ó ʽ÷Xc@`s¨ddlmZmZmZddlZddlmZddlmZddlmZddd d d d d dddddddddddddgZ d„Z dd„Z d„Z d„Zdddddded„Zdddddded „Zddddd!„Zddddd"„Zdddddd#„Zdddddd$„Zdddd%„Zddddd&d'dd(„Zddddd&d'dd)„Zddddd&d'dd*„Zddddd&d'dd+„Zddddd&d'dd,„Zddddd&d'dd-„Zddddd&d'dd.„Zddddd&d'dd/„Zd0deeddd1„Z d2eeddd3„Z!deeddd4„Z"dS(5i(tdivisiontprint_functiontabsolute_importNi(t _ni_support(t _nd_image(tfilterstiterate_structuretgenerate_binary_structuretbinary_erosiontbinary_dilationtbinary_openingtbinary_closingtbinary_hit_or_misstbinary_propagationtbinary_fill_holest grey_erosiont grey_dilationt grey_openingt grey_closingtmorphological_gradienttmorphological_laplacet white_tophatt black_tophattdistance_transform_bftdistance_transform_cdttdistance_transform_edtcC`sVtj|ƒ}tgt|j|ƒD]\}}||d^q%ƒ}t||ƒS(Ni(tnumpytarrayttupletziptshapetbool(t structuretorigintsstootcoor((s7/tmp/pip-build-7oUkmx/scipy/scipy/ndimage/morphology.pyt_center_is_true/s*c C`sVtj|ƒ}|dkr%|jƒS|d}g|jD]}|||d^q9}gtt|ƒƒD]}||j|d^qj}gtt|ƒƒD]+}t|||||j|dƒ^qž}tj|t ƒ}|dk||>> from scipy import ndimage >>> struct = ndimage.generate_binary_structure(2, 1) >>> struct.astype(int) array([[0, 1, 0], [1, 1, 1], [0, 1, 0]]) >>> ndimage.iterate_structure(struct, 2).astype(int) array([[0, 0, 1, 0, 0], [0, 1, 1, 1, 0], [1, 1, 1, 1, 1], [0, 1, 1, 1, 0], [0, 0, 1, 0, 0]]) >>> ndimage.iterate_structure(struct, 3).astype(int) array([[0, 0, 0, 1, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 1, 1, 1, 1, 1, 0], [1, 1, 1, 1, 1, 1, 1], [0, 1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 1, 0, 0, 0]]) iiit iterationsN(RtasarraytcopyRtrangetlentslicetNonetzerosRR Rt_normalize_sequencetndim( R R&R!tnitiiRtpostslctoutto((s7/tmp/pip-build-7oUkmx/scipy/scipy/ndimage/morphology.pyR6s 2   (4A cC`s§|dkrd}n|dkrV|dkr@tjddtƒStjddtƒSntjtjdg|ƒdƒ}tjj|dƒ}tj||kdtƒS(sŽ Generate a binary structure for binary morphological operations. Parameters ---------- rank : int Number of dimensions of the array to which the structuring element will be applied, as returned by `np.ndim`. connectivity : int `connectivity` determines which elements of the output array belong to the structure, i.e. are considered as neighbors of the central element. Elements up to a squared distance of `connectivity` from the center are considered neighbors. `connectivity` may range from 1 (no diagonal elements are neighbors) to `rank` (all elements are neighbors). Returns ------- output : ndarray of bools Structuring element which may be used for binary morphological operations, with `rank` dimensions and all dimensions equal to 3. See also -------- iterate_structure, binary_dilation, binary_erosion Notes ----- `generate_binary_structure` can only create structuring elements with dimensions equal to 3, i.e. minimal dimensions. For larger structuring elements, that are useful e.g. for eroding large objects, one may either use `iterate_structure`, or create directly custom arrays with numpy functions such as `numpy.ones`. Examples -------- >>> from scipy import ndimage >>> struct = ndimage.generate_binary_structure(2, 1) >>> struct array([[False, True, False], [ True, True, True], [False, True, False]], dtype=bool) >>> a = np.zeros((5,5)) >>> a[2, 2] = 1 >>> a array([[ 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0.], [ 0., 0., 1., 0., 0.], [ 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0.]]) >>> b = ndimage.binary_dilation(a, structure=struct).astype(a.dtype) >>> b array([[ 0., 0., 0., 0., 0.], [ 0., 0., 1., 0., 0.], [ 0., 1., 1., 1., 0.], [ 0., 0., 1., 0., 0.], [ 0., 0., 0., 0., 0.]]) >>> ndimage.binary_dilation(b, structure=struct).astype(a.dtype) array([[ 0., 0., 1., 0., 0.], [ 0., 1., 1., 1., 0.], [ 1., 1., 1., 1., 1.], [ 0., 1., 1., 1., 0.], [ 0., 0., 1., 0., 0.]]) >>> struct = ndimage.generate_binary_structure(2, 2) >>> struct array([[ True, True, True], [ True, True, True], [ True, True, True]], dtype=bool) >>> struct = ndimage.generate_binary_structure(3, 1) >>> struct # no diagonal elements array([[[False, False, False], [False, True, False], [False, False, False]], [[False, True, False], [ True, True, True], [False, True, False]], [[False, False, False], [False, True, False], [False, False, False]]], dtype=bool) iitdtypei(RRRtfabstindicestaddtreduceR'(trankt connectivitytoutput((s7/tmp/pip-build-7oUkmx/scipy/scipy/ndimage/morphology.pyR{sR    #c  C`sþtj|ƒ}tj|ƒr-tdƒ‚n|dkrNt|jdƒ}ntj|ƒ}|jtƒ}|j|jkrt dƒ‚n|j j s¨|j ƒ}ntj |jddƒdkrÕt dƒ‚n|dk rtj|ƒ}|j|jkrt dƒ‚qntj||jƒ}t||ƒ} t|tjƒrktj|ƒrqtdƒ‚qqnt}tj||ƒ\}} |dkr¾tj|||||||| dƒ | S| r| rtj|||||||| dƒ \} } |ttddd ƒg|jƒ}xMtt|ƒƒD]9} ||  || <|j| d@s1|| cd8>> from scipy import ndimage >>> a = np.zeros((7,7), dtype=int) >>> a[1:6, 2:5] = 1 >>> a array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0]]) >>> ndimage.binary_erosion(a).astype(a.dtype) array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]]) >>> #Erosion removes objects smaller than the structure >>> ndimage.binary_erosion(a, structure=np.ones((5,5))).astype(a.dtype) array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]]) i(RW(RKR