def test_resize_img_to_arbitrary_size(img, mask, resize_to):
# Setting up the data
kpts_data = np.array([[0, 0], [0, 2], [2, 2], [2, 0]]).reshape(
(4, 2)) # Top left corner
kpts = slc.Keypoints(kpts_data.copy(), img.shape[0], img.shape[1])
dc = slc.DataContainer((
img,
mask,
kpts,
), "IMP")
transf = slt.Resize(resize_to=resize_to)
res = transf(dc).data
if isinstance(resize_to, int):
resize_to = (resize_to, resize_to)
scale_x = resize_to[0] / img.shape[1]
scale_y = resize_to[1] / img.shape[0]
assert transf.resize_to == resize_to
assert (res[0].shape[0] == resize_to[1]) and (res[0].shape[1]
== resize_to[0])
assert (res[1].shape[0] == resize_to[1]) and (res[1].shape[1]
== resize_to[0])
assert (res[2].height == resize_to[1]) and (res[2].width == resize_to[0])
kpts_data = kpts_data.astype(float)
kpts_data[:, 0] *= scale_x
kpts_data[:, 1] *= scale_y
kpts_data = kpts_data.astype(int)
assert np.array_equal(res[2].data, kpts_data)
def __init__(self):
self.imgaug_transform = iaa.Scale(size=512, interpolation="linear")
self.solt_stream = slc.Stream([slt.Resize(resize_to=(512, 512))])
self.augmentor_op = Operations.Resize(probability=1,
width=512,
height=512,
resample_filter="BILINEAR")
def test_resize_img_to_arbitrary_size(img, mask, resize_to):
# Setting up the data
kpts_data = np.array([[0, 0], [0, 2], [2, 2], [2, 0]]).reshape(
(4, 2)) # Top left corner
kpts = slc.Keypoints(kpts_data.copy(), frame=(img.shape[:2]))
dc = slc.DataContainer((
img,
mask,
kpts,
), "IMP")
transf = slt.Resize(resize_to=resize_to)
res = transf(dc).data
if isinstance(resize_to, int):
resize_to = (resize_to, resize_to)
scales = tuple(resize_to[i] / img.shape[i] for i in range(img.ndim - 1))
assert transf.resize_to == resize_to
np.testing.assert_array_equal(res[0].shape[:-1], resize_to)
np.testing.assert_array_equal(res[1].shape, resize_to)
np.testing.assert_array_equal(res[2].frame, resize_to)
kpts_data = kpts_data.astype(float)
kpts_data = (kpts_data * np.asarray(scales)[None, ])
kpts_data = kpts_data.astype(int)
assert np.array_equal(res[2].data, kpts_data)
def test_different_interpolations_per_item_per_transform(img_6x6, transform_settings):
dc = slc.DataContainer((img_6x6,), "I", transform_settings=transform_settings)
dc_res = slt.Resize(resize_to=(10, 15), interpolation="bilinear")(dc)
interp = ALLOWED_INTERPOLATIONS["bilinear"]
if transform_settings is not None:
interp = ALLOWED_INTERPOLATIONS[transform_settings[0]["interpolation"][0]]
assert np.array_equal(cv2.resize(img_6x6, (10, 15), interpolation=interp).reshape(15, 10, 1), dc_res.data[0],)
def __init__(self):
self.augmentor_pipeline = Pipeline()
self.augmentor_pipeline.add_operation(
Operations.Crop(probability=1, width=64, height=64, centre=False))
self.augmentor_pipeline.add_operation(
Operations.Resize(probability=1,
width=512,
height=512,
resample_filter="BILINEAR"))
self.imgaug_transform = iaa.Sequential([
iaa.CropToFixedSize(width=64, height=64),
iaa.Scale(size=512, interpolation="linear")
])
self.solt_stream = slc.Stream([
slt.Crop(crop_to=(64, 64), crop_mode="r"),
slt.Resize(resize_to=(512, 512))
])
def create_train_transforms(size):
return solt.Stream([
slt.JPEGCompression(p=0.5,quality_range=(60,100)),
slt.Noise(p=0.25),
slt.Brightness(),
slt.Contrast(),
slt.Flip(),
slt.Rotate90(),
solt.SelectiveStream([
slt.GammaCorrection(gamma_range=0.5, p=1),
slt.Noise(gain_range=0.1, p=1),
slt.SaltAndPepper(),
slt.Blur(),
], n=3),
slt.Rotate(angle_range=(-10, 10), p=0.5),
slt.Resize((size,size)),
])
def create_val_transforms(size):
return solt.Stream([slt.Resize((size,size))])
def test_resize_does_not_change_labels():
trf = slt.Resize(resize_to=(5, 5))
dc = slc.DataContainer((1, ), "L")
dc = trf(dc)
assert dc.data[0] == 1
def test_wrong_resize_types(resize_to):
with pytest.raises(TypeError):
slt.Resize(resize_to=resize_to)
