def test_grid_distortion_interpolation(interpolation):
image = np.random.randint(low=0,
high=256,
size=(100, 100, 3),
dtype=np.uint8)
mask = np.random.randint(low=0, high=2, size=(100, 100), dtype=np.uint8)
aug = A.GridDistortion(num_steps=1,
distort_limit=(0.3, 0.3),
interpolation=interpolation,
p=1)
data = aug(image=image, mask=mask)
expected_image = F.grid_distortion(image,
num_steps=1,
xsteps=[1.3],
ysteps=[1.3],
interpolation=interpolation,
border_mode=cv2.BORDER_REFLECT_101)
expected_mask = F.grid_distortion(mask,
num_steps=1,
xsteps=[1.3],
ysteps=[1.3],
interpolation=cv2.INTER_NEAREST,
border_mode=cv2.BORDER_REFLECT_101)
assert np.array_equal(data['image'], expected_image)
assert np.array_equal(data['mask'], expected_mask)
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 __call__(self, sample):
# print('grid', np.random.uniform(0.0, 1.0))
if np.random.uniform(0.0, 1.0) < self.p:
return sample
h, w, _ = sample["img"].shape
# grid_distortion
if np.random.uniform(0.0, 1.0) < self.p:
num_steps=15
distort_limit=[-0.05,0.05]
stepsx = [1 + random.uniform(distort_limit[0], distort_limit[1]) for i in
range(num_steps + 1)]
stepsy = [1 + random.uniform(distort_limit[0], distort_limit[1]) for i in
range(num_steps + 1)]
sample["img"]=albumentations.grid_distortion(sample["img"],5,stepsx, stepsy)
# elastic_transform
else:
sample["img"]=albumentations.elastic_transform(sample["img"], alpha=5, sigma=1, alpha_affine=random.uniform(0,2),
interpolation=cv2.INTER_LINEAR, border_mode=cv2.BORDER_REFLECT_101,)
if np.random.uniform(0.0, 1.0) < self.p-0.2:
sample["img"]=albumentations.jpeg_compression(sample["img"], random.randint(20, 100))
return sample
