!pip install ninja
!pip install dlib
!pip uninstall -y torch!pip uninstall -y torchvision!pip install torch==1.6.0!pip install torchvision==0.7.0

%cd HFGI

#@title Setup Repositoryimport osfrom argparse import Namespaceimport timeimport osimport sysimport numpy as npfrom PIL import Imageimport torchimport torchvision.transforms as transforms  # from utils.common import tensor2imfrom models.psp import pSp  # we use the pSp framework to load the e4e encoder. %load_ext autoreload%autoreload 2

def tensor2im(var):    # var shape: (3, H, W)    var = var.cpu().detach().transpose(0, 2).transpose(0, 1).numpy()    var = ((var + 1) / 2)    var[var < 0] = 0    var[var > 1] = 1    var = var * 255    return Image.fromarray(var.astype('uint8'))

model_path = "checkpoint/ckpt.pt"ckpt = torch.load(model_path, map_location='cpu')opts = ckpt['opts']opts['is_train'] = Falseopts['checkpoint_path'] = model_pathopts= Namespace(**opts)net = pSp(opts)net.eval()net.cuda()print('Model successfully loaded!')

# Setup required image transformationsEXPERIMENT_ARGS = {        "image_path": "test_imgs/Lina.jpg"    } EXPERIMENT_ARGS['transform'] = transforms.Compose([    transforms.Resize((256, 256)),    transforms.ToTensor(),    transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])resize_dims = (256, 256)

image_path = EXPERIMENT_ARGS["image_path"]original_image = Image.open(image_path)original_image = original_image.convert("RGB") run_align = True

import numpy as npimport PILimport PIL.Imageimport scipyimport scipy.ndimageimport dlib  def get_landmark(filepath, predictor):    """get landmark with dlib    :return: np.array shape=(68, 2)    """    detector = dlib.get_frontal_face_detector()     img = dlib.load_rgb_image(filepath)    dets = detector(img, 1)     for k, d in enumerate(dets):        shape = predictor(img, d)     t = list(shape.parts())    a = []    for tt in t:        a.append([tt.x, tt.y])    lm = np.array(a)    return lm  def align_face(filepath, predictor):    """    :param filepath: str    :return: PIL Image    """     lm = get_landmark(filepath, predictor)     lm_chin = lm[0: 17]  # left-right    lm_eyebrow_left = lm[17: 22]  # left-right    lm_eyebrow_right = lm[22: 27]  # left-right    lm_nose = lm[27: 31]  # top-down    lm_nostrils = lm[31: 36]  # top-down    lm_eye_left = lm[36: 42]  # left-clockwise    lm_eye_right = lm[42: 48]  # left-clockwise    lm_mouth_outer = lm[48: 60]  # left-clockwise    lm_mouth_inner = lm[60: 68]  # left-clockwise     # Calculate auxiliary vectors.    eye_left = np.mean(lm_eye_left, axis=0)    eye_right = np.mean(lm_eye_right, axis=0)    eye_avg = (eye_left + eye_right) * 0.5    eye_to_eye = eye_right - eye_left    mouth_left = lm_mouth_outer[0]    mouth_right = lm_mouth_outer[6]    mouth_avg = (mouth_left + mouth_right) * 0.5    eye_to_mouth = mouth_avg - eye_avg     # Choose oriented crop rectangle.    x = eye_to_eye - np.flipud(eye_to_mouth) * [-1, 1]    x /= np.hypot(*x)    x *= max(np.hypot(*eye_to_eye) * 2.0, np.hypot(*eye_to_mouth) * 1.8)    y = np.flipud(x) * [-1, 1]    c = eye_avg + eye_to_mouth * 0.1    quad = np.stack([c - x - y, c - x + y, c + x + y, c + x - y])    qsize = np.hypot(*x) * 2     # read image    img = PIL.Image.open(filepath)     output_size = 256    transform_size = 256    enable_padding = True     # Shrink.    shrink = int(np.floor(qsize / output_size * 0.5))    if shrink > 1:        rsize = (int(np.rint(float(img.size[0]) / shrink)), int(np.rint(float(img.size[1]) / shrink)))        img = img.resize(rsize, PIL.Image.ANTIALIAS)        quad /= shrink        qsize /= shrink     # Crop.    border = max(int(np.rint(qsize * 0.1)), 3)    crop = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),            int(np.ceil(max(quad[:, 1]))))    crop = (max(crop[0] - border, 0), max(crop[1] - border, 0), min(crop[2] + border, img.size[0]),            min(crop[3] + border, img.size[1]))    if crop[2] - crop[0] < img.size[0] or crop[3] - crop[1] < img.size[1]:        img = img.crop(crop)        quad -= crop[0:2]     # Pad.    pad = (int(np.floor(min(quad[:, 0]))), int(np.floor(min(quad[:, 1]))), int(np.ceil(max(quad[:, 0]))),           int(np.ceil(max(quad[:, 1]))))    pad = (max(-pad[0] + border, 0), max(-pad[1] + border, 0), max(pad[2] - img.size[0] + border, 0),           max(pad[3] - img.size[1] + border, 0))    if enable_padding and max(pad) > border - 4:        pad = np.maximum(pad, int(np.rint(qsize * 0.3)))        img = np.pad(np.float32(img), ((pad[1], pad[3]), (pad[0], pad[2]), (0, 0)), 'reflect')        h, w, _ = img.shape        y, x, _ = np.ogrid[:h, :w, :1]        mask = np.maximum(1.0 - np.minimum(np.float32(x) / pad[0], np.float32(w - 1 - x) / pad[2]),                          1.0 - np.minimum(np.float32(y) / pad[1], np.float32(h - 1 - y) / pad[3]))        blur = qsize * 0.02        img += (scipy.ndimage.gaussian_filter(img, [blur, blur, 0]) - img) * np.clip(mask * 3.0 + 1.0, 0.0, 1.0)        img += (np.median(img, axis=(0, 1)) - img) * np.clip(mask, 0.0, 1.0)        img = PIL.Image.fromarray(np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB')        quad += pad[:2]     # Transform.    img = img.transform((transform_size, transform_size), PIL.Image.QUAD, (quad + 0.5).flatten(), PIL.Image.BILINEAR)    if output_size < transform_size:        img = img.resize((output_size, output_size), PIL.Image.ANTIALIAS)     # Return aligned image.    return img