R&RLR=RMR!RO((s7/tmp/pip-build-7oUkmx/scipy/scipy/ndimage/morphology.pyR"sWc C`sìtj|ƒ}|dkr0t|jdƒ}ntj||jƒ}tj|ƒ}|ttdddƒg|jƒ}xMt t |ƒƒD]9}|| ||<|j |d@s||cd8>> from scipy import ndimage >>> a = np.zeros((5, 5)) >>> a[2, 2] = 1 >>> a array([[ 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0.], [ 0., 0., 1., 0., 0.], [ 0., 0., 0., 0., 0.], [ 0., 0., 0., 0., 0.]]) >>> ndimage.binary_dilation(a) array([[False, False, False, False, False], [False, False, True, False, False], [False, True, True, True, False], [False, False, True, False, False], [False, False, False, False, False]], dtype=bool) >>> ndimage.binary_dilation(a).astype(a.dtype) array([[ 0., 0., 0., 0., 0.], [ 0., 0., 1., 0., 0.], [ 0., 1., 1., 1., 0.], [ 0., 0., 1., 0., 0.], [ 0., 0., 0., 0., 0.]]) >>> # 3x3 structuring element with connectivity 1, used by default >>> struct1 = ndimage.generate_binary_structure(2, 1) >>> struct1 array([[False, True, False], [ True, True, True], [False, True, False]], dtype=bool) >>> # 3x3 structuring element with connectivity 2 >>> struct2 = ndimage.generate_binary_structure(2, 2) >>> struct2 array([[ True, True, True], [ True, True, True], [ True, True, True]], dtype=bool) >>> ndimage.binary_dilation(a, structure=struct1).astype(a.dtype) array([[ 0., 0., 0., 0., 0.], [ 0., 0., 1., 0., 0.], [ 0., 1., 1., 1., 0.], [ 0., 0., 1., 0., 0.], [ 0., 0., 0., 0., 0.]]) >>> ndimage.binary_dilation(a, structure=struct2).astype(a.dtype) array([[ 0., 0., 0., 0., 0.], [ 0., 1., 1., 1., 0.], [ 0., 1., 1., 1., 0.], [ 0., 1., 1., 1., 0.], [ 0., 0., 0., 0., 0.]]) >>> ndimage.binary_dilation(a, structure=struct1,\ ... iterations=2).astype(a.dtype) array([[ 0., 0., 1., 0., 0.], [ 0., 1., 1., 1., 0.], [ 1., 1., 1., 1., 1.], [ 0., 1., 1., 1., 0.], [ 0., 0., 1., 0., 0.]]) iiÿÿÿÿN( RR'R,RR/RR.RR+R)R*RRW( RKR R&RLR=RMR!ROR1((s7/tmp/pip-build-7oUkmx/scipy/scipy/ndimage/morphology.pyR }sl cC`sptj|ƒ}|dkr6|j}t|dƒ}nt|||ddd|ƒ}t|||d|d|ƒS(sØ Multi-dimensional binary opening with the given structuring element. The *opening* of an input image by a structuring element is the *dilation* of the *erosion* of the image by the structuring element. Parameters ---------- input : array_like Binary array_like to be opened. Non-zero (True) elements form the subset to be opened. structure : array_like, optional Structuring element used for the opening. Non-zero elements are considered True. If no structuring element is provided an element is generated with a square connectivity equal to one (i.e., only nearest neighbors are connected to the center, diagonally-connected elements are not considered neighbors). iterations : {int, float}, optional The erosion step of the opening, then the dilation step are each repeated `iterations` times (one, by default). If `iterations` is less than 1, each operation is repeated until the result does not change anymore. output : ndarray, optional Array of the same shape as input, into which the output is placed. By default, a new array is created. origin : int or tuple of ints, optional Placement of the filter, by default 0. Returns ------- binary_opening : ndarray of bools Opening of the input by the structuring element. See also -------- grey_opening, binary_closing, binary_erosion, binary_dilation, generate_binary_structure Notes ----- *Opening* [1]_ is a mathematical morphology operation [2]_ that consists in the succession of an erosion and a dilation of the input with the same structuring element. Opening therefore removes objects smaller than the structuring element. Together with *closing* (`binary_closing`), opening can be used for noise removal. References ---------- .. [1] http://en.wikipedia.org/wiki/Opening_%28morphology%29 .. [2] http://en.wikipedia.org/wiki/Mathematical_morphology Examples -------- >>> from scipy import ndimage >>> a = np.zeros((5,5), dtype=int) >>> a[1:4, 1:4] = 1; a[4, 4] = 1 >>> a array([[0, 0, 0, 0, 0], [0, 1, 1, 1, 0], [0, 1, 1, 1, 0], [0, 1, 1, 1, 0], [0, 0, 0, 0, 1]]) >>> # Opening removes small objects >>> ndimage.binary_opening(a, structure=np.ones((3,3))).astype(int) array([[0, 0, 0, 0, 0], [0, 1, 1, 1, 0], [0, 1, 1, 1, 0], [0, 1, 1, 1, 0], [0, 0, 0, 0, 0]]) >>> # Opening can also smooth corners >>> ndimage.binary_opening(a).astype(int) array([[0, 0, 0, 0, 0], [0, 0, 1, 0, 0], [0, 1, 1, 1, 0], [0, 0, 1, 0, 0], [0, 0, 0, 0, 0]]) >>> # Opening is the dilation of the erosion of the input >>> ndimage.binary_erosion(a).astype(int) array([[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [0, 0, 1, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]) >>> ndimage.binary_dilation(ndimage.binary_erosion(a)).astype(int) array([[0, 0, 0, 0, 0], [0, 0, 1, 0, 0], [0, 1, 1, 1, 0], [0, 0, 1, 0, 0], [0, 0, 0, 0, 0]]) iiN(RR'R,R/RRR (RKR R&R=R!R;ttmp((s7/tmp/pip-build-7oUkmx/scipy/scipy/ndimage/morphology.pyR ùs_   cC`sptj|ƒ}|dkr6|j}t|dƒ}nt|||ddd|ƒ}t|||d|d|ƒS(sÚ Multi-dimensional binary closing with the given structuring element. The *closing* of an input image by a structuring