"""
MIT License

Copyright (c) 2019 Max Planck Institute of Molecular Physiology

Author: <NAME> (<EMAIL>)
Author: <NAME> (<EMAIL>)
Author: <NAME> (<EMAIL>)

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""

from . import models
from . import patch_pair_generator as gen
from . import utils
from keras.optimizers import Adam
import os
from . import utils
import mrcfile
import tifffile
import numpy as np

def train(
    even_path,
    odd_path,
    model_out_path,
    movie_path=None,
    learning_rate=0.001,
    epochs=50,
    model="unet",
    patch_size=(1024, 1024),
    batch_size=4,
    loss="mae",
    fbinning=utils.fourier_binning
):
    '''
    Does the complete noise2noise training and writes the model file to disk.
    :param even_path: Path where "even averages" will be written.
    :param odd_path: Path here "odd averages" will be written
    :param model_out_path: Filepath where model will be written.
    :param movie_path: Path to movie files. Supported are .mrc, .mrcs, .tiff and .tif.
    :param learning_rate: Learning rate used during training.
    :param epochs: Number of epochs to train the network
    :param model: Model indentifier. Right now only "unet" is supported.
    :param patch_size: Patch size in pixel. The network is trained on random patches of the images.
    :param batch_size: Mini-batch size used during training.
    :return: trained model
    '''

    print("Start training")
    # Read training even/odd micrographs
    trained_model = train_movie_dir(
        even_path=even_path,
        odd_path=odd_path,
        movie_path=movie_path,
        learning_rate=learning_rate,
        epochs=epochs,
        model=model,
        patch_size=patch_size,
        batch_size=batch_size,
        loss=loss,
        fbinning=fbinning
        )
    trained_model.save_weights(model_out_path)
    import h5py
    with h5py.File(model_out_path, mode='r+') as f:
        f["model_name"] = np.array((model), dtype=h5py.special_dtype(vlen=str))
        f["patch_size"] = patch_size

    print("Training done. Weights saved to " + model_out_path)

    return trained_model

def train_movie_dir(
    even_path,
    odd_path,
    movie_path=None,
    learning_rate=0.001,
    epochs=50,
    model="unet",
    patch_size=(1024, 1024),
    batch_size=4,
    loss="mae",
    fbinning=utils.fourier_binning
):
    '''
    Does the complete noise2noise training.
    :param even_path: Path where "even averages" will be written.
    :param odd_path: Path here "odd averages" will be written
    :param model_out_path: Filepath where model will be written.
    :param movie_path: Path to movie files. Supported are .mrc, .mrcs, .tiff and .tif.
    :param learning_rate: Learning rate used during training.
    :param epochs: Number of epochs to train the network
    :param model: Model indentifier. Right now only "unet" is supported.
    :param patch_size: Patch size in pixel. The network is trained on random patches of the images.
    :param batch_size: n
    :param loss: mae or mse are possible
    :return: trained model
    '''

    # Read training even/odd micrographs
    even_files, odd_files = calc_even_odd(
        movie_path, even_path, odd_path, recursive=True,fbinning=fbinning
    )
    trained_model = train_pairs(
        even_files,
        odd_files,
        model=model,
        learning_rate=learning_rate,
        patch_size=patch_size,
        batch_size=batch_size,
        epochs=epochs,
        valid_split=0.1,
        loss=loss
    )
    return trained_model



def calc_even_odd(movie_path,
                  even_path,
                  odd_path,
                  recursive=True,
                  fbinning=utils.fourier_binning):
    """
    Calculates averages based on the even/odd frames of the movies in movie_path and save the
    respective averages in even_path or odd_path.
    :param movie_path: Path to movie files. Supported are .mrc, .mrcs, .tiff and .tif.
    :param even_path: Path where "even averages" will be written.
    :param odd_path: Path here "odd averages" will be written
    :param recursive: If true, the movie_path is scanned recurively for movies.
    :return: Two lists with even filepaths and odd filepaths.
    """

