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import os
import shutil
import tempfile
import numpy as np
import pytest
from PIL import Image
from keras_preprocessing.image import image_data_generator
@pytest.fixture(scope='module')
def all_test_images():
img_w = img_h = 20
rgb_images = []
rgba_images = []
gray_images = []
gray_images_16bit = []
gray_images_32bit = []
for n in range(8):
bias = np.random.rand(img_w, img_h, 1) * 64
variance = np.random.rand(img_w, img_h, 1) * (255 - 64)
# RGB
imarray = np.random.rand(img_w, img_h, 3) * variance + bias
im = Image.fromarray(imarray.astype('uint8')).convert('RGB')
rgb_images.append(im)
# RGBA
imarray = np.random.rand(img_w, img_h, 4) * variance + bias
im = Image.fromarray(imarray.astype('uint8')).convert('RGBA')
rgba_images.append(im)
# 8-bit grayscale
imarray = np.random.rand(img_w, img_h, 1) * variance + bias
im = Image.fromarray(imarray.astype('uint8').squeeze()).convert('L')
gray_images.append(im)
# 16-bit grayscale
imarray = np.array(
np.random.randint(-2147483648, 2147483647, (img_w, img_h))
)
im = Image.fromarray(imarray.astype('uint16'))
gray_images_16bit.append(im)
# 32-bit grayscale
im = Image.fromarray(imarray.astype('uint32'))
gray_images_32bit.append(im)
return [rgb_images, rgba_images,
gray_images, gray_images_16bit, gray_images_32bit]
def test_directory_iterator(all_test_images, tmpdir):
num_classes = 2
# create folders and subfolders
paths = []
for cl in range(num_classes):
class_directory = 'class-{}'.format(cl)
classpaths = [
class_directory,
os.path.join(class_directory, 'subfolder-1'),
os.path.join(class_directory, 'subfolder-2'),
os.path.join(class_directory, 'subfolder-1', 'sub-subfolder')
]
for path in classpaths:
tmpdir.join(path).mkdir()
paths.append(classpaths)
# save the images in the paths
count = 0
filenames = []
for test_images in all_test_images:
for im in test_images:
# rotate image class
im_class = count % num_classes
# rotate subfolders
classpaths = paths[im_class]
filename = os.path.join(
classpaths[count % len(classpaths)],
'image-{}.png'.format(count))
filenames.append(filename)
im.save(str(tmpdir / filename))
count += 1
# create iterator
generator = image_data_generator.ImageDataGenerator()
dir_iterator = generator.flow_from_directory(str(tmpdir))
# check number of classes and images
assert len(dir_iterator.class_indices) == num_classes
assert len(dir_iterator.classes) == count
assert set(dir_iterator.filenames) == set(filenames)
# Test invalid use cases
with pytest.raises(ValueError):
generator.flow_from_directory(str(tmpdir), color_mode='cmyk')
with pytest.raises(ValueError):
generator.flow_from_directory(str(tmpdir), class_mode='output')
def preprocessing_function(x):
"""This will fail if not provided by a Numpy array.
Note: This is made to enforce backward compatibility.
"""
assert x.shape == (26, 26, 3)
assert type(x) is np.ndarray
return np.zeros_like(x)
# Test usage as Sequence
generator = image_data_generator.ImageDataGenerator(
preprocessing_function=preprocessing_function)
dir_seq = generator.flow_from_directory(str(tmpdir),
target_size=(26, 26),
color_mode='rgb',
batch_size=3,
class_mode='categorical')
assert len(dir_seq) == np.ceil(count / 3.)
x1, y1 = dir_seq[1]
assert x1.shape == (3, 26, 26, 3)
assert y1.shape == (3, num_classes)
x1, y1 = dir_seq[5]
assert (x1 == 0).all()
with pytest.raises(ValueError):
x1, y1 = dir_seq[14] # there are 40 images and batch size is 3
def test_directory_iterator_class_mode_input(all_test_images, tmpdir):
tmpdir.join('class-1').mkdir()
# save the images in the paths
count = 0
for test_images in all_test_images:
for im in test_images:
filename = str(
tmpdir / 'class-1' / 'image-{}.png'.format(count))
im.save(filename)
count += 1
# create iterator
generator = image_data_generator.ImageDataGenerator()
dir_iterator = generator.flow_from_directory(str(tmpdir),
class_mode='input')
batch = next(dir_iterator)
# check if input and output have the same shape
assert(batch[0].shape == batch[1].shape)
# check if the input and output images are not the same numpy array
input_img = batch[0][0]
output_img = batch[1][0]
output_img[0][0][0] += 1
assert(input_img[0][0][0] != output_img[0][0][0])
@pytest.mark.parametrize('validation_split,num_training', [
(0.25, 30),
(0.50, 20),
(0.75, 10),
])
def test_directory_iterator_with_validation_split(all_test_images,
validation_split,
num_training):
num_classes = 2
tmp_folder = tempfile.mkdtemp(prefix='test_images')
# create folders and subfolders
paths = []
for cl in range(num_classes):
class_directory = 'class-{}'.format(cl)
classpaths = [
class_directory,
os.path.join(class_directory, 'subfolder-1'),
os.path.join(class_directory, 'subfolder-2'),
os.path.join(class_directory, 'subfolder-1', 'sub-subfolder')
]
for path in classpaths:
os.mkdir(os.path.join(tmp_folder, path))
paths.append(classpaths)
# save the images in the paths
count = 0
filenames = []
for test_images in all_test_images:
for im in test_images:
# rotate image class
im_class = count % num_classes
# rotate subfolders
classpaths = paths[im_class]
filename = os.path.join(
classpaths[count % len(classpaths)],
'image-{}.png'.format(count))
filenames.append(filename)
im.save(os.path.join(tmp_folder, filename))
count += 1
# create iterator
generator = image_data_generator.ImageDataGenerator(
validation_split=validation_split
)
with pytest.raises(ValueError):
generator.flow_from_directory(tmp_folder, subset='foo')
train_iterator = generator.flow_from_directory(tmp_folder,
subset='training')
assert train_iterator.samples == num_training
valid_iterator = generator.flow_from_directory(tmp_folder,
subset='validation')
assert valid_iterator.samples == count - num_training
# check number of classes and images
assert len(train_iterator.class_indices) == num_classes
assert len(train_iterator.classes) == num_training
assert len(set(train_iterator.filenames) &
set(filenames)) == num_training
shutil.rmtree(tmp_folder)
if __name__ == '__main__':
pytest.main([__file__])
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