#copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
#
#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 sys
import os
import math
import random
import functools
import numpy as np
import cv2

import paddle
from paddle import fluid
from PIL import Image

policy = None

random.seed(0)
np.random.seed(0)


def random_crop(img, size, interpolation=Image.BILINEAR):
    """random crop image
        
    Args:
        img: image data
        size: crop size

    Returns:
        random cropped image data
    """
    lower_scale = 0.08
    lower_ratio = 3. / 4.
    upper_ratio = 4. / 3.
    scale = [lower_scale, 1.0]
    ratio = [lower_ratio, upper_ratio]

    aspect_ratio = math.sqrt(np.random.uniform(*ratio))
    w = 1. * aspect_ratio
    h = 1. / aspect_ratio

    bound = min((float(img.shape[0]) / img.shape[1]) / (h**2),
                (float(img.shape[1]) / img.shape[0]) / (w**2))

    scale_max = min(scale[1], bound)
    scale_min = min(scale[0], bound)

    target_area = img.shape[0] * img.shape[1] * np.random.uniform(scale_min,
                                                                  scale_max)
    target_size = math.sqrt(target_area)
    w = int(target_size * w)
    h = int(target_size * h)

    i = np.random.randint(0, img.shape[0] - h + 1)
    j = np.random.randint(0, img.shape[1] - w + 1)
    img = img[i:i + h, j:j + w, :]

    if interpolation:
        resized = cv2.resize(img, (size, size), interpolation=interpolation)
    else:
        resized = cv2.resize(img, (size, size))
    return resized


def resize_short(img, target_size, interpolation=None):
    """resize image
    
    Args:
        img: image data
        target_size: resize short target size
        interpolation: interpolation mode

    Returns:
        resized image data
    """
    percent = float(target_size) / min(img.shape[0], img.shape[1])
    resized_width = int(round(img.shape[1] * percent))
    resized_height = int(round(img.shape[0] * percent))
    if interpolation:
        resized = cv2.resize(
            img, (resized_width, resized_height), interpolation=interpolation)
    else:
        resized = cv2.resize(img, (resized_width, resized_height))
    return resized


def crop_image(img, target_size, center):
    """crop image 
    
    Args:
        img: images data
        target_size: crop target size
        center: crop mode
    
    Returns:
        img: cropped image data
    """
    height, width = img.shape[:2]
    size = target_size
    if center == True:
        w_start = (width - size) // 2
        h_start = (height - size) // 2
    else:
        w_start = np.random.randint(0, width - size + 1)
        h_start = np.random.randint(0, height - size + 1)
    w_end = w_start + size
    h_end = h_start + size
    img = img[h_start:h_end, w_start:w_end, :]
    return img


def process_image(sample, settings, mode):
    """ process_image """

    mean = [0.485, 0.456, 0.406]
    std = [0.229, 0.224, 0.225]
    crop_size = 224

    img_path = sample[0]
    img = cv2.imread(img_path)

    if mode == 'train':
        img = random_crop(img, crop_size)
        if np.random.randint(0, 2) == 1:
            img = img[:, ::-1, :]
    else:
        if crop_size > 0:
            target_size = settings.resize_short_size
            img = resize_short(
                img, target_size, interpolation=settings.interpolation)
            img = crop_image(img, target_size=crop_size, center=True)

    img = img[:, :, ::-1]

    img = img.astype('float32').transpose((2, 0, 1)) / 255
    img_mean = np.array(mean).reshape((3, 1, 1))
    img_std = np.array(std).reshape((3, 1, 1))
    img -= img_mean
    img /= img_std

    if mode == 'train' or mode == 'val':
        return (img, np.array([sample[1]]))
    elif mode == 'test':
        return (img, )


def process_batch_data(input_data, settings, mode):
    batch_data = []
    for sample in input_data:
        if os.path.isfile(sample[0]):
            batch_data.append(process_image(sample, settings, mode))
        else:
            print("File not exist : %s" % sample[0])
    return batch_data


class ImageNetReader:
    def __init__(self, seed=None):
        self.shuffle_seed = seed

    def set_shuffle_seed(self, seed):
        assert isinstance(seed, int), "shuffle seed must be int"
        self.shuffle_seed = seed

    def _reader_creator(self,
                        settings,
                        file_list,
                        mode,
                        shuffle=False,
                        data_dir=None):
        if mode == 'test':
            batch_size = 1
        else:
            batch_size = settings.batch_size

        def reader():
            def read_file_list():
                with open(file_list) as flist:
                    full_lines = [line.strip() for line in flist]
                    if mode != "test" and len(full_lines) < settings.batch_size:
                        print(
                            "Warning: The number of the whole data ({}) is smaller than the batch_size ({}), and drop_last is turnning on, so nothing  will feed in program, Terminated now. Please reset batch_size to a smaller number or feed more data!"
                            .format(len(full_lines), settings.batch_size))
                        os._exit(1)
                    if shuffle:
                        assert self.shuffle_seed is not None, "multiprocess train, shuffle seed must be set!"
                        np.random.RandomState(self.shuffle_seed).shuffle(
                            full_lines)

                batch_data = []
                for line in full_lines:
                    img_path, label = line.split()
                    img_path = os.path.join(data_dir, img_path)
                    batch_data.append([img_path, int(label)])
                    if len(batch_data) == batch_size:
                        if mode == 'train' or mode == 'val' or mde == 'test':
                            yield batch_data
                        batch_data = []

            return read_file_list

        data_reader = reader()
        mapper = functools.partial(
            process_batch_data, settings=settings, mode=mode)

        ret = fluid.io.xmap_readers(
            mapper,
            data_reader,
            settings.reader_thread,
            settings.reader_buf_size,
            order=False)

        return ret

    def train(self, settings):
        """Create a reader for trainning

        Args:
            settings: arguments

        Returns:
            train reader
        """
        file_list = os.path.join(settings.data_dir, 'train_list.txt')
        assert os.path.isfile(
            file_list), "{} doesn't exist, please check data list path".format(
                file_list)

        reader = self._reader_creator(
            settings,
            file_list,
            'train',
            shuffle=True,
            data_dir=settings.data_dir)

        return reader

    def val(self, settings):
        """Create a reader for eval

        Args:
            settings: arguments

        Returns:
            eval reader
        """
        file_list = os.path.join(settings.data_dir, 'val_list.txt')

        assert os.path.isfile(
            file_list), "{} doesn't exist, please check data list path".format(
                file_list)

        return self._reader_creator(
            settings,
            file_list,
            'val',
            shuffle=False,
            data_dir=settings.data_dir)

    def test(self, settings):
        """Create a reader for testing

        Args:
            settings: arguments

        Returns:
            test reader
        """
        file_list = os.path.join(settings.data_dir, 'val_list.txt')

        assert os.path.isfile(
            file_list), "{} doesn't exist, please check data list path".format(
                file_list)
        return self._reader_creator(
            settings,
            file_list,
            'test',
            shuffle=False,
            data_dir=settings.data_dir)