element is the *erosion* of the *dilation* of the image by the structuring element. Parameters ---------- input : array_like Binary array_like to be closed. Non-zero (True) elements form the subset to be closed. structure : array_like, optional Structuring element used for the closing. Non-zero elements are considered True. If no structuring element is provided an element is generated with a square connectivity equal to one (i.e., only nearest neighbors are connected to the center, diagonally-connected elements are not considered neighbors). iterations : {int, float}, optional The dilation step of the closing, then the erosion step are each repeated `iterations` times (one, by default). If iterations is less than 1, each operations is repeated until the result does not change anymore. output : ndarray, optional Array of the same shape as input, into which the output is placed. By default, a new array is created. origin : int or tuple of ints, optional Placement of the filter, by default 0. Returns ------- binary_closing : ndarray of bools Closing of the input by the structuring element. See also -------- grey_closing, binary_opening, binary_dilation, binary_erosion, generate_binary_structure Notes ----- *Closing* [1]_ is a mathematical morphology operation [2]_ that consists in the succession of a dilation and an erosion of the input with the same structuring element. Closing therefore fills holes smaller than the structuring element. Together with *opening* (`binary_opening`), closing can be used for noise removal. References ---------- .. [1] http://en.wikipedia.org/wiki/Closing_%28morphology%29 .. [2] http://en.wikipedia.org/wiki/Mathematical_morphology Examples -------- >>> from scipy import ndimage >>> a = np.zeros((5,5), dtype=int) >>> a[1:-1, 1:-1] = 1; a[2,2] = 0 >>> a array([[0, 0, 0, 0, 0], [0, 1, 1, 1, 0], [0, 1, 0, 1, 0], [0, 1, 1, 1, 0], [0, 0, 0, 0, 0]]) >>> # Closing removes small holes >>> ndimage.binary_closing(a).astype(int) array([[0, 0, 0, 0, 0], [0, 1, 1, 1, 0], [0, 1, 1, 1, 0], [0, 1, 1, 1, 0], [0, 0, 0, 0, 0]]) >>> # Closing is the erosion of the dilation of the input >>> ndimage.binary_dilation(a).astype(int) array([[0, 1, 1, 1, 0], [1, 1, 1, 1, 1], [1, 1, 1, 1, 1], [1, 1, 1, 1, 1], [0, 1, 1, 1, 0]]) >>> ndimage.binary_erosion(ndimage.binary_dilation(a)).astype(int) array([[0, 0, 0, 0, 0], [0, 1, 1, 1, 0], [0, 1, 1, 1, 0], [0, 1, 1, 1, 0], [0, 0, 0, 0, 0]]) >>> a = np.zeros((7,7), dtype=int) >>> a[1:6, 2:5] = 1; a[1:3,3] = 0 >>> a array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 1, 0, 0], [0, 0, 1, 0, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0]]) >>> # In addition to removing holes, closing can also >>> # coarsen boundaries with fine hollows. >>> ndimage.binary_closing(a).astype(int) array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0]]) >>> ndimage.binary_closing(a, structure=np.ones((2,2))).astype(int) array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0]]) iiN(RR'R,R/RR R(RKR R&R=R!R;RX((s7/tmp/pip-build-7oUkmx/scipy/scipy/ndimage/morphology.pyR csv   c C`s7tj|ƒ}|dkr0t|jdƒ}n|dkrNtj|ƒ}ntj||jƒ}|dkrx|}ntj||jƒ}t||dddd|dt ƒ }t |tj ƒ}t||dd|d|dt ƒ }|rtj||ƒtj |||ƒn tj||ƒtj ||ƒSdS(sã Multi-dimensional binary hit-or-miss transform. The hit-or-miss transform finds the locations of a given pattern inside the input image. Parameters ---------- input : array_like (cast to booleans) Binary image where a pattern is to be detected. structure1 : array_like (cast to booleans), optional Part of the structuring element to be fitted to the foreground (non-zero elements) of `input`. If no value is provided, a structure of square connectivity 1 is chosen. structure2 : array_like (cast to booleans), optional Second part of the structuring element that has to miss completely the foreground. If no value is provided, the complementary of `structure1` is taken. output : ndarray, optional Array of the same shape as input, into which the output is placed. By default, a new array is created. origin1 : int or tuple of ints, optional Placement of the first part of the structuring element `structure1`, by default 0 for a centered structure. origin2 : int or tuple of ints, optional Placement of the second part of the structuring element `structure2`, by default 0 for a centered structure. If a value is provided for `origin1` and not for `origin2`, then `origin2` is set to `origin1`. Returns ------- binary_hit_or_miss : ndarray Hit-or-miss transform of `input` with the given structuring element (`structure1`, `structure2`). See also -------- ndimage.morphology, binary_erosion References ---------- .. [1] http://en.wikipedia.org/wiki/Hit-or-miss_transform Examples -------- >>> from scipy import ndimage >>> a = np.zeros((7,7), dtype=int) >>> a[1, 1] = 1; a[2:4, 2:4] = 1; a[4:6, 4:6] = 1 >>> a array([[0, 0, 0, 0, 0, 0, 0], [0, 1, 0, 0, 0, 0, 0], [0, 0, 1, 1, 0, 0, 0], [0, 0, 1, 1, 0, 0, 0], [0, 0, 0, 0, 1, 1, 0], [0, 0, 0, 0, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0]]) >>> structure1 = np.array([[1, 0, 0], [0, 1, 1], [0, 1, 1]]) >>> structure1 array([[1, 0, 0], [0, 1, 1], [0, 1, 1]]) >>> # Find the matches of structure1 in the array a >>> ndimage.binary_hit_or_miss(a, structure1=structure1).astype(int) array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]]) >>> # Change the origin of the filter >>> # origin1=1 is equivalent to origin1=(1,1) here >>> ndimage.binary_hit_or_miss(a, structure1=structure1,\ ... origin1=1).astype(int) array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 1, 0], [0, 0, 0, 0, 0, 0, 0]]) iiN( RR'R,RR/t logical_notRR.RWtFalseRFRGt logical_and( RKt structure1t structure2R=torigin1torigin2ttmp1tinplacetresult((s7/tmp/pip-build-7oUkmx/scipy/scipy/ndimage/morphology.pyR äs&U     cC`st||d||||ƒS(s% Multi-dimensional binary propagation with the given structuring element. Parameters ---------- input : array_like Binary image to be propagated inside `mask`. structure : array_like, optional Structuring element used in the successive dilations. The output may depend on the structuring element, especially if `mask` has several connex components. If no structuring element is provided, an element is generated with a squared connectivity equal to one. mask : array_like, optional Binary mask defining the region into which `input` is allowed to propagate. output : ndarray, optional Array of the same shape as input, into which the output is placed. By default, a new array is created. border_value : int (cast to 0 or 1), optional Value at the border in the output array. origin : int or tuple of ints, optional Placement of the filter, by default 0. Returns ------- binary_propagation : ndarray Binary propagation of `input` inside `mask`. Notes ----- This function is functionally equivalent to calling binary_dilation with the number of iterations less then one: iterative dilation until the result does not change anymore. The succession of an erosion and propagation inside the original image can be used instead of an *opening* for deleting small objects while keeping the contours of larger objects untouched. References ---------- .. [1] http://cmm.ensmp.fr/~serra/cours/pdf/en/ch6en.pdf, slide 15. .. [2] http://www.qi.tnw.tudelft.nl/Courses/FIP/noframes/fip-Morpholo.html#Heading102 Examples -------- >>> from scipy import ndimage >>> input = np.zeros((8, 8), dtype=int) >>> input[2, 2] = 1 >>> mask = np.zeros((8, 8), dtype=int) >>> mask[1:4, 1:4] = mask[4, 4] = mask[6:8, 6:8] = 1 >>> input array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]]) >>> mask array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 0, 0, 0, 0], [0, 1, 1, 1, 0, 0, 0, 0], [0, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 1, 1], [0, 0, 0, 0, 0, 0, 1, 1]]) >>> ndimage.binary_propagation(input, mask=mask).astype(int) array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 0, 0, 0, 0], [0, 1, 1, 1, 0, 0, 0, 0], [0, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]]) >>> ndimage.binary_propagation(input, mask=mask,\ ... structure=np.ones((3,3))).astype(int) array([[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 0, 0, 0, 0], [0, 1, 1, 1, 0, 0, 0, 0], [0, 1, 1, 1, 0, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]]) >>> # Comparison between opening and erosion+propagation >>> a = np.zeros((6,6), dtype=int) >>> a[2:5, 2:5] = 1; a[0, 0] = 1; a[5, 5] = 1 >>> a array([[1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0], [0, 0, 0, 0, 0, 1]]) >>> ndimage.binary_opening(a).astype(int) array([[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0], [0, 0, 1, 1, 1, 0], [0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0]]) >>> b = ndimage.binary_erosion(a) >>> b.astype(int) array([[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0]]) >>> ndimage.binary_propagation(b, mask=a).astype(int) array([[0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0], [0, 0, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0]]) iÿÿÿÿ(R (RKR RLR=RMR!((s7/tmp/pip-build-7oUkmx/scipy/scipy/ndimage/morphology.pyR Qs}cC`s¡tj|ƒ}tj|jtƒ}t|tjƒ}|rkt||d||d|ƒtj||ƒn2t||d|dd|ƒ}tj||ƒ|SdS(s_ Fill the holes in binary objects. Parameters ---------- input : array_like n-dimensional binary array with holes to be filled structure : array_like, optional Structuring element used in the computation; large-size elements make computations faster but may miss holes separated from the background by thin regions. The default element (with a square connectivity equal to one) yields the intuitive result where all holes in the input have been filled. output : ndarray, optional Array of the same shape as input, into which the output is placed. By default, a new array is created. origin : int, tuple of ints, optional Position of the structuring element. Returns ------- out : ndarray Transformation of the initial image `input` where holes have been filled. See also -------- binary_dilation, binary_propagation, label Notes ----- The algorithm used in this function consists in invading the