commit all
This commit is contained in:
+466
@@ -0,0 +1,466 @@
|
||||
import os
|
||||
os.environ["CUDA_VISIBLE_DEVICES"] = "6"
|
||||
import tensorflow as tf
|
||||
|
||||
gpus = tf.config.experimental.list_physical_devices('GPU')
|
||||
if gpus:
|
||||
try:
|
||||
# Currently, memory growth needs to be the same across GPUs
|
||||
for gpu in gpus:
|
||||
tf.config.experimental.set_memory_growth(gpu, True)
|
||||
logical_gpus = tf.config.experimental.list_logical_devices('GPU')
|
||||
print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
|
||||
except RuntimeError as e:
|
||||
# Memory growth must be set before GPUs have been initialized
|
||||
print(e)
|
||||
|
||||
import time
|
||||
import argparse
|
||||
import wandb
|
||||
import random
|
||||
import cv2
|
||||
import tqdm
|
||||
import cmapy as cm
|
||||
import numpy as np
|
||||
import scipy as sp
|
||||
import trimesh as tm
|
||||
from custom_utils.custom_utils import UV2Mesh, maskFacesBatch, _maskFaces
|
||||
import plotly.graph_objects as go
|
||||
import plotly.express as ex
|
||||
from PIL import Image
|
||||
from plotly.subplots import make_subplots
|
||||
|
||||
from custom_utils.smpl import SMPL
|
||||
from custom_utils.custom_utils import load_model
|
||||
from plotly.subplots import make_subplots
|
||||
from scipy.spatial.transform import Rotation
|
||||
|
||||
from dataset import DatasetFactory, StaticDataset
|
||||
from custom_utils.custom_utils import batch_to_dict, UV2Mesh, _maskFaces, load_model, group_inputs_for_levels
|
||||
from custom_utils.smpl import SMPL
|
||||
from convert_to_uv_api import convert_to_uv_api
|
||||
from models.upscaler import UpscalerBasicConv as Upscaler
|
||||
from postprocessing import fix_collision
|
||||
from models.pyramid_model_optimized import Pyramid
|
||||
|
||||
# from configs.config_abl_overfit_dress_var_3 import config as config_main
|
||||
from configs.config_abl_overfit_jumpsuit_2 import config as config_main
|
||||
from configs.config_cvae_curv import config as config_curv
|
||||
# from models.cvae_model_overfit_jumpsuit import CVAE
|
||||
from models.cvae_model import CVAE
|
||||
from models.cvae_curv_model import CVAE as CVAECurv
|
||||
from convert_to_uv_api import convert_to_uv_api
|
||||
from tensorflow_graphics.geometry.representation.mesh.normals import vertex_normals
|
||||
|
||||
|
||||
uv2mesh = UV2Mesh(res=256, is_skirt=config_main['is_skirt'])
|
||||
|
||||
|
||||
def get_temp_batch(frame, config):
|
||||
with open(os.path.join(folder, 'frames_temp.txt'), 'w') as f:
|
||||
f.write(frame)
|
||||
|
||||
config['signature_dynamic']['mesh_curvature'] = (14475)
|
||||
|
||||
factory = DatasetFactory()
|
||||
dataset = factory.get_dataset(folder=folder,
|
||||
batch_size=1,
|
||||
signature=config['signature_dynamic'],
|
||||
in_memory=config['dataset_in_memory'],
|
||||
is_cape=config['is_cape'],
|
||||
frames_file='frames_temp.txt')
|
||||
static_dataset = StaticDataset(folder=folder,
|
