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import os
import torch
import torch.nn as nn
from torch.utils.data.dataset import Dataset
from PIL import Image
import fnmatch
import cv2
import sys
import numpy as np
# torch.set_printoptions(precision=10)
class _bn_relu_conv(nn.Module):
def __init__(self, in_filters, nb_filters, fw, fh, subsample=1):
super(_bn_relu_conv, self).__init__()
self.model = nn.Sequential(
nn.BatchNorm2d(in_filters, eps=1e-3),
nn.LeakyReLU(0.2),
nn.Conv2d(
in_filters,
nb_filters,
(fw, fh),
stride=subsample,
padding=(fw // 2, fh // 2),
padding_mode="zeros",
),
)
def forward(self, x):
return self.model(x)
# the following are for debugs
print(
"****",
np.max(x.cpu().numpy()),
np.min(x.cpu().numpy()),
np.mean(x.cpu().numpy()),
np.std(x.cpu().numpy()),
x.shape,
)
for i, layer in enumerate(self.model):
if i != 2:
x = layer(x)
else:
x = layer(x)
# x = nn.functional.pad(x, (1, 1, 1, 1), mode='constant', value=0)
print(
"____",
np.max(x.cpu().numpy()),
np.min(x.cpu().numpy()),
np.mean(x.cpu().numpy()),
np.std(x.cpu().numpy()),
x.shape,
)
print(x[0])
return x
class _u_bn_relu_conv(nn.Module):
def __init__(self, in_filters, nb_filters, fw, fh, subsample=1):
super(_u_bn_relu_conv, self).__init__()
self.model = nn.Sequential(
nn.BatchNorm2d(in_filters, eps=1e-3),
nn.LeakyReLU(0.2),
nn.Conv2d(
in_filters,
nb_filters,
(fw, fh),
stride=subsample,
padding=(fw // 2, fh // 2),
),
nn.Upsample(scale_factor=2, mode="nearest"),
)
def forward(self, x):
return self.model(x)
class _shortcut(nn.Module):
def __init__(self, in_filters, nb_filters, subsample=1):
super(_shortcut, self).__init__()
self.process = False
self.model = None
if in_filters != nb_filters or subsample != 1:
self.process = True
self.model = nn.Sequential(
nn.Conv2d(in_filters, nb_filters, (1, 1), stride=subsample)
)
def forward(self, x, y):
# print(x.size(), y.size(), self.process)
if self.process:
y0 = self.model(x)
# print("merge+", torch.max(y0+y), torch.min(y0+y),torch.mean(y0+y), torch.std(y0+y), y0.shape)
return y0 + y
else:
# print("merge", torch.max(x+y), torch.min(x+y),torch.mean(x+y), torch.std(x+y), y.shape)
return x + y
class _u_shortcut(nn.Module):
def __init__(self, in_filters, nb_filters, subsample):
super(_u_shortcut, self).__init__()
self.process = False
self.model = None
if in_filters != nb_filters:
self.process = True
self.model = nn.Sequential(
nn.Conv2d(
in_filters,
nb_filters,
(1, 1),
stride=subsample,
padding_mode="zeros",
),
nn.Upsample(scale_factor=2, mode="nearest"),
)
def forward(self, x, y):
if self.process:
return self.model(x) + y
else:
return x + y
class basic_block(nn.Module):
def __init__(self, in_filters, nb_filters, init_subsample=1):
super(basic_block, self).__init__()
self.conv1 = _bn_relu_conv(
in_filters, nb_filters, 3, 3, subsample=init_subsample
)
self.residual = _bn_relu_conv(nb_filters, nb_filters, 3, 3)
self.shortcut = _shortcut(in_filters, nb_filters, subsample=init_subsample)
def forward(self, x):
x1 = self.conv1(x)
x2 = self.residual(x1)
return self.shortcut(x, x2)
class _u_basic_block(nn.Module):
def __init__(self, in_filters, nb_filters, init_subsample=1):
super(_u_basic_block, self).__init__()
self.conv1 = _u_bn_relu_conv(
in_filters, nb_filters, 3, 3, subsample=init_subsample
)
self.residual = _bn_relu_conv(nb_filters, nb_filters, 3, 3)
self.shortcut = _u_shortcut(in_filters, nb_filters, subsample=init_subsample)
def forward(self, x):
y = self.residual(self.conv1(x))
return self.shortcut(x, y)
class _residual_block(nn.Module):
def __init__(self, in_filters, nb_filters, repetitions, is_first_layer=False):
super(_residual_block, self).__init__()
layers = []
for i in range(repetitions):
init_subsample = 1
if i == repetitions - 1 and not is_first_layer:
