1092 lines
45 KiB
Python
1092 lines
45 KiB
Python
import os
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import sys
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import argparse
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parser = argparse.ArgumentParser()
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# parser.add_argument("-f", "--folder", help = "end of range")
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parser.add_argument("-s", "--start", help = "start of range", type=int, default=0)
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parser.add_argument("-e", "--end", help = "end of range", type=int, default=1)
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parser.add_argument("-g", "--gpu", help = "gpu id", default='0')
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parser.add_argument("-d", "--debug", help = "end of range", default=False, action='store_true')
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args = parser.parse_args()
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os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
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os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
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os.environ["CUDA_LAUNCH_BLOCKING"] = '1'
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import math
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import shutil
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import warnings
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import tables as tb
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import numpy as np
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import scipy.io as sio
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import trimesh as tm
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from tqdm import tqdm
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from tables import NaturalNameWarning
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warnings.filterwarnings('ignore', category=NaturalNameWarning)
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import plotly.graph_objects as go
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import torch
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from scipy.spatial.transform import Rotation as R
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sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..')
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from data.cloth3d_io import readPC2, loadInfo, writePC2, readOBJ, quads2tris, readPC2Frame
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from data.utils import Frame
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from smpl.smpl_torch import SMPL
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from custom_utils.custom_utils import _maskFaces, remove_long_edges
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def create_extras_file(folder):
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# create a new pytables file
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root = '/'.join(folder.split('/')[:-1])
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type = folder.split('/')[-1]
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h5file = tb.open_file(os.path.join(root, f'{type}_extras_test.h5'), mode='w', title='Extras')
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frames = []
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with open(os.path.join(folder, 'frames_all.txt'), 'r') as f:
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lines = f.readlines()
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lines = [line.strip() for line in lines]
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frames = lines
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for frame in tqdm(frames[:1000]):
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# add a new empty group to the root group for each frame
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id, frame_nr, garment = frame.split()
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if not id in h5file.root:
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h5file.create_group('/', id)
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if not frame_nr in h5file.root[id]:
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h5file.create_group(f'/{id}', frame_nr)
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h5file.create_group(f'/{id}/{frame_nr}', f'{garment}')
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h5file.close()
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def deorient(verts, pose):
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rodr = smpl.rodrigues(torch.tensor(pose.T, device=smpl.device).reshape(-1, 1, 3)).reshape(verts.shape[0], -1, 3, 3).cpu().numpy()
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rotated = np.zeros_like(verts)
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for frame in range(verts.shape[0]):
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r = R.from_matrix(rodr[frame, 0, :, :])
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rotated[frame] = r.apply(verts[frame], inverse=True)
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return rotated
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def create_mesh_posed_no_orient_and_mask(folder, folder_reg, out_folder):
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ids = sorted(os.listdir(folder))
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os.makedirs(os.path.join(out_folder, 'mesh_posed_no_orient'), exist_ok=True)
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os.makedirs(os.path.join(out_folder, 'mesh_mask'), exist_ok=True)
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ids = ['00021']
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for id in tqdm(ids):
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# id = '01467'
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garment_dir = os.path.join(folder, id)
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if not os.path.isdir(garment_dir):
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continue
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garments = [f.replace('.obj','') for f in os.listdir(garment_dir) if f.endswith('.obj')]
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info = loadInfo(os.path.join(folder, id, 'info.mat'))
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for garment in garments:
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# if os.path.exists(os.path.join(out_folder, f'{id}_{garment}.pc16')):
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# continue
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verts = readPC2(os.path.join(garment_dir, garment + '.pc16'), True)['V']
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# if info['poses'].shape[1] == verts.shape[0]:
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# continue
