def main(weights, num, batch_file, opt_pose_steps, opt_shape_steps):
K.set_session(
tf.Session(config=tf.ConfigProto(gpu_options=tf.GPUOptions(
allow_growth=True))))
model = Octopus(num=num)
with open(batch_file, 'r') as f:
reader = csv.reader(f, delimiter=' ')
for name, segm_dir, pose_dir, out_dir in reader:
print('Processing {}...'.format(name))
model.load(weights)
segm_files = sorted(glob(os.path.join(segm_dir, '*.png')))
pose_files = sorted(glob(os.path.join(pose_dir, '*.json')))
if len(segm_files) != len(pose_files) or len(segm_files) == len(
pose_files) == 0:
print('> Inconsistent input.')
continue
segmentations = [read_segmentation(f) for f in segm_files]
joints_2d, face_2d = [], []
for f in pose_files:
j, f = openpose_from_file(f)
if len(j) != 25 or len(f) != 70:
print('> Invalid keypoints.')
continue
joints_2d.append(j)
face_2d.append(f)
if opt_pose_steps:
print('> Optimizing for pose...')
model.opt_pose(segmentations,
joints_2d,
opt_steps=opt_pose_steps)
if opt_shape_steps:
print('> Optimizing for shape...')
model.opt_shape(segmentations,
joints_2d,
face_2d,
opt_steps=opt_shape_steps)
print('> Estimating shape...')
pred = model.predict(segmentations, joints_2d)
write_mesh('{}/{}.obj'.format(out_dir, name), pred['vertices'][0],
pred['faces'])
print('> Done.')
def main(weights, name, segm_dir, pose_dir, out_dir, opt_pose_steps, opt_shape_steps):
segm_files = sorted(glob(os.path.join(segm_dir, '*.png')))
pose_files = sorted(glob(os.path.join(pose_dir, '*.json')))
if len(segm_files) != len(pose_files) or len(segm_files) == len(pose_files) == 0:
exit('Inconsistent input.')
K.set_session(tf.Session(config=tf.ConfigProto(
gpu_options=tf.GPUOptions(allow_growth=True))))
model = Octopus(num=len(segm_files))
model.load(weights)
segmentations = [read_segmentation(f) for f in segm_files]
joints_2d, face_2d = [], []
for f in pose_files:
j, f = openpose_from_file(f)
assert(len(j) == 25)
assert(len(f) == 70)
joints_2d.append(j)
face_2d.append(f)
if opt_pose_steps:
print('Optimizing for pose...')
model.opt_pose(segmentations, joints_2d, opt_steps=opt_pose_steps)
if opt_shape_steps:
print('Optimizing for shape...')
model.opt_shape(segmentations, joints_2d, face_2d,
opt_steps=opt_shape_steps)
print('Estimating shape...')
pred = model.predict(segmentations, joints_2d)
write_mesh('{}/{}.obj'.format(out_dir, name),
pred['vertices'][0], pred['faces'])
print('Done.')
def main(weights, name, segm_dir, pose_dir, out_dir, opt_pose_steps,
opt_shape_steps, opt_size):
segm_files = sorted(glob(os.path.join(segm_dir, '*.png')))
pose_files = sorted(glob(os.path.join(pose_dir, '*.json')))
if len(segm_files) != len(pose_files) or len(segm_files) == len(
pose_files) == 0:
exit('Inconsistent input.')
K.set_session(
tf.Session(config=tf.ConfigProto(gpu_options=tf.GPUOptions(
allow_growth=True))))
model = Octopus(num=len(segm_files))
#Octopus 모델은 그대로 1080,1080 기준으로 생성
model.load(weights)
segmentations = [read_segmentation(f) for f in segm_files]
joints_2d, face_2d = [], []
for f in pose_files:
#pose를 720 기준으로 스케일링
j, f = openpose_from_file(f, resolution=(opt_size, opt_size))
assert (len(j) == 25)
assert (len(f) == 70)
joints_2d.append(j)
face_2d.append(f)
if opt_pose_steps:
print('Optimizing for pose...')
model.opt_pose(segmentations, joints_2d, opt_steps=opt_pose_steps)
if opt_shape_steps:
print('Optimizing for shape...')
model.opt_shape(segmentations,
joints_2d,
face_2d,
opt_steps=opt_shape_steps)
print('Estimating shape...')
pred = model.predict(segmentations, joints_2d)
os.makedirs(out_dir, exist_ok=True)
write_mesh_custom(os.path.join(out_dir, name + '.obj'),
pred['vertices'][0], pred['faces'])
width = 1080
height = 1080
camera_c = [540.0, 540.0]
camera_f = [1080, 1080]
vertices = pred['vertices']
data_to_save = {
'width': width,
'camera_c': camera_c,
'vertices': vertices,
'camera_f': camera_f,
'height': height
}
pickle_out = open('{}/frame_data.pkl'.format(out_dir), "wb")
pickle.dump(data_to_save, pickle_out)
pickle_out.close()
print('Done.')
print('Done.')
def main(weights, name, segm_dir, pose_dir, out_dir, opt_pose_steps,
opt_shape_steps):
segm_files = sorted(glob(os.path.join(segm_dir, '*.png')))
pose_files = sorted(glob(os.path.join(pose_dir, '*.json')))
if len(segm_files) != len(pose_files) or len(segm_files) == len(
pose_files) == 0:
exit('Inconsistent input.')
