#   Copyright (c) 2018 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.

import os, sys
import numpy as np
from PIL import Image
import six
from six.moves import cStringIO as StringIO
import multiprocessing
import functools
import itertools

from paddle.utils.image_util import *
from paddle.trainer.config_parser import logger

try:
    import cv2
except ImportError:
    logger.warning("OpenCV2 is not installed, using PIL to process")
    cv2 = None

__all__ = ["CvTransformer", "PILTransformer", "MultiProcessImageTransformer"]


class CvTransformer(ImageTransformer):
    """
    CvTransformer used python-opencv to process image.
    """

    def __init__(
            self,
            min_size=None,
            crop_size=None,
            transpose=(2, 0, 1),  # transpose to C * H * W
            channel_swap=None,
            mean=None,
            is_train=True,
            is_color=True):
        ImageTransformer.__init__(self, transpose, channel_swap, mean, is_color)
        self.min_size = min_size
        self.crop_size = crop_size
        self.is_train = is_train

    def resize(self, im, min_size):
        row, col = im.shape[:2]
        new_row, new_col = min_size, min_size
        if row > col:
            new_row = min_size * row / col
        else:
            new_col = min_size * col / row
        im = cv2.resize(im, (new_row, new_col), interpolation=cv2.INTER_CUBIC)
        return im

    def crop_and_flip(self, im):
        """
        Return cropped image.
        The size of the cropped image is inner_size * inner_size.
        im: (H x W x K) ndarrays
        """
        row, col = im.shape[:2]
        start_h, start_w = 0, 0
        if self.is_train:
            start_h = np.random.randint(0, row - self.crop_size + 1)
            start_w = np.random.randint(0, col - self.crop_size + 1)
        else:
            start_h = (row - self.crop_size) / 2
            start_w = (col - self.crop_size) / 2
        end_h, end_w = start_h + self.crop_size, start_w + self.crop_size
        if self.is_color:
            im = im[start_h:end_h, start_w:end_w, :]
        else:
            im = im[start_h:end_h, start_w:end_w]
        if (self.is_train) and (np.random.randint(2) == 0):
            if self.is_color:
                im = im[:, ::-1, :]
            else:
                im = im[:, ::-1]
        return im

    def transform(self, im):
        im = self.resize(im, self.min_size)
        im = self.crop_and_flip(im)
        # transpose, swap channel, sub mean
        im = im.astype('float32')
        ImageTransformer.transformer(self, im)
        return im

    def load_image_from_string(self, data):
        flag = cv2.CV_LOAD_IMAGE_COLOR if self.is_color else cv2.CV_LOAD_IMAGE_GRAYSCALE
        im = cv2.imdecode(np.fromstring(data, np.uint8), flag)
        return im

    def transform_from_string(self, data):
        im = self.load_image_from_string(data)
        return self.transform(im)

    def load_image_from_file(self, file):
        flag = cv2.CV_LOAD_IMAGE_COLOR if self.is_color else cv2.CV_LOAD_IMAGE_GRAYSCALE
        im = cv2.imread(file, flag)
        return im

    def transform_from_file(self, file):
        im = self.load_image_from_file(file)
        return self.transform(im)


class PILTransformer(ImageTransformer):
    """
    PILTransformer used PIL to process image.
    """

    def __init__(
            self,
            min_size=None,
            crop_size=None,
            transpose=(2, 0, 1),  # transpose to C * H * W
            channel_swap=None,
            mean=None,
            is_train=True,
            is_color=True):
        ImageTransformer.__init__(self, transpose, channel_swap, mean, is_color)
        self.min_size = min_size
        self.crop_size = crop_size
        self.is_train = is_train

    def resize(self, im, min_size):
        row, col = im.size[:2]
        new_row, new_col = min_size, min_size
        if row > col:
            new_row = min_size * row / col
        else:
            new_col = min_size * col / row
        im = im.resize((new_row, new_col), Image.ANTIALIAS)
        return im