||||
signature=config['signature_static'],
|
||||
in_memory=True,
|
||||
is_cape=config['is_cape'])
|
||||
|
||||
data_iterator = iter(dataset)
|
||||
batch = data_iterator.get_next()
|
||||
batch = static_dataset.expand_batch(batch_to_dict(batch, config['signature_dynamic']))
|
||||
|
||||
return batch
|
||||
|
||||
|
||||
def plot_fixed(garment, body, faces, camera, colors=None):
|
||||
mesh = np.concatenate((body, garment), axis=0)
|
||||
faces = np.concatenate((F_body, faces + 14475), axis=0).astype(np.int32)
|
||||
if colors is None:
|
||||
colors = np.concatenate((np.ones_like(body, dtype=np.int32)*100, np.ones_like(garment, dtype=np.int32)*230), axis=0).astype(np.float32) / 255.0
|
||||
else:
|
||||
colors = np.concatenate((np.ones_like(body, dtype=np.int32)*100 / 255.0, colors), axis=0)
|
||||
|
||||
fig = go.Figure()
|
||||
|
||||
fig.add_trace(go.Mesh3d(
|
||||
x=mesh[:,0],
|
||||
y=mesh[:,2],
|
||||
z=mesh[:,1],
|
||||
i = faces[:,0],
|
||||
j = faces[:,1],
|
||||
k = faces[:,2],
|
||||
text="",
|
||||
vertexcolor=colors,
|
||||
lighting={ 'ambient':0.8, 'diffuse':0.5, 'roughness':0.5 },
|
||||
|
||||
),)
|
||||
|
||||
fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=1, z=1))
|
||||
helper = dict(xaxis = dict(range=[-1,1], showbackground=False, showticklabels=False, showaxeslabels=False, showspikes=False, visible=False),
|
||||
yaxis = dict(range=[-1,1], showbackground=False, showticklabels=False, showaxeslabels=False, showspikes=False, visible=False),
|
||||
zaxis = dict(range=[-1,1], showbackground=False, showticklabels=False, showaxeslabels=False, showspikes=False, visible=False),)
|
||||
fig.update_layout(scene = helper)
|
||||
|
||||
fig.update_layout(scene_camera=camera, width=800, height=1100, autosize=False,
|
||||
margin=dict(l=0, r=0, b=0, t=0, pad=0)
|
||||
)
|
||||
|
||||
return fig
|
||||
|
||||
|
||||
def custom_imshow(img):
|
||||
# fig = ex.imshow(np.flip(img, axis=0))
|
||||
fig = ex.imshow(img)
|
||||
fig.update_layout(margin=dict(l=0, r=0, b=0, t=0, pad=0), width=img.shape[1], height=img.shape[0])
|
||||
fig.update_xaxes(visible=False)
|
||||
fig.update_yaxes(visible=False)
|
||||
|
||||
return fig
|
||||
|
||||
|
||||
|
||||
def color_labels(img):
|
||||
colors = np.array([cm.color('jet', 1 - i) for i in np.linspace(0, 1, img.max()+1)]) / 255.0
|
||||
color_img = np.zeros((img.shape[0], img.shape[1], 3), np.float32)
|
||||
for i in range(img.max()+1):
|
||||
color_img[img == i, :] = colors[i]
|
||||
|
||||
return color_img
|
||||
|
||||
def color_labels_mesh(img):
|
||||
colors = np.array([cm.color('jet', 1 - i) for i in np.linspace(0, 1, img.max()+1)]) / 255.0
|
||||
color_img = np.zeros((img.shape[0], 3), np.float32)
|
||||
for i in range(img.max()+1):
|
||||
color_img[img == i, :] = colors[6-i]
|
||||
|
||||
return color_img
|
||||
|
||||
|
||||
def curvature_sampling(model: CVAECurv, batch, nr=4):
|
||||
