from . import train
from . import predict
from . import data
from . import misc
import numpy as np

""" YOLO framework __init__ equivalent"""


def constructor(self, meta, FLAGS):
    def _to_color(indx, base):
        """ return (b, r, g) tuple"""
        base2 = base * base
        b = 2 - indx / base2
        r = 2 - (indx % base2) / base
        g = 2 - (indx % base2) % base
        return (b * 127, r * 127, g * 127)

    if 'labels' not in meta:
        misc.labels(meta, FLAGS)  # We're not loading from a .pb so we do need to load the labels
    assert len(meta['labels']) == meta['classes'], (
            'labels.txt and {} indicate' + ' '
                                           'inconsistent class numbers'
    ).format(meta['model'])

    # assign a color for each label
    colors = list()
    base = int(np.ceil(pow(meta['classes'], 1. / 3)))
    for x in range(len(meta['labels'])):
        colors += [_to_color(x, base)]
    meta['colors'] = colors
    self.fetch = list()
    self.meta, self.FLAGS = meta, FLAGS

    # over-ride the threshold in meta if FLAGS has it.
    if FLAGS.threshold > 0.0:
        self.meta['thresh'] = FLAGS.threshold