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luni
2022-11-04 16:17:58 +00:00
parent ebe8522905
commit 1c6b805519
18 changed files with 1413 additions and 486 deletions
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custom_utils/custom_utils.py
+60 -8
View File
@@ -144,9 +144,25 @@ def maskFacesBatch(V, F, mask):
return Vs, Fs
def plot_3d(mesh_gt, mesh_pred_unposed, faces, collision, collision_faces, collision_color):
def flip_axis(x, ind=None):
y = x.copy()
if ind is not None:
y[ind:,1] = x[ind:,2]
y[ind:,2] = x[ind:,1]
else:
y[:,1] = x[:,2]
y[:,2] = x[:,1]
return y
def plot_3d(mesh_gt, mesh_pred_unposed, faces, collision, collision_faces, collision_color, flip_xy=False):
fig = make_subplots(rows=1, cols=3, start_cell="top-left", specs = [ [{ 'type': 'scene' } for _ in range(3)] ])
if flip_xy:
mesh_gt = flip_axis(mesh_gt)
mesh_pred_unposed = flip_axis(mesh_pred_unposed)
collision = flip_axis(collision, ind=14475)
fig.add_trace(go.Mesh3d(
# 8 vertices of a cube
x=mesh_gt[:,0],
@@ -200,8 +216,24 @@ def plot_3d(mesh_gt, mesh_pred_unposed, faces, collision, collision_faces, colli
return fig
def plot_3d_collision(collision_1, collision_2, collision_faces, collision_color):
fig = make_subplots(rows=1, cols=2, start_cell="top-left", specs = [ [{ 'type': 'scene' } for _ in range(2)] ])
def plot_3d_collision(gt, collision_1, collision_2, collision_faces, collision_color, flip_xy=False):
fig = make_subplots(rows=1, cols=3, start_cell="top-left", specs = [ [{ 'type': 'scene' } for _ in range(3)] ])
if flip_xy:
collision_1 = flip_axis(collision_1, ind=14475)
collision_2 = flip_axis(collision_2, ind=14475)
gt = flip_axis(gt, ind=14475)
fig.add_trace(go.Mesh3d(
x=gt[:,0],
y=gt[:,2],
z=gt[:,1],
i = collision_faces[:,0],
j = collision_faces[:,1],
k = collision_faces[:,2],
vertexcolor=collision_color
),
row=1, col=1)
fig.add_trace(go.Mesh3d(
x=collision_1[:,0],
@@ -212,7 +244,7 @@ def plot_3d_collision(collision_1, collision_2, collision_faces, collision_color
k = collision_faces[:,2],
vertexcolor=collision_color
),
row=1, col=1)
row=1, col=2)
fig.add_trace(go.Mesh3d(
x=collision_2[:,0],
@@ -223,17 +255,19 @@ def plot_3d_collision(collision_1, collision_2, collision_faces, collision_color
k = collision_faces[:,2],
vertexcolor=collision_color
),
row=1, col=2)
row=1, col=3)
fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=1, z=1),
scene2_aspectmode='manual', scene2_aspectratio=dict(x=1, y=1, z=1),
scene3_aspectmode='manual', scene3_aspectratio=dict(x=1, y=1, z=1),
)
helper = dict(xaxis = dict(nticks=4, range=[-1,1],),
yaxis = dict(nticks=4, range=[-1,1],),
zaxis = dict(nticks=4, range=[-1,1],),)
fig.update_layout(scene = helper,
scene2 = helper,
scene3 = helper,
)
return fig
@@ -258,7 +292,7 @@ def sparse_to_tensor(L):
return tf.SparseTensor(idx, data, shape)
def load_model(model, checkpoint, disable_assert=False, assert_consumed_on=False):
def load_model(model, checkpoint, disable_assert=False, assert_consumed_on=False, iteration=''):
ckpt = tf.train.Checkpoint(model=model)
manager = tf.train.CheckpointManager(ckpt, checkpoint, max_to_keep=1)
if not disable_assert:
@@ -267,7 +301,12 @@ def load_model(model, checkpoint, disable_assert=False, assert_consumed_on=False
if assert_consumed_on:
ckpt.restore(manager.latest_checkpoint).assert_consumed()
else:
ckpt.restore(manager.latest_checkpoint).assert_existing_objects_matched()#.assert_consumed()
if iteration != '':
name_base = manager.latest_checkpoint.split('-')[0]
ckpt_ind = [i for i in range(len(manager.checkpoints)) if manager.checkpoints[i].endswith(name_base + '-' + iteration)][0]
ckpt.restore(manager.checkpoints[ckpt_ind]).assert_existing_objects_matched()#.assert_consumed()
else:
ckpt.restore(manager.latest_checkpoint).assert_existing_objects_matched()#.assert_consumed()
def get_summary_str(model):
@@ -350,4 +389,17 @@ def replace_arrays(string):
out += string[i+1:]
return out
return out
def group_inputs_for_levels(batch, config):
levels_input = []
for res in config['pyramid']['resolutions']:
res_modifier = '_' + str(res) if res != 512 else ''
if config['use_skinning']:
uv_orig = batch['uv_unposed' + res_modifier]
else:
uv_orig = batch['uv_posed' + res_modifier]
levels_input.append([uv_orig, batch['uv_static' + res_modifier], batch['uv_body_posed' + res_modifier], batch['uv_curvature' + res_modifier], batch['uv_mask' + res_modifier], batch['uv_mask']])
conds = [batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature']]
return levels_input, conds
custom_utils/smpl.py
+19 -2
View File
@@ -34,12 +34,14 @@ eval_joints = {
class SMPL:
def __init__(self, orig_smpl=False, use_one_hot=False):
def __init__(self, orig_smpl=False, use_one_hot=False, is_skirt=False):
self.J_regressor = []
self.v_template = []
self.shapedirs = []
self.posedirs = []
self.weights = []
self.use_one_hot = use_one_hot
self.is_skirt = is_skirt
# 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:
@@ -299,7 +301,22 @@ class SMPL:
def run_garment(self, pose, betas, v_cloth, v_shaped=None):
J = self.get_joints_template(betas, v_shaped)
R = self.run_rodrigues(pose)
if self.use_one_hot and self.is_skirt:
# p_avg = (pose[:, 3:6] + pose[:, 6:9]) / 2
# pose_dress = tf.concat([pose[:, :3], p_avg, p_avg, pose[:, 9:]], axis=-1)
# R = self.run_rodrigues(pose_dress).numpy()
# for b in range(betas.shape[0]):
# R[b][[4,5,7,8,10,11],:,:] = np.eye(3)
pose_dress = pose
# p_avg = (pose_dress[:, 3:6] + pose_dress[:, 6:9]) / 2
# pose_dress[:,3:9] = np.concatenate([p_avg, p_avg], axis=-1)
R = self.run_rodrigues(pose_dress).numpy()
for b in range(betas.shape[0]):
R[b][[4,5,7,8,10,11],:,:] = np.eye(3)
else:
R = self.run_rodrigues(pose)
# if filter is not None:
# weights = tf.boolean_mask(self.weights, filter)