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update unposing smpl code based on other script

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luni
2022-09-16 16:48:21 +00:00
parent f0159d8ea4
commit 4eb9085f1e
1 changed files with 11 additions and 10 deletions
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custom_utils/smpl_2.py
+11 -10
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@@ -22,7 +22,7 @@ eval_joints = {
class SMPL:
def __init__(self):
def __init__(self, path):
self.J_regressor = []
self.v_template = []
self.shapedirs = []
@@ -30,7 +30,7 @@ class SMPL:
self.weights = []
# Load SMPL models
# path = os.path.abspath(os.path.dirname(__file__))
with open(os.path.join('custom_utils/super_model_f.pkl'), 'rb') as f:
with open(os.path.join(path, 'super_model_f.pkl'), 'rb') as f:
if sys.version_info[0]<3:
female = pickle.load(f)
else:
@@ -40,7 +40,7 @@ class SMPL:
self.posedirs.append(tf.constant(female['posedirs'], dtype=np.float32))
self.shapedirs.append(tf.constant(female['shapedirs'], dtype=np.float32))
self.weights.append(tf.constant(female['weights'], dtype=np.float32))
with open(os.path.join('custom_utils/super_model_m.pkl'), 'rb') as f:
with open(os.path.join(path, 'super_model_m.pkl'), 'rb') as f:
if sys.version_info[0]<3:
male = pickle.load(f)
else:
@@ -63,11 +63,11 @@ class SMPL:
self.weights_one_hot = tf.one_hot(self.weights_one_hot, 24)
# Topology no head/hands/feet
F = sio.loadmat('custom_utils/ssmpl_faces.mat')
F = sio.loadmat(path + '/ssmpl_faces.mat')
self.f = np.array(F['faces_default'])
self.f_skirt = np.array(F['faces_skirt'])
self.nn = np.array(sio.loadmat('custom_utils/NN.mat'))
self.to_keep = sio.loadmat(os.path.join('custom_utils/keep_remove.mat'))['keep'].reshape(-1)
self.nn = np.array(sio.loadmat(path + '/NN.mat'))
self.to_keep = sio.loadmat(os.path.join(path, 'keep_remove.mat'))['keep'].reshape(-1)
self.weights = tf.gather(self.weights, self.to_keep, axis=1)
# self.shapedirs = tf.gather(self.shapedirs, self.to_keep, axis=1)
@@ -246,14 +246,13 @@ class SMPL:
return J
def run_garment(self, pose, betas, gender, v_cloth, filter=None):
def run_garment(self, R, betas, gender, v_cloth, filter_idx=None):
batch_size = v_cloth.shape[0]
J = self.get_joints_template(betas, gender, batch_size)
R = self.run_rodrigues(pose)
if filter is not None:
weights = tf.boolean_mask(self.weights, filter, axis=1)
if filter_idx is not None:
weights = tf.gather(self.weights, filter_idx, axis=1)
R_cube_big = R
results = []
@@ -274,6 +273,8 @@ class SMPL:
v = tf.reshape(v, (batch_size, -1, 4))[:, :, :3]
J_out = tf.squeeze(J)
if J.shape[0] == 1:
J_out = J_out[tf.newaxis, :, :]
J0 = tf.reshape(J_out[:,0,:], (-1, 1, 3))
J_out = tf.subtract(J_out, J0)
T_out = tf.subtract(v, J0)