该项目旨在通过技术手段将“直男话术”转化为高情商表达，以增进情侣感情。其核心是让虚拟女友纠正不当话语，具体步骤如下：首先，输入直男语句（如“多喝热水”）转换为对应编号的高情商表达；接着用Pixel2Pixel模型将卡通照片真人化；再将真人化照片输入PaddleBoBo生成女友动画；最后让虚拟女友纠正话语。项目需32GB以上显卡环境，依赖相关模型和工具实现。
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虚拟女友纠正话语器
情侣之间相处少不了摩擦，但是据发现很多不必要的吵架，往往是词不达意造成的。比如关心她的身体健康，要注意身体，往往就只说了句“多喝热水”。如果换成另外一种表达，会让对方更容易接受，也更容易接收你给的爱意。因此“会说话”就变得十分重要了。这个项目就给大家一个初步的示范，怎么样的高情商的回答会让这段感情升温。
主要内容借鉴了我之前的项目：打造一个专属自己的卡通真人化主播
例如输入这张照片以及直男话术，你觉得会呈现出什么效果的视频呢?（doge）
直男语句：多喝热水。
效果展示
整体实现：
1.输入直男话语切换成高情商语句
2.利用Pixel2Pixel模型实现卡通照片真人化
3.把真人化输出的照片输入进PaddleBoBo生成女友动画
4.让虚拟女友纠正你的话语
PS：执行此项目请使用32GB显卡以上环境（看PaddleBoBo作者项目有提到，用16GB会爆内存导致跑不通，且本次项目也是在32GB显卡环境上制作的）
第一步、输入直男话语切换成高情商语句
请记住生成的编号等等用得着
阿里云-虚拟数字人
阿里云-虚拟数字人是什么？ …
2 查看详情 
In [ ]
huashu_dict={'多喝热水':'a', '你怎么又生气了':'b', '你又怎么了':'c', '你要这样想我也没办法':'d', '随便你！你定吧':'e', '哦':'f'}#请输入上面指定语句（粗糙版，请大家多多包涵）a = input('请输入直男语句:'+'n')if a in huashu_dict: print('已生成合适的话术'+'n'+'请记住生成编号'+':'+huashu_dict.get(a))else: print('不好意思，这句话我还没学会呢。')
请输入直男语句:已生成合适的话术请记住生成编号:a
第二步、利用Pixel2Pixel模型实现卡通照片真人化
主要是修改 image_name=’01503.png’，改成自己心仪的动漫照片（最好使用逆向思维：卡通照片真人化项目里面数据集的照片文件，其他动漫照片生成效果不好看，我不负责的哈）
In [ ]
import paddleimport paddle.nn as nnfrom paddle.io import Dataset, DataLoaderimport osimport cv2import numpy as npfrom tqdm import tqdmimport matplotlib.pyplot as pltimport PIL.Image as Image%matplotlib inlineclass UnetGenerator(nn.Layer): def __init__(self, input_nc=3, output_nc=3, ngf=64): super(UnetGenerator, self).__init__() self.down1 = nn.Conv2D(input_nc, ngf, kernel_size=4, stride=2, padding=1) self.down2 = Downsample(ngf, ngf*2) self.down3 = Downsample(ngf*2, ngf*4) self.down4 = Downsample(ngf*4, ngf*8) self.down5 = Downsample(ngf*8, ngf*8) self.down6 = Downsample(ngf*8, ngf*8) self.down7 = Downsample(ngf*8, ngf*8) self.center = Downsample(ngf*8, ngf*8) self.up7 = Upsample(ngf*8, ngf*8, use_dropout=True) self.up6 = Upsample(ngf*8*2, ngf*8, use_dropout=True) self.up5 = Upsample(ngf*8*2, ngf*8, use_dropout=True) self.up4 = Upsample(ngf*8*2, ngf*8) self.up3 = Upsample(ngf*8*2, ngf*4) self.up2 = Upsample(ngf*4*2, ngf*2) self.up1 = Upsample(ngf*2*2, ngf) self.output_block = nn.Sequential( nn.ReLU(), nn.Conv2DTranspose(ngf*2, output_nc, kernel_size=4, stride=2, padding=1), nn.Tanh() ) def forward(self, x): d1 = self.down1(x) d2 = self.down2(d1) d3 = self.down3(d2) d4 = self.down4(d3) d5 = self.down5(d4) d6 = self.down6(d5) d7 = self.down7(d6) c = self.center(d7) x = self.up7(c, d7) x = self.up6(x, d6) x = self.up5(x, d5) x = self.up4(x, d4) x = self.up3(x, d3) x = self.up2(x, d2) x = self.up1(x, d1) x = self.output_block(x) return xclass Downsample(nn.Layer): # LeakyReLU => conv => batch norm def __init__(self, in_dim, out_dim, kernel_size=4, stride=2, padding=1): super(Downsample, self).__init__() self.layers = nn.Sequential( nn.LeakyReLU(0.2), nn.Conv2D(in_dim, out_dim, kernel_size, stride, padding, bias_attr=False), nn.BatchNorm2D(out_dim) ) def forward(self, x): x = self.layers(x) return xclass Upsample(nn.Layer): # ReLU => deconv => batch norm => dropout def __init__(self, in_dim, out_dim, kernel_size=4, stride=2, padding=1, use_dropout=False): super(Upsample, self).