semiautoanno.py 文件源码

def evaluateToGT(self, Li, idxs):
        """
        Evaluate the current estimate to a ground truth
        :param Li: current estimates
        :param idxs: idxs to evaluate
        :return: mean error, max error and MD score
        """

        if not isinstance(idxs, numpy.ndarray):
            idxs = numpy.asarray(idxs)

        if self.gt3D is not None:
            gt3D_subset = self.gt3D[idxs]
            if Li.shape[0] == len(idxs):
                Li_subset = Li
            else:
                Li_subset = Li[idxs]
            mean_error = numpy.mean(numpy.sqrt(numpy.square((gt3D_subset - Li_subset.reshape(gt3D_subset.shape))*self.Di_scale[idxs, None, None]).sum(axis=2)), axis=1).mean()
            max_error = numpy.max(numpy.sqrt(numpy.square((gt3D_subset - Li_subset.reshape(gt3D_subset.shape))*self.Di_scale[idxs, None, None]).sum(axis=2)))
            vals = [(numpy.nanmax(numpy.sqrt(numpy.square((gt3D_subset - Li_subset.reshape(gt3D_subset.shape))*self.Di_scale[idxs, None, None]).sum(axis=2)), axis=1) <= j).sum() / float(gt3D_subset.shape[0]) for j in range(0, 80)]
            md_score = numpy.asarray(vals).sum() / float(80.)

            return mean_error, max_error, md_score
        else:
            return 0., 0., 0.