Python实现照片卡通化，一拳打破次元壁

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Python实现照片卡通化，一拳打破次元壁

2024-03-13 03:28| 来源: 网络整理| 查看: 265

前言

项目结构

核心代码

总结

前言

接着我上一篇开源机器学习的使用：如何将照片变成卡通图，animegan2-pytorch机器学习项目使用 | 机器学习_阿良的博客-CSDN博客

我还是继续把项目稍微魔改一下，依然变为一个python文件就可以执行单一图片的处理。变为可以直接拿去使用的工具。

项目github地址：github地址

项目结构

samples目录里面有一些样例图片，可以测试用。weights目录放了原项目的4个模型。python环境需要安装一些依赖，主要是pytorch。pytorch的环境安装可以参考我的另一篇文章：机器学习基础环境部署 | 机器学习系列_阿良的博客-CSDN博客

核心代码

不废话，上核心代码了。

#!/usr/bin/env python # -*- coding: utf-8 -*- # @Time : 2021/12/4 22:34 # @Author : 剑客阿良_ALiang # @Site : # @File : image_cartoon_tool.py from PIL import Image import torch from torchvision.transforms.functional import to_tensor, to_pil_image from torch import nn import os import torch.nn.functional as F import uuid # -------------------------- hy add 01 -------------------------- class ConvNormLReLU(nn.Sequential): def __init__(self, in_ch, out_ch, kernel_size=3, stride=1, padding=1, pad_mode="reflect", groups=1, bias=False): pad_layer = { "zero": nn.ZeroPad2d, "same": nn.ReplicationPad2d, "reflect": nn.ReflectionPad2d, } if pad_mode not in pad_layer: raise NotImplementedError super(ConvNormLReLU, self).__init__( pad_layer[pad_mode](padding), nn.Conv2d(in_ch, out_ch, kernel_size=kernel_size, stride=stride, padding=0, groups=groups, bias=bias), nn.GroupNorm(num_groups=1, num_channels=out_ch, affine=True), nn.LeakyReLU(0.2, inplace=True) ) class InvertedResBlock(nn.Module): def __init__(self, in_ch, out_ch, expansion_ratio=2): super(InvertedResBlock, self).__init__() self.use_res_connect = in_ch == out_ch bottleneck = int(round(in_ch * expansion_ratio)) layers = [] if expansion_ratio != 1: layers.append(ConvNormLReLU(in_ch, bottleneck, kernel_size=1, padding=0)) # dw layers.append(ConvNormLReLU(bottleneck, bottleneck, groups=bottleneck, bias=True)) # pw layers.append(nn.Conv2d(bottleneck, out_ch, kernel_size=1, padding=0, bias=False)) layers.append(nn.GroupNorm(num_groups=1, num_channels=out_ch, affine=True)) self.layers = nn.Sequential(*layers) def forward(self, input): out = self.layers(input) if self.use_res_connect: out = input + out return out class Generator(nn.Module): def __init__(self, ): super().__init__() self.block_a = nn.Sequential( ConvNormLReLU(3, 32, kernel_size=7, padding=3), ConvNormLReLU(32, 64, stride=2, padding=(0, 1, 0, 1)), ConvNormLReLU(64, 64) ) self.block_b = nn.Sequential( ConvNormLReLU(64, 128, stride=2, padding=(0, 1, 0, 1)), ConvNormLReLU(128, 128) ) self.block_c = nn.Sequential( ConvNormLReLU(128, 128), InvertedResBlock(128, 256, 2), InvertedResBlock(256, 256, 2), InvertedResBlock(256, 256, 2), InvertedResBlock(256, 256, 2), ConvNormLReLU(256, 128), ) self.block_d = nn.Sequential( ConvNormLReLU(128, 128), ConvNormLReLU(128, 128) ) self.block_e = nn.Sequential( ConvNormLReLU(128, 64), ConvNormLReLU(64, 64), ConvNormLReLU(64, 32, kernel_size=7, padding=3) ) self.out_layer = nn.Sequential( nn.Conv2d(32, 3, kernel_size=1, stride=1, padding=0, bias=False), nn.Tanh() ) def forward(self, input, align_corners=True): out = self.block_a(input) half_size = out.size()[-2:] out = self.block_b(out) out = self.block_c(out) if align_corners: out = F.interpolate(out, half_size, mode="bilinear", align_corners=True) else: out = F.interpolate(out, scale_factor=2, mode="bilinear", align_corners=False) out = self.block_d(out) if align_corners: out = F.interpolate(out, input.size()[-2:], mode="bilinear", align_corners=True) else: out = F.interpolate(out, scale_factor=2, mode="bilinear", align_corners=False) out = self.block_e(out) out = self.out_layer(out) return out # -------------------------- hy add 02 -------------------------- def load_image(image_path, x32=False): img = Image.open(image_path).convert("RGB") if x32: def to_32s(x): return 256 if x < 256 else x - x % 32 w, h = img.size img = img.resize((to_32s(w), to_32s(h))) return img def handle(image_path: str, output_dir: str, type: int, device='cpu'): _ext = os.path.basename(image_path).strip().split('.')[-1] if type == 1: _checkpoint = './weights/paprika.pt' elif type == 2: _checkpoint = './weights/face_paint_512_v2.pt' else: raise Exception('type not support') os.makedirs(output_dir, exist_ok=True) net = Generator() net.load_state_dict(torch.load(_checkpoint, map_location="cpu")) net.to(device).eval() image = load_image(image_path) with torch.no_grad(): image = to_tensor(image).unsqueeze(0) * 2 - 1 out = net(image.to(device), False).cpu() out = out.squeeze(0).clip(-1, 1) * 0.5 + 0.5 out = to_pil_image(out) result = os.path.join(output_dir, '{}.{}'.format(uuid.uuid1().hex, _ext)) out.save(result) return result if __name__ == '__main__': print(handle('samples/images/fengjing.jpg', 'samples/images_result/', 1)) print(handle('samples/images/renxiang.jpg', 'samples/images_result/', 2))

代码说明

1、handle方法可以将一张图片变为卡通化图片，入参为：图片路径、输出目录、类型（1为景色类型图片、2为人物人像图片）、设备类型（默认cpu，可以选择cuda）

2、按照我上一篇文章的测试，适合风景的模型和适合人像的模型不太一样，所以做了区分。

3、输出结果图片名字为了不重复，使用uuid。

验证一下

先发一下准备的图片