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import random
import torch
import numpy as np
import torch.nn.functional as F
import torchvision.transforms as transforms
import copy
def create_pseudoanomaly_cifar_smooth(img, cifar_img, max_size, h, w, dataset, max_move=0):
assert 0 <= max_size <= 1
pil_img = transforms.ToPILImage()(cifar_img)
pil_img = transforms.Grayscale(num_output_channels=1)(pil_img)
cifar_img = transforms.ToTensor()(pil_img)
cifar_img = transforms.Normalize(mean=[0.5], std=[0.5])(cifar_img)
cifar_patch = F.interpolate(cifar_img.unsqueeze(0), size=(h, w), mode='bilinear', align_corners=True)
x_mu, y_mu = random.randint(0, w), random.randint(0, h)
x_sigma = max(10, int(np.random.uniform(high=max_size) * w))
y_sigma = max(10, int(np.random.uniform(high=max_size) * h))
if max_move == 0:
mask = torch.tensor(_get_gaussian_mask(x_mu, y_mu, x_sigma, y_sigma, h, w)).to(img.device).float()
img = mask * cifar_patch.to(img.device) + (1-mask) * img
else:
mask = []
for frame_idx in range(img.size(1)):
delta_x = np.random.randint(-max_move, max_move + 1)
delta_y = np.random.randint(-max_move, max_move + 1)
mask_ = torch.tensor(_get_gaussian_mask(x_mu, y_mu, x_sigma, y_sigma, h, w)).to(img.device).float()
img[:, frame_idx] = mask_ * cifar_patch.to(img.device) + (1-mask_) * img[:, frame_idx]
mask.append(mask_)
x_mu = min(max(0, x_mu + delta_x), w)
y_mu = min(max(0, y_mu + delta_y), h)
mask = torch.stack(mask, dim=0)
return img, mask
def create_pseudoanomaly_cifar_smoothborder(img, cifar_img, max_size, h, w, dataset, max_move=0):
assert 0 <= max_size <= 1
pil_img = transforms.ToPILImage()(cifar_img)
pil_img = transforms.Grayscale(num_output_channels=1)(pil_img)
cifar_img = transforms.ToTensor()(pil_img)
cifar_img = transforms.Normalize(mean=[0.5], std=[0.5])(cifar_img)
cifar_patch = F.interpolate(cifar_img.unsqueeze(0), size=(h, w), mode='bilinear', align_corners=True)
cx, cy = np.random.randint(w), np.random.randint(h)
cut_w= max(10, int(np.random.uniform(high=max_size) * w))
cut_h = max(10, int(np.random.uniform(high=max_size) * h))
if max_move == 0:
mask = torch.tensor(_get_smoothborder_mask(cx, cy, cut_h, cut_w, h, w)).to(img.device).float()
img = mask * cifar_patch.to(img.device) + (1-mask) * img
else:
mask = []
for frame_idx in range(img.size(1)):
delta_x = np.random.randint(-max_move, max_move + 1)
delta_y = np.random.randint(-max_move, max_move + 1)
mask_ = torch.tensor(_get_smoothborder_mask(cx, cy, cut_h, cut_w, h, w)).to(img.device).float()
img[:, frame_idx] = mask_ * cifar_patch.to(img.device) + (1 - mask_) * img[:, frame_idx]
mask.append(mask_)
cx = min(max(0, cx + delta_x), w)
cy = min(max(0, cy + delta_y), h)
mask = torch.stack(mask, dim=0)
return img, mask
def create_pseudoanomaly_cifar_cutmix(img, cifar_img, max_size, h, w, dataset, max_move=0):
assert 0 <= max_size <= 1
pil_img = transforms.ToPILImage()(cifar_img)
pil_img = transforms.Grayscale(num_output_channels=1)(pil_img)
cifar_img = transforms.ToTensor()(pil_img)
cifar_img = transforms.Normalize(mean=[0.5], std=[0.5])(cifar_img)
cifar_patch = F.interpolate(cifar_img.unsqueeze(0), size=(h, w), mode='bilinear', align_corners=True)
