環境配置

win7+pycharm(pycharm-community-2017.1.3)+python2.7.5

1          安裝配置

1.1         下載

1.1.1    Anaconda 4.4.0下載

建議下載Python2.7 version

Anaconda是一個用於科學計算的Python發行版，支援 Linux,Mac, Windows系統，提供了包管理與環境管理的功能，可以很方便地解決多版本python並存、切換以及各種第三方包安裝問題。Anaconda利用工具/命令conda來進行package和environment的管理，並且已經包含了Python和相關的配套工具。

這裡先解釋下conda、anaconda這些概念的差別。conda可以理解為一個工具，也是一個可執行命令，其核心功能是包管理與環境管理。包管理與pip的使用類似，環境管理則允許使用者方便地安裝不同版本的python並可以快速切換。Anaconda則是一個打包的集合，裡面預裝好了conda、某個版本的python、眾多packages、科學計算工具等等，所以也稱為Python的一種發行版。

安裝說明見

1.1.2    pycharm下載

請選擇對應作業系統（windows、linux）、硬體（32位、64位）

建議下載

下載快

1.1.3    python庫下載

           可以選擇

1.2         安裝

1.2.1    Anaconda

1.2.2    pycharm

1.3          

1.4         配置

2          python科學計算庫介紹

2.1         Python用於科學計算的一些常用工具和庫

 IPython-增強的互動環境：支援變數自動補全，自動縮排，支援 bash shell 命令，內建了許多很有用的功能和函式

 Spyder、Wing IDE或Eclipse/Pydev：整合開發環境

NumPy-數學計算基礎庫：N維陣列、線性代數計算、傅立葉變換、隨機數等。

SciPy-數值計算庫：線性代數、擬合與優化、插值、數值積分、稀疏矩陣、影象處理、統計等。

SymPy-符號運算

Pandas-資料分析庫：資料匯入、整理、處理、分析等。

matplotlib-會相簿：繪製二維圖形和圖表

Chaco-互動式圖表

OpenCV-計算機視覺庫

TVTK-資料的三維視覺化

Cython-Python轉C的編譯器：編寫高效運算擴充套件庫的首選工具

BioPython-生物科學

2.2         Python科學計算髮行版

Python(x,y)
當前最新版本:2.7.6.1 (05/30/2014)，支援Windows和Python2.7.6。
其庫索引列出了所支援的170+Python27庫。

WinPython
當前最新版本:2.7.6.4和3.3.5.0 (04/2014)，支援Windows和Python2.7.6、3.3.5。
其庫索引列出了所支援的60+Python27庫。
其庫索引列出了所支援的60+Python33庫。

EnthoughtCanopy（EnthoughtPython Distribution）
當前最新版本:1.4.1 (06/11/2014)，支援Linux, Windows, Mac平臺和Python2.7.6。
其庫索引列出了所支援的150+測試過的Python庫。

Anaconda
當前最新版本:2.0.1 (06/12/2014)，支援Linux, Windows, Mac平臺和Python 2.6、2.7、3.3、3.4。
其庫索引列出了所支援的195+流行Python庫。

Sage不是Python發行版，而是一個由Python和Cython實現的開源數學軟體系統，將很多已有的（C 、C++、Fortran和Python編寫的）數學軟體包整合到一個通用介面（記事本文件介面和IPython命令列介面），使用者只需瞭解Python，就可以通過介面或包裝器(wrapper)使用NumPy、SciPy、matplotlib、Sympy、Maxima、GAP、 FLINT、R和其他已有軟體包（具體資訊見元件列表），完成代數、組合數學、計算數學和微積分等計算。其最初的目標是創造一個“Magma、Maple、Mathematica和MATLAB的開源替代品”。當前最新版本:6.3 (08/10/2014)，支援Linux, Windows, Mac平臺和Python2.x。
2.3         選擇和推薦

Python(x,y)和WinPython都是開源專案，其專案負責人都是Pierre Raybaut。

按Pierre自己的說法是“WinPython不是試圖取替Python(x,y)，而是出於不同動機和理念：更靈活、易於維護、可移動、對作業系統侵略性更小，但是使用者友好性更差、包更少、沒有同Windows資源管理器整合。”。參考1裡面說Python(x,y)不是很穩定，此外看它目前的更新不是很頻繁，確實有可能Pierre後來的工作重心放在WinPython上了。

Canopy和Anaconda是公司推的，帶免費版和商業版/外掛。這兩款發行版也牽扯到一個人，那就是Travis Oliphant。Travis是SciPy的原始作者，同時也是NumPy的貢獻者。Travis在2008年以副總裁身份加入Enthought，2012年以總裁的身份離開，創立了一個新公司continuum.io，並推出了Python的科學計算平臺Anaconda。Anaconda相對Canopy支援Python的版本更多，對Python新版本支援跟的很緊（Sage不支援Python3.x的理由是因為其依賴的SciPy還不支援Python3，而Anaconda卻實現了支援Python3.3和3.4，這就說明問題了），此外其在Linux平臺下（通過conda管理）安裝更方便。

不言而喻，我最後選擇了安裝科學計算髮行版Anaconda:)

opencv2.4.13

info.py

import cv2
import cv2 as cv

print  help(cv2)
print  help(cv)

cv2

Help on module cv2:

NAME
    cv2

FILE
    c:\programdata\anaconda2\lib\site-packages\cv2.pyd

SUBMODULES
    cv

CLASSES
    exceptions.Exception(exceptions.BaseException)
        error
    
    class error(exceptions.Exception)
     |  Method resolution order:
     |      error
     |      exceptions.Exception
     |      exceptions.BaseException
     |      __builtin__.object
     |  
     |  Data descriptors defined here:
     |  
     |  __weakref__
     |      list of weak references to the object (if defined)
     |  
     |  ----------------------------------------------------------------------
     |  Methods inherited from exceptions.Exception:
     |  
     |  __init__(...)
     |      x.__init__(...) initializes x; see help(type(x)) for signature
     |  
     |  ----------------------------------------------------------------------
     |  Data and other attributes inherited from exceptions.Exception:
     |  
     |  __new__ = <built-in method __new__ of type object>
     |      T.__new__(S, ...) -> a new object with type S, a subtype of T
     |  
     |  ----------------------------------------------------------------------
     |  Methods inherited from exceptions.BaseException:
     |  
     |  __delattr__(...)
     |      x.__delattr__('name') <==> del x.name
     |  
     |  __getattribute__(...)
     |      x.__getattribute__('name') <==> x.name
     |  
     |  __getitem__(...)
     |      x.__getitem__(y) <==> x[y]
     |  
     |  __getslice__(...)
     |      x.__getslice__(i, j) <==> x[i:j]
     |      
     |      Use of negative indices is not supported.
     |  
     |  __reduce__(...)
     |  
     |  __repr__(...)
     |      x.__repr__() <==> repr(x)
     |  
     |  __setattr__(...)
     |      x.__setattr__('name', value) <==> x.name = value
     |  
     |  __setstate__(...)
     |  
     |  __str__(...)
     |      x.__str__() <==> str(x)
     |  
     |  __unicode__(...)
     |  
     |  ----------------------------------------------------------------------
     |  Data descriptors inherited from exceptions.BaseException:
     |  
     |  __dict__
     |  
     |  args
     |  
     |  message

FUNCTIONS
    ANN_MLP(...)
        ANN_MLP([layerSizes[, activateFunc[, fparam1[, fparam2]]]]) -> <ANN_MLP object>
    
    Algorithm__create(...)
        Algorithm__create(name) -> retval
    
    Algorithm_getList(...)
        Algorithm_getList() -> algorithms
    
    BFMatcher(...)
        BFMatcher([, normType[, crossCheck]]) -> <BFMatcher object>
    
    BOWImgDescriptorExtractor(...)
        BOWImgDescriptorExtractor(dextractor, dmatcher) -> <BOWImgDescriptorExtractor object>
    
    BOWKMeansTrainer(...)
        BOWKMeansTrainer(clusterCount[, termcrit[, attempts[, flags]]]) -> <BOWKMeansTrainer object>
    
    BRISK(...)
        BRISK([, thresh[, octaves[, patternScale]]]) -> <BRISK object>  or  BRISK(radiusList, numberList[, dMax[, dMin[, indexChange]]]) -> <BRISK object>
    
    BackgroundSubtractorMOG(...)
        BackgroundSubtractorMOG([history, nmixtures, backgroundRatio[, noiseSigma]]) -> <BackgroundSubtractorMOG object>
    
    BackgroundSubtractorMOG2(...)
        BackgroundSubtractorMOG2([history, varThreshold[, bShadowDetection]]) -> <BackgroundSubtractorMOG2 object>
    
    Boost(...)
        Boost([trainData, tflag, responses[, varIdx[, sampleIdx[, varType[, missingDataMask[, params]]]]]]) -> <Boost object>
    
    CamShift(...)
        CamShift(probImage, window, criteria) -> retval, window
    
    Canny(...)
        Canny(image, threshold1, threshold2[, edges[, apertureSize[, L2gradient]]]) -> edges
    
    CascadeClassifier(...)
        CascadeClassifier([filename]) -> <CascadeClassifier object>
    
    DMatch(...)
        DMatch() -> <DMatch object>  or  DMatch(_queryIdx, _trainIdx, _distance) -> <DMatch object>  or  DMatch(_queryIdx, _trainIdx, _imgIdx, _distance) -> <DMatch object>
    
    DTree(...)
        DTree() -> <DTree object>
    
    DescriptorExtractor_create(...)
        DescriptorExtractor_create(descriptorExtractorType) -> retval
    
    DescriptorMatcher_create(...)
        DescriptorMatcher_create(descriptorMatcherType) -> retval
    
    EM(...)
        EM([, nclusters[, covMatType[, termCrit]]]) -> <EM object>
    
    ERTrees(...)
        ERTrees() -> <ERTrees object>
    
    FastFeatureDetector(...)
        FastFeatureDetector([, threshold[, nonmaxSuppression]]) -> <FastFeatureDetector object>
    
    Feature2D_create(...)
        Feature2D_create(name) -> retval
    
    FeatureDetector_create(...)
        FeatureDetector_create(detectorType) -> retval
    
    FileNode(...)
        FileNode() -> <FileNode object>
    
    FileStorage(...)
        FileStorage([source, flags[, encoding]]) -> <FileStorage object>
    
    FlannBasedMatcher(...)
        FlannBasedMatcher([, indexParams[, searchParams]]) -> <FlannBasedMatcher object>
    
