IlyaMichlin

image_path = 'path to images'
mask_path = 'path to masks'

training_generator = DataGenerator(train_idx, labels, image_path, mask_path)
validation_generator = DataGenerator(val_idx, labels, image_path, mask_path)

# Design model
model = Sequential()
[...] # Architecture
model.compile()

IlyaMichlin

def __getitem__(self, index):
    'Generate one batch of data'
    # Generate indexes of the batch
    indexes = self.indexes[index*self.batch_size: (index+1)*self.batch_size]
    
    # Find list of IDs
    list_IDs_temp = [self.list_IDs[k] for k in indexes]
    
    # Generate data
    X = self._generate_X(list_IDs_temp)

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def on_epoch_end(self):
    'Updates indexes after each epoch'
    self.indexes = np.arange(len(self.list_IDs))
    if self.shuffle == True:
        np.random.shuffle(self.indexes)

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def __len__(self):
    'Denotes the number of batches per epoch'
    return int(np.floor(len(self.list_IDs) / self.batch_size))

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class DataGenerator(Sequence):
    'Generates data for Keras'
    def __init__(self, list_IDs, labels, image_path, mask_path,
    to_fit=True, batch_size=32, dim=(256,256),
    n_channels=1, n_classes=10, shuffle=True):
        'Initialization'
        self.list_IDs = list_IDs
        self.labels = labels
        self.image_path = image_path
        self.mask_path = mask_path

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model.fit_generator(
    train_generator,
    steps_per_epoch=2000,
    epochs=50,
    validation_data=validation_generator,
    validation_steps=800)

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from tensorflow.keras.preprocessing.image import ImageDataGenerator

train_datagen = ImageDataGenerator(
    rescale=1./255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True)

test_datagen = ImageDataGenerator(rescale=1./255)

IlyaMichlin

import numpy as np
import cv2
from tensorflow.keras.utils import Sequence


class DataGenerator(Sequence):
    """Generates data for Keras
    Sequence based data generator. Suitable for building data generator for training and prediction.
    """
    def __init__(self, list_IDs, labels, image_path, mask_path,