ó  ‰\c @sƒddlZddlZddlmZddlmZmZm Z m Z ddl m Z ddl mZddlmZmZmZmZeee fZd „Zddd eeddd „Zddd eedddd „Zeddd d eed„Zd ed„Zd„Zdddd dddd eed„ Zd„Zej d„Z!d„Z"iddd deed„Z#dS(iÿÿÿÿN(tndimagei(tSimilarityTransformtAffineTransformtProjectiveTransformt_to_ndimage_mode(t _warp_fasti(t block_reduce(tget_bound_method_classt safe_as_inttwarntconvert_to_floatcCs2|dk r|Stdƒ|dkr*tStSdS(Ns¨The default multichannel argument (None) is deprecated. Please specify either True or False explicitly. multichannel will default to False starting with release 0.16.i(tNoneR tTruetFalse(t multichanneltndim((s7lib/python2.7/site-packages/skimage/transform/_warps.pyt_multichannel_defaults    ic Cs*|dkrd}tdƒn|dkr>t}tdƒnt|ƒ}t|ƒ} |j} | |jkr˜| d| |j} tj|| ƒ}nL| |jdkrÂ||jdf}n"| |jdkrät dƒ‚ntj | dt ƒtj |dt ƒ} |rÈ|dkr;tj d| dd ƒ}nltj |ƒtj| ƒ}tj|dkƒr{t d ƒ‚n,tj|dk| dk@ƒr§td ƒntj||d |d |ƒ}nt|ƒd kst|ƒdkrl|d | d krl|d} |d} | d}| d}| dkrh| dkrhtd|dd|ddfƒ}nÎtjddgd| g| | ggƒd}tj|jdtjƒ}| d|dd…dfdd|dd…df<| d|dd…dfdd|dd…df>> from skimage import data >>> from skimage.transform import resize >>> image = data.camera() >>> resize(image, (100, 100), mode='reflect').shape (100, 100) tconstantsKThe default mode, 'constant', will be changed to 'reflect' in skimage 0.15.soAnti-aliasing will be enabled by default in skimage 0.15 to avoid aliasing artifacts when down-sampling images.iiÿÿÿÿs=len(output_shape) cannot be smaller than the image dimensionstdtypeiisFAnti-aliasing standard deviation must be greater than or equal to zerosWAnti-aliasing standard deviation greater than zero but not down-sampling along all axestcvaltmodeit translationg@gà?Nt output_shapetordertcliptpreserve_rangetsparsetindexingtij(i(R R R ttupletlentshapeRtnptreshapet ValueErrortasarraytfloattmaximumt atleast_1dt ones_liketanytnditgaussian_filterRtarraytzerostdoubletestimatetwarpt enumeratetarangetmeshgridR Rtmap_coordinatest_clip_warp_output(timageRRRRRRt anti_aliasingtanti_aliasing_sigmat output_ndimt input_shapetfactorstrowstcolst input_rowst input_colsttformt src_cornerst dst_cornerstouttitdt coord_arrayst coord_maptndi_mode((s7lib/python2.7/site-packages/skimage/transform/_warps.pytresize"srE         $    .66  ; c Cst||jƒ}tj|ƒ}t|ƒdkr¡| rOt|ƒ|jksn|r}t|ƒ|jdkr}tdƒ‚n|r¡tj|dgfƒ}q¡ntj|jƒ} tj || ƒ} |rÝ| d| d>> from skimage import data >>> from skimage.transform import rescale >>> image = data.camera() >>> rescale(image, 0.1, mode='reflect').shape (51, 51) >>> rescale(image, 0.5, mode='reflect').shape (256, 256) is4Supply a single scale, or one value per spatial axisiÿÿÿÿRRRRRR6R7( RRR R&RR"t concatenateR#RtroundRH( R5tscaleRRRRRRR6R7t orig_shapeR((s7lib/python2.7/site-packages/skimage/transform/_warps.pytrescale¾s K Rc Csó|jd|jd} } |dkrGtj| | fƒdd}ntj|ƒ}td|ƒ} tdtj|ƒƒ} td| ƒ} | | | }d}|rÂtjddgd| dg| d| dg| ddggƒ}|j|ƒ}|dd…dfjƒ}|dd…dfjƒ}|dd…dfj ƒ}|dd…dfj ƒ}||d}||d}tj ||fƒ}||f}td|ƒ}||}nt ||d|d |d |d |d |d |ƒS(ss Rotate image by a certain angle around its center. Parameters ---------- image : ndarray Input image. angle : float Rotation angle in degrees in counter-clockwise direction. resize : bool, optional Determine whether the shape of the output image will be automatically calculated, so the complete rotated image exactly fits. Default is False. center : iterable of length 2 The rotation center. If ``center=None``, the image is rotated around its center, i.e. ``center=(cols / 2 - 0.5, rows / 2 - 0.5)``. Please note that this parameter is (cols, rows), contrary to normal skimage ordering. Returns ------- rotated : ndarray Rotated version of the input. Other parameters ---------------- order : int, optional The order of the spline interpolation, default is 1. The order has to be in the range 0-5. See `skimage.transform.warp` for detail. mode : {'constant', 'edge', 'symmetric', 'reflect', 'wrap'}, optional Points outside the boundaries of the input are filled according to the given mode. Modes match the behaviour