ó  ‰\c@sdZddlZddlZddlmZddlmZddl m Z dd d d d d gZ d„Z d„Z d„Zeddeed„ƒZeddeed„ƒZeeddd„ƒƒZeeddd„ƒƒZeddd„ƒZeddd„ƒZdS(s$ Grayscale morphological operations iÿÿÿÿN(tndimagei(t default_selemi(tcropterosiontdilationtopeningtclosingt white_tophatt black_tophatcCsò|jdkr|S|j\}}|ddkrtjd|f|jƒ}|rktj||fƒ}ntj||fƒ}|d7}n|ddkrîtj|df|jƒ}|rÖtj||fƒ}qîtj||fƒ}n|S(sÉShift the binary image `selem` in the left and/or up. This only affects 2D structuring elements with even number of rows or columns. Parameters ---------- selem : 2D array, shape (M, N) The input structuring element. shift_x, shift_y : bool Whether to move `selem` along each axis. Returns ------- out : 2D array, shape (M + int(shift_x), N + int(shift_y)) The shifted structuring element. iii(tndimtshapetnptzerostdtypetvstackthstack(tselemtshift_xtshift_ytmtnt extra_rowt extra_col((s6lib/python2.7/site-packages/skimage/morphology/grey.pyt _shift_selems cCs$|tdddƒf|j}|S(sÌChange the order of the values in `selem`. This is a patch for the *weird* footprint inversion in `ndi.grey_morphology` [1]_. Parameters ---------- selem : array The input structuring element. Returns ------- inverted : array, same shape and type as `selem` The structuring element, in opposite order. Examples -------- >>> selem = np.array([[0, 0, 0], [0, 1, 1], [0, 1, 1]], np.uint8) >>> _invert_selem(selem) 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 iÿÿÿÿN(tslicetNoneR (Rtinverted((s6lib/python2.7/site-packages/skimage/morphology/grey.pyt _invert_selem4s cs%tjˆƒd‡fd†ƒ}|S(súPad input images for certain morphological operations. Parameters ---------- func : callable A morphological function, either opening or closing, that supports eccentric structuring elements. Its parameters must include at least `image`, `selem`, and `out`. Returns ------- func_out : callable The same function, but correctly padding the input image before applying the input function. See Also -------- opening, closing. c sïg}t}|dkr*tj|ƒ}nxN|jD]C}|ddkr]|d}t}nd}|j|fdƒq4W|r«tj||ddƒ}tj|ƒ} n|} ˆ||d| ||Ž} |råt| |ƒ|(n| }|S(Niiitmodetedgetout( tFalseRR t empty_likeR tTruetappendtpadR( timageRRtargstkwargst pad_widthstpaddingtaxis_lentaxis_pad_widthtout_temp(tfunc(s6lib/python2.7/site-packages/skimage/morphology/grey.pytfunc_ouths&   N(t functoolstwrapsR(R,R-((R,s6lib/python2.7/site-packages/skimage/morphology/grey.pytpad_for_eccentric_selemsTs cCs\tj|ƒ}t|||ƒ}|dkr?tj|ƒ}ntj|d|d|ƒ|S(sìReturn greyscale 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. selem : ndarray, optional The neighborhood expressed as an array of 1's and 0's. If None, use cross-shaped structuring element (connectivity=1). 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 structuring element about center point. This only affects eccentric structuring elements (i.e. selem 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 structuring elements. 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) t footprinttoutputN(R tarrayRRR tndit grey_erosion(R$RRRR((s6lib/python2.7/site-packages/skimage/morphology/grey.pyR‚s 3 cCshtj|ƒ}t|||ƒ}t|ƒ}|dkrKtj|ƒ}ntj|d|d|ƒ|S(sýReturn greyscale morphological dilation of an image. Morphological dilation sets a pixel at (i,j) to the maximum over all pixels in the neighborhood centered at (i,j). Dilation enlarges bright regions and shrinks dark regions. Parameters ---------- image : ndarray Image array. selem : ndarray, optional The neighborhood expressed as a 2-D array of 1's and 0's. If None, use cross-shaped structuring element (connectivity=1). 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 structuring element about center point. This only affects eccentric structuring elements (i.e. selem 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 structuring elements. 