def evaluate_Blender():
ds = load_model_ds('BlenderFiles/model_frames')
cfg = yaml.load(open('configs/mb1_120x120.yml'), Loader=yaml.SafeLoader)
os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'
os.environ['OMP_NUM_THREADS'] = '4'
tddfa = TDDFA_ONNX(**cfg)
face_boxes = FaceBoxes_ONNX()
for artifact_id in ds:
for img, id in zip(ds[artifact_id]['img'], ds[artifact_id]['ids']):
wfp = f'examples/results/' + artifact_id + '_' + str(
id) + '_2d_sparse.jpg'
boxes = face_boxes(img)
print(boxes)
n = len(boxes)
if n == 0:
print(f'No face detected, exit')
sys.exit(-1)
print(f'Detect {n} faces')
param_lst, roi_box_lst = tddfa(img, boxes)
ver_lst = tddfa.recon_vers(param_lst,
roi_box_lst,
dense_flag=False)
draw_landmarks(img, ver_lst, dense_flag=False, wfp=wfp)
def main(args):
cfg = yaml.load(open(args.config), Loader=yaml.FullLoader)
gpu_mode = args.mode == 'gpu'
tddfa = TDDFA(gpu_mode=gpu_mode, **cfg)
# Initialize FaceBoxes
face_boxes = FaceBoxes()
# Given a still image path and load to BGR channel
img = cv2.imread(args.img_fp)
# Detect faces, get 3DMM params and roi boxes
boxes = face_boxes(img)
print(f'Detect {len(boxes)} faces')
param_lst, roi_box_lst = tddfa(img, boxes)
# Visualization
ver_lst = tddfa.recon_vers(param_lst,
roi_box_lst,
dense_flag=args.dense_flag)
wfp = f'examples/results/{args.img_fp.split("/")[-1].replace(get_suffix(args.img_fp), "")}_dense{args.dense_flag}.jpg'
draw_landmarks(img,
ver_lst,
show_flg=args.show_flg,
dense_flg=args.dense_flag,
wfp=wfp)
def main(args):
# Init TDDFA
cfg = yaml.load(open(args.config), Loader=yaml.SafeLoader)
gpu_mode = args.mode == 'gpu'
tddfa = TDDFA(gpu_mode=gpu_mode, **cfg)
# Init FaceBoxes
face_boxes = FaceBoxes()
# Given a still image path and load to BGR channel
img = cv2.imread(args.img_fp)
# Detect faces, get 3DMM params and roi boxes
boxes = face_boxes(img)
n = len(boxes)
print(f'Detect {n} faces')
if n == 0:
print(f'No face detected, exit')
sys.exit(-1)
param_lst, roi_box_lst = tddfa(img, boxes)
# Visualization and serialization
dense_flag = args.opt in ('2d_dense', '3d', 'depth', 'pncc', 'uv_tex', 'ply', 'obj')
old_suffix = get_suffix(args.img_fp)
new_suffix = f'.{args.opt}' if args.opt in ('ply', 'obj') else '.jpg'
wfp = f'examples/results/{args.img_fp.split("/")[-1].replace(old_suffix, "")}_{args.opt}' + new_suffix
ver_lst = tddfa.recon_vers(param_lst, roi_box_lst, dense_flag=dense_flag)
if args.opt == '2d_sparse':
draw_landmarks(img, ver_lst, show_flag=args.show_flag, dense_flag=dense_flag, wfp=wfp)
elif args.opt == '2d_dense':
draw_landmarks(img, ver_lst, show_flag=args.show_flag, dense_flag=dense_flag, wfp=wfp)
elif args.opt == '3d':
render(img, ver_lst, alpha=0.6, show_flag=args.show_flag, wfp=wfp)
elif args.opt == 'depth':
# if `with_bf_flag` is False, the background is black
depth(img, ver_lst, show_flag=args.show_flag, wfp=wfp, with_bg_flag=True)
elif args.opt == 'pncc':
pncc(img, ver_lst, show_flag=args.show_flag, wfp=wfp, with_bg_flag=True)
elif args.opt == 'uv_tex':
uv_tex(img, ver_lst, show_flag=args.show_flag, wfp=wfp)
elif args.opt == 'pose':
viz_pose(img, param_lst, ver_lst, show_flag=args.show_flag, wfp=wfp)
elif args.opt == 'ply':
ser_to_ply(ver_lst, height=img.shape[0], wfp=wfp)
