def mead(args):
mead_data = args.data
device = args.device
savefolder = args.savefolder
t = tarfile.open(mead_data)
for member in t.getmembers():
if 'front/' in member.name and '.mp4' in member.name:
t.extract(member, cur_dir)
video_name = f'{cur_dir}/{member.name}'
if args.gen_audio:
clip = mp.VideoFileClip(video_name)
audio_path = os.path.join(
savefolder,
member.name.replace('.mp4', '.wav',
1)[len('video/front/'):])
util.check_mkdir(os.path.dirname(audio_path))
clip.audio.write_audiofile(audio_path)
testdata = datasets.TestData(video_name,
iscrop=True,
face_detector=args.detector)
os.remove(video_name)
deca_cfg.model.use_tex = False
deca = DECA(config=deca_cfg, device=device)
save_path = os.path.join(savefolder,
member.name[len("video/front/"):-4])
Path(save_path).mkdir(parents=True, exist_ok=True)
for i in tqdm(range(len(testdata))):
images = testdata[i]['image'].to(device)[None, ...]
codedict = deca.encode(images)
opdict, visdict = deca.decode(codedict) #tensor
deca.save_obj(save_path + '/%04d.obj' % i, opdict, simple=True)
shutil.rmtree(video_name.split('.')[0])
print(f'-- please check the results in {savefolder}')
def create_deca(args, video, ext_dir='', audio_path=''):
device = args.device
savefolder = args.savefolder
if args.gen_audio and audio_path != '':
clip = mp.VideoFileClip(video)
util.check_mkdir(os.path.dirname(audio_path))
clip.audio.write_audiofile(audio_path)
testdata = datasets.TestData(video,
iscrop=True,
face_detector=args.detector)
os.remove(video)
deca_cfg.model.use_tex = False
deca = DECA(config=deca_cfg, device=device)
data_file = os.path.join(savefolder, video[len(ext_dir) + 1:-4]) + '.pkl'
Path(os.path.dirname(data_file)).mkdir(parents=True, exist_ok=True)
obj_path = os.path.join(savefolder, video[len(ext_dir) + 1:-4])
if args.save_obj:
Path(obj_path).mkdir(parents=True, exist_ok=True)
vertices = []
for i in tqdm(range(len(testdata))):
images = testdata[i]['image'].to(device)[None, ...]
codedict = deca.encode(images)
opdict, visdict = deca.decode(codedict)
vertices.append(opdict['vertices'])
if args.save_obj:
deca.save_obj(obj_path + '/%04d.obj' % i, opdict, simple=True)
torch.save(torch.cat(vertices), data_file)
shutil.rmtree(video.split('.')[0])
print(f'-- please check the results in {savefolder}')
def main(args):
savefolder = args.savefolder
device = args.device
os.makedirs(savefolder, exist_ok=True)
# load test images
testdata = datasets.TestData(args.inputpath,
iscrop=args.iscrop,
face_detector=args.detector)
# run DECA
deca_cfg.model.use_tex = args.useTex
deca = DECA(config=deca_cfg, device=device)
# for i in range(len(testdata)):
for i in tqdm(range(len(testdata))):
name = testdata[i]['imagename']
images = testdata[i]['image'].to(device)[None, ...]
