传统的照片转漫画，使用边缘检测、双边滤波器和降采样，得到图像如下，可以看到，噪点很多，有些关键线条也没有展现出来。
本次采用GAN，GAN网络使用的方法是根据图像对去不断地学习，如输入图像1和对应已有的漫画B，GAN网络从图片1中获取关键特征，不停地生成一张图像C，当C与B的差值很小时停止，当有很多这样地图像对时，我们就有了一个模型。输入一张图像，就可以生成一张对应地漫画图像，我这次使用的GAN（White-box Cartoon）生成。生成效果：
图片.png
图片.png
原始图片大小建议为256*256像素
完整程序代码
import os
import cv2
import torch
import numpy as np
import torch.nn as nn
class ResBlock(nn.Module):
def __init__(self, num_channel):
super(ResBlock, self).__init__()
self.conv_layer = nn.Sequential(
nn.Conv2d(num_channel, num_channel, 3, 1, 1),
nn.BatchNorm2d(num_channel),
nn.ReLU(inplace=True),
nn.Conv2d(num_channel, num_channel, 3, 1, 1),
nn.BatchNorm2d(num_channel))
self.activation = nn.ReLU(inplace=True)
def forward(self, inputs):
output = self.conv_layer(inputs)
output = self.activation(output + inputs)
return output
class DownBlock(nn.Module):
def __init__(self, in_channel, out_channel):
super(DownBlock, self).__init__()
self.conv_layer = nn.Sequential(
nn.Conv2d(in_channel, out_channel, 3, 2, 1),
nn.BatchNorm2d(out_channel),
nn.ReLU(inplace=True),
nn.Conv2d(out_channel, out_channel, 3, 1, 1),
nn.BatchNorm2d(out_channel),
nn.ReLU(inplace=True))
def forward(self, inputs):
output = self.conv_layer(inputs)
return output
class UpBlock(nn.Module):
def __init__(self, in_channel, out_channel, is_last=False):
super(UpBlock, self).__init__()
self.is_last = is_last
self.conv_layer = nn.Sequential(
nn.Conv2d(in_channel, in_channel, 3, 1, 1),
nn.BatchNorm2d(in_channel),
nn.ReLU(inplace=True),
nn.Upsample(scale_factor=2),
nn.Conv2d(in_channel, out_channel, 3, 1, 1))
self.act = nn.Sequential(
nn.BatchNorm2d(out_channel),
nn.ReLU(inplace=True))
self.last_act = nn.Tanh()
def forward(self, inputs):
output = self.conv_layer(inputs)
if self.is_last:
output = self.last_act(output)
else:
output = self.act(output)
return output
class SimpleGenerator(nn.Module):
def __init__(self, num_channel=32, num_blocks=4):
super(SimpleGenerator, self).__init__()
self.down1 = DownBlock(3, num_channel)
self.down2 = DownBlock(num_channel, num_channel*2)
self.down3 = DownBlock(num_channel*2, num_channel*3)
self.down4 = DownBlock(num_channel*3, num_channel*4)
res_blocks = [ResBlock(num_channel*4)]*num_blocks
self.res_blocks = nn.Sequential(*res_blocks)
self.up1 = UpBlock(num_channel*4, num_channel*3)
self.up2 = UpBlock(num_channel*3, num_channel*2)
self.up3 = UpBlock(num_channel*2, num_channel)
self.up4 = UpBlock(num_channel, 3, is_last=True)
def forward(self, inputs):
down1 = self.down1(inputs)
down2 = self.down2(down1)
down3 = self.down3(down2)
down4 = self.down4(down3)
down4 = self.res_blocks(down4)
up1 = self.up1(down4)
up2 = self.up2(up1+down3)
up3 = self.up3(up2+down2)
up4 = self.up4(up3+down1)
return up4
weight = torch.load('weight.pth', map_location='cpu')
model = SimpleGenerator()
model.load_state_dict(weight)
model.eval()
img = cv2.imread(r'input.jpg')
image = img/127.5 - 1
image = image.transpose(2, 0, 1)
image = torch.tensor(image).unsqueeze(0)
output = model(image.float())
output = output.squeeze(0).detach().numpy()
output = output.transpose(1, 2, 0)
output = (output + 1) * 127.5
output = np.clip(output, 0, 255).astype(np.uint8)
cv2.imwrite('output.jpg', output)### unterminated keywords
需要完整源码，可以私信。
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