关于图像:想知道你未来宝宝长什么样吗

摘要： 本案例可依据一张父亲和母亲的正脸照片，生成孩子的照片，并且能够调节参数，看看不同性别和年龄孩子的长相。

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

本案例可依据一张父亲和母亲的正脸照片，生成孩子的照片，并且能够调节参数，看看不同性别和年龄孩子的长相。

为保障照片的生成成果，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换，请勿用于其余用处。

另外，因为技术不欠缺的起因，生成的孩子照片可能会有扭曲或失真，你能够更换不同的父母照片，从新生成孩子照片，直到达到称心的生成成果为止。

上面开始按步骤运行本案例。

1. 装置所需的模块

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. 加载相干模块及模型

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，如果产生报错，请 restart kernel，从新运行所有代码

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. 筹备好父亲和母亲的照片

本案例已各筹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：

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 目录下筹备一张父亲的照片，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 目录下筹备一张母亲的照片，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.')

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.')

7. 提取人脸特色

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

请批改上面代码中的 gender_influence 和 person_age 参数，

gender_influence：性别影响因子，取值范畴 [0.01, 0.99]，取值越靠近 0，父亲的模样影响越大，反之母亲影响越大；

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

每次批改该参数值后，从新运行上面的代码块，即可生成孩子的新照片

genes_influence = 0.8 # 性别影响因子，取值范畴 [0.01, 0.99]，取值越靠近 0，父亲的模样影响越大，反之母亲影响越大
person_age = 10 # 年龄影响因子，取值范畴 [10, 50]，设置该值后，将生成对应年龄的小孩的模样

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)

9. 查看孩子各年龄段的模样

请批改上面代码中的 gender_influence 参数，该参数是性别影响因子，取值范畴 [0.01, 0.99]，取值越靠近 0，父亲的模样影响越大，反之母亲影响越大。

每次批改该参数值后，要从新运行上面的代码块

gender_influence = 0.8  # 性别影响因子，取值范畴 [0.01, 0.99]，取值越靠近 0，父亲的模样影响越大，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))

10. 查看孩子不同性别的模样

请批改上面代码中的 person_age 参数，该参数是年龄影响因子，取值范畴 [10, 50]，设置该值后，将生成对应年龄的小孩的模样。

每次批改该参数值后，要从新运行上面的代码块

person_age = 10  # 小孩的年龄，取值范畴 [10, 50]，设置该值后，将生成对应年龄的小孩的模样

!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  # 模样变动的图像数，取值范畴 [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))

11. 更换父亲和母亲的照片

接下来，你能够上传本人感兴趣的父母亲照片到 father_image 和 mother_image 目录下，从新运行代码，即可生成新的孩子照片。

你须要依照如下规定和步骤进行：

1、参考下图的操作，进入到 ma_share/BabyGAN 目录；

2、筹备一张父亲的照片，上传到 father_image 目录下，命名必须为 father.jpg；（如果你不晓得上传文件到 JupyterLab 的办法，请查看此文档）

3、筹备一张母亲的照片，上传到 mother_image 目录下，命名必须为 mother.jpg；

4、father_image 和 mother_image 目录都只容许存在一张照片；

5、从新运行步骤 4~10 的代码。

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