File size: 8,677 Bytes
c2f174c
 
 
 
 
ddafa9f
c2f174c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4bda5a0
c2f174c
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
from matplotlib import pyplot as plt
import torch
import torch.nn.functional as F
import os
import cv2
import dlib
from PIL import Image
import numpy as np
import math
import torchvision
import scipy
import scipy.ndimage
import torchvision.transforms as transforms

google_drive_paths = {
    "models/stylegan2-ffhq-config-f.pt": "https://drive.google.com/uc?id=1Yr7KuD959btpmcKGAUsbAk5rPjX2MytK",
    "models/dlibshape_predictor_68_face_landmarks.dat": "https://drive.google.com/uc?id=11BDmNKS1zxSZxkgsEvQoKgFd8J264jKp",
    "models/e4e_ffhq_encode.pt": "https://drive.google.com/uc?id=1o6ijA3PkcewZvwJJ73dJ0fxhndn0nnh7",
    "models/restyle_psp_ffhq_encode.pt": "https://drive.google.com/uc?id=1nbxCIVw9H3YnQsoIPykNEFwWJnHVHlVd",
    "models/arcane_caitlyn.pt": "https://drive.google.com/uc?id=1gOsDTiTPcENiFOrhmkkxJcTURykW1dRc",
    "models/arcane_caitlyn_preserve_color.pt": "https://drive.google.com/uc?id=1cUTyjU-q98P75a8THCaO545RTwpVV-aH",
    "models/arcane_jinx_preserve_color.pt": "https://drive.google.com/uc?id=1jElwHxaYPod5Itdy18izJk49K1nl4ney",
    "models/arcane_jinx.pt": "https://drive.google.com/uc?id=1quQ8vPjYpUiXM4k1_KIwP4EccOefPpG_",
    "models/disney.pt": "https://drive.google.com/uc?id=1zbE2upakFUAx8ximYnLofFwfT8MilqJA",
    "models/disney_preserve_color.pt": "https://drive.google.com/uc?id=1Bnh02DjfvN_Wm8c4JdOiNV4q9J7Z_tsi",
    "models/jojo.pt": "https://drive.google.com/uc?id=13cR2xjIBj8Ga5jMO7gtxzIJj2PDsBYK4",
    "models/jojo_preserve_color.pt": "https://drive.google.com/uc?id=1ZRwYLRytCEKi__eT2Zxv1IlV6BGVQ_K2",
    "models/jojo_yasuho.pt": "https://drive.google.com/uc?id=1grZT3Gz1DLzFoJchAmoj3LoM9ew9ROX_",
    "models/jojo_yasuho_preserve_color.pt": "https://drive.google.com/uc?id=1SKBu1h0iRNyeKBnya_3BBmLr4pkPeg_L",
    "models/supergirl.pt": "https://drive.google.com/uc?id=1L0y9IYgzLNzB-33xTpXpecsKU-t9DpVC",
    "models/supergirl_preserve_color.pt": "https://drive.google.com/uc?id=1VmKGuvThWHym7YuayXxjv0fSn32lfDpE",
}

@torch.no_grad()
def load_model(generator, model_file_path):
    ensure_checkpoint_exists(model_file_path)
    ckpt = torch.load(model_file_path, map_location=lambda storage, loc: storage)
    generator.load_state_dict(ckpt["g_ema"], strict=False)
    return generator.mean_latent(50000)

def ensure_checkpoint_exists(model_weights_filename):
    if not os.path.isfile(model_weights_filename) and (
        model_weights_filename in google_drive_paths
    ):
        gdrive_url = google_drive_paths[model_weights_filename]
        try:
            from gdown import download as drive_download

            drive_download(gdrive_url, model_weights_filename, quiet=False)
        except ModuleNotFoundError:
            print(
                "gdown module not found.",
                "pip3 install gdown or, manually download the checkpoint file:",
                gdrive_url
            )

    if not os.path.isfile(model_weights_filename) and (
        model_weights_filename not in google_drive_paths
    ):
        print(
            model_weights_filename,
            " not found, you may need to manually download the model weights."
        )

# given a list of filenames, load the inverted style code
@torch.no_grad()
def load_source(files, generator, device='cuda'):
    sources = []
    
    for file in files:
        source = torch.load(f'./inversion_codes/{file}.pt')['latent'].to(device)

        if source.size(0) != 1:
            source = source.unsqueeze(0)

        if source.ndim == 3:
            source = generator.get_latent(source, truncation=1, is_latent=True)
            source = list2style(source)
            
        sources.append(source)
        
    sources = torch.cat(sources, 0)
    if type(sources) is not list:
        sources = style2list(sources)
        
    return sources

def display_image(image, size=None, mode='nearest', unnorm=False, title=''):
    # image is [3,h,w] or [1,3,h,w] tensor [0,1]
    if not isinstance(image, torch.Tensor):
        image = transforms.ToTensor()(image).unsqueeze(0)
    if image.is_cuda:
        image = image.cpu()
    if size is not None and image.size(-1) != size:
        image = F.interpolate(image, size=(size,size), mode=mode)
    if image.dim() == 4:
        image = image[0]
    image = image.permute(1, 2, 0).detach().numpy()
    plt.figure()
    plt.title(title)
    plt.axis('off')
    plt.imshow(image)

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)
    assert len(dets) > 0, "Face not detected, try another face image"

    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, output_size=256, transform_size=1024, enable_padding=True):

    """
    :param filepath: str
    :return: PIL Image
    """
    ensure_checkpoint_exists("models/dlibshape_predictor_68_face_landmarks.dat")
    predictor = dlib.shape_predictor("models/dlibshape_predictor_68_face_landmarks.dat")
    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 = Image.open(filepath)

    transform_size = output_size
    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, 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 = Image.fromarray(np.uint8(np.clip(np.rint(img), 0, 255)), 'RGB')
        quad += pad[:2]

    # Transform.
    img = img.transform((transform_size, transform_size), Image.QUAD, (quad + 0.5).flatten(), Image.BILINEAR)
    if output_size < transform_size:
        img = img.resize((output_size, output_size), Image.ANTIALIAS)

    # Return aligned image.
    return img

def strip_path_extension(path):
   return  os.path.splitext(path)[0]