K.set_session(
tf.Session(config=tf.ConfigProto(gpu_options=tf.GPUOptions(
allow_growth=True))))
model = Octopus(num=len(segm_files))
model.load(weights)
segmentations = [read_segmentation(f) for f in segm_files]
joints_2d, face_2d = [], []
for f in pose_files:
j, f = openpose_from_file(f)
assert (len(j) == 25)
assert (len(f) == 70)
joints_2d.append(j)
face_2d.append(f)
if opt_pose_steps:
print('Optimizing for pose...')
model.opt_pose(segmentations, joints_2d, opt_steps=opt_pose_steps)
if opt_shape_steps:
print('Optimizing for shape...')
model.opt_shape(segmentations,
joints_2d,
face_2d,
opt_steps=opt_shape_steps)
print('Estimating shape...')
pred = model.predict(segmentations, joints_2d)
ff = np.load("assets/basicModel_f.npy")
ft = np.load("assets/basicModel_ft.npy")
vt = np.load("assets/basicModel_vt.npy")
v = pred['vertices'][0]
# write_mesh('{}/{}.obj'.format(out_dir, name), pred['vertices'][0], pred['faces'],ff,ft,vt)
with open('{}/{}.obj'.format(out_dir, name), 'w') as fp:
fp.write(('v {:f} {:f} {:f}\n' * len(v)).format(*v.reshape(-1)))
# fp.write(('f {:d} {:d} {:d}\n' * len(f)).format(*(f.reshape(-1) + 1)))
for i in range(len(vt)):
fp.write("vt " + str(vt[i][0]) + " " + str(vt[i][1]) + " " +
str(1) + "\n")
for i in range(len(ft)):
fp.write("f " + str(ff[i][0] + 1) + '/' + str(ft[i][0] + 1) +
" " + str(ff[i][1] + 1) + '/' + str(ft[i][1] + 1) + " " +
str(ff[i][2] + 1) + '/' + str(ft[i][2] + 1) + "\n")
width = 1080
height = 1080
camera_c = [540.0, 540.0]
camera_f = [1080, 1080]
vertices = pred['vertices']
data_to_save = {
'width': width,
'camera_c': camera_c,
'vertices': vertices,
'camera_f': camera_f,
'height': height
}
pickle_out = open("5.pkl", "wb")
pickle.dump(data_to_save, pickle_out)
pickle_out.close()
print('Done.')
print('Done.')
def main(weights, name, in_dir, segm_dir, pose_dir, out_dir, opt_pose_steps,
opt_shape_steps, openpose_model_dir, frame_data_name):
segm_files = sorted(
glob(os.path.join(segm_dir, '*.png')) +
glob(os.path.join(segm_dir, '*.jpg')))
if pose_dir is None and openpose_model_dir is None:
exit('No pose information available.')
joints_2d, face_2d = [], []
if pose_dir is not None:
pose_files = sorted(glob(os.path.join(pose_dir, '*.json')))
if len(segm_files) != len(pose_files) or len(segm_files) == len(
pose_files) == 0:
exit('Inconsistent input.')
for f in pose_files:
j, f = openpose_from_file(f)
assert (len(j) == 25)
assert (len(f) == 70)
joints_2d.append(j)
face_2d.append(f)
elif openpose_model_dir is not None:
from lib import openpose
joints_2d, face_2d = openpose.process(in_dir, openpose_model_dir)
K.set_session(
tf.Session(config=tf.ConfigProto(gpu_options=tf.GPUOptions(
allow_growth=True))))
model = Octopus(num=len(segm_files))
model.load(weights)
segmentations = [read_segmentation(f) for f in segm_files]
if opt_pose_steps:
print('Optimizing for pose...')
model.opt_pose(segmentations, joints_2d, opt_steps=opt_pose_steps)
if opt_shape_steps:
print('Optimizing for shape...')
model.opt_shape(segmentations,
joints_2d,
face_2d,
opt_steps=opt_shape_steps)
print('Estimating shape...')
pred = model.predict(segmentations, joints_2d)
# Include texture coords in mesh
vt = np.load('assets/basicModel_vt.npy')
ft = np.load('assets/basicModel_ft.npy')
write_mesh('{}/{}.obj'.format(out_dir, name),
pred['vertices_tposed'],
pred['faces'],
vt=vt,
ft=ft)
# Output frame data if specified
write_frame_data('{}/{}.pkl'.format(out_dir, frame_data_name),
pred['vertices'])
print('Done.')