    GBTrees(...)
        GBTrees([trainData, tflag, responses[, varIdx[, sampleIdx[, varType[, missingDataMask[, params]]]]]]) -> <GBTrees object>
    
    GFTTDetector(...)
        GFTTDetector([, maxCorners[, qualityLevel[, minDistance[, blockSize[, useHarrisDetector[, k]]]]]]) -> <GFTTDetector object>
    
    GaussianBlur(...)
        GaussianBlur(src, ksize, sigmaX[, dst[, sigmaY[, borderType]]]) -> dst
    
    GridAdaptedFeatureDetector(...)
        GridAdaptedFeatureDetector([, detector[, maxTotalKeypoints[, gridRows[, gridCols]]]]) -> <GridAdaptedFeatureDetector object>
    
    HOGDescriptor(...)
        HOGDescriptor() -> <HOGDescriptor object>  or  HOGDescriptor(_winSize, _blockSize, _blockStride, _cellSize, _nbins[, _derivAperture[, _winSigma[, _histogramNormType[, _L2HysThreshold[, _gammaCorrection[, _nlevels]]]]]]) -> <HOGDescriptor object>  or  HOGDescriptor(filename) -> <HOGDescriptor object>
    
    HOGDescriptor_getDaimlerPeopleDetector(...)
        HOGDescriptor_getDaimlerPeopleDetector() -> retval
    
    HOGDescriptor_getDefaultPeopleDetector(...)
        HOGDescriptor_getDefaultPeopleDetector() -> retval
    
    HoughCircles(...)
        HoughCircles(image, method, dp, minDist[, circles[, param1[, param2[, minRadius[, maxRadius]]]]]) -> circles
    
    HoughLines(...)
        HoughLines(image, rho, theta, threshold[, lines[, srn[, stn]]]) -> lines
    
    HoughLinesP(...)
        HoughLinesP(image, rho, theta, threshold[, lines[, minLineLength[, maxLineGap]]]) -> lines
    
    HuMoments(...)
        HuMoments(m[, hu]) -> hu
    
    KDTree(...)
        KDTree() -> <KDTree object>  or  KDTree(points[, copyAndReorderPoints]) -> <KDTree object>  or  KDTree(points, _labels[, copyAndReorderPoints]) -> <KDTree object>
    
    KNearest(...)
        KNearest([trainData, responses[, sampleIdx[, isRegression[, max_k]]]]) -> <KNearest object>
    
    KalmanFilter(...)
        KalmanFilter([dynamParams, measureParams[, controlParams[, type]]]) -> <KalmanFilter object>
    
    KeyPoint(...)
        KeyPoint([x, y, _size[, _angle[, _response[, _octave[, _class_id]]]]]) -> <KeyPoint object>
    
    LUT(...)
        LUT(src, lut[, dst[, interpolation]]) -> dst
    
    Laplacian(...)
        Laplacian(src, ddepth[, dst[, ksize[, scale[, delta[, borderType]]]]]) -> dst
    
    MSER(...)
        MSER([, _delta[, _min_area[, _max_area[, _max_variation[, _min_diversity[, _max_evolution[, _area_threshold[, _min_margin[, _edge_blur_size]]]]]]]]]) -> <MSER object>
    
    Mahalanobis(...)
        Mahalanobis(v1, v2, icovar) -> retval
    
    NormalBayesClassifier(...)
        NormalBayesClassifier([trainData, responses[, varIdx[, sampleIdx]]]) -> <NormalBayesClassifier object>
    
    ORB(...)
        ORB([, nfeatures[, scaleFactor[, nlevels[, edgeThreshold[, firstLevel[, WTA_K[, scoreType[, patchSize]]]]]]]]) -> <ORB object>
    
    PCABackProject(...)
        PCABackProject(data, mean, eigenvectors[, result]) -> result
    
    PCACompute(...)
        PCACompute(data[, mean[, eigenvectors[, maxComponents]]]) -> mean, eigenvectors
    
    PCAComputeVar(...)
        PCAComputeVar(data, retainedVariance[, mean[, eigenvectors]]) -> mean, eigenvectors
    
    PCAProject(...)
        PCAProject(data, mean, eigenvectors[, result]) -> result
    
    PSNR(...)
        PSNR(src1, src2) -> retval
    
    PyramidAdaptedFeatureDetector(...)
        PyramidAdaptedFeatureDetector(detector[, maxLevel]) -> <PyramidAdaptedFeatureDetector object>
    
    RQDecomp3x3(...)
        RQDecomp3x3(src[, mtxR[, mtxQ[, Qx[, Qy[, Qz]]]]]) -> retval, mtxR, mtxQ, Qx, Qy, Qz
    
    RTrees(...)
        RTrees() -> <RTrees object>
    
    Rodrigues(...)
        Rodrigues(src[, dst[, jacobian]]) -> dst, jacobian
    
    SIFT(...)
        SIFT([, nfeatures[, nOctaveLayers[, contrastThreshold[, edgeThreshold[, sigma]]]]]) -> <SIFT object>
    
    SURF(...)
        SURF([hessianThreshold[, nOctaves[, nOctaveLayers[, extended[, upright]]]]]) -> <SURF object>
    
    SVBackSubst(...)
        SVBackSubst(w, u, vt, rhs[, dst]) -> dst
    
    SVDecomp(...)
        SVDecomp(src[, w[, u[, vt[, flags]]]]) -> w, u, vt
    
    SVM(...)
        SVM([trainData, responses[, varIdx[, sampleIdx[, params]]]]) -> <SVM object>
    
    Scharr(...)
        Scharr(src, ddepth, dx, dy[, dst[, scale[, delta[, borderType]]]]) -> dst
    
    SimpleBlobDetector(...)
        SimpleBlobDetector([, parameters]) -> <SimpleBlobDetector object>
    
    SimpleBlobDetector_Params(...)
        SimpleBlobDetector_Params() -> <SimpleBlobDetector_Params object>
    
    Sobel(...)
        Sobel(src, ddepth, dx, dy[, dst[, ksize[, scale[, delta[, borderType]]]]]) -> dst
    
    StarDetector(...)
        StarDetector([, _maxSize[, _responseThreshold[, _lineThresholdProjected[, _lineThresholdBinarized[, _suppressNonmaxSize]]]]]) -> <StarDetector object>
    
    StereoBM(...)
        StereoBM([preset[, ndisparities[, SADWindowSize]]]) -> <StereoBM object>
    
    StereoSGBM(...)
        StereoSGBM([minDisparity, numDisparities, SADWindowSize[, P1[, P2[, disp12MaxDiff[, preFilterCap[, uniquenessRatio[, speckleWindowSize[, speckleRange[, fullDP]]]]]]]]]) -> <StereoSGBM object>
    
    StereoVar(...)
        StereoVar([levels, pyrScale, nIt, minDisp, maxDisp, poly_n, poly_sigma, fi, lambda, penalization, cycle, flags]) -> <StereoVar object>
    
    Subdiv2D(...)
        Subdiv2D([rect]) -> <Subdiv2D object>
    
    VideoCapture(...)
        VideoCapture() -> <VideoCapture object>  or  VideoCapture(filename) -> <VideoCapture object>  or  VideoCapture(device) -> <VideoCapture object>
    
    VideoWriter(...)
        VideoWriter([filename, fourcc, fps, frameSize[, isColor]]) -> <VideoWriter object>
    
    absdiff(...)
        absdiff(src1, src2[, dst]) -> dst
    
    accumulate(...)
        accumulate(src, dst[, mask]) -> None
    
    accumulateProduct(...)
        accumulateProduct(src1, src2, dst[, mask]) -> None
    
    accumulateSquare(...)
        accumulateSquare(src, dst[, mask]) -> None
    
    accumulateWeighted(...)
        accumulateWeighted(src, dst, alpha[, mask]) -> None
    
    adaptiveBilateralFilter(...)
        adaptiveBilateralFilter(src, ksize, sigmaSpace[, dst[, maxSigmaColor[, anchor[, borderType]]]]) -> dst
    
    adaptiveThreshold(...)
        adaptiveThreshold(src, maxValue, adaptiveMethod, thresholdType, blockSize, C[, dst]) -> dst
    
    add(...)
        add(src1, src2[, dst[, mask[, dtype]]]) -> dst
    
    addWeighted(...)
        addWeighted(src1, alpha, src2, beta, gamma[, dst[, dtype]]) -> dst
    
    applyColorMap(...)
        applyColorMap(src, colormap[, dst]) -> dst
    
    approxPolyDP(...)
        approxPolyDP(curve, epsilon, closed[, approxCurve]) -> approxCurve
    
    arcLength(...)
        arcLength(curve, closed) -> retval
    
    arrowedLine(...)
        arrowedLine(img, pt1, pt2, color[, thickness[, line_type[, shift[, tipLength]]]]) -> None
    
    batchDistance(...)
        batchDistance(src1, src2, dtype[, dist[, nidx[, normType[, K[, mask[, update[, crosscheck]]]]]]]) -> dist, nidx
    
    bilateralFilter(...)
        bilateralFilter(src, d, sigmaColor, sigmaSpace[, dst[, borderType]]) -> dst
    
    bitwise_and(...)
        bitwise_and(src1, src2[, dst[, mask]]) -> dst
    
    bitwise_not(...)
        bitwise_not(src[, dst[, mask]]) -> dst
    
    bitwise_or(...)
        bitwise_or(src1, src2[, dst[, mask]]) -> dst
    
    bitwise_xor(...)
        bitwise_xor(src1, src2[, dst[, mask]]) -> dst
    
    blur(...)
        blur(src, ksize[, dst[, anchor[, borderType]]]) -> dst
    
    borderInterpolate(...)
        borderInterpolate(p, len, borderType) -> retval
    
    boundingRect(...)
        boundingRect(points) -> retval
    
    boxFilter(...)
        boxFilter(src, ddepth, ksize[, dst[, anchor[, normalize[, borderType]]]]) -> dst
    
    buildOpticalFlowPyramid(...)
        buildOpticalFlowPyramid(img, winSize, maxLevel[, pyramid[, withDerivatives[, pyrBorder[, derivBorder[, tryReuseInputImage]]]]]) -> retval, pyramid
    
    calcBackProject(...)
        calcBackProject(images, channels, hist, ranges, scale[, dst]) -> dst
    
    calcCovarMatrix(...)
        calcCovarMatrix(samples, flags[, covar[, mean[, ctype]]]) -> covar, mean
    
    calcGlobalOrientation(...)
        calcGlobalOrientation(orientation, mask, mhi, timestamp, duration) -> retval
    
