from ...utils.pascal_voc_clean_xml import pascal_voc_clean_xml
from numpy.random import permutation as perm
from ..yolo.predict import preprocess
from ..yolo.data import shuffle
from copy import deepcopy
import pickle
import numpy as np
import os


def _batch(self, chunk):
    """
    Takes a chunk of parsed annotations
    returns value for placeholders of net's 
    input & loss layer correspond to this chunk
    """
    meta = self.meta
    labels = meta['labels']

    H, W, _ = meta['out_size']
    C, B = meta['classes'], meta['num']
    anchors = meta['anchors']

    # preprocess
    jpg = chunk[0];
    w, h, allobj_ = chunk[1]
    allobj = deepcopy(allobj_)
    path = os.path.join(self.FLAGS.dataset, jpg)
    img = self.preprocess(path, allobj)

    # Calculate regression target
    cellx = 1. * w / W
    celly = 1. * h / H
    for obj in allobj:
        centerx = .5 * (obj[1] + obj[3])  # xmin, xmax
        centery = .5 * (obj[2] + obj[4])  # ymin, ymax
        cx = centerx / cellx
        cy = centery / celly
        if cx >= W or cy >= H: return None, None
        obj[3] = float(obj[3] - obj[1]) / w
        obj[4] = float(obj[4] - obj[2]) / h
        obj[3] = np.sqrt(obj[3])
        obj[4] = np.sqrt(obj[4])
        obj[1] = cx - np.floor(cx)  # centerx
        obj[2] = cy - np.floor(cy)  # centery
        obj += [int(np.floor(cy) * W + np.floor(cx))]

    # show(im, allobj, S, w, h, cellx, celly) # unit test

    # Calculate placeholders' values
    probs = np.zeros([H * W, B, C])
    confs = np.zeros([H * W, B])
    coord = np.zeros([H * W, B, 4])
    proid = np.zeros([H * W, B, C])
    prear = np.zeros([H * W, 4])
    for obj in allobj:
        probs[obj[5], :, :] = [[0.] * C] * B
        probs[obj[5], :, labels.index(obj[0])] = 1.
        proid[obj[5], :, :] = [[1.] * C] * B
        coord[obj[5], :, :] = [obj[1:5]] * B
        prear[obj[5], 0] = obj[1] - obj[3] ** 2 * .5 * W  # xleft
        prear[obj[5], 1] = obj[2] - obj[4] ** 2 * .5 * H  # yup
        prear[obj[5], 2] = obj[1] + obj[3] ** 2 * .5 * W  # xright
        prear[obj[5], 3] = obj[2] + obj[4] ** 2 * .5 * H  # ybot
        confs[obj[5], :] = [1.] * B

    # Finalise the placeholders' values
    upleft = np.expand_dims(prear[:, 0:2], 1)
    botright = np.expand_dims(prear[:, 2:4], 1)
    wh = botright - upleft;
    area = wh[:, :, 0] * wh[:, :, 1]
    upleft = np.concatenate([upleft] * B, 1)
    botright = np.concatenate([botright] * B, 1)
    areas = np.concatenate([area] * B, 1)

    # value for placeholder at input layer
    inp_feed_val = img
    # value for placeholder at loss layer 
    loss_feed_val = {
        'probs': probs, 'confs': confs,
        'coord': coord, 'proid': proid,
        'areas': areas, 'upleft': upleft,
        'botright': botright
    }

    return inp_feed_val, loss_feed_val