309 lines
10 KiB
Python
309 lines
10 KiB
Python
import os
|
|
import sys
|
|
import time
|
|
import argparse
|
|
|
|
import numpy as np
|
|
import scipy.io as sio
|
|
import scipy as sp
|
|
import plotly.graph_objects as go
|
|
from scipy.spatial.transform import Rotation
|
|
|
|
from tqdm import tqdm
|
|
|
|
|
|
os.environ["CUDA_VISIBLE_DEVICES"] = "1"
|
|
import tensorflow as tf
|
|
gpus = tf.config.experimental.list_physical_devices('GPU')
|
|
if gpus:
|
|
try:
|
|
for gpu in gpus:
|
|
tf.config.experimental.set_memory_growth(gpu, True)
|
|
|
|
except RuntimeError as e:
|
|
print(e)
|
|
|
|
|
|
from custom_utils.smpl import SMPL
|
|
from custom_utils.supersmpl_io import readPC2
|
|
|
|
|
|
|
|
# run = wandb.init(config=tf.compat.v1.flags.FLAGS, sync_tensorboard=True, project='unpose', name='new_smpl_2', notes='')
|
|
# artifact = wandb.Artifact('code', type='code')
|
|
# artifact.add_file('Data/unpose.py')
|
|
# artifact.add_file('Data/verify_unposed.py')
|
|
# artifact.add_file('FeatureLines/superSMPL/smpl.py')
|
|
# artifact.add_file('FeatureLines/superSMPL/smpl_2.py')
|
|
# run.log_artifact(artifact)
|
|
|
|
|
|
def read_garment(folder, id):
|
|
min_body = np.load(os.path.join(folder, id, id + '_minimal.npy'))
|
|
# data = np.load(os.path.join('/home/workspace/data/dataset/cape_release', 'sequences', id))
|
|
|
|
return min_body
|
|
|
|
|
|
def plot_3d(mesh_orig, mesh_pred, unposed, faces):
|
|
_mesh_pred = mesh_pred.copy()
|
|
_unposed = unposed.copy()
|
|
_mesh_pred[:,1] += 1
|
|
_unposed[:,1] += 3
|
|
fig = go.Figure(data=[
|
|
go.Mesh3d(
|
|
# 8 vertices of a cube
|
|
x=mesh_orig[:,0],
|
|
y=mesh_orig[:,1],
|
|
z=mesh_orig[:,2],
|
|
# i, j and k give the vertices of triangles
|
|
i = faces[:,0],
|
|
j = faces[:,1],
|
|
k = faces[:,2]
|
|
),
|
|
go.Mesh3d(
|
|
# 8 vertices of a cube
|
|
x=_mesh_pred[:,0],
|
|
y=_mesh_pred[:,1],
|
|
z=_mesh_pred[:,2],
|
|
# i, j and k give the vertices of triangles
|
|
i = faces[:,0],
|
|
j = faces[:,1],
|
|
k = faces[:,2],
|
|
),
|
|
go.Mesh3d(
|
|
# 8 vertices of a cube
|
|
x=_unposed[:,0],
|
|
y=_unposed[:,1],
|
|
z=_unposed[:,2],
|
|
# i, j and k give the vertices of triangles
|
|
i = faces[:,0],
|
|
j = faces[:,1],
|
|
k = faces[:,2],
|
|
# vertexcolor=collision_color
|
|
)
|
|
])
|
|
|
|
fig.update_layout(scene_aspectmode='manual',
|
|
scene_aspectratio=dict(x=1, y=2, z=1))
|
|
fig.update_layout(
|
|
scene = dict(
|
|
xaxis = dict(nticks=4, range=[-1,1],),
|
|
yaxis = dict(nticks=4, range=[-1,3],),
|
|
zaxis = dict(nticks=4, range=[-1,1],),)
|
|
)
|
|
|
|
return fig
|
|
|
|
|
|
|
|
|
|
|
|
if __name__ == '__main__':
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument("-f", "--folder", help = "end of range", default='/home/workspace/data/dataset/cape_release/minimal_body_shape')
|
|
parser.add_argument("-s", "--start", help = "start of range", default=0)
|
|
parser.add_argument("-e", "--end", help = "end of range", default=200)
|
|
parser.add_argument("-n", "--name", help = "end of range", default='lhunor')