    calcHist(...)
        calcHist(images, channels, mask, histSize, ranges[, hist[, accumulate]]) -> hist
    
    calcMotionGradient(...)
        calcMotionGradient(mhi, delta1, delta2[, mask[, orientation[, apertureSize]]]) -> mask, orientation
    
    calcOpticalFlowFarneback(...)
        calcOpticalFlowFarneback(prev, next, pyr_scale, levels, winsize, iterations, poly_n, poly_sigma, flags[, flow]) -> flow
    
    calcOpticalFlowPyrLK(...)
        calcOpticalFlowPyrLK(prevImg, nextImg, prevPts[, nextPts[, status[, err[, winSize[, maxLevel[, criteria[, flags[, minEigThreshold]]]]]]]]) -> nextPts, status, err
    
    calcOpticalFlowSF(...)
        calcOpticalFlowSF(from, to, flow, layers, averaging_block_size, max_flow) -> None  or  calcOpticalFlowSF(from, to, flow, layers, averaging_block_size, max_flow, sigma_dist, sigma_color, postprocess_window, sigma_dist_fix, sigma_color_fix, occ_thr, upscale_averaging_radius, upscale_sigma_dist, upscale_sigma_color, speed_up_thr) -> None
    
    calibrateCamera(...)
        calibrateCamera(objectPoints, imagePoints, imageSize[, cameraMatrix[, distCoeffs[, rvecs[, tvecs[, flags[, criteria]]]]]]) -> retval, cameraMatrix, distCoeffs, rvecs, tvecs
    
    calibrationMatrixValues(...)
        calibrationMatrixValues(cameraMatrix, imageSize, apertureWidth, apertureHeight) -> fovx, fovy, focalLength, principalPoint, aspectRatio
    
    cartToPolar(...)
        cartToPolar(x, y[, magnitude[, angle[, angleInDegrees]]]) -> magnitude, angle
    
    chamerMatching(...)
        chamerMatching(img, templ[, templScale[, maxMatches[, minMatchDistance[, padX[, padY[, scales[, minScale[, maxScale[, orientationWeight[, truncate]]]]]]]]]]) -> retval, results, cost
    
    checkHardwareSupport(...)
        checkHardwareSupport(feature) -> retval
    
    checkRange(...)
        checkRange(a[, quiet[, minVal[, maxVal]]]) -> retval, pos
    
    circle(...)
        circle(img, center, radius, color[, thickness[, lineType[, shift]]]) -> None
    
    clipLine(...)
        clipLine(imgRect, pt1, pt2) -> retval, pt1, pt2
    
    compare(...)
        compare(src1, src2, cmpop[, dst]) -> dst
    
    compareHist(...)
        compareHist(H1, H2, method) -> retval
    
    completeSymm(...)
        completeSymm(mtx[, lowerToUpper]) -> None
    
    composeRT(...)
        composeRT(rvec1, tvec1, rvec2, tvec2[, rvec3[, tvec3[, dr3dr1[, dr3dt1[, dr3dr2[, dr3dt2[, dt3dr1[, dt3dt1[, dt3dr2[, dt3dt2]]]]]]]]]]) -> rvec3, tvec3, dr3dr1, dr3dt1, dr3dr2, dr3dt2, dt3dr1, dt3dt1, dt3dr2, dt3dt2
    
    computeCorrespondEpilines(...)
        computeCorrespondEpilines(points, whichImage, F[, lines]) -> lines
    
    contourArea(...)
        contourArea(contour[, oriented]) -> retval
    
    convertMaps(...)
        convertMaps(map1, map2, dstmap1type[, dstmap1[, dstmap2[, nninterpolation]]]) -> dstmap1, dstmap2
    
    convertPointsFromHomogeneous(...)
        convertPointsFromHomogeneous(src[, dst]) -> dst
    
    convertPointsToHomogeneous(...)
        convertPointsToHomogeneous(src[, dst]) -> dst
    
    convertScaleAbs(...)
        convertScaleAbs(src[, dst[, alpha[, beta]]]) -> dst
    
    convexHull(...)
        convexHull(points[, hull[, clockwise[, returnPoints]]]) -> hull
    
    convexityDefects(...)
        convexityDefects(contour, convexhull[, convexityDefects]) -> convexityDefects
    
    copyMakeBorder(...)
        copyMakeBorder(src, top, bottom, left, right, borderType[, dst[, value]]) -> dst
    
    cornerEigenValsAndVecs(...)
        cornerEigenValsAndVecs(src, blockSize, ksize[, dst[, borderType]]) -> dst
    
    cornerHarris(...)
        cornerHarris(src, blockSize, ksize, k[, dst[, borderType]]) -> dst
    
    cornerMinEigenVal(...)
        cornerMinEigenVal(src, blockSize[, dst[, ksize[, borderType]]]) -> dst
    
    cornerSubPix(...)
        cornerSubPix(image, corners, winSize, zeroZone, criteria) -> None
    
    correctMatches(...)
        correctMatches(F, points1, points2[, newPoints1[, newPoints2]]) -> newPoints1, newPoints2
    
    countNonZero(...)
        countNonZero(src) -> retval
    
    createCLAHE(...)
        createCLAHE([, clipLimit[, tileGridSize]]) -> retval
    
    createEigenFaceRecognizer(...)
        createEigenFaceRecognizer([, num_components[, threshold]]) -> retval
    
    createFisherFaceRecognizer(...)
        createFisherFaceRecognizer([, num_components[, threshold]]) -> retval
    
    createHanningWindow(...)
        createHanningWindow(winSize, type[, dst]) -> dst
    
    createLBPHFaceRecognizer(...)
        createLBPHFaceRecognizer([, radius[, neighbors[, grid_x[, grid_y[, threshold]]]]]) -> retval
    
    createTrackbar(...)
        createTrackbar(trackbarName, windowName, value, count, onChange) -> None
    
    cubeRoot(...)
        cubeRoot(val) -> retval
    
    cvtColor(...)
        cvtColor(src, code[, dst[, dstCn]]) -> dst
    
    dct(...)
        dct(src[, dst[, flags]]) -> dst
    
    decomposeProjectionMatrix(...)
        decomposeProjectionMatrix(projMatrix[, cameraMatrix[, rotMatrix[, transVect[, rotMatrixX[, rotMatrixY[, rotMatrixZ[, eulerAngles]]]]]]]) -> cameraMatrix, rotMatrix, transVect, rotMatrixX, rotMatrixY, rotMatrixZ, eulerAngles
    
    destroyAllWindows(...)
        destroyAllWindows() -> None
    
    destroyWindow(...)
        destroyWindow(winname) -> None
    
    determinant(...)
        determinant(mtx) -> retval
    
    dft(...)
        dft(src[, dst[, flags[, nonzeroRows]]]) -> dst
    
    dilate(...)
        dilate(src, kernel[, dst[, anchor[, iterations[, borderType[, borderValue]]]]]) -> dst
    
    distanceTransform(...)
        distanceTransform(src, distanceType, maskSize[, dst]) -> dst
    
    distanceTransformWithLabels(...)
        distanceTransformWithLabels(src, distanceType, maskSize[, dst[, labels[, labelType]]]) -> dst, labels
    
    divide(...)
        divide(src1, src2[, dst[, scale[, dtype]]]) -> dst  or  divide(scale, src2[, dst[, dtype]]) -> dst
    
    drawChessboardCorners(...)
        drawChessboardCorners(image, patternSize, corners, patternWasFound) -> None
    
    drawContours(...)
        drawContours(image, contours, contourIdx, color[, thickness[, lineType[, hierarchy[, maxLevel[, offset]]]]]) -> None
    
    drawDataMatrixCodes(...)
        drawDataMatrixCodes(image, codes, corners) -> None
    
    drawKeypoints(...)
        drawKeypoints(image, keypoints[, outImage[, color[, flags]]]) -> outImage
    
    drawMarker(...)
        drawMarker(img, position, color[, markerType[, markerSize[, thickness[, line_type]]]]) -> None
    
    eigen(...)
        eigen(src, computeEigenvectors[, eigenvalues[, eigenvectors]]) -> retval, eigenvalues, eigenvectors
    
    ellipse(...)
        ellipse(img, center, axes, angle, startAngle, endAngle, color[, thickness[, lineType[, shift]]]) -> None  or  ellipse(img, box, color[, thickness[, lineType]]) -> None
    
    ellipse2Poly(...)
        ellipse2Poly(center, axes, angle, arcStart, arcEnd, delta) -> pts
    
    equalizeHist(...)
        equalizeHist(src[, dst]) -> dst
    
    erode(...)
        erode(src, kernel[, dst[, anchor[, iterations[, borderType[, borderValue]]]]]) -> dst
    
    estimateAffine3D(...)
        estimateAffine3D(src, dst[, out[, inliers[, ransacThreshold[, confidence]]]]) -> retval, out, inliers
    
    estimateRigidTransform(...)
        estimateRigidTransform(src, dst, fullAffine) -> retval
    
    exp(...)
        exp(src[, dst]) -> dst
    
    extractChannel(...)
        extractChannel(src, coi[, dst]) -> dst
    
    fastAtan2(...)
        fastAtan2(y, x) -> retval
    
    fastNlMeansDenoising(...)
        fastNlMeansDenoising(src[, dst[, h[, templateWindowSize[, searchWindowSize]]]]) -> dst
    
    fastNlMeansDenoisingColored(...)
        fastNlMeansDenoisingColored(src[, dst[, h[, hColor[, templateWindowSize[, searchWindowSize]]]]]) -> dst
    
    fastNlMeansDenoisingColoredMulti(...)
        fastNlMeansDenoisingColoredMulti(srcImgs, imgToDenoiseIndex, temporalWindowSize[, dst[, h[, hColor[, templateWindowSize[, searchWindowSize]]]]]) -> dst
    
    fastNlMeansDenoisingMulti(...)
        fastNlMeansDenoisingMulti(srcImgs, imgToDenoiseIndex, temporalWindowSize[, dst[, h[, templateWindowSize[, searchWindowSize]]]]) -> dst
    
    fillConvexPoly(...)
        fillConvexPoly(img, points, color[, lineType[, shift]]) -> None
    
    fillPoly(...)
        fillPoly(img, pts, color[, lineType[, shift[, offset]]]) -> None
    
    filter2D(...)
        filter2D(src, ddepth, kernel[, dst[, anchor[, delta[, borderType]]]]) -> dst
    
    filterSpeckles(...)
        filterSpeckles(img, newVal, maxSpeckleSize, maxDiff[, buf]) -> None
    
