a =b @s4ddlmZddlmZdgZd dddddZdS) )check_shape_equality)contingency_tableadapted_rand_errorN))table ignore_labelscCs|dur|durt|||dur4t|||dd}n|}|j|j|}|jddj}|jddj}|||}|||} ||} || } d| | | | } d| } | | | fS) a3Compute Adapted Rand error as defined by the SNEMI3D contest. [1]_ Parameters ---------- image_true : ndarray of int Ground-truth label image, same shape as im_test. image_test : ndarray of int Test image. table : scipy.sparse array in crs format, optional A contingency table built with skimage.evaluate.contingency_table. If None, it will be computed on the fly. ignore_labels : sequence of int, optional Labels to ignore. Any part of the true image labeled with any of these values will not be counted in the score. Returns ------- are : float The adapted Rand error; equal to :math:`1 - \frac{2pr}{p + r}`, where ``p`` and ``r`` are the precision and recall described below. prec : float The adapted Rand precision: this is the number of pairs of pixels that have the same label in the test label image *and* in the true image, divided by the number in the test image. rec : float The adapted Rand recall: this is the number of pairs of pixels that have the same label in the test label image *and* in the true image, divided by the number in the true image. Notes ----- Pixels with label 0 in the true segmentation are ignored in the score. References ---------- .. [1] Arganda-Carreras I, Turaga SC, Berger DR, et al. (2015) Crowdsourcing the creation of image segmentation algorithms for connectomics. Front. Neuroanat. 9:142. :DOI:`10.3389/fnana.2015.00142` NF)rZ normalizer)Zaxisrg@g?)rrdatasumAZravel)Z image_trueZ image_testrrZp_ijZ sum_p_ij2Za_iZb_iZsum_a2Zsum_b2Z precisionZrecallZfscoreZarer Blib/python3.9/site-packages/skimage/metrics/_adapted_rand_error.pyrs"* )NN)Z _shared.utilsrZ_contingency_tabler__all__rr r r r s