import numpy as np import pytest from PIL import Image from keras_preprocessing.image import image_data_generator from keras_preprocessing.image import utils @pytest.fixture(scope='module') def all_test_images(): img_w = img_h = 20 rgb_images = [] rgba_images = [] gray_images = [] 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) imarray = np.random.rand(img_w, img_h, 3) * variance + bias im = Image.fromarray(imarray.astype('uint8')).convert('RGB') rgb_images.append(im) imarray = np.random.rand(img_w, img_h, 4) * variance + bias im = Image.fromarray(imarray.astype('uint8')).convert('RGBA') rgba_images.append(im) imarray = np.random.rand(img_w, img_h, 1) * variance + bias im = Image.fromarray( imarray.astype('uint8').squeeze()).convert('L') gray_images.append(im) return [rgb_images, rgba_images, gray_images] def test_image_data_generator(all_test_images): for test_images in all_test_images: img_list = [] for im in test_images: img_list.append(utils.img_to_array(im)[None, ...]) image_data_generator.ImageDataGenerator( featurewise_center=True, samplewise_center=True, featurewise_std_normalization=True, samplewise_std_normalization=True, zca_whitening=True, 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., brightness_range=(1, 5), fill_mode='nearest', cval=0.5, horizontal_flip=True, vertical_flip=True, interpolation_order=1 ) def test_image_data_generator_with_validation_split(all_test_images): for test_images in all_test_images: img_list = [] for im in test_images: img_list.append(utils.img_to_array(im)[None, ...]) images = np.vstack(img_list) labels = np.concatenate([ np.zeros((int(len(images) / 2),)), np.ones((int(len(images) / 2),))]) generator = image_data_generator.ImageDataGenerator(validation_split=0.5) # training and validation sets would have different # number of classes, because labels are sorted with pytest.raises(ValueError, match='Training and validation subsets ' 'have different number of classes after ' 'the split.*'): generator.flow(images, labels, shuffle=False, batch_size=10, subset='validation') labels = np.concatenate([ np.zeros((int(len(images) / 4),)), np.ones((int(len(images) / 4),)), np.zeros((int(len(images) / 4),)), np.ones((int(len(images) / 4),)) ]) seq = generator.flow(images, labels, shuffle=False, batch_size=10, subset='validation') x, y = seq[0] assert 2 == len(np.unique(y)) seq = generator.flow(images, labels, shuffle=False, batch_size=10, subset='training') x2, y2 = seq[0] assert 2 == len(np.unique(y2)) with pytest.raises(ValueError): generator.flow(images, np.arange(images.shape[0]), shuffle=False, batch_size=3, subset='foo') def test_image_data_generator_with_split_value_error(): with pytest.raises(ValueError): image_data_generator.ImageDataGenerator(validation_split=5) def test_image_data_generator_invalid_data(): generator = image_data_generator.ImageDataGenerator( featurewise_center=True, samplewise_center=True, featurewise_std_normalization=True, samplewise_std_normalization=True, zca_whitening=True, data_format='channels_last') # Test fit with invalid data with pytest.raises(ValueError): x = np.random.random((3, 10, 10)) generator.fit(x) # Test flow with invalid data with pytest.raises(ValueError): x = np.random.random((32, 10, 10)) generator.flow(np.arange(x.shape[0])) def test_image_data_generator_fit(): generator = image_data_generator.ImageDataGenerator( featurewise_center=True, samplewise_center=True, featurewise_std_normalization=True, samplewise_std_normalization=True, zca_whitening=True, rotation_range=90., width_shift_range=0.1, height_shift_range=0.1, shear_range=0.5, zoom_range=(0.2, 0.2), channel_shift_range=0., brightness_range=(1, 5), fill_mode='nearest', cval=0.5, horizontal_flip=True, vertical_flip=True, interpolation_order=1, data_format='channels_last' ) x = np.random.random((32, 10, 10, 3)) generator.fit(x, augment=True) # Test grayscale x = np.random.random((32, 10, 10, 1)) generator.fit(x) # Test RBG x = np.random.random((32, 10, 10, 3)) generator.fit(x) # Test more samples than dims x = np.random.random((32, 4, 4, 1)) generator.fit(x) generator = image_data_generator.ImageDataGenerator( featurewise_center=True, samplewise_center=True, featurewise_std_normalization=True, samplewise_std_normalization=True, zca_whitening=True, rotation_range=90., width_shift_range=0.1, height_shift_range=0.1, shear_range=0.5, zoom_range=(0.2, 0.2), channel_shift_range=0., brightness_range=(1, 5), fill_mode='nearest', cval=0.5, horizontal_flip=True, vertical_flip=True, interpolation_order=1, data_format='channels_first' ) x = np.random.random((32, 10, 10, 3)) generator.fit(x, augment=True) # Test grayscale x = np.random.random((32, 1, 10, 10)) generator.fit(x) # Test RBG x = np.random.random((32, 3, 10, 10)) generator.fit(x) # Test more samples than dims x = np.random.random((32, 1, 4, 4)) generator.fit(x) def test_image_data_generator_flow(all_test_images, tmpdir): for test_images in all_test_images: img_list = [] for im in test_images: img_list.append(utils.img_to_array(im)[None, ...]) images = np.vstack(img_list) dsize = images.shape[0] generator = image_data_generator.ImageDataGenerator( featurewise_center=True, samplewise_center=True, featurewise_std_normalization=True, samplewise_std_normalization=True, zca_whitening=True, 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., brightness_range=(1, 