U ?h@s ddlZddZefddZdS)Nc s,t|}t|}tt|}t|}tt|}||krPtdf|Stj|||d|dd}||k rt |D]^}||||<tt||dd}||d||d||dd<||k rqq|d t }t| t }ddt||Dtfd d |DS) 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?Zfloat64dtypecSsg|]\}}t|d|qSN)slice).0startstepr L/var/www/html/venv/lib/python3.8/site-packages/skimage/util/_regular_grid.py Psz regular_grid..c3s|]}|VqdSrr )riZslicesr r Rszregular_grid..)npZ asanyarraylenZargsortsortfloatprodrfullanyrangeallZastypeintroundziptuple) ar_shapen_pointsndimZunsort_dim_idxsZ sorted_dimsZ space_sizeZ stepsizesdimZstartsr rr regular_grids49      r"cCsBt||}tj||d}dtt||j||j||<|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)r"rZzerosZreshapeZarangesizeshape)rrrgridZseed_imgr r r regular_seedsVs   r&)numpyrr"rr&r r r r sR