    def crop_and_flip(self, im):
        """
        Return cropped image.
        The size of the cropped image is inner_size * inner_size.
        """
        row, col = im.size[:2]
        start_h, start_w = 0, 0
        if self.is_train:
            start_h = np.random.randint(0, row - self.crop_size + 1)
            start_w = np.random.randint(0, col - self.crop_size + 1)
        else:
            start_h = (row - self.crop_size) / 2
            start_w = (col - self.crop_size) / 2
        end_h, end_w = start_h + self.crop_size, start_w + self.crop_size
        im = im.crop((start_h, start_w, end_h, end_w))
        if (self.is_train) and (np.random.randint(2) == 0):
            im = im.transpose(Image.FLIP_LEFT_RIGHT)
        return im

    def transform(self, im):
        im = self.resize(im, self.min_size)
        im = self.crop_and_flip(im)
        im = np.array(im, dtype=np.float32)  # convert to numpy.array
        # transpose, swap channel, sub mean
        ImageTransformer.transformer(self, im)
        return im

    def load_image_from_string(self, data):
        im = Image.open(StringIO(data))
        return im

    def transform_from_string(self, data):
        im = self.load_image_from_string(data)
        return self.transform(im)

    def load_image_from_file(self, file):
        im = Image.open(file)
        return im

    def transform_from_file(self, file):
        im = self.load_image_from_file(file)
        return self.transform(im)


def job(is_img_string, transformer, data_label_pack):
    (data, label) = data_label_pack
    if is_img_string:
        return transformer.transform_from_string(data), label
    else:
        return transformer.transform_from_file(data), label


class MultiProcessImageTransformer(object):
    def __init__(self,
                 procnum=10,
                 resize_size=None,
                 crop_size=None,
                 transpose=(2, 0, 1),
                 channel_swap=None,
                 mean=None,
                 is_train=True,
                 is_color=True,
                 is_img_string=True):
        """
        Processing image with multi-process. If it is used in PyDataProvider,
        the simple usage for CNN is as follows:

        .. code-block:: python

            def hool(settings, is_train,  **kwargs):
                settings.is_train = is_train
                settings.mean_value = np.array([103.939,116.779,123.68], dtype=np.float32)
                settings.input_types = [
                    dense_vector(3 * 224 * 224),
                    integer_value(1)]
                settings.transformer = MultiProcessImageTransformer(
                    procnum=10,
                    resize_size=256,
                    crop_size=224,
                    transpose=(2, 0, 1),
                    mean=settings.mean_values,
                    is_train=settings.is_train)


            @provider(init_hook=hook, pool_size=20480)
            def process(settings, file_list):
                with open(file_list, 'r') as fdata:
                    for line in fdata:
                        data_dic = np.load(line.strip()) # load the data batch pickled by Pickle.
                        data = data_dic['data']
                        labels = data_dic['label']
                        labels = np.array(labels, dtype=np.float32)
                        for im, lab in settings.dp.run(data, labels):
                            yield [im.astype('float32'), int(lab)]

        :param procnum: processor number.
        :type procnum: int
        :param resize_size: the shorter edge size of image after resizing.
        :type resize_size: int
        :param crop_size: the croping size.
        :type crop_size: int
        :param transpose: the transpose order, Paddle only allow C * H * W order.
        :type transpose: tuple or list
        :param channel_swap: the channel swap order, RGB or BRG.
        :type channel_swap: tuple or list
        :param mean: the mean values of image, per-channel mean or element-wise mean.
        :type mean: array, The dimension is 1 for per-channel mean.
                    The dimension is 3 for element-wise mean.
        :param is_train: training peroid or testing peroid.
        :type is_train: bool.
        :param is_color: the image is color or gray.
        :type is_color: bool.
        :param is_img_string: The input can be the file name of image or image string.
        :type is_img_string: bool.
        """

        self.procnum = procnum
        self.pool = multiprocessing.Pool(procnum)
        self.is_img_string = is_img_string
        if cv2 is not None:
            self.transformer = CvTransformer(resize_size, crop_size, transpose,
                                             channel_swap, mean, is_train,
                                             is_color)
        else:
            self.transformer = PILTransformer(resize_size, crop_size, transpose,
                                              channel_swap, mean, is_train,
                                              is_color)

    def run(self, data, label):
        fun = functools.partial(job, self.is_img_string, self.transformer)
        return self.pool.imap_unordered(
            fun, six.moves.zip(data, label), chunksize=100 * self.procnum)