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) R1R2N(R R3RRRR R4t grey_dilation(R$RRRR((s6lib/python2.7/site-packages/skimage/morphology/grey.pyR½s4  c Cs4t||ƒ}t||d|dtdtƒ}|S(s¶Return greyscale morphological opening of an image. The morphological opening on an image is defined as an erosion followed by a dilation. Opening can remove small bright spots (i.e. "salt") and connect small dark cracks. This tends to "open" up (dark) gaps between (bright) features. Parameters ---------- image : ndarray Image array. selem : ndarray, optional The neighborhood expressed as an array of 1's and 0's. If None, use cross-shaped structuring element (connectivity=1). out : ndarray, optional The array to store the result of the morphology. If None is passed, a new array will be allocated. Returns ------- opening : array, same shape and type as `image` The result of the morphological opening. Examples -------- >>> # Open up gap between two bright regions (but also shrink regions) >>> import numpy as np >>> from skimage.morphology import square >>> bad_connection = np.array([[1, 0, 0, 0, 1], ... [1, 1, 0, 1, 1], ... [1, 1, 1, 1, 1], ... [1, 1, 0, 1, 1], ... [1, 0, 0, 0, 1]], dtype=np.uint8) >>> opening(bad_connection, square(3)) array([[0, 0, 0, 0, 0], [1, 1, 0, 1, 1], [1, 1, 0, 1, 1], [1, 1, 0, 1, 1], [0, 0, 0, 0, 0]], dtype=uint8) RRR(RRR!(R$RRteroded((s6lib/python2.7/site-packages/skimage/morphology/grey.pyRÿs,!c Cs4t||ƒ}t||d|dtdtƒ}|S(sŒReturn greyscale morphological closing of an image. The morphological closing on 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. selem : ndarray, optional The neighborhood expressed as an array of 1's and 0's. If None, use cross-shaped structuring element (connectivity=1). out : ndarray, optional The array to store the result of the morphology. If None, is passed, a new array will be allocated. Returns ------- closing : array, same shape and type as `image` The result of the morphological closing. 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) RRR(RRR!(R$RRtdilated((s6lib/python2.7/site-packages/skimage/morphology/grey.pyR1s,!cCs*tj|ƒ}||krit||ƒ}tj|jtjƒr[tj||d|ƒn ||8}|S|dkr‡tj|ƒ}nt |tj ƒrÃ|jtj krÃ|j dtj ƒ}n|}t |tj ƒr|jtj kr|j dtj ƒ}n|}tj|d|d|ƒ}|S(sbReturn white top hat of an image. The white top hat of an image is defined as the image minus its morphological opening. This operation returns the bright spots of the image that are smaller than the structuring element. Parameters ---------- image : ndarray Image array. selem : ndarray, optional The neighborhood expressed as an array of 1's and 0's. If None, use cross-shaped structuring element (connectivity=1). out : ndarray, optional The array to store the result of the morphology. If None is passed, a new array will be allocated. Returns ------- out : array, same shape and type as `image` The result of the morphological white top hat. Examples -------- >>> # Subtract grey background from bright peak >>> import numpy as np >>> from skimage.morphology import square >>> bright_on_grey = np.array([[2, 3, 3, 3, 2], ... [3, 4, 5, 4, 3], ... [3, 5, 9, 5, 3], ... [3, 4, 5, 4, 3], ... [2, 3, 3, 3, 2]], dtype=np.uint8) >>> white_tophat(bright_on_grey, square(3)) array([[0, 0, 0, 0, 0], [0, 0, 1, 0, 0], [0, 1, 5, 1, 0], [0, 0, 1, 0, 0], [0, 0, 0, 0, 0]], dtype=uint8) RR R1R2N(R R3Rt issubdtypeR tbool_t logical_xorRR t isinstancetndarraytbooltviewtuint8R4R(R$RRtopenedtimage_tout_((s6lib/python2.7/site-packages/skimage/morphology/grey.pyRcs"*   $$cCsu||kr|jƒ}n|}t||d|ƒ}tj|jtjƒrgtj||d|ƒn ||8}|S(sÇReturn 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 structuring element. Note that dark spots in the original image are bright spots after the black top hat. Parameters ---------- image : ndarray Image array. selem : ndarray, optional The neighborhood expressed as a 2-D array of 1's and 0's. If None, use cross-shaped structuring element (connectivity=1). 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. Examples -------- >>> # Change dark peak to bright peak and subtract background >>> import numpy as np >>> from skimage.morphology import square >>> dark_on_grey = 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_grey, 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) R(tcopyRR R9R R:R;(R$RRtoriginal((s6lib/python2.7/site-packages/skimage/morphology/grey.pyR¥s+  (t__doc__R.tnumpyR tscipyRR4tmiscRtutilRt__all__RRR0RRRRRRRR(((s6lib/python2.7/site-packages/skimage/morphology/grey.pyts0    & .:A00A