0-Ph)AdZddlZddlZddlmZddlmZddl m Z ddl m Z d Z d e DZd Z ed e Dd ZdZddddZddddZddddddZdS)z#Miscellaneous morphology functions.N)ndimage)cKDTree)warn)_remove_objects_by_distance)erosiondilationopeningclosingci|]}|d|z S)grey_.0xs W/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/skimage/morphology/misc.py rs111a1gk111)binary_erosionbinary_dilationbinary_openingbinary_closing black_tophat white_tophatci|]}||Srrrs rrrs,,,1,,,rcHtjdfd }|S)aDecorator to add a default footprint to morphology functions. Parameters ---------- func : function A morphology function such as erosion, dilation, opening, closing, white_tophat, or black_tophat. Returns ------- func_out : function The function, using a default footprint of same dimension as the input image with connectivity 1. NcX|tj|jd}|g|Rd|i|S)Nr footprint)ndigenerate_binary_structurendim)imagerargskwargsfuncs rfunc_outz#default_footprint..func_out.sB  5ej!DDItE@@@@Y@@@@rN) functoolswraps)r&r's` rdefault_footprintr+sE"_TAAAAAA Orc|jtks>> from skimage import morphology >>> a = np.array([[0, 0, 0, 1, 0], ... [1, 1, 1, 0, 0], ... [1, 1, 1, 0, 1]], bool) >>> b = morphology.remove_small_objects(a, 6) >>> b array([[False, False, False, False, False], [ True, True, True, False, False], [ True, True, True, False, False]]) >>> c = morphology.remove_small_objects(a, 7, connectivity=2) >>> c array([[False, False, False, True, False], [ True, True, True, False, False], [ True, True, True, False, False]]) >>> d = morphology.remove_small_objects(a, 6, out=a) >>> d is a True Nrr.)outputz{Negative value labels are not supported. Try relabeling the input with `scipy.ndimage.label` or `skimage.morphology.label`.rz[Only one label was provided to `remove_small_objects`. Did you mean to use a boolean array?)r5copyr.r/r r!r"r0 zeros_likeint32labelbincountravel ValueErrorlenr) r4min_size connectivityr8rccscomponent_sizes too_smalltoo_small_masks rremove_small_objectsrJ?sDx2 {ggiiAAA1}}  yD1"'<HH mBbh/// "i,,,,, +ciikk22     *     ?q  SY$%6%6  3    (*Is^NC Js &CCcht||jtkrtdt| |jtkrt dn|td}tj||t||||}tj|||S)apRemove contiguous holes smaller than the specified size. Parameters ---------- ar : ndarray (arbitrary shape, int or bool type) The array containing the connected components of interest. area_threshold : int, optional (default: 64) The maximum area, in pixels, of a contiguous hole that will be filled. Replaces `min_size`. connectivity : int, {1, 2, ..., ar.ndim}, optional (default: 1) The connectivity defining the neighborhood of a pixel. out : ndarray Array of the same shape as `ar` and bool dtype, into which the output is placed. By default, a new array is created. Raises ------ TypeError If the input array is of an invalid type, such as float or string. ValueError If the input array contains negative values. Returns ------- out : ndarray, same shape and type as input `ar` The input array with small holes within connected components removed. Examples -------- >>> from skimage import morphology >>> a = np.array([[1, 1, 1, 1, 1, 0], ... [1, 1, 1, 0, 1, 0], ... [1, 0, 0, 1, 1, 0], ... [1, 1, 1, 1, 1, 0]], bool) >>> b = morphology.remove_small_holes(a, 2) >>> b array([[ True, True, True, True, True, False], [ True, True, True, True, True, False], [ True, False, False, True, True, False], [ True, True, True, True, True, False]]) >>> c = morphology.remove_small_holes(a, 2, connectivity=2) >>> c array([[ True, True, True, True, True, False], [ True, True, True, False, True, False], [ True, False, False, True, True, False], [ True, True, True, True, True, False]]) >>> d = morphology.remove_small_holes(a, 2, out=a) >>> d is a True Notes ----- If the array type is int, it is assumed that it contains already-labeled objects. The labels are not kept in the output image (this function always outputs a bool image). It is suggested that labeling is completed after using this function. z\Any labeled images will be returned as a boolean array. Did you mean to use a boolean array?Nzout dtype must be boolTr<r7) r5r.r/r UserWarningr3astyper0 logical_notrJ)r4area_thresholdrEr8s rremove_small_holesrQsv2 x4  3      9  455 5 ii4i((N23 sNLc J J JCN3C    Jr)priorityp_normspacingr8c |dkrtd|tj|jtjstd|j||d}n ||ur||dd<|d}Ltjj|j fks dkrtdtj |}tj dkr!|dkrtj|j d }ntj|j |j }tj|| tj|| k|} || } || } | jdkr|S| tj|| } | tj|| } |ntj|jtjd s4tj|tjd }ntj|}|| d} | dkrtd|  | tj||| dddd} nR#t.$rE} tj|d zf}|j|krtd|d|jd| d} ~ wwxYwtj| |j.t5fdt7|j Dt9tjtjj}tA|| | ||||j|j!