codedict = deca.encode(images)
opdict, visdict = deca.decode(codedict) #tensor
if args.saveDepth or args.saveKpt or args.saveObj or args.saveMat or args.saveImages:
os.makedirs(os.path.join(savefolder, name), exist_ok=True)
# -- save results
if args.saveDepth:
depth_image = deca.render.render_depth(
opdict['transformed_vertices']).repeat(1, 3, 1, 1)
visdict['depth_images'] = depth_image
cv2.imwrite(os.path.join(savefolder, name, name + '_depth.jpg'),
util.tensor2image(depth_image[0]))
if args.saveKpt:
np.savetxt(os.path.join(savefolder, name, name + '_kpt2d.txt'),
opdict['landmarks2d'][0].cpu().numpy())
np.savetxt(os.path.join(savefolder, name, name + '_kpt3d.txt'),
opdict['landmarks3d'][0].cpu().numpy())
if args.saveObj:
deca.save_obj(os.path.join(savefolder, name, name + '.obj'),
opdict)
if args.saveMat:
opdict = util.dict_tensor2npy(opdict)
savemat(os.path.join(savefolder, name, name + '.mat'), opdict)
if args.saveVis:
cv2.imwrite(os.path.join(savefolder, 'vis_' + name + '.jpg'),
deca.visualize(visdict))
cv2.imwrite(
os.path.join(savefolder, 'detailed_images_' + name + '.jpg'),
deca.visualize_detailed_image(visdict))
if args.saveImages:
for vis_name in [
'inputs', 'rendered_images', 'albedo_images',
'shape_images', 'shape_detail_images'
]:
if vis_name not in visdict.keys():
continue
image = util.tensor2image(visdict[vis_name][0])
cv2.imwrite(
os.path.join(savefolder, name,
name + '_' + vis_name + '.jpg'),
util.tensor2image(visdict[vis_name][0]))
print(f'-- please check the results in {savefolder}')
def main(args):
# if args.rasterizer_type != 'standard':
# args.render_orig = False
savefolder = args.savefolder
device = args.device
os.makedirs(savefolder, exist_ok=True)
# load test images
testdata = datasets.TestData(args.inputpath, iscrop=args.iscrop, face_detector=args.detector, sample_step=args.sample_step)
# run DECA
deca_cfg.model.use_tex = args.useTex
deca_cfg.rasterizer_type = args.rasterizer_type
deca_cfg.model.extract_tex = args.extractTex
deca = DECA(config = deca_cfg, device=device)
# for i in range(len(testdata)):
for i in tqdm(range(len(testdata))):
name = testdata[i]['imagename']
images = testdata[i]['image'].to(device)[None,...]
with torch.no_grad():
codedict = deca.encode(images)
opdict, visdict = deca.decode(codedict) #tensor
if args.render_orig:
tform = testdata[i]['tform'][None, ...]
tform = torch.inverse(tform).transpose(1,2).to(device)
original_image = testdata[i]['original_image'][None, ...].to(device)
_, orig_visdict = deca.decode(codedict, render_orig=True, original_image=original_image, tform=tform)
orig_visdict['inputs'] = original_image
if args.saveDepth or args.saveKpt or args.saveObj or args.saveMat or args.saveImages:
os.makedirs(os.path.join(savefolder, name), exist_ok=True)
# -- save results
if args.saveDepth:
depth_image = deca.render.render_depth(opdict['trans_verts']).repeat(1,3,1,1)
visdict['depth_images'] = depth_image
cv2.imwrite(os.path.join(savefolder, name, name + '_depth.jpg'), util.tensor2image(depth_image[0]))
if args.saveKpt:
np.savetxt(os.path.join(savefolder, name, name + '_kpt2d.txt'), opdict['landmarks2d'][0].cpu().numpy())
np.savetxt(os.path.join(savefolder, name, name + '_kpt3d.txt'), opdict['landmarks3d'][0].cpu().numpy())
if args.saveObj:
deca.save_obj(os.path.join(savefolder, name, name + '.obj'), opdict)
if args.saveMat:
opdict = util.dict_tensor2npy(opdict)
savemat(os.path.join(savefolder, name, name + '.mat'), opdict)
if args.saveVis:
cv2.imwrite(os.path.join(savefolder, name + '_vis.jpg'), deca.visualize(visdict))
if args.render_orig:
cv2.imwrite(os.path.join(savefolder, name + '_vis_original_size.jpg'), deca.visualize(orig_visdict))
if args.saveImages:
for vis_name in ['inputs', 'rendered_images', 'albedo_images', 'shape_images', 'shape_detail_images', 'landmarks2d']:
if vis_name not in visdict.keys():
continue
image = util.tensor2image(visdict[vis_name][0])
cv2.imwrite(os.path.join(savefolder, name, name + '_' + vis_name +'.jpg'), util.tensor2image(visdict[vis_name][0]))
if args.render_orig:
image = util.tensor2image(orig_visdict[vis_name][0])
cv2.imwrite(os.path.join(savefolder, name, 'orig_' + name + '_' + vis_name +'.jpg'), util.tensor2image(orig_visdict[vis_name][0]))
print(f'-- please check the results in {savefolder}')
def main(args):
savefolder = args.savefolder
device = args.device
os.makedirs(savefolder, exist_ok=True)
# load test images
testdata = datasets.TestData(args.image_path,
iscrop=args.iscrop,
face_detector=args.detector)
expdata = datasets.TestData(args.exp_path,
iscrop=args.iscrop,
face_detector=args.detector)
# run DECA
i = 0
deca = DECA(device=device)
name = testdata[i]['imagename']
savepath = '{}/{}.jpg'.format(savefolder, name)
images = testdata[i]['image'].to(device)[None, ...]
