import argparse

import json

import os

from pathlib import Path

import nibabel as nib

import fetal_net.preprocess

from fetal.preprocess import window_intensities_data

from fetal.utils import get_last_model_path

from fetal_net.normalize import normalize_data

from fetal_net.postprocess import postprocess_prediction as process_pred

from fetal_net.prediction import patch_wise_prediction, predict_augment, predict_flips

from fetal_net.preprocess import *

from fetal_net.training import load_old_model

from fetal_net.utils.cut_relevant_areas import find_bounding_box, check_bounding_box

from fetal_net.utils.utils import read_img, get_image

def save_nifti(data, path):

    nifti = get_image(data)

    nib.save(nifti, path)

def secondary_prediction(mask, vol, config2, model2_path=None,

                         preprocess_method2=None, norm_params2=None,

                         overlap_factor=0.9, augment2=None, num_augment=32, return_all_preds=False):

    model2 = load_old_model(get_last_model_path(model2_path), config=config2)

    pred = mask

    bbox_start, bbox_end = find_bounding_box(pred)

    check_bounding_box(pred, bbox_start, bbox_end)

    padding = [16, 16, 8]

    if padding is not None:

        bbox_start = np.maximum(bbox_start - padding, 0)

        bbox_end = np.minimum(bbox_end + padding, mask.shape)

    data = vol.astype(np.float)[

           bbox_start[0]:bbox_end[0],

           bbox_start[1]:bbox_end[1],

           bbox_start[2]:bbox_end[2]

           ]

    data = preproc_and_norm(data, preprocess_method2, norm_params2)

    prediction = get_prediction(data, model2, augment=augment2, num_augments=num_augment, return_all_preds=return_all_preds,

                                overlap_factor=overlap_factor, config=config2)

    padding2 = list(zip(bbox_start, np.array(vol.shape) - bbox_end))

    if return_all_preds:

        padding2 = [(0, 0)] + padding2

    print(padding2)

    print(prediction.shape)

    prediction = np.pad(prediction, padding2, mode='constant', constant_values=0)

    return prediction

def preproc_and_norm(data, preprocess_method=None, norm_params=None, scale=None, preproc=None):

    if preprocess_method is not None:

        print('Applying preprocess by {}...'.format(preprocess_method))

        if preprocess_method == 'window_1_99':

            data = window_intensities_data(data)

        else:

            raise Exception('Unknown preprocess: {}'.format(preprocess_method))

    if scale is not None:

        data = ndimage.zoom(data, scale)

    if preproc is not None:

        preproc_func = getattr(fetal_net.preprocess, preproc)

        data = preproc_func(data)

    # data = normalize_data(data, mean=data.mean(), std=data.std())

    if norm_params is not None and any(norm_params.values()):

        data = normalize_data(data, mean=norm_params['mean'], std=norm_params['std'])

    return data

def get_prediction(data, model, augment, num_augments, return_all_preds, overlap_factor, config):

    if augment is not None:

        patch_shape = config["patch_shape"] + [config["patch_depth"]]

        if augment == 'all':

            prediction = predict_augment(data, model=model, overlap_factor=overlap_factor, num_augments=num_augments, patch_shape=patch_shape)

        elif augment == 'flip':

            prediction = predict_flips(data, model=model, overlap_factor=overlap_factor, patch_shape=patch_shape, config=config)

        else:

            raise ("Unknown augmentation {}".format(augment))

        if not return_all_preds:

            prediction = np.median(prediction, axis=0)

    else:

        prediction = \

            patch_wise_prediction(model=model,

                                  data=np.expand_dims(data, 0),

                                  overlap_factor=overlap_factor,

                                  patch_shape=config["patch_shape"] + [config["patch_depth"]])

    prediction = prediction.squeeze()

    return prediction

def main(input_path, output_path, overlap_factor,

         config, model_path, preprocess_method=None, norm_params=None, augment=None, num_augment=0,

         config2=None, model2_path=None, preprocess_method2=None, norm_params2=None, augment2=None, num_augment2=0,

         z_scale=None, xy_scale=None, return_all_preds=False):

    print(model_path)

    model = load_old_model(get_last_model_path(model_path), config=config)

    print('Loading nifti from {}...'.format(input_path))

    nifti = read_img(input_path)

    print('Predicting mask...')

    data = nifti.get_fdata().astype(np.float).squeeze()

    print('original_shape: ' + str(data.shape))

    scan_name = Path(input_path).name.split('.')[0]

    if (z_scale is None):

        z_scale = 1.0

    if (xy_scale is None):

        xy_scale = 1.0

    if z_scale != 1.0 or xy_scale != 1.0:

        data = ndimage.zoom(data, [xy_scale, xy_scale, z_scale])

    data = preproc_and_norm(data, preprocess_method, norm_params,

                            scale=config.get('scale_data', None),

                            preproc=config.get('preproc', None))

    save_nifti(data, os.path.join(output_path, scan_name + '_data.nii.gz'))

    data = np.pad(data, 3, 'constant', constant_values=data.min())

    print('Shape: ' + str(data.shape))

    prediction = get_prediction(data=data, model=model, augment=augment,

                                num_augments=num_augment, return_all_preds=return_all_preds,

                                overlap_factor=overlap_factor, config=config)