|
|
args = parser.parse_args()
|
|
|
|
F = np.load('/home/workspace/data/dataset/cape_release/misc/smpl_tris.npy')
|
|
gender_table = np.load('/home/workspace/data/dataset/cape_release/misc/subj_genders.pkl', allow_pickle=True)
|
|
folder = args.folder
|
|
smpl = SMPL()
|
|
iterations = 10000
|
|
|
|
ids = os.listdir(folder)[int(args.start):int(args.end)]
|
|
|
|
for id in tqdm(ids):
|
|
if not os.path.isdir(os.path.join(folder, id)):
|
|
continue
|
|
min_body = read_garment(folder, id)
|
|
|
|
pose = tf.constant(np.zeros((1, 72)), dtype=np.float32)
|
|
R = smpl.run_rodrigues(pose)
|
|
|
|
|
|
|
|
# lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(10.0, decay_steps=40000, decay_rate=0.1, staircase=False)
|
|
optimizer = tf.keras.optimizers.SGD(learning_rate=25.0)
|
|
betas = tf.Variable(np.zeros((1, 10), np.float32))
|
|
gender = np.zeros((1,1), np.float32) if gender_table[id] == 'female' else np.ones((1,1), np.float32)
|
|
|
|
|
|
|
|
@tf.function
|
|
def shaping(shape):
|
|
_shape = tf.concat((shape, gender), axis=-1)
|
|
result = smpl.run(R, _shape, remove=False)[0][0, :6890, :]
|
|
return result
|
|
|
|
|
|
@tf.function
|
|
def error():
|
|
pred = shaping(betas)
|
|
loss = tf.reduce_mean(tf.math.abs(pred - min_body))
|
|
|
|
return loss
|
|
|
|
start = time.perf_counter()
|
|
euclid_error = 10000
|
|
|
|
|
|
while True:
|
|
optimizer.minimize(error, [betas])
|
|
|
|
i = optimizer.iterations.numpy()
|
|
if i % 100 == 0:
|
|
pred = shaping(betas)
|
|
# loss = tf.reduce_mean(tf.reduce_sum(tf.math.abs(pred - posed) ** 2, axis=-1))
|
|
loss = tf.reduce_mean(tf.math.abs(pred - min_body))
|
|
euclid_error = tf.reduce_mean(tf.math.sqrt(tf.reduce_sum((min_body - pred) ** 2, axis=-1)))
|
|
print('id: ' + id + ' iteration: ' + str(i) + ' loss: ' + str(loss.numpy()) + ' error: ' + str(euclid_error.numpy()) + ' time: ' + str(time.perf_counter() - start))
|
|
start = time.perf_counter()
|
|
|
|
if euclid_error < 0.0001:
|
|
break
|
|
|
|
if euclid_error < 0.0005 and i > 20000:
|
|
break
|
|
|
|
if euclid_error < 0.001 and i > 40000:
|
|
break
|
|
|
|
if i > 60000:
|
|
break
|
|
|
|
x = min_body
|
|
y = pred
|
|
faces = F
|
|
fig = go.Figure(data=[
|
|
go.Mesh3d(
|
|
# 8 vertices of a cube
|
|
x=x[:,0],
|
|
y=x[:,1],
|
|
z=x[:,2],
|
|
# i, j and k give the vertices of triangles
|
|
i = faces[:,0],
|
|
j = faces[:,1],
|
|
k = faces[:,2]
|
|
),
|
|
go.Mesh3d(
|
|
# 8 vertices of a cube
|
|
x=y[:,0],
|
|
y=y[:,1],
|
|
z=y[:,2],
|
|
# i, j and k give the vertices of triangles
|
|
i = faces[:,0],
|
|
j = faces[:,1],
|
|
k = faces[:,2]
|
|
),
|
|
])
|
|
|
|
fig.update_layout(scene_aspectmode='manual',
|
|
scene_aspectratio=dict(x=1, y=1, z=1))
|
|
fig.update_layout(
|
|
scene = dict(
|
|
xaxis = dict(nticks=4, range=[-1.5,1.5],),
|
|
yaxis = dict(nticks=4, range=[-1.5,1.5],),
|
|
zaxis = dict(nticks=4, range=[-1.5,1.5],),)
|
|
)
|
|
|
|
fig.show()
|
|
|
|
|
|
continue
|
|
|
|
|
|
|
|
offset = 1
|
|
|
|
|
|
for frame in range(posed.shape[0]):
|
|
|
|
lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(10.0, decay_steps=10000, decay_rate=0.1, staircase=False)
|
|
optimizer = tf.keras.optimizers.SGD(learning_rate=lr_schedule)
|
|
unposed = tf.Variable(np.random.rand(offset, posed.shape[1], 3).astype(np.float32) * 2 - 1)
|
|
# unposed = tf.Variable(init)
|
|
|
|
@tf.function
|
|
def skinning(verts):
|
|
# verts = smpl.run(R[frame:frame+offset], tf.tile(shape[tf.newaxis,:], [offset,1]))[0].numpy()[:,mask,:]
|
|
|
|
|
|
# start = time.perf_counter()
|
|
result = smpl.run_garment(R[frame:frame+offset], tf.tile(shape[tf.newaxis,:], [offset,1]), verts, filter_idx=mask_idx)
|
|
# print(time.perf_counter() - start)
|
|
|
|
# start = time.perf_counter()
|
|
# result_2 = smpl_2.run_garment(R[frame:frame+offset], shape[:10], gender, verts, filter_idx=mask_idx)
|
|
# print(time.perf_counter() - start)
|
|
|
|
# test = np.abs(result.numpy() - result_2.numpy()) > 1e-4
|
|
# x = np.sum(test, axis=-1).sum(-1).sum(-1)
|
|
# euclid_error = tf.reduce_mean(tf.math.sqrt(tf.reduce_sum((result - result_2) ** 2, axis=-1)))
|
|
|
|
# batch = 25
|
|
# _, faces = _maskFaces(np.zeros((14475,3)), F, mask)
|
|
# go.Figure(data=[
|
|
# go.Mesh3d(
|
|
# # 8 vertices of a cube
|
|
# x=result[batch,:,0],
|
|
# y=result[batch,:,1],
|
|
# z=result[batch,:,2],
|
|
# # i, j and k give the vertices of triangles
|
|
# i = faces[:,0],
|
|
# j = faces[:,1],
|
|
# k = faces[:,2]
|
|
# ),
|
|
# go.Mesh3d(
|
|
# # 8 vertices of a cube
|
|
# x=result_2[batch,:,0],
|
|
# y=result_2[batch,:,1],
|
|
# z=result_2[batch,:,2],
|
|
# # i, j and k give the vertices of triangles
|
|
# i = faces[:,0],
|
|
# j = faces[:,1],
|
|
# k = faces[:,2],
|
|
# )]).show()
|
|
|
|
return result
|
|
|
|
@tf.function
|
|
def error():
|
|
pred = skinning(unposed)
|
|
loss = tf.reduce_mean(tf.math.abs(pred - posed[frame]))
|
|
|
|
return loss
|
|
|
|
start = time.perf_counter()
|
|
euclid_error = 10000
|
|
|
|
for i in range(iterations):
|
|
optimizer.minimize(error, [unposed])
|
|
if i % 50 == 0:
|
|
pred = skinning(unposed)
|
|
# loss = tf.reduce_mean(tf.reduce_sum(tf.math.abs(pred - posed) ** 2, axis=-1))
|
|
loss = tf.reduce_mean(tf.math.abs(pred - posed[frame]))
|
|
euclid_error = tf.reduce_mean(tf.math.sqrt(tf.reduce_sum((posed[frame] - pred) ** 2, axis=-1)))
|
|
print('id: ' + id + ' frame: ' + str(frame) + ' iteration: ' + str(i) + ' loss: ' + str(loss.numpy()) + ' error: ' + str(euclid_error.numpy()) + ' time: ' + str(time.perf_counter() - start))
|
|
start = time.perf_counter()
|
|
|
|
if euclid_error < 0.001:
|
|
break
|
|
|
|
result = np.zeros((14475, 3), dtype = np.float32)
|
|
result[mask,:] = unposed.numpy()
|
|
np.savez_compressed(os.path.join(folder, id, 'mesh_unposed', id + '_' + str(frame) + '.npz'), mesh_unposed=result)
|
|
|
|
init = unposed
|
|
|
|
# pred, unposed = pred.numpy(), unposed.numpy()
|
|
# _, faces = _maskFaces(np.zeros((14475,3)), F, mask)
|
|
# k = 0
|
|
# plot_3d(posed[k], pred[k], unposed[k], faces).show()
|
|
|
|
# print()
|
|
|
|
|
|
|