|ur|"|j|dd<|S)aURemove objects, in specified order, until remaining are a minimum distance apart. Remove labeled objects from an image until the remaining ones are spaced more than a given distance from one another. By default, smaller objects are removed first. Parameters ---------- label_image : ndarray of integers An n-dimensional array containing object labels, e.g. as returned by :func:`~.label`. A value of zero is considered background, all other object IDs must be positive integers. min_distance : int or float Remove objects whose distance to other objects is not greater than this positive value. Objects with a lower `priority` are removed first. priority : ndarray, optional Defines the priority with which objects are removed. Expects a 1-dimensional array of length :func:`np.amax(label_image) + 1 ` that contains the priority for each object's label at the respective index. Objects with a lower value are removed first until all remaining objects fulfill the distance requirement. If not given, priority is given to objects with a higher number of samples and their label value second. p_norm : int or float, optional The Minkowski distance of order p, used to calculate the distance between objects. The default ``2`` corresponds to the Euclidean distance, ``1`` to the "Manhattan" distance, and ``np.inf`` to the Chebyshev distance. spacing : sequence of float, optional The pixel spacing along each axis of `label_image`. If not specified, a grid spacing of unity (1) is implied. out : ndarray, optional Array of the same shape and dtype as `image`, into which the output is placed. By default, a new array is created. Returns ------- out : ndarray Array of the same shape as `label_image`, for which objects that violate the `min_distance` condition were removed. See Also -------- skimage.morphology.remove_small_objects Remove objects smaller than the specified size. Notes ----- The basic steps of this algorithm work as follows: 1. Find the indices for of all given objects and separate them depending on if they point to an object's border or not. 2. Sort indices by their label value, ensuring that indices which point to the same object are next to each other. This optimization allows finding all parts of an object, simply by stepping to the neighboring indices. 3. Sort boundary indices by `priority`. Use a stable-sort to preserve the ordering from the previous sorting step. If `priority` is not given, use :func:`numpy.bincount` as a fallback. 4. Construct a :class:`scipy.spatial.cKDTree` from the boundary indices. 5. Iterate across boundary indices in priority-sorted order, and query the kd-tree for objects that are too close. Remove ones that are and don't take them into account when evaluating other objects later on. The performance of this algorithm depends on the number of samples in `label_image` that belong to an object's border. Examples -------- >>> import skimage as ski >>> ski.morphology.remove_objects_by_distance(np.array([2, 0, 1, 1]), 2) array([0, 0, 1, 1]) >>> ski.morphology.remove_objects_by_distance( ... np.array([2, 0, 1, 1]), 2, priority=np.array([0, 1, 9]) ... ) array([2, 0, 0, 0]) >>> label_image = np.array( ... [[8, 0, 0, 0, 0, 0, 0, 0, 0, 9, 9], ... [8, 8, 8, 0, 0, 0, 0, 0, 0, 9, 9], ... [0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0], ... [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], ... [0, 0, 3, 0, 0, 0, 1, 0, 0, 0, 0], ... [2, 0, 0, 0, 0, 1, 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, 7, 7]] ... ) >>> ski.morphology.remove_objects_by_distance( ... label_image, min_distance=3 ... ) array([[8, 0, 0, 0, 0, 0, 0, 0, 0, 9, 9], [8, 8, 8, 0, 0, 0, 0, 0, 0, 9, 9], [0, 0, 0, 0, 0, 0, 0, 0, 9, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 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, 7, 7]]) rzmin_distance must be >= 0, was z,`label_image` must be of integer dtype, got NC)orderzV`spacing` must contain exactly one positive factor for each dimension of `label_image`rr)rsafe)castingFrLzfound object with negative ID stable)kindzBshape of `priority` must be (np.amax(label_image) + 1,), expected z, got z insteadc3:K|]}||zVdSr(r)rdimrTunraveled_indicess r z-remove_objects_by_distance..sC" " 69 c "WS\ 1" " " " " " rr:)data)r8border_indices inner_indiceskdtree min_distancerSshape)#rBr0r1r.r2r<rAarrayrfr"min flatnonzeroallr r!maximum_filterminimum_filtersizeargsortcan_castintpr@rN IndexErroramax unravel_indextuplerangerasarrayfloat64Trbasereshape) label_imagererRrSrTr8 out_raveledindicesrborderrbrc smallest_iderrorexpected_shaperdr_s ` @rremove_objects_by_distancers3TaI<IIJJJ =*BJ 7 7  N;;L N N    {S)) K  AAA))#)&&K(7## =SXK ' '7;;==A+=+=6  n[))G 26'!*"788fkk1#(A>> 1#(CHEE  3)444  cY 7 7 7 8 egggFV_NVG$Ma $BJ{>/J$K$KLN!"*[-G"H"HIM{39bgv>>> 0{;#5#5bgE#5#J#JKKHH{;//HnQ/0KQI+IIJJJ( Jx N ;<8 L L LTTrT R     '+..24 >^ + +K*KK2:.KKK   (CC!" " " " " =B38__" " "   "*%6bjIIIK L L LF %#!s""$$SY//AAA Js 1J?? L AL  L)r6r)__doc__numpyr0r)scipyrr scipy.spatialr _shared.utilsr_misc_cyrfuncsskimage2ndimageupdater+r5rJrQrrrrrsU)) !!!!!! 111111 6115111 ,,e,,,---4   ``````FSTSSSSSt  NNNNNNNr