未来宝宝长什么样子测试（想知道你未来宝宝长什么样吗）

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本文分享自华为云社区《BabyGAN：根据父母照片生成孩子照片》，作者：山海之光。

本案例可根据一张父亲和母亲的正脸照片，生成孩子的照片，并且可以调节参数，看看不同性别和年龄孩子的长相。

为保证照片的生成效果，上传父母的照片时尽量上传能露出五官且浅色底的照片。

本案例仅用于学习交流，请勿用于其他用途。

另外，由于技术不完善的原因，生成的孩子照片可能会有扭曲或失真，你可以更换不同的父母照片，重新生成孩子照片，直到达到满意的生成效果为止。

下面开始按步骤运行本案例。

1. 安装所需的模块

本步骤耗时约 4 分钟

!pip install imutils moviepy dlib

2. 下载代码和模型文件

import os import moxing as mox root_dir = '/home/ma-user/work/ma_share/' code_dir = os.path.join(root_dir, 'BabyGAN') if not os.path.exists(os.path.join(root_dir, 'BabyGAN.zip')): mox.file.copy('obs://arthur-1/BabyGAN/BabyGAN.zip', os.path.join(root_dir, 'BabyGAN.zip')) os.system('cd %s; unzip BabyGAN.zip' % root_dir) os.chdir(code_dir)

3. 加载相关模块及模型

import cv2 import math import pickle import imageio import warnings import PIL.Image import numpy as np from glob import glob from PIL import Image import tensorflow as tf from random import randrange import moviepy.editor as mpy import matplotlib.pyplot as plt from IPython.display import clear_output from moviepy.video.io.ffmpeg_writer import FFMPEG_VideoWriter import config import dnnlib import dnnlib.tflib as tflib from encoder.generator_model import Generator %matplotlib inline warnings.filterwarnings("ignore")

加载模型文件，本代码块只可执行一次，如果发生报错，请 restart kernel，重新运行所有代码

tflib.init_tf() URL_FFHQ = "./karras2019stylegan-ffhq-1024x1024.pkl" with dnnlib.util.open_url(URL_FFHQ, cache_dir=config.cache_dir) as f: generator_network, discriminator_network, Gs_network = pickle.load(f) generator = Generator(Gs_network, batch_size=1, randomize_noise=False) model_scale = int(2 * (math.log(1024, 2) - 1))

age_direction = np.load('./ffhq_dataset/latent_directions/age.npy') horizontal_direction = np.load('./ffhq_dataset/latent_directions/angle_horizontal.npy') vertical_direction = np.load('./ffhq_dataset/latent_directions/angle_vertical.npy') eyes_open_direction = np.load('./ffhq_dataset/latent_directions/eyes_open.npy') gender_direction = np.load('./ffhq_dataset/latent_directions/gender.npy') smile_direction = np.load('./ffhq_dataset/latent_directions/smile.npy')

