詳解python中GPU版本的opencv常用方法介紹
引言
本篇是以python的視角介紹相關的函式還有自我使用中的一些問題,本想在這篇之前總結一下opencv編譯的全過程,但遇到了太多坑,暫時不太想回看做過的筆記,所以這裡主要總結python下GPU版本的opencv。
主要函式說明
threshold():二值化,但要指定設定閾值
blendLinear():兩幅圖片的線形混合
calcHist()
createBoxFilter ():建立一個規範化的2D框過濾器
canny邊緣檢測
createGaussianFilter():建立一個Gaussian過濾器
createLaplacianFilter():建立一個Laplacian過濾器
createMorphologyFilter():形態學運算濾波器;腐蝕、開、關等操作
createSolbelFilter():建立一個solbel過濾器
createHougnCirclesDetector():建立一個霍夫原檢測器
createMedianFilter():建立一箇中值濾波過濾器
createTemplateMatching():模板匹配
cvtColor():顏色空間轉換
dft():執行浮點矩陣的正向或逆離散傅立葉變換
drawColorDisp():顏色差異影象
equalizeHist():將灰度影象的直方圖均衡化
findMinMaxLoc()
flip():翻轉二維矩陣
merge():用幾個單通道矩陣構成一個多通道矩陣
split():將多通道矩陣分離成多個單通道矩陣
getCudaEnabledDeviceCount():獲取可用的gpu數目
getDevice():返回由cuda::setDevice或預設初始化的當前裝置索引
printCudaDeviceInfo()
resetDevice():顯示地銷燬和清理與當前程序中當前裝置相關的所有資源
setDevice():設定一個device併為當前執行緒初始化它;如果省略次函式的呼叫,則在第一次CUDA使用時初始化預設裝置
resize():調整一個影象大小
rotate():在原點(0,0)周圍旋轉一個影象,然後移動它
sum():返回矩陣元素的和
上述是在cv2.cuda下總結出來的一些可以使用的方法,因為底層是C++,並且python主要呼叫的是cv2.so檔案,如果我們想要看到一個物件到底有什麼樣的作用還有一系列的使用方式,我們可以利用python的魔法方法__dir__ 與help檢視:
$ dir(cv2.cuda) """ ['ALPHA_ATOP','ALPHA_ATOP_PREMUL','ALPHA_IN','ALPHA_IN_PREMUL','ALPHA_OUT','ALPHA_OUT_PREMUL','ALPHA_OVER','ALPHA_OVER_PREMUL','ALPHA_PLUS','ALPHA_PLUS_PREMUL','ALPHA_PREMUL','ALPHA_XOR','ALPHA_XOR_PREMUL','BroxOpticalFlow_create','COLOR_BAYER_BG2BGR_MHT','COLOR_BAYER_BG2GRAY_MHT','COLOR_BAYER_BG2RGB_MHT','COLOR_BAYER_GB2BGR_MHT','COLOR_BAYER_GB2GRAY_MHT','COLOR_BAYER_GB2RGB_MHT','COLOR_BAYER_GR2BGR_MHT','COLOR_BAYER_GR2GRAY_MHT','COLOR_BAYER_GR2RGB_MHT','COLOR_BAYER_RG2BGR_MHT','COLOR_BAYER_RG2GRAY_MHT','COLOR_BAYER_RG2RGB_MHT','COLOR_BayerBG2BGR_MHT','COLOR_BayerBG2GRAY_MHT','COLOR_BayerBG2RGB_MHT','COLOR_BayerGB2BGR_MHT','COLOR_BayerGB2GRAY_MHT','COLOR_BayerGB2RGB_MHT','COLOR_BayerGR2BGR_MHT','COLOR_BayerGR2GRAY_MHT','COLOR_BayerGR2RGB_MHT','COLOR_BayerRG2BGR_MHT','COLOR_BayerRG2GRAY_MHT','COLOR_BayerRG2RGB_MHT','CascadeClassifier_create','DEVICE_INFO_COMPUTE_MODE_DEFAULT','DEVICE_INFO_COMPUTE_MODE_EXCLUSIVE','DEVICE_INFO_COMPUTE_MODE_EXCLUSIVE_PROCESS','DEVICE_INFO_COMPUTE_MODE_PROHIBITED','DYNAMIC_PARALLELISM','DensePyrLKOpticalFlow_create','DescriptorMatcher_createBFMatcher','DeviceInfo_ComputeModeDefault','DeviceInfo_ComputeModeExclusive','DeviceInfo_ComputeModeExclusiveProcess','DeviceInfo_ComputeModeProhibited','EVENT_BLOCKING_SYNC','EVENT_DEFAULT','EVENT_DISABLE_TIMING','EVENT_INTERPROCESS','Event_BLOCKING_SYNC','Event_DEFAULT','Event_DISABLE_TIMING','Event_INTERPROCESS','Event_elapsedTime','FEATURE_SET_COMPUTE_10','FEATURE_SET_COMPUTE_11','FEATURE_SET_COMPUTE_12','FEATURE_SET_COMPUTE_13','FEATURE_SET_COMPUTE_20','FEATURE_SET_COMPUTE_21','FEATURE_SET_COMPUTE_30','FEATURE_SET_COMPUTE_32','FEATURE_SET_COMPUTE_35','FEATURE_SET_COMPUTE_50','FarnebackOpticalFlow_create','FastFeatureDetector_create','GLOBAL_ATOMICS','GpuMat_defaultAllocator','GpuMat_setDefaultAllocator','HOG_create','HOST_MEM_PAGE_LOCKED','HOST_MEM_SHARED','HOST_MEM_WRITE_COMBINED','HostMem_PAGE_LOCKED','HostMem_SHARED','HostMem_WRITE_COMBINED','NATIVE_DOUBLE','NVIDIA_OPTICAL_FLOW_1_0_NV_OF_PERF_LEVEL_FAST','NVIDIA_OPTICAL_FLOW_1_0_NV_OF_PERF_LEVEL_MAX','NVIDIA_OPTICAL_FLOW_1_0_NV_OF_PERF_LEVEL_MEDIUM','NVIDIA_OPTICAL_FLOW_1_0_NV_OF_PERF_LEVEL_SLOW','NVIDIA_OPTICAL_FLOW_1_0_NV_OF_PERF_LEVEL_UNDEFINED','NvidiaOpticalFlow_1_0_NV_OF_PERF_LEVEL_FAST','NvidiaOpticalFlow_1_0_NV_OF_PERF_LEVEL_MAX','NvidiaOpticalFlow_1_0_NV_OF_PERF_LEVEL_MEDIUM','NvidiaOpticalFlow_1_0_NV_OF_PERF_LEVEL_SLOW','NvidiaOpticalFlow_1_0_NV_OF_PERF_LEVEL_UNDEFINED','NvidiaOpticalFlow_1_0_create','ORB_create','OpticalFlowDual_TVL1_create','SHARED_ATOMICS','SURF_CUDA_ANGLE_ROW','SURF_CUDA_HESSIAN_ROW','SURF_CUDA_LAPLACIAN_ROW','SURF_CUDA_OCTAVE_ROW','SURF_CUDA_ROWS_COUNT','SURF_CUDA_SIZE_ROW','SURF_CUDA_X_ROW','SURF_CUDA_Y_ROW','SparsePyrLKOpticalFlow_create','StereoBeliefPropagation_estimateRecommendedParams','StereoConstantSpaceBP_estimateRecommendedParams','Stream_Null','TargetArchs_has','TargetArchs_hasBin','TargetArchs_hasEqualOrGreater','TargetArchs_hasEqualOrGreaterBin','TargetArchs_hasEqualOrGreaterPtx','TargetArchs_hasEqualOrLessPtx','TargetArchs_hasPtx','WARP_SHUFFLE_FUNCTIONS','__doc__','__loader__','__name__','__package__','__spec__','abs','absSum','absdiff','add','addWeighted','alphaComp','bilateralFilter','bitwise_and','bitwise_not','bitwise_or','bitwise_xor','blendLinear','buildWarpAffineMaps','buildWarpPerspectiveMaps','calcAbsSum','calcHist','calcNorm','calcNormDiff','calcSqrSum','calcSum','cartToPolar','compare','copyMakeBorder','countNonZero','createBackgroundSubtractorMOG','createBackgroundSubtractorMOG2','createBoxFilter','createBoxMaxFilter','createBoxMinFilter','createCLAHE','createCannyEdgeDetector','createColumnSumFilter','createContinuous','createConvolution','createDFT','createDerivFilter','createDisparityBilateralFilter','createGaussianFilter','createGeneralizedHoughBallard','createGeneralizedHoughGuil','createGoodFeaturesToTrackDetector','createHarrisCorner','createHoughCirclesDetector','createHoughLinesDetector','createHoughSegmentDetector','createLaplacianFilter','createLinearFilter','createLookUpTable','createMedianFilter','createMinEigenValCorner','createMorphologyFilter','createRowSumFilter','createScharrFilter','createSeparableLinearFilter','createSobelFilter','createStereoBM','createStereoBeliefPropagation','createStereoConstantSpaceBP','createTemplateMatching','cvtColor','demosaicing','dft','divide','drawColorDisp','ensureSizeIsEnough','equalizeHist','evenLevels','exp','findMinMax','findMinMaxLoc','flip','gammaCorrection','gemm','getCudaEnabledDeviceCount','getDevice','histEven','histRange','integral','log','magnitude','magnitudeSqr','max','meanShiftFiltering','meanShiftProc','meanShiftSegmentation','meanStdDev','merge','min','minMax','minMaxLoc','mulAndScaleSpectrums','mulSpectrums','multiply','norm','normalize','phase','polarToCart','pow','printCudaDeviceInfo','printShortCudaDeviceInfo','pyrDown','pyrUp','rectStdDev','reduce','registerPageLocked','remap','reprojectImageTo3D','resetDevice','resize','rotate','setBufferPoolConfig','setBufferPoolUsage','setDevice','split','sqr','sqrIntegral','sqrSum','sqrt','subtract','sum','threshold','transpose','unregisterPageLocked','warpAffine','warpPerspective'] """ $ GPU_mat = cv2.cuda_GpuMat() $ dir(GPU_mat) """ ['__class__','__delattr__','__dir__','__eq__','__format__','__ge__','__getattribute__','__gt__','__hash__','__init__','__init_subclass__','__le__','__lt__','__ne__','__new__','__reduce__','__reduce_ex__','__repr__','__setattr__','__sizeof__','__str__','__subclasshook__','adjustROI','assignTo','channels','clone','col','colRange','convertTo','copyTo','create','defaultAllocator','depth','download','elemSize','elemSize1','empty','isContinuous','locateROI','reshape','row','rowRange','setDefaultAllocator','setTo','size','step','step1','swap','type','updateContinuityFlag','upload'] """ $ dir(cv2) """ 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ipseDirect','fitLine','flann','flann_Index','floodFill','ft','getAffineTransform','getBuildInformation','getCPUTickCount','getDefaultNewCameraMatrix','getDerivKernels','getFontScaleFromHeight','getGaborKernel','getGaussianKernel','getHardwareFeatureName','getNumThreads','getNumberOfCPUs','getOptimalDFTSize','getOptimalNewCameraMatrix','getPerspectiveTransform','getRectSubPix','getRotationMatrix2D','getStructuringElement','getTextSize','getThreadNum','getTickCount','getTickFrequency','getTrackbarPos','getValidDisparityROI','getVersionMajor','getVersionMinor','getVersionRevision','getVersionString','getWindowImageRect','getWindowProperty','goodFeaturesToTrack','grabCut','groupRectangles','haveImageReader','haveImageWriter','haveOpenVX','hconcat','hdf','hdf_HDF5','hfs','hfs_HfsSegment','idct','idft','illuminationChange','imdecode','imencode','img_hash','img_hash_AverageHash','img_hash_BlockMeanHash','img_hash_ColorMomentHash','img_hash_ImgHashBase','img_hash_MarrHildrethHash','img_hash_PHash','img_hash_RadialVarianceHash','imread','imreadmulti','imshow','imwrite','inRange','initCameraMatrix2D','initUndistortRectifyMap','inpaint','insertChannel','instr','integral2','integral3','intersectConvexConvex','invert','invertAffineTransform','ipp','isContourConvex','kinfu','kinfu_KinFu','kinfu_Params','kmeans','line','line_descriptor','line_descriptor_BinaryDescriptor','line_descriptor_BinaryDescriptorMatcher','line_descriptor_DrawLinesMatchesFlags','line_descriptor_KeyLine','line_descriptor_LSDDetector','line_descriptor_LSDParam','linearPolar','linemod','linemod_ColorGradient','linemod_DepthNormal','linemod_Detector','linemod_Feature','linemod_Match','linemod_Modality','linemod_QuantizedPyramid','linemod_Template','logPolar','matMulDeriv','matchShapes','matchTemplate','mean','meanShift','medianBlur','minAreaRect','minEnclosingCircle','minEnclosingTriangle','mixChannels','ml','ml_ANN_MLP','ml_Boost','ml_DTrees','ml_EM','ml_KNearest','ml_LogisticRegression','ml_NormalBayesClassifier','ml_ParamGrid','ml_RTrees','ml_SVM','ml_SVMSGD','ml_StatModel','ml_TrainData','moments','morphologyEx','motempl','moveWindow','mulTransposed','multicalib','namedWindow','ocl','ocl_Device','ogl','omnidir','optflow','optflow_DenseRLOFOpticalFlow','optflow_DualTVL1OpticalFlow','optflow_GPCDetails','optflow_GPCPatchDescriptor','optflow_GPCPatchSample','optflow_GPCTrainingSamples','optflow_GPCTree','optflow_OpticalFlowPCAFlow','optflow_PCAPrior','optflow_RLOFOpticalFlowParameter','optflow_SparseRLOFOpticalFlow','patchNaNs','pencilSketch','perspectiveTransform','phaseCorrelate','phase_unwrapping_HistogramPhaseUnwrapping','phase_unwrapping_PhaseUnwrapping','plot','plot_Plot2d','pointPolygonTest','polylines','ppf_match_3d','ppf_match_3d_ICP','ppf_match_3d_PPF3DDetector','ppf_match_3d_Pose3D','ppf_match_3d_PoseCluster3D','preCornerDetect','projectPoints','putText','pyrMeanShiftFiltering','quality','quality_QualityBRISQUE','quality_QualityBase','quality_QualityGMSD','quality_QualityMSE','quality_QualityPSNR','quality_QualitySSIM','randShuffle','randn','randu','readOpticalFlow','recoverPose','rectangle','rectify3Collinear','redirectError','reg','reg_Map','reg_MapAffine','reg_MapProjec','reg_MapShift','reg_MapTypeCaster','reg_Mapper','reg_MapperGradAffine','reg_MapperGradEuclid','reg_MapperGradProj','reg_MapperGradShift','reg_MapperGradSimilar','reg_MapperPyramid','repeat','resizeWindow','rgbd','rgbd_DepthCleaner','rgbd_FastICPOdometry','rgbd_ICPOdometry','rgbd_Odometry','rgbd_OdometryFrame','rgbd_RgbdFrame','rgbd_RgbdICPOdometry','rgbd_RgbdNormals','rgbd_RgbdOdometry','rgbd_RgbdPlane','rotatedRectangleIntersection','saliency','saliency_MotionSaliency','saliency_MotionSaliencyBinWangApr2014','saliency_Objectness','saliency_ObjectnessBING','saliency_Saliency','saliency_StaticSaliency','saliency_StaticSaliencyFineGrained','saliency_StaticSaliencySpectralResidual','samples','sampsonDistance','scaleAdd','seamlessClone','selectROI','selectROIs','sepFilter2D','setIdentity','setMouseCallback','setNumThreads','setRNGSeed','setTrackbarMax','setTrackbarMin','setTrackbarPos','setUseOpenVX','setUseOptimized','setWindowProperty','setWindowTitle','sfm','sfm_BaseSFM','sfm_SFMLibmvEuclideanReconstruction','sfm_libmv_CameraIntrinsicsOptions','sfm_libmv_ReconstructionOptions','solve','solveCubic','solveLP','solveP3P','solvePnP','solvePnPGeneric','solvePnPRansac','solvePnPRefineLM','solvePnPRefineVVS','solvePoly','sort','sortIdx','spatialGradient','sqrBoxFilter','startWindowThread','stereoCalibrate','