of `numpy.pad`. cval : float, optional Used in conjunction with mode 'constant', the value outside the image boundaries. clip : bool, optional Whether to clip the output to the range of values of the input image. This is enabled by default, since higher order interpolation may produce values outside the given input range. preserve_range : bool, optional Whether to keep the original range of values. Otherwise, the input image is converted according to the conventions of `img_as_float`. Notes ----- Modes 'reflect' and 'symmetric' are similar, but differ in whether the edge pixels are duplicated during the reflection. As an example, if an array has values [0, 1, 2] and was padded to the right by four values using symmetric, the result would be [0, 1, 2, 2, 1, 0, 0], while for reflect it would be [0, 1, 2, 1, 0, 1, 2]. Examples -------- >>> from skimage import data >>> from skimage.transform import rotate >>> image = data.camera() >>> rotate(image, 2).shape (512, 512) >>> rotate(image, 2, resize=True).shape (530, 530) >>> rotate(image, 90, resize=True).shape (512, 512) iig@gà?RtrotationNRRRRRR( RR R R+R#Rtdeg2radtinversetmintmaxtceilR/(R5tangleRHtcenterRRRRRR;R<ttform1ttform2ttform3R?Rtcornerstminctminrtmaxctmaxrtout_rowstout_colsRttform4((s7lib/python2.7/site-packages/skimage/transform/_warps.pytrotates8B      cCst||tj|ƒS(sYDown-sample N-dimensional image by local averaging. The image is padded with `cval` if it is not perfectly divisible by the integer factors. In contrast to the 2-D interpolation in `skimage.transform.resize` and `skimage.transform.rescale` this function may be applied to N-dimensional images and calculates the local mean of elements in each block of size `factors` in the input image. Parameters ---------- image : ndarray N-dimensional input image. factors : array_like Array containing down-sampling integer factor along each axis. cval : float, optional Constant padding value if image is not perfectly divisible by the integer factors. Returns ------- image : ndarray Down-sampled image with same number of dimensions as input image. Examples -------- >>> a = np.arange(15).reshape(3, 5) >>> a array([[ 0, 1, 2, 3, 4], [ 5, 6, 7, 8, 9], [10, 11, 12, 13, 14]]) >>> downscale_local_mean(a, (2, 3)) array([[ 3.5, 4. ], [ 5.5, 4.5]]) (RR tmean(R5R:RR((s7lib/python2.7/site-packages/skimage/transform/_warps.pytdownscale_local_mean†s&c CsÃ|j\}}|\}}tj||d||dƒ} |dtjdƒ}||tj| |ƒtj||||ƒ} || tj| ƒ|d<|| tj| ƒ|d<|S(Nii.ii(.i(.i(tTR tsqrttlogtexptarctan2tcostsin( txyRURNtstrengthtradiustxtytx0ty0trhottheta((s7lib/python2.7/site-packages/skimage/transform/_warps.pyt_swirl_mapping¯s #idc Cs®|dkrtdƒd}n|dkrUtj|jƒd ddd…d}ni|d6|d6|d6|d 6} t|td | d |d |d |d|d| d| ƒS(sÖPerform a swirl transformation. Parameters ---------- image : ndarray Input image. center : (column, row) tuple or (2,) ndarray, optional Center coordinate of transformation. strength : float, optional The amount of swirling applied. radius : float, optional The extent of the swirl in pixels. The effect dies out rapidly beyond `radius`. rotation : float, optional Additional rotation applied to the image. Returns ------- swirled : ndarray Swirled version of the input. Other parameters ---------------- output_shape : tuple (rows, cols), optional Shape of the output image generated. By default the shape of the input image is preserved. order : int, optional The order of the spline interpolation, default is 1. The order has to be in the range 0-5. See `skimage.transform.warp` for detail. mode : {'constant', 'edge', 'symmetric', 'reflect', 'wrap'}, optional Points outside the boundaries of the input are filled according to the given mode, with 'constant' used as the default. Modes match the behaviour of `numpy.pad`. cval : float, optional Used in conjunction with mode 'constant', the value outside the image boundaries. clip : bool, optional Whether to clip the output to