def test_center_crop(target):
img = np.array([[1, 1, 1, 1], [0, 1, 1, 1], [0, 0, 1, 1], [0, 0, 0, 1]],
dtype=np.uint8)
expected = np.array([[1, 1], [0, 1]], dtype=np.uint8)
img, expected = convert_2d_to_target_format([img, expected], target=target)
cropped_img = A.center_crop(img, 2, 2)
assert np.array_equal(cropped_img, expected)
def test_shift_y_float_from_shift_scale_rotate(target):
img = np.array(
[
[0.01, 0.02, 0.03, 0.04],
[0.05, 0.06, 0.07, 0.08],
[0.09, 0.10, 0.11, 0.12],
[0.13, 0.14, 0.15, 0.16],
],
dtype=np.float32,
)
expected = np.array(
[
[0.00, 0.00, 0.00, 0.00],
[0.00, 0.00, 0.00, 0.00],
[0.01, 0.02, 0.03, 0.04],
[0.05, 0.06, 0.07, 0.08],
],
dtype=np.float32,
)
img, expected = convert_2d_to_target_format([img, expected], target=target)
shifted_along_y_img = F.shift_scale_rotate(
img,
angle=0,
scale=1,
dx=0,
dy=0.5,
interpolation=cv2.INTER_NEAREST,
border_mode=cv2.BORDER_CONSTANT,
)
assert_array_almost_equal_nulp(shifted_along_y_img, expected)
def test_rot90_float(target):
img = np.array([[0.0, 0.0, 0.4], [0.0, 0.0, 0.4], [0.0, 0.0, 0.4]],
dtype=np.float32)
expected = np.array([[0.4, 0.4, 0.4], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
dtype=np.float32)
img, expected = convert_2d_to_target_format([img, expected], target=target)
rotated = F.rot90(img, factor=1)
assert_array_almost_equal_nulp(rotated, expected)
def test_pad(target):
img = np.array([[1, 2], [3, 4]], dtype=np.uint8)
expected = np.array(
[[4, 3, 4, 3], [2, 1, 2, 1], [4, 3, 4, 3], [2, 1, 2, 1]],
dtype=np.uint8)
img, expected = convert_2d_to_target_format([img, expected], target=target)
padded = F.pad(img, min_height=4, min_width=4)
assert np.array_equal(padded, expected)
def test_pad_float(target):
img = np.array([[0.1, 0.2], [0.3, 0.4]], dtype=np.float32)
expected = np.array([[0.4, 0.3, 0.4, 0.3], [0.2, 0.1, 0.2, 0.1],
[0.4, 0.3, 0.4, 0.3], [0.2, 0.1, 0.2, 0.1]],
dtype=np.float32)
img, expected = convert_2d_to_target_format([img, expected], target=target)
padded_img = F.pad(img, min_height=4, min_width=4)
assert_array_almost_equal_nulp(padded_img, expected)
def test_hflip_float(target):
img = np.array([[0.4, 0.4, 0.4], [0.0, 0.4, 0.4], [0.0, 0.0, 0.4]],
dtype=np.float32)
expected = np.array([[0.4, 0.4, 0.4], [0.4, 0.4, 0.0], [0.4, 0.0, 0.0]],
dtype=np.float32)
img, expected = convert_2d_to_target_format([img, expected], target=target)
flipped_img = F.hflip(img)
assert_array_almost_equal_nulp(flipped_img, expected)
def test_center_crop_float(target):
img = np.array([[0.4, 0.4, 0.4, 0.4], [0.0, 0.4, 0.4, 0.4],
[0.0, 0.0, 0.4, 0.4], [0.0, 0.0, 0.0, 0.4]],
dtype=np.float32)
expected = np.array([[0.4, 0.4], [0.0, 0.4]], dtype=np.float32)
img, expected = convert_2d_to_target_format([img, expected], target=target)
cropped_img = A.center_crop(img, 2, 2)
assert_array_almost_equal_nulp(cropped_img, expected)
def test_longest_max_size(target):
img = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]],
dtype=np.uint8)
expected = np.array([[2, 3], [6, 7], [10, 11]], dtype=np.uint8)
img, expected = convert_2d_to_target_format([img, expected], target=target)
scaled = FGeometric.longest_max_size(img,
max_size=3,
