学习Python和OpenCV, 用gAWK抽出OpenCV函数方便查找，
从《The OpenCV Reference Manual, Release 2.4.8.0 》输出： 章节行， 函数行 ，页码
C++开头 1632行
C开头 469 行
Python开头 599行
以下是Python API，文字自动生成，很是粗糙，记录一下，看自己能走多远。2014.03.19
2.1 Basic Structures
2.2 Basic C Structures and Operations
cv.ClearND (arr, idx ) None 67
cv.CloneImage (image ) image 67
cv.CloneMat (mat ) mat 68
cv.CloneMatND (mat ) matND 68
cv.ConvertScale (src, dst, scale=1.0, shift=0.0 ) None 68
cv.Convert (src, dst ) None 68
cv.Copy (src, dst, mask=None ) None 69
cv.CreateData (arr ) None 69
cv.CreateImage (size, depth, channels ) image 70
cv.CreateImageHeader (size, depth, channels ) image 70
cv.CreateMat (rows, cols, type ) mat 70
cv.CreateMatHeader (rows, cols, type ) mat 71
cv.CreateMatND (dims, type ) matND 71
cv.CreateMatNDHeader (dims, type ) matND 71
cv.CrossProduct (src1, src2, dst ) None 72
cv.DotProduct (src1, src2 ) float 72
cv.Get1D (arr, idx ) scalar 73
cv.Get2D (arr, idx0, idx1 ) scalar 73
cv.Get3D (arr, idx0, idx1, idx2 ) scalar 73
cv.GetND (arr, indices ) scalar 73
cv.GetCol (arr, col ) submat 73
cv.GetCols (arr, startCol, endCol ) submat 73
cv.GetDiag (arr, diag=0 ) submat 74
cv.GetDims (arr) -> (dim1, dim2, ... ) 74
cv.GetElemType (arr ) int 75
cv.GetImage (arr ) iplimage 75
cv.GetImageCOI (image ) int 75
cv.GetImageROI (image ) CvRect 75
cv.GetMat (arr, allowND=0 ) mat 76
cv.GetReal1D (arr, idx0 ) float 78
cv.GetReal2D (arr, idx0, idx1 ) float 78
cv.GetReal3D (arr, idx0, idx1, idx2 ) float 78
cv.GetRealND (arr, idx ) float 78
cv.GetRow (arr, row ) submat 78
cv.GetRows (arr, startRow, endRow, deltaRow=1 ) submat 78
cv.GetSize (arr)-> (width, height ) 79
cv.GetSubRect (arr, rect ) submat 79
cv.ResetImageROI (image ) None 84
cv.Reshape (arr, newCn, newRows=0 ) mat 84
cv.ReshapeMatND (arr, newCn, newDims ) mat 85
cv.Set (arr, value, mask=None ) None 86
cv.Set1D (arr, idx, value ) None 86
cv.Set2D (arr, idx0, idx1, value ) None 86
cv.Set3D (arr, idx0, idx1, idx2, value ) None 86
cv.SetND (arr, indices, value ) None 86
cv.SetData (arr, data, step ) None 87
cv.SetImageCOI (image, coi ) None 87
cv.SetImageROI (image, rect ) None 87
cv.SetReal1D (arr, idx, value ) None 88
cv.SetReal2D (arr, idx0, idx1, value ) None 88
cv.SetReal3D (arr, idx0, idx1, idx2, value ) None 88
cv.SetRealND (arr, indices, value ) None 88
cv.SetZero (arr ) None 88
cv.mGet (mat, row, col ) float 88
cv.mSet (mat, row, col, value ) None 89
cv.RNG (seed=-1LL ) CvRNG 90
cv.RandArr (rng, arr, distType, param1, param2 ) None 90
cv.RandInt (rng ) unsigned 90
cv.RandReal (rng ) float 91
cv.fromarray (array, allowND=False ) mat 91
2.3 Dynamic Structures
cv.CloneSeq (seq, storage ) None 96
cv.CreateMemStorage (blockSize=0 ) memstorage 99
2.4 Operations on Arrays
cv2.absdiff (src1, src2[, dst ]) dst 119
cv.AbsDiff (src1, src2, dst ) None 119
cv.AbsDiffS (src, dst, value ) None 120
cv2.add (src1, src2[, dst[, mask[, dtype ]]]) dst 120
cv.Add (src1, src2, dst, mask=None ) None 120
cv.AddS (src, value, dst, mask=None ) None 120
cv2.addWeighted (src1, alpha, src2, beta, gamma[, dst[, dtype ]]) dst 121
cv.AddWeighted (src1, alpha, src2, beta, gamma, dst ) None 122
cv2.bitwise_and (src1, src2[, dst[, mask ]]) dst 122
cv.And (src1, src2, dst, mask=None ) None 122
cv.AndS (src, value, dst, mask=None ) None 122
cv2.bitwise_ not (src[, dst[, mask ]]) dst 123
cv.Not (src, dst ) None 123
cv2.bitwise_or (src1, src2[, dst[, mask ]]) dst 123
cv.Or (src1, src2, dst, mask=None ) None 124
cv.OrS (src, value, dst, mask=None ) None 124
cv2.bitwise_xor (src1, src2[, dst[, mask ]]) dst 124
cv.Xor (src1, src2, dst, mask=None ) None 124
cv.XorS (src, value, dst, mask=None ) None 124
cv2.calcCovarMatrix (samples, flags[, covar[, mean[, ctype ]]]) covar, mean 125
cv.CalcCovarMatrix (vects, covMat, avg, flags ) None 125
cv2.cartToPolar (x, y [, magnitude[, angle[, angleInDegrees ]]]) magnitude, angle 127
cv.CartToPolar (x, y, magnitude, angle=None, angleInDegrees=0 ) None 127
cv2.checkRange (a [, quiet[, minVal[, maxVal ]]]) retval, pos 127
cv2.compare (src1, src2, cmpop[, dst ]) dst 128
cv.Cmp (src1, src2, dst, cmpOp ) None 128
cv.CmpS (src, value, dst, cmpOp ) None 128
cv2.completeSymm (mtx [, lowerToUpper ]) None 129
cv2.convertScaleAbs (src[, dst[, alpha[, beta ]]]) dst 129
cv.ConvertScaleAbs (src, dst, scale=1.0, shift=0.0 ) None 129
cv2.countNonZero (src ) retval 130
cv.CountNonZero (arr ) int 130
cv2.dct (src[, dst[, flags ]]) dst 131
cv.DCT (src, dst, flags ) None 131
cv2.dft (src[, dst[, flags[, nonzeroRows ]]]) dst 133
cv.DFT (src, dst, flags, nonzeroRows=0 ) None 133
cv2.divide (src1, src2[, dst[, scale[, dtype ]]]) dst 136
cv2.divide (scale, src2[, dst[, dtype ]]) dst 136
cv.Div (src1, src2, dst, scale=1 ) None 136
cv2.determinant (mtx ) retval 137
cv.Det (mat ) float 137
cv2.eigen (src, computeEigenvectors[, eigenvalues[, eigenvectors ]]) retval, eigenvalues, eigen- 137
