"""
Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany

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 random
from pathlib import Path

import numpy as np
import SimpleITK as sitk
from loguru import logger

from nndet.io import save_json
from nndet.utils.info import env_guard


# # 2D example
# [Ignore, Not supported]
# dim = 2
# image_size = [512, 512]
# object_size = [32, 64]
# object_width = 6
# num_images_tr = 100
# num_images_ts = 100

# 3D example

dim = 3
image_size = [256, 256, 256]
object_size = [16, 32]
object_width = 4
num_images_tr = 10
num_images_ts = 10


def generate_image(image_dir, label_dir, idx):
    logger.info(f"Generating case_{idx}")
    selected_size = np.random.randint(object_size[0], object_size[1])
    selected_class = np.random.randint(0, 2)

    data = np.random.rand(*image_size)
    mask = np.zeros_like(data)

    top_left = [np.random.randint(0, image_size[i] - selected_size) for i in range(dim)]

    if selected_class == 0:
        slicing = tuple([slice(tp, tp + selected_size) for tp in top_left])
        data[slicing] = data[slicing] + 0.4
        data = data.clip(0, 1)
        mask[slicing] = 1
    elif selected_class == 1:
        slicing = tuple([slice(tp, tp + selected_size) for tp in top_left])

        inner_slicing = [slice(tp + object_width, tp + selected_size - object_width) for tp in top_left]
        if len(inner_slicing) == 3:
            inner_slicing[0] = slice(0, image_size[0])
        inner_slicing = tuple(inner_slicing)

        object_mask = np.zeros_like(mask).astype(bool)
        object_mask[slicing] = 1
        object_mask[inner_slicing] = 0

        data[object_mask] = data[object_mask] + 0.4
        data = data.clip(0, 1)
        mask[object_mask] = 1
    else:
        raise NotImplementedError

    if dim == 2:
        data = data[None]
        mask = mask[None]

    data_itk = sitk.GetImageFromArray(data)
    mask_itk = sitk.GetImageFromArray(mask)
    mask_meta = {
        "instances": {
            "1": selected_class
        },
    }
    sitk.WriteImage(data_itk, str(image_dir / f"case_{idx}_0000.nii.gz"))
    sitk.WriteImage(mask_itk, str(label_dir / f"case_{idx}.nii.gz"))
    save_json(mask_meta, label_dir / f"case_{idx}.json")


@env_guard
def main():
    """
    Generate an example dataset for nnDetection to test the installation or
    experiment with ideas.
    """
    random.seed(0)
    np.random.seed(0)

    meta = {
        "task": f"Task000D{dim}_Example",
        "name": "Example",
        "target_class": None,
        "test_labels": True,
        "labels": {"0": "Square", "1": "SquareHole"},
        "modalities": {"0": "MRI"},
        "dim": dim,
    }

    # setup paths
    data_task_dir = Path(os.getenv("det_data")) / meta["task"]
    data_task_dir.mkdir(parents=True, exist_ok=True)
    save_json(meta, data_task_dir / "dataset.json")

    raw_splitted_dir = data_task_dir / "raw_splitted"
    images_tr_dir = raw_splitted_dir / "imagesTr"
    images_tr_dir.mkdir(parents=True, exist_ok=True)
    labels_tr_dir = raw_splitted_dir / "labelsTr"
    labels_tr_dir.mkdir(parents=True, exist_ok=True)
    images_ts_dir = raw_splitted_dir / "imagesTs"
    images_ts_dir.mkdir(parents=True, exist_ok=True)
    labels_ts_dir = raw_splitted_dir / "labelsTs"
    labels_ts_dir.mkdir(parents=True, exist_ok=True)

    for idx in range(num_images_tr):
        generate_image(images_tr_dir, labels_tr_dir, idx)

    for idx in range(num_images_ts):
        generate_image(images_ts_dir, labels_ts_dir, idx)


if __name__ == '__main__':
    main()