out = model.sample(tf.tile(batch['uv_static'], [nr,1,1,1]), tf.tile(batch['uv_body_posed'], [nr,1,1,1]), tf.tile(batch['uv_mask'], [nr,1,1]))
|
||||
|
||||
img = np.concatenate(out.numpy(), axis=1)
|
||||
img = color_labels(img.argmax(-1))
|
||||
|
||||
custom_imshow(img).show()
|
||||
|
||||
return out
|
||||
|
||||
|
||||
def convert_to_normal_uv(mesh, mask):
|
||||
uv = (convert_to_uv_api(np.squeeze(vertex_normals(mesh, tf.tile(F[None], [1, 1, 1])).numpy()) * mask[:,np.newaxis], mask) + 2) / 4
|
||||
return uv
|
||||
|
||||
|
||||
def get_posed_mesh(uv, pose_shape, body, mask):
|
||||
shape = pose_shape[:, 72:]
|
||||
pose = pose_shape[:, :72]
|
||||
gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32)
|
||||
shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])])
|
||||
|
||||
if len(uv.shape) == 3:
|
||||
mesh = uv2mesh(uv[tf.newaxis,:,:,:])
|
||||
else:
|
||||
mesh = uv2mesh(uv)
|
||||
|
||||
mesh_shaped = mesh + shape_offsets * mask[tf.newaxis,:,tf.newaxis]
|
||||
mesh_posed = smpl.run_garment(pose, shape, mesh_shaped)
|
||||
|
||||
# mesh_fixed = fix_collision(mesh_posed, body, mask[np.newaxis,:])[0].numpy()
|
||||
# return mesh_fixed
|
||||
return mesh_posed
|
||||
|
||||
|
||||
|
||||
def mm_norm(x):
|
||||
return (x - x.min()) / (x.max() - x.min())
|
||||
|
||||
|
||||
def sample_with_curvature(model: CVAE, batch, sampled_curvature, nr=4):
|
||||
noise = tf.random.normal(shape=(1, 128))
|
||||
noise = tf.tile(noise, [nr,1])
|
||||
out_sampled = model.sample(tf.tile(batch['uv_static'], [nr,1,1,1]), tf.tile(batch['uv_body_posed'], [nr,1,1,1]), sampled_curvature, tf.tile(batch['uv_mask'], [nr,1,1]), noise)
|
||||
out_orig = model.sample(batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask'], noise[:1])
|
||||
# out_sampled, _, _ = model([tf.tile(batch['uv_unposed'], [nr,1,1,1]), tf.tile(batch['uv_static'], [nr,1,1,1]), tf.tile(batch['uv_body_posed'], [nr,1,1,1]), sampled_curvature, tf.tile(batch['uv_mask'], [nr,1,1])], training=False)
|
||||
# out_orig, _, _ = model([batch['uv_unposed'], batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False)
|
||||
|
||||
out = tf.concat((out_orig, out_sampled), axis=0)
|
||||
|
||||
mask = batch['mesh_mask'][0].numpy().astype(bool)
|
||||
|
||||
posed_mesh = [get_posed_mesh(uv, batch['pose_shape'], batch['mesh_body_posed'], mask) for uv in tf.unstack(out)]
|
||||
# posed_mesh = [uv2mesh(uv[tf.newaxis,:,:,:]) for uv in tf.unstack(out)]
|
||||
|
||||
plot_fixed(posed_mesh[0][0].numpy()[mask, :], batch['mesh_body_posed'][0].numpy(), faces, camera_1).show()
|
||||
|
||||
normal_uvs = np.concatenate([convert_to_normal_uv(mesh, mask) for mesh in posed_mesh], axis=1)
|
||||
|
||||
out_img = np.concatenate(out.numpy(), axis=1)
|
||||
curv_img = color_labels(np.concatenate(np.concatenate((batch['uv_curvature'].numpy(), sampled_curvature), axis=0), axis=1).argmax(-1))
|
||||
|
||||
img = np.concatenate((curv_img, normal_uvs), axis=0)
|
||||
custom_imshow(img).show()
|
||||
|
||||
print()
|
||||
|
||||
|
||||
|
||||
