def wrap_border(mask_src: np.ndarray) -> np.ndarray:
mask = mask_src.copy()
mask[14:27, :] = (mask[14:27, :] + vflip(mask[:13, :])) / 2
mask[:, 14:27] = (mask[:, 14:27] + hflip(mask[:, :13])) / 2
mask[99:113, :] = (mask[99:113, :] + vflip(mask[114:, :])) / 2
mask[:, 99:113] = (mask[:, 99:113] + hflip(mask[:, 114:])) / 2
return center_crop(mask, 101, 101)
def spacialAg(img1,img2):
# 水平翻转
if np.random.random() < 0.5:
img1 = functional.hflip(img1)
img2 = functional.hflip(img2)
# 垂直翻转
if np.random.random() < 0.5:
img1 = functional.vflip(img1)
img2 = functional.vflip(img2)
# 绕图像中心旋转
if np.random.random() < 0.5:
angle = np.random.uniform(-20, 20)
scale = np.random.uniform(1 - 0.1, 1 + 0.1)
dx = np.random.uniform(-0.0625, 0.0625)
dy = np.random.uniform(-0.0625, 0.0625)
img1 = functional.shift_scale_rotate(img1, angle, scale, dx, dy, interpolation=cv2.INTER_LINEAR,
border_mode=cv2.BORDER_CONSTANT)
img2 = functional.shift_scale_rotate(img2, angle, scale, dx, dy, interpolation=cv2.INTER_LINEAR,
border_mode=cv2.BORDER_CONSTANT)
# 网格扭曲
if np.random.random() < 0.5:
num_steps = 5
distort_limit = (-0.3, 0.3)
stepsx = [1 + np.random.uniform(distort_limit[0], distort_limit[1]) for i in
range(num_steps + 1)]
stepsy = [1 + np.random.uniform(distort_limit[0], distort_limit[1]) for i in
range(num_steps + 1)]
img1 = functional.grid_distortion(img1, num_steps, stepsx, stepsy, interpolation=cv2.INTER_LINEAR,
border_mode=cv2.BORDER_CONSTANT)
img2 = functional.grid_distortion(img2, num_steps, stepsx, stepsy, interpolation=cv2.INTER_LINEAR,
border_mode=cv2.BORDER_CONSTANT)
# 弹性扭曲
if np.random.random() < 0.5:
alpha = 1
sigma = 50
alpha_affine = 50
interpolation = cv2.INTER_LINEAR
random_state = np.random.randint(0, 10000)
img1 = functional.elastic_transform_fast(img1, alpha, sigma, alpha_affine, interpolation,
cv2.BORDER_CONSTANT, np.random.RandomState(random_state))
img2 = functional.elastic_transform_fast(img2, alpha, sigma, alpha_affine, interpolation, cv2.BORDER_CONSTANT,
np.random.RandomState(random_state))
return img1, img2
def predict(self, x, ave='temperture'):
for i in range(x.shape[0]):
out1 = self.model.predict(np.expand_dims(F.vflip(x[i, ]), axis=0))
out2 = self.model.predict(np.expand_dims(F.hflip(x[i, ]), axis=0))
out3 = self.model.predict(
np.expand_dims(F.vflip(F.hflip(x[i, ])), axis=0))
out4 = self.model.predict(np.expand_dims(x[i, ], axis=0))
ave = (F.vflip(out1) + F.hflip(out2) + F.hflip(F.vflip(out3)) +
out4) / 4
if i == 0:
output = ave
else:
output = np.concatenate((output, ave), axis=0)
return output
def ten_crop(img: numpy.ndarray,
size: List[int],
vertical_flip: bool = False) -> List[numpy.ndarray]:
"""Generate ten cropped images from the given image.
Crop the given image into four corners and the central crop plus the
flipped version of these (horizontal flipping is used by default).
Args:
img (numpy.ndarray): Image to be cropped.
size (sequence or int): Desired output size of the crop. If size is an
int instead of sequence like (h, w), a square crop (size, size) is
made. If provided a sequence of length 1, it will be interpreted
as (size[0], size[0]).
vertical_flip (bool): Use vertical flipping instead of horizontal
Returns:
tuple: tuple (tl, tr, bl, br, center, tl_flip, tr_flip, bl_flip, br_flip, center_flip)
Corresponding top left, top right, bottom left, bottom right and
center crop and same for the flipped image.