codedict = deca.encode(images)
_, visdict = deca.decode(codedict)
visdict = {x: visdict[x] for x in ['inputs', 'shape_detail_images']}
# -- expression transfer
# exp code from image
exp_images = expdata[i]['image'].to(device)[None, ...]
exp_codedict = deca.encode(exp_images)
# transfer exp code
codedict['pose'][:, 3:] = exp_codedict['pose'][:, 3:]
codedict['exp'] = exp_codedict['exp']
_, exp_visdict = deca.decode(codedict)
visdict['transferred_shape'] = exp_visdict['shape_detail_images']
cv2.imwrite(os.path.join(savefolder, name + '_animation.jpg'),
deca.visualize(visdict))
print(f'-- please check the results in {savefolder}')
def main(args):
savefolder = args.savefolder
device = args.device
os.makedirs(savefolder, exist_ok=True)
# load test images
testdata = datasets.TestData(args.image_path, iscrop=args.iscrop, face_detector=args.detector)
expdata = datasets.TestData(args.exp_path, iscrop=args.iscrop, face_detector=args.detector)
# run DECA
deca_cfg.model.use_tex = args.useTex
deca = DECA(config = deca_cfg, device=device)
# identity reference
i = 0
name = testdata[i]['imagename']
savepath = '{}/{}.jpg'.format(savefolder, name)
images = testdata[i]['image'].to(device)[None,...]
id_codedict = deca.encode(images)
id_opdict, id_visdict = deca.decode(id_codedict)
id_visdict = {x:id_visdict[x] for x in ['inputs', 'shape_detail_images']}
# -- expression transfer
# exp code from image
exp_images = expdata[i]['image'].to(device)[None,...]
exp_codedict = deca.encode(exp_images)
# transfer exp code
id_codedict['pose'][:,3:] = exp_codedict['pose'][:,3:]
id_codedict['exp'] = exp_codedict['exp']
transfer_opdict, transfer_visdict = deca.decode(id_codedict)
id_visdict['transferred_shape'] = transfer_visdict['shape_detail_images']
cv2.imwrite(os.path.join(savefolder, name + '_animation.jpg'), deca.visualize(id_visdict))
transfer_opdict['uv_texture_gt'] = id_opdict['uv_texture_gt']
if args.saveDepth or args.saveKpt or args.saveObj or args.saveMat or args.saveImages:
os.makedirs(os.path.join(savefolder, name, 'reconstruction'), exist_ok=True)
os.makedirs(os.path.join(savefolder, name, 'animation'), exist_ok=True)
# -- save results
image_name = name
for save_type in ['reconstruction', 'animation']:
if save_type == 'reconstruction':
visdict = id_codedict; opdict = id_opdict
else:
visdict = transfer_visdict; opdict = transfer_opdict
if args.saveDepth:
depth_image = deca.render.render_depth(opdict['transformed_vertices']).repeat(1,3,1,1)
visdict['depth_images'] = depth_image
cv2.imwrite(os.path.join(savefolder, name, save_type, name + '_depth.jpg'), util.tensor2image(depth_image[0]))
if args.saveKpt:
np.savetxt(os.path.join(savefolder, name, save_type, name + '_kpt2d.txt'), opdict['landmarks2d'][0].cpu().numpy())
np.savetxt(os.path.join(savefolder, name, save_type, name + '_kpt3d.txt'), opdict['landmarks3d'][0].cpu().numpy())
if args.saveObj:
deca.save_obj(os.path.join(savefolder, name, save_type, name + '.obj'), opdict)
