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)
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))