elif args.opt == 'obj':
ser_to_obj(img, ver_lst, height=img.shape[0], wfp=wfp)
else:
raise ValueError(f'Unknown opt {args.opt}')
def main(args):
# Init TDDFA
cfg = yaml.load(open(args.config), Loader=yaml.SafeLoader)
gpu_mode = args.mode == 'gpu'
tddfa = TDDFA(gpu_mode=gpu_mode, **cfg)
# Init FaceBoxes
face_boxes = FaceBoxes()
# Given a still image path and load to BGR channel
img = cv2.imread(args.img_fp)
# Detect faces, get 3DMM params and roi boxes
boxes = face_boxes(img)
n = len(boxes)
print(f'Detect {n} faces')
if n == 0:
print(f'No face detected, exit')
sys.exit(-1)
param_lst, roi_box_lst = tddfa(img, boxes)
# Visualization and serialization
dense_flag = args.opt in (
'2d_dense', '3d', 'depth'
) # if opt is 2d_dense or 3d, reconstruct dense vertices
ver_lst = tddfa.recon_vers(param_lst, roi_box_lst, dense_flag=dense_flag)
suffix = get_suffix(args.img_fp)
wfp = f'examples/results/{args.img_fp.split("/")[-1].replace(suffix, "")}_{args.opt}.jpg'
if args.opt == '2d_sparse':
draw_landmarks(img,
ver_lst,
show_flag=args.show_flag,
dense_flag=dense_flag,
wfp=wfp)
elif args.opt == '2d_dense':
draw_landmarks(img,
ver_lst,
show_flag=args.show_flag,
dense_flag=dense_flag,
wfp=wfp)
elif args.opt == '3d':
render(img, ver_lst, alpha=0.6, show_flag=args.show_flag, wfp=wfp)
elif args.opt == 'depth':
depth(img, ver_lst, show_flag=args.show_flag, wfp=wfp)
else:
raise Exception(f'Unknown opt {args.opt}')
def main(args):
cfg = yaml.load(open(args.config), Loader=yaml.SafeLoader)
# Init FaceBoxes and TDDFA, recommend using onnx flag
if args.onnx:
import os
os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'
os.environ['OMP_NUM_THREADS'] = '4'
from FaceBoxes.FaceBoxes_ONNX import FaceBoxes_ONNX
from TDDFA_ONNX import TDDFA_ONNX
face_boxes = FaceBoxes_ONNX()
tddfa = TDDFA_ONNX(**cfg)
else:
gpu_mode = args.mode == 'gpu'
tddfa = TDDFA(gpu_mode=gpu_mode, **cfg)
face_boxes = FaceBoxes()
# Given a still image path and load to BGR channel
img = cv2.imread(args.img_fp)
# Detect faces, get 3DMM params and roi boxes
boxes = face_boxes(img)
n = len(boxes)
if n == 0:
print('No face detected, exit')
sys.exit(-1)
print(f'Detect {n} faces')
param_lst, roi_box_lst = tddfa(img, boxes)
# Visualization and serialization
dense_flag = args.opt in ('2d_dense', '3d', 'depth', 'pncc', 'uv_tex',
'ply', 'obj')
old_suffix = get_suffix(args.img_fp)
new_suffix = f'.{args.opt}' if args.opt in ('ply', 'obj') else '.jpg'
wfp = f'{args.img_fp.split("/")[-1].replace(old_suffix, "")}_{args.opt}' +\
new_suffix
wfp = base_path / 'examples' / 'results' / wfp
ver_lst = tddfa.recon_vers(param_lst, roi_box_lst, dense_flag=dense_flag)
if args.opt == '2d_sparse':
draw_landmarks(img,
ver_lst,
show_flag=args.show_flag,
dense_flag=dense_flag,
wfp=wfp)
elif args.opt == '2d_dense':
draw_landmarks(img,
ver_lst,
show_flag=args.show_flag,
dense_flag=dense_flag,
wfp=wfp)
elif args.opt == '3d':
render(img,
ver_lst,
tddfa.tri,
alpha=0.6,
show_flag=args.show_flag,
wfp=wfp)
elif args.opt == 'depth':
# if `with_bf_flag` is False, the background is black
depth(img,
ver_lst,
tddfa.tri,
show_flag=args.show_flag,
wfp=wfp,
with_bg_flag=True)
elif args.opt == 'pncc':
pncc(img,
ver_lst,
tddfa.tri,
show_flag=args.show_flag,
