# Adapted from score written by wkentaro
# https://github.com/wkentaro/pytorch-fcn/blob/master/torchfcn/utils.py

import numpy as np

label = ['road',
         'sidewalk',
         'building',
         'wall',
         'fence',
         'pole',
         'light',
         'sign',
         'vegetation',
         'terrain',
         'sky',
         'person',
         'rider',
         'car',
         'truck',
         'bus',
         'train',
         'motorcycle',
         'bycycle']

class runningScore(object):

    def __init__(self, n_classes):
        self.n_classes = n_classes
        self.confusion_matrix = np.zeros((n_classes, n_classes))

    def _fast_hist(self, label_true, label_pred, n_class):
        mask = (label_true >= 0) & (label_true < n_class)
        hist = np.bincount(
            n_class * label_true[mask].astype(int) +
            label_pred[mask], minlength=n_class**2).reshape(n_class, n_class)
        return hist

    def update(self, label_trues, label_preds):
        for lt, lp in zip(label_trues, label_preds):
            self.confusion_matrix += self._fast_hist(lt.flatten(), lp.flatten(), self.n_classes)

    def get_scores(self):
        """Returns accuracy score evaluation result.
            - overall accuracy
            - mean accuracy
            - mean IU
            - fwavacc
        """
        hist = self.confusion_matrix
        acc = np.diag(hist).sum() / hist.sum()
        acc_cls = np.diag(hist) / hist.sum(axis=1)
        acc_cls = np.nanmean(acc_cls)
        iu = np.diag(hist) / (hist.sum(axis=1) + hist.sum(axis=0) - np.diag(hist))
        for id in range(19):
            print ('===>' + label[id] + ':' + str(iu[id]))
        mean_iu = np.nanmean(iu)
        freq = hist.sum(axis=1) / hist.sum()
        fwavacc = (freq[freq > 0] * iu[freq > 0]).sum()
        cls_iu = dict(zip(range(self.n_classes), iu))

        return {'Overall Acc: \t': acc,
                'Mean Acc : \t': acc_cls,
                'FreqW Acc : \t': fwavacc,
                'Mean IoU : \t': mean_iu,}, cls_iu

    def reset(self):
        self.confusion_matrix = np.zeros((self.n_classes, self.n_classes))