complementary of the shapes in `input` from the outer boundary of the image, using binary dilations. Holes are not connected to the boundary and are therefore not invaded. The result is the complementary subset of the invaded region. References ---------- .. [1] http://en.wikipedia.org/wiki/Mathematical_morphology Examples -------- >>> from scipy import ndimage >>> a = np.zeros((5, 5), dtype=int) >>> a[1:4, 1:4] = 1 >>> a[2,2] = 0 >>> a array([[0, 0, 0, 0, 0], [0, 1, 1, 1, 0], [0, 1, 0, 1, 0], [0, 1, 1, 1, 0], [0, 0, 0, 0, 0]]) >>> ndimage.binary_fill_holes(a).astype(int) array([[0, 0, 0, 0, 0], [0, 1, 1, 1, 0], [0, 1, 1, 1, 0], [0, 1, 1, 1, 0], [0, 0, 0, 0, 0]]) >>> # Too big structuring element >>> ndimage.binary_fill_holes(a, structure=np.ones((5,5))).astype(int) array([[0, 0, 0, 0, 0], [0, 1, 1, 1, 0], [0, 1, 0, 1, 0], [0, 1, 1, 1, 0], [0, 0, 0, 0, 0]]) iÿÿÿÿiN( RRYR-RRRFRGR R,(RKR R=R!RLRXRa((s7/tmp/pip-build-7oUkmx/scipy/scipy/ndimage/morphology.pyRÒsG treflectgc C`sX|dkr3|dkr3|dkr3tdƒ‚ntj||||||||dƒ S(sy Calculate a greyscale erosion, using either a structuring element, or a footprint corresponding to a flat structuring element. Grayscale erosion is a mathematical morphology operation. For the simple case of a full and flat structuring element, it can be viewed as a minimum filter over a sliding window. Parameters ---------- input : array_like Array over which the grayscale erosion is to be computed. size : tuple of ints Shape of a flat and full structuring element used for the grayscale erosion. Optional if `footprint` or `structure` is provided. footprint : array of ints, optional Positions of non-infinite elements of a flat structuring element used for the grayscale erosion. Non-zero values give the set of neighbors of the center over which the minimum is chosen. structure : array of ints, optional Structuring element used for the grayscale erosion. `structure` may be a non-flat structuring element. output : array, optional An array used for storing the ouput of the erosion may be provided. mode : {'reflect','constant','nearest','mirror', 'wrap'}, optional The `mode` parameter determines how the array borders are handled, where `cval` is the value when mode is equal to 'constant'. Default is 'reflect' cval : scalar, optional Value to fill past edges of input if `mode` is 'constant'. Default is 0.0. origin : scalar, optional The `origin` parameter controls the placement of the filter. Default 0 Returns ------- output : ndarray Grayscale erosion of `input`. See also -------- binary_erosion, grey_dilation, grey_opening, grey_closing generate_binary_structure, ndimage.minimum_filter Notes ----- The grayscale erosion of an image input by a structuring element s defined over a domain E is given by: (input+s)(x) = min {input(y) - s(x-y), for y in E} In particular, for structuring elements defined as s(y) = 0 for y in E, the grayscale erosion computes the minimum of the input image inside a sliding window defined by E. Grayscale erosion [1]_ is a *mathematical morphology* operation [2]_. References ---------- .. [1] http://en.wikipedia.org/wiki/Erosion_%28morphology%29 .. [2] http://en.wikipedia.org/wiki/Mathematical_morphology Examples -------- >>> from scipy import ndimage >>> a = np.zeros((7,7), dtype=int) >>> a[1:6, 1:6] = 3 >>> a[4,4] = 2; a[2,3] = 1 >>> a array([[0, 0, 0, 0, 0, 0, 0], [0, 3, 3, 3, 3, 3, 0], [0, 3, 3, 1, 3, 3, 0], [0, 3, 3, 3, 3, 3, 0], [0, 3, 3, 3, 2, 3, 0], [0, 3, 3, 3, 3, 3, 0], [0, 0, 0, 0, 0, 0, 0]]) >>> ndimage.grey_erosion(a, size=(3,3)) array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 3, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]]) >>> footprint = ndimage.generate_binary_structure(2, 1) >>> footprint array([[False, True, False], [ True, True, True], [False, True, False]], dtype=bool) >>> # Diagonally-connected elements are not considered neighbors >>> ndimage.grey_erosion(a, size=(3,3), footprint=footprint) array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 3, 1, 2, 0, 0], [0, 0, 3, 2, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]]) s.size, footprint or structure must be specifiediN(R,t ValueErrorRt_min_or_max_filter(RKtsizet footprintR R=tmodetcvalR!((s7/tmp/pip-build-7oUkmx/scipy/scipy/ndimage/morphology.pyR&sg$c C`s§|dkr3|dkr3|dkr3tdƒ‚n|dk rwtj|ƒ}|ttdddƒg|jƒ}n|dk r»tj|ƒ}|ttdddƒg|jƒ}ntj|ƒ}tj||jƒ}x t t |ƒƒD]Œ}|| ||<|dk r#|j |} n>|dk r?