    findChessboardCorners(...)
        findChessboardCorners(image, patternSize[, corners[, flags]]) -> retval, corners
    
    findCirclesGrid(...)
        findCirclesGrid(image, patternSize[, centers[, flags[, blobDetector]]]) -> retval, centers
    
    findCirclesGridDefault(...)
        findCirclesGridDefault(image, patternSize[, centers[, flags]]) -> retval, centers
    
    findContours(...)
        findContours(image, mode, method[, contours[, hierarchy[, offset]]]) -> contours, hierarchy
    
    findDataMatrix(...)
        findDataMatrix(image[, corners[, dmtx]]) -> codes, corners, dmtx
    
    findFundamentalMat(...)
        findFundamentalMat(points1, points2[, method[, param1[, param2[, mask]]]]) -> retval, mask
    
    findHomography(...)
        findHomography(srcPoints, dstPoints[, method[, ransacReprojThreshold[, mask]]]) -> retval, mask
    
    findNonZero(...)
        findNonZero(src[, idx]) -> idx
    
    fitEllipse(...)
        fitEllipse(points) -> retval
    
    fitLine(...)
        fitLine(points, distType, param, reps, aeps[, line]) -> line
    
    flann_Index(...)
        flann_Index([features, params[, distType]]) -> <flann_Index object>
    
    flip(...)
        flip(src, flipCode[, dst]) -> dst
    
    floodFill(...)
        floodFill(image, mask, seedPoint, newVal[, loDiff[, upDiff[, flags]]]) -> retval, rect
    
    gemm(...)
        gemm(src1, src2, alpha, src3, beta[, dst[, flags]]) -> dst
    
    getAffineTransform(...)
        getAffineTransform(src, dst) -> retval
    
    getBuildInformation(...)
        getBuildInformation() -> retval
    
    getCPUTickCount(...)
        getCPUTickCount() -> retval
    
    getDefaultNewCameraMatrix(...)
        getDefaultNewCameraMatrix(cameraMatrix[, imgsize[, centerPrincipalPoint]]) -> retval
    
    getDerivKernels(...)
        getDerivKernels(dx, dy, ksize[, kx[, ky[, normalize[, ktype]]]]) -> kx, ky
    
    getGaborKernel(...)
        getGaborKernel(ksize, sigma, theta, lambd, gamma[, psi[, ktype]]) -> retval
    
    getGaussianKernel(...)
        getGaussianKernel(ksize, sigma[, ktype]) -> retval
    
    getNumThreads(...)
        getNumThreads() -> retval
    
    getNumberOfCPUs(...)
        getNumberOfCPUs() -> retval
    
    getOptimalDFTSize(...)
        getOptimalDFTSize(vecsize) -> retval
    
    getOptimalNewCameraMatrix(...)
        getOptimalNewCameraMatrix(cameraMatrix, distCoeffs, imageSize, alpha[, newImgSize[, centerPrincipalPoint]]) -> retval, validPixROI
    
    getPerspectiveTransform(...)
        getPerspectiveTransform(src, dst) -> retval
    
    getRectSubPix(...)
        getRectSubPix(image, patchSize, center[, patch[, patchType]]) -> patch
    
    getRotationMatrix2D(...)
        getRotationMatrix2D(center, angle, scale) -> retval
    
    getStructuringElement(...)
        getStructuringElement(shape, ksize[, anchor]) -> retval
    
    getTextSize(...)
        getTextSize(text, fontFace, fontScale, thickness) -> retval, baseLine
    
    getThreadNum(...)
        getThreadNum() -> retval
    
    getTickCount(...)
        getTickCount() -> retval
    
    getTickFrequency(...)
        getTickFrequency() -> retval
    
    getTrackbarPos(...)
        getTrackbarPos(trackbarname, winname) -> retval
    
    getValidDisparityROI(...)
        getValidDisparityROI(roi1, roi2, minDisparity, numberOfDisparities, SADWindowSize) -> retval
    
    getWindowProperty(...)
        getWindowProperty(winname, prop_id) -> retval
    
    goodFeaturesToTrack(...)
        goodFeaturesToTrack(image, maxCorners, qualityLevel, minDistance[, corners[, mask[, blockSize[, useHarrisDetector[, k]]]]]) -> corners
    
    grabCut(...)
        grabCut(img, mask, rect, bgdModel, fgdModel, iterCount[, mode]) -> None
    
    groupRectangles(...)
        groupRectangles(rectList, groupThreshold[, eps]) -> rectList, weights
    
    hconcat(...)
        hconcat(src[, dst]) -> dst
    
    idct(...)
        idct(src[, dst[, flags]]) -> dst
    
    idft(...)
        idft(src[, dst[, flags[, nonzeroRows]]]) -> dst
    
    imdecode(...)
        imdecode(buf, flags) -> retval
    
    imencode(...)
        imencode(ext, img[, params]) -> retval, buf
    
    imread(...)
        imread(filename[, flags]) -> retval
    
    imshow(...)
        imshow(winname, mat) -> None
    
    imwrite(...)
        imwrite(filename, img[, params]) -> retval
    
    inRange(...)
        inRange(src, lowerb, upperb[, dst]) -> dst
    
    initCameraMatrix2D(...)
        initCameraMatrix2D(objectPoints, imagePoints, imageSize[, aspectRatio]) -> retval
    
    initModule_nonfree(...)
        initModule_nonfree() -> retval
    
    initUndistortRectifyMap(...)
        initUndistortRectifyMap(cameraMatrix, distCoeffs, R, newCameraMatrix, size, m1type[, map1[, map2]]) -> map1, map2
    
    initWideAngleProjMap(...)
        initWideAngleProjMap(cameraMatrix, distCoeffs, imageSize, destImageWidth, m1type[, map1[, map2[, projType[, alpha]]]]) -> retval, map1, map2
    
    inpaint(...)
        inpaint(src, inpaintMask, inpaintRadius, flags[, dst]) -> dst
    
    insertChannel(...)
        insertChannel(src, dst, coi) -> None
    
    integral(...)
        integral(src[, sum[, sdepth]]) -> sum
    
    integral2(...)
        integral2(src[, sum[, sqsum[, sdepth]]]) -> sum, sqsum
    
    integral3(...)
        integral3(src[, sum[, sqsum[, tilted[, sdepth]]]]) -> sum, sqsum, tilted
    
    intersectConvexConvex(...)
        intersectConvexConvex(_p1, _p2[, _p12[, handleNested]]) -> retval, _p12
    
    invert(...)
        invert(src[, dst[, flags]]) -> retval, dst
    
    invertAffineTransform(...)
        invertAffineTransform(M[, iM]) -> iM
    
    isContourConvex(...)
        isContourConvex(contour) -> retval
    
    kmeans(...)
        kmeans(data, K, criteria, attempts, flags[, bestLabels[, centers]]) -> retval, bestLabels, centers
    
    line(...)
        line(img, pt1, pt2, color[, thickness[, lineType[, shift]]]) -> None
    
    log(...)
        log(src[, dst]) -> dst
    
    magnitude(...)
        magnitude(x, y[, magnitude]) -> magnitude
    
    matMulDeriv(...)
        matMulDeriv(A, B[, dABdA[, dABdB]]) -> dABdA, dABdB
    
    matchShapes(...)
        matchShapes(contour1, contour2, method, parameter) -> retval
    
    matchTemplate(...)
        matchTemplate(image, templ, method[, result]) -> result
    
    max(...)
        max(src1, src2[, dst]) -> dst
    
    mean(...)
        mean(src[, mask]) -> retval
    
    meanShift(...)
        meanShift(probImage, window, criteria) -> retval, window
    
    meanStdDev(...)
        meanStdDev(src[, mean[, stddev[, mask]]]) -> mean, stddev
    
    medianBlur(...)
        medianBlur(src, ksize[, dst]) -> dst
    
    merge(...)
        merge(mv[, dst]) -> dst
    
    min(...)
        min(src1, src2[, dst]) -> dst
    
    minAreaRect(...)
        minAreaRect(points) -> retval
    
    minEnclosingCircle(...)
        minEnclosingCircle(points) -> center, radius
    
    minMaxLoc(...)
        minMaxLoc(src[, mask]) -> minVal, maxVal, minLoc, maxLoc
    
    mixChannels(...)
        mixChannels(src, dst, fromTo) -> None
    
    moments(...)
        moments(array[, binaryImage]) -> retval
    
    morphologyEx(...)
        morphologyEx(src, op, kernel[, dst[, anchor[, iterations[, borderType[, borderValue]]]]]) -> dst
    
    moveWindow(...)
        moveWindow(winname, x, y) -> None
    
    mulSpectrums(...)
        mulSpectrums(a, b, flags[, c[, conjB]]) -> c
    
    mulTransposed(...)
        mulTransposed(src, aTa[, dst[, delta[, scale[, dtype]]]]) -> dst
    
    multiply(...)
        multiply(src1, src2[, dst[, scale[, dtype]]]) -> dst
    
    namedWindow(...)
        namedWindow(winname[, flags]) -> None
    
    norm(...)
        norm(src1[, normType[, mask]]) -> retval  or  norm(src1, src2[, normType[, mask]]) -> retval
    
    normalize(...)
        normalize(src[, dst[, alpha[, beta[, norm_type[, dtype[, mask]]]]]]) -> dst
    
    patchNaNs(...)
        patchNaNs(a[, val]) -> None
    
    perspectiveTransform(...)
        perspectiveTransform(src, m[, dst]) -> dst
    
    phase(...)
        phase(x, y[, angle[, angleInDegrees]]) -> angle
    
    phaseCorrelate(...)
        phaseCorrelate(src1, src2[, window]) -> retval
    
    phaseCorrelateRes(...)
        phaseCorrelateRes(src1, src2, window) -> retval, response
    
    pointPolygonTest(...)
        pointPolygonTest(contour, pt, measureDist) -> retval
    
    polarToCart(...)
        polarToCart(magnitude, angle[, x[, y[, angleInDegrees]]]) -> x, y
    
    polylines(...)
        polylines(img, pts, isClosed, color[, thickness[, lineType[, shift]]]) -> None
    
    pow(...)
        pow(src, power[, dst]) -> dst
    
    preCornerDetect(...)
        preCornerDetect(src, ksize[, dst[, borderType]]) -> dst
    
    projectPoints(...)
        projectPoints(objectPoints, rvec, tvec, cameraMatrix, distCoeffs[, imagePoints[, jacobian[, aspectRatio]]]) -> imagePoints, jacobian
    
    putText(...)
        putText(img, text, org, fontFace, fontScale, color[, thickness[, lineType[, bottomLeftOrigin]]]) -> None
    
