9iHe<SrSSKrSSKrSSKJr SSKJrJ r J r SSK J r SSK Jr /S QrS rS rS rS r1Skr\ SS\\4SSS.Sjj5r\ SS\\4SSS.Sjj5r\ SSSS.Sjj5r\ SSSS.Sjj5r\ SSSS.Sjj5r\ SSSS.Sjj5rg)z$ Grayscale morphological operations N)ndimage)_footprint_is_sequencemirror_footprint pad_footprint)default_footprint) DEPRECATED)erosiondilationopeningclosing white_tophat black_tophatc USupgU"XX4US9 [SU5HnU"UR5XcXES9 M USSH-upg[U5HnU"UR5XcXES9 M M/ U$)zHelper to call `gray_func` for each footprint in a sequence. `gray_func` is a morphology function that accepts `footprint`, `output`, `mode` and `cval` keyword arguments (e.g. `scipy.ndimage.grey_erosion`). r) footprintoutputmodecvalrN)rangecopy) gray_funcimage footprintsoutrrfpnum_iter_s W/var/www/html/land-doc-ocr/venv/lib/python3.13/site-packages/skimage/morphology/gray.py_iterate_gray_funcr sw a=LB e#tD 1h #((*TM "12 xA chhjB Q!' Jc[R"U5nURS:waU$URup4US-S:Xa^[R"SU4UR 5nU(a[R "X45nO[R "XP45nUS- nUS-S:XaZ[R"US4UR 5nU(a[R"X45nU$[R"X`45nU$)aShift the binary image `footprint` in the left and/or up. This only affects 2D footprints with even number of rows or columns. Parameters ---------- footprint : 2D array, shape (M, N) The input footprint. shift_x, shift_y : bool or None Whether to move `footprint` along each axis. If ``None``, the array is not modified along that dimension. Returns ------- out : 2D array, shape (M + int(shift_x), N + int(shift_y)) The shifted footprint. r rr)npasarrayndimshapezerosdtypevstackhstack)rshift_xshift_ymn extra_row extra_cols r_shift_footprintr1#s& 9%I~~ ??DA1uzHHaVY__5  9"89I 9"89I Q1uzHHaVY__5  9"89I  9"89I r!c^^T[La T[LaU$Sn[R"U[SS9 [ U5(a[ UU4SjU55$[ UTT5$)zShifts the footprints, whether it's a single array or a sequence. See `_shift_footprint`, which is called for each array in the sequence. zThe parameters `shift_x` and `shift_y` are deprecated since v0.23 and will be removed in v0.26. Use `pad_footprint` or modify the footprintmanually instead.) stacklevelc3F># UHup[UTT5U4v M g7f)N)r1).0rr.r+r,s r $_shift_footprints..[s"XiUR&r7GKsa *J!6   MM+};i((XiXXX Iw 88r!c:US:XaSn[R"U[5(aSnX4$[R"U[R5(a#[R"U5R nX4$[R nX4$US:XaSn[R"U[5(aSnX4$[R"U[R5(a#[R"U5RnX4$[R *nX4$)zDReplace 'max' and 'min' with appropriate 'cval' and 'constant' mode.maxconstantTminF)r# issubdtypeboolintegeriinfor@infrB)r(rrs r_min_max_to_constant_moderH_s u} == % %D :]]5"** - -88E?&&D :66D :  == % %D : ]]5"** - -88E?&&D :FF7D :r!>r@rBwrapignoremirrornearestreflectrArMgrrc 8Uc[R"U5nU[;a[SU<35eUS:XaSn[ UR XV5upV[ XU5n[USS9n[U5(dUS4/n[[RUUUUUS9nU$)a Return grayscale morphological erosion of an image. Morphological erosion sets a pixel at (i,j) to the minimum over all pixels in the neighborhood centered at (i,j). Erosion shrinks bright regions and enlarges dark regions. Parameters ---------- image : ndarray Image array. footprint : ndarray or tuple, optional The neighborhood expressed as a 2-D array of 1's and 0's. If None, use a cross-shaped footprint (connectivity=1). The footprint can also be provided as a sequence of smaller footprints as described in the notes below. out : ndarrays, optional The array to store the result of the morphology. If None is passed, a new array will be allocated. mode : str, optional The `mode` parameter determines how the array borders are handled. Valid modes are: 'reflect', 'constant', 'nearest', 'mirror', 'wrap', 'max', 'min', or 'ignore'. If 'max' or 'ignore', pixels outside the image domain are assumed to be the maximum for the image's dtype, which causes them to not influence the result. Default is 'reflect'. cval : scalar, optional Value to fill past edges of input if `mode` is 'constant'. Default is 0.0. .. versionadded:: 0.23 `mode` and `cval` were added in 0.23. Returns ------- eroded : array, same shape as `image` The result of the morphological erosion. Other Parameters ---------------- shift_x, shift_y : DEPRECATED .. deprecated:: 0.23 Notes ----- For ``uint8`` (and ``uint16`` up to a certain bit-depth) data, the lower algorithm complexity makes the :func:`skimage.filters.rank.minimum` function more efficient for larger images and footprints. The footprint can also be a provided as a sequence of 2-tuples where the first element of each 2-tuple is a footprint ndarray and the