def test_smart_resize(self):
test_input = np.random.random((20, 40, 3))
output = image_utils.smart_resize(test_input, size=(50, 50))
self.assertIsInstance(output, np.ndarray)
self.assertListEqual(list(output.shape), [50, 50, 3])
output = image_utils.smart_resize(test_input, size=(10, 10))
self.assertListEqual(list(output.shape), [10, 10, 3])
output = image_utils.smart_resize(test_input, size=(100, 50))
self.assertListEqual(list(output.shape), [100, 50, 3])
output = image_utils.smart_resize(test_input, size=(5, 15))
self.assertListEqual(list(output.shape), [5, 15, 3])
def test_smart_resize_tf_dataset(self, size):
test_input_np = np.random.random((2, 20, 40, 3))
test_ds = tf.data.Dataset.from_tensor_slices(test_input_np)
resize = lambda img: image_utils.smart_resize(img, size=size)
test_ds = test_ds.map(resize)
for sample in test_ds.as_numpy_iterator():
self.assertIsInstance(sample, np.ndarray)
self.assertListEqual(list(sample.shape), [size[0], size[1], 3])
def test_smart_resize_errors(self):
with self.assertRaisesRegex(ValueError, 'a tuple of 2 integers'):
image_utils.smart_resize(np.random.random((20, 20, 2)), size=(10, 5, 3))
with self.assertRaisesRegex(ValueError, 'incorrect rank'):
image_utils.smart_resize(np.random.random((2, 4)), size=(10, 5))
with self.assertRaisesRegex(ValueError, 'incorrect rank'):
image_utils.smart_resize(np.random.random((2, 4, 4, 5, 3)), size=(10, 5))
def load_image(
path, image_size, num_channels, interpolation, crop_to_aspect_ratio=False
):
"""Load an image from a path and resize it."""
img = tf.io.read_file(path)
img = tf.image.decode_image(
img, channels=num_channels, expand_animations=False
)
if crop_to_aspect_ratio:
img = image_utils.smart_resize(
img, image_size, interpolation=interpolation
)
else:
img = tf.image.resize(img, image_size, method=interpolation)
img.set_shape((image_size[0], image_size[1], num_channels))
return img
def test_smart_resize_batch(self):
img = np.random.random((2, 20, 40, 3))
out = image_utils.smart_resize(img, size=(20, 20))
self.assertListEqual(list(out.shape), [2, 20, 20, 3])
self.assertAllClose(out, img[:, :, 10:-10, :])
