import numpy as np


def mean(input_masks):
    zeros = np.zeros_like(input_masks[0]).astype(np.float64)
    float_mask = sum(input_masks, zeros) / len(input_masks)
    return float_mask.astype(np.uint8)


def intersection(input_masks):
    m = np.copy(input_masks[0])
    mmean = mean(input_masks)
    m[mmean < 255.] = 0.
    m[mmean == 255.] = 255.
    return m


def majority(input_masks):
    m = np.copy(input_masks[0])
    mmean = mean(input_masks)
    m[mmean < 127] = 0
    m[mmean > 127] = 255
    return m



MEAN = "mean"
INTERSECTION = "intersection"
MAJORITY = "majority"

consensus_methods = {}
consensus_methods[MEAN] = mean
consensus_methods[INTERSECTION] = intersection
consensus_methods[MAJORITY] = majority


def get_consensus(consensus_name):
    return consensus_methods[consensus_name]