"""
first_five = five_crop(img, size)
if vertical_flip:
img = F.vflip(img)
else:
img = F.hflip(img)
second_five = five_crop(img, size)
return first_five + second_five
def test_vflip_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([[0.0, 0.0, 0.4], [0.0, 0.4, 0.4], [0.4, 0.4, 0.4]],
dtype=np.float32)
img, expected = convert_2d_to_target_format([img, expected], target=target)
flipped_img = F.vflip(img)
assert_array_almost_equal_nulp(flipped_img, expected)
def test_vflip(target):
img = np.array([[1, 1, 1], [0, 1, 1], [0, 0, 1]], dtype=np.uint8)
expected_output = np.array([[0, 0, 1], [0, 1, 1], [1, 1, 1]],
dtype=np.uint8)
if target == 'image':
img = convert_2d_to_3d(img)
expected_output = convert_2d_to_3d(expected_output)
flipped_img = F.vflip(img)
assert np.array_equal(flipped_img, expected_output)
def test_vflip(target):
img = np.array(
[[1, 1, 1],
[0, 1, 1],
[0, 0, 1]], dtype=np.uint8)
expected = np.array(
[[0, 0, 1],
[0, 1, 1],
[1, 1, 1]], dtype=np.uint8)
img, expected = convert_2d_to_target_format([img, expected], target=target)
flipped_img = F.vflip(img)
assert np.array_equal(flipped_img, expected)
def test_vflip(target):
img = np.array(
[[1, 1, 1],
[0, 1, 1],
[0, 0, 1]], dtype=np.uint8)
expected_output = np.array(
[[0, 0, 1],
[0, 1, 1],
[1, 1, 1]], dtype=np.uint8)
if target == 'image':
img = convert_2d_to_3d(img)
expected_output = convert_2d_to_3d(expected_output)
flipped_img = F.vflip(img)
assert np.array_equal(flipped_img, expected_output)
def albumentations(self, img):
return albumentations.vflip(img)
@pytest.mark.parametrize("target", ["image", "image_4_channels"])
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)
@pytest.mark.parametrize("target", ["image", "mask"])
@pytest.mark.parametrize(
["code", "func"],
[[0, F.vflip], [1, F.hflip], [-1, lambda img: F.vflip(F.hflip(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))
@pytest.mark.parametrize("target", ["image", "image_4_channels"])
@pytest.mark.parametrize(
["code", "func"],
[[0, F.vflip], [1, F.hflip], [-1, lambda img: F.vflip(F.hflip(img))]])
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))
expected_output = np.array(
[[1, 1, 1],
[1, 1, 0],
[1, 0, 0]], dtype=np.uint8)
if target == 'image':
img = convert_2d_to_3d(img)
expected_output = convert_2d_to_3d(expected_output)
flipped_img = F.hflip(img)
assert np.array_equal(flipped_img, expected_output)
@pytest.mark.parametrize('target', ['image', 'mask'])
@pytest.mark.parametrize(['code', 'func'], [
[0, F.vflip],
[1, F.hflip],
[-1, lambda img: F.vflip(F.hflip(img))],
])
def test_random_flip(code, func, target):
img = np.array(
[[1, 1, 1],
[0, 1, 1],
[0, 0, 1]], dtype=np.uint8)
if target == 'image':
img = convert_2d_to_3d(img)
assert np.array_equal(F.random_flip(img, code), func(img))
@pytest.mark.parametrize(['input_shape', 'expected_shape'], [
[(128, 64), (64, 128)],
[(128, 64, 3), (64, 128, 3)],
@pytest.mark.parametrize('target', ['image', 'image_4_channels'])
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)
@pytest.mark.parametrize('target', ['image', 'mask'])
@pytest.mark.parametrize(['code', 'func'], [
[0, F.vflip],
[1, F.hflip],
[-1, lambda img: F.vflip(F.hflip(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))
@pytest.mark.parametrize('target', ['image', 'image_4_channels'])
@pytest.mark.parametrize(['code', 'func'], [
[0, F.vflip],
[1, F.hflip],
[-1, lambda img: F.vflip(F.hflip(img))],
])
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]],
def albumentations(self, img):
return albumentations.vflip(img)
def albumentations_fn(self, image, arg):
return F.vflip(image)
def vflip_image(image, **kwargs):
return F.vflip(image)
def vflip_mask(mask, **kwargs):
return F.vflip(mask)
img, expected = convert_2d_to_target_format([img, expected], target=target)
flipped_img = F.hflip(img)
assert np.array_equal(flipped_img, expected)
@pytest.mark.parametrize("target", ["image", "image_4_channels"])
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)
@pytest.mark.parametrize("target", ["image", "mask"])
@pytest.mark.parametrize(["code", "func"], [[0, F.vflip], [1, F.hflip], [-1, lambda img: F.vflip(F.hflip(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))
@pytest.mark.parametrize("target", ["image", "image_4_channels"])
@pytest.mark.parametrize(["code", "func"], [[0, F.vflip], [1, F.hflip], [-1, lambda img: F.vflip(F.hflip(img))]])
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))
@pytest.mark.parametrize(["input_shape", "expected_shape"], [[(128, 64), (64, 128)], [(128, 64, 3), (64, 128, 3)]])