def curv_output_samples(frames):
|
||||
out = []
|
||||
for frame in frames:
|
||||
batch = get_temp_batch(frame, config_main)
|
||||
mask = batch['mesh_mask'][0].numpy().astype(bool)
|
||||
# _, faces = _maskFaces(np.zeros((14475, 3)), F, mask)
|
||||
curv_img = color_labels(batch['uv_curvature'][0].numpy().argmax(-1))
|
||||
pred, _, _ = model([batch['uv_unposed'], batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False)
|
||||
pred_img = convert_to_normal_uv(uv2mesh(pred), mask)
|
||||
img = np.concatenate((curv_img, pred_img), axis=0)
|
||||
# custom_imshow(img).show()
|
||||
out.append(img)
|
||||
|
||||
return custom_imshow(np.concatenate(out, axis=1))
|
||||
|
||||
|
||||
def curv_output_samples_mesh(frames):
|
||||
out = []
|
||||
for frame in tqdm.tqdm(frames):
|
||||
batch = get_temp_batch(frame, config_main)
|
||||
mask = batch['mesh_mask'][0].numpy().astype(bool)
|
||||
_, faces = _maskFaces(np.zeros((14475, 3)), F, mask)
|
||||
curv_colors = color_labels_mesh(batch['mesh_curvature'][0].numpy() + np.ones((14475), np.int32) * mask)
|
||||
pred, _, _ = model([batch['uv_unposed'], batch['uv_static'], batch['uv_body_posed'], batch['uv_curvature'], batch['uv_mask']], training=False)
|
||||
# pred_mesh = uv2mesh(pred)
|
||||
|
||||
pred_mesh = get_posed_mesh(pred, batch['pose_shape'], batch['mesh_body_posed'], mask)
|
||||
|
||||
body = batch['mesh_body_posed'][0].numpy()
|
||||
pred_mesh_fixed = postprocess(pred_mesh, body, faces, mask, do_smoothing=False)
|
||||
|
||||
fig = plot_fixed(pred_mesh_fixed, np.zeros_like(body), faces, camera_1)
|
||||
fig.write_image('/tmp/exp_temp_fasdsavcs.png')
|
||||
img = cv2.imread('/tmp/exp_temp_fasdsavcs.png')
|
||||
fig = plot_fixed(pred_mesh_fixed, np.zeros_like(body), faces, camera_1, curv_colors[mask, :])
|
||||
fig.write_image('/tmp/exp_temp_fasdsavcs.png')
|
||||
img_curv = cv2.imread('/tmp/exp_temp_fasdsavcs.png')
|
||||
|
||||
custom_imshow(np.concatenate((img[:,100:-100,:], img_curv[:,100:-100,:]), axis=1)).write_image(os.path.join(out_folder, frame + '_curv.png'))
|
||||
|
||||
# print()
|
||||
|
||||
# return custom_imshow(np.concatenate(out, axis=1))
|
||||
|
||||
|
||||
def get_closest_n_points(verts, idx, n):
|
||||
dist = sp.spatial.distance.cdist(verts[idx, :], verts)
|
||||
dist[dist == 0] = 1e9
|
||||
idx_sort = np.argsort(dist, axis=-1)
|
||||
closest = idx_sort[:, :n]
|
||||
return closest
|
||||
|
||||
|
||||
def postprocess(mesh, body, faces, mask, do_smoothing=True):
|
||||
mesh_fixed = fix_collision(mesh, body[np.newaxis,:,:], mask[np.newaxis,:])[0].numpy()[mask, :]
|
||||
|
||||
if do_smoothing:
|
||||
m = tm.Trimesh(mesh_fixed, faces, process=False, validate=False)
|
||||
outliers = np.array([len(v) for v in m.vertex_neighbors]) == 0
|
||||
outliers = np.nonzero(outliers)[0]
|
||||
outliers_n_neighbours = get_closest_n_points(mesh_fixed, outliers, 2)
|
||||
outliers_faces = np.concatenate((outliers[:, np.newaxis], outliers_n_neighbours), axis=-1)