cv.EigenVV (mat, evects, evals, eps, lowindex=-1, highindex=-1 ) None 137
cv2.exp (src[, dst ]) dst 138
cv.Exp (src, dst ) None 138
cv2.flip (src, flipCode[, dst ]) dst 139
cv.Flip (src, dst=None, flipMode=0 ) None 139
cv2.gemm (src1, src2, alpha, src3, gamma[, dst[, flags ]]) dst 140
cv.GEMM (src1, src2, alpha, src3, beta, dst, tABC=0 ) None 140
cv2.getOptimalDFTSize (vecsize ) retval 142
cv.GetOptimalDFTSize (size0 ) int 142
cv2.idct (src[, dst[, flags ]]) dst 142
cv2.idft (src[, dst[, flags[, nonzeroRows ]]]) dst 142
cv2.inRange (src, lowerb, upperb[, dst ]) dst 143
cv.InRange (src, lower, upper, dst ) None 143
cv.InRangeS (src, lower, upper, dst ) None 143
cv2.invert (src[, dst[, flags ]]) retval, dst 144
cv.Invert (src, dst, method=CV_LU ) float 144
cv2.log (src[, dst ]) dst 144
cv.Log (src, dst ) None 144
cv2.LUT (src, lut[, dst[, interpolation ]]) dst 145
cv.LUT (src, dst, lut ) None 145
cv2.magnitude (x, y [, magnitude ]) magnitude 145
cv2.Mahalanobis (v1, v2, icovar ) retval 146
cv.Mahalonobis (vec1, vec2, mat ) None 146
cv2.max (src1, src2[, dst ]) dst 146
cv.Max (src1, src2, dst ) None 147
cv.MaxS (src, value, dst ) None 147
cv2.mean (src[, mask ]) retval 147
cv.Avg (arr, mask=None ) scalar 147
cv2.meanStdDev (src[, mean[, stddev[, mask ]]]) mean, stddev 148
cv.AvgSdv (arr, mask=None) -> (mean, stdDev ) 148
cv2.merge (mv [, dst ]) dst 148
cv.Merge (src0, src1, src2, src3, dst ) None 148
cv2.min (src1, src2[, dst ]) dst 149
cv.Min (src1, src2, dst ) None 149
cv.MinS (src, value, dst ) None 149
cv2.minMaxLoc (src[, mask ]) minVal, maxVal, minLoc, maxLoc 150
cv.MinMaxLoc (arr, mask=None)-> (minVal, maxVal, minLoc, maxLoc ) 150
cv2.mixChannels (src, dst, fromTo ) None 151
cv.MixChannels (src, dst, fromTo ) None 151
cv2.mulSpectrums (a, b, flags [, c[, conjB ]]) c 152
cv.MulSpectrums (src1, src2, dst, flags ) None 152
cv2.multiply (src1, src2[, dst[, scale[, dtype ]]]) dst 152
cv.Mul (src1, src2, dst, scale=1 ) None 153
cv2.mulTransposed (src, aTa[, dst[, delta[, scale[, dtype ]]]]) dst 153
cv.MulTransposed (src, dst, order, delta=None, scale=1.0 ) None 153
cv2.norm (src1[, normType[, mask ]]) retval 154
cv2.norm (src1, src2[, normType[, mask ]]) retval 154
cv.Norm (arr1, arr2, normType=CV_L2, mask=None ) float 154
cv2.normalize (src[, dst[, alpha[, beta[, norm_type[, dtype[, mask ]]]]]]) dst 155
cv2.PCACompute (data [, mean[, eigenvectors[, maxComponents ]]]) mean, eigenvectors 157
cv2.PCAComputeVar (data, retainedVariance [, mean[, eigenvectors ]]) mean, eigenvectors 157
cv2.PCAProject (data, mean, eigenvectors [, result ]) result 158
cv2.PCABackProject (data, mean, eigenvectors [, result ]) result 158
cv2.perspectiveTransform (src, m[, dst ]) dst 159
cv.PerspectiveTransform (src, dst, mat ) None 159
cv2.phase (x, y [, angle[, angleInDegrees ]]) angle 159
cv2.polarToCart (magnitude, angle [, x[, y[, angleInDegrees ]]]) x, y 160
cv.PolarToCart (magnitude, angle, x, y, angleInDegrees=0 ) None 160
cv2.pow (src, power[, dst ]) dst 161
cv.Pow (src, dst, power ) None 161
cv2.randu(dst, low, high ) None 164
cv2.randn(dst, mean, stddev ) None 164
cv2.randShuffle(dst [, iterFactor ]) None 165
cv2.reduce(src, dim, rtype[, dst[, dtype ]]) dst 165
cv.Reduce (src, dst, dim=-1, op=CV_REDUCE_SUM ) None 165
cv2.repeat(src, ny, nx[, dst ]) dst 166
cv.Repeat (src, dst ) None 166
cv2.scaleAdd (src1, alpha, src2[, dst ]) dst 166
cv.ScaleAdd (src1, scale, src2, dst ) None 167
cv2.setIdentity (mtx [, s ]) None 167
cv.SetIdentity (mat, value=1 ) None 167
cv2.solve (src1, src2[, dst[, flags ]]) retval, dst 168
cv.Solve (A, B, X, method=CV_LU ) None 168
cv2.solveCubic (coeffs [, roots ]) retval, roots 168
cv.SolveCubic (coeffs, roots ) None 169
cv2.solvePoly (coeffs [, roots[, maxIters ]]) retval, roots 169
cv2.sort (src, flags[, dst ]) dst 169
cv2.sortIdx (src, flags[, dst ]) dst 170
cv2.split (m [, mv ]) mv 170
cv.Split (src, dst0, dst1, dst2, dst3 ) None 171
cv2.sqrt (src[, dst ]) dst 171
cv.Sqrt (value ) float 171
cv2.subtract (src1, src2[, dst[, mask[, dtype ]]]) dst 171
cv.Sub (src1, src2, dst, mask=None ) None 171
cv.SubRS (src, value, dst, mask=None ) None 171
cv.SubS (src, value, dst, mask=None ) None 172
cv2.SVDecomp (src[, w[, u[, vt[, flags ]]]]) w, u, vt 174
cv.SVD (A, W, U=None, V=None, flags=0 ) None 174
cv2.SVBackSubst (w, u, vt, rhs [, dst ]) dst 175
cv.SVBkSb (W, U, V, B, X, flags ) None 175
cv2.sumElems (src ) retval 175
cv.Sum (arr ) scalar 175
cv2.trace (mtx ) retval 176
cv.Trace (mat ) scalar 176
cv2.transform (src, m[, dst ]) dst 176
cv.Transform (src, dst, transmat, shiftvec=None ) None 176
cv2.transpose (src[, dst ]) dst 177
cv.Transpose (src, dst ) None 177
2.5 Drawing Functions
cv2.circle (img, center, radius, color [, thickness[, lineType[, shift ]]]) None 178
cv.Circle (img, center, radius, color, thickness=1, lineType=8, shift=0 ) None 178
cv2.clipLine (imgRect, pt1, pt2 ) retval, pt1, pt2 179
