def test_random_proj_and_selective_stream(img_5x5):
img = img_5x5
dc = slc.DataContainer((img, ), "I")
ppl = slt.Projection(
slc.SelectiveStream(
[
slt.Rotate(angle_range=(90, 90), p=0),
slt.Scale(range_y=(0, 0.1), same=True, p=0),
slt.Shear(range_y=(-0.1, 0.1), p=0),
],
n=3,
),
v_range=(0, 0),
)
dc_res = ppl(dc)
assert np.array_equal(dc.data, dc_res.data)
def test_rotate_90_img_mask_keypoints_destructive(img, mask,
transform_settings,
ignore_state):
# Setting up the data
kpts_data = np.array([[0, 0], [0, 2], [2, 2], [2, 0]]).reshape((4, 2))
kpts = slc.Keypoints(kpts_data, frame=(3, 3))
H, W = mask.shape
dc = slc.DataContainer(
(img, mask, kpts, 1),
"IMPL",
transform_settings=copy.deepcopy(transform_settings),
)
# Defining the 90 degrees transform (clockwise)
stream = slt.Rotate(angle_range=(90, 90), p=1, ignore_state=ignore_state)
dc_res = stream(dc)
img_res, _, _ = dc_res[0]
mask_res, _, _ = dc_res[1]
kpts_res, _, _ = dc_res[2]
label_res, _, _ = dc_res[3]
M = cv2.getRotationMatrix2D((W // 2, H // 2), -90, 1)
img_inter = ALLOWED_INTERPOLATIONS["bicubic"]
img_pad = ALLOWED_PADDINGS["z"]
if transform_settings is not None:
img_inter = ALLOWED_INTERPOLATIONS[transform_settings[0]
["interpolation"]]
img_pad = ALLOWED_PADDINGS[transform_settings[0]["padding"]]
expected_img_res = cv2.warpAffine(img,
M, (W, H),
flags=img_inter,
borderMode=img_pad).reshape((H, W, 1))
expected_mask_res = cv2.warpAffine(mask, M, (W, H))
expected_kpts_res = np.array([[2, 0], [0, 0], [0, 2], [2, 2]]).reshape(
(4, 2))
assert np.array_equal(expected_img_res, img_res)
assert np.array_equal(expected_mask_res, mask_res)
np.testing.assert_array_almost_equal(expected_kpts_res, kpts_res.data)
assert label_res == 1
],
}
},
}
},
]
}
},
slc.Stream([
slc.Stream([slt.Pad(34),
slt.Crop(32, "r"),
slt.CutOut(2)]),
slt.Pad(34),
slt.Crop(32, "c"),
slt.CutOut(4),
slt.Projection(slc.Stream([slt.Rotate(30),
slt.Scale(2)]),
v_range=(0, 1e-3)),
]),
],
],
)
def test_deserialize_from_dict_nested(serialized: dict, stream: slc.Stream):
serialized_trfs = json.dumps(stream.to_dict())
serialized_from_deserialized = json.dumps(
slu.from_dict(serialized).to_dict())
assert serialized_trfs == serialized_from_deserialized
def test_stream_serializes_all_args_are_set():
def test_projection_empty_sampling_low_prob_trf(img_6x6_rgb):
dc = slc.DataContainer(img_6x6_rgb, "I")
trf = slt.Projection(affine_transforms=slc.Stream([slt.Rotate(p=0)]), p=1)
trf.sample_transform(dc)
assert np.array_equal(trf.state_dict["transform_matrix"], np.eye(3))
def test_rotate_range_conversion_from_number(angle):
trf = slt.Rotate(angle_range=angle)
assert trf.angle_range == (-angle, angle)
def test_rotate_range_none():
trf = slt.Rotate(None)
assert trf.angle_range == (0, 0)
def test_fusion_rotate_360_flip_rotate_360(img_5x5):
img = img_5x5
dc = slc.DataContainer((img, ), 'I')
ppl = slc.Stream([
slc.Stream([
slt.Rotate((45, 45), padding='z', p=1),
slt.Rotate((45, 45), padding='z', p=1),
slt.Rotate((45, 45), padding='z', p=1),
slt.Rotate((45, 45), padding='z', p=1),
slt.Rotate((45, 45), padding='z', p=1),
slt.Rotate((45, 45), padding='z', p=1),
slt.Rotate((45, 45), padding='z', p=1),
slt.Rotate((45, 45), padding='z', p=1),
],
optimize_stack=True),
slt.Flip(p=1, axis=1),
slc.Stream([
slt.Rotate((45, 45), padding='z', p=1),
slt.Rotate((45, 45), padding='z', p=1),
slt.Rotate((45, 45), padding='z', p=1),
slt.Rotate((45, 45), padding='z', p=1),
slt.Rotate((45, 45), padding='z', p=1),
slt.Rotate((45, 45), padding='z', p=1),
slt.Rotate((45, 45), padding='z', p=1),
slt.Rotate((45, 45), padding='z', p=1),
],
optimize_stack=True)
])
img_res = ppl(dc, return_torch=False)[0][0]
np.testing.assert_array_almost_equal(
cv2.flip(img, 1).reshape(5, 5, 1), img_res)
def test_stream_with_zero_prob_transform(img, mask):
dc = slc.DataContainer((img, mask, 1), "IML")
stream = slc.Stream([slt.Rotate(p=0)])
res = stream(dc, return_torch=False)
assert dc == res