5), fill_mode='nearest', cval=0.5, horizontal_flip=True, vertical_flip=True, interpolation_order=1 ) generator.flow( images, np.arange(images.shape[0]), shuffle=False, save_to_dir=str(tmpdir), batch_size=3 ) generator.flow( images, np.arange(images.shape[0]), shuffle=False, sample_weight=np.arange(images.shape[0]) + 1, save_to_dir=str(tmpdir), batch_size=3 ) # Test with `shuffle=True` generator.flow( images, np.arange(images.shape[0]), shuffle=True, save_to_dir=str(tmpdir), batch_size=3, seed=42 ) # Test without y generator.flow( images, None, shuffle=True, save_to_dir=str(tmpdir), batch_size=3 ) # Test with a single miscellaneous input data array x_misc1 = np.random.random(dsize) generator.flow( (images, x_misc1), np.arange(dsize), shuffle=False, batch_size=2 ) # Test with two miscellaneous inputs x_misc2 = np.random.random((dsize, 3, 3)) generator.flow( (images, [x_misc1, x_misc2]), np.arange(dsize), shuffle=False, batch_size=2 ) # Test cases with `y = None` generator.flow(images, None, batch_size=3) generator.flow((images, x_misc1), None, batch_size=3, shuffle=False) generator.flow( (images, [x_misc1, x_misc2]), None, batch_size=3, shuffle=False ) generator = image_data_generator.ImageDataGenerator(validation_split=0.2) generator.flow(images, batch_size=3) # Test some failure cases: x_misc_err = np.random.random((dsize + 1, 3, 3)) with pytest.raises(ValueError) as e_info: generator.flow((images, x_misc_err), np.arange(dsize), batch_size=3) assert str(e_info.value).find('All of the arrays in') != -1 with pytest.raises(ValueError) as e_info: generator.flow((images, x_misc1), np.arange(dsize + 1), batch_size=3) assert str(e_info.value).find('`x` (images tensor) and `y` (labels) ') != -1 # Test `flow` behavior as Sequence generator.flow( images, np.arange(images.shape[0]), shuffle=False, save_to_dir=str(tmpdir), batch_size=3 ) # Test with `shuffle=True` generator.flow( images, np.arange(images.shape[0]), shuffle=True, save_to_dir=str(tmpdir), batch_size=3, seed=123 ) # test order_interpolation labels = np.array([[2, 2, 0, 2, 2], [1, 3, 2, 3, 1], [2, 1, 0, 1, 2], [3, 1, 0, 2, 0], [3, 1, 3, 2, 1]]) label_generator = image_data_generator.ImageDataGenerator( rotation_range=90., interpolation_order=0 ) label_generator.flow( x=labels[np.newaxis, ..., np.newaxis], seed=123 ) def test_valid_args(): with pytest.raises(ValueError): image_data_generator.ImageDataGenerator(brightness_range=0.1) def test_batch_standardize(all_test_images): # ImageDataGenerator.standardize should work on batches for test_images in all_test_images: img_list = [] for im in test_images: img_list.append(utils.img_to_array(im)[None, ...]) images = np.vstack(img_list) generator = image_data_generator.ImageDataGenerator( featurewise_center=True, samplewise_center=True, featurewise_std_normalization=True, samplewise_std_normalization=True, zca_whitening=True, 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., brightness_range=(1, 5), fill_mode='nearest', cval=0.5, horizontal_flip=True, vertical_flip=True) generator.fit(images, augment=True) transformed = np.copy(images) for i, im in enumerate(transformed): transformed[i] = generator.random_transform(im) transformed = generator.standardize(transformed) def test_deterministic_transform(): x = np.ones((32, 32, 3)) generator = image_data_generator.ImageDataGenerator( rotation_range=90, fill_mode='constant') x = np.random.random((32, 32, 3)) assert np.allclose(generator.apply_transform(x, {'flip_vertical': True}), x[::-1, :, :]) assert np.allclose(generator.apply_transform(x, {'flip_horizontal': True}), x[:, ::-1, :]) x = np.ones((3, 3, 3)) x_rotated = np.array([[[0., 0., 0.], [0., 0., 0.], [1., 1., 1.]], [[0., 0., 0.], [1., 1., 1.], [1., 1., 1.]], [[0., 0., 0.], [0., 0., 0.], [1., 1., 1.]]]) assert np.allclose(generator.apply_transform(x, {'theta': 45}), x_rotated) def test_random_transforms(): x = np.random.random((2, 28, 28)) # Test get_random_transform with predefined seed seed = 1 generator = image_data_generator.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_data_generator.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 test_fit_rescale(all_test_images): rescale = 1. / 255 for test_images in all_test_images: img_list = [] for im in test_images: img_list.append(utils.img_to_array(im)[None, ...]) images = np.vstack(img_list) # featurewise_center test generator = image_data_generator.ImageDataGenerator( rescale=rescale, featurewise_center=True, dtype='float64') generator.fit(images) batch = generator.flow(images, batch_size=8).next() assert abs(np.mean(batch)) < 1e-6 # featurewise_std_normalization test generator = image_data_generator.ImageDataGenerator( rescale=rescale, featurewise_center=True, featurewise_std_normalization=True, dtype='float64') generator.fit(images) batch = generator.flow(images, batch_size=8).next() assert abs(np.mean(batch)) < 1e-6 assert abs(1 - np.std(batch)) < 1e-5 # zca_whitening test generator = image_data_generator.ImageDataGenerator( rescale=rescale, featurewise_center=True, zca_whitening=True, dtype='float64') generator.fit(images) batch = generator.flow(images, batch_size=8).next() batch = np.reshape(batch, (batch.shape[0], batch.shape[1] * batch.shape[2] * batch.shape[3])) # Y * Y_T = n * I, where Y = W * X identity = np.dot(batch, batch.T) / batch.shape[0] assert ((np.abs(identity) - np.identity(identity.shape[0])) < 1e-6).all() if __name__ == '__main__': pytest.main([__file__])