-
Notifications
You must be signed in to change notification settings - Fork 13
/
demo.py
executable file
·194 lines (137 loc) · 8.73 KB
/
demo.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
187
188
189
190
191
192
193
import torch
import cv2
import numpy as np
from skimage.transform import estimate_transform, warp
from src.smirk_encoder import SmirkEncoder
from src.FLAME.FLAME import FLAME
from src.renderer.renderer import Renderer
import argparse
import os
import src.utils.masking as masking_utils
from utils.mediapipe_utils import run_mediapipe
from datasets.base_dataset import create_mask
import torch.nn.functional as F
def crop_face(frame, landmarks, scale=1.0, image_size=224):
left = np.min(landmarks[:, 0])
right = np.max(landmarks[:, 0])
top = np.min(landmarks[:, 1])
bottom = np.max(landmarks[:, 1])
h, w, _ = frame.shape
old_size = (right - left + bottom - top) / 2
center = np.array([right - (right - left) / 2.0, bottom - (bottom - top) / 2.0])
size = int(old_size * scale)
# crop image
src_pts = np.array([[center[0] - size / 2, center[1] - size / 2], [center[0] - size / 2, center[1] + size / 2],
[center[0] + size / 2, center[1] - size / 2]])
DST_PTS = np.array([[0, 0], [0, image_size - 1], [image_size - 1, 0]])
tform = estimate_transform('similarity', src_pts, DST_PTS)
return tform
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--input_path', type=str, default='samples/mead_90.png', help='Path to the input image/video')
parser.add_argument('--device', type=str, default='cuda', help='Device to run the model on')
parser.add_argument('--checkpoint', type=str, default='trained_models/SMIRK_em1.pt', help='Path to the checkpoint')
parser.add_argument('--crop', action='store_true', help='Crop the face using mediapipe')
parser.add_argument('--out_path', type=str, default='output', help='Path to save the output (will be created if not exists)')
parser.add_argument('--use_smirk_generator', action='store_true', help='Use SMIRK neural image to image translator to reconstruct the image')
parser.add_argument('--render_orig', action='store_true', help='Present the result w.r.t. the original image/video size')
args = parser.parse_args()
image_size = 224
# ----------------------- initialize configuration ----------------------- #
smirk_encoder = SmirkEncoder().to(args.device)
checkpoint = torch.load(args.checkpoint)
checkpoint_encoder = {k.replace('smirk_encoder.', ''): v for k, v in checkpoint.items() if 'smirk_encoder' in k} # checkpoint includes both smirk_encoder and smirk_generator
smirk_encoder.load_state_dict(checkpoint_encoder)
smirk_encoder.eval()
if args.use_smirk_generator:
from src.smirk_generator import SmirkGenerator
smirk_generator = SmirkGenerator(in_channels=6, out_channels=3, init_features=32, res_blocks=5).to(args.device)
checkpoint_generator = {k.replace('smirk_generator.', ''): v for k, v in checkpoint.items() if 'smirk_generator' in k} # checkpoint includes both smirk_encoder and smirk_generator
smirk_generator.load_state_dict(checkpoint_generator)
smirk_generator.eval()
# ---- visualize the results ---- #
flame = FLAME().to(args.device)
renderer = Renderer().to(args.device)
# check if input is an image or a video or webcam or directory
image = cv2.imread(args.input_path)
orig_image_height, orig_image_width, _ = image.shape
kpt_mediapipe = run_mediapipe(image)
# crop face if needed
if args.crop:
if (kpt_mediapipe is None):
print('Could not find landmarks for the image using mediapipe and cannot crop the face. Exiting...')
