def main(args):
cfg = yaml.load(open(args.config), Loader=yaml.SafeLoader)
gpu_mode = args.mode == 'gpu'
tddfa = TDDFA(gpu_mode=gpu_mode, **cfg)
# Initialize FaceBoxes
face_boxes = FaceBoxes()
# Given a camera
# before run this line, make sure you have installed `imageio-ffmpeg`
reader = imageio.get_reader("<video0>")
# the simple implementation of average smoothing by looking ahead by n_next frames
# assert the frames of the video >= n
n_pre, n_next = args.n_pre, args.n_next
n = n_pre + n_next + 1
queue_ver = deque()
queue_frame = deque()
# run
dense_flag = args.opt in ('2d_dense', '3d')
pre_ver = None
for i, frame in tqdm(enumerate(reader)):
frame_bgr = frame[..., ::-1] # RGB->BGR
if i == 0:
# the first frame, detect face, here we only use the first face, you can change depending on your need
boxes = face_boxes(frame_bgr)
boxes = [boxes[0]]
param_lst, roi_box_lst = tddfa(frame_bgr, boxes)
ver = tddfa.recon_vers(param_lst, roi_box_lst, dense_flag=dense_flag)[0]
# refine
param_lst, roi_box_lst = tddfa(frame_bgr, [ver], crop_policy='landmark')
ver = tddfa.recon_vers(param_lst, roi_box_lst, dense_flag=dense_flag)[0]
# padding queue
for _ in range(n_pre):
queue_ver.append(ver.copy())
queue_ver.append(ver.copy())
for _ in range(n_pre):
queue_frame.append(frame_bgr.copy())
queue_frame.append(frame_bgr.copy())
else:
param_lst, roi_box_lst = tddfa(frame_bgr, [pre_ver], crop_policy='landmark')
roi_box = roi_box_lst[0]
# todo: add confidence threshold to judge the tracking is failed
if abs(roi_box[2] - roi_box[0]) * abs(roi_box[3] - roi_box[1]) < 2020:
boxes = face_boxes(frame_bgr)
boxes = [boxes[0]]
param_lst, roi_box_lst = tddfa(frame_bgr, boxes)
ver = tddfa.recon_vers(param_lst, roi_box_lst, dense_flag=dense_flag)[0]
queue_ver.append(ver.copy())
queue_frame.append(frame_bgr.copy())
pre_ver = ver # for tracking
# smoothing: enqueue and dequeue ops
if len(queue_ver) >= n:
ver_ave = np.mean(queue_ver, axis=0)
if args.opt == '2d_sparse':
img_draw = cv_draw_landmark(queue_frame[n_pre], ver_ave) # since we use padding
elif args.opt == '2d_dense':
img_draw = cv_draw_landmark(queue_frame[n_pre], ver_ave, size=1)
elif args.opt == '3d':
img_draw = render(queue_frame[n_pre], [ver_ave], alpha=0.7)
else:
raise ValueError(f'Unknown opt {args.opt}')
cv2.imshow('image', img_draw)
k = cv2.waitKey(20)
if (k & 0xff == ord('q')):
break
queue_ver.popleft()
queue_frame.popleft()
def main(args):
cfg = yaml.load(open(args.config), Loader=yaml.SafeLoader)
gpu_mode = args.mode == 'gpu'
tddfa = TDDFA(gpu_mode=gpu_mode, **cfg)
# Initialize FaceBoxes
face_boxes = FaceBoxes()
# Given a video path
fn = args.video_fp.split('/')[-1]
reader = imageio.get_reader(args.video_fp)
fps = reader.get_meta_data()['fps']
suffix = get_suffix(args.video_fp)
video_wfp = f'examples/results/videos/{fn.replace(suffix, "")}_{args.opt}_smooth.mp4'
writer = imageio.get_writer(video_wfp, fps=fps)
# the simple implementation of average smoothing by looking ahead by n_next frames
# assert the frames of the video >= n
n_pre, n_next = args.n_pre, args.n_next
n = n_pre + n_next + 1
queue_ver = deque()
queue_frame = deque()
# run
dense_flag = args.opt in (
'2d_dense',
'3d',
)
pre_ver = None
for i, frame in tqdm(enumerate(reader)):
if args.start > 0 and i < args.start:
continue
if args.end > 0 and i > args.end:
break
frame_bgr = frame[..., ::-1] # RGB->BGR
if i == 0:
# detect
boxes = face_boxes(frame_bgr)
boxes = [boxes[0]]
param_lst, roi_box_lst = tddfa(frame_bgr, boxes)
ver = tddfa.recon_vers(param_lst,
roi_box_lst,
dense_flag=dense_flag)[0]
# refine
param_lst, roi_box_lst = tddfa(frame_bgr, [ver],