second element is an integer describing the number of times it should be iterated. For example ``footprint=[(np.ones((9, 1)), 1), (np.ones((1, 9)), 1)]`` would apply a 9x1 footprint followed by a 1x9 footprint resulting in a net effect that is the same as ``footprint=np.ones((9, 9))``, but with lower computational cost. Most of the builtin footprints such as :func:`skimage.morphology.disk` provide an option to automatically generate a footprint sequence of this type. For even-sized footprints, :func:`skimage.morphology.binary_erosion` and this function produce an output that differs: one is shifted by one pixel compared to the other. Examples -------- >>> # Erosion shrinks bright regions >>> import numpy as np >>> from skimage.morphology import footprint_rectangle >>> bright_square = np.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]], dtype=np.uint8) >>> erosion(bright_square, footprint_rectangle((3, 3))) 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]], dtype=uint8) unsupported mode, got rJr@Fpad_endrrrrrrr) r# empty_like_SUPPORTED_MODES ValueErrorrHr(r>rrr ndi grey_erosionrrrr+r,rrs rr r sx {mmE" ##1$:;; x*5;;CJD!)g>Ii7I !) , ,^$ ""     C Jr!c NUc[R"U5nU[;a[SU<35eUS:XaSn[ UR XV5upV[ XU5n[USS9n[U5n[U5(dUS4/n[[RUUUUUS9nU$)aN Return grayscale morphological dilation of an image. Morphological dilation sets the value of a pixel to the maximum over all pixel values within a local neighborhood centered about it. The values where the footprint is 1 define this neighborhood. Dilation enlarges bright regions and shrinks dark regions. Parameters ---------- image : ndarray Image array. footprint : ndarray or tuple, optional The neighborhood expressed as a 2-D array of 1's and 0's. If None, use a cross-shaped footprint (connectivity=1). The footprint can also be provided as a sequence of smaller footprints as described in the notes below. out : ndarray, optional The array to store the result of the morphology. If None is passed, a new array will be allocated. mode : str, optional The `mode` parameter determines how the array borders are handled. Valid modes are: 'reflect', 'constant', 'nearest', 'mirror', 'wrap', 'max', 'min', or 'ignore'. If 'min' or 'ignore', pixels outside the image domain are assumed to be the maximum for the image's dtype, which causes them to not influence the result. Default is 'reflect'. cval : scalar, optional Value to fill past edges of input if `mode` is 'constant'. Default is 0.0. .. versionadded:: 0.23 `mode` and `cval` were added in 0.23. Returns ------- dilated : uint8 array, same shape and type as `image` The result of the morphological dilation. Other Parameters ---------------- shift_x, shift_y : DEPRECATED .. deprecated:: 0.23 Notes ----- For ``uint8`` (and ``uint16`` up to a certain bit-depth) data, the lower algorithm complexity makes the :func:`skimage.filters.rank.maximum` function more efficient for larger images and footprints. The footprint can also be a provided as a sequence of 2-tuples where the first element of each 2-tuple is a footprint ndarray and the second element is an integer describing the number of times it should be iterated. For example ``footprint=[(np.ones((9, 1)), 1), (np.ones((1, 9)), 1)]`` would apply a 9x1 footprint followed by a 1x9 footprint resulting in a net effect that is the same as ``footprint=np.ones((9, 9))``, but with lower computational cost. Most of the builtin footprints such as :func:`skimage.morphology.disk` provide an option to automatically generate a footprint sequence of this type. For non-symmetric footprints, :func:`skimage.morphology.binary_dilation` and :func:`skimage.morphology.dilation` produce an output that differs: `binary_dilation` mirrors the footprint, whereas `dilation` does not. Examples -------- >>> # Dilation enlarges bright regions >>> import numpy as np >>> from skimage.morphology import footprint_rectangle >>> bright_pixel = np.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]], dtype=np.uint8) >>> dilation(bright_pixel, footprint_rectangle((3, 3))) 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]], dtype=uint8) rPrJrBFrQrrS) r#rTrUrVrHr(r>rrrr rW grey_dilationrYs rr r sz {mmE" ##1$:;; x*5;;CJD!)g>Ii7I!