    pyrDown(...)
        pyrDown(src[, dst[, dstsize[, borderType]]]) -> dst
    
    pyrMeanShiftFiltering(...)
        pyrMeanShiftFiltering(src, sp, sr[, dst[, maxLevel[, termcrit]]]) -> dst
    
    pyrUp(...)
        pyrUp(src[, dst[, dstsize[, borderType]]]) -> dst
    
    randShuffle(...)
        randShuffle(dst[, iterFactor]) -> None
    
    randn(...)
        randn(dst, mean, stddev) -> None
    
    randu(...)
        randu(dst, low, high) -> None
    
    rectangle(...)
        rectangle(img, pt1, pt2, color[, thickness[, lineType[, shift]]]) -> None
    
    rectify3Collinear(...)
        rectify3Collinear(cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, cameraMatrix3, distCoeffs3, imgpt1, imgpt3, imageSize, R12, T12, R13, T13, alpha, newImgSize, flags[, R1[, R2[, R3[, P1[, P2[, P3[, Q]]]]]]]) -> retval, R1, R2, R3, P1, P2, P3, Q, roi1, roi2
    
    reduce(...)
        reduce(src, dim, rtype[, dst[, dtype]]) -> dst
    
    remap(...)
        remap(src, map1, map2, interpolation[, dst[, borderMode[, borderValue]]]) -> dst
    
    repeat(...)
        repeat(src, ny, nx[, dst]) -> dst
    
    reprojectImageTo3D(...)
        reprojectImageTo3D(disparity, Q[, _3dImage[, handleMissingValues[, ddepth]]]) -> _3dImage
    
    resize(...)
        resize(src, dsize[, dst[, fx[, fy[, interpolation]]]]) -> dst
    
    resizeWindow(...)
        resizeWindow(winname, width, height) -> None
    
    scaleAdd(...)
        scaleAdd(src1, alpha, src2[, dst]) -> dst
    
    segmentMotion(...)
        segmentMotion(mhi, timestamp, segThresh[, segmask]) -> segmask, boundingRects
    
    sepFilter2D(...)
        sepFilter2D(src, ddepth, kernelX, kernelY[, dst[, anchor[, delta[, borderType]]]]) -> dst
    
    setIdentity(...)
        setIdentity(mtx[, s]) -> None
    
    setMouseCallback(...)
        setMouseCallback(windowName, onMouse [, param]) -> None
    
    setNumThreads(...)
        setNumThreads(nthreads) -> None
    
    setRNGSeed(...)
        setRNGSeed(seed) -> None
    
    setTrackbarPos(...)
        setTrackbarPos(trackbarname, winname, pos) -> None
    
    setUseOptimized(...)
        setUseOptimized(onoff) -> None
    
    setWindowProperty(...)
        setWindowProperty(winname, prop_id, prop_value) -> None
    
    solve(...)
        solve(src1, src2[, dst[, flags]]) -> retval, dst
    
    solveCubic(...)
        solveCubic(coeffs[, roots]) -> retval, roots
    
    solvePnP(...)
        solvePnP(objectPoints, imagePoints, cameraMatrix, distCoeffs[, rvec[, tvec[, useExtrinsicGuess[, flags]]]]) -> retval, rvec, tvec
    
    solvePnPRansac(...)
        solvePnPRansac(objectPoints, imagePoints, cameraMatrix, distCoeffs[, rvec[, tvec[, useExtrinsicGuess[, iterationsCount[, reprojectionError[, minInliersCount[, inliers[, flags]]]]]]]]) -> rvec, tvec, inliers
    
    solvePoly(...)
        solvePoly(coeffs[, roots[, maxIters]]) -> retval, roots
    
    sort(...)
        sort(src, flags[, dst]) -> dst
    
    sortIdx(...)
        sortIdx(src, flags[, dst]) -> dst
    
    split(...)
        split(m[, mv]) -> mv
    
    sqrt(...)
        sqrt(src[, dst]) -> dst
    
    startWindowThread(...)
        startWindowThread() -> retval
    
    stereoCalibrate(...)
        stereoCalibrate(objectPoints, imagePoints1, imagePoints2, imageSize[, cameraMatrix1[, distCoeffs1[, cameraMatrix2[, distCoeffs2[, R[, T[, E[, F[, criteria[, flags]]]]]]]]]]) -> retval, cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, R, T, E, F
    
    stereoRectify(...)
        stereoRectify(cameraMatrix1, distCoeffs1, cameraMatrix2, distCoeffs2, imageSize, R, T[, R1[, R2[, P1[, P2[, Q[, flags[, alpha[, newImageSize]]]]]]]]) -> R1, R2, P1, P2, Q, validPixROI1, validPixROI2
    
    stereoRectifyUncalibrated(...)
        stereoRectifyUncalibrated(points1, points2, F, imgSize[, H1[, H2[, threshold]]]) -> retval, H1, H2
    
    subtract(...)
        subtract(src1, src2[, dst[, mask[, dtype]]]) -> dst
    
    sumElems(...)
        sumElems(src) -> retval
    
    threshold(...)
        threshold(src, thresh, maxval, type[, dst]) -> retval, dst
    
    trace(...)
        trace(mtx) -> retval
    
    transform(...)
        transform(src, m[, dst]) -> dst
    
    transpose(...)
        transpose(src[, dst]) -> dst
    
    triangulatePoints(...)
        triangulatePoints(projMatr1, projMatr2, projPoints1, projPoints2[, points4D]) -> points4D
    
    undistort(...)
        undistort(src, cameraMatrix, distCoeffs[, dst[, newCameraMatrix]]) -> dst
    
    undistortPoints(...)
        undistortPoints(src, cameraMatrix, distCoeffs[, dst[, R[, P]]]) -> dst
    
    updateMotionHistory(...)
        updateMotionHistory(silhouette, mhi, timestamp, duration) -> None
    
    useOptimized(...)
        useOptimized() -> retval
    
    validateDisparity(...)
        validateDisparity(disparity, cost, minDisparity, numberOfDisparities[, disp12MaxDisp]) -> None
    
    vconcat(...)
        vconcat(src[, dst]) -> dst
    
    waitKey(...)
        waitKey([, delay]) -> retval
    
    warpAffine(...)
        warpAffine(src, M, dsize[, dst[, flags[, borderMode[, borderValue]]]]) -> dst
    
    warpPerspective(...)
        warpPerspective(src, M, dsize[, dst[, flags[, borderMode[, borderValue]]]]) -> dst
    