cx, cy = np.random.randint(w), np.random.randint(h)
cut_w= max(10, int(np.random.uniform(high=max_size) * w))
cut_h = max(10, int(np.random.uniform(high=max_size) * h))
if max_move == 0:
mask = torch.tensor(_get_cutmix_mask(cx, cy, cut_h, cut_w, h, w)).to(img.device).float()
img = mask * cifar_patch.to(img.device) + (1-mask) * img
else:
mask = []
for frame_idx in range(img.size(1)):
delta_x = np.random.randint(-max_move, max_move + 1)
delta_y = np.random.randint(-max_move, max_move + 1)
mask_ = torch.tensor(_get_cutmix_mask(cx, cy, cut_h, cut_w, h, w)).to(img.device).float()
img[:, frame_idx] = mask_ * cifar_patch.to(img.device) + (1 - mask_) * img[:, frame_idx]
mask.append(mask_)
cx = min(max(0, cx + delta_x), w)
cy = min(max(0, cy + delta_y), h)
mask = torch.stack(mask, dim=0)
return img, mask
def create_pseudoanomaly_cifar_mixupcutmix(img, cifar_img, max_size, h, w, dataset, max_move=0):
assert 0 <= max_size <= 1
pil_img = transforms.ToPILImage()(cifar_img)
pil_img = transforms.Grayscale(num_output_channels=1)(pil_img)
cifar_img = transforms.ToTensor()(pil_img)
cifar_img = transforms.Normalize(mean=[0.5], std=[0.5])(cifar_img)
cifar_patch = F.interpolate(cifar_img.unsqueeze(0), size=(h, w), mode='bilinear', align_corners=True)
cx, cy = np.random.randint(w), np.random.randint(h)
cut_w= max(10, int(np.random.uniform(high=max_size) * w))
cut_h = max(10, int(np.random.uniform(high=max_size) * h))
if max_move == 0:
mask = torch.tensor(_get_cutmix_mask(cx, cy, cut_h, cut_w, h, w)).to(img.device).float()
img = mask * 0.5 * cifar_patch.to(img.device) + mask * 0.5 * img + (1-mask) * img
else:
mask = []
for frame_idx in range(img.size(1)):
delta_x = np.random.randint(-max_move, max_move + 1)
delta_y = np.random.randint(-max_move, max_move + 1)
mask_ = torch.tensor(_get_cutmix_mask(cx, cy, cut_h, cut_w, h, w)).to(img.device).float()
img[:, frame_idx] = mask_ * 0.5 * cifar_patch.to(img.device) + mask_ * 0.5 * img[:, frame_idx] + (1 - mask_) * img[:, frame_idx]
mask.append(mask_)
cx = min(max(0, cx + delta_x), w)
cy = min(max(0, cy + delta_y), h)
mask = torch.stack(mask, dim=0)
return img, mask
def create_pseudoanomaly_seq_smoothborder(img, seq, max_size, h, w, dataset, max_move=0):
assert 0 <= max_size <= 1
cx, cy = np.random.randint(w), np.random.randint(h)
cut_w= max(10, int(np.random.uniform(high=max_size) * w))
cut_h = max(10, int(np.random.uniform(high=max_size) * h))
if max_move == 0:
mask = torch.tensor(_get_smoothborder_mask(cx, cy, cut_h, cut_w, h, w)).to(img.device).float()
img = mask * seq.to(img.device) + (1-mask) * img
else:
mask = []
for frame_idx in range(img.size(1)):
delta_x = np.random.randint(-max_move, max_move + 1)
delta_y = np.random.randint(-max_move, max_move + 1)
mask_ = torch.tensor(_get_smoothborder_mask(cx, cy, cut_h, cut_w, h, w)).to(img.device).float()
img[:, frame_idx] = mask_ * seq[:, frame_idx].to(img.device) + (1 - mask_) * img[:, frame_idx]
mask.append(mask_)
cx = min(max(0, cx + delta_x), w)
cy = min(max(0, cy + delta_y), h)
mask = torch.stack(mask, dim=0)
return img, mask
def _get_gaussian_mask(x_mu, y_mu, x_sigma, y_sigma, h, w):