|j |} n"tj |ƒrW|} n ||} | d@sò||cd8>> from scipy import ndimage >>> a = np.zeros((7,7), dtype=int) >>> a[2:5, 2:5] = 1 >>> a[4,4] = 2; a[2,3] = 3 >>> a array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 3, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]]) >>> ndimage.grey_dilation(a, size=(3,3)) array([[0, 0, 0, 0, 0, 0, 0], [0, 1, 3, 3, 3, 1, 0], [0, 1, 3, 3, 3, 1, 0], [0, 1, 3, 3, 3, 2, 0], [0, 1, 1, 2, 2, 2, 0], [0, 1, 1, 2, 2, 2, 0], [0, 0, 0, 0, 0, 0, 0]]) >>> ndimage.grey_dilation(a, footprint=np.ones((3,3))) array([[0, 0, 0, 0, 0, 0, 0], [0, 1, 3, 3, 3, 1, 0], [0, 1, 3, 3, 3, 1, 0], [0, 1, 3, 3, 3, 2, 0], [0, 1, 1, 2, 2, 2, 0], [0, 1, 1, 2, 2, 2, 0], [0, 0, 0, 0, 0, 0, 0]]) >>> s = ndimage.generate_binary_structure(2,1) >>> s array([[False, True, False], [ True, True, True], [False, True, False]], dtype=bool) >>> ndimage.grey_dilation(a, footprint=s) array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 3, 1, 0, 0], [0, 1, 3, 3, 3, 1, 0], [0, 1, 1, 3, 2, 1, 0], [0, 1, 1, 2, 2, 2, 0], [0, 0, 1, 1, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0]]) >>> ndimage.grey_dilation(a, size=(3,3), structure=np.ones((3,3))) array([[1, 1, 1, 1, 1, 1, 1], [1, 2, 4, 4, 4, 2, 1], [1, 2, 4, 4, 4, 2, 1], [1, 2, 4, 4, 4, 3, 1], [1, 2, 2, 3, 3, 3, 1], [1, 2, 2, 3, 3, 3, 1], [1, 1, 1, 1, 1, 1, 1]]) s.size, footprint or structure must be specifiediÿÿÿÿiiN(R,RdRR'RR+R/RR.R)R*RtisscalarRRe( RKRfRgR R=RhRiR!R1tsz((s7/tmp/pip-build-7oUkmx/scipy/scipy/ndimage/morphology.pyR”s2v$       c C`s@t||||d|||ƒ}t||||||||ƒS(sì Multi-dimensional greyscale opening. A greyscale opening consists in the succession of a greyscale erosion, and a greyscale dilation. Parameters ---------- input : array_like Array over which the grayscale opening is to be computed. size : tuple of ints Shape of a flat and full structuring element used for the grayscale opening. Optional if `footprint` or `structure` is provided. footprint : array of ints, optional Positions of non-infinite elements of a flat structuring element used for the grayscale opening. structure : array of ints, optional Structuring element used for the grayscale opening. `structure` may be a non-flat structuring element. output : array, optional An array used for storing the ouput of the opening may be provided. mode : {'reflect', 'constant', 'nearest', 'mirror', 'wrap'}, optional The `mode` parameter determines how the array borders are handled, where `cval` is the value when mode is equal to 'constant'. Default is 'reflect' cval : scalar, optional Value to fill past edges of input if `mode` is 'constant'. Default is 0.0. origin : scalar, optional The `origin` parameter controls the placement of the filter. Default 0 Returns ------- grey_opening : ndarray Result of the grayscale opening of `input` with `structure`. See also -------- binary_opening, grey_dilation, grey_erosion, grey_closing generate_binary_structure Notes ----- The action of a grayscale opening with a flat structuring element amounts to smoothen high local maxima, whereas binary opening erases small objects. References ---------- .. [1] http://en.wikipedia.org/wiki/Mathematical_morphology Examples -------- >>> from scipy import ndimage >>> a = np.arange(36).reshape((6,6)) >>> a[3, 3] = 50 >>> a array([[ 0, 1, 2, 3, 4, 5], [ 6, 7, 8, 9, 10, 11], [12, 13, 14, 15, 16, 17], [18, 19, 20, 50, 22, 23], [24, 25, 26, 27, 28, 29], [30, 31, 32, 33, 34, 35]]) >>> ndimage.grey_opening(a, size=(3,3)) array([[ 0, 1, 2, 3, 4, 4], [ 6, 7, 8, 9, 10, 10], [12, 13, 14, 15, 16, 16], [18, 19, 20, 22, 22, 22], [24, 25, 26, 27, 28, 28], [24, 25, 26, 27, 28, 28]]) >>> # Note that the local maximum a[3,3] has disappeared N(RR,R( RKRfRgR R=RhRiR!RX((s7/tmp/pip-build-7oUkmx/scipy/scipy/ndimage/morphology.pyR(sK c C`s@t||||d|||ƒ}t||||||||ƒS(sè Multi-dimensional greyscale closing. A greyscale closing consists in the succession of a greyscale dilation, and a greyscale erosion. Parameters ---------- input : array_like Array over which the grayscale closing is to be computed. size : tuple of ints Shape of a flat and full structuring element used for the grayscale closing. Optional if `footprint` or `structure` is provided. footprint : array of ints, optional Positions of non-infinite elements of a flat structuring element used for the grayscale closing. structure : array of ints, optional Structuring element used for the grayscale closing. `structure` may be a non-flat structuring element. output : array, optional An array used for storing the ouput of the closing may be provided. mode : {'reflect', 'constant', 'nearest', 'mirror', 'wrap'}, optional The `mode` parameter determines how the array borders are handled, where `cval` is the value when mode is equal to 'constant'. Default is 'reflect' cval : scalar, optional Value to fill past edges of input if `mode` is 'constant'. Default is 0.0. origin : scalar, optional The `origin` parameter controls the placement of the filter. Default 0 Returns ------- grey_closing : ndarray Result of the grayscale closing of `input` with `structure`. See also -------- binary_closing, grey_dilation, grey_erosion, grey_opening, generate_binary_structure Notes ----- The action of a grayscale closing with a flat structuring element amounts to smoothen deep local minima, whereas binary closing fills small holes. References ---------- .. [1] http://en.wikipedia.org/wiki/Mathematical_morphology Examples -------- >>> from scipy import ndimage >>> a = np.arange(36).reshape((6,6)) >>> a[3,3] = 0 >>> a array([[ 0, 1, 2, 3, 4, 5], [ 6, 7, 8, 9, 10, 11], [12, 13, 14, 15, 16, 17], [18, 19, 20, 0, 22, 