+I !) , ,^$ ##     C Jr!cX[USS9n[XX4S9n[U[U5X#US9nU$)a Return grayscale morphological opening of an image. The morphological opening of an image is defined as an erosion followed by a dilation. Opening can remove small bright spots (i.e. "salt") and connect small dark cracks. This tends to "open" up (dark) gaps between (bright) features. Parameters ---------- image : ndarray Image array. footprint : ndarray or tuple, optional The neighborhood expressed as a 2-D array of 1's and 0's. If None, use a cross-shaped footprint (connectivity=1). The footprint can also be provided as a sequence of smaller footprints as described in the notes below. out : ndarray, optional The array to store the result of the morphology. If None is passed, a new array will be allocated. mode : str, optional The `mode` parameter determines how the array borders are handled. Valid modes are: 'reflect', 'constant', 'nearest', 'mirror', 'wrap', 'max', 'min', or 'ignore'. If 'ignore', pixels outside the image domain are assumed to be the maximum for the image's dtype in the erosion, and minimum in the dilation, which causes them to not influence the result. Default is 'reflect'. cval : scalar, optional Value to fill past edges of input if `mode` is 'constant'. Default is 0.0. .. versionadded:: 0.23 `mode` and `cval` were added in 0.23. Returns ------- opening : array, same shape and type as `image` The result of the morphological opening. Notes ----- The footprint can also be a provided as a sequence of 2-tuples where the first element of each 2-tuple is a footprint ndarray and the second element is an integer describing the number of times it should be iterated. For example ``footprint=[(np.ones((9, 1)), 1), (np.ones((1, 9)), 1)]`` would apply a 9x1 footprint followed by a 1x9 footprint resulting in a net effect that is the same as ``footprint=np.ones((9, 9))``, but with lower computational cost. Most of the builtin footprints such as :func:`skimage.morphology.disk` provide an option to automatically generate a footprint sequence of this type. Examples -------- >>> # Open up gap between two bright regions (but also shrink regions) >>> import numpy as np >>> from skimage.morphology import footprint_rectangle >>> bad_connection = np.array([[1, 0, 0, 0, 1], ... [1, 1, 0, 1, 1], ... [1, 1, 1, 1, 1], ... [1, 1, 0, 1, 1], ... [1, 0, 0, 0, 1]], dtype=np.uint8) >>> opening(bad_connection, footprint_rectangle((3, 3))) array([[0, 0, 0, 0, 0], [1, 1, 0, 1, 1], [1, 1, 0, 1, 1], [1, 1, 0, 1, 1], [0, 0, 0, 0, 0]], dtype=uint8) FrQrNrrr)rr r r)rrrrrerodeds rr r ns9Ni7I UD >> # Close a gap between two bright lines >>> import numpy as np >>> from skimage.morphology import footprint_rectangle >>> broken_line = np.array([[0, 0, 0, 0, 0], ... [0, 0, 0, 0, 0], ... [1, 1, 0, 1, 1], ... [0, 0, 0, 0, 0], ... [0, 0, 0, 0, 0]], dtype=np.uint8) >>> closing(broken_line, footprint_rectangle((3, 3))) array([[0, 0, 0, 0, 0], [0, 0, 0, 0, 0], [1, 1, 1, 1, 1], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]], dtype=uint8) FrQrNr])rr r r)rrrrrdilateds rrrs9Ni7Iud>G '+I6CQU VC Jr!crX LaQ[XX4S9n[R"UR[5(a[R "X%US9 U$X%-nU$[XX#US9n[R"UR[5(a[R "XUS9 U$[R "XUS9 U$)a Return white top hat of an image. The white top hat of an image is defined as the image minus its morphological opening. This operation returns the bright spots of the image that are smaller than the footprint. Parameters ---------- image : ndarray Image array. footprint : ndarray or tuple, optional The neighborhood expressed as a 2-D array of 1's and 0's. If None, use a cross-shaped footprint (connectivity=1). The footprint can also be provided as a sequence of smaller footprints as described in the notes below. out : ndarray, optional The array to store the result of the morphology. If None is passed, a new array will be allocated. mode : str, optional The `mode` parameter determines how the array borders are handled. Valid modes are: 'reflect', 'constant', 'nearest', 'mirror', 'wrap', 'max', 'min', or 'ignore'. See :func:`skimage.morphology.opening`. Default is 'reflect'. cval : scalar, optional Value to fill past edges of input if `mode` is 'constant'. Default is 0.0. .. versionadded:: 0.23 `mode` and `cval` were added in 0.23. Returns ------- out : array, same shape and type as `image` The result of the morphological white top hat. Notes ----- The footprint can also be a provided as a sequence of 2-tuples where the first element of each 2-tuple is a footprint ndarray and the second element is an integer describing the number of times it should be iterated. For example ``footprint=[(np.ones((9, 1)), 1), (np.ones((1, 9)), 1)]`` would apply a 9x1 footprint followed by a 1x9 footprint resulting in a net effect that is the same as ``footprint=np.ones((9, 9))``, but with lower computational cost. Most of the builtin footprints such as :func:`skimage.morphology.disk` provide an option to automatically generate a footprint sequence of this type. See Also -------- black_tophat References ---------- .. [1] https://en.wikipedia.org/wiki/Top-hat_transform Examples -------- >>> # Subtract gray background from bright peak >>> import numpy as np >>> from skimage.morphology import footprint_rectangle >>> bright_on_gray = np.array([[2, 3, 3, 3, 2], ... [3, 4, 5, 4, 3], ... [3, 5, 9, 5, 3], ... [3, 4, 5, 4, 3], ... [2, 3, 3, 3, 2]], dtype=np.uint8) >>> white_tophat(bright_on_gray, footprint_rectangle((3, 3))) array([[0, 0, 0, 0, 0], [0, 0, 1, 0, 0], [0, 1, 5, 1, 0], [0, 0, 1, 0, 0], [0, 0, 0, 0, 0]], dtype=uint8) rNrr])r r#rCr(rD logical_xorsubtract)rrrrropeneds rrrsV |@ ==t , , NN3C 0  MC  %T BC }}SYY%% us+ J EC( Jr!cX Lab[XX4S9n[R"UR[5(a[R "XRUS9 U$[R "XRUS9 U$[XX#US9n[R"UR[R5(a[R "X US9 U$X -nU$)aY Return black top hat of an image. The black top hat of an image is defined as its morphological closing minus the original image. This operation returns the dark spots of the image that are smaller than the footprint. Note that dark spots in the original image are bright spots after the black top hat. Parameters ---------- image : ndarray Image array. footprint : ndarray or tuple, optional The neighborhood expressed as a 2-D array of 1's and 0's. If None, use a cross-shaped footprint (connectivity=1). The footprint can also be provided as a sequence of smaller footprints as described in the notes below. out : ndarray, optional The array to store the result of the morphology. If None is passed, a new array will be allocated. mode : str, optional The `mode` parameter determines how the array borders are handled. Valid modes are: 'reflect', 'constant', 'nearest', 'mirror', 'wrap', 'max', 'min', or 'ignore'. See :func:`skimage.morphology.closing`. Default is 'reflect'. cval : scalar, optional Value to fill past edges of input if `mode` is 'constant'. Default is 0.0. .. versionadded:: 0.23 `mode` and `cval` were added in 0.23. Returns ------- out : array, same shape and type as `image` The result of the morphological black top hat. Notes ----- The footprint can also be a provided as a sequence of 2-tuples where the first element of each 2-tuple is a footprint ndarray and the second element is an integer describing the number of times it should be iterated. For example ``footprint=[(np.ones((9, 1)), 1), (np.ones((1, 9)), 1)]`` would apply a 9x1 footprint followed by a 1x9 footprint resulting in a net effect that is the same as ``footprint=np.ones((9, 9))``, but with lower computational cost. Most of the builtin footprints such as :func:`skimage.morphology.disk` provide an option to automatically generate a footprint sequence of this type. See Also -------- white_tophat References ---------- .. [1] https://en.wikipedia.org/wiki/Top-hat_transform Examples -------- >>> # Change dark peak to bright peak and subtract background >>> import numpy as np >>> from skimage.morphology import footprint_rectangle >>> dark_on_gray = np.array([[7, 6, 6, 6, 7], ... [6, 5, 4, 5, 6], ... [6, 4, 0, 4, 6], ... [6, 5, 4, 5, 6], ... [7, 6, 6, 6, 7]], dtype=np.uint8) >>> black_tophat(dark_on_gray, footprint_rectangle((3, 3))) array([[0, 0, 0, 0, 0], [0, 0, 1, 0, 0], [0, 1, 5, 1, 0], [0, 0, 1, 0, 0], [0, 0, 0, 0, 0]], dtype=uint8) rNrbr])rr#rCr(rDrcrdbool_)rrrrrcloseds rrresX |@ ==t , , NN6C 0  KK - %T BC }}SYY)) ss+ J  Jr!)NN)__doc__r9numpyr#scipyrrWrrrrmiscr _shared.utilsr __all__r r1r>rHrUr r r rrrr!rrps/ OO#& X"%P9(*    q  qqh   u  uupIYSIIXIYSIIXY)#YYxY)#YYr!