h :SSKJr SSKJr S/rS SSSS.S jjrg) )check_shape_equality)contingency_tableadapted_rand_errorN)g?)table ignore_labelsalphacUbUb [X5 Uc[UUUSSS9nOUnUS:dUS:a [S5eURUR-UR 5- nUR SS9R 5nUR S S9R 5nXw-UR 5- n X-UR 5- n Xi- n Xj- n XdU -SU- U --- n SU - nXU 4$) a$Compute 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. alpha : float, optional Relative weight given to precision and recall in the adapted Rand error calculation. Returns ------- are : float The adapted Rand error. 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. The adapted Rand error is calculated as follows: :math:`1 - \frac{\sum_{ij} p_{ij}^{2}}{\alpha \sum_{k} s_{k}^{2} + (1-\alpha)\sum_{k} t_{k}^{2}}`, where :math:`p_{ij}` is the probability that a pixel has the same label in the test image *and* in the true image, :math:`t_{k}` is the probability that a pixel has label :math:`k` in the true image, and :math:`s_{k}` is the probability that a pixel has label :math:`k` in the test image. Default behavior is to weight precision and recall equally in the adapted Rand error calculation. When alpha = 0, adapted Rand error = recall. When alpha = 1, adapted Rand error = precision. 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` Farray)r normalize sparse_typegg?zalpha must be between 0 and 1r)axisr)rr ValueErrordatasumravel) image_true image_testrr r p_ij sum_p_ij2a_ib_isum_a2sum_b2 precisionrecallfscoreares ]/var/www/fran/franai/venv/lib/python3.13/site-packages/skimage/metrics/_adapted_rand_error.pyrrs z*"8Z4 }  '   s{eck899 DII% 2I (((  "C (((  "CY "F Y "F"I  F &.AI+?? @F ,C 6 !!)NN) _shared.utilsr_contingency_tabler__all__rr!r r&s+01  !%`"/34s`"r!