“gerador de imagem 3D personalizado para segmentação” Respostas de código

gerador de imagem 3D personalizado para segmentação

import glob
import os

import keras
import numpy as np
import skimage
from imgaug import augmenters as iaa


class DataGenerator(keras.utils.Sequence):
    """Generates data for Keras"""
    """This structure guarantees that the network will only train once on each sample per epoch"""

    def __init__(self, list_IDs, im_path, label_path, batch_size=4, dim=(128, 128, 128),
                 n_classes=4, shuffle=True, augment=False):
        'Initialization'
        self.dim = dim
        self.batch_size = batch_size
        self.list_IDs = list_IDs
        self.im_path = im_path
        self.label_path = label_path
        self.n_classes = n_classes
        self.shuffle = shuffle
        self.augment = augment
        self.on_epoch_end()

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

    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, y = self.__data_generation(list_IDs_temp)

        return X, y

    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)

    def __data_generation(self, list_IDs_temp):

        if self.augment:
            pass

        if not self.augment:

            X = np.empty([self.batch_size, *self.dim])
            Y = np.empty([self.batch_size, *self.dim, self.n_classes])

            # Generate data
            for i, ID in enumerate(list_IDs_temp):
                img_X = skimage.io.imread(os.path.join(im_path, ID))
                X[i,] = img_X

                img_Y = skimage.io.imread(os.path.join(label_path, ID))
                Y[i,] = keras.utils.to_categorical(img_Y, num_classes=self.n_classes)

            X = X.reshape(self.batch_size, *self.dim, 1)
            return X, Y

params = {'dim': (128, 128, 128),
          'batch_size': 4,
          'im_path': "some/path/for/the/images/",
          'label_path': "some/path/for/the/label_images",
          'n_classes': 4,
          'shuffle': True,
          'augment': True}



partition = {}
im_path = "some/path/for/the/images/"
label_path = "some/path/for/the/label_images/"

images = glob.glob(os.path.join(im_path, "*.tif"))
images_IDs = [name.split("/")[-1] for name in images]

partition['train'] = images_IDs

training_generator = DataGenerator(partition['train'], **params)