cv.ClipLine (imgSize, pt1, pt2) -> (point1, point2 ) 179
cv2.ellipse (img, center, axes, angle, startAngle, endAngle, color [, thickness[, lineType[, shift ]] 179
cv2.ellipse (img, box, color [, thickness[, lineType ]]) None 179
cv.Ellipse (img, center, axes, angle, start_angle, end_angle, color, thickness=1, lineType=8, 179
cv.EllipseBox (img, box, color, thickness=1, lineType=8, shift=0 ) None 179
cv2.ellipse2Poly (center, axes, angle, arcStart, arcEnd, delta ) pts 180
cv2.fillConvexPoly (img, points, color [, lineType[, shift ]]) None 181
cv.FillConvexPoly (img, pn, color, lineType=8, shift=0 ) None 181
cv2.fillPoly (img, pts, color [, lineType[, shift[, offset ]]]) None 181
cv.FillPoly (img, polys, color, lineType=8, shift=0 ) None 181
cv2.getTextSize (text, fontFace, fontScale, thickness ) retval, baseLine 182
cv.GetTextSize (textString, font)-> (textSize, baseline ) 182
cv2.line (img, pt1, pt2, color [, thickness[, lineType[, shift ]]]) None 184
cv.Line (img, pt1, pt2, color, thickness=1, lineType=8, shift=0 ) None 184
cv2.rectangle(img, pt1, pt2, color [, thickness[, lineType[, shift ]]]) None 185
cv.Rectangle (img, pt1, pt2, color, thickness=1, lineType=8, shift=0 ) None 185
cv2.polylines (img, pts, isClosed, color [, thickness[, lineType[, shift ]]]) None 186
cv.PolyLine (img, polys, is_closed, color, thickness=1, lineType=8, shift=0 ) None 186
cv2.putText (img, text, org, fontFace, fontScale, color [, thickness[, lineType[, bottomLeftOrigin ] 186
cv.PutText (img, text, org, font, color ) None 186
2.6 XML/YAML Persistence
2.7 XML/YAML Persistence (C API)
cv.Load (filename, storage=None, name=None ) generic 206
cv.Save (filename, structPtr, name=None, comment=None ) None 211
2.8 Clustering
cv2.kmeans (data, K, criteria, attempts, flags [, bestLabels[, centers ]]) retval, bestLabels, centers 217
cv.KMeans2 (samples, nclusters, labels, termcrit, attempts=1, flags=0, centers=None ) float 217
2.9 Utility and System Functions and Macros
cv2.fastAtan2 (y, x ) retval 219
cv.FastArctan (y, x ) float 219
cv2.cubeRoot (val ) retval 220
cv.Cbrt (value ) float 220
cv.Ceil (value ) int 220
cv.Floor (value ) int 220
cv.Round (value ) int 220
cv.IsInf (value ) int 221
cv.IsNaN (value ) int 221
cv2.checkHardwareSupport (feature ) retval 224
cv2.getTickCount () retval 225
cv2.getTickFrequency () retval 225
cv2.getCPUTickCount () retval 226
cv2.setUseOptimized (onoff ) None 227
cv2.useOptimized () retval 227
3.1 Image Filtering
cv2.bilateralFilter (src, d, sigmaColor, sigmaSpace[, dst[, borderType ]]) dst 243
cv2.adaptiveBilateralFilter (src, ksize, sigmaSpace[, dst[, anchor[, borderType ]]]) dst 244
cv2.blur (src, ksize[, dst[, anchor[, borderType ]]]) dst 245
cv2.borderInterpolate (p, len, borderType ) retval 245
cv2.boxFilter (src, ddepth, ksize[, dst[, anchor[, normalize[, borderType ]]]]) dst 246
cv2.copyMakeBorder (src, top, bottom, left, right, borderType[, dst[, value ]]) dst 247
cv.CopyMakeBorder (src, dst, offset, bordertype, value=(0, 0, 0, 0) ) None 247
cv2.dilate (src, kernel[, dst[, anchor[, iterations[, borderType[, borderValue ]]]]]) dst 252
cv.Dilate (src, dst, element=None, iterations=1 ) None 252
cv2.erode (src, kernel[, dst[, anchor[, iterations[, borderType[, borderValue ]]]]]) dst 253
cv.Erode (src, dst, element=None, iterations=1 ) None 253
cv2.filter2D (src, ddepth, kernel[, dst[, anchor[, delta[, borderType ]]]]) dst 253
cv.Filter2D (src, dst, kernel, anchor=(-1, -1) ) None 254
cv2.GaussianBlur (src, ksize, sigmaX[, dst[, sigmaY[, borderType ]]]) dst 254
cv2.getDerivKernels (dx, dy, ksize [, kx[, ky[, normalize[, ktype ]]]]) kx, ky 255
cv2.getGaussianKernel (ksize, sigma [, ktype ]) retval 256
cv2.getStructuringElement (shape, ksize[, anchor ]) retval 256
cv.CreateStructuringElementEx (cols, rows, anchorX, anchorY, shape, values=None ) kernel 257
cv2.medianBlur (src, ksize[, dst ]) dst 257
cv2.morphologyEx (src, op, kernel[, dst[, anchor[, iterations[, borderType[, borderValue ]]]]]) 258
cv.MorphologyEx (src, dst, temp, element, operation, iterations=1 ) None 258
cv2.Laplacian (src, ddepth[, dst[, ksize[, scale[, delta[, borderType ]]]]]) dst 259
cv.Laplace (src, dst, apertureSize=3 ) None 259
cv2.pyrDown (src[, dst[, dstsize[, borderType ]]]) dst 260
cv.PyrDown (src, dst, filter=CV_GAUSSIAN_5X5 ) None 260
cv2.pyrUp (src[, dst[, dstsize[, borderType ]]]) dst 260
cv.PyrUp (src, dst, filter=CV_GAUSSIAN_5X5 ) None 260
cv2.pyrMeanShiftFiltering (src, sp, sr[, dst[, maxLevel[, termcrit ]]]) dst 261
cv.PyrMeanShiftFiltering (src, dst, sp, sr, max_level=1, term- 261
cv2.sepFilter2D (src, ddepth, kernelX, kernelY[, dst[, anchor[, delta[, borderType ]]]]) dst 262
cv.Smooth (src, dst, smoothtype=CV_GAUSSIAN, param1=3, param2=0, param3=0, param4=0 ) 263
cv2.Sobel (src, ddepth, dx, dy[, dst[, ksize[, scale[, delta[, borderType ]]]]]) dst 264
cv.Sobel (src, dst, xorder, yorder, apertureSize=3 ) None 264
