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luni
2023-02-09 16:54:16 +00:00
parent 2f4dc477bd
commit cac3ee03d9
4 changed files with 106 additions and 80 deletions
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data/cloth3d_io.py
+17 -8
View File
@@ -236,7 +236,7 @@ def read_posed_garment_frame(folder, id, garment, frame, map=None, mask=None, sh
verts[mask, :] = verts[mask, :] - np.squeeze(shape_offsets)[mask, :]
if deorient:
R = scipy.spatial.transform.Rotation.from_euler(pose[:3]).inv()
R = scipy.spatial.transform.Rotation.from_euler('xyz', pose[:3]).inv()
# R = scipy.spatial.transform.Rotation.from_euler([0,0,-zrot])
verts = R.apply(verts)
@@ -244,13 +244,12 @@ def read_posed_garment_frame(folder, id, garment, frame, map=None, mask=None, sh
# can i cache map?
def read_posed_garment(folder, id, garment, map=None, mask=None, shape=None, shapedirs=None, poses=None, unshape=False, deorient=False):
pc16_path = os.path.join(folder, id, garment + '.pc16')
verts = readPC2(pc16_path, True)
# NOT WORKING WELL
def read_posed_garment(folder, id, garment, mask=None, shape=None, shapedirs=None, poses=None, unshape=False, deorient=False):
body_posed = readPC2(os.path.join(folder, id, 'Body_posed.pc16'), True)['V']
offsets = readPC2(os.path.join(folder, id, garment + '_offsets_dynamic.pc16'), True)['V']
verts = (body_posed + offsets) * mask[:, None]
if map is None:
map = sio.loadmat(os.path.join(folder, id, garment + '_map.mat'))['map'].reshape((-1))
verts = verts[:, map, :]
if unshape:
shape_offsets = np.tensordot(shape, shapedirs, axes=[[1], [2]])
@@ -258,7 +257,7 @@ def read_posed_garment(folder, id, garment, map=None, mask=None, shape=None, sha
if deorient:
for i in range(verts.shape[0]):
R = scipy.spatial.transform.Rotation.from_euler(poses[:3, i]).inv()
R = scipy.spatial.transform.Rotation.from_euler('xyz', poses[:3, i]).inv()
# R = scipy.spatial.transform.Rotation.from_euler([0,0,-zrot])
verts[i] = R.apply(verts[i])
@@ -304,6 +303,16 @@ def read_posed_body(folder, id, poses=None, deorient=False):
return verts
def read_curvatures(h5file, id, frames, neighbourhood=None):
frames_filtered = [frame for frame in frames if frame.split()[0] == id]
garment = frames_filtered[0].split()[2]
curvatures_all = []
for frame_nr in range(len(frames_filtered)):
curv = h5file.get_node(f'/{id}/{frame_nr}/{garment}/curvature_raw').read()
curvatures_all.append(curv)
return np.array(curvatures_all)
# import os
# import sys
# import numpy as np
data/convert_to_uv.py
+59 -45
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@@ -13,6 +13,7 @@ import numpy as np
import scipy as sp
import trimesh as tm
import cmapy as cm
import tables as tb
import scipy.io as sio
import scipy.interpolate as spi
import matplotlib.pyplot as plt
@@ -22,7 +23,8 @@ import plotly.express as ex
import plotly.graph_objects as go
sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..')