|
||||
faces_fixed = np.concatenate((faces, outliers_faces), axis=0)
|
||||
m = tm.Trimesh(mesh_fixed, faces_fixed, process=False, validate=False)
|
||||
tm.smoothing.filter_humphrey(m, iterations=1, alpha=0.6, beta=0.4)
|
||||
|
||||
mesh = m.vertices
|
||||
else:
|
||||
mesh = mesh_fixed
|
||||
|
||||
return mesh
|
||||
|
||||
|
||||
def sampling_example(frame, camera, nr=10):
|
||||
|
||||
batch = get_temp_batch(frame, config_main)
|
||||
mask = batch['mesh_mask'][0].numpy().astype(bool)
|
||||
_, faces = _maskFaces(np.zeros((14475, 3)), F, mask)
|
||||
|
||||
noise = tf.random.normal(shape=(nr, 128))
|
||||
out = model.sample(tf.tile(batch['uv_static'], [nr,1,1,1]), tf.tile(batch['uv_body_posed'], [nr,1,1,1]), tf.tile(batch['uv_curvature'], [nr,1,1,1]), tf.tile(batch['uv_mask'], [nr,1,1]), noise)
|
||||
# out,_,_ = model([tf.tile(batch['uv_unposed'], [nr,1,1,1]), tf.tile(batch['uv_static'], [nr,1,1,1]), tf.tile(batch['uv_body_posed'], [nr,1,1,1]), tf.tile(batch['uv_curvature'], [nr,1,1,1]), tf.tile(batch['uv_mask'], [nr,1,1])], training=False)
|
||||
|
||||
|
||||
posed_mesh = [get_posed_mesh(uv, batch['pose_shape'], batch['mesh_body_posed'], mask) for uv in tf.unstack(out)]
|
||||
# posed_mesh = [uv2mesh(uv[tf.newaxis,:,:,:]) for uv in tf.unstack(out)]
|
||||
gt_mesh = batch['mesh_posed'][0].numpy()[mask, :]
|
||||
|
||||
body = batch['mesh_body_posed'][0].numpy()
|
||||
posed_mesh_fixed = [postprocess(mesh, body, faces, mask, do_smoothing=False) for mesh in posed_mesh]
|
||||
# posed_mesh_fixed = [mesh[0].numpy()[mask, :] for mesh in posed_mesh]
|
||||
|
||||
# error = [np.sqrt(((gt_mesh-m) ** 2).sum(-1)).mean() for m in posed_mesh_fixed]
|
||||
|
||||
# row_sampling = []
|
||||
# fig = plot_fixed(gt_mesh, body, faces, camera)
|
||||
# fig.write_image('/tmp/exp_temp_7.png')
|
||||
# img = cv2.imread('/tmp/exp_temp_7.png')
|
||||
# row_sampling.append(img[:, 100:700, :])
|
||||
# for mesh in posed_mesh_fixed:
|
||||
# fig = plot_fixed(mesh, body, faces, camera)
|
||||
# fig.write_image('/tmp/exp_temp_7.png')
|
||||
# img = cv2.imread('/tmp/exp_temp_7.png')
|
||||
# row_sampling.append(img[:, 100:700, :])
|
||||
|
||||
|
||||
# custom_imshow(np.concatenate(row_sampling, axis=1)).show()
|
||||
|
||||
plot_fixed(np.zeros_like(gt_mesh), body, faces, camera).write_image(os.path.join(out_folder, frame + '_body.png'))
|
||||
plot_fixed(gt_mesh, body, faces, camera).write_image(os.path.join(out_folder, frame + '_gt.png'))
|
||||
for i in range(nr):
|
||||
plot_fixed(posed_mesh_fixed[i], body, faces, camera).write_image(os.path.join(out_folder, frame + '_' + str(i) + '.png'))
|
||||
|
||||
# custom_imshow(np.concatenate(row_sampling, axis=1)).write_image(os.path.join(out_folder, frame + '_sampling.png'))
|
||||
print()
|
||||
|
||||
|
||||
|
||||
def sampling_poses(frame, camera):
|
||||
nr = 5
|
||||
frame_nr = [frame.split('_')[1], '25', '35', '50', '0']
|
||||