def get_watermarked(pil_image: Image) -> Image: try: image = cv2.cvtColor(np.array(pil_image), cv2.COLOR_RGB2BGR) (h, w) = image.shape[:2] image = np.dstack([image, np.ones((h, w), dtype="uint8") * 255]) pct = 0.08 full_watermark = cv2.imread('./media/logo.png', cv2.IMREAD_UNCHANGED) (fwH, fwW) = full_watermark.shape[:2] wH = int(pct * h * 2) wW = int((wH * fwW) / fwH * 0.1) watermark = cv2.resize(full_watermark, (wH, wW), interpolation=cv2.INTER_AREA) overlay = np.zeros((h, w, 4), dtype="uint8") (wH, wW) = watermark.shape[:2] overlay[h - wH - 10: h - 10, 10: 10 wW] = watermark output = image.copy() cv2.addWeighted(overlay, 0.5, output, 1.0, 0, output) rgb_image = cv2.cvtColor(output, cv2.COLOR_BGR2RGB) return Image.fromarray(rgb_image) except: return pil_image def generate_final_images(latent_vector, direction, coeffs, i): new_latent_vector = latent_vector.copy() new_latent_vector[:8] = (latent_vector coeffs * direction)[:8] new_latent_vector = new_latent_vector.reshape((1, 18, 512)) generator.set_dlatents(new_latent_vector) img_array = generator.generate_images()[0] img = PIL.Image.fromarray(img_array, 'RGB') if size[0] >= 512: img = get_watermarked(img) img_path = "./for_animation/" str(i) ".png" img.thumbnail(animation_size, PIL.Image.ANTIALIAS) img.save(img_path) face_img.append(imageio.imread(img_path)) clear_output() return img def generate_final_image(latent_vector, direction, coeffs): new_latent_vector = latent_vector.copy() new_latent_vector[:8] = (latent_vector coeffs * direction)[:8] new_latent_vector = new_latent_vector.reshape((1, 18, 512)) generator.set_dlatents(new_latent_vector) img_array = generator.generate_images()[0] img = PIL.Image.fromarray(img_array, 'RGB') if size[0] >= 512: img = get_watermarked(img) img.thumbnail(size, PIL.Image.ANTIALIAS) img.save("face.png") if download_image == True: files.download("face.png") return img def plot_three_images(imgB, fs=10): f, axarr = plt.subplots(1, 3, figsize=(fs, fs)) axarr[0].imshow(Image.open('./aligned_images/father_01.png')) axarr[0].title.set_text("Father's photo") axarr[1].imshow(imgB) axarr[1].title.set_text("Child's photo") axarr[2].imshow(Image.open('./aligned_images/mother_01.png')) axarr[2].title.set_text("Mother's photo") plt.setp(plt.gcf().get_axes(), xticks=[], yticks=[]) plt.show()

4. 准备好父亲和母亲的照片

本案例已各准备好一张默认的父母亲照片，可在左侧边栏的文件资源管理窗口中，进入到 ma_share/BabyGAN 目录，再进入到 father_image 或 mother_image 目录即可看到已提供的父母亲照片，如下图所示：

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如果你需更换父母亲的照片，请查看本文第 11 节“更换父亲和母亲的照片”

if len(glob(os.path.join('./father_image', '*.jpg'))) != 1 or (not os.path.exists('./father_image/father.jpg')): raise Exception('请在 ma_share/BabyGAN/father_image 目录下准备一张父亲的照片，且命名为father.jpg') if len(glob(os.path.join('./mother_image', '*.jpg'))) != 1 or (not os.path.exists('./mother_image/mother.jpg')): raise Exception('请在 ma_share/BabyGAN/father_image 目录下准备一张母亲的照片，且命名为mother.jpg')

5. 获取父亲的脸部区域，并进行人脸对齐

!python align_images.py ./father_image ./aligned_images

查看父亲的人脸

if os.path.isfile('./aligned_images/father_01.png'): pil_father = Image.open('./aligned_images/father_01.png') (fat_width, fat_height) = pil_father.size resize_fat = max(fat_width, fat_height) / 256 display(pil_father.resize((int(fat_width / resize_fat), int(fat_height / resize_fat)))) else: raise ValueError('No face was found or there is more than one in the photo.')

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6. 获取母亲的脸部区域，并进行人脸对齐

!python align_images.py ./mother_image ./aligned_images

查看母亲的人脸

if os.path.isfile('./aligned_images/mother_01.png'): pil_mother = Image.open('./aligned_images/mother_01.png') (mot_width, mot_height) = pil_mother.size resize_mot = max(mot_width, mot_height) / 256 display(pil_mother.resize((int(mot_width / resize_mot), int(mot_height / resize_mot)))) else: raise ValueError('No face was found or there is more than one in the photo.')

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7. 提取人脸特征

本步骤耗时约 3 分钟

!python encode_images.py \ --early_stopping False \ --lr=0.25 \ --batch_size=2 \ --iterations=100 \ --output_video=False \ ./aligned_images \ ./generated_images \ ./latent_representations if len(glob(os.path.join('./generated_images', '*.png'))) == 2: first_face = np.load('./latent_representations/father_01.npy') second_face = np.load('./latent_representations/mother_01.npy') print("Generation of latent representation is complete! Now comes the fun part.") else: raise ValueError('Something wrong. It may be impossible to read the face in the photos. Upload other photos and try again.')

