# Copyright (c) 2021 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 math

import cv2
import numpy as np
import paddle

from paddleseg import utils
from core import infer_generate_autolabel
from paddleseg.utils import logger, progbar


def mkdir(path):
    sub_dir = os.path.dirname(path)
    if not os.path.exists(sub_dir):
        os.makedirs(sub_dir)


def partition_list(arr, m):
    """split the list 'arr' into m pieces"""
    n = int(math.ceil(len(arr) / float(m)))
    return [arr[i:i + n] for i in range(0, len(arr), n)]


def predictAutolabel(model,
                     model_path,
                     transforms,
                     image_list,
                     image_dir=None,
                     save_dir='output',
                     aug_pred=False,
                     scales=1.0,
                     flip_horizontal=True,
                     flip_vertical=False,
                     is_slide=False,
                     stride=None,
                     crop_size=None):
    """
    predict and visualize the image_list.

    Args:
        model (nn.Layer): Used to predict for input image.
        model_path (str): The path of pretrained model.
        transforms (transform.Compose): Preprocess for input image.
        image_list (list): A list of image path to be predicted.
        image_dir (str, optional): The root directory of the images predicted. Default: None.
        save_dir (str, optional): The directory to save the visualized results. Default: 'output'.
        aug_pred (bool, optional): Whether to use mulit-scales and flip augment for predition. Default: False.
        scales (list|float, optional): Scales for augment. It is valid when `aug_pred` is True. Default: 1.0.
        flip_horizontal (bool, optional): Whether to use flip horizontally augment. It is valid when `aug_pred` is True. Default: True.
        flip_vertical (bool, optional): Whether to use flip vertically augment. It is valid when `aug_pred` is True. Default: False.
        is_slide (bool, optional): Whether to predict by sliding window. Default: False.
        stride (tuple|list, optional): The stride of sliding window, the first is width and the second is height.
            It should be provided when `is_slide` is True.
        crop_size (tuple|list, optional):  The crop size of sliding window, the first is width and the second is height.
            It should be provided when `is_slide` is True.

    """
    utils.utils.load_entire_model(model, model_path)
    model.eval()
    nranks = paddle.distributed.get_world_size()
    local_rank = paddle.distributed.get_rank()
    if nranks > 1:
        img_lists = partition_list(image_list, nranks)
    else:
        img_lists = [image_list]

    added_saved_dir = os.path.join(save_dir, 'added_prediction')
    pred_saved_dir = os.path.join(save_dir, 'pseudo_color_prediction')

    logger.info("Start to predict...")
    progbar_pred = progbar.Progbar(target=len(img_lists[0]), verbose=1)
    with paddle.no_grad():
        for i, im_path in enumerate(img_lists[local_rank]):
            im = cv2.imread(im_path)
            ori_shape = im.shape[:2]
            im, _ = transforms(im)
            im = im[np.newaxis, ...]
            im = paddle.to_tensor(im)

            if aug_pred:
                pred = infer_generate_autolabel.aug_inference(
                    model,
                    im,
                    ori_shape=ori_shape,
                    transforms=transforms.transforms,
                    scales=scales,
                    flip_horizontal=flip_horizontal,
                    flip_vertical=flip_vertical,
                    is_slide=is_slide,
                    stride=stride,
                    crop_size=crop_size)
            else:
                pred = infer_generate_autolabel.inference(
                    model,
                    im,
                    ori_shape=ori_shape,
                    transforms=transforms.transforms,
                    is_slide=is_slide,
                    stride=stride,
                    crop_size=crop_size)
            pred = paddle.squeeze(pred)
            pred = pred.numpy().astype('uint8')

            # get the saved name
            if image_dir is not None:
                im_file = im_path.replace(image_dir, '')
            else:
                im_file = os.path.basename(im_path)
            if im_file[0] == '/':
                im_file = im_file[1:]

            # save added image
            added_image = utils.visualize.visualize(im_path, pred, weight=0.6)
            added_image_path = os.path.join(added_saved_dir, im_file)
            mkdir(added_image_path)
            cv2.imwrite(added_image_path, added_image)

            # save pseudo color prediction
            pred_mask = utils.visualize.get_pseudo_color_map(pred)
            pred_saved_path = os.path.join(pred_saved_dir,
                                           im_file.rsplit(".")[0] + ".png")
            mkdir(pred_saved_path)
            pred_mask.save(pred_saved_path)

            # pred_im = utils.visualize(im_path, pred, weight=0.0)
            # pred_saved_path = os.path.join(pred_saved_dir, im_file)
            # mkdir(pred_saved_path)
            # cv2.imwrite(pred_saved_path, pred_im)

            progbar_pred.update(i + 1)
