def test_random_transforms(self):
x = np.random.random((2, 28, 28))
assert image.random_rotation(x, 45).shape == (2, 28, 28)
assert image.random_shift(x, 1, 1).shape == (2, 28, 28)
assert image.random_shear(x, 20).shape == (2, 28, 28)
assert image.random_zoom(x, (5, 5)).shape == (2, 28, 28)
assert image.random_channel_shift(x, 20).shape == (2, 28, 28)
# Test get_random_transform with predefined seed
seed = 1
generator = image.ImageDataGenerator(
rotation_range=90.,
width_shift_range=0.1,
height_shift_range=0.1,
shear_range=0.5,
zoom_range=0.2,
channel_shift_range=0.1,
brightness_range=(1, 5),
horizontal_flip=True,
vertical_flip=True)
transform_dict = generator.get_random_transform(x.shape, seed)
transform_dict2 = generator.get_random_transform(x.shape, seed * 2)
assert transform_dict['theta'] != 0
assert transform_dict['theta'] != transform_dict2['theta']
assert transform_dict['tx'] != 0
assert transform_dict['tx'] != transform_dict2['tx']
assert transform_dict['ty'] != 0
assert transform_dict['ty'] != transform_dict2['ty']
assert transform_dict['shear'] != 0
assert transform_dict['shear'] != transform_dict2['shear']
assert transform_dict['zx'] != 0
assert transform_dict['zx'] != transform_dict2['zx']
assert transform_dict['zy'] != 0
assert transform_dict['zy'] != transform_dict2['zy']
assert transform_dict['channel_shift_intensity'] != 0
assert (transform_dict['channel_shift_intensity'] !=
transform_dict2['channel_shift_intensity'])
assert transform_dict['brightness'] != 0
assert transform_dict['brightness'] != transform_dict2['brightness']
# Test get_random_transform without any randomness
generator = image.ImageDataGenerator()
transform_dict = generator.get_random_transform(x.shape, seed)
assert transform_dict['theta'] == 0
assert transform_dict['tx'] == 0
assert transform_dict['ty'] == 0
assert transform_dict['shear'] == 0
assert transform_dict['zx'] == 1
assert transform_dict['zy'] == 1
assert transform_dict['channel_shift_intensity'] is None
assert transform_dict['brightness'] is None
def get_batch():
index = 1
global current_index
B = np.zeros(shape=(batch_size, IMAGE_SIZE, IMAGE_SIZE, 3))
L = np.zeros(shape=(batch_size))
while index < batch_size:
try:
img = load_img(images[current_index],
target_size=(IMAGE_SIZE, IMAGE_SIZE))
img = img_to_array(img)
img /= 255.
# if cnn == 'ResNet50': # imagenet pretrained
# mean = np.array([0.485, 0.456, 0.406])
# std = np.array([0.229, 0.224, 0.225])
# img = (img - mean)/std
## data augmentation
# random width and height shift
img = random_shift(img, 0.2, 0.2)
# random rotation
img = random_rotation(img, 10)
# random horizental flip
flip_horizontal = (np.random.random() < 0.5)
if flip_horizontal:
img = flip_axis(img, axis=1)
# # random vertical flip
# flip_vertical = (np.random.random() < 0.5)
# if flip_vertical:
# img = flip_axis(img, axis=0)
# #cutout
# eraser = get_random_eraser(v_l=0, v_h=1, pixel_level=False)
# img = eraser(img)
B[index] = img
L[index] = labels[current_index]
index = index + 1
current_index = current_index + 1
except:
traceback.print_exc()
# print("Ignore image {}".format(images[current_index]))
current_index = current_index + 1
# B = np.rollaxis(B, 3, 1)
return B, np_utils.to_categorical(L, num_classes)