File size: 3,227 Bytes
e5461d8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 | import imgaug.augmenters as iaa
import numpy as np
import torch
import math
def generate_thr(img_shape, min=0, max=4):
min_perlin_scale = min
max_perlin_scale = max
perlin_scalex = 2 ** np.random.randint(min_perlin_scale, max_perlin_scale)
perlin_scaley = 2 ** np.random.randint(min_perlin_scale, max_perlin_scale)
perlin_noise_np = rand_perlin_2d_np((img_shape[1], img_shape[2]), (perlin_scalex, perlin_scaley))
threshold = 0.5
perlin_noise_np = iaa.Sequential([iaa.Affine(rotate=(-90, 90))])(image=perlin_noise_np)
perlin_thr = np.where(perlin_noise_np > threshold, np.ones_like(perlin_noise_np), np.zeros_like(perlin_noise_np))
return perlin_thr
def perlin_mask(img_shape, feat_size, min, max, mask_fg, flag=0):
mask = np.zeros((feat_size, feat_size))
while np.max(mask) == 0:
perlin_thr_1 = generate_thr(img_shape, min, max)
perlin_thr_2 = generate_thr(img_shape, min, max)
temp = torch.rand(1).numpy()[0]
if temp > 2 / 3:
perlin_thr = perlin_thr_1 + perlin_thr_2
perlin_thr = np.where(perlin_thr > 0, np.ones_like(perlin_thr), np.zeros_like(perlin_thr))
elif temp > 1 / 3:
perlin_thr = perlin_thr_1 * perlin_thr_2
else:
perlin_thr = perlin_thr_1
perlin_thr = torch.from_numpy(perlin_thr)
perlin_thr_fg = perlin_thr * mask_fg
down_ratio_y = int(img_shape[1] / feat_size)
down_ratio_x = int(img_shape[2] / feat_size)
mask_ = perlin_thr_fg
mask = torch.nn.functional.max_pool2d(perlin_thr_fg.unsqueeze(0).unsqueeze(0), (down_ratio_y, down_ratio_x)).float()
mask = mask.numpy()[0, 0]
mask_s = mask
if flag != 0:
mask_l = mask_.numpy()
if flag == 0:
return mask_s
else:
return mask_s, mask_l
def lerp_np(x, y, w):
fin_out = (y - x) * w + x
return fin_out
def rand_perlin_2d_np(shape, res, fade=lambda t: 6 * t ** 5 - 15 * t ** 4 + 10 * t ** 3):
delta = (res[0] / shape[0], res[1] / shape[1])
d = (shape[0] // res[0], shape[1] // res[1])
grid = np.mgrid[0:res[0]:delta[0], 0:res[1]:delta[1]].transpose(1, 2, 0) % 1
angles = 2 * math.pi * np.random.rand(res[0] + 1, res[1] + 1)
gradients = np.stack((np.cos(angles), np.sin(angles)), axis=-1)
tt = np.repeat(np.repeat(gradients, d[0], axis=0), d[1], axis=1)
tile_grads = lambda slice1, slice2: np.repeat(np.repeat(gradients[slice1[0]:slice1[1], slice2[0]:slice2[1]], d[0], axis=0), d[1],
axis=1)
dot = lambda grad, shift: (
np.stack((grid[:shape[0], :shape[1], 0] + shift[0], grid[:shape[0], :shape[1], 1] + shift[1]),
axis=-1) * grad[:shape[0], :shape[1]]).sum(axis=-1)
n00 = dot(tile_grads([0, -1], [0, -1]), [0, 0])
n10 = dot(tile_grads([1, None], [0, -1]), [-1, 0])
n01 = dot(tile_grads([0, -1], [1, None]), [0, -1])
n11 = dot(tile_grads([1, None], [1, None]), [-1, -1])
t = fade(grid[:shape[0], :shape[1]])
return math.sqrt(2) * lerp_np(lerp_np(n00, n10, t[..., 0]), lerp_np(n01, n11, t[..., 0]), t[..., 1])
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