interpolation=cv2.INTER_LINEAR)
assert np.array_equal(scaled, expected)
def test_resize_different_height_and_width(target):
img = np.ones((100, 100), dtype=np.uint8)
img = convert_2d_to_target_format([img], target=target)
resized_img = FGeometric.resize(img, height=20, width=30)
height, width = resized_img.shape[:2]
assert height == 20
assert width == 30
if target == "image":
num_channels = resized_img.shape[2]
assert num_channels == 3
def test_resize_nearest_interpolation(target):
img = np.array([[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
dtype=np.uint8)
expected = np.array([[1, 1], [3, 3]], dtype=np.uint8)
img, expected = convert_2d_to_target_format([img, expected], target=target)
resized_img = FGeometric.resize(img, 2, 2, interpolation=cv2.INTER_NEAREST)
height, width = resized_img.shape[:2]
assert height == 2
assert width == 2
assert np.array_equal(resized_img, expected)
def test_resize_default_interpolation(target):
img = np.array([[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
dtype=np.uint8)
expected = np.array([[2, 2], [4, 4]], dtype=np.uint8)
img, expected = convert_2d_to_target_format([img, expected], target=target)
resized_img = F.resize(img, 2, 2)
height, width = resized_img.shape[:2]
assert height == 2
assert width == 2
assert np.array_equal(resized_img, expected)
def test_resize_default_interpolation_float(target):
img = np.array([[0.1, 0.1, 0.1, 0.1], [0.2, 0.2, 0.2, 0.2],
[0.3, 0.3, 0.3, 0.3], [0.4, 0.4, 0.4, 0.4]],
dtype=np.float32)
expected = np.array([[0.15, 0.15], [0.35, 0.35]], dtype=np.float32)
img, expected = convert_2d_to_target_format([img, expected], target=target)
resized_img = FGeometric.resize(img, 2, 2)
height, width = resized_img.shape[:2]
assert height == 2
assert width == 2
assert_array_almost_equal_nulp(resized_img, expected)
def test_resize_nearest_interpolation_float(target):
img = np.array([[0.1, 0.1, 0.1, 0.1], [0.2, 0.2, 0.2, 0.2],
[0.3, 0.3, 0.3, 0.3], [0.4, 0.4, 0.4, 0.4]],
dtype=np.float32)
expected = np.array([[0.1, 0.1], [0.3, 0.3]], dtype=np.float32)
img, expected = convert_2d_to_target_format([img, expected], target=target)
resized_img = FGeometric.resize(img, 2, 2, interpolation=cv2.INTER_NEAREST)
height, width = resized_img.shape[:2]
assert height == 2
assert width == 2
assert np.array_equal(resized_img, expected)
def test_smallest_max_size(target):
img = np.array([[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12],
[12, 13, 14, 15, 16, 17], [18, 19, 20, 21, 22, 23]],
dtype=np.uint8)
expected = np.array([[2, 4, 5, 7], [10, 11, 13, 14], [17, 19, 20, 22]],
dtype=np.uint8)
img, expected = convert_2d_to_target_format([img, expected], target=target)
scaled = FGeometric.smallest_max_size(img,
max_size=3,
interpolation=cv2.INTER_LINEAR)
assert np.array_equal(scaled, expected)
def test_random_crop(target):
img = np.array(
[[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16]],
dtype=np.uint8)
expected = np.array([[5, 6], [9, 10]], dtype=np.uint8)
img, expected = convert_2d_to_target_format([img, expected], target=target)
cropped_img = A.random_crop(img,
crop_height=2,
crop_width=2,
h_start=0.5,
w_start=0)
