Spaces:
Runtime error
Runtime error
File size: 13,765 Bytes
5238ef9 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 |
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 pandas as pd
import math
import scipy
import scipy.ndimage
import gc
# Number of style channels per StyleGAN layer
style2list_len = [512, 512, 512, 512, 512, 512, 512, 512, 512, 512,
512, 512, 512, 512, 512, 256, 256, 256, 128, 128,
128, 64, 64, 64, 32, 32]
# Layer indices of ToRGB modules
rgb_layer_idx = [1,4,7,10,13,16,19,22,25]
google_drive_paths = {
"stylegan2-ffhq-config-f.pt": "https://drive.google.com/uc?id=1Yr7KuD959btpmcKGAUsbAk5rPjX2MytK",
"inversion_stats.npz": "https://drive.google.com/uc?id=1oE_mIKf-Vr7b3J04l2UjsSrxZiw-UuFg",
"model_ir_se50.pt": "https://drive.google.com/uc?id=1KW7bjndL3QG3sxBbZxreGHigcCCpsDgn",
"dlibshape_predictor_68_face_landmarks.dat": "https://drive.google.com/uc?id=11BDmNKS1zxSZxkgsEvQoKgFd8J264jKp",
"e4e_ffhq_encode.pt": "https://drive.google.com/uc?id=1o6ijA3PkcewZvwJJ73dJ0fxhndn0nnh7"
}
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/{files}.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
'''
Given M, we zero out the first 2048 dimensions for non pose or hair features.
The reason is that the first 2048 mostly contain hair and pose information and rarely
anything related to other classes.
'''
def remove_2048(M, labels2idx):
M_hair = M[:,labels2idx['hair']].clone()
# zero out first 2048 channels (4 style layers) for non hair and pose features
M[...,:2048] = 0
M[:,labels2idx['hair']] = M_hair
return M
# Compute pose M and append it as the last index of M
def add_pose(M, labels2idx):
M = remove_2048(M, labels2idx)
# Add pose to the very last index of M
pose = 1-M[:,labels2idx['hair']]
M = torch.cat([M, pose.view(-1,1,9088)], 1)
#zero out rest of the channels after 2048 as pose should not affect other features
M[:,-1, 2048:] = 0
return M
# add direction specified by q from source to reference, scaled by a
def add_direction(s, r, q, a):
if isinstance(s, list):
s = list2style(s)
if isinstance(r, list):
r = list2style(r)
if s.ndim == 1:
s = s.unsqueeze(0)
if r.ndim == 1:
r = r.unsqueeze(0)
if q.ndim == 1:
q = q.unsqueeze(0)
if len(s) != len(r):
if s.size(0)< r.size(0):
s = s.expand(r.size(0), -1)
else:
r = r.expand(s.size(0), -1)
q = q.float()
old_norm = (q*s).norm(2,dim=1, keepdim=True)+1e-8
new_dir = q*r
new_dir = new_dir/(new_dir.norm(2,dim=1, keepdim=True)+1e-8) * old_norm
return s -a*q*s + a*new_dir
# convert a style vector [B, 9088] into a suitable format (list) for our generator's input
def style2list(s):
output = []
count = 0
for size in style2list_len:
output.append(s[:, count:count+size])
count += size
return output
# convert the list back to a style vector
def list2style(s):
return torch.cat(s, 1)
# flatten spatial activations to vectors
def flatten_act(x):
b,c,h,w = x.size()
x = x.pow(2).permute(0,2,3,1).contiguous().view(-1, c) # [b,c]
return x.cpu().numpy()
def show(imgs, title=None):
plt.figure(figsize=(5 * len(imgs), 5))
if title is not None:
plt.suptitle(title + '\n', fontsize=24).set_y(1.05)
for i in range(len(imgs)):
plt.subplot(1, len(imgs), i + 1)
plt.imshow(imgs[i])
plt.axis('off')
plt.gca().set_axis_off()
plt.subplots_adjust(top=1, bottom=0, right=1, left=0,
hspace=0, wspace=0.02)
plt.savefig(title + '.png', bbox_inches='tight', pad_inches=0)
def part_grid(target_image, refernce_images, part_images, file_name, score=None):
def proc(img):
return (img * 255).permute(1, 2, 0).squeeze().cpu().numpy().astype('uint8')
rows, cols = len(part_images) + 1, len(refernce_images) + 1
fig = plt.figure(figsize=(cols*4, rows*4))
sz = target_image.shape[-1]