    # Read training even/odd micrographs
    even_files = []
    odd_files = []
    for (dirpath, dirnames, filenames) in os.walk(even_path):
        for filename in filenames:
            if filename.endswith(utils.SUPPORTED_FILES):
                even_files.append(os.path.join(dirpath, filename))

    for (dirpath, dirnames, filenames) in os.walk(odd_path):
        for filename in filenames:
            if filename.endswith(utils.SUPPORTED_FILES):
                odd_files.append(os.path.join(dirpath, filename))

    # Create training data for movies which are not in even/odd directory
    try:
        os.makedirs(even_path)
    except FileExistsError:
        pass
    try:
        os.makedirs(odd_path)
    except FileExistsError:
        pass

    movies_to_split = []
    filenames_even = list(map(os.path.basename, even_files))
    filenames_odd = list(map(os.path.basename, odd_files))
    if movie_path:
        for (dirpath, dirnames, filenames) in os.walk(movie_path):
            for filename in filenames:
                if filename.endswith(utils.SUPPORTED_FILES):
                    if filename not in filenames_even and filename not in filenames_odd:
                        path = os.path.join(dirpath, filename)
                        movies_to_split.append((path,filename))

            if recursive == False:
                break

    for tuble_index, movie_tuble in enumerate(movies_to_split):
        path, filename = movie_tuble
        print("Create even/odd average for:", path, "( Progress: ",int(100*tuble_index/len(movies_to_split)),"% )")
        even, odd = utils.create_image_pair(path,fbinning)
        out_even_path = os.path.join(even_path, filename)
        out_odd_path = os.path.join(odd_path, filename)
        if path.endswith(("mrcs", "mrc")):
            with mrcfile.new(out_even_path, overwrite=True) as mrc:
                mrc.set_data(even)

            with mrcfile.new(out_odd_path, overwrite=True) as mrc:
                mrc.set_data(odd)

        elif path.endswith((".tif", ".tiff")):
            tifffile.imwrite(out_even_path, even)
            tifffile.imwrite(out_odd_path, odd)

        even_files.append(out_even_path)
        odd_files.append(out_odd_path)
        filenames_even.append(filename)
        filenames_odd.append(filename)

    print("Reset progress bar: ( Progress: -1 % )")
    return even_files, odd_files


def train_pairs(
    pair_files_a,
    pari_files_b,
    model="unet",
    learning_rate=0.001,
    epochs=50,
    patch_size=(1024, 1024),
    callbacks=[],
    batch_size=4,
    valid_split=0.1,
    loss="mae"
):
    """
    Training noise2noise model.
    :param pair_files_a: List with paths to the first images of the image pairs.
    :param pari_files_b: List with paths to the second images of the image pairs.
    :param model: Model indentifier. Right now only "unet" is supported.
    :param learning_rate: Learning rate used during training.
    :param epochs: Number of epochs to train the network
    :param patch_size: Patch size in pixel. The network is trained on random patches of the images.
    :param callbacks: Optional callbacks during training. See Keras callbacks for more information.
    :param batch_size: Mini-batch size used during training
    :param valid_split: training-validion split.
    :return: Trained keras model
    """
    #train_valid_split = int(valid_split * len(pair_files_a))
    train_pair_a_files = pair_files_a
    #valid_pair_a_files = pair_files_a[:train_valid_split]
    train_pair_b_files = pari_files_b
    #valid_pair_b_files = pari_files_b[:train_valid_split]

    train_gen = gen.patch_pair_batch_generator(
        pair_a_images=train_pair_a_files,
        pair_b_images=train_pair_b_files,
        patch_size=patch_size,
        batch_size=batch_size,
        augment=True,
    )

