endif } const char *CUDA_CC_LBP = "LBP"; String featureTypeStr = (String)fs.getFirstTopLevelNode()["featureType"]; if (featureTypeStr == CUDA_CC_LBP) - impl = new LbpCascade(); + { + return makePtr(fs); + } else - impl = new HaarCascade(); + { + #ifndef HAVE_OPENCV_CUDALEGACY + CV_Error(Error::StsUnsupportedFormat, "OpenCV CUDA objdetect was built without HaarCascade"); + return Ptr(); + #else + return makePtr(filename); + #endif + } - impl->read(filename); - return !this->empty(); + CV_Error(Error::StsUnsupportedFormat, "Unsupported format for CUDA CascadeClassifier"); + return Ptr(); +} + +Ptr cv::cuda::CascadeClassifier::create(const FileStorage& file) +{ + return makePtr(file); } #endif diff --git a/modules/cudaobjdetect/test/test_objdetect.cpp b/modules/cudaobjdetect/test/test_objdetect.cpp index 79b1f07..336d6e0 100644 --- a/modules/cudaobjdetect/test/test_objdetect.cpp +++ b/modules/cudaobjdetect/test/test_objdetect.cpp @@ -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 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 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 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(); - 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, diff --git a/samples/gpu/cascadeclassifier.cpp b/samples/gpu/cascadeclassifier.cpp index ff19c1d..f6209f9 100644 --- a/samples/gpu/cascadeclassifier.cpp +++ b/samples/gpu/cascadeclassifier.cpp @@ -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 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 facesBuf_cpu; + Mat frame, frame_cpu, gray_cpu, resized_cpu, frameDisp; + vector 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 (int i = 0; i < detections_num; ++i) - { - rectangle(resized_cpu, facesBuf_cpu[i], Scalar(255)); - } } - if (useGPU) + for (size_t i = 0; i < faces.size(); ++i) { - resized_gpu.download(resized_cpu); - - for (int i = 0; i < detections_num; ++i) - { - rectangle(resized_cpu, faces_downloaded.ptr()[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() : &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; -- 2.7.4