#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import print_function

import io
import tarfile
import numpy as np
from PIL import Image

import paddle
from paddle.io import Dataset
from paddle.dataset.common import _check_exists_and_download

__all__ = []

VOC_URL = 'https://dataset.bj.bcebos.com/voc/VOCtrainval_11-May-2012.tar'

VOC_MD5 = '6cd6e144f989b92b3379bac3b3de84fd'
SET_FILE = 'VOCdevkit/VOC2012/ImageSets/Segmentation/{}.txt'
DATA_FILE = 'VOCdevkit/VOC2012/JPEGImages/{}.jpg'
LABEL_FILE = 'VOCdevkit/VOC2012/SegmentationClass/{}.png'

CACHE_DIR = 'voc2012'

MODE_FLAG_MAP = {'train': 'trainval', 'test': 'train', 'valid': "val"}


class VOC2012(Dataset):
    """
    Implementation of `VOC2012 <http://host.robots.ox.ac.uk/pascal/VOC/voc2012/>`_ dataset.

    Args:
        data_file (str, optional): Path to data file, can be set None if
            :attr:`download` is True. Default: None, default data path: ~/.cache/paddle/dataset/voc2012.
        mode (str, optional): Either train or test mode. Default 'train'.
        transform (Callable, optional): Transform to perform on image, None for no transform. Default: None.
        download (bool, optional): Download dataset automatically if :attr:`data_file` is None. Default: True.
        backend (str, optional): Specifies which type of image to be returned:
            PIL.Image or numpy.ndarray. Should be one of {'pil', 'cv2'}.
            If this option is not set, will get backend from :ref:`paddle.vision.get_image_backend <api_vision_image_get_image_backend>`,
            default backend is 'pil'. Default: None.

    Returns:
        :ref:`api_paddle_io_Dataset`. An instance of VOC2012 dataset.

    Examples:

        .. code-block:: python

            import itertools
            import paddle.vision.transforms as T
            from paddle.vision.datasets import VOC2012


            voc2012 = VOC2012()
            print(len(voc2012))
            # 2913

            for i in range(5):  # only show first 5 images
                img, label = voc2012[i]
                # do something with img and label
                print(type(img), img.size)
                # <class 'PIL.JpegImagePlugin.JpegImageFile'> (500, 281)
                print(type(label), label.size)
                # <class 'PIL.PngImagePlugin.PngImageFile'> (500, 281)


            transform = T.Compose(
                [
                    T.ToTensor(),
                    T.Normalize(
                        mean=[0.5, 0.5, 0.5],
                        std=[0.5, 0.5, 0.5],
                        to_rgb=True,
                    ),
                ]
            )

            voc2012_test = VOC2012(
                mode="test",
                transform=transform,  # apply transform to every image
                backend="cv2",  # use OpenCV as image transform backend
            )
            print(len(voc2012_test))
            # 1464

            for img, label in itertools.islice(iter(voc2012_test), 5):  # only show first 5 images
                # do something with img and label
                print(type(img), img.shape)
                # <class 'paddle.Tensor'> [3, 281, 500]
                print(type(label), label.shape)
                # <class 'numpy.ndarray'> (281, 500)
    """

    def __init__(self,
                 data_file=None,
                 mode='train',
                 transform=None,
                 download=True,
                 backend=None):
        assert mode.lower() in ['train', 'valid', 'test'], \
            "mode should be 'train', 'valid' or 'test', but got {}".format(mode)

        if backend is None:
            backend = paddle.vision.get_image_backend()
        if backend not in ['pil', 'cv2']:
            raise ValueError(
                "Expected backend are one of ['pil', 'cv2'], but got {}".format(
                    backend))
        self.backend = backend

        self.flag = MODE_FLAG_MAP[mode.lower()]

        self.data_file = data_file
        if self.data_file is None:
            assert download, "data_file is not set and downloading automatically is disabled"
            self.data_file = _check_exists_and_download(data_file, VOC_URL,
                                                        VOC_MD5, CACHE_DIR,
                                                        download)
        self.transform = transform

        # read dataset into memory
        self._load_anno()

        self.dtype = paddle.get_default_dtype()

    def _load_anno(self):
        self.name2mem = {}
        self.data_tar = tarfile.open(self.data_file)
        for ele in self.data_tar.getmembers():
            self.name2mem[ele.name] = ele

        set_file = SET_FILE.format(self.flag)
        sets = self.data_tar.extractfile(self.name2mem[set_file])

        self.data = []
        self.labels = []

        for line in sets:
            line = line.strip()
            data = DATA_FILE.format(line.decode('utf-8'))
            label = LABEL_FILE.format(line.decode('utf-8'))
            self.data.append(data)
            self.labels.append(label)

    def __getitem__(self, idx):
        data_file = self.data[idx]
        label_file = self.labels[idx]

        data = self.data_tar.extractfile(self.name2mem[data_file]).read()
        label = self.data_tar.extractfile(self.name2mem[label_file]).read()
        data = Image.open(io.BytesIO(data))
        label = Image.open(io.BytesIO(label))

        if self.backend == 'cv2':
            data = np.array(data)
            label = np.array(label)

        if self.transform is not None:
            data = self.transform(data)

        if self.backend == 'cv2':
            return data.astype(self.dtype), label.astype(self.dtype)

        return data, label

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

    def __del__(self):
        if self.data_tar:
            self.data_tar.close()
