-
Notifications
You must be signed in to change notification settings - Fork 0
/
render.py
186 lines (148 loc) · 8.7 KB
/
render.py
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
import os
import sys
import numpy as np
import torch
from torchvision import transforms
from skimage.io import imsave
import skvideo.io
from pathlib import Path
from tqdm import auto
import argparse
import cv2
from utils import torch_img_to_np, _fix_image, torch_img_to_np2
from external.FaceVerse import get_faceverse
from external.PIRender import FaceGenerator
def obtain_seq_index(index, num_frames, semantic_radius = 13):
seq = list(range(index - semantic_radius, index + semantic_radius + 1))
seq = [min(max(item, 0), num_frames - 1) for item in seq]
return seq
def transform_semantic(semantic):
semantic_list = []
for i in range(semantic.shape[0]):
index = obtain_seq_index(i, semantic.shape[0])
semantic_item = semantic[index, :].unsqueeze(0)
semantic_list.append(semantic_item)
semantic = torch.cat(semantic_list, dim = 0)
return semantic.transpose(1,2)
class Render(object):
"""Computes and stores the average and current value"""
def __init__(self, device = 'cpu'):
self.faceverse, _ = get_faceverse(device=device, img_size=224)
self.faceverse.init_coeff_tensors()
self.id_tensor = torch.from_numpy(np.load('external/FaceVerse/reference_full.npy')).float().view(1,-1)[:,:150]
self.pi_render = FaceGenerator().to(device)
self.pi_render.eval()
checkpoint = torch.load('external/PIRender/cur_model_fold.pth')
self.pi_render.load_state_dict(checkpoint['state_dict'])
self.mean_face = torch.FloatTensor(
np.load('external/FaceVerse/mean_face.npy').astype(np.float32)).view(1, 1, -1).to(device)
self.std_face = torch.FloatTensor(
np.load('external/FaceVerse/std_face.npy').astype(np.float32)).view(1, 1, -1).to(device)
self._reverse_transform_3dmm = transforms.Lambda(lambda e: e + self.mean_face)
def rendering(self, path, ind, listener_vectors, speaker_video_clip, listener_reference):
'''
path: val_offline/val
ind: 第epoch_第batch_第i个视频
listener_vectors: model预测的听者3dmm参数
speaker_video_clip: 说话者的视频帧数图
listener_reference: 真实听者video第一帧
'''
# 3D video
T = listener_vectors.shape[0]
listener_vectors = self._reverse_transform_3dmm(listener_vectors)[0]
self.faceverse.batch_size = T
self.faceverse.init_coeff_tensors()
self.faceverse.exp_tensor = listener_vectors[:,:52].view(T,-1).to(listener_vectors.get_device())
self.faceverse.rot_tensor = listener_vectors[:,52:55].view(T, -1).to(listener_vectors.get_device())
self.faceverse.trans_tensor = listener_vectors[:,55:].view(T, -1).to(listener_vectors.get_device())
self.faceverse.id_tensor = self.id_tensor.view(1,150).repeat(T,1).view(T,150).to(listener_vectors.get_device())
pred_dict = self.faceverse(self.faceverse.get_packed_tensors(), render=True, texture=False)
rendered_img_r = pred_dict['rendered_img']
rendered_img_r = np.clip(rendered_img_r.cpu().numpy(), 0, 255)
rendered_img_r = rendered_img_r[:, :, :, :3].astype(np.uint8)
# 2D video
# listener_vectors = torch.cat((listener_exp.view(T,-1), listener_trans.view(T, -1), listener_rot.view(T, -1)))
semantics = transform_semantic(listener_vectors.detach()).to(listener_vectors.get_device())
C, H, W = listener_reference.shape
output_dict_list = []
duration = listener_vectors.shape[0] // 20
listener_reference_frames = listener_reference.repeat(listener_vectors.shape[0], 1, 1).view(
listener_vectors.shape[0], C, H, W)
for i in range(20):
if i != 19:
listener_reference_copy = listener_reference_frames[i * duration:(i + 1) * duration]
semantics_copy = semantics[i * duration:(i + 1) * duration]
else:
listener_reference_copy = listener_reference_frames[i * duration:]
semantics_copy = semantics[i * duration:]
output_dict = self.pi_render(listener_reference_copy, semantics_copy)