init_subsample = 2
if i == 0:
l = basic_block(
in_filters=in_filters,
nb_filters=nb_filters,
init_subsample=init_subsample,
)
else:
l = basic_block(
in_filters=nb_filters,
nb_filters=nb_filters,
init_subsample=init_subsample,
)
layers.append(l)
self.model = nn.Sequential(*layers)
def forward(self, x):
return self.model(x)
class _upsampling_residual_block(nn.Module):
def __init__(self, in_filters, nb_filters, repetitions):
super(_upsampling_residual_block, self).__init__()
layers = []
for i in range(repetitions):
l = None
if i == 0:
l = _u_basic_block(
in_filters=in_filters, nb_filters=nb_filters
) # (input)
else:
l = basic_block(in_filters=nb_filters, nb_filters=nb_filters) # (input)
layers.append(l)
self.model = nn.Sequential(*layers)
def forward(self, x):
return self.model(x)
class res_skip(nn.Module):
def __init__(self):
super(res_skip, self).__init__()
self.block0 = _residual_block(
in_filters=1, nb_filters=24, repetitions=2, is_first_layer=True
) # (input)
self.block1 = _residual_block(
in_filters=24, nb_filters=48, repetitions=3
) # (block0)
self.block2 = _residual_block(
in_filters=48, nb_filters=96, repetitions=5
) # (block1)
self.block3 = _residual_block(
in_filters=96, nb_filters=192, repetitions=7
) # (block2)
self.block4 = _residual_block(
in_filters=192, nb_filters=384, repetitions=12
) # (block3)
self.block5 = _upsampling_residual_block(
in_filters=384, nb_filters=192, repetitions=7
) # (block4)
self.res1 = _shortcut(
in_filters=192, nb_filters=192
) # (block3, block5, subsample=(1,1))
self.block6 = _upsampling_residual_block(
in_filters=192, nb_filters=96, repetitions=5
) # (res1)
self.res2 = _shortcut(
in_filters=96, nb_filters=96
) # (block2, block6, subsample=(1,1))
self.block7 = _upsampling_residual_block(
in_filters=96, nb_filters=48, repetitions=3
) # (res2)
self.res3 = _shortcut(
in_filters=48, nb_filters=48
) # (block1, block7, subsample=(1,1))
self.block8 = _upsampling_residual_block(
in_filters=48, nb_filters=24, repetitions=2
) # (res3)
self.res4 = _shortcut(
in_filters=24, nb_filters=24
) # (block0,block8, subsample=(1,1))
self.block9 = _residual_block(
in_filters=24, nb_filters=16, repetitions=2, is_first_layer=True
) # (res4)
self.conv15 = _bn_relu_conv(
in_filters=16, nb_filters=1, fh=1, fw=1, subsample=1
) # (block7)
def forward(self, x):
x0 = self.block0(x)
x1 = self.block1(x0)
x2 = self.block2(x1)
x3 = self.block3(x2)
x4 = self.block4(x3)
x5 = self.block5(x4)
res1 = self.res1(x3, x5)
x6 = self.block6(res1)
res2 = self.res2(x2, x6)
x7 = self.block7(res2)
res3 = self.res3(x1, x7)
x8 = self.block8(res3)
res4 = self.res4(x0, x8)
x9 = self.block9(res4)
y = self.conv15(x9)
return y
class MangaLineExtractor:
def __init__(self, model_path: str = "erika.pth", device: str = "cpu"):
self.model = res_skip()
self.model.load_state_dict(torch.load(model_path))
self.is_cuda = torch.cuda.is_available() and device == "cuda"
if self.is_cuda:
self.model.cuda()
else:
self.model.cpu()
self.model.eval()
def predict(self, image):
src = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
rows = int(np.ceil(src.shape[0] / 16)) * 16
cols = int(np.ceil(src.shape[1] / 16)) * 16
# manually construct a batch. You can change it based on your usecases.
patch = np.ones((1, 1, rows, cols), dtype=np.float32)
patch[0, 0, 0 : src.shape[0], 0 : src.shape[1]] = src
if self.is_cuda:
tensor = torch.from_numpy(patch).cuda()
else:
tensor = torch.from_numpy(patch).cpu()
y = self.model(tensor)
yc = y.detach().numpy()[0, 0, :, :]
yc[yc > 255] = 255
yc[yc < 0] = 0
yc = yc / 255.0
output = yc[0 : src.shape[0], 0 : src.shape[1]]
output = cv2.cvtColor(output, cv2.COLOR_GRAY2BGR)
return output
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