i = 1
plt.subplot(rows, cols, i)
plt.imshow(proc(target_image[0]))
plt.axis('off')
plt.gca().set_axis_off()
plt.title('Source', fontdict={'size': 26})
for img in refernce_images:
i += 1
plt.subplot(rows, cols, i)
plt.imshow(proc(img))
plt.axis('off')
plt.gca().set_axis_off()
plt.title('Reference', fontdict={'size': 26})
# plt.text(0, sz, 'Perceptual loss: {:.2f}'.format(score[i-2]), fontdict={'size': 25}, color='red')
for j, label in enumerate(part_images.keys()):
i += 1
plt.subplot(rows, cols, i)
plt.imshow(proc(target_image[0]) * 0 + 255)
# plt.text(sz // 2, sz // 2, label.capitalize(), fontdict={'size': 30})
if score is not None:
plt.text(0 , sz//6, f'ID: {score[0]:.2f}', fontdict={'size': 30})
plt.text(0 , sz//6*2, f'Face_LPIPS:{score[1]:.2f}', fontdict={'size': 30})
plt.text(0 , sz//6*3, f'Hair_LPIPS:{score[2]:.2f}', fontdict={'size': 30})
plt.text(0 , sz//6*4, f'Total_LPIPS:{score[3]:.2f}', fontdict={'size': 30})
plt.text(0 , sz//6*5, f'FACE_SSIM: {score[4]:.2f}', fontdict={'size': 30})
plt.text(0 , sz//6*6, f'Hair_SSIM: {score[5]:.2f}', fontdict={'size': 30})
plt.text(0 , sz//6*7, f'Total_SSIM: {score[6]:.2f}', fontdict={'size': 30})
plt.axis('off')
plt.gca().set_axis_off()
for img in part_images[label]:
i += 1
plt.subplot(rows, cols, i)
plt.imshow(proc(img))
plt.axis('off')
plt.gca().set_axis_off()
plt.tight_layout(pad=0, w_pad=0, h_pad=0)
plt.subplots_adjust(wspace=0, hspace=0)
## Put 5 lines of text beside the image
# plt.text(0, sz+5, 'Perceptual loss: {:.2f}'.format(score[i-2]), fontdict={'size': 25}, color='red')
plt.savefig(file_name , bbox_inches='tight', pad_inches=0)
plt.close()
gc.collect()
return fig
def display_image(image, size=256, mode='nearest', unnorm=False, title=''):
# image is [3,h,w] or [1,3,h,w] tensor [0,1]
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.clamp(-1,1)+1)/2).permute(1, 2, 0).detach().numpy()
plt.figure()
plt.title(title)
plt.axis('off')
plt.imshow(image)
def get_parsing_labels():
color = torch.FloatTensor([[0, 0, 0],
[128, 0, 0], [0, 128, 0], [128, 128, 0], [0, 0, 128], [128, 0, 128],
[0, 128, 128], [128, 128, 128], [64, 0, 0], [192, 0, 0], [64, 128, 0],
[192, 128, 0], [64, 0, 128], [192, 0, 128], [64, 128, 128], [192,128,128],
[0, 64, 0], [0, 0, 64], [128, 0, 192], [0, 192, 128], [64,128,192], [64,64,64]])
return (color/255 * 2)-1
def decode_segmap(seg):
seg = seg.float()
label_colors = get_parsing_labels()
r = seg.clone()
g = seg.clone()
b = seg.clone()
for l in range(label_colors.size(0)):
r[seg == l] = label_colors[l, 0]
g[seg == l] = label_colors[l, 1]
b[seg == l] = label_colors[l, 2]
output = torch.stack([r,g,b], 1)
return output
def remove_idx(act, i):
# act [N, 128]
return torch.cat([act[:i], act[i+1:]], 0)
def interpolate_style(s, t, q):
if isinstance(s, list):
s = list2style(s)
if isinstance(t, list):
t = list2style(t)
if s.ndim == 1:
s = s.unsqueeze(0)
if t.ndim == 1:
t = t.unsqueeze(0)
if q.ndim == 1:
q = q.unsqueeze(0)
if len(s) != len(t):
s = s.expand(t.size(0), -1)
q = q.float()
return (1 - q) * s + q * t
def index_layers(w, i):
return [w[j][[i]] for j in range(len(w))]
def normalize_im(x):
return (x.clamp(-1,1)+1)/2
def l2(a, b):
return (a-b).pow(2).sum(1)
def cos_dist(a,b):
return -F.cosine_similarity(a, b, 1)
def downsample(x):
return F.interpolate(x, size=(256,256), mode='bilinear')
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,output_size=512):
# def align_face(filepath,output_size=512):
"""
:param filepath: str
:return: PIL Image
"""
ensure_checkpoint_exists("dlibshape_predictor_68_face_landmarks.dat")
predictor = dlib.shape_predictor("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
|