evalution_segmentaion.py

from __future__ import division
import numpy as np
import six


def calc_semantic_segmentation_confusion(pred_labels, gt_labels):

    pred_labels = iter(pred_labels)     # (352, 480)
    gt_labels = iter(gt_labels)     # (352, 480)

    n_class = 4
    confusion = np.zeros((n_class, n_class), dtype=np.int64)    # (12, 12)
    for pred_label, gt_label in six.moves.zip(pred_labels, gt_labels):
        if pred_label.ndim != 2 or gt_label.ndim != 2:
            raise ValueError('ndim of labels should be two.')
        if pred_label.shape != gt_label.shape:
            raise ValueError('Shape of ground truth and prediction should'
                             ' be same.')
        pred_label = pred_label.flatten()   # (168960, )
        gt_label = gt_label.flatten()   # (168960, )

        # Dynamically expand the confusion matrix if necessary.
        lb_max = np.max((pred_label, gt_label))
        # print(lb_max)
        if lb_max >= n_class:
            expanded_confusion = np.zeros(
                (lb_max + 1, lb_max + 1), dtype=np.int64)
            expanded_confusion[0:n_class, 0:n_class] = confusion

            n_class = lb_max + 1
            confusion = expanded_confusion

        # Count statistics from valid pixels.  极度巧妙 × class_nums 正好使得每个ij能够对应.
        mask = gt_label >= 0
        confusion += np.bincount(
            n_class * gt_label[mask].astype(int) + pred_label[mask],
            minlength=n_class ** 2)\
            .reshape((n_class, n_class))

    for iter_ in (pred_labels, gt_labels):
        # This code assumes any iterator does not contain None as its items.
        if next(iter_, None) is not None:
            raise ValueError('Length of input iterables need to be same')

    return confusion


def calc_semantic_segmentation_iou(confusion):

    iou_denominator = (confusion.sum(axis=1) + confusion.sum(axis=0)
                       - np.diag(confusion))
    iou = np.diag(confusion) / iou_denominator
    return iou[:-1]
    # return iou


def eval_semantic_segmentation(pred_labels, gt_labels):


    confusion = calc_semantic_segmentation_confusion(
        pred_labels, gt_labels)
    iou = calc_semantic_segmentation_iou(confusion)     # (11, )
    pixel_accuracy = np.diag(confusion).sum() / confusion.sum()
    class_accuracy = np.diag(confusion) / (np.sum(confusion, axis=1) + 1e-10)

    return {'iou': iou, 'miou': np.nanmean(iou),
            'pixel_accuracy': pixel_accuracy,
            'class_accuracy': class_accuracy,
            'mean_class_accuracy': np.nanmean(class_accuracy[:-1])}
            # 'mean_class_accuracy': np.nanmean(class_accuracy)}