23], [24, 25, 26, 27, 28, 29], [30, 31, 32, 33, 34, 35]]) >>> ndimage.grey_closing(a, size=(3,3)) array([[ 7, 7, 8, 9, 10, 11], [ 7, 7, 8, 9, 10, 11], [13, 13, 14, 15, 16, 17], [19, 19, 20, 20, 22, 23], [25, 25, 26, 27, 28, 29], [31, 31, 32, 33, 34, 35]]) >>> # Note that the local minimum a[3,3] has disappeared N(RR,R( RKRfRgR R=RhRiR!RX((s7/tmp/pip-build-7oUkmx/scipy/scipy/ndimage/morphology.pyRysK c C`sŒt||||d|||ƒ}t|tjƒret||||||||ƒtj|||ƒS|t||||d|||ƒSdS(s$ Multi-dimensional morphological gradient. The morphological gradient is calculated as the difference between a dilation and an erosion of the input with a given structuring element. Parameters ---------- input : array_like Array over which to compute the morphlogical gradient. size : tuple of ints Shape of a flat and full structuring element used for the mathematical morphology operations. Optional if `footprint` or `structure` is provided. A larger `size` yields a more blurred gradient. footprint : array of ints, optional Positions of non-infinite elements of a flat structuring element used for the morphology operations. Larger footprints give a more blurred morphological gradient. structure : array of ints, optional Structuring element used for the morphology operations. `structure` may be a non-flat structuring element. output : array, optional An array used for storing the ouput of the morphological gradient may be provided. mode : {'reflect', 'constant', 'nearest', 'mirror', 'wrap'}, optional The `mode` parameter determines how the array borders are handled, where `cval` is the value when mode is equal to 'constant'. Default is 'reflect' cval : scalar, optional Value to fill past edges of input if `mode` is 'constant'. Default is 0.0. origin : scalar, optional The `origin` parameter controls the placement of the filter. Default 0 Returns ------- morphological_gradient : ndarray Morphological gradient of `input`. See also -------- grey_dilation, grey_erosion, ndimage.gaussian_gradient_magnitude Notes ----- For a flat structuring element, the morphological gradient computed at a given point corresponds to the maximal difference between elements of the input among the elements covered by the structuring element centered on the point. References ---------- .. [1] http://en.wikipedia.org/wiki/Mathematical_morphology Examples -------- >>> from scipy import ndimage >>> a = np.zeros((7,7), dtype=int) >>> a[2:5, 2:5] = 1 >>> ndimage.morphological_gradient(a, size=(3,3)) array([[0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 0], [0, 1, 1, 0, 1, 1, 0], [0, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0]]) >>> # The morphological gradient is computed as the difference >>> # between a dilation and an erosion >>> ndimage.grey_dilation(a, size=(3,3)) -\ ... ndimage.grey_erosion(a, size=(3,3)) array([[0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 0], [0, 1, 1, 0, 1, 1, 0], [0, 1, 1, 1, 1, 1, 0], [0, 1, 1, 1, 1, 1, 0], [0, 0, 0, 0, 0, 0, 0]]) >>> a = np.zeros((7,7), dtype=int) >>> a[2:5, 2:5] = 1 >>> a[4,4] = 2; a[2,3] = 3 >>> a array([[0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 3, 1, 0, 0], [0, 0, 1, 1, 1, 0, 0], [0, 0, 1, 1, 2, 0, 0], [0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0]]) >>> ndimage.morphological_gradient(a, size=(3,3)) array([[0, 0, 0, 0, 0, 0, 0], [0, 1, 3, 3, 3, 1, 0], [0, 1, 3, 3, 3, 1, 0], [0, 1, 3, 2, 3, 2, 0], [0, 1, 1, 2, 2, 2, 0], [0, 1, 1, 2, 2, 2, 0], [0, 0, 0, 0, 0, 0, 0]]) N(RR,RFRRGRtsubtract( RKRfRgR R=RhRiR!RX((s7/tmp/pip-build-7oUkmx/scipy/scipy/ndimage/morphology.pyRÊsg  c C`sít||||d|||ƒ}t|tjƒr‹t||||||||ƒtj|||ƒtj|||ƒtj|||ƒSt||||d|||ƒ} tj|| | ƒtj| || ƒtj| || ƒ| SdS(sÙ Multi-dimensional morphological laplace. Parameters ---------- input : array_like Input. size : int or sequence of ints, optional See `structure`. footprint : bool or ndarray, optional See `structure`. structure : structure, optional Either `size`, `footprint`, or the `structure` must be provided. output : ndarray, optional An output array can optionally be provided. mode : {'reflect','constant','nearest','mirror', 'wrap'}, optional The mode parameter determines how the array borders are handled. For 'constant' mode, values beyond borders are set to be `cval`. Default is 'reflect'. cval : scalar, optional Value to fill past edges of input if mode is 'constant'. Default is 0.0 origin : origin, optional The origin parameter controls the placement of the filter. Returns ------- morphological_laplace : ndarray Output N(RR,RFRRGRR9Rl( RKRfRgR R=RhRiR!R`ttmp2((s7/tmp/pip-build-7oUkmx/scipy/scipy/ndimage/morphology.pyR<s"   c C`s’t||||d|||ƒ}t|tjƒret||||||||ƒtj|||ƒSt||||d|||ƒ}||SdS(s Multi-dimensional white tophat filter. Parameters ---------- input : array_like Input. size : tuple of ints Shape of a flat and full structuring element used for the filter. Optional if `footprint` or `structure` is provided. footprint : array of ints, optional Positions of elements of a flat structuring element used for the white tophat