from data.cloth3d_io import read_garment_mask, read_garment_template, read_posed_garment_frame, loadInfo, read_posed_body_frame, read_posed_body, read_posed_garment
from data.cloth3d_io import read_garment_mask, read_garment_template, read_posed_garment_frame, loadInfo, read_posed_body_frame, read_posed_body, read_posed_garment, read_curvatures
from data.utils import save_to_h5file
os.environ["CUDA_VISIBLE_DEVICES"] = "6"
@@ -119,6 +121,8 @@ def convert_to_uv_batch(res, m2uv, uv_map, verts, neighbours=15, mask=None, meth
rbf_dest_current = np.nonzero(rbf_dest_current)
rbf_dest_current = np.array(rbf_dest_current).T
out = []
for i in range(batch):
print('processing frame: ' + str(i))
@@ -139,7 +143,9 @@ def convert_to_uv_batch(res, m2uv, uv_map, verts, neighbours=15, mask=None, meth
if garment_uv_mask is not None:
result *= garment_uv_mask[:,:,np.newaxis]
np.savez_compressed(os.path.join(folder, 'uv_posed_no_orient', id + '_' + str(i) + '.npz'), uv_posed_no_orient=result)
out.append(result)
return out
def convert_to_uv_batch_label(res, m2uv, uv_map, values, neighbours=15, mask=None, method='rbf', garment_uv_mask=None, garment_uv_mask_border=None):
@@ -323,25 +329,16 @@ def get_shapedirs():
shapedirs.append(male['shapedirs'][to_keep, :])
return shapedirs
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("-f", "--folder", help = "end of range", default='/home/workspace/data/dataset/test_skirt_ready_2')
parser.add_argument("-x", "--extras", help = "end of range", default='/home/workspace/data/dataset/test_skirt_ready_2_extras')
parser.add_argument("-f", "--folder", help = "end of range", default='/home/workspace/data/dataset/cloth3d/train')
parser.add_argument("-s", "--start", help = "start of range", default=0)
parser.add_argument("-e", "--end", help = "end of range", default=30000)
parser.add_argument("-e", "--end", help = "end of range", default=1000)
parser.add_argument("-n", "--name", help = "end of range", default='lhunor')
parser.add_argument("-sk", "--skirt", help = "end of range", default=1)
parser.add_argument("-sk", "--skirt", help = "end of range", default=0)
args = parser.parse_args()
# smpl = SMPL('FeatureLines/superSMPL')
folder = args.folder
extras_folder = args.extras
os.makedirs(extras_folder, exist_ok=True)
print("skirt" if args.skirt == 1 else "not skirt")
F = np.load("custom_utils/smpl_faces" + ("_skirt" if args.skirt == 1 else "") + ".npy")
uv_map = np.load('custom_utils/stretched_uv_map_' + ("skirt" if args.skirt == 1 else "3") + '.npy')
m2uv = np.load('custom_utils/m2uv' + ("_skirt" if args.skirt == 1 else "") + '.npy', allow_pickle=True).item()
@@ -369,49 +366,64 @@ if __name__ == '__main__':
rbf_dest[uv_map_discreet_unique[:, 0], uv_map_discreet_unique[:, 1]] = 0
rbf_dest[np.invert(uv_mask[:,:,0])] = 0
is_skirt = True if args.skirt == 1 else False
skirt_modifier = '_skirt' if is_skirt else ''
#########################################################################################
use_no_orient = False
no_shape = False
is_skirt = True if args.skirt == 1 else False
folder = args.folder
neighbourhood = 25
frames_file = 'frames_all.txt'
data_to_convert = ['mesh_posed', 'mesh_static', 'mesh_curvature', 'body_posed'] # 'mesh_unposed',
print("skirt" if args.skirt == 1 else "not skirt")
print(f'params: -f {args.folder} -s {args.start} -e {args.end} -n {args.name} -skirt {args.skirt}')
skirt_modifier = '_skirt' if is_skirt else ''
orient_mod = '_no_orient' if use_no_orient else ''