__init__() sequence = [ nn.ReLU(), nn.Conv2DTranspose(in_dim, out_dim, kernel_size, stride, padding, bias_attr=False), nn.BatchNorm2D(out_dim) ] if use_dropout: sequence.append(nn.Dropout(p=0.5)) self.layers = nn.Sequential(*sequence) def forward(self, x, skip): x = self.layers(x) x = paddle.concat([x, skip], axis=1) return x#实例化生成器generator = UnetGenerator()#加载权重last_weights_path = 'data/data148534/epoch100.pdparams'print('加载权重:', last_weights_path)model_state_dict = paddle.load(last_weights_path)generator.load_dict(model_state_dict)generator.eval()#读取数据image_name='01503.png'img_A2B = cv2.imread('work/'+image_name)img_A = img_A2B[:, 256:] # 卡通图（即输入）img_B = img_A2B[:, :256] # 真人图（即预测结果）g_input = img_A.astype('float32') / 127.5 - 1 # 归一化g_input = g_input[np.newaxis, ...].transpose(0, 3, 1, 2) # NHWC -> NCHWg_input = paddle.to_tensor(g_input) # numpy -> tensorg_output = generator(g_input)g_output = g_output.detach().numpy() # tensor -> numpyg_output = g_output.transpose(0, 2, 3, 1)[0] # NCHW -> NHWCg_output = g_output * 127.5 + 127.5 # 反归一化g_output = g_output.astype(np.uint8)#只保存生成真人图像img = np.asarray(g_output).copy()img = Image.fromarray(cv2.cvtColor(img,cv2.COLOR_BGR2RGB))# cv2 to Imageimg.save('work/'+'output_'+image_name)img_show = np.hstack([img_A, g_output])[:,:,::-1]plt.figure(figsize=(8, 8))plt.imshow(img_show)plt.show()
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/__init__.py:107: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working from collections import MutableMapping/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/rcsetup.py:20: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working from collections import Iterable, Mapping/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/colors.py:53: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working from collections import SizedW0728 22:24:44.614435 192 gpu_resources.cc:61] Please NOTE: device: 0, GPU Compute Capability: 7.0, Driver API Version: 11.2, Runtime API Version: 10.1W0728 22:24:44.619457 192 gpu_resources.cc:91] device: 0, cuDNN Version: 7.6.
加载权重: data/data148534/epoch100.pdparams
/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:2349: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working if isinstance(obj, collections.Iterator):/opt/conda/envs/python35-paddle120-env/lib/python3.7/site-packages/matplotlib/cbook/__init__.py:2366: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working return list(data) if isinstance(data, collections.MappingView) else data
效果展示
变身！！！
第三步、把真人化输出的照片输入进paddlebobo生成虚拟女友动画
3.1解压压缩包
In [11]
!tar xzvf bobo.tar.gz PaddleBoBo data nltk_data work
3.2安装PaddleGAN和PaddleSpeech依赖
In [ ]
#这一步执行时间会比较久!pip install ppgan paddlespeech
3.3动漫真人化图像生成虚拟女友动画
这一步用到了default.yaml的配置文件，如果你只是尝试的话使用默认配置即可，如果你需要生成另一个人像，请修改default.yaml配置。主要是修改输入照片的位置：PaddleBoBo/default.yaml 里面的FOM_INPUT_IMAGE: ‘/home/aistudio/work/output_01503.png’
In [ ]
%cd PaddleBoBo!python create_virtual_human.py --config default.yaml
第四步、让虚拟女友纠正你的话语
–text 请输入之前生成的编号
In [ ]
!python general_demo.py --human ./file/input/test.mp4 --output ../output.mp4 --text a
效果展示
以上就是【情人节特辑】：虚拟女友教你如何正确“回答”的详细内容，更多请关注创想鸟其它相关文章！
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