cv2.Scharr (src, ddepth, dx, dy[, dst[, scale[, delta[, borderType ]]]]) dst 265
3.2 Geometric Image Transformations
cv2.convertMaps (map1, map2, dstmap1type [, dstmap1[, dstmap2[, nninterpolation ]]]) 266
cv2.getAffineTransform (src, dst ) retval 267
cv.GetAffineTransform (src, dst, mapMatrix ) None 267
cv2.getPerspectiveTransform (src, dst ) retval 268
cv.GetPerspectiveTransform (src, dst, mapMatrix ) None 268
cv2.getRectSubPix (image, patchSize, center [, patch[, patchType ]]) patch 268
cv.GetRectSubPix (src, dst, center ) None 268
cv2.getRotationMatrix2D (center, angle, scale ) retval 269
cv.GetRotationMatrix2D (center, angle, scale, mapMatrix ) None 269
cv2.invertAffineTransform (M [, iM ]) iM 269
cv.LogPolar (src, dst, center, M, flags=CV_INNER_LINEAR+CV_WARP_FILL_OUTLIERS ) 270
cv2.remap(src, map1, map2, interpolation[, dst[, borderMode[, borderValue ]]]) dst 271
cv.Remap (src, dst, mapx, mapy, flags=CV_INNER_LINEAR+CV_WARP_FILL_OUTLIERS, fill- 271
cv2.resize(src, dsize[, dst[, fx[, fy[, interpolation ]]]]) dst 271
cv.Resize (src, dst, interpolation=CV_INTER_LINEAR ) None 271
cv2.warpAffine (src, M, dsize[, dst[, flags[, borderMode[, borderValue ]]]]) dst 273
cv.WarpAffine (src, dst, mapMatrix, flags=CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS, 273
cv.GetQuadrangleSubPix (src, dst, mapMatrix ) None 273
cv2.warpPerspective (src, M, dsize[, dst[, flags[, borderMode[, borderValue ]]]]) dst 273
cv.WarpPerspective (src, dst, mapMatrix, flags=CV_INNER_LINEAR+CV_WARP_FILL_OUTLIERS, 273
cv2.initUndistortRectifyMap (cameraMatrix, distCoeffs, R, newCameraMatrix, size, m1type [, 274
cv.InitUndistortRectifyMap (cameraMatrix, distCoeffs, R, newCameraMatrix, map1, map2 ) 274
cv.InitUndistortMap (cameraMatrix, distCoeffs, map1, map2 ) None 274
cv2.getDefaultNewCameraMatrix (cameraMatrix [, imgsize[, centerPrincipalPoint ]]) retval 275
cv2.undistort (src, cameraMatrix, distCoeffs[, dst[, newCameraMatrix ]]) dst 276
cv.Undistort2 (src, dst, cameraMatrix, distCoeffs ) None 276
cv.UndistortPoints (src, dst, cameraMatrix, distCoeffs, R=None, P=None ) None 277
3.3 Miscellaneous Image Transformations
cv2.adaptiveThreshold (src, maxValue, adaptiveMethod, thresholdType, blockSize, C[, dst ]) 278
cv.AdaptiveThreshold (src, dst, maxValue, adaptive_method=CV_ADAPTIVE_THRESH_MEAN_C, 278
cv2.cvtColor (src, code[, dst[, dstCn ]]) dst 279
cv.CvtColor (src, dst, code ) None 279
cv2.distanceTransform (src, distanceType, maskSize[, dst ]) dst 284
cv.DistTransform (src, dst, distance_type=CV_DIST_L2, mask_size=3, mask=None, la- 284
cv2.floodFill (image, mask, seedPoint, newVal [, loDiff[, upDiff[, flags ]]]) retval, rect 285
cv.FloodFill (image, seed_point, new_val, lo_diff=(0, 0, 0, 0), up_diff=(0, 0, 0, 0), flags=4, 285
cv2.integral (src[, sum[, sdepth ]]) sum 287
cv2.integral2 (src[, sum[, sqsum[, sdepth ]]]) sum, sqsum 287
cv2.integral3 (src[, sum[, sqsum[, tilted[, sdepth ]]]]) sum, sqsum, tilted 287
cv.Integral (image, sum, sqsum=None, tiltedSum=None ) None 287
cv2.threshold (src, thresh, maxval, type[, dst ]) retval, dst 288
cv.Threshold (src, dst, threshold, maxValue, thresholdType ) None 289
cv2.watershed (image, markers ) None 291
cv2.grabCut (img, mask, rect, bgdModel, fgdModel, iterCount [, mode ]) None 291
3.4 Histograms
cv2.calcHist (images, channels, mask, histSize, ranges [, hist[, accumulate ]]) hist 292
cv.CalcHist (image, hist, accumulate=0, mask=None ) None 293
cv2.calcBackProject (images, channels, hist, ranges, scale [, dst ]) dst 295
cv.CalcBackProject (image, back_project, hist ) None 295
cv2.compareHist (H1, H2, method ) retval 296
cv.CompareHist (hist1, hist2, method ) float 296
cv.CalcEMD2 (signature1, signature2, distance_type, distance_func=None, cost_matrix=None, 297
cv2.equalizeHist (src[, dst ]) dst 298
cv.CalcBackProjectPatch (images, dst, patch_size, hist, method, factor ) None 298
cv.CalcProbDensity (hist1, hist2, dst_hist, scale=255 ) None 299
cv.ClearHist (hist ) None 300
cv.CreateHist (dims, type, ranges=None, uniform=1 ) hist 300
cv.GetMinMaxHistValue (hist)-> (min_value, max_value, min_idx, max_idx ) 301
cv.NormalizeHist (hist, factor ) None 302
cv.ThreshHist (hist, threshold ) None 302
3.5 Structural Analysis and Shape Descriptors
cv2.moments (array [, binaryImage ]) retval 303
cv.Moments (arr, binary=0 ) moments 303
cv2.HuMoments (m [, hu ]) hu 304
cv.GetHuMoments (moments ) hu 304
cv2.findContours (image, mode, method [, contours[, hierarchy[, offset ]]]) contours, hierar- 305
cv.FindContours (image, storage, mode=CV_RETR_LIST, method=CV_CHAIN_APPROX_SIMPLE, 305
cv2.drawContours (image, contours, contourIdx, color [, thickness[, lineType[, hierarchy[, 306
cv.DrawContours (img, contour, external_color, hole_color, max_level, thickness=1, lineType=8, 306
cv2.approxPolyDP (curve, epsilon, closed [, approxCurve ]) approxCurve 308