bodies = np.stack([np.load(os.path.join(folder, 'uv_posed_body_no_orient_2', frame.split('_')[0] + '_' + f_nr + '.npz'))['uv_posed_body_no_orient'] for f_nr in frame_nr])
|
||||
bodies_mesh = [np.load(os.path.join(folder, 'mesh_posed_body_no_orient', frame.split('_')[0] + '_' + f_nr + '.npz'))['mesh_posed_body_no_orient'] for f_nr in frame_nr]
|
||||
batch = get_temp_batch(frame, config_main)
|
||||
mask = batch['mesh_mask'][0].numpy().astype(bool)
|
||||
_, faces = _maskFaces(np.zeros((14475, 3)), F, mask)
|
||||
|
||||
noise = tf.random.normal(shape=(1, 128))
|
||||
noise = tf.tile(noise, [nr,1])
|
||||
|
||||
out = model.sample(tf.tile(batch['uv_static'], [nr,1,1,1]), bodies, tf.tile(batch['uv_curvature'], [nr,1,1,1]), tf.tile(batch['uv_mask'], [nr,1,1]), noise)
|
||||
|
||||
# posed_mesh = [get_posed_mesh(uv, batch['pose_shape'], batch['mesh_body_posed'], mask) for uv in tf.unstack(out)]
|
||||
posed_mesh = [uv2mesh(uv[tf.newaxis,:,:,:]) for uv in tf.unstack(out)]
|
||||
|
||||
# body = batch['mesh_body_posed'][0].numpy()
|
||||
posed_mesh_fixed = [postprocess(mesh, body, faces, mask, do_smoothing=False) for mesh, body in zip(posed_mesh, bodies_mesh)]
|
||||
|
||||
|
||||
row_sampling = []
|
||||
for mesh, body in zip(posed_mesh_fixed, bodies_mesh):
|
||||
fig = plot_fixed(mesh, body, faces, camera)
|
||||
fig.write_image('/tmp/exp_temp_7.png')
|
||||
img = cv2.imread('/tmp/exp_temp_7.png')
|
||||
row_sampling.append(img[:, 100:700, :])
|
||||
|
||||
|
||||
custom_imshow(np.concatenate(row_sampling, axis=1)).show()
|
||||
|
||||
print()
|
||||
|
||||
|
||||
|
||||
def sampling_garment(frame, camera):
|
||||
nr = 5
|
||||
frame_nr = [frame.split('_')[0], '03265', '02869', '00300', '01763']
|
||||
templates = np.stack([np.load(os.path.join(folder, 'uv_static', f_nr + '.npz'))['uv_static'] for f_nr in frame_nr])
|
||||
# bodies_mesh = [np.load(os.path.join(folder, 'mesh_posed_body_no_orient', frame.split('_')[0] + '_' + f_nr + '.npz'))['mesh_posed_body_no_orient'] for f_nr in frame_nr]
|
||||
masks = np.stack([np.load(os.path.join(folder, 'mesh_mask', f_nr + '.npy'))['uv_static'] for f_nr in frame_nr])
|
||||
batch = get_temp_batch(frame, config_main)
|
||||
mask = batch['mesh_mask'][0].numpy().astype(bool)
|
||||
_, faces = _maskFaces(np.zeros((14475, 3)), F, mask)
|
||||
|
||||
noise = tf.random.normal(shape=(1, 128))
|
||||
noise = tf.tile(noise, [nr,1])
|
||||
|
||||
out = model.sample(templates, tf.tile(batch['uv_body_posed'], [nr,1,1,1]), tf.tile(batch['uv_curvature'], [nr,1,1,1]), tf.tile(batch['uv_mask'], [nr,1,1]), noise)
|
||||
|
||||
# posed_mesh = [get_posed_mesh(uv, batch['pose_shape'], batch['mesh_body_posed'], mask) for uv in tf.unstack(out)]
|
||||
posed_mesh = [uv2mesh(uv[tf.newaxis,:,:,:]) for uv in tf.unstack(out)]
|
||||
|
||||
body = batch['mesh_body_posed'][0].numpy()
|
||||
posed_mesh_fixed = [postprocess(mesh, body, faces, mask, do_smoothing=False) for mesh, mask in zip(posed_mesh, masks)]
|
||||
|
||||
|
||||
row_sampling = []
|
||||