    '''
    valid_gen = gen.patch_pair_batch_generator(
        pair_a_images=valid_pair_a_files,
        pair_b_images=valid_pair_b_files,
        patch_size=patch_size,
        batch_size=batch_size,
    )
    '''

    if model == "unet":
        model = models.get_model_unet(input_size=patch_size, kernel_size=(3, 3))
    opt = Adam(lr=learning_rate, epsilon=10 ** -8, amsgrad=True)
    model.compile(optimizer=opt, loss=loss)

    history = model.fit_generator(
        generator=train_gen,
        epochs=epochs,
        callbacks=callbacks,
        workers=4,
        use_multiprocessing=True
    )
    return model

"""
MIT License

Copyright (c) 2019 Max Planck Institute of Molecular Physiology

Author: <NAME> (<EMAIL>)
Author: <NAME> (<EMAIL>)
Author: <NAME> (<EMAIL>)

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""

from . import models
from . import patch_pair_generator as gen
from . import utils
from keras.optimizers import Adam
import os
from . import utils
import mrcfile
import tifffile
import numpy as np

def train(
    even_path,
    odd_path,
    model_out_path,
    movie_path=None,
    learning_rate=0.001,
    epochs=50,
    model="unet",
    patch_size=(1024, 1024),
    batch_size=4,
    loss="mae",
    fbinning=utils.fourier_binning
):
    '''
    Does the complete noise2noise training and writes the model file to disk.
    :param even_path: Path where "even averages" will be written.
    :param odd_path: Path here "odd averages" will be written
    :param model_out_path: Filepath where model will be written.
    :param movie_path: Path to movie files. Supported are .mrc, .mrcs, .tiff and .tif.
    :param learning_rate: Learning rate used during training.
    :param epochs: Number of epochs to train the network
    :param model: Model indentifier. Right now only "unet" is supported.
    :param patch_size: Patch size in pixel. The network is trained on random patches of the images.
    :param batch_size: Mini-batch size used during training.
    :return: trained model
    '''

    print("Start training")
    # Read training even/odd micrographs
    trained_model = train_movie_dir(
        even_path=even_path,
        odd_path=odd_path,
        movie_path=movie_path,
        learning_rate=learning_rate,
        epochs=epochs,
        model=model,
        patch_size=patch_size,
        batch_size=batch_size,
        loss=loss,
        fbinning=fbinning
        )
    trained_model.save_weights(model_out_path)
    import h5py
    with h5py.File(model_out_path, mode='r+') as f:
        f["model_name"] = np.array((model), dtype=h5py.special_dtype(vlen=str))
        f["patch_size"] = patch_size

    print("Training done. Weights saved to " + model_out_path)

    return trained_model

def train_movie_dir(
    even_path,
    odd_path,
    movie_path=None,
    learning_rate=0.001,
    epochs=50,
    model="unet",
    patch_size=(1024, 1024),
    batch_size=4,
    loss="mae",
    fbinning=utils.fourier_binning
):
    '''
    Does the complete noise2noise training.
    :param even_path: Path where "even averages" will be written.
    :param odd_path: Path here "odd averages" will be written
    :param model_out_path: Filepath where model will be written.
    :param movie_path: Path to movie files. Supported are .mrc, .mrcs, .tiff and .tif.
    :param learning_rate: Learning rate used during training.
    :param epochs: Number of epochs to train the network
    :param model: Model indentifier. Right now only "unet" is supported.
    :param patch_size: Patch size in pixel. The network is trained on random patches of the images.
    :param batch_size: n
    :param loss: mae or mse are possible
    :return: trained model
    '''