x, y = np.arange(w), np.arange(h)
# x_mu, y_mu = random.randint(0, w), random.randint(0, h)
# x_sigma = max(10, int(np.random.uniform(high=max_size) * w))
# y_sigma = max(10, int(np.random.uniform(high=max_size) * h))
gx = np.exp(-(x - x_mu) ** 2 / (2 * x_sigma ** 2))
gy = np.exp(-(y - y_mu) ** 2 / (2 * y_sigma ** 2))
g = np.outer(gx, gy)
# g /= np.sum(g) # normalize, if you want that
# sum_g = np.sum(g)
# lam = sum_g / (w * h)
# print(lam)
# plt.imshow(g, interpolation="nearest", origin="lower")
# plt.show()
# g = np.dstack([g, g, g])
return g
# a = _get_gaussian_mask(0.5, 256, 256)
def _get_smoothborder_mask(cx, cy, Cut_h, Cut_w, h, w):
lam = np.random.beta(1, 1)
percentage = 0.1
cut_rat = np.sqrt(1. - lam)
# Cut_w = min(np.int(max_size*w), max(2, np.int(w * cut_rat)))
# Cut_h = min(np.int(max_size*h), max(2, np.int(h * cut_rat)))
# cx, cy = np.random.randint(w), np.random.randint(h)
bbx1 = np.clip(cx - Cut_w // 2, 0, w) # top left x
bby1 = np.clip(cy - Cut_h // 2, 0, h) # top left y
bbx2 = np.clip(cx + Cut_w // 2, 0, w) # bottom right x
bby2 = np.clip(cy + Cut_h // 2, 0, h) # bottom right y
img = np.zeros((w, h))
img2, img3 = np.ones_like(img), np.ones_like(img)
img[bbx1:bbx2, bby1:bby2] = img2[bbx1:bbx2, bby1:bby2]
lo = bbx1 - (Cut_w // 2) * percentage # left side: beginning linear from 0
li = bbx1 # + (Cut_w // 2) * percentage # left side: end of linear at 1
ri = bbx2 # - (Cut_w // 2) * percentage # right : start linear from 1
ro = bbx2 + (Cut_w // 2) * percentage # right: end linear at 0
to = bby1 - (Cut_h // 2) * percentage # top: start linear from 0
ti = bby1 # + (Cut_h // 2) * percentage # top: end linear at 1
bi = bby2 # - (Cut_h // 2) * percentage # bottom: start linear from 1
bo = bby2 + (Cut_h // 2) * percentage # bottom: end linear at 0
# glx = lambda x: ((x - lo) / (li - lo))
# grx = lambda x: (-(x - ro) / (ro - ri))
# gtx = lambda x: ((x - to) / (ti - to))
# gbx = lambda x: (-(x - bo) / (bo - bi))
for i in range(w):
for j in range(h):
if i < cx:
img2[j][i] = ((i - lo) / (li - lo)) # linear going up
else:
img2[j][i] = (-(i - ro) / (ro - ri)) # linear going down
if j < cy:
img3[j][i] = ((j - to) / (ti - to))
else:
img3[j][i] = (-(j - bo) / (bo - bi))
img2[img2 < 0] = 0
img2[img2 > 1] = 1
img3[img3 < 0] = 0
img3[img3 > 1] = 1
# plt.figure()
# plt.subplot(131)
# plt.imshow(img2)
# # plt.show()
# plt.subplot(132)
# plt.imshow(img3)
# # plt.show()
img4 = np.multiply(img2, img3)
# sum_img4 = np.sum(img4)
# lam = sum_img4 / (w * h)
# plt.subplot(133)
# plt.imshow(img4)
# plt.show()
return img4 #, lam
# a = _get_smoothborder_mask(0.5, 256, 256)
def _get_cutmix_mask(cx, cy, Cut_h, Cut_w, h, w):
lam = np.random.beta(1, 1)
bbx1 = np.clip(cx - Cut_w // 2, 0, w) # top left x
bby1 = np.clip(cy - Cut_h // 2, 0, h) # top left y
bbx2 = np.clip(bbx1 + Cut_w, 0, w) # bottom right x
bby2 = np.clip(bby1 + Cut_h, 0, h) # bottom right y
img = np.zeros((w, h))
img2 = np.ones_like(img)
img[bby1:bby2, bbx1:bbx2] = img2[bby1:bby2, bbx1:bbx2]
return img #, lam
# a = _get_cutmix_mask(100, 100, 15, 30, 256, 256)
# a = _get_smoothborder_mask(100, 100, 15, 30, 256, 256) |