# data generator source from https://medium.com/@ensembledme/writing-custom-keras-generators-fe815d992c5a
from skimage.io import imread
import numpy as np

def get_input(path):
    """get specific image from path"""
    img = imread(path)
    return img

def get_output(path, label_file = None):
    """get all the labels relative to the image of path"""
    img_id = path.split('/')[-1]
    labels = label_file.loc[img_id].values
    return labels
def preprocess_input(img):
    # convert between 0 and 1
    return img.astype('float32') / 127.5 -1

def image_generator(files, label_file, batch_size = 32):
    while True:
        batch_paths = np.random.choice(a = files, size = batch_size)
        batch_input = []
        batch_output = []
        for input_path in batch_paths:
            input = get_input(input_path)
            input = preprocess_input(input)
            output = get_output(input_path, label_file = label_file)
            batch_input += [input]
            batch_output += [output]
        batch_x = np.array(batch_input)
        batch_y = np.array(batch_output)
        yield batch_x, batch_y

def auto_encoder_generator(files, batch_size = 32):
    while True:
        batch_paths = np.random.choice(a = files, size = batch_size)
        batch_input = []
        batch_output = []
        for input_path in batch_paths:
            input = get_input(input_path)
            input = preprocess_input(input)
            output = input
            batch_input += [input]
            batch_output += [output]
        batch_x = np.array(batch_input)
        batch_y = np.array(batch_output)
        yield batch_x, batch_y


4769ea207793a6229138a058ce16cc9a55a6db31
<mask token>


def preprocess_input(img):
    return img.astype('float32') / 127.5 - 1


def image_generator(files, label_file, batch_size=32):
    while True:
        batch_paths = np.random.choice(a=files, size=batch_size)
        batch_input = []
        batch_output = []
        for input_path in batch_paths:
            input = get_input(input_path)
            input = preprocess_input(input)
            output = get_output(input_path, label_file=label_file)
            batch_input += [input]
            batch_output += [output]
        batch_x = np.array(batch_input)
        batch_y = np.array(batch_output)
        yield batch_x, batch_y


def auto_encoder_generator(files, batch_size=32):
    while True:
        batch_paths = np.random.choice(a=files, size=batch_size)
        batch_input = []
        batch_output = []
        for input_path in batch_paths:
            input = get_input(input_path)
            input = preprocess_input(input)
            output = input
            batch_input += [input]
            batch_output += [output]
        batch_x = np.array(batch_input)
        batch_y = np.array(batch_output)
        yield batch_x, batch_y

<mask token>


def get_output(path, label_file=None):
    """get all the labels relative to the image of path"""
    img_id = path.split('/')[-1]
    labels = label_file.loc[img_id].values
    return labels


def preprocess_input(img):
    return img.astype('float32') / 127.5 - 1


def image_generator(files, label_file, batch_size=32):
    while True:
        batch_paths = np.random.choice(a=files, size=batch_size)
        batch_input = []
        batch_output = []
        for input_path in batch_paths:
            input = get_input(input_path)
            input = preprocess_input(input)
            output = get_output(input_path, label_file=label_file)
            batch_input += [input]
            batch_output += [output]
        batch_x = np.array(batch_input)
        batch_y = np.array(batch_output)
        yield batch_x, batch_y


def auto_encoder_generator(files, batch_size=32):
    while True:
        batch_paths = np.random.choice(a=files, size=batch_size)
        batch_input = []
        batch_output = []
        for input_path in batch_paths:
            input = get_input(input_path)
            input = preprocess_input(input)
            output = input
            batch_input += [input]
            batch_output += [output]
        batch_x = np.array(batch_input)
        batch_y = np.array(batch_output)
        yield batch_x, batch_y

<mask token>


def get_input(path):
    """get specific image from path"""
    img = imread(path)
    return img


def get_output(path, label_file=None):
    """get all the labels relative to the image of path"""
    img_id = path.split('/')[-1]
    labels = label_file.loc[img_id].values
    return labels


def preprocess_input(img):
    return img.astype('float32') / 127.5 - 1


def image_generator(files, label_file, batch_size=32):
    while True:
        batch_paths = np.random.choice(a=files, size=batch_size)
        batch_input = []
        batch_output = []
        for input_path in batch_paths:
            input = get_input(input_path)
            input = preprocess_input(input)
            output = get_output(input_path, label_file=label_file)
            batch_input += [input]
            batch_output += [output]
        batch_x = np.array(batch_input)
        batch_y = np.array(batch_output)
        yield batch_x, batch_y


def auto_encoder_generator(files, batch_size=32):
    while True:
        batch_paths = np.random.choice(a=files, size=batch_size)
        batch_input = []
        batch_output = []
        for input_path in batch_paths:
            input = get_input(input_path)
            input = preprocess_input(input)
            output = input
            batch_input += [input]
            batch_output += [output]
        batch_x = np.array(batch_input)
        batch_y = np.array(batch_output)
        yield batch_x, batch_y

from skimage.io import imread
import numpy as np


def get_input(path):
    """get specific image from path"""
    img = imread(path)
    return img


def get_output(path, label_file=None):
    """get all the labels relative to the image of path"""
    img_id = path.split('/')[-1]
    labels = label_file.loc[img_id].values
    return labels


def preprocess_input(img):
    return img.astype('float32') / 127.5 - 1


def image_generator(files, label_file, batch_size=32):
    while True:
        batch_paths = np.random.choice(a=files, size=batch_size)
        batch_input = []
        batch_output = []
        for input_path in batch_paths:
            input = get_input(input_path)
            input = preprocess_input(input)
            output = get_output(input_path, label_file=label_file)
            batch_input += [input]
            batch_output += [output]
        batch_x = np.array(batch_input)
        batch_y = np.array(batch_output)
        yield batch_x, batch_y


def auto_encoder_generator(files, batch_size=32):
    while True:
        batch_paths = np.random.choice(a=files, size=batch_size)
        batch_input = []
        batch_output = []
        for input_path in batch_paths:
            input = get_input(input_path)
            input = preprocess_input(input)
            output = input
            batch_input += [input]
            batch_output += [output]
        batch_x = np.array(batch_input)
        batch_y = np.array(batch_output)
        yield batch_x, batch_y

# data generator source from https://medium.com/@ensembledme/writing-custom-keras-generators-fe815d992c5a
from skimage.io import imread
import numpy as np

def get_input(path):
    """get specific image from path"""
    img = imread(path)
    return img

def get_output(path, label_file = None):
    """get all the labels relative to the image of path"""
    img_id = path.split('/')[-1]
    labels = label_file.loc[img_id].values
    return labels
def preprocess_input(img):
    # convert between 0 and 1
    return img.astype('float32') / 127.5 -1

def image_generator(files, label_file, batch_size = 32):
    while True:
        batch_paths = np.random.choice(a = files, size = batch_size)
        batch_input = []
        batch_output = []
        for input_path in batch_paths:
            input = get_input(input_path)
            input = preprocess_input(input)
            output = get_output(input_path, label_file = label_file)
            batch_input += [input]
            batch_output += [output]
        batch_x = np.array(batch_input)
        batch_y = np.array(batch_output)
        yield batch_x, batch_y

def auto_encoder_generator(files, batch_size = 32):
    while True:
        batch_paths = np.random.choice(a = files, size = batch_size)
        batch_input = []
        batch_output = []
        for input_path in batch_paths:
            input = get_input(input_path)
            input = preprocess_input(input)
            output = input
            batch_input += [input]
            batch_output += [output]
        batch_x = np.array(batch_input)
        batch_y = np.array(batch_output)
        yield batch_x, batch_y

