import numpy as np
import cv2
import random


def get_training_data(dataset):
    """ Reads in the predicted pos/rotation data as well as the ground truth """
    print(dataset)
    images_batch = []
    timestamps = []
    po1 = []
    po2 = []
    with open(dataset) as f:
        for line in f:
            fname, p0, p1, p2, p3, p4, p5, p6 = line.split()
            timestamps.append(fname)
            img = np.load("path/to/txt_files/with/data/" + fname)
            images_batch.append(img)
            po1.append(np.array((np.float(p0), np.float(p1), np.float(p2))))
            po2.append(np.array((np.float(p3), np.float(p4), np.float(p5), np.float(p6))))
    return (images_batch, [np.asarray(po1), np.asarray(po2)]), timestamps


def limited_gen_data(source):
    """ Yields the input/ground truth data in batches of size 1. Can be used for evaluating the test dataset"""
    indices = range(len(source[0]))
    for i in indices:
        image = source[0][i]
        pose_x_rel = source[1][0][i]
        pose_q_rel = source[1][1][i]
        yield image, pose_x_rel, pose_q_rel


def gen_data(source):
    """ Data generator that randomly shuffles the training data and yields them as single elements. """
    while True:
        indices = list(range(len(source[0])))
        random.shuffle(indices)
        for i in indices:
            image = source[0][i]
            pose_x_rel = source[1][0][i]
            pose_q_rel = source[1][1][i]
            yield image, pose_x_rel, pose_q_rel
            

def gen_data_batch(source, batchSize, im_shape):
    """ Uses the gen_data and yields batches of the size batchSize. """
    data_gen = gen_data(source)
    while True:
        image_batch = []
        pose_x_batch = []
        pose_q_batch = []
        for _ in range(batchSize):
            image, pose_x, pose_q = next(data_gen)
            if image.shape[0] != im_shape[1] or image.shape[1] != im_shape[2]:
                image = cv2.resize(image, (im_shape[2], im_shape[1]))
            image_batch.append(image)
            pose_x_batch.append(pose_x)
            pose_q_batch.append(pose_q)
        yield np.asarray(image_batch), [np.asarray(pose_x_batch), np.asarray(pose_q_batch)]