voi8)DSrSSKrSSKJr SSjrSSjrSSjrSSjr g) z! Binary morphological operations N)ndimagecT[R"XS9n[R"XAUS9$)aReturn binary morphological erosion of an image. Compared to the more general :func:`skimage.morphology.erosion`, this function only supports binary inputs and circular footprints. However, it performs typically faster for large (circular) footprints. This works by applying a threshold to the exact Euclidean distance map of the image [1]_, [2]_. The implementation is based on: func:`scipy.ndimage.distance_transform_edt`. Parameters ---------- image : ndarray Binary input image. radius : float The radius of the footprint used for the operation. out : ndarray of bool, optional The array to store the result of the morphology. If None, a new array will be allocated. spacing : float, or sequence of float, optional Spacing of elements along each dimension. If a sequence, must be of length equal to the input's dimension (number of axes). If a single number, this value is used for all axes. If not specified, a grid spacing of unity is implied. Returns ------- eroded : ndarray of bool The result of the morphological erosion taking values in ``[False, True]``. References ---------- .. [1] Cuisenaire, O. and Macq, B., "Fast Euclidean morphological operators using local distance transformation by propagation, and applications," Image Processing And Its Applications, 1999. Seventh International Conference on (Conf. Publ. No. 465), 1999, pp. 856-860 vol.2. :DOI:`10.1049/cp:19990446` .. [2] Ingemar Ragnemalm, Fast erosion and dilation by contour processing and thresholding of distance maps, Pattern Recognition Letters, Volume 13, Issue 3, 1992, Pages 161-166. :DOI:`10.1016/0167-8655(92)90055-5` Examples -------- Erosion shrinks bright regions >>> import numpy as np >>> import skimage as ski >>> image = np.array([[0, 0, 1, 0, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 1, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=bool) >>> result = ski.morphology.isotropic_erosion(image, radius=1) >>> result.view(np.uint8) array([[0, 0, 0, 0, 0], [0, 0, 1, 0, 0], [0, 0, 1, 0, 0], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0]], dtype=uint8) samplingout)ndidistance_transform_edtnpgreaterimageradiusrspacingdists X/var/www/html/land-ocr/venv/lib/python3.13/site-packages/skimage/morphology/isotropic.pyisotropic_erosionr s&@  % %e >D ::d ,,c~[R"[R"U5US9n[R"XAUS9$)aReturn binary morphological dilation of an image. Compared to the more general :func:`skimage.morphology.dilation`, this function only supports binary inputs and circular footprints. However, it performs typically faster for large (circular) footprints. This works by applying a threshold to the exact Euclidean distance map of the inverted image [1]_, [2]_. The implementation is based on: func:`scipy.ndimage.distance_transform_edt`. Parameters ---------- image : ndarray Binary input image. radius : float The radius of the footprint used for the operation. out : ndarray of bool, optional The array to store the result of the morphology. If None is passed, a new array will be allocated. spacing : float, or sequence of float, optional Spacing of elements along each dimension. If a sequence, must be of length equal to the input's dimension (number of axes). If a single number, this value is used for all axes. If not specified, a grid spacing of unity is implied. Returns ------- dilated : ndarray of bool The result of the morphological dilation with values in ``[False, True]``. References ---------- .. [1] Cuisenaire, O. and Macq, B., "Fast Euclidean morphological operators using local distance transformation by propagation, and applications," Image Processing And Its Applications, 1999. Seventh International Conference on (Conf. Publ. No. 465), 1999, pp. 856-860 vol.2. :DOI:`10.1049/cp:19990446` .. [2] Ingemar Ragnemalm, Fast erosion and dilation by contour processing and thresholding of distance maps, Pattern Recognition Letters, Volume 13, Issue 3, 1992, Pages 161-166. :DOI:`10.1016/0167-8655(92)90055-5` Examples -------- Dilation enlarges bright regions >>> import numpy as np >>> import skimage as ski >>> image = np.array([[0, 0, 0, 0, 0], ... [0, 0, 0, 0, 0], ... [0, 0, 1, 0, 0], ... [0, 0, 1, 1, 0], ... [0, 0, 0, 0, 