if  'shape_predictor_68_face_landmarks.dat' not in os.listdir():#     !wget http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2    !bzip2 -dk shape_predictor_68_face_landmarks.dat.bz2 def run_alignment(image_path):  import dlib  predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")  aligned_image = align_face(filepath=image_path, predictor=predictor)   print("Aligned image has shape: {}".format(aligned_image.size))  return aligned_image  if run_align:  input_image = run_alignment(image_path)else:  input_image = original_image input_image.resize(resize_dims)

def display_alongside_source_image(result_image, source_image):    res = np.concatenate([np.array(source_image.resize(resize_dims)),                          np.array(result_image.resize(resize_dims))], axis=1)    return Image.fromarray(res) def get_latents(net, x, is_cars=False):    codes = net.encoder(x)    if net.opts.start_from_latent_avg:        if codes.ndim == 2:            codes = codes + net.latent_avg.repeat(codes.shape[0], 1, 1)[:, 0, :]        else:            codes = codes + net.latent_avg.repeat(codes.shape[0], 1, 1)    if codes.shape[1] == 18 and is_cars:        codes = codes[:, :16, :]    return codeswith torch.no_grad():    x = transformed_image.unsqueeze(0).cuda()     tic = time.time()    latent_codes = get_latents(net, x)        # calculate the distortion map    imgs, _ = net.decoder([latent_codes[0].unsqueeze(0).cuda()],None, input_is_latent=True, randomize_noise=False, return_latents=True)    res = x -  torch.nn.functional.interpolate(torch.clamp(imgs, -1., 1.), size=(256,256) , mode='bilinear')     # ADA    img_edit = torch.nn.functional.interpolate(torch.clamp(imgs, -1., 1.), size=(256,256) , mode='bilinear')    res_align  = net.grid_align(torch.cat((res, img_edit  ), 1))     # consultation fusion    conditions = net.residue(res_align)     result_image, _ = net.decoder([latent_codes],conditions, input_is_latent=True, randomize_noise=False, return_latents=True)    toc = time.time()    print('Inference took {:.4f} seconds.'.format(toc - tic)) # Display inversion:display_alongside_source_image(tensor2im(result_image[0]), input_image)

from editings import latent_editoreditor = latent_editor.LatentEditor(net.decoder)# interface-GANinterfacegan_directions = {        'age': './editings/interfacegan_directions/age.pt',        'smile': './editings/interfacegan_directions/smile.pt' }edit_direction = torch.load(interfacegan_directions['smile']).cuda() edit_degree = 1.5 # 设置微笑幅度img_edit, edit_latents = editor.apply_interfacegan(latent_codes[0].unsqueeze(0).cuda(), edit_direction, factor=edit_degree)  # 设置微笑# align the distortion mapimg_edit = torch.nn.functional.interpolate(torch.clamp(img_edit, -1., 1.), size=(256,256) , mode='bilinear')res_align  = net.grid_align(torch.cat((res, img_edit  ), 1)) # fusionconditions = net.residue(res_align)result, _ = net.decoder([edit_latents],conditions, input_is_latent=True, randomize_noise=False, return_latents=True) result = torch.nn.functional.interpolate(result, size=(256,256) , mode='bilinear')display_alongside_source_image(tensor2im(result[0]), input_image)