import random
import numpy as np
import cv2
import lmdb
import torch
import torch.utils.data as data
import data.util as util


class DownsampleDataset(data.Dataset):
    """
    Reads an unpaired HQ and LQ image. Clips both images to the expected input sizes of the model. Produces a
    downsampled LQ image from the HQ image and feeds that as well.
    """

    def __init__(self, opt):
        super(DownsampleDataset, self).__init__()
        self.opt = opt
        self.data_type = self.opt['data_type']
        self.paths_LQ, self.paths_GT = None, None
        self.sizes_LQ, self.sizes_GT = None, None
        self.LQ_env, self.GT_env = None, None  # environments for lmdb
        self.doCrop = self.opt['doCrop']

        self.paths_GT, self.sizes_GT = util.get_image_paths(self.data_type, opt['dataroot_GT'])
        self.paths_LQ, self.sizes_LQ = util.get_image_paths(self.data_type, opt['dataroot_LQ'])

        self.data_sz_mismatch_ok = opt['mismatched_Data_OK']
        assert self.paths_GT, 'Error: GT path is empty.'
        assert self.paths_LQ, 'LQ is required for downsampling.'
        if not self.data_sz_mismatch_ok:
            assert len(self.paths_LQ) == len(
                self.paths_GT
            ), 'GT and LQ datasets have different number of images - {}, {}.'.format(
                len(self.paths_LQ), len(self.paths_GT))
        self.random_scale_list = [1]

    def _init_lmdb(self):
        # https://github.com/chainer/chainermn/issues/129
        self.GT_env = lmdb.open(self.opt['dataroot_GT'], readonly=True, lock=False, readahead=False,
                                meminit=False)
        self.LQ_env = lmdb.open(self.opt['dataroot_LQ'], readonly=True, lock=False, readahead=False,
                                meminit=False)

    def __getitem__(self, index):
        if self.data_type == 'lmdb' and (self.GT_env is None or self.LQ_env is None):
            self._init_lmdb()
        scale = self.opt['scale']
        GT_size = self.opt['target_size'] * scale

        # get GT image
        GT_path = self.paths_GT[index]
        resolution = [int(s) for s in self.sizes_GT[index].split('_')
                      ] if self.data_type == 'lmdb' else None
        img_GT = util.read_img(self.GT_env, GT_path, resolution)
        if self.opt['phase'] != 'train':  # modcrop in the validation / test phase
            img_GT = util.modcrop(img_GT, scale)
        if self.opt['color']:  # change color space if necessary
            img_GT = util.channel_convert(img_GT.shape[2], self.opt['color'], [img_GT])[0]

        # get LQ image
        lqind = index % len(self.paths_LQ)
        LQ_path = self.paths_LQ[index % len(self.paths_LQ)]
        resolution = [int(s) for s in self.sizes_LQ[index].split('_')
                      ] if self.data_type == 'lmdb' else None
        img_LQ = util.read_img(self.LQ_env, LQ_path, resolution)

        # Create a downsampled version of the HQ image using matlab imresize.
        img_Downsampled = util.imresize_np(img_GT, 1 / scale)
        assert img_Downsampled.ndim == 3

        if self.opt['phase'] == 'train':
            H, W, _ = img_GT.shape
            assert H >= GT_size and W >= GT_size

            H, W, C = img_LQ.shape
            LQ_size = GT_size // scale

            if self.doCrop:
                # randomly crop
                rnd_h = random.randint(0, max(0, H - LQ_size))
                rnd_w = random.randint(0, max(0, W - LQ_size))
                img_LQ = img_LQ[rnd_h:rnd_h + LQ_size, rnd_w:rnd_w + LQ_size, :]
                img_Downsampled = img_Downsampled[rnd_h:rnd_h + LQ_size, rnd_w:rnd_w + LQ_size, :]
                rnd_h_GT, rnd_w_GT = int(rnd_h * scale), int(rnd_w * scale)
                img_GT = img_GT[rnd_h_GT:rnd_h_GT + GT_size, rnd_w_GT:rnd_w_GT + GT_size, :]
            else:
                img_LQ = cv2.resize(img_LQ, (LQ_size, LQ_size), interpolation=cv2.INTER_LINEAR)
                img_Downsampled = cv2.resize(img_Downsampled, (LQ_size, LQ_size), interpolation=cv2.INTER_LINEAR)
                img_GT = cv2.resize(img_GT, (GT_size, GT_size), interpolation=cv2.INTER_LINEAR)

            # augmentation - flip, rotate
            img_LQ, img_GT, img_Downsampled = util.augment([img_LQ, img_GT, img_Downsampled], self.opt['use_flip'],
                                          self.opt['use_rot'])

        if self.opt['color']:  # change color space if necessary
            img_Downsampled = util.channel_convert(C, self.opt['color'],
                                          [img_Downsampled])[0]  # TODO during val no definition

        # BGR to RGB, HWC to CHW, numpy to tensor
        if img_GT.shape[2] == 3:
            img_GT = img_GT[:, :, [2, 1, 0]]
            img_LQ = img_LQ[:, :, [2, 1, 0]]
            img_Downsampled = img_Downsampled[:, :, [2, 1, 0]]
        img_GT = torch.from_numpy(np.ascontiguousarray(np.transpose(img_GT, (2, 0, 1)))).float()
        img_Downsampled = torch.from_numpy(np.ascontiguousarray(np.transpose(img_Downsampled, (2, 0, 1)))).float()
        img_LQ = torch.from_numpy(np.ascontiguousarray(np.transpose(img_LQ, (2, 0, 1)))).float()

        # This may seem really messed up, but let me explain:
        #  The goal is to re-use existing code as much as possible. SRGAN_model was coded to supersample, not downsample,
        #  but it can be retrofitted. To do so, we need to "trick" it. In this case the "input" is the HQ image and the
        #  "output" is the LQ image. SRGAN_model will be using a Generator and a Discriminator which already know this,
        #  we just need to trick its logic into following this rules.
        #  Do this by setting LQ(which is the input into the models)=img_GT and GT(which is the expected outpuut)=img_LQ.
        #  PIX is used as a reference for the pixel loss. Use the manually downsampled image for this.
        return {'LQ': img_GT, 'GT': img_LQ, 'PIX': img_Downsampled, 'LQ_path': LQ_path, 'GT_path': GT_path}

    def __len__(self):
        return len(self.paths_GT)