stereoCalibrateExtended','stereoRectify','stereoRectifyUncalibrated','structured_light','structured_light_GrayCodePattern','structured_light_SinusoidalPattern','structured_light_SinusoidalPattern_Params','structured_light_StructuredLightPattern','stylization','sumElems','text','text_BaseOCR','text_ERFilter','text_ERFilter_Callback','text_OCRBeamSearchDecoder','text_OCRBeamSearchDecoder_ClassifierCallback','text_OCRHMMDecoder','text_OCRHMMDecoder_ClassifierCallback','text_OCRT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"""
我將上述dir顯示的所有方法都列印在了上面,可以複製下來對比哪些方法能用,哪些不能。對於進行GPU編譯過的opencv來講,cv2.cuda下的基本都是GPU方法,而cv2裡的方法還是CPU的,這也導致了opencv的侷限性,因為目前所提供的編譯版本的cuda方法都是基於影象的變化和演算法,對流的部分是一個空缺,所以如果如果要對視訊做操作的話,很大概率會導致記憶體洩漏:
這是我之前的valgrind測試報告,可以很明顯的看出在用opencv進行操作的時候有記憶體洩漏。所以如果只是對圖片做某些演算法操作,比如DNN、canny等,速度提升是很明顯的。但只是resize或者read這樣的操作,都無法發揮出GPU本來的優勢,執行效率反而不如直接CPU快,具體原因可以看我下面的三種寫法:
OpenCV Mat與UMat
OpenCV3以後引入了一個新的影象容器物件UMat,它的使用與功能基本和Mat類似,但一個代表著將圖片存進GPU,一個是CPU,UMat格式需要支援openCL,OpenCL是一個面向異構系統通用的並行程式設計標準,也是基於此,才能在opencv中實現UMat和Mat的轉換:
cv::Mat inMat,outMat; vidInput >> inMat; cv::cvtColor(inMat,outMat,cv::COLOR_RGB2GRAY); vidOutput << outMat;
上面是C++的虛擬碼,而python的寫法和這個類似:
img_Mat = cv2.imread('test.jpg') # 16-bit float,<class 'numpy.ndarray'> img_UMat = cv2.UMat(img_Mat) # 16-bit float,<class 'cv2.UMat'>
在opencv-python中,目前我已知的4.1.0以上版本是都有同意OpenGL,如果想知道到底支不支援,可以輸入如下程式碼測試:
import cv2 print('OpenCL available:',cv2.ocl.haveOpenCL())
返回True即為支援openCL,而如果專案中報錯"cv::ocl::OpenCLAllocator::upload",這是因為當前版本的openCL與顯示卡驅動或者說cuda不相容,所以安全起見,可以設定cv2.ocl.setUseOpenCL(False)。
另外,在GPU enabled OpenCV in Python中,寫了一個例子:
""" cpu_gpu.py An OpenCL-OpenCV-Python CPU vs GPU comparison """ import cv2 import timeit # A simple image pipeline that runs on both Mat and Umat def img_cal(img,mode): if mode=='UMat': img = cv2.UMat(img) img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY) img = cv2.GaussianBlur(img,(7,7),1.5) img = cv2.Canny(img,50) if type(img) == 'cv2.UMat': img = cv2.UMat.get(img) return img # Timing function def run(processor,function,n_threads,N): cv2.setNumThreads(n_threads) t = timeit.timeit(function,globals=globals(),number=N)/N*1000 print('%s avg. with %d threads: %0.2f ms' % (processor,n,t)) return t img = cv2.imread('ct.tif') N = 1000 threads = [1,16] processor = {'GPU': "img_cal(img_UMat)",'CPU': "img_cal(img)"} results = {} for n in n_threads: for pro in processor.keys(): results[pro,n] = run(processor=pro,function= processor[pro],n_threads=n,N=N) print('\nGPU speed increase over 1 CPU thread [%%]: %0.2f' % \ (results[('CPU',1)]/results[('GPU',1)]*100)) print('CPU speed increase on 4 threads versus 1 thread [%%]: %0.2f' % \ (results[('CPU',1)]/results[('CPU',16)]*100)) print('GPU speed increase versus 4 threads [%%]: %0.2f' % \ (results[('CPU',4)]/results[('CPU',1)]*100))
這段程式碼分別計算了GPU和CPU對於影象進行灰度化到邊緣檢測的時間,然後看起來測試結果是比較讓人滿意的:
