For the label_images (which have the same dimensions and are single channel coded (value 1 = label 1, value 2 = label 2, value 3 = label 3 and value 0 = label 4 or background)) I get a binary representation of the labels with the to_categorical() function from keras and end up with a 5D, e.g. My images have the size (128, 128, 128) and when I load them in I get a 5D tensor of size (batch_size, depth, heigt, width, number_of_channels), e.g. Training_generator = DataGenerator(partition, **params) Images = glob.glob(os.path.join(im_path, "*.tif")) Label_path = "some/path/for/the/label_images/" 'label_path': "some/path/for/the/label_images", X = X.reshape(self.batch_size, *self.dim, 1) Y = _categorical(img_Y, num_classes=self.n_classes) Img_Y = skimage.io.imread(os.path.join(label_path, ID)) Img_X = skimage.io.imread(os.path.join(im_path, ID)) Self.indexes = np.arange(len(self.list_IDs))ĭef _data_generation(self, list_IDs_temp): X, y = self._data_generation(list_IDs_temp) Return int(np.floor(len(self.list_IDs) / self.batch_size)) 'Denotes the number of batches per epoch' N_classes=4, shuffle=True, augment=False): """This structure guarantees that the network will only train once on each sample per epoch"""ĭef _init_(self, list_IDs, im_path, label_path, batch_size=4, dim=(128, 128, 128),
KERAS DATA AUGMENTATION 3D GENERATOR
Since the ImageDataGenerator by keras is not suitable for 3D volumes, I started to write my own generator for keras (semantic segmentation, not classification!).ġ) If there is anybody out there that has adapted the ImageDataGenerator code to work with 3D volumes, please share it! This guy has done it for videos.Ģ) According to this tutorial I wrote a custom generator.Ĭlass DataGenerator(): imshow ( image ) # Displaying the figure pyplot. astype ( 'uint8' ) # Plotting the data pyplot. next () # Remember to convert these images to unsigned integers for viewing image = batch. subplot ( 330 + 1 + i ) # generating images in batches batch = it. flow ( samples, batch_size = 1 ) # Preparing the Samples and Plot for displaying output for i in range ( 9 ): # preparing the subplot pyplot. datagen = ImageDataGenerator ( rotation_range = 90 ) # Creating an iterator for data augmentation it = datagen. # Importing the required libraries from numpy import expand_dims from import load_img from import img_to_array from import ImageDataGenerator from matplotlib import pyplot # Loading desired images img = load_img ( 'Car.jpg' ) # For processing, we are converting the image(s) to an array data = img_to_array ( img ) # Expanding dimension to one sample samples = expand_dims ( data, 0 ) # Calling ImageDataGenerator for creating data augmentation generator. There are mainly five different techniques for applying image augmentation, we will discuss these techniques in the coming section. And it does all this with better memory management so that you can train a huge dataset efficiently with lesser memory consumption. But here ImageDataGenerator takes care of this automatically during the training phase. Then in that case we would have to manually generate the augmented image as a preprocessing step and include them in our training dataset. To appreciate this Keras capability of image data generator we need to imagine if this class was not present. This simply means it can generate augmented images dynamically during the training of the model making the overall mode more robust and accurate. The major advantage of the Keras ImageDataGenerator class is its ability to produce real-time image augmentation. The ImageDataGenerator class in Keras is used for implementing image augmentation. What is Image Data Generator (ImageDataGenerator) in Keras?
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