想知道你未来宝宝长什么样吗？

!pip install imutils moviepy dlib

import osimport 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)

import cv2import mathimport pickleimport imageioimport warningsimport PIL.Imageimport numpy as npfrom glob import globfrom PIL import Imageimport tensorflow as tffrom random import randrangeimport moviepy.editor as mpyimport matplotlib.pyplot as pltfrom IPython.display import clear_outputfrom moviepy.video.io.ffmpeg_writer import FFMPEG_VideoWriter
import configimport dnnlibimport dnnlib.tflib as tflibfrom encoder.generator_model import Generator
%matplotlib inlinewarnings.filterwarnings("ignore")

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_imagedef 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 imgdef 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 imgdef 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()

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')

!python align_images.py ./father_image ./aligned_images

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.')

!python align_images.py ./mother_image ./aligned_images

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.')

!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.')

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 = Falseface = generate_final_image(hybrid_face, age_direction, intensity)plot_three_images(face, fs=15)

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_animationface_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 = 50download_image = Falsefor 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))

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_animationface_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))