if args.saveMat:
opdict = util.dict_tensor2npy(opdict)
savemat(os.path.join(savefolder, name, save_type, name + '.mat'), opdict)
if args.saveImages:
for vis_name in ['inputs', 'rendered_images', 'albedo_images', 'shape_images', 'shape_detail_images']:
if vis_name not in visdict.keys():
continue
image =util.tensor2image(visdict[vis_name][0])
cv2.imwrite(os.path.join(savefolder, name, save_type, name + '_' + vis_name +'.jpg'), util.tensor2image(visdict[vis_name][0]))
print(f'-- please check the results in {savefolder}')
def main(args):
savefolder = args.savefolder
device = args.device
os.makedirs(savefolder, exist_ok=True)
# load test images
testdata = datasets.TestData(args.inputpath,
iscrop=args.iscrop,
face_detector=args.detector)
expdata = datasets.TestData(args.exp_path,
iscrop=args.iscrop,
face_detector=args.detector)
# DECA
deca_cfg.rasterizer_type = args.rasterizer_type
deca = DECA(config=deca_cfg, device=device)
visdict_list_list = []
for i in range(len(testdata)):
name = testdata[i]['imagename']
images = testdata[i]['image'].to(device)[None, ...]
with torch.no_grad():
codedict = deca.encode(images)
opdict, visdict = deca.decode(codedict) #tensor
### show shape with different views and expressions
visdict_list = []
max_yaw = 30
yaw_list = list(range(0, max_yaw, 5)) + list(
range(max_yaw, -max_yaw, -5)) + list(range(-max_yaw, 0, 5))
for k in yaw_list: #jaw angle from -50 to 50
## yaw angle
euler_pose = torch.randn((1, 3))
euler_pose[:, 1] = k #torch.rand((self.batch_size))*160 - 80
euler_pose[:,
0] = 0 #(torch.rand((self.batch_size))*60 - 30)*(2./euler_pose[:,1].abs())
euler_pose[:,
2] = 0 #(torch.rand((self.batch_size))*60 - 30)*(2./euler_pose[:,1].abs())
global_pose = batch_euler2axis(deg2rad(euler_pose[:, :3].cuda()))
codedict['pose'][:, :3] = global_pose
codedict['cam'][:, :] = 0.
codedict['cam'][:, 0] = 8
_, visdict_view = deca.decode(codedict)
visdict = {
x: visdict[x]
for x in ['inputs', 'shape_detail_images']
}
visdict['pose'] = visdict_view['shape_detail_images']
visdict_list.append(visdict)
euler_pose = torch.zeros((1, 3))
global_pose = batch_euler2axis(deg2rad(euler_pose[:, :3].cuda()))
codedict['pose'][:, :3] = global_pose
for (i, k) in enumerate(range(0, 31, 2)): #jaw angle from -50 to 50
# expression: jaw pose
euler_pose = torch.randn((1, 3))
euler_pose[:, 0] = k #torch.rand((self.batch_size))*160 - 80
euler_pose[:,
1] = 0 #(torch.rand((self.batch_size))*60 - 30)*(2./euler_pose[:,1].abs())
euler_pose[:,
2] = 0 #(torch.rand((self.batch_size))*60 - 30)*(2./euler_pose[:,1].abs())
jaw_pose = batch_euler2axis(deg2rad(euler_pose[:, :3].cuda()))
codedict['pose'][:, 3:] = jaw_pose
_, visdict_view = deca.decode(codedict)
visdict_list[i]['exp'] = visdict_view['shape_detail_images']
count = i
for (i,
k) in enumerate(range(len(expdata))): #jaw angle from -50 to 50
# expression: jaw pose
exp_images = expdata[i]['image'].to(device)[None, ...]
exp_codedict = deca.encode(exp_images)
# transfer exp code
codedict['pose'][:, 3:] = exp_codedict['pose'][:, 3:]
codedict['exp'] = exp_codedict['exp']
_, exp_visdict = deca.decode(codedict)
visdict_list[i + count]['exp'] = exp_visdict['shape_detail_images']
visdict_list_list.append(visdict_list)
### write gif
writer = imageio.get_writer(os.path.join(savefolder, 'teaser.gif'),
mode='I')
for i in range(len(yaw_list)):
grid_image_list = []
for j in range(len(testdata)):
grid_image = deca.visualize(visdict_list_list[j][i])
grid_image_list.append(grid_image)
grid_image_all = np.concatenate(grid_image_list, 0)
grid_image_all = rescale(
grid_image_all, 0.6,
multichannel=True) # resize for showing in github
writer.append_data(grid_image_all[:, :, [2, 1, 0]])
print(f'-- please check the teaser figure in {savefolder}')
def main(args):
savefolder = args.savefolder
device = args.device
os.makedirs(savefolder, exist_ok=True)
print(str(args.iscrop))
# load test images
testdata = datasets.TestData(args.inputpath,
iscrop=args.iscrop,
face_detector=args.detector)
test_data_loader = DataLoader(testdata,
batch_size=3,
num_workers=4,
shuffle=True,
pin_memory=True,
drop_last=True)
# run DECA
deca_cfg.model.use_tex = args.useTex
if args.model_path != '':
deca_cfg.pretrained_modelpath = args.model_path
deca = DECA(config=deca_cfg, device=device, eval_detail=False)
# for i in range(len(testdata)):
for i, sample in enumerate(test_data_loader):
names = sample['imagename']
images = sample['image'].to(device)
codedict = deca.encode(images)
opdict, visdict = deca.decode(codedict) #tensor
if args.saveDepth or args.saveKpt or args.saveObj or args.saveMat or args.saveImages:
for name in names:
os.makedirs(os.path.join(savefolder, name), exist_ok=True)
# -- save results
if args.saveDepth:
depth_images = deca.render.render_depth(
opdict['transformed_vertices']).repeat(1, 3, 1, 1)
visdict['depth_images'] = depth_images
for j in range(len(names)):
cv2.imwrite(
os.path.join(savefolder, names[j],
names[j] + '_depth.jpg'),
util.tensor2image(depth_image[j]))
if args.saveKpt:
for j in range(len(names)):
np.savetxt(
os.path.join(savefolder, names[j],
names[j] + '_kpt2d.txt'),
opdict['landmarks2d'][j].cpu().numpy())
np.savetxt(
os.path.join(savefolder, names[j],
names[j] + '_kpt3d.txt'),
opdict['landmarks3d'][j].cpu().numpy())
if args.saveObj:
deca.save_obj(savefolder, names, opdict)
if args.saveMat:
opdict = util.dict_tensor2npy(opdict)
savemat(os.path.join(savefolder, name, name + '.mat'), opdict)
if args.saveVis:
vis_imgs = deca.visualize(visdict)
for j in range(len(names)):
cv2.imwrite(os.path.join(savefolder, names[j] + '_vis.jpg'),
vis_imgs[j])
if args.saveImages:
for vis_name in [
'inputs', 'rendered_images', 'albedo_images',
'shape_images', 'shape_detail_images'
]:
if vis_name not in visdict.keys():
continue
image = util.tensor2image(visdict[vis_name][0])
cv2.imwrite(
os.path.join(savefolder, name,
name + '_' + vis_name + '.jpg'),
util.tensor2image(visdict[vis_name][0]))
print(f'-- please check the results in {savefolder}')