wfp=wfp,
with_bg_flag=True)
elif args.opt == 'uv_tex':
uv_tex(img, ver_lst, tddfa.tri, show_flag=args.show_flag, wfp=wfp)
elif args.opt == 'pose':
viz_pose(img, param_lst, ver_lst, show_flag=args.show_flag, wfp=wfp)
elif args.opt == 'ply':
ser_to_ply(ver_lst, tddfa.tri, height=img.shape[0], wfp=wfp)
elif args.opt == 'obj':
ser_to_obj(img, ver_lst, tddfa.tri, height=img.shape[0], wfp=wfp)
else:
raise ValueError(f'Unknown opt {args.opt}')
def main(args):
cfg = yaml.load(open(args.config), Loader=yaml.SafeLoader)
# Init FaceBoxes and TDDFA, recommend using onnx flag
if args.onnx:
import os
os.environ['KMP_DUPLICATE_LIB_OK'] = 'True'
os.environ['OMP_NUM_THREADS'] = '4'
from FaceBoxes.FaceBoxes_ONNX import FaceBoxes_ONNX
from TDDFA_ONNX import TDDFA_ONNX
face_boxes = FaceBoxes_ONNX()
tddfa = TDDFA_ONNX(**cfg)
else:
gpu_mode = args.mode == 'gpu'
tddfa = TDDFA(gpu_mode=gpu_mode, **cfg)
face_boxes = FaceBoxes()
# Given a still image path and load to BGR channel
img = cv2.imread(args.img_fp)
# Detect faces, get 3DMM params and roi boxes
boxes = face_boxes(img)
n = len(boxes)
if n == 0:
print(f'No face detected, exit')
sys.exit(-1)
print(f'Detect {n} faces')
param_lst, roi_box_lst = tddfa(img, boxes)
# Visualization and serialization
dense_flag = args.opt in ('2d_dense', '3d', 'depth', 'pncc', 'uv_tex',
'ply', 'obj')
old_suffix = get_suffix(args.img_fp)
new_suffix = f'.{args.opt}' if args.opt in ('ply', 'obj') else '.jpg'
wfp = f'examples/results/{args.img_fp.split("/")[-1].replace(old_suffix, "")}_{args.opt}' + new_suffix
ver_lst = tddfa.recon_vers(param_lst, roi_box_lst, dense_flag=dense_flag)
print(ver_lst[0].shape)
print(tddfa.bfm.u.shape)
lm68 = np.reshape(
np.reshape(ver_lst[0].T, (-1, 1))[tddfa.bfm.keypoints], (-1, 3))
print(lm68.shape)
for i in range(lm68.shape[0]):
lm68[i, 1] = img.shape[0] - lm68[i, 1]
for i in range(ver_lst[0].shape[1]):
ver_lst[0][1, i] = img.shape[0] - ver_lst[0][1, i]
useful_tri = np.copy(tddfa.tri)
for i in range(useful_tri.shape[0]):
tmp = useful_tri[i, 2]
useful_tri[i, 2] = useful_tri[i, 0]
useful_tri[i, 0] = tmp
useful_tri = useful_tri + 1
np.save("asd_lm.npy", lm68)
np.save("asd_v.npy", ver_lst[0].T)
np.save("asd_f.npy", useful_tri)
if args.opt == '2d_sparse':
draw_landmarks(img,
ver_lst,
show_flag=args.show_flag,
dense_flag=dense_flag,
wfp=wfp)
elif args.opt == '2d_dense':
draw_landmarks(img,
ver_lst,
show_flag=args.show_flag,
dense_flag=dense_flag,
wfp=wfp)
elif args.opt == '3d':
render(img,
ver_lst,
tddfa.tri,
alpha=0.6,
show_flag=args.show_flag,
wfp=wfp)
elif args.opt == 'depth':
# if `with_bf_flag` is False, the background is black
depth(img,
ver_lst,
tddfa.tri,
show_flag=args.show_flag,
wfp=wfp,
with_bg_flag=True)
elif args.opt == 'pncc':
pncc(img,
ver_lst,
tddfa.tri,
show_flag=args.show_flag,
wfp=wfp,
with_bg_flag=True)
elif args.opt == 'uv_tex':
uv_tex(img, ver_lst, tddfa.tri, show_flag=args.show_flag, wfp=wfp)
elif args.opt == 'pose':
viz_pose(img, param_lst, ver_lst, show_flag=args.show_flag, wfp=wfp)
elif args.opt == 'ply':
ser_to_ply(ver_lst, tddfa.tri, height=img.shape[0], wfp=wfp)
elif args.opt == 'obj':
ser_to_obj(img, ver_lst, tddfa.tri, height=img.shape[0], wfp=wfp)
else:
raise ValueError(f'Unknown opt {args.opt}')