import numpy as np
import cv2
import random
import settings
from tqdm import tqdm
from similarityMeasures import getError
imageDistance = 1


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 tqdm(f):
            fname, p0, p1, p2, p3, p4, p5, p6 = line.split()
            timestamps.append(fname)
            img = cv2.imread("../../../Self_loc_synthetic/dataset_real/dataset/" + 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. Each input
    uses 3 images. The current one, the previous one and the next one.
    """
    indices = range(len(source[0]))
    for i in indices:
        image = source[0][i]
        image_left = source[0][max(0, i - imageDistance)]
        image_right = source[0][min(i + imageDistance, len(source[0]) - 1)]
        pose_x = source[1][0][i]
        pose_q = source[1][1][i]
        pose_x_left = source[1][0][max(0, i - imageDistance)]
        pose_q_left = source[1][1][max(0, i - imageDistance)]
        pose_x_right = source[1][0][min(i + imageDistance, len(source[0]) - 1)]
        pose_q_right = source[1][1][min(i + imageDistance, len(source[0]) - 1)]
        if settings.saveMean:
            m1_2, a1_2 = getError(pose_x, pose_q, pose_x_left, pose_q_left)
            m2_3, a2_3 = getError(pose_x, pose_q, pose_x_right, pose_q_right)
            m1_3, a1_3 = getError(pose_x_left, pose_q_left, pose_x_right, pose_q_right)
            if m1_2 > settings.distanceThreshold or m2_3 > settings.distanceThreshold:
                continue
            if a1_2 > settings.angleThreshold or a2_3 > settings.angleThreshold:
                continue
        yield np.asarray([image_left, image, image_right]), np.asarray([pose_x_left, pose_x, pose_x_right]), np.asarray([pose_q_left, pose_q, pose_q_right])


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]
            image_left = source[0][max(0, i - imageDistance)]
            image_right = source[0][min(i + imageDistance, len(source[0]) - 1)]
            pose_x = source[1][0][i]
            pose_q = source[1][1][i]
            pose_x_left = source[1][0][max(0, i - imageDistance)]
            pose_q_left = source[1][1][max(0, i - imageDistance)]
            pose_x_right = source[1][0][min(i + imageDistance, len(source[0]) - 1)]
            pose_q_right = source[1][1][min(i + imageDistance, len(source[0]) - 1)]
            m1_2, a1_2 = getError(pose_x, pose_q, pose_x_left, pose_q_left)
            m2_3, a2_3 = getError(pose_x, pose_q, pose_x_right, pose_q_right)
            m1_3, a1_3 = getError(pose_x_left, pose_q_left, pose_x_right, pose_q_right)
            if m1_2 > settings.distanceThreshold or m2_3 > settings.distanceThreshold:
                continue
            if a1_2 > settings.angleThreshold or a2_3 > settings.angleThreshold:
                continue
            yield np.asarray([image_left, image, image_right]), np.asarray([pose_x_left, pose_x, pose_x_right]), np.asarray([pose_q_left, pose_q, pose_q_right])
            

def gen_data_batch(source, batchSize):
    """ 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)
            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)]