def apply(self, img, blur_type, **params):
ksize = random.choice(
np.arange(self.blur_limit[0], self.blur_limit[1] + 1, 2))
# print('ksize:', ksize)
# blur_type = random.randint(0, 90)
# blur_type = 1
if blur_type < 10:
img = F.motion_blur(img, kernel=self.get_motion_kernel())
elif blur_type < 20:
img = F.blur(img, random.choice(range(3, 16, 2)))
elif blur_type < 30:
img = F.median_blur(img, random.choice(range(3, 16, 2)))
elif blur_type < 40:
img = F.gaussian_blur(img, ksize)
elif blur_type < 50:
img = ghosting(img,
random.uniform(0.01, 0.1),
angle=random.randint(-7, 7),
scale=random.uniform(0., 0.1))
elif blur_type < 60:
kernel, anchor = genaratePsf(random.randint(5, 30),
random.randint(-90, 90))
img = cv2.filter2D(img, -1, kernel, anchor=anchor)
elif blur_type < 70: # 速度会很慢
if random.random() < 0.5:
radial_num = random.randint(8, 30)
radial_type = 'angle'
else:
radial_num = random.randint(5, 13)
radial_type = 'direction'
if os.name == 'posix':
h, w, c = img.shape
img_arr = img.ctypes.data_as(c_char_p)
clib.apply_radial_blur(img_arr, h, w, radial_num,
0 if radial_type == 'angle' else 1)
else:
img = apply_radial_blur(img, num=radial_num, type=radial_type)
elif blur_type < 80:
if blur_type < 75:
img = F.gaussian_blur(img, random.choice(range(3, 9, 2)))
img = F.image_compression(img, random.randint(0, 15), '.jpg')
elif blur_type < 85:
img = gasuss_noise(img, 0, random.uniform(0.05, 0.15))
elif blur_type < 90:
img = apply_resize(img, random.randint(2, 7))
else:
img = F.gaussian_blur(img, ksize)
end_plus = random.random()
if end_plus < 0.05:
img = F.gaussian_blur(img, random.choice(range(3, 9, 2)))
elif end_plus < 0.1:
kernel, anchor = genaratePsf(random.randint(3, 10),
random.randint(-90, 90))
img = cv2.filter2D(img, -1, kernel, anchor=anchor)
return img
def ghosting(img, dis, alpha=0.5, angle=0, scale=0):
dx = random.uniform(-dis, dis)
dy = dis - abs(dx)
dy = random.choice([-dy, dy])
tmp1 = F.shift_scale_rotate(img, angle, scale + 1, dx, dy, value=0)
if random.random() < 0.5:
tmp1 = F.gaussian_blur(tmp1, 3)
tmp2 = img
if random.random() < 0.5:
tmp2 = F.gaussian_blur(tmp2, 3)
beta = 1 - alpha
gamma = 0
return cv2.addWeighted(tmp1, alpha, tmp2, beta, gamma)
def test_gaus_blur_limits(blur_limit, sigma, result_blur, result_sigma):
img = np.zeros([100, 100, 3], dtype=np.uint8)
aug = A.Compose([A.GaussianBlur(blur_limit=blur_limit, sigma_limit=sigma, p=1)])
res = aug(image=img)["image"]
assert np.allclose(res, F.gaussian_blur(img, result_blur, result_sigma))
def applyBlur(img):
blur_type = random.choice([1, 2, 3, 4, 5, 6, 7, 8, 9])
level = np.random.choice([0, 1, 2, 3, 4, 5], size=2, replace=False)
# blur_type = 1
if blur_type == 1:
l_value = [0, 3, 7, 9, 11, 13]
img_first = img.copy() if level[0] == 0 else F.motion_blur(
img, kernel=get_motion_kernel(l_value[level[0]]))
img_second = img if level[1] == 0 else F.motion_blur(
img, kernel=get_motion_kernel(l_value[level[1]]))
elif blur_type == 2:
l_value = [0, 3, 5, 7, 9, 11]
img_first = img.copy() if level[0] == 0 else F.blur(
img, l_value[level[0]])
img_second = img if level[1] == 0 else F.blur(img, l_value[level[1]])
elif blur_type == 3:
l_value = [0, 3, 5, 7, 9, 11]
img_first = img.copy() if level[0] == 0 else F.median_blur(
img, l_value[level[0]])
img_second = img if level[1] == 0 else F.median_blur(
img, l_value[level[1]])
elif blur_type == 4:
l_value = [0, 5, 9, 11, 15, 17]
img_first = img.copy() if level[0] == 0 else F.gaussian_blur(
img, l_value[level[0]])
img_second = img if level[1] == 0 else F.gaussian_blur(
img, l_value[level[1]])
elif blur_type == 5:
l_value = [0, 5, 11, 17, 23, 25]
kernel_first, anchor_first = genaratePsf(l_value[level[0]],
random.randint(-90, 90))
kernel_second, anchor_second = genaratePsf(l_value[level[1]],
random.randint(-90, 90))
img_first = img.copy() if level[0] == 0 else cv2.filter2D(
img, -1, kernel_first, anchor=anchor_first)
img_second = img if level[1] == 0 else cv2.filter2D(
img, -1, kernel_second, anchor=anchor_second)
elif blur_type == 6:
l_value = [0, 20, 15, 10, 5, 0]
img_first = img.copy() if level[0] == 0 else F.image_compression(
img, l_value[level[0]], '.jpg')
img_second = img if level[1] == 0 else F.image_compression(
img, l_value[level[1]], '.jpg')
elif blur_type == 7:
l_value = [0, 0.04, 0.05, 0.06, 0.07, 0.08]
img_first = img.copy() if level[0] == 0 else ghosting(
img, l_value[level[0]])
img_second = img if level[1] == 0 else ghosting(img, l_value[level[1]])
elif blur_type == 8:
if random.random() < 0.3:
l_value = [0, 8, 12, 15, 20, 25]
radial_type = 'angle'
else:
l_value = [0, 5, 7, 9, 11, 12]
radial_type = 'direction'
if os.name == 'posix':
h, w, c = img.shape
img_first = img.copy()
img_second = img
if level[0] != 0:
img_arr_f = img_first.ctypes.data_as(c_char_p)
clib.apply_radial_blur(img_arr_f, h, w, l_value[level[0]],
0 if radial_type == 'angle' else 1)
if level[1] != 0:
img_arr_s = img_second.ctypes.data_as(c_char_p)
clib.apply_radial_blur(img_arr_s, h, w, l_value[level[1]],
0 if radial_type == 'angle' else 1)
else:
img_first = img.copy() if level[0] == 0 else apply_radial_blur(
img.copy(), num=l_value[level[0]], type=radial_type)
img_second = img if level[1] == 0 else apply_radial_blur(
img, num=l_value[level[1]], type=radial_type)
else:
l_value = [0, 0.025, 0.05, 0.075, 0.1, 0.125]
img_first = img.copy() if level[0] == 0 else gasuss_noise(
img, 0, l_value[level[0]])
img_second = img if level[1] == 0 else gasuss_noise(
img, 0, l_value[level[1]])
return img_first, img_second, 1 if level[0] > level[1] else -1