cv.ApproxChains (src_seq, storage, method=CV_CHAIN_APPROX_SIMPLE, parameter=0, mini- 309
cv2.arcLength (curve, closed ) retval 309
cv.ArcLength (curve, slice=CV_WHOLE_SEQ, isClosed=-1 ) float 309
cv2.boundingRect (points ) retval 310
cv.BoundingRect (points, update=0 ) CvRect 310
cv2.contourArea (contour [, oriented ]) retval 310
cv.ContourArea (contour, slice=CV_WHOLE_SEQ ) float 310
cv2.convexHull (points [, hull[, clockwise[, returnPoints ]]]) hull 311
cv.ConvexHull2 (points, storage, orientation=CV_CLOCKWISE, return_points=0 ) convexHull 311
cv2.convexityDefects (contour, convexhull [, convexityDefects ]) convexityDefects 311
cv.ConvexityDefects (contour, convexhull, storage ) convexityDefects 311
cv2.fitEllipse (points ) retval 313
cv.FitEllipse2 (points ) Box2D 313
cv2.fitLine (points, distType, param, reps, aeps [, line ]) line 313
cv.FitLine (points, dist_type, param, reps, aeps ) line 313
cv2.isContourConvex (contour ) retval 314
cv.CheckContourConvexity (contour ) int 314
cv2.minAreaRect (points ) retval 315
cv.MinAreaRect2 (points, storage=None ) Box2D 315
cv2.minEnclosingCircle (points ) center, radius 315
cv.MinEnclosingCircle (points)-> (int, center, radius ) 315
cv2.matchShapes (contour1, contour2, method, parameter ) retval 316
cv.MatchShapes (object1, object2, method, parameter=0 ) float 316
cv2.pointPolygonTest (contour, pt, measureDist ) retval 316
cv.PointPolygonTest (contour, pt, measure_dist ) float 317
3.6 Motion Analysis and Object Tracking
cv2.accumulate (src, dst[, mask ]) None 318
cv.Acc (image, sum, mask=None ) None 318
cv2.accumulateSquare (src, dst[, mask ]) None 319
cv.SquareAcc (image, sqsum, mask=None ) None 319
cv2.accumulateProduct (src1, src2, dst[, mask ]) None 319
cv.MultiplyAcc (image1, image2, acc, mask=None ) None 320
cv2.accumulateWeighted (src, dst, alpha[, mask ]) None 320
cv.RunningAvg (image, acc, alpha, mask=None ) None 320
3.7 Feature Detection
cv2.Canny (image, threshold1, threshold2 [, edges[, apertureSize[, L2gradient ]]]) edges 322
cv.Canny (image, edges, threshold1, threshold2, aperture_size=3 ) None 322
cv2.cornerEigenValsAndVecs (src, blockSize, ksize[, dst[, borderType ]]) dst 323
cv.CornerEigenValsAndVecs (image, eigenvv, blockSize, aperture_size=3 ) None 323
cv2.cornerHarris (src, blockSize, ksize, k[, dst[, borderType ]]) dst 324
cv.CornerHarris (image, harris_dst, blockSize, aperture_size=3, k=0.04 ) None 324
cv2.cornerMinEigenVal (src, blockSize[, dst[, ksize[, borderType ]]]) dst 324
cv.CornerMinEigenVal (image, eigenval, blockSize, aperture_size=3 ) None 324
cv2.cornerSubPix (image, corners, winSize, zeroZone, criteria ) None 325
cv.FindCornerSubPix (image, corners, win, zero_zone, criteria ) corners 325
cv2.goodFeaturesToTrack (image, maxCorners, qualityLevel, minDistance [, corners[, mask[, 326
cv.GoodFeaturesToTrack (image, eigImage, tempImage, cornerCount, qualityLevel, minDistance, 326
cv2.HoughCircles (image, method, dp, minDist [, circles[, param1[, param2[, minRadius[, maxRa- 328
cv2.HoughLines (image, rho, theta, threshold [, lines[, srn[, stn ]]]) lines 329
cv.HoughLines2 (image, storage, method, rho, theta, threshold, param1=0, param2=0 ) lines 329
cv2.HoughLinesP (image, rho, theta, threshold [, lines[, minLineLength[, maxLineGap ]]]) 330
cv2.preCornerDetect (src, ksize[, dst[, borderType ]]) dst 333
cv.PreCornerDetect (image, corners, apertureSize=3 ) None 333
3.8 Object Detection
cv2.matchTemplate (image, templ, method [, result ]) result 334
cv.MatchTemplate (image, templ, result, method ) None 334
4.1 User Interface
cv.CreateTrackbar (trackbarName, windowName, value, count, onChange ) None 337
cv2.getTrackbarPos (trackbarname, winname ) retval 338
cv.GetTrackbarPos (trackbarName, windowName ) retval 338
cv2.imshow (winname, mat ) None 338
cv.ShowImage (name, image ) None 338
cv2.namedWindow (winname [, flags ]) None 339
cv.NamedWindow (name, flags=CV_WINDOW_AUTOSIZE ) None 339
cv2.destroyWindow (winname ) None 340
cv.DestroyWindow (name ) None 340
cv2.destroyAllWindows () None 340
cv.DestroyAllWindows () None 340
cv2.moveWindow (winname, x, y ) None 340
cv.MoveWindow (name, x, y ) None 340
cv2.resizeWindow(winname, width, height ) None 340
cv.ResizeWindow (name, width, height ) None 340
cv.SetMouseCallback (windowName, onMouse, param=None ) None 341
cv2.setTrackbarPos (trackbarname, winname, pos ) None 341
cv.SetTrackbarPos (trackbarName, windowName, pos ) None 341
cv2.waitKey ([delay ]) retval 341
cv.WaitKey (delay=0 ) int 342
4.2 Reading and Writing Images and Video
cv2.imdecode (buf, flags ) retval 343
cv2.imencode (ext, img [, params ]) retval, buf 343
cv2.imread (filename [, flags ]) retval 344
cv.LoadImage (filename, iscolor=CV_LOAD_IMAGE_COLOR ) None 344
cv.LoadImageM (filename, iscolor=CV_LOAD_IMAGE_COLOR ) None 344
cv2.imwrite (filename, img [, params ]) retval 345
cv.SaveImage (filename, image ) None 345
