utils.py

# import torch
import numpy as np


class AverageMeter(object):
    """Computes and stores the average and current value"""
    def __init__(self):
        self.initialized = False
        self.val = None
        self.avg = None
        self.sum = None
        self.count = None

    def initialize(self, val, weight):
        self.val = val
        self.avg = val
        self.sum = val * weight
        self.count = weight
        self.initialized = True

    def update(self, val, weight=1):
        if not self.initialized:
            self.initialize(val, weight)
        else:
            self.add(val, weight)

    def add(self, val, weight):
        self.val = val
        self.sum += val * weight
        self.count += weight
        self.avg = self.sum / self.count

    def value(self):
        return self.val

    def average(self):
        return self.avg


def unique(ar, return_index=False, return_inverse=False, return_counts=False):
    ar = np.asanyarray(ar).flatten()

    optional_indices = return_index or return_inverse
    optional_returns = optional_indices or return_counts

    if ar.size == 0:
        if not optional_returns:
            ret = ar
        else:
            ret = (ar,)
            if return_index:
                ret += (np.empty(0, np.bool),)
            if return_inverse:
                ret += (np.empty(0, np.bool),)
            if return_counts:
                ret += (np.empty(0, np.intp),)
        return ret
    if optional_indices:
        perm = ar.argsort(kind='mergesort' if return_index else 'quicksort')
        aux = ar[perm]
    else:
        ar.sort()
        aux = ar
    flag = np.concatenate(([True], aux[1:] != aux[:-1]))

    if not optional_returns:
        ret = aux[flag]
    else:
        ret = (aux[flag],)
        if return_index:
            ret += (perm[flag],)
        if return_inverse:
            iflag = np.cumsum(flag) - 1
            inv_idx = np.empty(ar.shape, dtype=np.intp)
            inv_idx[perm] = iflag
            ret += (inv_idx,)
        if return_counts:
            idx = np.concatenate(np.nonzero(flag) + ([ar.size],))
            ret += (np.diff(idx),)
    return ret


def colorEncode(labelmap, colors, mode='BGR'):
    labelmap = labelmap.astype('int')
    labelmap_rgb = np.zeros((labelmap.shape[0], labelmap.shape[1], 3),
                            dtype=np.uint8)
    for label in unique(labelmap):
        if label < 0:
            continue
        labelmap_rgb += (labelmap == label)[:, :, np.newaxis] * \
            np.tile(colors[label],
                    (labelmap.shape[0], labelmap.shape[1], 1))

    if mode == 'BGR':
        return labelmap_rgb[:, :, ::-1]
    else:
        return labelmap_rgb


def accuracy(preds, label):
    valid = (label >= 0)
    acc_sum = (valid * (preds == label)).sum()
    valid_sum = valid.sum()
    acc = float(acc_sum) / (valid_sum + 1e-10)
    return acc, valid_sum


def intersectionAndUnion(imPred, imLab, numClass):
    imPred = np.asarray(imPred).copy()
    imLab = np.asarray(imLab).copy()

    imPred += 1
    imLab += 1
    # Remove classes from unlabeled pixels in gt image.
    # We should not penalize detections in unlabeled portions of the image.
    imPred = imPred * (imLab > 0)

    # Compute area intersection:
    intersection = imPred * (imPred == imLab)
    (area_intersection, _) = np.histogram(
        intersection, bins=numClass, range=(1, numClass))

    # Compute area union:
    (area_pred, _) = np.histogram(imPred, bins=numClass, range=(1, numClass))
    (area_lab, _) = np.histogram(imLab, bins=numClass, range=(1, numClass))
    area_union = area_pred + area_lab - area_intersection

    return (area_intersection, area_union)