U !?h@shddlZddlmZmZeddddddZeddddd d Zed dddd d dZddZdS)N)njitprangez(f8[:],)T)cachenogilcCsvt|}td|D]&}|d}t||||d||<qt|dddD]&}|d}t||||d||<qJdS)N)lenrangemin)dni1i2rI/var/www/html/venv/lib/python3.8/site-packages/pymatting/util/distance.py_propagate_1d_first_passsrz(f8[:], i4[:], f8[:], f8[:])c Cs"t|}d}tj }t|D]}||}|tjkr4q||} | ||<|dkr||} d| |||| | | || }|||krq|d8}qD|d7}|||<|||<tj||d<q|dkrdSd}t|D]H}||d|kr|d7}q||} || } t| | || ||<qdS)Nrrg?r)r npinfr sqrt) r vzfnxksxZd_yxZfxZvkZdxrrr _propagate_1ds8  (    rz (f8[:, :],))rparallelrcCs|j\}}t|D]}t|dd|fqtj||ftjd}t||df}t||f}t|D]"}t||||||||qhdS)N)dtyper)shaperrrzerosZint32r)distancenyrrrrryrrr_propagate_distance=s   r%cCsF|jtjkr|dk}|j\}}t||ftj}tj||<t||S)a For every non-zero value, compute the distance to the closest zero value. Based on :cite:`felzenszwalb2012distance`. Parameters ---------- mask: numpy.ndarray 2D matrix of zero and nonzero values. Returns ------- distance: numpy.ndarray Distance to closest zero-valued pixel. Example ------- >>> from pymatting import * >>> import numpy as np >>> mask = np.random.rand(10, 20) < 0.9 >>> distance = distance_transform(mask) r)rrZbool_r r!Zfloat64rr%)maskr#rr"rrrdistance_transformLs   r') numpyrZnumbarrrrr%r'rrrrs   +