    watershed(...)
        watershed(image, markers) -> None

DATA
    ADAPTIVE_SKIN_DETECTOR_MORPHING_METHOD_ERODE = 1L
    ADAPTIVE_SKIN_DETECTOR_MORPHING_METHOD_ERODE_DILATE = 3L
    ADAPTIVE_SKIN_DETECTOR_MORPHING_METHOD_ERODE_ERODE = 2L
    ADAPTIVE_SKIN_DETECTOR_MORPHING_METHOD_NONE = 0L
    ADAPTIVE_THRESH_GAUSSIAN_C = 1L
    ADAPTIVE_THRESH_MEAN_C = 0L
    ANN_MLP_GAUSSIAN = 2L
    ANN_MLP_IDENTITY = 0L
    ANN_MLP_NO_INPUT_SCALE = 2L
    ANN_MLP_NO_OUTPUT_SCALE = 4L
    ANN_MLP_SIGMOID_SYM = 1L
    ANN_MLP_TRAIN_PARAMS_BACKPROP = 0L
    ANN_MLP_TRAIN_PARAMS_RPROP = 1L
    ANN_MLP_UPDATE_WEIGHTS = 1L
    BOOST_DEFAULT = 0L
    BOOST_DISCRETE = 0L
    BOOST_GENTLE = 3L
    BOOST_GINI = 1L
    BOOST_LOGIT = 2L
    BOOST_MISCLASS = 3L
    BOOST_REAL = 1L
    BOOST_SQERR = 4L
    BORDER_CONSTANT = 0L
    BORDER_DEFAULT = 4L
    BORDER_ISOLATED = 16L
    BORDER_REFLECT = 2L
    BORDER_REFLECT101 = 4L
    BORDER_REFLECT_101 = 4L
    BORDER_REPLICATE = 1L
    BORDER_TRANSPARENT = 5L
    BORDER_WRAP = 3L
    CALIB_CB_ADAPTIVE_THRESH = 1L
    CALIB_CB_ASYMMETRIC_GRID = 2L
    CALIB_CB_CLUSTERING = 4L
    CALIB_CB_FAST_CHECK = 8L
    CALIB_CB_FILTER_QUADS = 4L
    CALIB_CB_NORMALIZE_IMAGE = 2L
    CALIB_CB_SYMMETRIC_GRID = 1L
    CALIB_FIX_ASPECT_RATIO = 2L
    CALIB_FIX_FOCAL_LENGTH = 16L
    CALIB_FIX_INTRINSIC = 256L
    CALIB_FIX_K1 = 32L
    CALIB_FIX_K2 = 64L
    CALIB_FIX_K3 = 128L
    CALIB_FIX_K4 = 2048L
    CALIB_FIX_K5 = 4096L
    CALIB_FIX_K6 = 8192L
    CALIB_FIX_PRINCIPAL_POINT = 4L
    CALIB_RATIONAL_MODEL = 16384L
    CALIB_SAME_FOCAL_LENGTH = 512L
    CALIB_USE_INTRINSIC_GUESS = 1L
    CALIB_ZERO_DISPARITY = 1024L
    CALIB_ZERO_TANGENT_DIST = 8L
    CASCADE_DO_CANNY_PRUNING = 1L
    CASCADE_DO_ROUGH_SEARCH = 8L
    CASCADE_FIND_BIGGEST_OBJECT = 4L
    CASCADE_SCALE_IMAGE = 2L
    CHAIN_APPROX_NONE = 1L
    CHAIN_APPROX_SIMPLE = 2L
    CHAIN_APPROX_TC89_KCOS = 4L
    CHAIN_APPROX_TC89_L1 = 3L
    CMP_EQ = 0L
    CMP_GE = 2L
    CMP_GT = 1L
    CMP_LE = 4L
    CMP_LT = 3L
    CMP_NE = 5L
    COLORMAP_AUTUMN = 0L
    COLORMAP_BONE = 1L
    COLORMAP_COOL = 8L
    COLORMAP_HOT = 11L
    COLORMAP_HSV = 9L
    COLORMAP_JET = 2L
    COLORMAP_OCEAN = 5L
    COLORMAP_PINK = 10L
    COLORMAP_RAINBOW = 4L
    COLORMAP_SPRING = 7L
    COLORMAP_SUMMER = 6L
    COLORMAP_WINTER = 3L
    COLOR_BAYER_BG2BGR = 46L
    COLOR_BAYER_BG2BGR_VNG = 62L
    COLOR_BAYER_BG2GRAY = 86L
    COLOR_BAYER_BG2RGB = 48L
    COLOR_BAYER_BG2RGB_VNG = 64L
    COLOR_BAYER_GB2BGR = 47L
    COLOR_BAYER_GB2BGR_VNG = 63L
    COLOR_BAYER_GB2GRAY = 87L
    COLOR_BAYER_GB2RGB = 49L
    COLOR_BAYER_GB2RGB_VNG = 65L
    COLOR_BAYER_GR2BGR = 49L
    COLOR_BAYER_GR2BGR_VNG = 65L
    COLOR_BAYER_GR2GRAY = 89L
    COLOR_BAYER_GR2RGB = 47L
    COLOR_BAYER_GR2RGB_VNG = 63L
    COLOR_BAYER_RG2BGR = 48L
    COLOR_BAYER_RG2BGR_VNG = 64L
    COLOR_BAYER_RG2GRAY = 88L
    COLOR_BAYER_RG2RGB = 46L
    COLOR_BAYER_RG2RGB_VNG = 62L
    COLOR_BGR2BGR555 = 22L
    COLOR_BGR2BGR565 = 12L
    COLOR_BGR2BGRA = 0L
    COLOR_BGR2GRAY = 6L
    COLOR_BGR2HLS = 52L
    COLOR_BGR2HLS_FULL = 68L
    COLOR_BGR2HSV = 40L
    COLOR_BGR2HSV_FULL = 66L
    COLOR_BGR2LAB = 44L
    COLOR_BGR2LUV = 50L
    COLOR_BGR2RGB = 4L
    COLOR_BGR2RGBA = 2L
    COLOR_BGR2XYZ = 32L
    COLOR_BGR2YCR_CB = 36L
    COLOR_BGR2YUV = 82L
    COLOR_BGR2YUV_I420 = 128L
    COLOR_BGR2YUV_IYUV = 128L
    COLOR_BGR2YUV_YV12 = 132L
    COLOR_BGR5552BGR = 24L
    COLOR_BGR5552BGRA = 28L
    COLOR_BGR5552GRAY = 31L
    COLOR_BGR5552RGB = 25L
    COLOR_BGR5552RGBA = 29L
    COLOR_BGR5652BGR = 14L
    COLOR_BGR5652BGRA = 18L
    COLOR_BGR5652GRAY = 21L
    COLOR_BGR5652RGB = 15L
    COLOR_BGR5652RGBA = 19L
    COLOR_BGRA2BGR = 1L
    COLOR_BGRA2BGR555 = 26L
    COLOR_BGRA2BGR565 = 16L
    COLOR_BGRA2GRAY = 10L
    COLOR_BGRA2RGB = 3L
    COLOR_BGRA2RGBA = 5L
    COLOR_BGRA2YUV_I420 = 130L
    COLOR_BGRA2YUV_IYUV = 130L
    COLOR_BGRA2YUV_YV12 = 134L
    COLOR_COLORCVT_MAX = 135L
    COLOR_GRAY2BGR = 8L
    COLOR_GRAY2BGR555 = 30L
    COLOR_GRAY2BGR565 = 20L
    COLOR_GRAY2BGRA = 9L
    COLOR_GRAY2RGB = 8L
    COLOR_GRAY2RGBA = 9L
    COLOR_HLS2BGR = 60L
    COLOR_HLS2BGR_FULL = 72L
    COLOR_HLS2RGB = 61L
    COLOR_HLS2RGB_FULL = 73L
    COLOR_HSV2BGR = 54L
    COLOR_HSV2BGR_FULL = 70L
    COLOR_HSV2RGB = 55L
    COLOR_HSV2RGB_FULL = 71L
    COLOR_LAB2BGR = 56L
    COLOR_LAB2LBGR = 78L
    COLOR_LAB2LRGB = 79L
    COLOR_LAB2RGB = 57L
    COLOR_LBGR2LAB = 74L
    COLOR_LBGR2LUV = 76L
    COLOR_LRGB2LAB = 75L
    COLOR_LRGB2LUV = 77L
    COLOR_LUV2BGR = 58L
    COLOR_LUV2LBGR = 80L
    COLOR_LUV2LRGB = 81L
    COLOR_LUV2RGB = 59L
    COLOR_M_RGBA2RGBA = 126L
    COLOR_RGB2BGR = 4L
    COLOR_RGB2BGR555 = 23L
    COLOR_RGB2BGR565 = 13L
    COLOR_RGB2BGRA = 2L
    COLOR_RGB2GRAY = 7L
    COLOR_RGB2HLS = 53L
    COLOR_RGB2HLS_FULL = 69L
    COLOR_RGB2HSV = 41L
    COLOR_RGB2HSV_FULL = 67L
    COLOR_RGB2LAB = 45L
    COLOR_RGB2LUV = 51L
    COLOR_RGB2RGBA = 0L
    COLOR_RGB2XYZ = 33L
    COLOR_RGB2YCR_CB = 37L
    COLOR_RGB2YUV = 83L
    COLOR_RGB2YUV_I420 = 127L
    COLOR_RGB2YUV_IYUV = 127L
    COLOR_RGB2YUV_YV12 = 131L
    COLOR_RGBA2BGR = 3L
    COLOR_RGBA2BGR555 = 27L
    COLOR_RGBA2BGR565 = 17L
    COLOR_RGBA2BGRA = 5L
    COLOR_RGBA2GRAY = 11L
    COLOR_RGBA2M_RGBA = 125L
    COLOR_RGBA2RGB = 1L
    COLOR_RGBA2YUV_I420 = 129L
    COLOR_RGBA2YUV_IYUV = 129L
    COLOR_RGBA2YUV_YV12 = 133L
    COLOR_XYZ2BGR = 34L
    COLOR_XYZ2RGB = 35L
    COLOR_YCR_CB2BGR = 38L
    COLOR_YCR_CB2RGB = 39L
    COLOR_YUV2BGR = 84L
    COLOR_YUV2BGRA_I420 = 105L
    COLOR_YUV2BGRA_IYUV = 105L
    COLOR_YUV2BGRA_NV12 = 95L
    COLOR_YUV2BGRA_NV21 = 97L
    COLOR_YUV2BGRA_UYNV = 112L
    COLOR_YUV2BGRA_UYVY = 112L
    COLOR_YUV2BGRA_Y422 = 112L
    COLOR_YUV2BGRA_YUNV = 120L
    COLOR_YUV2BGRA_YUY2 = 120L
    COLOR_YUV2BGRA_YUYV = 120L
    COLOR_YUV2BGRA_YV12 = 103L
    COLOR_YUV2BGRA_YVYU = 122L
    COLOR_YUV2BGR_I420 = 101L
    COLOR_YUV2BGR_IYUV = 101L
    COLOR_YUV2BGR_NV12 = 91L
    COLOR_YUV2BGR_NV21 = 93L
    COLOR_YUV2BGR_UYNV = 108L
    COLOR_YUV2BGR_UYVY = 108L
    COLOR_YUV2BGR_Y422 = 108L
    COLOR_YUV2BGR_YUNV = 116L
    COLOR_YUV2BGR_YUY2 = 116L
    COLOR_YUV2BGR_YUYV = 116L
    COLOR_YUV2BGR_YV12 = 99L
    COLOR_YUV2BGR_YVYU = 118L
    COLOR_YUV2GRAY_420 = 106L
    COLOR_YUV2GRAY_I420 = 106L
    COLOR_YUV2GRAY_IYUV = 106L
    COLOR_YUV2GRAY_NV12 = 106L
    COLOR_YUV2GRAY_NV21 = 106L
    COLOR_YUV2GRAY_UYNV = 123L
    COLOR_YUV2GRAY_UYVY = 123L
