def next(self): batch_idx = self.get_permuted_batch_idx() batch_files = self.files[batch_idx] batch_X = data_util.load_images(batch_files) batch_X = self.process_func(batch_X, (self.mean, self.std), self.testing) batch_y = self.labels[batch_idx] return (batch_X, batch_y)
import numpy as np from time import time import pdb import skimage import matplotlib.pyplot as plt import data_util DATA_DIR = "converted" files = data_util.get_image_files(DATA_DIR) images = data_util.load_images(files) MEAN = data_util.compute_mean(files) STD = data_util.compute_std(files) images_normalized = [] for img in images: img = img - MEAN[:, np.newaxis, np.newaxis] img = img / STD[:, np.newaxis, np.newaxis] images_normalized.append(img) images_normalized = np.array(images_normalized) original_augmented = data_util.parallel_augment(images) normalized_augmented = data_util.parallel_augment(images_normalized) original = images[3] normalized = images_normalized[3] original = original.transpose(1, 2, 0) normalized = normalized.transpose(1, 2, 0)
from time import time import data import data_util from matplotlib import pyplot as plt aug_params = { 'zoom_range': (1 / 1.15, 1.15), 'rotation_range': (0, 360), 'shear_range': (0, 0), 'translation_range': (-40, 40), 'do_flip': True, 'allow_stretch': True, } files = data_util.get_image_files('testing') X = data_util.load_images(files) mean, std = data_util.compute_mean_and_std(files) print(mean, std) print("Number of images: {}".format(len(X))) # start = time() # result = data.batch_perturb_and_augment(X, 500, 500, aug_params=aug_params, sigma=0.5) # end = time() # print("Processing without parallelization took {} seconds".format(end - start)) #start = time() #result = data.parallel_perturb_and_augment(X, 500, 500, aug_params=aug_params, sigma=0.5) #result = data.parallel_perturb_and_augment(X, 500, 500) #end = time() #print("Processing with parallelization took {} seconds".format(end - start))