    # Read training even/odd micrographs
    even_files, odd_files = calc_even_odd(
        movie_path, even_path, odd_path, recursive=True,fbinning=fbinning
    )
    trained_model = train_pairs(
        even_files,
        odd_files,
        model=model,
        learning_rate=learning_rate,
        patch_size=patch_size,
        batch_size=batch_size,
        epochs=epochs,
        valid_split=0.1,
        loss=loss
    )
    return trained_model



def calc_even_odd(movie_path,
                  even_path,
                  odd_path,
                  recursive=True,
                  fbinning=utils.fourier_binning):
    """
    Calculates averages based on the even/odd frames of the movies in movie_path and save the
    respective averages in even_path or odd_path.
    :param movie_path: Path to movie files. Supported are .mrc, .mrcs, .tiff and .tif.
    :param even_path: Path where "even averages" will be written.
    :param odd_path: Path here "odd averages" will be written
    :param recursive: If true, the movie_path is scanned recurively for movies.
    :return: Two lists with even filepaths and odd filepaths.
    """

    # Read training even/odd micrographs
    even_files = []
    odd_files = []
    for (dirpath, dirnames, filenames) in os.walk(even_path):
        for filename in filenames:
            if filename.endswith(utils.SUPPORTED_FILES):
                even_files.append(os.path.join(dirpath, filename))

    for (dirpath, dirnames, filenames) in os.walk(odd_path):
        for filename in filenames:
            if filename.endswith(utils.SUPPORTED_FILES):
                odd_files.append(os.path.join(dirpath, filename))

    # Create training data for movies which are not in even/odd directory
    try:
        os.makedirs(even_path)
    except FileExistsError:
        pass
    try:
        os.makedirs(odd_path)
    except FileExistsError:
        pass

    movies_to_split = []
    filenames_even = list(map(os.path.basename, even_files))
    filenames_odd = list(map(os.path.basename, odd_files))
    if movie_path:
        for (dirpath, dirnames, filenames) in os.walk(movie_path):
            for filename in filenames:
                if filename.endswith(utils.SUPPORTED_FILES):
                    if filename not in filenames_even and filename not in filenames_odd:
                        path = os.path.join(dirpath, filename)
                        movies_to_split.append((path,filename))

            if recursive == False:
                break

    for tuble_index, movie_tuble in enumerate(movies_to_split):
        path, filename = movie_tuble
        print("Create even/odd average for:", path, "( Progress: ",int(100*tuble_index/len(movies_to_split)),"% )")
        even, odd = utils.create_image_pair(path,fbinning)
        out_even_path = os.path.join(even_path, filename)
        out_odd_path = os.path.join(odd_path, filename)
        if path.endswith(("mrcs", "mrc")):
            with mrcfile.new(out_even_path, overwrite=True) as mrc:
                mrc.set_data(even)

            with mrcfile.new(out_odd_path, overwrite=True) as mrc:
                mrc.set_data(odd)

        elif path.endswith((".tif", ".tiff")):
            tifffile.imwrite(out_even_path, even)
            tifffile.imwrite(out_odd_path, odd)

        even_files.append(out_even_path)
        odd_files.append(out_odd_path)
        filenames_even.append(filename)
        filenames_odd.append(filename)

    print("Reset progress bar: ( Progress: -1 % )")
    return even_files, odd_files


def train_pairs(
    pair_files_a,
    pari_files_b,
    model="unet",
    learning_rate=0.001,
    epochs=50,
    patch_size=(1024, 1024),
    callbacks=[],
    batch_size=4,
    valid_split=0.1,
    loss="mae"
):
    """
    Training noise2noise model.
    :param pair_files_a: List with paths to the first images of the image pairs.
    :param pari_files_b: List with paths to the second images of the image pairs.
    :param model: Model indentifier. Right now only "unet" is supported.
    :param learning_rate: Learning rate used during training.
    :param epochs: Number of epochs to train the network
    :param patch_size: Patch size in pixel. The network is trained on random patches of the images.
    :param callbacks: Optional callbacks during training. See Keras callbacks for more information.
    :param batch_size: Mini-batch size used during training
    :param valid_split: training-validion split.
    :return: Trained keras model
    """
    #train_valid_split = int(valid_split * len(pair_files_a))
    train_pair_a_files = pair_files_a
    #valid_pair_a_files = pair_files_a[:train_valid_split]
    train_pair_b_files = pari_files_b
    #valid_pair_b_files = pari_files_b[:train_valid_split]

    train_gen = gen.patch_pair_batch_generator(
        pair_a_images=train_pair_a_files,
        pair_b_images=train_pair_b_files,
        patch_size=patch_size,
        batch_size=batch_size,
        augment=True,
    )