the range of values of the input image. This is enabled by default, since higher order interpolation may produce values outside the given input range. preserve_range : bool, optional Whether to keep the original range of values. Otherwise, the input image is converted according to the conventions of `img_as_float`. s\The default of `mode` in `skimage.transform.swirl` will change to `reflect` in version 0.15.RiNiÿÿÿÿRURNRlRmtmap_argsRRRRRR(R R R R+RR/Rt( R5RURlRmRNRRRRRRt warp_args((s7lib/python2.7/site-packages/skimage/transform/_warps.pytswirlÂs0    *  cCs@|jdkr5|dd…dd…tjf|(n||(dS(s5Copy b into each color layer of a, such that:: a[:,:,0] = a[:,:,1] = ... = b Parameters ---------- a : (M, N) or (M, N, P) ndarray Target array. b : (M, N) Source array. Notes ----- Color images are stored as an ``(M, N, 3)`` or ``(M, N, 4)`` arrays. iN(RR tnewaxis(tatb((s7lib/python2.7/site-packages/skimage/transform/_warps.pyt _stackcopys&cCs%t|ƒ}|d|d}}t|ƒ||g}t|ƒdkr\|j|dƒntj|d|ƒ}tj||fd|ƒjddƒj}||ƒ}|jjd||fƒjddƒ}t |d|d ƒt |d |d ƒt|ƒdkr!t |dƒ|d >> from skimage import data >>> from scipy.ndimage import map_coordinates >>> >>> def shift_up10_left20(xy): ... return xy - np.array([-20, 10])[None, :] >>> >>> image = data.astronaut().astype(np.float32) >>> coords = warp_coords(shift_up10_left20, image.shape) >>> warped_image = map_coordinates(image, coords) iiiiRiÿÿÿÿ.(i.(i.(i.(i.(i.( RRtappendR temptytindicesR!RdtswapaxesR{trange(RFRRR;R<t coords_shapetcoordst tf_coords((s7lib/python2.7/site-packages/skimage/transform/_warps.pyt warp_coordss1 * 'c Cs›|r—|dkr—|jƒ}|jƒ}|dkoP||koM|kn }|rh||k} ntj|||d|ƒ|r—||| } | jt|d | f| d|d |d |d |ƒƒqWtj| ƒ} qçqên| dkrtˆtjƒr)ˆjdkr)td ˆƒ‰ntˆtjƒrDˆ}n|jdksb|jdkrqtdƒ‚n‡‡fd†}t| ƒdkrÅt|ƒdkrÅ|d|d| df}nt||ƒ}|dk}t|ƒ}tj||d|d |d |d |ƒ} nt || ||||ƒ| S(s=Warp an image according to a given coordinate transformation. Parameters ---------- image : ndarray Input image. inverse_map : transformation object, callable ``cr = f(cr, **kwargs)``, or ndarray Inverse coordinate map, which transforms coordinates in the output images into their corresponding coordinates in the input image. There are a number of different options to define this map, depending on the dimensionality of the input image. A 2-D image can have 2 dimensions for gray-scale images, or 3 dimensions with color information. - For 2-D images, you can directly pass a transformation object, e.g. `skimage.transform.SimilarityTransform`, or its inverse. - For 2-D images, you can pass a ``(3, 3)`` homogeneous transformation matrix, e.g. `skimage.transform.SimilarityTransform.params`. - For 2-D images, a function that transforms a ``(M, 2)`` array of ``(col, row)`` coordinates in the output image to their corresponding coordinates in the input image. Extra parameters to the function can be specified through `map_args`. - For N-D images, you can directly pass an array of coordinates. The first dimension specifies the coordinates in the input image, while the subsequent dimensions determine the position in the output image. E.g. in case of 2-D images, you need to pass an array of shape ``(2, rows, cols)``, where `rows` and `cols` determine the shape of the output image, and the first dimension contains the ``(row, col)`` coordinate in the input image. See `scipy.ndimage.map_coordinates` for further documentation. Note, that a ``(3, 3)`` matrix is interpreted as a homogeneous transformation matrix, so you cannot interpolate values from a 3-D input, if the output is of shape ``(3,)``. See example section for usage. map_args : dict, optional Keyword arguments passed to `inverse_map`. output_shape : tuple (rows, cols), optional Shape of the output image generated. By default the shape of the input image is preserved. Note that, even for multi-band images, only rows and columns need to be specified. order : int, optional The order of interpolation. The order has to be in the range 0-5: - 0: Nearest-neighbor - 1: Bi-linear (default) - 2: Bi-quadratic - 3: Bi-cubic - 4: Bi-quartic - 5: Bi-quintic mode : {'constant', 'edge', 'symmetric', 'reflect', 'wrap'}, optional Points outside the boundaries of the input are filled according to the given mode. Modes match the behaviour of `numpy.pad`. cval : float, optional Used in conjunction with mode 'constant', the value outside the image boundaries. clip : bool, optional Whether to clip the output to the range of values of the input image. This is enabled by default, since higher order interpolation may produce values outside the given input range. preserve_range : bool, optional Whether to keep the original range of values. Otherwise, the input image is converted according to the conventions of `img_as_float`. Returns ------- warped : double ndarray The warped input image. Notes ----- - The input image is converted to a `double` image. - In case of a `SimilarityTransform`, `AffineTransform` and `ProjectiveTransform` and `order` in [0, 3] this function uses the underlying transformation matrix to warp the image with a much faster routine. Examples -------- >>> from skimage.transform import warp >>> from skimage import data >>> image = data.camera() The following image warps are all equal but differ substantially in execution time. The image is shifted to the bottom. Use a geometric transform to warp an image (fast): >>> from skimage.transform import SimilarityTransform >>> tform = SimilarityTransform(translation=(0, -10)) >>> warped = warp(image, tform) Use a callable (slow): >>> def shift_down(xy): ... xy[:, 1] -= 10 ... return xy >>> warped = warp(image, shift_down) Use a transformation matrix to warp an image (fast): >>> matrix = np.array([[1, 0, 0], [0, 1, -10], [0, 0, 1]]) >>> warped = warp(image, matrix) >>> from skimage.transform import ProjectiveTransform >>> warped = warp(image, ProjectiveTransform(matrix=matrix)) You can also use the inverse of a geometric transformation (fast): >>> warped = warp(image, tform.inverse) For N-D images you can pass a coordinate array, that specifies the coordinates in the input image for every element in the output image. E.g. if you want to rescale a 3-D cube, you can do: >>> cube_shape = np.array([30, 30, 30]) >>> cube = np.random.rand(*cube_shape) Setup the coordinate array, that defines the scaling: >>> scale = 0.1 >>> output_shape = (scale * cube_shape).astype(int) >>> coords0, coords1, coords2 = np.mgrid[:output_shape[0], ... :output_shape[1], :output_shape[2]] >>> coords = np.array([coords0, coords1, coords2]) Assume that the cube contains spatial data, where the first array element center is at coordinate (0.5, 0.5, 0.5) in real space, i.e. we have to account for this extra offset when scaling the image: >>> coords = (coords + 0.5) / scale - 0.5 >>> warped = warp(cube, coords) isZBi-quadratic interpolation behavior has changed due to a bug in the implementation of scikit-image. The new version now serves as a wrapper around SciPy's interpolation functions, which itself is not verified to be a correct implementation. Until skimage's implementation is fixed, we recommend to use bi-linear or bi-cubic interpolation instead.iiit__name__RPRRRR.tmatrixs\Only 2-D images (grayscale or color) are supported, when providing a callable `inverse_map`.cs ˆ|ˆŽS(N((targs(t inverse_mapRu(s7lib/python2.7/site-packages/skimage/transform/_warps.pyRFmst prefilterN(iii(ii(ii(!R R R+RR RR t isinstancetndarraytHOMOGRAPHY_TRANSFORMStparamsthasattrR‹Rtlinalgtinvtsixtget_method_selftastypeR-RRR€R|tdstackRR"RR„RR)R3R4(R5RŽRuRRRRRRR9twarpedRŒtdimstdimR‚RFRRG((RŽRus7lib/python2.7/site-packages/skimage/transform/_warps.pyR/—sd‰     !  !    $  ($R—tnumpyR tscipyRR)t _geometricRRRRt _warps_cyRtmeasureRt _shared.utilsRRR R R’RR R R RHRMRaRcRtRwR{tfloat64R„R4R/(((s7lib/python2.7/site-packages/skimage/transform/_warps.pyts6  ""   › ] h)   @ N .