8. 生成一家三口照片

请修改下面代码中的 gender_influence 和 person_age 参数

gender_influence：性别影响因子，取值范围[0.01, 0.99]，取值越接近 0，父亲的容貌影响越大，反之母亲影响越大；

person_age：年龄影响因子，取值范围[10, 50]，设置该值后，将生成对应年龄的小孩的容貌。

每次修改该参数值后，重新运行下面的代码块，即可生成孩子的新照片

genes_influence = 0.8 # 性别影响因子，取值范围[0.01, 0.99]，取值越接近0，父亲的容貌影响越大，反之母亲影响越大 person_age = 10 # 年龄影响因子，取值范围[10, 50]，设置该值后，将生成对应年龄的小孩的容貌 style = "Default" if style == "Father's photo": lr = ((np.arange(1, model_scale 1) / model_scale) ** genes_influence).reshape((model_scale, 1)) rl = 1 - lr hybrid_face = (lr * first_face) (rl * second_face) elif style == "Mother's photo": lr = ((np.arange(1, model_scale 1) / model_scale) ** (1 - genes_influence)).reshape((model_scale, 1)) rl = 1 - lr hybrid_face = (rl * first_face) (lr * second_face) else: hybrid_face = ((1 - genes_influence) * first_face) (genes_influence * second_face) intensity = -((person_age / 5) - 6) resolution = "512" size = int(resolution), int(resolution) download_image = False face = generate_final_image(hybrid_face, age_direction, intensity) plot_three_images(face, fs=15)

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9. 查看孩子各年龄段的容貌

请修改下面代码中的 gender_influence 参数，该参数是性别影响因子，取值范围[0.01, 0.99]，取值越接近 0，父亲的容貌影响越大，反之母亲影响越大。

每次修改该参数值后，要重新运行下面的代码块

gender_influence = 0.8 # 性别影响因子，取值范围[0.01, 0.99]，取值越接近0，父亲的容貌影响越大，反之母亲影响越大 !rm -rf ./for_animation !mkdir ./for_animation face_img = [] hybrid_face = ((1 - gender_influence) * first_face) (gender_influence * second_face) animation_resolution = "512" animation_size = int(animation_resolution), int(animation_resolution) frames_number = 50 download_image = False for i in range(0, frames_number, 1): intensity = (8 * (i / (frames_number - 1))) - 4 generate_final_images(hybrid_face, age_direction, intensity, i) clear_output() print(str(i) " of {} photo generated".format(str(frames_number))) for j in reversed(face_img): face_img.append(j) automatic_download = False if gender_influence <= 0.3: animation_name = "boy.mp4" elif gender_influence >= 0.7: animation_name = "girl.mp4" else: animation_name = "animation.mp4" imageio.mimsave('./for_animation/' animation_name, face_img) clear_output() display(mpy.ipython_display('./for_animation/' animation_name, height=400, autoplay=1, loop=1))

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10. 查看孩子不同性别的容貌

请修改下面代码中的 person_age 参数，该参数是年龄影响因子，取值范围[10, 50]，设置该值后，将生成对应年龄的小孩的容貌。

每次修改该参数值后，要重新运行下面的代码块

person_age = 10 # 小孩的年龄，取值范围[10, 50]，设置该值后，将生成对应年龄的小孩的容貌 !rm -rf ./for_animation !mkdir ./for_animation face_img = [] intensity = -((person_age / 5) - 6) animation_resolution = "512" animation_size = int(animation_resolution), int(animation_resolution) frames_number = 50 # 容貌变化的图像数，取值范围[10, 50] download_image = False for i in range(1, frames_number): gender_influence = i / frames_number hybrid_face = ((1 - gender_influence) * first_face) (gender_influence * second_face) face = generate_final_images(hybrid_face, age_direction, intensity, i) clear_output() print(str(i) " of {} photo generated".format(str(frames_number))) for j in reversed(face_img): face_img.append(j) animation_name = str(person_age) "_years.mp4" imageio.mimsave('./for_animation/' animation_name, face_img) clear_output() display(mpy.ipython_display('./for_animation/' animation_name, height=400, autoplay=1, loop=1))

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