fake_videos = output_dict['fake_image']
fake_videos = torch_img_to_np2(fake_videos)
output_dict_list.append(fake_videos)
listener_videos = np.concatenate(output_dict_list, axis=0)
speaker_video_clip = torch_img_to_np2(speaker_video_clip)
out = cv2.VideoWriter(os.path.join(path, ind + "_val.avi"), cv2.VideoWriter_fourcc(*"MJPG"), 25, (672, 224))
for i in range(rendered_img_r.shape[0]):
combined_img = np.zeros((224, 672, 3), dtype=np.uint8)
combined_img[0:224, 0:224] = speaker_video_clip[i]
combined_img[0:224, 224:448] = rendered_img_r[i]
combined_img[0:224, 448:] = listener_videos[i]
out.write(combined_img)
out.release()
def rendering_for_fid(self, path, ind, listener_vectors, speaker_video_clip, listener_reference, listener_video_clip):
# 3D video
T = listener_vectors.shape[0]
# rendering_listener_vectors: torch.Size([750, 58])
listener_vectors = self._reverse_transform_3dmm(listener_vectors)[0]
self.faceverse.batch_size = T
self.faceverse.init_coeff_tensors()
self.faceverse.exp_tensor = listener_vectors[:, :52].view(T, -1).to(listener_vectors.get_device())
self.faceverse.rot_tensor = listener_vectors[:, 52:55].view(T, -1).to(listener_vectors.get_device())
self.faceverse.trans_tensor = listener_vectors[:, 55:].view(T, -1).to(listener_vectors.get_device())
self.faceverse.id_tensor = self.id_tensor.view(1, 150).repeat(T, 1).view(T, 150).to(listener_vectors.get_device())
pred_dict = self.faceverse(self.faceverse.get_packed_tensors(), render=True, texture=False)
rendered_img_r = pred_dict['rendered_img']
rendered_img_r = np.clip(rendered_img_r.cpu().numpy(), 0, 255)
rendered_img_r = rendered_img_r[:, :, :, :3].astype(np.uint8)
# rendered_img_r: (750, 224, 224, 3)
# 2D video
# listener_vectors = torch.cat((listener_exp.view(T,-1), listener_trans.view(T, -1), listener_rot.view(T, -1)))
semantics = transform_semantic(listener_vectors.detach()).to(listener_vectors.get_device())
C, H, W = listener_reference.shape
output_dict_list = []
duration = listener_vectors.shape[0] // 20 ## 32
listener_reference_frames = listener_reference.repeat(listener_vectors.shape[0], 1, 1).view(
listener_vectors.shape[0], C, H, W) ## 750张同样的第一帧
for i in range(20):
if i != 19:
listener_reference_copy = listener_reference_frames[i * duration:(i + 1) * duration]
semantics_copy = semantics[i * duration:(i + 1) * duration]
else:
listener_reference_copy = listener_reference_frames[i * duration:]
semantics_copy = semantics[i * duration:]
output_dict = self.pi_render(listener_reference_copy, semantics_copy) ## 3D人脸 -> 2D人脸
fake_videos = output_dict['fake_image']
fake_videos = torch_img_to_np2(fake_videos)
output_dict_list.append(fake_videos)
listener_videos = np.concatenate(output_dict_list, axis=0)
speaker_video_clip = torch_img_to_np2(speaker_video_clip)
if not os.path.exists(os.path.join(path, 'results_videos')):
os.makedirs(os.path.join(path, 'results_videos'))
out = cv2.VideoWriter(os.path.join(path, 'results_videos', ind + "_val.avi"), cv2.VideoWriter_fourcc(*"MJPG"), 25, (672, 224))
for i in range(rendered_img_r.shape[0]):
combined_img = np.zeros((224, 672, 3), dtype=np.uint8)
combined_img[0:224, 0:224] = speaker_video_clip[i]
combined_img[0:224, 224:448] = rendered_img_r[i]
combined_img[0:224, 448:] = listener_videos[i]
out.write(combined_img)
out.release()
listener_video_clip = torch_img_to_np2(listener_video_clip)
path_real = os.path.join(path, 'fid', 'real')
if not os.path.exists(path_real):
os.makedirs(path_real)
path_fake = os.path.join(path, 'fid', 'fake')
if not os.path.exists(path_fake):
os.makedirs(path_fake)
for i in range(0, rendered_img_r.shape[0], 30):
cv2.imwrite(os.path.join(path_fake, ind+'_'+str(i+1)+'.png'), listener_videos[i]) ## 预测听者视频
cv2.imwrite(os.path.join(path_real, ind+'_'+str(i+1)+'.png'), listener_video_clip[i]) ## 真实听者视频