exit()
kpt_mediapipe = kpt_mediapipe[..., :2]
tform = crop_face(image,kpt_mediapipe,scale=1.4,image_size=image_size)
cropped_image = warp(image, tform.inverse, output_shape=(224, 224), preserve_range=True).astype(np.uint8)
cropped_kpt_mediapipe = np.dot(tform.params, np.hstack([kpt_mediapipe, np.ones([kpt_mediapipe.shape[0],1])]).T).T
cropped_kpt_mediapipe = cropped_kpt_mediapipe[:,:2]
else:
cropped_image = image
cropped_kpt_mediapipe = kpt_mediapipe
cropped_image = cv2.cvtColor(cropped_image, cv2.COLOR_BGR2RGB)
cropped_image = cv2.resize(cropped_image, (224,224))
cropped_image = torch.tensor(cropped_image).permute(2,0,1).unsqueeze(0).float()/255.0
cropped_image = cropped_image.to(args.device)
outputs = smirk_encoder(cropped_image)
flame_output = flame.forward(outputs)
renderer_output = renderer.forward(flame_output['vertices'], outputs['cam'],
landmarks_fan=flame_output['landmarks_fan'], landmarks_mp=flame_output['landmarks_mp'])
rendered_img = renderer_output['rendered_img']
if args.render_orig:
if args.crop:
rendered_img_numpy = (rendered_img.squeeze(0).permute(1,2,0).detach().cpu().numpy()*255.0).astype(np.uint8)
rendered_img_orig = warp(rendered_img_numpy, tform, output_shape=(orig_image_height, orig_image_width), preserve_range=True).astype(np.uint8)
# back to pytorch to concatenate with full_image
rendered_img_orig = torch.Tensor(rendered_img_orig).permute(2,0,1).unsqueeze(0).float()/255.0
else:
rendered_img_orig = F.interpolate(rendered_img, (orig_image_height, orig_image_width), mode='bilinear').cpu()
full_image = torch.Tensor(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)).permute(2,0,1).unsqueeze(0).float()/255.0
grid = torch.cat([full_image, rendered_img_orig], dim=3)
else:
grid = torch.cat([cropped_image, rendered_img], dim=3)
# ---- create the neural renderer reconstructed img ---- #
if args.use_smirk_generator:
if (kpt_mediapipe is None):
print('Could not find landmarks for the image using mediapipe and cannot create the hull mask for the smirk generator. Exiting...')
exit()
mask_ratio_mul = 5
mask_ratio = 0.01
mask_dilation_radius = 10
hull_mask = create_mask(cropped_kpt_mediapipe, (224, 224))
face_probabilities = masking_utils.load_probabilities_per_FLAME_triangle()
rendered_mask = 1 - (rendered_img == 0).all(dim=1, keepdim=True).float()
tmask_ratio = mask_ratio * mask_ratio_mul # upper bound on the number of points to sample
npoints, _ = masking_utils.mesh_based_mask_uniform_faces(renderer_output['transformed_vertices'], # sample uniformly from the mesh
flame_faces=flame.faces_tensor,
face_probabilities=face_probabilities,
mask_ratio=tmask_ratio)
pmask = torch.zeros_like(rendered_mask)
rsing = torch.randint(0, 2, (npoints.size(0),)).to(npoints.device) * 2 - 1
rscale = torch.rand((npoints.size(0),)).to(npoints.device) * (mask_ratio_mul - 1) + 1
rbound =(npoints.size(1) * (1/mask_ratio_mul) * (rscale ** rsing)).long()
for bi in range(npoints.size(0)):
pmask[bi, :, npoints[bi, :rbound[bi], 1], npoints[bi, :rbound[bi], 0]] = 1
hull_mask = torch.from_numpy(hull_mask).type(dtype = torch.float32).unsqueeze(0).to(args.device)
extra_points = cropped_image * pmask
masked_img = masking_utils.masking(cropped_image, hull_mask, extra_points, mask_dilation_radius, rendered_mask=rendered_mask)
smirk_generator_input = torch.cat([rendered_img, masked_img], dim=1)
reconstructed_img = smirk_generator(smirk_generator_input)
if args.render_orig:
if args.crop:
reconstructed_img_numpy = (reconstructed_img.squeeze(0).permute(1,2,0).detach().cpu().numpy()*255.0).astype(np.uint8)
reconstructed_img_orig = warp(reconstructed_img_numpy, tform, output_shape=(orig_image_height, orig_image_width), preserve_range=True).astype(np.uint8)
# back to pytorch to concatenate with full_image
reconstructed_img_orig = torch.Tensor(reconstructed_img_orig).permute(2,0,1).unsqueeze(0).float()/255.0
else:
reconstructed_img_orig = F.interpolate(reconstructed_img, (orig_image_height, orig_image_width), mode='bilinear').cpu()
grid = torch.cat([grid, reconstructed_img_orig], dim=3)
else:
grid = torch.cat([grid, reconstructed_img], dim=3)
grid_numpy = grid.squeeze(0).permute(1,2,0).detach().cpu().numpy()*255.0
grid_numpy = grid_numpy.astype(np.uint8)
grid_numpy = cv2.cvtColor(grid_numpy, cv2.COLOR_BGR2RGB)
if not os.path.exists(args.out_path):
os.makedirs(args.out_path)
image_name = args.input_path.split('/')[-1]
cv2.imwrite(f"{args.out_path}/{image_name}", grid_numpy)