cv2.VideoCapture () <VideoCapture object> 347
cv2.VideoCapture (filename ) <VideoCapture object> 347
cv2.VideoCapture (device ) <VideoCapture object> 347
cv.CaptureFromCAM (index ) CvCapture 347
cv.CaptureFromFile (filename ) CvCapture 347
cv2.VideoCapture.open (filename ) retval 348
cv2.VideoCapture.open (device ) retval 348
cv2.VideoCapture.isOpened () retval 348
cv2.VideoCapture.release() None 348
cv2.VideoCapture.grab () retval 348
cv.GrabFrame (capture ) int 348
cv2.VideoCapture.retrieve([image [, channel ]]) retval, image 349
cv.RetrieveFrame (capture ) image 349
cv2.VideoCapture.read([image ]) retval, image 349
cv.QueryFrame (capture ) image 349
cv2.VideoCapture.get (propId ) retval 350
cv.GetCaptureProperty (capture, property_id ) float 350
cv2.VideoCapture.set (propId, value ) retval 350
cv.SetCaptureProperty (capture, property_id, value ) retval 350
cv2.VideoWriter ([filename, fourcc, fps, frameSize [, isColor ]]) <VideoWriter object> 351
cv.CreateVideoWriter (filename, fourcc, fps, frame_size, is_color=true ) CvVideoWriter 352
cv2.VideoWriter.isOpened () retval 352
cv2.VideoWriter.open (filename, fourcc, fps, frameSize [, isColor ]) retval 352
cv2.VideoWriter.write (image ) None 352
cv2.VideoWriter.open (filename, fourcc, fps, frameSize [, isColor ]) retval 352
cv2.VideoWriter.isOpened () retval 352
cv2.VideoWriter.write (image ) None 353
cv.WriteFrame (writer, image ) int 353
4.3 Qt New Functions
cv2.setWindowProperty (winname, prop_id, prop_value ) None 354
cv2.getWindowProperty (winname, prop_id ) retval 355
5.1 Motion Analysis and Object Tracking
cv2.calcOpticalFlowPyrLK (prevImg, nextImg, prevPts [, nextPts[, status[, err[, winSize[, 361
cv.CalcOpticalFlowPyrLK (prev, curr, prevPyr, currPyr, prevFeatures, winSize, level, criteria, flags, 361
cv2.buildOpticalFlowPyramid (img, winSize, maxLevel [, pyramid[, withDerivatives[, pyrBor- 362
cv2.calcOpticalFlowFarneback (prev, next, pyr_scale, levels, winsize, iterations, poly_n, 363
cv2.estimateRigidTransform (src, dst, fullAffine ) retval 364
cv2.updateMotionHistory (silhouette, mhi, timestamp, duration ) None 365
cv.UpdateMotionHistory (silhouette, mhi, timestamp, duration ) None 365
cv2.calcMotionGradient (mhi, delta1, delta2 [, mask[, orientation[, apertureSize ]]]) mask, 365
cv.CalcMotionGradient (mhi, mask, orientation, delta1, delta2, apertureSize=3 ) None 365
cv2.calcGlobalOrientation (orientation, mask, mhi, timestamp, duration ) retval 366
cv.CalcGlobalOrientation (orientation, mask, mhi, timestamp, duration ) float 366
cv2.segmentMotion (mhi, timestamp, segThresh [, segmask ]) segmask, boundingRects 367
cv.SegmentMotion (mhi, seg_mask, storage, timestamp, seg_thresh ) boundingRects 367
cv2.CamShift (probImage, window, criteria ) retval, window 367
cv.CamShift (prob_image, window, criteria) -> (int, comp, box ) 367
cv2.meanShift (probImage, window, criteria ) retval, window 368
cv.MeanShift (prob_image, window, criteria ) comp 368
cv2.KalmanFilter ([dynamParams, measureParams [, controlParams[, type ]]]) <KalmanFilter 369
cv.CreateKalman (dynam_params, measure_params, control_params=0 ) CvKalman 369
cv2.KalmanFilter.predict ([control ]) retval 369
cv.KalmanPredict (kalman, control=None ) mat 369
cv2.KalmanFilter.correct (measurement ) retval 370
cv.KalmanCorrect (kalman, measurement ) mat 370
cv2.BackgroundSubtractor.apply (image [, fgmask[, learningRate ]]) fgmask 370
cv2.BackgroundSubtractorMOG ([history, nmixtures, backgroundRatio [, noiseSigma ]]) 371
6.1 Camera Calibration and 3D Reconstruction
cv2.calibrateCamera (objectPoints, imagePoints, imageSize [, cameraMatrix[, distCoeffs[, rvecs[, 379
cv.CalibrateCamera2 (objectPoints, imagePoints, pointCounts, imageSize, cameraMatrix, distCo- 379
cv2.calibrationMatrixValues (cameraMatrix, imageSize, apertureWidth, apertureHeight ) 381
cv2.composeRT (rvec1, tvec1, rvec2, tvec2 [, rvec3[, tvec3[, dr3dr1[, dr3dt1[, dr3dr2[, dr3dt2[, 382
cv.ComputeCorrespondEpilines (points, whichImage, F, lines ) None 382
cv2.convertPointsToHomogeneous (src[, dst ]) dst 383
cv2.convertPointsFromHomogeneous (src[, dst ]) dst 383
cv.ConvertPointsHomogeneous (src, dst ) None 383
cv2.correctMatches (F, points1, points2 [, newPoints1[, newPoints2 ]]) newPoints1, new- 384
cv2.decomposeProjectionMatrix (projMatrix [, cameraMatrix[, rotMatrix[, transVect[, rotMa- 384
cv.DecomposeProjectionMatrix (projMatrix, cameraMatrix, rotMatrix, transVect, rotMa- 384
cv2.drawChessboardCorners (image, patternSize, corners, patternWasFound ) None 385
cv.DrawChessboardCorners (image, patternSize, corners, patternWasFound ) None 385
cv2.findChessboardCorners (image, patternSize [, corners[, flags ]]) retval, corners 386
cv.FindChessboardCorners (image, patternSize, flags=CV_CALIB_CB_ADAPTIVE_THRESH ) 386
cv2.findCirclesGridDefault (image, patternSize [, centers[, flags ]]) retval, centers 387