shape_mod = '_no_shape' if no_shape else ''
frames = []
with open(os.path.join(folder, frames_file), 'r') as f:
lines = f.readlines()
lines = [line.strip() for line in lines]
frames = lines#[int(args.start):int(args.end)]
ids = [frame.split('_')[0] for frame in frames]
print(f'params: -f {args.folder} -s {args.start} -e {args.end} -n {args.name} -skirt {args.skirt}')
# ids = os.listdir(os.path.join(local_folder, 'pose_shape'))
# ids = [id.split('_')[0] for id in ids]
frames = lines
ids = [frame.split()[0] for frame in frames]
ids = sorted(list(set(ids)))[int(args.start):int(args.end)]
shapedirs = get_shapedirs()
use_no_orient = True
no_shape = False
data_to_convert = ['mesh_posed', 'mesh_unposed', 'mesh_static', 'mesh_curvature', 'body_posed']
root = '/'.join(folder.split('/')[:-1])
type = folder.split('/')[-1]
h5file = tb.open_file(os.path.join(root, f'{type}_extras.h5'), mode='a')
for id in tqdm.tqdm(ids):
info = loadInfo(os.path.join(folder, id, 'info.mat'))
garments = info['outfit']
garments = list(info['outfit'].keys())
assert info['poses'].shape[1] <= 300
# put all this in a function and call for all garments
# also masking is not the same as just filling the empty values with neighbouring
# values, at least the plots are different
garment = garments[0]
mask = read_garment_mask(folder, id, garment)
if 'mesh_posed' in data_to_convert:
mesh_posed = read_posed_garment(folder, id, garment, mask=mask, shape=info['shape'], poses=info['poses'], use_no_orient=use_no_orient, no_shape=no_shape)
mesh_posed_frame = read_posed_garment_frame(folder, id, garment, 0, mask=mask, shape=info['shape'], pose=info['poses'][:,0], deorient=use_no_orient, unshape=no_shape)
mesh_posed = read_posed_garment(folder, id, garment, mask=mask, shape=info['shape'], poses=info['poses'], deorient=use_no_orient, unshape=no_shape)
if 'mesh_unposed' in data_to_convert:
mesh_unposed = read_unposed_garment(folder, id, garment, mask=mask, shape=info['shape'], no_shape=no_shape)
mesh_unposed = read_unposed_garment(folder, id, garment, mask=mask, shape=info['shape'], unshape=no_shape)
if 'mesh_static' in data_to_convert:
mesh_static = read_garment_template(folder, id, garment, mask)
if 'mesh_curvature' in data_to_convert:
mesh_curvature = compute_curvature(mesh_posed, F)
mesh_curvature = read_curvatures(h5file, id, frames, neighbourhood=None)
if 'body_posed' in data_to_convert:
body_posed = read_posed_body(folder, id, info['poses'], use_no_orient)
@@ -442,33 +454,35 @@ if __name__ == '__main__':
start = time.perf_counter()
uv_verts_static, garment_uv_mask, garment_uv_mask_border = convert_to_uv(uv_res, m2uv, uv_map, verts_static, neighbours=3, mask=mask, method='rbf', garment_uv_mask=None)
uv_verts_static = uv_verts_static.astype(np.float32)
uv_static, uv_mask, garment_uv_mask_border = convert_to_uv(uv_res, m2uv, uv_map, mesh_static, neighbours=3, mask=mask, method='rbf', garment_uv_mask=None)
uv_static = uv_static.astype(np.float32)
if 'mesh_posed' in data_to_convert:
uv_posed = convert_to_uv_batch(uv_res, m2uv, uv_map, gt[:, :, :], neighbours=3, mask=mask, method='rbf', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
uv_posed = convert_to_uv_batch(uv_res, m2uv, uv_map, mesh_posed[:, :, :], neighbours=3, mask=mask, method='rbf', garment_uv_mask=uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
if 'mesh_unposed' in data_to_convert:
uv_unposed = convert_to_uv_batch(uv_res, m2uv, uv_map, mesh_unposed[:, :, :], neighbours=3, mask=mask, method='rbf', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
uv_unposed = convert_to_uv_batch(uv_res, m2uv, uv_map, mesh_unposed[:, :, :], neighbours=3, mask=mask, method='rbf', garment_uv_mask=uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
if 'mesh_curvature' in data_to_convert:
uv_curvature = convert_to_uv_batch_label(uv_res, m2uv, uv_map, curvature[:, :], neighbours=3, mask=mask, method='nearest', garment_uv_mask=garment_uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
uv_curvature = convert_to_uv_batch_label(uv_res, m2uv, uv_map, mesh_curvature[:, :], neighbours=3, mask=mask, method='nearest', garment_uv_mask=uv_mask, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
if 'body_posed' in data_to_convert:
uv_body_posed = convert_to_uv_batch(uv_res, m2uv, uv_map, body_posed[:, :, :], neighbours=3, mask=None, method='rbf', garment_uv_mask=None, garment_uv_mask_border=garment_uv_mask_border).astype(np.float32)
print(time.perf_counter() - start)
if 'mesh_static' in data_to_convert:
# np.save(os.path.join(folder, id, garment + '_offsets_static_uv.npy'), uv_verts_static)
# np.save(os.path.join(folder, id, 'mask_uv.npy'), garment_uv_mask)
# mesh_static
save_to_h5file(h5file, id, 0, garment, uv_static, 'uv_static', 'float32')
save_to_h5file(h5file, id, 0, garment, uv_mask, 'uv_mask', 'float32')
if 'mesh_posed' in data_to_convert:
mesh_posed = read_posed_garment(folder, id, garment, mask=mask, shape=info['shape'], poses=info['poses'], use_no_orient=use_no_orient, no_shape=no_shape)
for i in range(uv_posed.shape[0]):
save_to_h5file(h5file, id, i, garment, uv_posed[i, :, :, :], 'uv_posed' + orient_mod + shape_mod, 'float32')
if 'mesh_unposed' in data_to_convert:
mesh_unposed = read_unposed_garment(folder, id, garment, mask=mask, shape=info['shape'], no_shape=no_shape)
for i in range(uv_unposed.shape[0]):
save_to_h5file(h5file, id, i, garment, uv_unposed[i, :, :, :], 'uv_unposed' + orient_mod + shape_mod, 'float32')
if 'mesh_curvature' in data_to_convert:
mesh_curvature = compute_curvature(mesh_posed, F)
for i in range(uv_curvature.shape[0]):
save_to_h5file(h5file, id, i, garment, uv_curvature[i, :, :, :], 'uv_curvature', 'uint8')
if 'body_posed' in data_to_convert:
# for i in range(uv_body_posed.shape[0]):
# np.savez_compressed(os.path.join(folder, id, 'uv_body_posed', garment + '_' + str(i) + '.npz'), uv_body=uv_body[i, :, :, :])
for i in range(uv_body_posed.shape[0]):
save_to_h5file(h5file, id, i, garment, uv_body_posed[i, :, :, :], 'uv_body_posed', 'float32')
# start = time.perf_counter()
data/curvatures.py
+8 -25
View File
@@ -114,14 +114,6 @@ class Cache:
mask = read_garment_mask(self.folder, id, garment)
if self.include_neighbours:
k_neighbors = np.argsort(dist[mask, :][:, mask], axis=-1)[:, :self.neighbourhood]
self.cache[id].k_neighbors = k_neighbors
G = tm.graph.vertex_adjacency_graph(m)
for i in range(v.shape[0]):
for k in range(25):
assert nx.has_path(G, i, k_neighbors[i, k]), f'No path between {i} and {k_neighbors[i, k]}'
_, faces = _maskFaces(np.zeros((14475,3)), F, mask)
v = read_posed_garment_frame(folder, id, garment, 0)[mask,:]
m = tm.Trimesh(v, faces, process=False, validate=False)
@@ -132,6 +124,14 @@ class Cache:
faces = np.concatenate((faces, outliers_faces), axis=0)
m = tm.Trimesh(v, faces, process=False, validate=False)
if self.include_neighbours:
k_neighbors = np.argsort(dist[mask, :][:, mask], axis=-1)[:, :self.neighbourhood]