assert np.array_equal(cropped_img, expected)
def test_random_crop_float(target):
img = np.array(
[[0.01, 0.02, 0.03, 0.04], [0.05, 0.06, 0.07, 0.08],
[0.09, 0.10, 0.11, 0.12], [0.13, 0.14, 0.15, 0.16]],
dtype=np.float32,
)
expected = np.array([[0.05, 0.06], [0.09, 0.10]], dtype=np.float32)
img, expected = convert_2d_to_target_format([img, expected], target=target)
cropped_img = A.random_crop(img,
crop_height=2,
crop_width=2,
h_start=0.5,
w_start=0)
assert_array_almost_equal_nulp(cropped_img, expected)
def test_shift_x_from_shift_scale_rotate(target):
img = np.array(
[[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16]],
dtype=np.uint8)
expected = np.array(
[[0, 0, 1, 2], [0, 0, 5, 6], [0, 0, 9, 10], [0, 0, 13, 14]],
dtype=np.uint8)
img, expected = convert_2d_to_target_format([img, expected], target=target)
shifted_along_x_img = F.shift_scale_rotate(img,
angle=0,
scale=1,
dx=0.5,
dy=0,
interpolation=cv2.INTER_NEAREST,
border_mode=cv2.BORDER_CONSTANT)
assert np.array_equal(shifted_along_x_img, expected)
def test_scale_from_shift_scale_rotate(target):
img = np.array(
[[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16]],
dtype=np.uint8)
expected = np.array(
[[6, 7, 7, 8], [10, 11, 11, 12], [10, 11, 11, 12], [14, 15, 15, 16]],
dtype=np.uint8)
img, expected = convert_2d_to_target_format([img, expected], target=target)
scaled_img = FGeometric.shift_scale_rotate(img,
angle=0,
scale=2,
dx=0,
dy=0,
interpolation=cv2.INTER_NEAREST,
border_mode=cv2.BORDER_CONSTANT)
assert np.array_equal(scaled_img, expected)
def test_scale(target):
img = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]],
dtype=np.uint8)
expected = np.array(
[
[1, 1, 2, 2, 3, 3],
[2, 2, 2, 3, 3, 4],
[3, 3, 4, 4, 5, 5],
[5, 5, 5, 6, 6, 7],
[6, 6, 7, 7, 8, 8],
[8, 8, 8, 9, 9, 10],
[9, 9, 10, 10, 11, 11],
[10, 10, 11, 11, 12, 12],
],
dtype=np.uint8,
)
img, expected = convert_2d_to_target_format([img, expected], target=target)
scaled = FGeometric.scale(img, scale=2, interpolation=cv2.INTER_LINEAR)
assert np.array_equal(scaled, expected)
def test_random_flip_float(code, func, target):
img = np.array([[0.4, 0.4, 0.4], [0.0, 0.4, 0.4], [0.0, 0.0, 0.4]],
dtype=np.float32)
img = convert_2d_to_target_format([img], target=target)
assert_array_almost_equal_nulp(F.random_flip(img, code), func(img))
def test_random_flip(code, func, target):
img = np.array([[1, 1, 1], [0, 1, 1], [0, 0, 1]], dtype=np.uint8)
img = convert_2d_to_target_format([img], target=target)
assert np.array_equal(F.random_flip(img, code), func(img))
def test_rot90(target):
img = np.array([[0, 0, 1], [0, 0, 1], [0, 0, 1]], dtype=np.uint8)
expected = np.array([[1, 1, 1], [0, 0, 0], [0, 0, 0]], dtype=np.uint8)
img, expected = convert_2d_to_target_format([img, expected], target=target)
rotated = F.rot90(img, factor=1)
assert np.array_equal(rotated, expected)
def test_hflip(target):
img = np.array([[1, 1, 1], [0, 1, 1], [0, 0, 1]], dtype=np.uint8)
expected = np.array([[1, 1, 1], [1, 1, 0], [1, 0, 0]], dtype=np.uint8)
img, expected = convert_2d_to_target_format([img, expected], target=target)
flipped_img = F.hflip(img)
assert np.array_equal(flipped_img, expected)