GPU avg. with 1 threads: 2.07 ms CPU avg. with 1 threads: 5.70 ms GPU avg. with 16 threads: 2.09 ms CPU avg. with 16 threads: 2.70 ms GPU speed increase over 1 CPU thread [%]: 275.11 CPU speed increase on 16 threads versus 1 CPU thread [%]: 210.82 GPU speed increase versus 16 CPU threads [%]: 47.43
但實際上,我在多程序讀取影象做分析的時候發現,如果用UMat的格式,其生產圖片的效率遠遠沒有我消費速度快,因為我沒有做什麼演算法相關操作,並且在程序數達到一定量,它會出現異常,也就是說,在我的測試中,它並不太適用於多程序或者多執行緒,雖然這個是最簡單的方案。
使用GPU下的opencv
使用的步驟與上面方法類似,只是OpenCV中GPU模組,已經封裝的核心函式的呼叫,其使用步驟如下:
1.驗證OpenCV是否已啟用GPU模組。
2.上傳待處理資料到GPU (Mat --> GpuMat)。
3.呼叫OpenCV支援的GPU的處理函式。
4.下載處理結果到CPU (GpuMat —> Mat)。
其示例程式如下,完成顏色轉換,BGR2GRAY。
這個版本的方法就比上面Umat的方式多很多,也是我在開頭引用的所有函式以及相關物件方法都是經過GPU編譯的opencv,這個版本是一定要經過編譯的,編譯的坑我會在之後的博文中說明,那麼在編譯成功後,就可以看到我介紹的函式,以及GitHub中有一個demo可以執行:
#!/usr/bin/env python ''' CUDA-accelerated Computer Vision functions ''' # Python 2/3 compatibility from __future__ import print_function import numpy as np import cv2 as cv import os from tests_common import NewOpenCVTests,unittest class cuda_test(NewOpenCVTests): def setUp(self): super(cuda_test,self).setUp() if not cv.cuda.getCudaEnabledDeviceCount(): self.skipTest("No CUDA-capable device is detected") def test_cuda_upload_download(self): npMat = (np.random.random((128,128,3)) * 255).astype(np.uint8) cuMat = cv.cuda_GpuMat() cuMat.upload(npMat) self.assertTrue(np.allclose(cuMat.download(),npMat)) def test_cuda_interop(self): npMat = (np.random.random((128,3)) * 255).astype(np.uint8) cuMat = cv.cuda_GpuMat() cuMat.upload(npMat) self.assertTrue(cuMat.cudaPtr() != 0) stream = cv.cuda_Stream() self.assertTrue(stream.cudaPtr() != 0) if __name__ == '__main__': NewOpenCVTests.bootstrap()
關於上述更詳細的引數介紹,可以通過opencv官方的C++文件進行比對:
cv::cuda::GpuMat Class Reference
上面的程式碼如果理解了UMat格式的定義,那麼這裡的opencv產生的資料結構就是gpu_mat,相對應的get方法換成了download,而圖片首先要upload進這個gpu_mat容器裡,它要比UMat更可靠與完整,在opencv4.1以後已經建立了相關的生態體系,除了解碼之外,我看到大多數用opencv的都是在用GPU的dnn演算法,從issue的實驗結果上看,確實提升是巨大的。
但這裡還是有一個問題,如果出現記憶體洩漏,怎樣去規避和降低,這個只能說因程式碼而異了。。我在kaggle和opencv官網上找到了兩種方案來解決這個問題,但對我來說沒啥作用,但如果出現了洩漏,這兩種可以參考:
使用gc手動回收:
import cv2 import os import gc images_path = "../input/aptos2019-blindness-detection/train_images/" image_names = os.listdir(images_path) for image_name in image_names: img = cv2.imread(images_path + image_name) del img gc.collect()
https://www.kaggle.com/c/aptos2019-blindness-detection/discussion/106622
使用qimage2ndarray庫:
def display_video_stream(self): """Read frame from camera and repaint QLabel widget. """ _,frame = self.capture.read() frame = cv2.cvtColor(frame,cv2.COLOR_BGR2RGB) # frame = cv2.flip(frame,1) image = qimage2ndarray.array2qimage(frame) #Solution for memory leak self.image_label.setPixmap(QPixmap.fromImage(image))
https://answers.opencv.org/question/136206/memory-leak-while-using-cv2videocapture/
最後,我引用一張opencv使用cuda執行程式的流程圖來說明內部過程,取自【OpenCV】OpenCV中GPU模組使用,我覺得是比較生動形象了:
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