def test_get_ImageDataGenerator(tmpdir):
file_path = tmpdir.join('Image2.jpg')
img = Image.new('RGB', (30, 30), color=(255, 0, 0))
img.save(file_path.strpath)
data_gen = utils.get_ImageDataGenerator(['Image2.jpg'], (32, 24, 3),
file_path.dirname)
assert (isinstance(data_gen, ImageDataGenerator))
x = img_to_array(img, data_format=keras.backend.image_data_format())
x = x.reshape((1, ) + x.shape)
X_batch = data_gen.flow(x).next()
assert X_batch.shape[0] == x.shape[0]
assert X_batch.shape[1:] == x.shape[1:]
assert (X_batch >= 0).all() and (X_batch <= 1).all()
print(c.shape)
return [f, c]
images_train = random.sample(all_image_names, int(len(all_image_names) * 0.9))
images_test = [i for i in all_image_names if i not in set(images_train)]
def couples_generator(images):
for i in images:
yield load_pair(i), labels[i]
def gen_func(images):
return couples_generator(images)
train_couples_len = sum(1 for e in gen_func(images_train))
test_couples_len = sum(1 for e in gen_func(images_test))
data_gen = utils.get_ImageDataGenerator(all_images, input_shape)
train_iterator = utils.CouplesIterator(utils.make_infinite(gen_func, images_train), input_shape, data_gen, BATCH_SIZE)
test_iterator = utils.CouplesIterator(utils.make_infinite(gen_func, images_test), input_shape, data_gen, BATCH_SIZE)
model = network.create(input_shape, args.network)
network.compile(model, args.optimizer)
model.fit_generator(train_iterator, steps_per_epoch=train_couples_len / BATCH_SIZE, epochs=EPOCHS)
score = model.evaluate_generator(test_iterator, steps=test_couples_len / BATCH_SIZE)
print(score)