    COLOR_YUV2GRAY_Y422 = 123L
    COLOR_YUV2GRAY_YUNV = 124L
    COLOR_YUV2GRAY_YUY2 = 124L
    COLOR_YUV2GRAY_YUYV = 124L
    COLOR_YUV2GRAY_YV12 = 106L
    COLOR_YUV2GRAY_YVYU = 124L
    COLOR_YUV2RGB = 85L
    COLOR_YUV2RGBA_I420 = 104L
    COLOR_YUV2RGBA_IYUV = 104L
    COLOR_YUV2RGBA_NV12 = 94L
    COLOR_YUV2RGBA_NV21 = 96L
    COLOR_YUV2RGBA_UYNV = 111L
    COLOR_YUV2RGBA_UYVY = 111L
    COLOR_YUV2RGBA_Y422 = 111L
    COLOR_YUV2RGBA_YUNV = 119L
    COLOR_YUV2RGBA_YUY2 = 119L
    COLOR_YUV2RGBA_YUYV = 119L
    COLOR_YUV2RGBA_YV12 = 102L
    COLOR_YUV2RGBA_YVYU = 121L
    COLOR_YUV2RGB_I420 = 100L
    COLOR_YUV2RGB_IYUV = 100L
    COLOR_YUV2RGB_NV12 = 90L
    COLOR_YUV2RGB_NV21 = 92L
    COLOR_YUV2RGB_UYNV = 107L
    COLOR_YUV2RGB_UYVY = 107L
    COLOR_YUV2RGB_Y422 = 107L
    COLOR_YUV2RGB_YUNV = 115L
    COLOR_YUV2RGB_YUY2 = 115L
    COLOR_YUV2RGB_YUYV = 115L
    COLOR_YUV2RGB_YV12 = 98L
    COLOR_YUV2RGB_YVYU = 117L
    COLOR_YUV420P2BGR = 99L
    COLOR_YUV420P2BGRA = 103L
    COLOR_YUV420P2GRAY = 106L
    COLOR_YUV420P2RGB = 98L
    COLOR_YUV420P2RGBA = 102L
    COLOR_YUV420SP2BGR = 93L
    COLOR_YUV420SP2BGRA = 97L
    COLOR_YUV420SP2GRAY = 106L
    COLOR_YUV420SP2RGB = 92L
    COLOR_YUV420SP2RGBA = 96L
    COVAR_COLS = 16L
    COVAR_NORMAL = 1L
    COVAR_ROWS = 8L
    COVAR_SCALE = 4L
    COVAR_SCRAMBLED = 0L
    COVAR_USE_AVG = 2L
    CV_16S = 3
    CV_16SC1 = 3
    CV_16SC2 = 11
    CV_16SC3 = 19
    CV_16SC4 = 27
    CV_16U = 2
    CV_16UC1 = 2
    CV_16UC2 = 10
    CV_16UC3 = 18
    CV_16UC4 = 26
    CV_32F = 5
    CV_32FC1 = 5
    CV_32FC2 = 13
    CV_32FC3 = 21
    CV_32FC4 = 29
    CV_32S = 4
    CV_32SC1 = 4
    CV_32SC2 = 12
    CV_32SC3 = 20
    CV_32SC4 = 28
    CV_64F = 6
    CV_64FC1 = 6
    CV_64FC2 = 14
    CV_64FC3 = 22
    CV_64FC4 = 30
    CV_8S = 1
    CV_8SC1 = 1
    CV_8SC2 = 9
    CV_8SC3 = 17
    CV_8SC4 = 25
    CV_8U = 0
    CV_8UC1 = 0
    CV_8UC2 = 8
    CV_8UC3 = 16
    CV_8UC4 = 24
    CV_AA = 16
    CV_EPNP = 1L
    CV_HIST_ARRAY = 0
    CV_HIST_SPARSE = 1
    CV_ITERATIVE = 0L
    CV_LOAD_IMAGE_COLOR = 1
    CV_LOAD_IMAGE_GRAYSCALE = 0
    CV_LOAD_IMAGE_UNCHANGED = -1
    CV_NEXT_AROUND_DST = 34
    CV_NEXT_AROUND_LEFT = 19
    CV_NEXT_AROUND_ORG = 0
    CV_NEXT_AROUND_RIGHT = 49
    CV_P3P = 2L
    CV_PREV_AROUND_DST = 51
    CV_PREV_AROUND_LEFT = 32
    CV_PREV_AROUND_ORG = 17
    CV_PREV_AROUND_RIGHT = 2
    CV_PTLOC_INSIDE = 0
    CV_PTLOC_ON_EDGE = 2
    CV_PTLOC_OUTSIDE_RECT = -1
    CV_PTLOC_VERTEX = 1
    CV_ROW_SAMPLE = 1
    CV_VAR_CATEGORICAL = 1
    CV_VAR_NUMERICAL = 0
    CV_VAR_ORDERED = 0
    CV_WINDOW_AUTOSIZE = 1
    DCT_INVERSE = 1L
    DCT_ROWS = 4L
    DECOMP_CHOLESKY = 3L
    DECOMP_EIG = 2L
    DECOMP_LU = 0L
    DECOMP_NORMAL = 16L
    DECOMP_QR = 4L
    DECOMP_SVD = 1L
    DEPTH_MASK = 7L
    DEPTH_MASK_16S = 8L
    DEPTH_MASK_16U = 4L
    DEPTH_MASK_32F = 32L
    DEPTH_MASK_32S = 16L
    DEPTH_MASK_64F = 64L
    DEPTH_MASK_8S = 2L
    DEPTH_MASK_8U = 1L
    DEPTH_MASK_ALL = 127L
    DEPTH_MASK_ALL_BUT_8S = 125L
    DEPTH_MASK_FLT = 96L
    DFT_COMPLEX_OUTPUT = 16L
    DFT_INVERSE = 1L
    DFT_REAL_OUTPUT = 32L
    DFT_ROWS = 4L
    DFT_SCALE = 2L
    DIST_LABEL_CCOMP = 0L
    DIST_LABEL_PIXEL = 1L
    DRAW_MATCHES_FLAGS_DEFAULT = 0L
    DRAW_MATCHES_FLAGS_DRAW_OVER_OUTIMG = 1L
    DRAW_MATCHES_FLAGS_DRAW_RICH_KEYPOINTS = 4L
    DRAW_MATCHES_FLAGS_NOT_DRAW_SINGLE_POINTS = 2L
    EM_COV_MAT_DEFAULT = 1L
    EM_COV_MAT_DIAGONAL = 1L
    EM_COV_MAT_GENERIC = 2L
    EM_COV_MAT_SPHERICAL = 0L
    EM_DEFAULT_MAX_ITERS = 100L
    EM_DEFAULT_NCLUSTERS = 5L
    EM_START_AUTO_STEP = 0L
    EM_START_E_STEP = 1L
    EM_START_M_STEP = 2L
    EPNP = 1L
    EVENT_FLAG_ALTKEY = 32L
    EVENT_FLAG_CTRLKEY = 8L
    EVENT_FLAG_LBUTTON = 1L
    EVENT_FLAG_MBUTTON = 4L
    EVENT_FLAG_RBUTTON = 2L
    EVENT_FLAG_SHIFTKEY = 16L
    EVENT_LBUTTONDBLCLK = 7L
    EVENT_LBUTTONDOWN = 1L
    EVENT_LBUTTONUP = 4L
    EVENT_MBUTTONDBLCLK = 9L
    EVENT_MBUTTONDOWN = 3L
    EVENT_MBUTTONUP = 6L
    EVENT_MOUSEMOVE = 0L
    EVENT_RBUTTONDBLCLK = 8L
    EVENT_RBUTTONDOWN = 2L
    EVENT_RBUTTONUP = 5L
    FAST_FEATURE_DETECTOR_TYPE_5_8 = 0L
    FAST_FEATURE_DETECTOR_TYPE_7_12 = 1L
    FAST_FEATURE_DETECTOR_TYPE_9_16 = 2L
    FEATURE_EVALUATOR_HAAR = 0L
    FEATURE_EVALUATOR_HOG = 2L
    FEATURE_EVALUATOR_LBP = 1L
    FILE_NODE_EMPTY = 32L
    FILE_NODE_FLOAT = 2L
    FILE_NODE_FLOW = 8L
    FILE_NODE_INT = 1L
    FILE_NODE_MAP = 6L
    FILE_NODE_NAMED = 64L
    FILE_NODE_NONE = 0L
    FILE_NODE_REAL = 2L
    FILE_NODE_REF = 4L
    FILE_NODE_SEQ = 5L
    FILE_NODE_STR = 3L
    FILE_NODE_STRING = 3L
    FILE_NODE_TYPE_MASK = 7L
    FILE_NODE_USER = 16L
    FILE_STORAGE_APPEND = 2L
    FILE_STORAGE_FORMAT_AUTO = 0L
    FILE_STORAGE_FORMAT_MASK = 56L
    FILE_STORAGE_FORMAT_XML = 8L
    FILE_STORAGE_FORMAT_YAML = 16L
    FILE_STORAGE_INSIDE_MAP = 4L
    FILE_STORAGE_MEMORY = 4L
    FILE_STORAGE_NAME_EXPECTED = 2L
    FILE_STORAGE_READ = 0L
    FILE_STORAGE_UNDEFINED = 0L
    FILE_STORAGE_VALUE_EXPECTED = 1L
    FILE_STORAGE_WRITE = 1L
    FISHEYE_CALIB_CHECK_COND = 4L
    FISHEYE_CALIB_FIX_INTRINSIC = 256L
    FISHEYE_CALIB_FIX_K1 = 16L
    FISHEYE_CALIB_FIX_K2 = 32L
    FISHEYE_CALIB_FIX_K3 = 64L
    FISHEYE_CALIB_FIX_K4 = 128L
    FISHEYE_CALIB_FIX_SKEW = 8L
    FISHEYE_CALIB_RECOMPUTE_EXTRINSIC = 2L
    FISHEYE_CALIB_USE_INTRINSIC_GUESS = 1L
    FLOODFILL_FIXED_RANGE = 65536L
    FLOODFILL_MASK_ONLY = 131072L
    FM_7POINT = 1L
    FM_8POINT = 2L
    FM_LMEDS = 4L
    FM_RANSAC = 8L
    FONT_HERSHEY_COMPLEX = 3L
    FONT_HERSHEY_COMPLEX_SMALL = 5L
    FONT_HERSHEY_DUPLEX = 2L
    FONT_HERSHEY_PLAIN = 1L
    FONT_HERSHEY_SCRIPT_COMPLEX = 7L
    FONT_HERSHEY_SCRIPT_SIMPLEX = 6L
    FONT_HERSHEY_SIMPLEX = 0L
    FONT_HERSHEY_TRIPLEX = 4L
    FONT_ITALIC = 16L
    FREAK_NB_ORIENPAIRS = 45L
    FREAK_NB_PAIRS = 512L
    FREAK_NB_SCALES = 64L
    FUZZY_MEAN_SHIFT_TRACKER_MIN_KERNEL_MASS = 1000L
    FUZZY_MEAN_SHIFT_TRACKER_RM_EDGE_DENSITY_FUZZY = 1L
    FUZZY_MEAN_SHIFT_TRACKER_RM_EDGE_DENSITY_LINEAR = 0L
    FUZZY_MEAN_SHIFT_TRACKER_RM_INNER_DENSITY = 2L
    FUZZY_MEAN_SHIFT_TRACKER_TS_DISABLED = 10L
    FUZZY_MEAN_SHIFT_TRACKER_TS_NONE = 0L
    FUZZY_MEAN_SHIFT_TRACKER_TS_SEARCHING = 1L
    FUZZY_MEAN_SHIFT_TRACKER_TS_SET_WINDOW = 3L
    FUZZY_MEAN_SHIFT_TRACKER_TS_TRACKING = 2L
    GBTREES_ABSOLUTE_LOSS = 1L
    GBTREES_DEVIANCE_LOSS = 4L
    GBTREES_HUBER_LOSS = 3L
    GBTREES_SQUARED_LOSS = 0L
    GC_BGD = 0L
    GC_EVAL = 2L