for mesh in posed_mesh_fixed:
|
||||
fig = plot_fixed(mesh, body, faces, camera)
|
||||
fig.write_image('/tmp/exp_temp_7.png')
|
||||
img = cv2.imread('/tmp/exp_temp_7.png')
|
||||
row_sampling.append(img[:, 100:700, :])
|
||||
|
||||
|
||||
custom_imshow(np.concatenate(row_sampling, axis=1)).show()
|
||||
|
||||
print()
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser(description='')
|
||||
parser.add_argument('--name', type=str)
|
||||
args = vars(parser.parse_args())
|
||||
|
||||
smpl = SMPL()
|
||||
F = np.load("custom_utils/smpl_faces.npy")
|
||||
# F = np.load("custom_utils/smpl_faces_skirt.npy")
|
||||
F_body = np.load("custom_utils/smpl_faces.npy")
|
||||
# folder = os.path.join('/home/workspace/data/dataset', ('test_skirt_ready_2' if is_skirt else 'test_ready'))
|
||||
# folder = os.path.join('/home/workspace/local-data/train_skirt')
|
||||
folder = os.path.join('/home/workspace/local-data/train')
|
||||
|
||||
|
||||
model = CVAE(config_main)
|
||||
curv_model = CVAECurv(config_curv)
|
||||
|
||||
load_model(model, 'checkpoints/' + config_main['name'])
|
||||
load_model(curv_model, 'checkpoints/' + config_curv['name'])
|
||||
|
||||
camera_1 = dict(up=dict(x=0, y=0, z=1), center=dict(x=0, y=0, z=-0.05), eye=dict(x=0.3, y=1.0, z=0.05))
|
||||
# faces_folder = '/home/workspace/data/results/support/faces'
|
||||
# faces = np.load(os.path.join(faces_folder, '01763' + '.npy'))
|
||||
|
||||
|
||||
|
||||
frame = '00761_150'
|
||||
body_frames = ['01763_259' , '007292_282']
|
||||
curv_frames = ['01763_259' , '007292_282']
|
||||
static_frames = ['01763_259' , '007292_282']
|
||||
|
||||
with open('/home/workspace/local-data/train_skirt/frames_dress_30k.txt') as f:
|
||||
frames = f.readlines()
|
||||
|
||||
random.shuffle(frames)
|
||||
frames = [frame.strip() for frame in frames]
|
||||
frames = frames[:150]
|
||||
|
||||
# input_batch = get_temp_batch(frame, config_main)
|
||||
# _, faces = _maskFaces(np.zeros((14475, 3)), F, input_batch['mesh_mask'][0].numpy().astype(bool))
|
||||
|
||||
# sample_with_curvature(model, input_batch, sampled_curvature)
|
||||
|
||||
# out_folder = '/home/workspace/data/results/curv_pred_plots_skirt'
|
||||
# out_folder = '/home/workspace/local-data/results/curv_pred_plots_skirt'
|
||||
# os.makedirs(out_folder, exist_ok=True)
|
||||
# curv_output_samples_mesh(frames)
|
||||
|
||||
# frame = '01082_109'
|
||||
# camera_01082_109 = dict(up=dict(x=0.0, y=0, z=1), center=dict(x=-0.15, y=0, z=-0.05), eye=dict(x=0.9, y=0.8, z=-0.1))
|
||||
# frame = '04925_272'
|
||||
|
||||
frames = ['01070_13']
|
||||
out_folder = '/home/workspace/local-data/results/sampling_plots_jumpsuits'
|
||||
os.makedirs(out_folder, exist_ok=True)
|
||||
for frame in tqdm.tqdm(frames):
|
||||
sampling_example(frame, camera_1)
|
||||
|
||||
# sampling_poses(frame, camera_01082_109)
|
||||
# sampling_garment(frame, camera_1)
|
||||
|
||||
|
||||
print()
|
||||
Reference in New Issue
Block a user