cv2.solvePnP (objectPoints, imagePoints, cameraMatrix, distCoeffs [, rvec[, tvec[, useExtrin- 388
cv.FindExtrinsicCameraParams2 (objectPoints, imagePoints, cameraMatrix, distCoeffs, rvec, 388
cv2.solvePnPRansac (objectPoints, imagePoints, cameraMatrix, distCoeffs [, rvec[, tvec[, use- 389
cv2.findFundamentalMat (points1, points2 [, method[, param1[, param2[, mask ]]]]) retval, 390
cv.FindFundamentalMat (points1, points2, fundamentalMatrix, method=CV_FM_RANSAC, 390
cv2.findHomography (srcPoints, dstPoints[, method[, ransacReprojThreshold[, mask ]]]) ret- 391
cv.FindHomography (srcPoints, dstPoints, H, method=0, ransacReprojThreshold=3.0, status=None ) 391
cv2.estimateAffine3D (src, dst[, out[, inliers[, ransacThreshold[, confidence ]]]]) retval, 392
cv2.filterSpeckles (img, newVal, maxSpeckleSize, maxDiff [, buf ]) None 393
cv2.getOptimalNewCameraMatrix (cameraMatrix, distCoeffs, imageSize, alpha [, newImgSize[, 393
cv.GetOptimalNewCameraMatrix (cameraMatrix, distCoeffs, imageSize, alpha, newCameraMatrix, 393
cv2.initCameraMatrix2D (objectPoints, imagePoints, imageSize [, aspectRatio ]) retval 394
cv.InitIntrinsicParams2D (objectPoints, imagePoints, npoints, imageSize, cameraMatrix, aspec- 394
cv2.matMulDeriv (A, B [, dABdA[, dABdB ]]) dABdA, dABdB 395
cv2.projectPoints (objectPoints, rvec, tvec, cameraMatrix, distCoeffs [, imagePoints[, jacobian[, 395
cv.ProjectPoints2 (objectPoints, rvec, tvec, cameraMatrix, distCoeffs, imagePoints, dpdrot=None, 395
cv2.reprojectImageTo3D(disparity, Q [, _3dImage[, handleMissingValues[, ddepth ]]]) 396
cv.ReprojectImageTo3D (disparity, _3dImage, Q, handleMissingValues=0 ) None 396
cv2.RQDecomp3x3 (src[, mtxR[, mtxQ[, Qx[, Qy[, Qz ]]]]]) retval, mtxR, mtxQ, Qx, Qy, Qz 397
cv.RQDecomp3x3 (M, R, Q, Qx=None, Qy=None, Qz=None ) eulerAngles 397
cv2.Rodrigues (src[, dst[, jacobian ]]) dst, jacobian 397
cv.Rodrigues2 (src, dst, jacobian=0 ) None 397
cv2.StereoBM ([preset [, ndisparities[, SADWindowSize ]]]) <StereoBM object> 399
cv.CreateStereoBMState (preset=CV_STEREO_BM_BASIC, numberOfDisparities=0 ) 399
cv2.StereoBM.compute (left, right [, disparity[, disptype ]]) disparity 399
cv.FindStereoCorrespondenceBM (left, right, disparity, state ) None 399
cv2.StereoSGBM ([minDisparity, numDisparities, SADWindowSize [, P1[, P2[, disp12MaxDiff[, 401
cv2.StereoSGBM.compute (left, right [, disp ]) disp 402
cv2.stereoCalibrate (objectPoints, imagePoints1, imagePoints2, imageSize [, cameraMatrix1[, 402
cv.StereoCalibrate (objectPoints, imagePoints1, imagePoints2, pointCounts, cameraMatrix1, 402
cv.StereoRectify (cameraMatrix1, cameraMatrix2, distCoeffs1, distCoeffs2, imageSize, R, T, 404
cv2.stereoRectifyUncalibrated (points1, points2, F, imgSize [, H1[, H2[, threshold ]]]) ret- 407
cv.StereoRectifyUncalibrated (points1, points2, F, imageSize, H1, H2, threshold=5 ) None 407
cv2.triangulatePoints (projMatr1, projMatr2, projPoints1, projPoints2 [, points4D ]) 407
7.1 Feature Detection and Description
7.2 Common Interfaces of Feature Detectors
cv2.KeyPoint ([x, y, _size [, _angle[, _response[, _octave[, _class_id ]]]]]) <KeyPoint ob- 414
7.3 Common Interfaces of Descriptor Extractors
7.4 Common Interfaces of Descriptor Matchers
7.5 Common Interfaces of Generic Descriptor Matchers
7.6 Drawing Function of Keypoints and Matches
7.7 Object Categorization
8.1 Cascade Classification
cv2.CascadeClassifier ([filename ]) <CascadeClassifier object> 448
cv2.CascadeClassifier.empty () retval 449
cv2.CascadeClassifier.load (filename ) retval 449
cv2.CascadeClassifier.detectMultiScale (image [, scaleFactor[, minNeighbors[, flags[, min- 449
cv2.CascadeClassifier.detectMultiScale (image, rejectLevels, levelWeights [, scaleFactor[, 449
cv.HaarDetectObjects (image, cascade, storage, scale_factor=1.1, min_neighbors=3, flags=0, 449
cv2.groupRectangles (rectList, groupThreshold [, eps ]) rectList, weights 451
8.2 Latent SVM
9.1 Statistical Models
cv2.StatModel.save (filename [, name ]) None 459
cv2.StatModel.load (filename [, name ]) None 459
9.2 Normal Bayes Classifier
cv2.NormalBayesClassifier ([trainData, responses [, varIdx[, sampleIdx ]]]) <Normal- 461
cv2.NormalBayesClassifier.train (trainData, responses [, varIdx[, sampleIdx[, update ]]]) 461
cv2.NormalBayesClassifier.predict (samples ) retval, results 462
9.3 K-Nearest Neighbors
cv2.KNearest.train (trainData, responses [, sampleIdx[, isRegression[, maxK[, updateBase ]]] 463
cv2.KNearest.find_ nearest (samples, k[, results[, neighborResponses[, dists ]]]) retval, re- 463
9.4 Support Vector Machines
cv2.SVM ([trainData, responses [, varIdx[, sampleIdx[, params ]]]]) <SVM object> 469
cv2.SVM.train (trainData, responses [, varIdx[, sampleIdx[, params ]]]) retval 469
cv2.SVM.train_auto (trainData, responses, varIdx, sampleIdx, params [, k_fold[, Cgrid[, gamma- 470
cv2.SVM.predict (sample[, returnDFVal ]) retval 471
cv2.SVM.predict_all (samples[, results ]) results 471
