# -*- coding:utf-8 -*-
# copyright (c) 2020 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 os
import time
from collections import OrderedDict

import cv2
import numpy as np
from PIL import Image

__all__ = ['reader']

DATA_DIM = 224
img_mean = np.array([0.485, 0.456, 0.406]).reshape((3, 1, 1))
img_std = np.array([0.229, 0.224, 0.225]).reshape((3, 1, 1))


def resize_short(img, target_size):
    percent = float(target_size) / min(img.size[0], img.size[1])
    resized_width = int(round(img.size[0] * percent))
    resized_height = int(round(img.size[1] * percent))
    img = img.resize((resized_width, resized_height), Image.LANCZOS)
    return img


def crop_image(img, target_size, center):
    width, height = img.size
    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.crop((w_start, h_start, w_end, h_end))
    return img


def process_image(img):
    img = resize_short(img, target_size=256)
    img = crop_image(img, target_size=DATA_DIM, center=True)
    if img.mode != 'RGB':
        img = img.convert('RGB')
    img = np.array(img).astype('float32').transpose((2, 0, 1)) / 255
    img -= img_mean
    img /= img_std
    return img


def reader(images=None, paths=None):
    """
    Preprocess to yield image.

    Args:
        images (list[numpy.ndarray]): images data, shape of each is [H, W, C].
        paths (list[str]): paths to images.

    Yield:
        each (collections.OrderedDict): info of original image, preprocessed image.
    """
    component = list()
    if paths:
        for im_path in paths:
            each = OrderedDict()
            assert os.path.isfile(
                im_path), "The {} isn't a valid file path.".format(im_path)
            each['org_im_path'] = im_path
            each['org_im'] = Image.open(im_path)
            each['org_im_width'], each['org_im_height'] = each['org_im'].size
            component.append(each)
    if images is not None:
        assert type(images), "images is a list."
        for im in images:
            each = OrderedDict()
            each['org_im'] = Image.fromarray(im[:, :, ::-1])
            each['org_im_path'] = 'ndarray_time={}'.format(
                round(time.time(), 6) * 1e6)
            each['org_im_width'], each['org_im_height'] = each['org_im'].size
            component.append(each)

    for element in component:
        element['image'] = process_image(element['org_im'])
        yield element