# 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.

import os
import cv2
import math
import random
import numpy as np

from datasets.folder import DatasetFolder


def center_crop_resize(img):
    h, w = img.shape[:2]
    c = int(224 / 256 * min((h, w)))
    i = (h + 1 - c) // 2
    j = (w + 1 - c) // 2
    img = img[i:i + c, j:j + c, :]
    return cv2.resize(img, (224, 224), 0, 0, cv2.INTER_LINEAR)


def random_crop_resize(img):
    height, width = img.shape[:2]
    area = height * width

    for attempt in range(10):
        target_area = random.uniform(0.08, 1.) * area
        log_ratio = (math.log(3 / 4), math.log(4 / 3))
        aspect_ratio = math.exp(random.uniform(*log_ratio))

        w = int(round(math.sqrt(target_area * aspect_ratio)))
        h = int(round(math.sqrt(target_area / aspect_ratio)))

        if w <= width and h <= height:
            i = random.randint(0, height - h)
            j = random.randint(0, width - w)
            img = img[i:i + h, j:j + w, :]
            return cv2.resize(img, (224, 224), 0, 0, cv2.INTER_LINEAR)

    return center_crop_resize(img)


def random_flip(img):
    if np.random.randint(0, 2) == 1:
        img = img[:, ::-1, :]
    return img


def normalize_permute(img):
    # transpose and convert to RGB from BGR
    img = img.astype(np.float32).transpose((2, 0, 1))[::-1, ...]
    mean = np.array([123.675, 116.28, 103.53], dtype=np.float32)
    std = np.array([58.395, 57.120, 57.375], dtype=np.float32)
    invstd = 1. / std
    for v, m, s in zip(img, mean, invstd):
        v.__isub__(m).__imul__(s)
    return img


def compose(functions):
    def process(sample):
        img, label = sample
        for fn in functions:
            img = fn(img)
        return img, label

    return process


class ImageNetDataset(DatasetFolder):
    def __init__(self, path, mode='train'):
        super(ImageNetDataset, self).__init__(path)
        self.mode = mode
        if self.mode == 'train':
            self.transform = compose([
                cv2.imread, random_crop_resize, random_flip, normalize_permute
            ])
        else:
            self.transform = compose(
                [cv2.imread, center_crop_resize, normalize_permute])

    def __getitem__(self, idx):
        img, label = self.samples[idx]
        return self.transform((img, [label]))

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