cv2.SVM.get_ support_vector_count () retval 472
cv2.SVM.get_var_count () retval 472
9.5 Decision Trees
cv2.DTree.train (trainData, tflag, responses [, varIdx[, sampleIdx[, varType[, missingDataMask[, 477
cv2.DTree.predict (sample[, missingDataMask[, preprocessedInput ]]) retval 477
cv2.DTree.getVarImportance () retval 478
9.6 Boosting
cv2.Boost ([trainData, tflag, responses [, varIdx[, sampleIdx[, varType[, missingDataMask[, 481
cv2.Boost.train (trainData, tflag, responses [, varIdx[, sampleIdx[, varType[, missingDataMask[, 482
cv2.Boost.predict (sample[, missing[, slice[, rawMode[, returnSum ]]]]) retval 482
cv2.Boost.prune (slice ) None 483
9.7 Gradient Boosted Trees
cv2.GBTrees ([trainData, tflag, responses [, varIdx[, sampleIdx[, varType[, missingDataMask[, 486
cv2.GBTrees.train (trainData, tflag, responses [, varIdx[, sampleIdx[, varType[, missingData- 486
cv2.GBTrees.predict (sample[, missing[, slice[, k ]]]) retval 487
cv2.GBTrees.clear () None 487
9.8 Random Trees
cv2.RTrees.train (trainData, tflag, responses [, varIdx[, sampleIdx[, varType[, missingData- 490
cv2.RTrees.predict (sample[, missing ]) retval 490
cv2.RTrees.predict_prob (sample[, missing ]) retval 491
cv2.RTrees.getVarImportance () retval 491
9.9 Extremely randomized trees
cv2.EM ([nclusters [, covMatType[, termCrit ]]]) <EM object> 494
cv2.EM.train (samples[, logLikelihoods[, labels[, probs ]]]) retval, logLikelihoods, labels, 495
cv2.EM.trainE (samples, means0[, covs0[, weights0[, logLikelihoods[, labels[, probs ]]]]]) 495
cv2.EM.trainM (samples, probs0[, logLikelihoods[, labels[, probs ]]]) retval, logLikelihoods, 495
cv2.EM.predict (sample[, probs ]) retval, probs 496
cv2.EM.isTrained () retval 496
9.11 Neural Networks
cv2.ANN_MLP ([layerSizes [, activateFunc[, fparam1[, fparam2 ]]]]) <ANN_MLP object> 500
cv2.ANN_MLP.create (layerSizes [, activateFunc[, fparam1[, fparam2 ]]]) None 500
cv2.ANN_MLP.train (inputs, outputs, sampleWeights [, sampleIdx[, params[, flags ]]]) retval 501
cv2.ANN_MLP.predict (inputs [, outputs ]) retval, outputs 502
9.12 MLData
10.1 Fast Approximate Nearest Neighbor Search
10.2 Clustering
11.1 GPU Module Introduction
11.2 Initalization and Information
11.3 Data Structures
11.4 Operations on Matrices
11.5 Per-element Operations
11.6 Image Processing
11.7 Matrix Reductions
11.8 Object Detection
11.9 Feature Detection and Description
11.11 Camera Calibration and 3D Reconstruction
11.12 Video Analysis
12.1 Inpainting
cv2.inpaint (src, inpaintMask, inpaintRadius, flags[, dst ]) dst 629
cv.Inpaint (src, mask, dst, inpaintRadius, flags ) None 629
12.2 Denoising
13.1 Stitching Pipeline
13.2 References
13.3 High Level Functionality
13.4 Camera
13.5 Features Finding and Images Matching
13.6 Rotation Estimation
13.7 Autocalibration
13.8 Images Warping
13.9 Seam Estimation
13.11 Image Blenders
14.1 Feature Detection and Description
cv2.SURF ([hessianThreshold [, nOctaves[, nOctaveLayers[, extended[, upright ]]]]]) <SURF 663
cv2.SURF.detect (image [, mask ]) keypoints 663
cv.ExtractSURF (image, mask, storage, params)-> (keypoints, descriptors ) 663
15.1 Stereo Correspondence
15.2 FaceRecognizer - Face Recognition with OpenCV
15.3 Retina : a Bio mimetic human retina model
15.4 OpenFABMAP
16.1 Motion Analysis
cv.CalcOpticalFlowBM (prev, curr, blockSize, shiftSize, max_range, usePrevious, velx, vely ) 759
cv.CalcOpticalFlowHS (prev, curr, usePrevious, velx, vely, lambda, criteria ) None 760
cv.CalcOpticalFlowLK (prev, curr, winSize, velx, vely ) None 760
16.2 Expectation Maximization
16.3 Histograms
cv.QueryHistValue_ 1D (hist, idx0 ) float 765
cv.QueryHistValue_2D (hist, idx0, idx1 ) float 765
cv.QueryHistValue_3D (hist, idx0, idx1, idx2 ) float 765
cv.QueryHistValue_ nD (hist, idx ) float 765
16.4 Planar Subdivisions (C API)
cv.CalcSubdivVoronoi2D (subdiv ) None 769
cv.ClearSubdivVoronoi2D (subdiv ) None 769
cv.CreateSubdivDelaunay2D (rect, storage ) CvSubdiv2D 769
cv.FindNearestPoint2D (subdiv, pt ) point 769
cv.Subdiv2DEdgeDst (edge ) point 770
cv.Subdiv2DGetEdge (edge, type ) CvSubdiv2DEdge 770
cv.Subdiv2DNextEdge (edge ) CvSubdiv2DEdge 771
cv.Subdiv2DLocate (subdiv, pt) -> (loc, where ) 772
cv.Subdiv2DRotateEdge (edge, rotate ) CvSubdiv2DEdge 772
cv.SubdivDelaunay2DInsert (subdiv, pt ) point 772
16.5 Feature Detection and Description
16.6 Common Interfaces of Descriptor Extractors
16.7 Common Interfaces of Generic Descriptor Matchers
17.1 OpenCL Module Introduction
17.2 Data Structures and Utility Functions
17.3 Data Structures
17.4 Operations on Matrics
17.5 Matrix Reductions
17.6 Image Filtering
17.7 Image Processing
17.8 ml.Machine Learning
17.9 Object Detection
17.11 Video Analysis
17.12 Camera Calibration and 3D Reconstruction
18.1 Super Resolution --------------------- 本文来自 northelec 的CSDN 博客 ，全文地址请点击：https://blog.csdn.net/hyb0106/article/details/21489827?utm_source=copy