1-Ph1ddlZdZefdZdS)Nc tj|}t|}tjtj|}tj|}t tj|}||krtdf|zStj|||z d|z zd}||k r|t|D]l}||||<t tj||dzd}||z d||z dz z z||dzd<||k rnm|dz t}tj| t}dt||D t! fd|D S) aFind `n_points` regularly spaced along `ar_shape`. The returned points (as slices) should be as close to cubically-spaced as possible. Essentially, the points are spaced by the Nth root of the input array size, where N is the number of dimensions. However, if an array dimension cannot fit a full step size, it is "discarded", and the computation is done for only the remaining dimensions. Parameters ---------- ar_shape : array-like of ints The shape of the space embedding the grid. ``len(ar_shape)`` is the number of dimensions. n_points : int The (approximate) number of points to embed in the space. Returns ------- slices : tuple of slice objects A slice along each dimension of `ar_shape`, such that the intersection of all the slices give the coordinates of regularly spaced points. .. versionchanged:: 0.14.1 In scikit-image 0.14.1 and 0.15, the return type was changed from a list to a tuple to ensure `compatibility with Numpy 1.15`_ and higher. If your code requires the returned result to be a list, you may convert the output of this function to a list with: >>> result = list(regular_grid(ar_shape=(3, 20, 40), n_points=8)) .. _compatibility with NumPy 1.15: https://github.com/numpy/numpy/blob/master/doc/release/1.15.0-notes.rst#deprecations Examples -------- >>> ar = np.zeros((20, 40)) >>> g = regular_grid(ar.shape, 8) >>> g (slice(5, None, 10), slice(5, None, 10)) >>> ar[g] = 1 >>> ar.sum() 8.0 >>> ar = np.zeros((20, 40)) >>> g = regular_grid(ar.shape, 32) >>> g (slice(2, None, 5), slice(2, None, 5)) >>> ar[g] = 1 >>> ar.sum() 32.0 >>> ar = np.zeros((3, 20, 40)) >>> g = regular_grid(ar.shape, 8) >>> g (slice(1, None, 3), slice(5, None, 10), slice(5, None, 10)) >>> ar[g] = 1 >>> ar.sum() 8.0 Ng?float64dtypec6g|]\}}t|d|SN)slice).0startsteps Z/var/www/html/test/jupyter/venv/lib/python3.11/site-packages/skimage/util/_regular_grid.py z regular_grid..Ns( Q Q Q;5$eE4&& Q Q Qc3(K|] }|V dSr )r islicess r zregular_grid..Os'666!9666666r)np asanyarraylenargsortsortfloatprodr fullanyrangeallastypeintroundziptuple) ar_shapen_pointsndimunsort_dim_idxs sorted_dims space_size stepsizesdimstartsrs @r regular_gridr0sr}X&&H x==DjH!5!566O'(##Krwx(())JXd ~$$zH4#*EYWWWIi$$&&;;  C(-IcNrw{3799'=>>??J$.$9sdSjSTn?U#VIcAgii y(--//  1n $ $S ) )F##**3//I Q Q#fi:P:P Q Q QF 6666o666 6 6F Mrct||}tj||}dtjtj||j||jz||<|S)aeReturn an image with ~`n_points` regularly-spaced nonzero pixels. Parameters ---------- ar_shape : tuple of int The shape of the desired output image. n_points : int The desired number of nonzero points. dtype : numpy data type, optional The desired data type of the output. Returns ------- seed_img : array of int or bool The desired image. Examples -------- >>> regular_seeds((5, 5), 4) array([[0, 0, 0, 0, 0], [0, 1, 0, 2, 0], [0, 0, 0, 0, 0], [0, 3, 0, 4, 0], [0, 0, 0, 0, 0]]) rr)r0rzerosreshapearangesizeshape)r'r(rgridseed_imgs r regular_seedsr9Ssg4 ( + +Dx...H (4.%&&(<HTN Or)numpyrr0r#r9rrrr;sGLLL^-0r