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reg_data = sio.loadmat(os.path.join(folder_reg, id, garment + '_weights_fixed.mat'))
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verts = (verts[:, reg_data['F'], :] * reg_data['W'][None, :, :, None]).sum(axis=2)
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mask_idx = sio.loadmat(os.path.join(folder_reg, id, garment + '_mask.mat'))['mask_idx'].reshape((-1)) - 1
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mask = np.zeros((14475), dtype=bool)
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mask[mask_idx] = True
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if info['poses'].shape[1] != verts.shape[0]:
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assert False, 'Somethign wrong here'
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info['poses'] = info['poses'][:, :verts.shape[0]]
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verts = deorient(verts, info['poses'])
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# # plot as scatter plot with plotly
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# import plotly.graph_objects as go
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# frame = 5
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# fig = go.Figure(data=[go.Scatter3d(x=verts[frame,:,0], y=verts[frame,:,2], z=verts[frame,:,1], mode='markers', marker=dict(size=1))])
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# fig.show()
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writePC2(os.path.join(out_folder, 'mesh_posed_no_orient', f'{id}_{garment}.pc16'), verts, True)
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np.save(os.path.join(out_folder, 'mesh_mask', f'{id}_{garment}.npy'), mask)
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def create_pose_shape(folder, out_folder):
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ids = sorted(os.listdir(folder))
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os.makedirs(os.path.join(out_folder, 'pose'), exist_ok=True)
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os.makedirs(os.path.join(out_folder, 'shape'), exist_ok=True)
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for id in tqdm(ids):
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garment_dir = os.path.join(folder, id)
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if not os.path.isdir(garment_dir):
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continue
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info = loadInfo(os.path.join(folder, id, 'info.mat'))
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shape = np.concatenate([info['shape'], np.asarray([info['gender']])]).astype(np.float32)
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np.save(os.path.join(out_folder, 'pose', f'{id}.npy'), info['poses'].T.astype(np.float32))
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np.save(os.path.join(out_folder, 'shape', f'{id}.npy'), shape)
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def create_mesh_static(folder, out_folder):
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ids = sorted(os.listdir(folder))
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os.makedirs(os.path.join(out_folder, 'mesh_static'), exist_ok=True)
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for id in tqdm(ids):
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garment_dir = os.path.join(folder, id)
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if not os.path.isdir(garment_dir):
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continue
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garments = [f.replace('.obj','') for f in os.listdir(garment_dir) if f.endswith('.obj')]
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for garment in garments:
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if os.path.exists(os.path.join(out_folder, f'{id}_{garment}.pc16')):
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continue
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verts_static, _, _, _ = readOBJ(os.path.join(garment_dir, f'{garment}.obj'))
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reg_data = sio.loadmat(os.path.join(folder_reg, id, garment + '_weights_fixed.mat'))
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verts_static = (verts_static[reg_data['F'], :] * reg_data['W'][:, :, None]).sum(axis=1).astype(np.float32)
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# rotate 90 degrees around x axis
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verts_static = np.matmul(verts_static, np.array([[1,0,0],[0,0,-1],[0,1,0]]))
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# fig = go.Figure(data=[go.Scatter3d(x=verts_static[:,0], y=verts_static[:,2], z=verts_static[:,1], mode='markers', marker=dict(size=1))])
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# fig.update_layout(scene_aspectmode='cube')
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# fig.update_layout(scene=dict(xaxis=dict(range=[-1,1]), yaxis=dict(range=[-1,1]), zaxis=dict(range=[-1,1])))
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# fig.show()
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np.save(os.path.join(out_folder, 'mesh_static', f'{id}_{garment}.npy'), verts_static.astype(np.float32))
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def create_mesh_body_posed_no_orient(folder, out_folder):
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ids = sorted(os.listdir(folder))
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os.makedirs(os.path.join(out_folder, 'mesh_body_posed_no_orient'), exist_ok=True)
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for id in tqdm(ids):
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garment_dir = os.path.join(folder, id)
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if not os.path.isdir(garment_dir):
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continue
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info = sio.loadmat(os.path.join(folder, id, 'info.mat'))
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pose, shape, gender = info['poses'], info['shape'], info['gender']
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# if pose.shape[0] == 72:
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pose = np.transpose(pose, [1, 0]).astype(np.float32)
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pose[:, :3] = 0
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beta = np.squeeze(np.concatenate((shape, gender), axis=1)).astype(np.float32)
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smpl = SMPL(path='smpl', device='cuda', batch_size=pose.shape[0])
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T = smpl(torch.tensor(pose, device=smpl.device), torch.tensor(np.tile(beta[np.newaxis, :], [pose.shape[0], 1]), device=smpl.device))[0]
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T = T.cpu().numpy()