0]], dtype=bool) >>> result = ski.morphology.isotropic_dilation(image, radius=1) >>> result.view(np.uint8) array([[0, 0, 0, 0, 0], [0, 0, 1, 0, 0], [0, 1, 1, 1, 0], [0, 1, 1, 1, 1], [0, 0, 1, 1, 0]], dtype=uint8) rr)r r r logical_not less_equalr s risotropic_dilationrMs1@  % %bnnU&;g ND ==3 //rc*[XX#S9n[XAX#S9$)a|Return binary morphological opening of an image. Compared to the more general :func:`skimage.morphology.opening`, this function only supports binary inputs and circular footprints. However, it performs typically faster for large (circular) footprints. This works by thresholding the exact Euclidean distance map [1]_, [2]_. The implementation is based on: func:`scipy.ndimage.distance_transform_edt`. Parameters ---------- image : ndarray Binary input image. radius : float The radius of the footprint used for the operation. out : ndarray of bool, optional The array to store the result of the morphology. If None is passed, a new array will be allocated. spacing : float, or sequence of float, optional Spacing of elements along each dimension. If a sequence, must be of length equal to the input's dimension (number of axes). If a single number, this value is used for all axes. If not specified, a grid spacing of unity is implied. Returns ------- opened : ndarray of bool The result of the morphological opening. References ---------- .. [1] Cuisenaire, O. and Macq, B., "Fast Euclidean morphological operators using local distance transformation by propagation, and applications," Image Processing And Its Applications, 1999. Seventh International Conference on (Conf. Publ. No. 465), 1999, pp. 856-860 vol.2. :DOI:`10.1049/cp:19990446` .. [2] Ingemar Ragnemalm, Fast erosion and dilation by contour processing and thresholding of distance maps, Pattern Recognition Letters, Volume 13, Issue 3, 1992, Pages 161-166. :DOI:`10.1016/0167-8655(92)90055-5` Examples -------- Remove connection between two bright regions >>> import numpy as np >>> import skimage as ski >>> image = 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=bool) >>> result = ski.morphology.isotropic_opening(image, radius=1) >>> result.view(np.uint8) 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=uint8) rr)rr)rrrrerodeds risotropic_openingrs|u# GF f# GGrc*[XX#S9n[XAX#S9$)asReturn binary morphological closing of an image. Compared to the more general :func:`skimage.morphology.closing`, this function only supports binary inputs and circular footprints. However, it performs typically faster for large (circular) footprints. This works by thresholding the exact Euclidean distance map [1]_, [2]_. The implementation is based on: func:`scipy.ndimage.distance_transform_edt`. Parameters ---------- image : ndarray Binary input image. radius : float The radius of the footprint used for the operation. out : ndarray of bool, optional The array to store the result of the morphology. If None, is passed, a new array will be allocated. spacing : float, or sequence of float, optional Spacing of elements along each dimension. If a sequence, must be of length equal to the input's dimension (number of axes). If a single number, this value is used for all axes. If not specified, a grid spacing of unity is implied. Returns ------- closed : ndarray of bool The result of the morphological closing. References ---------- .. [1] Cuisenaire, O. and Macq, B., "Fast Euclidean morphological operators using local distance transformation by propagation, and applications," Image Processing And Its Applications, 1999. Seventh International Conference on (Conf. Publ. No. 465), 1999, pp. 856-860 vol.2. :DOI:`10.1049/cp:19990446` .. [2] Ingemar Ragnemalm, Fast erosion and dilation by contour processing and thresholding of distance maps, Pattern Recognition Letters, Volume 13, Issue 3, 1992, Pages 161-166. :DOI:`10.1016/0167-8655(92)90055-5` Examples -------- Close gap between two bright lines >>> import numpy as np >>> import skimage as ski >>> image = 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=bool) >>> result = ski.morphology.isotropic_closing(image, radius=1) >>> result.view(np.uint8) 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=uint8) r)rr)rrrrdilateds risotropic_closingrs|!CIG W# GGr)NN) __doc__numpyr scipyrr rrrrrrr$s- A-HA0H?HD?Hr