self.cache[id].k_neighbors = k_neighbors
G = tm.graph.vertex_adjacency_graph(m)
for i in range(v.shape[0]):
for k in range(25):
assert nx.has_path(G, i, k_neighbors[i, k]), f'No path between {i} and {k_neighbors[i, k]}'
self.cache[id].mask = mask
self.cache[id].faces = faces
@@ -162,7 +162,6 @@ def calculate_dist(folder, neighbourhood, frames):
def generate_curvatures(folder, neighbourhood, frames):
# ids = os.listdir(folder)[int(args.start):int(args.end)]
# frames = frames[int(args.start):int(args.end)]
os.makedirs(os.path.join('/'.join(folder.split('/')[:-1]), 'backup', f'mesh_curvature_raw'), exist_ok=True)
@@ -176,9 +175,6 @@ def generate_curvatures(folder, neighbourhood, frames):
frame_nr = int(frame.split()[1])
garment = frame.split()[2]
# bodies = readPC2(os.path.join(folder, id, 'Body_posed.pc16'), True)['V']
# offsetss = readPC2(os.path.join(folder, id, garment + '_registered.pc16'), True)['V']
# mask = np.load(os.path.join(folder, 'mesh_mask', f'{id}.npy')).astype(bool)
mask = cache[id].mask
faces = cache[id].faces
v = read_posed_garment_frame(folder, id, garment, frame_nr)[mask,:]
@@ -194,7 +190,6 @@ def generate_curvatures(folder, neighbourhood, frames):
out = np.zeros((14475), dtype=np.float32)
out[mask] = curvature
np.savez_compressed(os.path.join('/'.join(folder.split('/')[:-1]), 'backup', f'mesh_curvature_raw', f'{id}_{frame_nr}_{garment}.npz'), mesh_curvature_raw=out)
group = h5file.get_node(f'/{id}/{frame_nr}/{garment}')
if f'curvature_raw' in group:
@@ -204,7 +199,6 @@ def generate_curvatures(folder, neighbourhood, frames):
carray = h5file.create_carray(group, "curvature_raw", atom=tb.Float32Atom(), shape=out.shape, filters=filters)
carray[:] = out
return None
@@ -283,23 +277,12 @@ def visualize_curvature_labels(folder, frames):
verts = read_posed_garment_frame(folder, id, garment, frame_nr)[mask, :]
curv = h5file.get_node(f'/{id}/{frame_nr}/{garment}/curvature_mesh_global').read()[mask]
# m = tm.Trimesh(verts, 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(verts, outliers, 2)
# outliers_faces = np.concatenate((outliers[:, np.newaxis], outliers_n_neighbours), axis=-1)
# faces = np.concatenate((faces, outliers_faces), axis=0)
colors = np.array([cm.color('jet', 1 - i) for i in np.linspace(0, 1, int(curv.max()))])
colors_binned = np.zeros((curv.shape[0], 3))
for i in range(1, int(curv.max())+1):
# print(np.count_nonzero(curv == i))
colors_binned[curv == i, :] = colors[i-1]
# colors_binned = colors_binned[mask, :]
# m = tm.Trimesh(verts, faces, process=False, validate=False, vertex_colors=colors_binned)
# m.show()
x = verts
meshes = [go.Mesh3d(
x=x[:,0],
data/utils.py
+21 -1
View File
@@ -1,4 +1,4 @@
import tables as tb
class Frame:
def __init__(self, id, frame_nr, garment):
@@ -12,3 +12,23 @@ class Frame:
def __eq__(self, other):
return (self.id, self.frame_nr) == (other.id, other.frame_nr)
def save_to_h5file(h5file, id, frame_nr, garment, data, name, type):
filters = tb.Filters(complevel=9, complib='blosc2')
group = h5file.get_node(f'/{id}/{frame_nr}/{garment}')
if type == 'float32':
atom = tb.Float32Atom()
elif type == 'uint8':
atom = tb.UInt8Atom()
elif type == 'bool':
atom = tb.BoolAtom()
else:
raise Exception('Unknown type')
if f'{name}' in group:
array = h5file.get_node(f'/{id}/{frame_nr}/{garment}/{name}')
array[:] = data
else:
carray = h5file.create_carray(group, name, atom=atom, shape=data.shape, filters=filters)
carray[:] = data