    '''
    valid_gen = gen.patch_pair_batch_generator(
        pair_a_images=valid_pair_a_files,
        pair_b_images=valid_pair_b_files,
        patch_size=patch_size,
        batch_size=batch_size,
    )
    '''

    if model == "unet":
        model = models.get_model_unet(input_size=patch_size, kernel_size=(3, 3))
    opt = Adam(lr=learning_rate, epsilon=10 ** -8, amsgrad=True)
    model.compile(optimizer=opt, loss=loss)

    history = model.fit_generator(
        generator=train_gen,
        epochs=epochs,
        callbacks=callbacks,
        workers=4,
        use_multiprocessing=True
    )
    return model

MIT License  Copyright (c) 2019 Max Planck Institute of Molecular Physiology  Author: <NAME> (<EMAIL>) Author: <NAME> (<EMAIL>) Author: <NAME> (<EMAIL>)  Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:  The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.  THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. Does the complete noise2noise training and writes the model file to disk.     :param even_path: Path where "even averages" will be written.     :param odd_path: Path here "odd averages" will be written     :param model_out_path: Filepath where model will be written.     :param movie_path: Path to movie files. Supported are .mrc, .mrcs, .tiff and .tif.     :param learning_rate: Learning rate used during training.     :param epochs: Number of epochs to train the network     :param model: Model indentifier. Right now only "unet" is supported.     :param patch_size: Patch size in pixel. The network is trained on random patches of the images.     :param batch_size: Mini-batch size used during training.     :return: trained model # Read training even/odd micrographs Does the complete noise2noise training.     :param even_path: Path where "even averages" will be written.     :param odd_path: Path here "odd averages" will be written     :param model_out_path: Filepath where model will be written.     :param movie_path: Path to movie files. Supported are .mrc, .mrcs, .tiff and .tif.     :param learning_rate: Learning rate used during training.     :param epochs: Number of epochs to train the network     :param model: Model indentifier. Right now only "unet" is supported.     :param patch_size: Patch size in pixel. The network is trained on random patches of the images.     :param batch_size: n     :param loss: mae or mse are possible     :return: trained model # Read training even/odd micrographs Calculates averages based on the even/odd frames of the movies in movie_path and save the     respective averages in even_path or odd_path.     :param movie_path: Path to movie files. Supported are .mrc, .mrcs, .tiff and .tif.     :param even_path: Path where "even averages" will be written.     :param odd_path: Path here "odd averages" will be written     :param recursive: If true, the movie_path is scanned recurively for movies.     :return: Two lists with even filepaths and odd filepaths. # Read training even/odd micrographs # Create training data for movies which are not in even/odd directory Training noise2noise model.     :param pair_files_a: List with paths to the first images of the image pairs.     :param pari_files_b: List with paths to the second images of the image pairs.     :param model: Model indentifier. Right now only "unet" is supported.     :param learning_rate: Learning rate used during training.     :param epochs: Number of epochs to train the network     :param patch_size: Patch size in pixel. The network is trained on random patches of the images.     :param callbacks: Optional callbacks during training. See Keras callbacks for more information.     :param batch_size: Mini-batch size used during training     :param valid_split: training-validion split.     :return: Trained keras model #train_valid_split = int(valid_split * len(pair_files_a)) #valid_pair_a_files = pair_files_a[:train_valid_split] #valid_pair_b_files = pari_files_b[:train_valid_split] valid_gen = gen.patch_pair_batch_generator(         pair_a_images=valid_pair_a_files,         pair_b_images=valid_pair_b_files,         patch_size=patch_size,         batch_size=batch_size,     )