    GC_FGD = 1L
    GC_INIT_WITH_MASK = 1L
    GC_INIT_WITH_RECT = 0L
    GC_PR_BGD = 2L
    GC_PR_FGD = 3L
    GEMM_1_T = 1L
    GEMM_2_T = 2L
    GEMM_3_T = 4L
    GHT_POSITION = 0L
    GHT_ROTATION = 2L
    GHT_SCALE = 1L
    HAMMING_NORM_TYPE = 6L
    HOGDESCRIPTOR_DEFAULT_NLEVELS = 64L
    HOGDESCRIPTOR_L2HYS = 0L
    IMREAD_ANYCOLOR = 4L
    IMREAD_ANYDEPTH = 2L
    IMREAD_COLOR = 1L
    IMREAD_GRAYSCALE = 0L
    IMREAD_UNCHANGED = -1L
    IMWRITE_JPEG_QUALITY = 1L
    IMWRITE_PNG_BILEVEL = 18L
    IMWRITE_PNG_COMPRESSION = 16L
    IMWRITE_PNG_STRATEGY = 17L
    IMWRITE_PNG_STRATEGY_DEFAULT = 0L
    IMWRITE_PNG_STRATEGY_FILTERED = 1L
    IMWRITE_PNG_STRATEGY_FIXED = 4L
    IMWRITE_PNG_STRATEGY_HUFFMAN_ONLY = 2L
    IMWRITE_PNG_STRATEGY_RLE = 3L
    IMWRITE_PXM_BINARY = 32L
    INPAINT_NS = 0L
    INPAINT_TELEA = 1L
    INTER_AREA = 3L
    INTER_BITS = 5L
    INTER_BITS2 = 10L
    INTER_CUBIC = 2L
    INTER_LANCZOS4 = 4L
    INTER_LINEAR = 1L
    INTER_MAX = 7L
    INTER_NEAREST = 0L
    INTER_TAB_SIZE = 32L
    INTER_TAB_SIZE2 = 1024L
    IPL_DEPTH_16S = 2147483664L
    IPL_DEPTH_16U = 16L
    IPL_DEPTH_32F = 32L
    IPL_DEPTH_32S = 2147483680L
    IPL_DEPTH_64F = 64L
    IPL_DEPTH_8S = 2147483656L
    IPL_DEPTH_8U = 8L
    ITERATIVE = 0L
    KERNEL_ASYMMETRICAL = 2L
    KERNEL_GENERAL = 0L
    KERNEL_INTEGER = 8L
    KERNEL_SMOOTH = 4L
    KERNEL_SYMMETRICAL = 1L
    KMEANS_PP_CENTERS = 2L
    KMEANS_RANDOM_CENTERS = 0L
    KMEANS_USE_INITIAL_LABELS = 1L
    LEV_MARQ_CALC_J = 2L
    LEV_MARQ_CHECK_ERR = 3L
    LEV_MARQ_DONE = 0L
    LEV_MARQ_STARTED = 1L
    LMEDS = 4L
    MAGIC_MASK = -65536L
    MARKER_CROSS = 0L
    MARKER_DIAMOND = 3L
    MARKER_SQUARE = 4L
    MARKER_STAR = 2L
    MARKER_TILTED_CROSS = 1L
    MARKER_TRIANGLE_DOWN = 6L
    MARKER_TRIANGLE_UP = 5L
    MAT_AUTO_STEP = 0L
    MAT_CONTINUOUS_FLAG = 16384L
    MAT_MAGIC_VAL = 1124007936L
    MAT_SUBMATRIX_FLAG = 32768L
    MORPH_BLACKHAT = 6L
    MORPH_CLOSE = 3L
    MORPH_CROSS = 1L
    MORPH_DILATE = 1L
    MORPH_ELLIPSE = 2L
    MORPH_ERODE = 0L
    MORPH_GRADIENT = 4L
    MORPH_HITMISS = 7L
    MORPH_OPEN = 2L
    MORPH_RECT = 0L
    MORPH_TOPHAT = 5L
    NORM_HAMMING = 6L
    NORM_HAMMING2 = 7L
    NORM_INF = 1L
    NORM_L1 = 2L
    NORM_L2 = 4L
    NORM_L2SQR = 5L
    NORM_MINMAX = 32L
    NORM_RELATIVE = 8L
    NORM_TYPE_MASK = 7L
    OPTFLOW_FARNEBACK_GAUSSIAN = 256L
    OPTFLOW_LK_GET_MIN_EIGENVALS = 8L
    OPTFLOW_USE_INITIAL_FLOW = 4L
    ORB_FAST_SCORE = 1L
    ORB_HARRIS_SCORE = 0L
    ORB_K_BYTES = 32L
    P3P = 2L
    PARAM_ALGORITHM = 6L
    PARAM_BOOLEAN = 1L
    PARAM_FLOAT = 7L
    PARAM_GRID_SVM_C = 0L
    PARAM_GRID_SVM_COEF = 4L
    PARAM_GRID_SVM_DEGREE = 5L
    PARAM_GRID_SVM_GAMMA = 1L
    PARAM_GRID_SVM_NU = 3L
    PARAM_GRID_SVM_P = 2L
    PARAM_INT = 0L
    PARAM_MAT = 4L
    PARAM_MAT_VECTOR = 5L
    PARAM_REAL = 2L
    PARAM_SHORT = 10L
    PARAM_STRING = 3L
    PARAM_UCHAR = 11L
    PARAM_UINT64 = 9L
    PARAM_UNSIGNED_INT = 8L
    PROJ_SPHERICAL_EQRECT = 1L
    PROJ_SPHERICAL_ORTHO = 0L
    RANSAC = 8L
    RETR_CCOMP = 2L
    RETR_EXTERNAL = 0L
    RETR_FLOODFILL = 4L
    RETR_LIST = 1L
    RETR_TREE = 3L
    RIGID_BODY_MOTION = 4L
    RNG_NORMAL = 1L
    RNG_UNIFORM = 0L
    ROTATION = 1L
    SELF_SIM_DESCRIPTOR_DEFAULT_LARGE_SIZE = 41L
    SELF_SIM_DESCRIPTOR_DEFAULT_NUM_ANGLES = 20L
    SELF_SIM_DESCRIPTOR_DEFAULT_NUM_DISTANCE_BUCKETS = 7L
    SELF_SIM_DESCRIPTOR_DEFAULT_SMALL_SIZE = 5L
    SELF_SIM_DESCRIPTOR_DEFAULT_START_DISTANCE_BUCKET = 3L
    SORT_ASCENDING = 0L
    SORT_DESCENDING = 16L
    SORT_EVERY_COLUMN = 1L
    SORT_EVERY_ROW = 0L
    SPARSE_MAT_HASH_BIT = -2147483648L
    SPARSE_MAT_HASH_SCALE = 1540483477L
    SPARSE_MAT_MAGIC_VAL = 1123876864L
    SPARSE_MAT_MAX_DIM = 32L
    STEREO_BM_BASIC_PRESET = 0L
    STEREO_BM_FISH_EYE_PRESET = 1L
    STEREO_BM_NARROW_PRESET = 2L
    STEREO_BM_PREFILTER_NORMALIZED_RESPONSE = 0L
    STEREO_BM_PREFILTER_XSOBEL = 1L
    STEREO_SGBM_DISP_SCALE = 16L
    STEREO_SGBM_DISP_SHIFT = 4L
    STEREO_VAR_CYCLE_O = 0L
    STEREO_VAR_CYCLE_V = 1L
    STEREO_VAR_PENALIZATION_CHARBONNIER = 1L
    STEREO_VAR_PENALIZATION_PERONA_MALIK = 2L
    STEREO_VAR_PENALIZATION_TICHONOV = 0L
    STEREO_VAR_USE_AUTO_PARAMS = 8L
    STEREO_VAR_USE_EQUALIZE_HIST = 2L
    STEREO_VAR_USE_INITIAL_DISPARITY = 1L
    STEREO_VAR_USE_MEDIAN_FILTERING = 16L
    STEREO_VAR_USE_SMART_ID = 4L
    SUBDIV2D_NEXT_AROUND_DST = 34L
    SUBDIV2D_NEXT_AROUND_LEFT = 19L
    SUBDIV2D_NEXT_AROUND_ORG = 0L
    SUBDIV2D_NEXT_AROUND_RIGHT = 49L
    SUBDIV2D_PREV_AROUND_DST = 51L
    SUBDIV2D_PREV_AROUND_LEFT = 32L
    SUBDIV2D_PREV_AROUND_ORG = 17L
    SUBDIV2D_PREV_AROUND_RIGHT = 2L
    SUBDIV2D_PTLOC_ERROR = -2L
    SUBDIV2D_PTLOC_INSIDE = 0L
    SUBDIV2D_PTLOC_ON_EDGE = 2L
    SUBDIV2D_PTLOC_OUTSIDE_RECT = -1L
    SUBDIV2D_PTLOC_VERTEX = 1L
    SVD_FULL_UV = 4L
    SVD_MODIFY_A = 1L
    SVD_NO_UV = 2L
    SVM_C = 0L
    SVM_COEF = 4L
    SVM_C_SVC = 100L
    SVM_DEGREE = 5L
    SVM_EPS_SVR = 103L
    SVM_GAMMA = 1L
    SVM_LINEAR = 0L
    SVM_NU = 3L
    SVM_NU_SVC = 101L
    SVM_NU_SVR = 104L
    SVM_ONE_CLASS = 102L
    SVM_P = 2L
    SVM_POLY = 1L
    SVM_RBF = 2L
    SVM_SIGMOID = 3L
    TERM_CRITERIA_COUNT = 1L
    TERM_CRITERIA_EPS = 2L
    TERM_CRITERIA_MAX_ITER = 1L
    THRESH_BINARY = 0L
    THRESH_BINARY_INV = 1L
    THRESH_MASK = 7L
    THRESH_OTSU = 8L
    THRESH_TOZERO = 3L
    THRESH_TOZERO_INV = 4L
    THRESH_TRUNC = 2L
    TM_CCOEFF = 4L
    TM_CCOEFF_NORMED = 5L
    TM_CCORR = 2L
    TM_CCORR_NORMED = 3L
    TM_SQDIFF = 0L
    TM_SQDIFF_NORMED = 1L
    TRANSLATION = 2L
    TYPE_MASK = 4095L
    WARP_INVERSE_MAP = 16L
    WINDOW_AUTOSIZE = 1L
    WINDOW_NORMAL = 0L
    WINDOW_OPENGL = 4096L
    WND_PROP_ASPECT_RATIO = 2L
    WND_PROP_AUTOSIZE = 1L
    WND_PROP_FULLSCREEN = 0L
    WND_PROP_OPENGL = 3L
    __version__ = '2.4.13.2'

VERSION
    2.4.13.2



Process finished with exit code 0

cv

Help on module cv2.cv in cv2:

NAME
    cv2.cv

FILE
    (built-in)

CLASSES
    __builtin__.object
        cvmat
        iplimage
    
    class cvmat(__builtin__.object)
     |  Methods defined here:
     |  
     |  __delitem__(...)
     |      x.__delitem__(y) <==> del x[y]
     |  
     |  __getitem__(...)
     |      x.__getitem__(y) <==> x[y]
     |  
     |  __init__(...)
     |      x.__init__(...) initializes x; see help(type(x)) for signature
     |  
     |  __repr__(...)
     |      x.__repr__() <==> repr(x)
     |