crop_policy='landmark')
ver = tddfa.recon_vers(param_lst,
roi_box_lst,
dense_flag=dense_flag)[0]
# padding queue
for j in range(n_pre):
queue_ver.append(ver.copy())
queue_ver.append(ver.copy())
for j in range(n_pre):
queue_frame.append(frame_bgr.copy())
queue_frame.append(frame_bgr.copy())
else:
param_lst, roi_box_lst = tddfa(frame_bgr, [pre_ver],
crop_policy='landmark')
roi_box = roi_box_lst[0]
# todo: add confidence threshold to judge the tracking is failed
if abs(roi_box[2] - roi_box[0]) * abs(roi_box[3] -
roi_box[1]) < 2020:
boxes = face_boxes(frame_bgr)
boxes = [boxes[0]]
param_lst, roi_box_lst = tddfa(frame_bgr, boxes)
ver = tddfa.recon_vers(param_lst,
roi_box_lst,
dense_flag=dense_flag)[0]
queue_ver.append(ver.copy())
queue_frame.append(frame_bgr.copy())
pre_ver = ver # for tracking
# smoothing: enqueue and dequeue ops
if len(queue_ver) >= n:
ver_ave = np.mean(queue_ver, axis=0)
if args.opt == '2d_sparse':
img_draw = cv_draw_landmark(queue_frame[n_pre],
ver_ave) # since we use padding
elif args.opt == '2d_dense':
img_draw = cv_draw_landmark(queue_frame[n_pre],
ver_ave,
size=1)
elif args.opt == '3d':
img_draw = render(queue_frame[n_pre], [ver_ave], alpha=0.7)
writer.append_data(img_draw[:, :, ::-1]) # BGR->RGB
queue_ver.popleft()
queue_frame.popleft()
# we will lost the last n_next frames, still padding
for j in range(n_next):
queue_ver.append(ver.copy())
queue_frame.append(frame_bgr.copy()) # the last frame
ver_ave = np.mean(queue_ver, axis=0)
if args.opt == '2d_sparse':
img_draw = cv_draw_landmark(queue_frame[n_pre],
ver_ave) # since we use padding
elif args.opt == '2d_dense':
img_draw = cv_draw_landmark(queue_frame[n_pre], ver_ave, size=1)
elif args.opt == '3d':
img_draw = render(queue_frame[n_pre], [ver_ave], alpha=0.7)
writer.append_data(img_draw[..., ::-1]) # BGR->RGB
queue_ver.popleft()
queue_frame.popleft()
writer.close()
print(f'Dump to {video_wfp}')
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)
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):
# input and output folder
image_path = "G:\\BU-3DFE\\Extracted"
save_path = "G:\\bu3dfe_3ddfa_proc"
if not os.path.exists(save_path):
os.makedirs(save_path)
cfg = yaml.load(open(args.config), Loader=yaml.SafeLoader)
# Init FaceBoxes and TDDFA, recommend using onnx flag
gpu_mode = args.mode == 'gpu'
tddfa = TDDFA(gpu_mode=gpu_mode, **cfg)
face_boxes = FaceBoxes()
img_list = sorted(
[str(x) for x in pathlib.Path(image_path).rglob("*_F2D.bmp")])
print("Reconstructing:\n")
for file in img_list:
out_file = save_path + file[len(image_path):-4] + ".pickle"
if os.path.isfile(out_file):
continue
out_dir = str(pathlib.Path(out_file).parent)
if not os.path.isdir(out_dir):
os.makedirs(out_dir)
# Given a still image path and load to BGR channel
img = cv2.imread(file)
# Detect faces, get 3DMM params and roi boxes
boxes = face_boxes(img)
n = len(boxes)
if n == 0:
print(f'No face detected, skipping \"' + file + '\".')
continue
#print(f'Detect {n} faces')
param_lst, roi_box_lst = tddfa(img, boxes)
# Visualization and serialization
dense_flag = True
ver_lst = tddfa.recon_vers(param_lst,
roi_box_lst,
dense_flag=dense_flag)
# repair all the bs because of the matrices' axis alignment...
lm68 = np.reshape(
np.reshape(ver_lst[0].T, (-1, 1))[tddfa.bfm.keypoints], (-1, 3))
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]
vertices = ver_lst[0].T
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
# save
mesh = dict()
mesh["vertices"] = vertices
mesh["faces"] = useful_tri
mesh["lm68"] = lm68
with open(out_file, "wb+") as f:
_pickle.dump(mesh, f)
print(out_file)