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)
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温馨提示
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
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收起资源包目录
照片生成漫画.zip (3个子文件)
照片生成漫画
weight.pth 4.7MB
input.jpg 22KB
AIpic.py 4KB
共 3 条
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资源评论
- zzff132022-07-18支持这个资源,内容详细,主要是能解决当下的问题,感谢大佬分享~
- qq_364523882022-07-26感谢大佬,让我及时解决了当下的问题,解燃眉之急,必须支持!
- m0_616345662023-12-05资源是宝藏资源,实用也是真的实用,感谢大佬分享~
- 熊熊捕手2024-04-24感谢大佬分享的资源,对我启发很大,给了我新的灵感。
- 2301_767244242023-03-06资源太好了,解决了我当下遇到的难题,抱紧大佬的大腿~
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