filter. structure : array of ints, optional Structuring element used for the filter. `structure` may be a non-flat structuring element. output : array, optional An array used for storing the output of the filter may be provided. mode : {'reflect', 'constant', 'nearest', 'mirror', 'wrap'}, optional The `mode` parameter determines how the array borders are handled, where `cval` is the value when mode is equal to 'constant'. Default is 'reflect' cval : scalar, optional Value to fill past edges of input if `mode` is 'constant'. Default is 0.0. origin : scalar, optional The `origin` parameter controls the placement of the filter. Default is 0. Returns ------- output : ndarray Result of the filter of `input` with `structure`. See also -------- black_tophat N(RR,RFRRGRRl( RKRfRgR R=RhRiR!RX((s7/tmp/pip-build-7oUkmx/scipy/scipy/ndimage/morphology.pyRos)  c C`s’t||||d|||ƒ}t|tjƒret||||||||ƒtj|||ƒSt||||d|||ƒ}||SdS(sO Multi-dimensional black tophat filter. Parameters ---------- input : array_like Input. size : tuple of ints, optional Shape of a flat and full structuring element used for the filter. Optional if `footprint` or `structure` is provided. footprint : array of ints, optional Positions of non-infinite elements of a flat structuring element used for the black tophat filter. structure : array of ints, optional Structuring element used for the filter. `structure` may be a non-flat structuring element. output : array, optional An array used for storing the output of the filter may be provided. mode : {'reflect', 'constant', 'nearest', 'mirror', 'wrap'}, optional The `mode` parameter determines how the array borders are handled, where `cval` is the value when mode is equal to 'constant'. Default is 'reflect' cval : scalar, optional Value to fill past edges of input if `mode` is 'constant'. Default is 0.0. origin : scalar, optional The `origin` parameter controls the placement of the filter. Default 0 Returns ------- black_tophat : ndarray Result of the filter of `input` with `structure`. See also -------- white_tophat, grey_opening, grey_closing N(RR,RFRRGRRl( RKRfRgR R=RhRiR!RX((s7/tmp/pip-build-7oUkmx/scipy/scipy/ndimage/morphology.pyR¤s*  t euclideancC`sò| r#| r#d}t|ƒ‚ntj|ƒdk}t|j|jƒ} t|| ƒ} tj|| ƒ} |jtjƒ| jtjƒ}|j ƒ}|dkr±d}n6|dkrÆd}n!|d krÛd }n td ƒ‚|dk r>t j ||jƒ}tj|d tj ƒ}|jjs>|jƒ}q>n|rbtj|jd tjƒ} nd} |rC|dkrÂ|dkr¤tj|jd tj ƒ} q@tj|jd tjƒ} qI|j|jkrãtd ƒ‚n|dkr|jjtj kr:tdƒ‚q:n$|jjtjkr:tdƒ‚n|} nd} tj|||| | ƒ|r\t|tjƒrÒ|jjtjkržtdƒ‚n|j|jf|jkrÉtdƒ‚n|} ntj|jd tjƒ} tj| ƒ} xTt| jdƒD]?} tj| | dfƒ| }|j|_|| | dfR6sindices must of int32 typesindices has wrong shapeiN(staxicabs cityblocks manhattan(RBRFRRGR'R/RRRCRDR(R6RxRvtwhereRAREtarangeR,Rtdistance_transform_opRR+RyR8R)RzR*(RKR{R}R~RR8R€t ft_inplacet dt_inplaceR;tsRƒRkR‚RXR1R„Rb((s7/tmp/pip-build-7oUkmx/scipy/scipy/ndimage/morphology.pyRcs„%      (! #&##      c C`s | r#| r#d}t|ƒ‚nt|tjƒ}t|tjƒ}tjtj|ddƒjtjƒƒ}|d k rÈt j ||j ƒ}tj |dtj ƒ}|jjsÈ|jƒ}qÈn|r&|} | j|j f|jkrÿtdƒ‚n| jjtjkrKtdƒ‚qKn%tj|j f|jdtjƒ} tj||| ƒ|rŽ| tj|jd| jƒ} | jtj ƒ} |d k rÛx7tt|ƒƒD] } | | dfc|| 9>> from scipy import ndimage >>> a = np.array(([0,1,1,1,1], ... [0,0,1,1,1], ... [0,1,1,1,1], ... [0,1,1,1,0], ... [0,1,1,0,0])) >>> ndimage.distance_transform_edt(a) array([[ 0. , 1. , 1.4142, 2.2361, 3. ], [ 0. , 0. , 1. , 2. , 2. ], [ 0. , 1. , 1.4142, 1.4142, 1. ], [ 0. , 1. , 1.4142, 1. , 0. ], [ 0. , 1. , 1. , 0. , 0. ]]) With a sampling of 2 units along x, 1 along y: >>> ndimage.distance_transform_edt(a, sampling=[2,1]) array([[ 0. , 1. , 2. , 2.8284, 3.6056], [ 0. , 0. , 1. , 2. , 3. ], [ 0. , 1. , 2. , 2.2361, 2. ], [ 0. , 1. , 2. , 1. , 0. ], [ 0. , 1. , 1. , 0. , 0. ]]) Asking for indices as well: >>> edt, inds = ndimage.distance_transform_edt(a, return_indices=True) >>> inds array([[[0, 0, 1, 1, 3], [1, 1, 1, 1, 3], [2, 2, 1, 3, 3], [3, 3, 4, 4, 3], [4, 4, 4, 4, 4]], [[0, 0, 1, 1, 4], [0, 1, 1, 1, 4], [0, 0, 1, 4, 4], [0, 0, 3, 3, 4], [0, 0, 3, 3, 4]]]) With arrays provided for inplace outputs: >>> indices = np.zeros(((np.ndim(a),) + a.shape), dtype=np.int32) >>> ndimage.distance_transform_edt(a, return_indices=True, indices=indices) array([[ 0. , 1. , 1.4142, 2.2361, 3. ], [ 0. , 0. , 1. , 2. , 2. ], [ 0. , 1. , 1.4142, 1.4142, 1. ], [ 0. , 1. , 1.4142, 1. , 0. ], [ 0. , 1. , 1. , 0. , 0. ]]) >>> indices array([[[0, 0, 1, 1, 3], [1, 1, 1, 1, 3], [2, 2, 1, 3, 3], [3, 3, 4, 4, 3], [4, 4, 4, 4, 4]], [[0, 0, 1, 1, 4], [0, 1, 1, 1, 4], [0, 0, 1, 4, 4], [0, 0, 3, 3, 4], [0, 0, 3, 3, 4]]]) s3at least one of distances/indices must be specifiediiR6sindices has wrong shapesindices must be of int32 type.R>sindices must be of float64 typeiN(!RBRFRRGt atleast_1dR…RARIR,RR.R/R'RuRCRDR(RR6RxRvR-Rteuclidean_feature_transformR8R)R*tmultiplyR9R:tsqrtRzR( RKR|R}R~RR8R€RˆR‰R‚RƒR1Rb((s7/tmp/pip-build-7oUkmx/scipy/scipy/ndimage/morphology.pyRØs\n*    !   (#t __future__RRRRtRRRt__all__R%R,RRRWRZRR R R R R RRRRRRRRRtTrueRRR(((s7/tmp/pip-build-7oUkmx/scipy/scipy/ndimage/morphology.pytsp          E ^ I Z {  i  €l€T m “ P P p1 4 4‡s