U ?hZ@sdZddlZddlZddlmZddlmZddl m Z m Z ddl m Z d d d d d dgZddZddZddZddZe d dd Ze d!dd Ze ed"dd Ze ed#dd ZddZe d$dd Ze d%ddZdS)&z$ Grayscale morphological operations N)ndimage)crop)_footprint_is_sequence_shape_from_sequence)default_footprinterosiondilationopeningclosing white_tophat black_tophatcCsv|d\}}||||dtd|D]}||||dq$|ddD](\}}t|D]}||||dqXqH|S)zHelper to call `binary_func` for each footprint in a sequence. binary_func is a binary morphology function that accepts "structure", "output" and "iterations" keyword arguments (e.g. `scipy.ndimage.binary_erosion`). r footprintoutputrN)rangecopy)Z gray_funcimage footprintsoutfpZnum_iter_rI/var/www/html/venv/lib/python3.8/site-packages/skimage/morphology/gray.py_iterate_gray_funcs  rcCs|jdkr|S|j\}}|ddkr`td|f|j}|rJt||f}nt||f}|d7}|ddkrt|df|j}|rt||f}nt||f}|S)aShift the binary image `footprint` in the left and/or up. This only affects 2D footprints with even number of rows or columns. Parameters ---------- footprint : 2D array, shape (M, N) The input footprint. shift_x, shift_y : bool Whether to move `footprint` along each axis. Returns ------- out : 2D array, shape (M + int(shift_x), N + int(shift_y)) The shifted footprint. rrr)ndimshapenpZzerosdtypeZvstackZhstack)rshift_xshift_ymnZ extra_rowZ extra_colrrr_shift_footprint#s    r$cCs|tdddf|j}|S)aChange the order of the values in `footprint`. This is a patch for the *weird* footprint inversion in `ndi.grey_morphology` [1]_. Parameters ---------- footprint : array The input footprint. Returns ------- inverted : array, same shape and type as `footprint` The footprint, in opposite order. Examples -------- >>> footprint = np.array([[0, 0, 0], [0, 1, 1], [0, 1, 1]], np.uint8) >>> _invert_footprint(footprint) array([[1, 1, 0], [1, 1, 0], [0, 0, 0]], dtype=uint8) References ---------- .. [1] https://github.com/scipy/scipy/blob/ec20ababa400e39ac3ffc9148c01ef86d5349332/scipy/ndimage/morphology.py#L1285 # noqa N)slicer)rinvertedrrr_invert_footprintIsr(cstdfdd }|S)aPad input images for certain morphological operations. Parameters ---------- func : callable A morphological function, either opening or closing, that supports eccentric footprints. Its parameters must include at least `image`, `footprint`, and `out`. Returns ------- func_out : callable The same function, but correctly padding the input image before applying the input function. See Also -------- opening, closing. Nc sg}d}|dkrt|}t|r,t|}n|j}|D]2}|ddkrT|d} d}nd} || fdq6|rtj||dd}t|} n|} ||f|d| i|} |rt| ||dd<n| }|S) NFrrrTedge)moder)r empty_likerrrappendpadr) rrrargskwargs pad_widthspaddingZfootprint_shapeZaxis_lenZaxis_pad_widthZout_tempfuncrrfunc_out}s,    z.pad_for_eccentric_footprints..func_out)N) functoolswraps)r3r4rr2rpad_for_eccentric_footprintsisr7Fcsl|dkrt|}t|rBtfdd|D}ttj|||St|}t|}tj|||d|S)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. shift_x, shift_y : bool, optional shift footprint about center point. This only affects eccentric footprints (i.e. footprint with even numbered sides). Returns ------- eroded : array, same shape as `image` The result of the morphological erosion. Notes ----- For ``uint8`` (and ``uint16`` up to a certain bit-depth) data, the lower algorithm complexity makes the `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 ``skimage.morphology.disk`` provide an option to automatically generate a footprint sequence of this type. Examples -------- >>> # Erosion shrinks bright regions >>> import numpy as np >>> from skimage.morphology import square >>> 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, square(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) Nc3s"|]\}}t||fVqdSN)r$.0rr#r r!rr szerosion..r) rr+rtuplerndi grey_erosionarrayr$rrrr r!rrr;rr s@   cst|dkrt|}t|rBtfdd|D}ttj|||St|}t|}t |}tj|||d|S)a> 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. shift_x, shift_y : bool, optional Shift footprint about center point. This only affects 2D eccentric footprints (i.e., footprints with even-numbered sides). Returns ------- dilated : uint8 array, same shape and type as `image` The result of the morphological dilation. Notes ----- For `uint8` (and `uint16` up to a certain bit-depth) data, the lower algorithm complexity makes the `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 ``skimage.morphology.disk`` provide an option to automatically generate a footprint sequence of this type. Examples -------- >>> # Dilation enlarges bright regions >>> import numpy as np >>> from skimage.morphology import square >>> 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, square(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) Nc3s&|]\}}tt||fVqdSr8)r(r$r9r;rrr<2szdilation..r) rr+rr=rr> grey_dilationr@r$r(rArr;rr sA   cCs t||}t|||ddd}|S)aReturn 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. 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 ``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 square >>> 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, square(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) Trr r!)r r )rrrZerodedrrrr Es: cCs t||}t|||ddd}|S)aReturn grayscale morphological closing of an image. The morphological closing of an image is defined as a dilation followed by an erosion. Closing can remove small dark spots (i.e. "pepper") and connect small bright cracks. This tends to "close" 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, a new array will be allocated. Returns ------- closing : array, same shape and type as `image` The result of the morphological closing. 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 ``skimage.morphology.disk`` provide an option to automatically generate a footprint sequence of this type. Examples -------- >>> # Close a gap between two bright lines >>> import numpy as np >>> from skimage.morphology import square >>> 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, square(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) TrC)r r )rrrZdilatedrrrr s: cCsnttj|||}ttj|||}|dkr0|}|jtjkrZ|jtjkrZtj|||dntj |||d|S)zReturn white top hat for a sequence of footprints. Like SciPy's implementation, but with ``ndi.grey_erosion`` and ``ndi.grey_dilation`` wrapped with ``_iterate_gray_func``. Nr) rr>r?rBrrrbool_Z bitwise_xorsubtract)rrrtmprrr_white_tophat_seqencesrHcCs||kr>t||}t|jtr2tj|||dn||8}|S|dkrPt|}t|tjrv|jtkrv|j tj d}n|}t|tjr|jtkr|j tj d}n|}t |rt |||St |}tj|||d}|S)aReturn 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. 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 ``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 square >>> 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, square(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) rDN)rr)r r issubdtyperbool logical_xorr+ isinstanceZndarrayviewZuint8rrHr@r>r )rrrZopenedZimage_Zout_rrrr s&@    cCsR||kr|}n|}t|||d}t|jtjrFtj|||dn||8}|S)aS 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. 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 ``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 square >>> 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, square(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) rD)rr rrIrrErK)rrroriginalrrrr/sA )NNFF)NNFF)NN)NN)NN)NN)__doc__r5numpyrZscipyrr>utilrrrrmiscr__all__rr$r(r7r r r r rHr rrrrrs8    & 5 M X>> X