    # unpad

    prediction = prediction[3:-3, 3:-3, 3:-3]

    # revert to original size

    if config.get('scale_data', None) is not None:

        prediction = ndimage.zoom(prediction.squeeze(), np.divide([1, 1, 1], config.get('scale_data', None)), order=0)[..., np.newaxis]

    save_nifti(prediction, os.path.join(output_path, scan_name + '_pred.nii.gz'))

    if z_scale != 1.0 or xy_scale != 1.0:

        prediction = ndimage.zoom(prediction.squeeze(), [1.0 / xy_scale, 1.0 / xy_scale, 1.0 / z_scale], order=1)[..., np.newaxis]

    # if prediction.shape[-1] > 1:

    #    prediction = prediction[..., 1]

    if config2 is not None:

        prediction = prediction.squeeze()

        mask = process_pred(prediction, gaussian_std=0.5, threshold=0.5)  # .astype(np.uint8)

        nifti = read_img(input_path)

        prediction = secondary_prediction(mask, vol=nifti.get_fdata().astype(np.float),

                                          config2=config2, model2_path=model2_path,

                                          preprocess_method2=preprocess_method2, norm_params2=norm_params2,

                                          overlap_factor=overlap_factor, augment2=augment2, num_augment=num_augment2,

                                          return_all_preds=return_all_preds)

        save_nifti(prediction, os.path.join(output_path, scan_name + 'pred_roi.nii.gz'))

    print('Saving to {}'.format(output_path))

    print('Finished.')

def get_params(config_dir):

    with open(os.path.join(config_dir, 'config.json'), 'r') as f:

        __config = json.load(f)

    with open(os.path.join(config_dir, 'norm_params.json'), 'r') as f:

        __norm_params = json.load(f)

    __model_path = os.path.join(config_dir, os.path.basename(__config['model_file']))

    return __config, __norm_params, __model_path

if __name__ == '__main__':

    parser = argparse.ArgumentParser()

    parser.add_argument("--input_nii", help="specifies mat file dir path",

                        type=str, required=True)

    parser.add_argument("--output_folder", help="specifies mat file dir path",

                        type=str, required=True)

    parser.add_argument("--overlap_factor", help="specifies overlap between prediction patches",

                        type=float, default=0.9)

    parser.add_argument("--z_scale", help="specifies overlap between prediction patches",

                        type=float, default=1)

    parser.add_argument("--xy_scale", help="specifies overlap between prediction patches",

                        type=float, default=1)

    parser.add_argument("--return_all_preds", help="output std for prediction",

                        type=int, default=0)

    # Params for primary prediction

    parser.add_argument("--config_dir", help="specifies config dir path",

                        type=str, required=True)

    parser.add_argument("--preprocess", help="what preprocess to do",

                        type=str, required=False, default=None)

    parser.add_argument("--augment", help="what augment to do",

                        type=str, required=False, default=None)  # one of 'flip, all'

    parser.add_argument("--num_augment", help="what augment to do",

                        type=int, required=False, default=0)  # one of 'flip, all'

    # Params for secondary prediction

    parser.add_argument("--config2_dir", help="specifies config dir path",

                        type=str, required=False, default=None)

    parser.add_argument("--preprocess2", help="what preprocess to do",

                        type=str, required=False, default=None)

    parser.add_argument("--augment2", help="what augment to do",

                        type=str, required=False, default=None)  # one of 'flip, all'

    parser.add_argument("--num_augment2", help="what augment to do",

                        type=int, required=False, default=0)  # one of 'flip, all'

    opts = parser.parse_args()

    Path(opts.output_folder).mkdir(exist_ok=True)

    # 1

    _config, _norm_params, _model_path = get_params(opts.config_dir)

    # 2

    if opts.config2_dir is not None:

        _config2, _norm_params2, _model2_path = get_params(opts.config2_dir)

    else:

        _config2, _norm_params2, _model2_path = None, None, None

    main(opts.input_nii, opts.output_folder, overlap_factor=opts.overlap_factor,

         config=_config, model_path=_model_path, preprocess_method=opts.preprocess, norm_params=_norm_params, augment=opts.augment,

         num_augment=opts.num_augment,

         config2=_config2, model2_path=_model2_path, preprocess_method2=opts.preprocess2, norm_params2=_norm_params2, augment2=opts.augment2,

         num_augment2=opts.num_augment2,

         z_scale=opts.z_scale, xy_scale=opts.xy_scale, return_all_preds=opts.return_all_preds)