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writePC2(os.path.join(out_folder, 'mesh_body_posed_no_orient', f'{id}.pc16'), T, True)
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# verts = readPC2(os.path.join(out_folder, 'mesh_body_posed_no_orient', f'{id}.pc16'), True)['V']
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# # plot as scatter plot with plotly
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# import plotly.graph_objects as go
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# frame = 5
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# fig = go.Figure(data=[go.Scatter3d(x=verts[frame,:,0], y=verts[frame,:,2], z=verts[frame,:,1], mode='markers', marker=dict(size=1))])
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# fig.show()
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pass
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def calculate_normals(vertices, faces):
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normals = []
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for i in range(vertices.shape[0]):
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mesh = tm.Trimesh(vertices=vertices[i], faces=faces, process=False, validate=False)
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normals.append(mesh.vertex_normals)
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return np.stack(normals)
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def create_mesh_normals(folder, out_folder):
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ids = sorted(os.listdir(folder))
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os.makedirs(os.path.join(out_folder, 'mesh_normals'), exist_ok=True)
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# F = np.load('smpl/smpl_faces.npy')
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# F_skirt = np.load('smpl/smpl_faces_skirt.npy')
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# ids = ['00438']
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for id in tqdm(ids):
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garment_dir = os.path.join(folder, id)
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if not os.path.isdir(garment_dir):
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continue
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garments = [f.replace('.obj','') for f in os.listdir(garment_dir) if f.endswith('.obj')]
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info = sio.loadmat(os.path.join(folder, id, 'info.mat'))
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for garment in garments:
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if os.path.exists(os.path.join(out_folder, f'{id}_{garment}.pc16')):
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continue
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verts = readPC2(os.path.join(garment_dir, garment + '.pc16'), True)['V']
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verts = deorient(verts, info['poses'][:, :verts.shape[0]])
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_, faces, _, _ = readOBJ(os.path.join(garment_dir, garment + '.obj'))
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faces = quads2tris(faces)
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normals = calculate_normals(verts, faces.astype(np.int32))
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reg_data = sio.loadmat(os.path.join(folder_reg, id, garment + '_weights_fixed.mat'))
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normals = (normals[:, reg_data['F'], :] * reg_data['W'][None, :, :, None]).sum(axis=2)
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writePC2(os.path.join(out_folder, 'mesh_normals', f'{id}_{garment}.pc16'), normals, True)
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# normals = readPC2(os.path.join(out_folder, 'mesh_normals', f'{id}_{garment}.pc16'), True)['V']
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# colors = np.clip(np.abs(normals), 0, 1)
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# verts = (verts[:, reg_data['F'], :] * reg_data['W'][None, :, :, None]).sum(axis=2)
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# # plot as scatter plot with plotly
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# import plotly.graph_objects as go
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# frame = 5
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# fig = go.Figure(data=[go.Scatter3d(
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# x=verts[frame,:,0],
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# y=verts[frame,:,2],
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# z=verts[frame,:,1],
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# mode='markers',
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# marker=dict(
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# size=1,
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# color=normals[frame,:,:],
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# colorscale='Viridis',
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# opacity=0.8
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# )
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# )])
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# moved_verts = verts + 0.05 * normals
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# # Create the scatter plot for the moved vertices
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# fig.add_trace(go.Scatter3d(
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# x=moved_verts[frame,:,0],
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# y=moved_verts[frame,:,2],
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# z=moved_verts[frame,:,1],
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# mode='markers',
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# marker=dict(
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# size=1,
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# color='red',
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# opacity=0.8
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# )
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# ))
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# fig.show()
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# # create a plotly mesh plot for verts and faces
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# fig = go.Figure(data=[go.Mesh3d(x=verts[frame,:,0], y=verts[frame,:,2], z=verts[frame,:,1], i=faces[:,0], j=faces[:,1], k=faces[:,2], color='lightpink', opacity=1)])
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# fig.show()
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pass
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def create_mesh_normals_cape(folder, out_folder):
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frames = os.listdir(os.path.join(folder, 'mesh_posed'))
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os.makedirs(os.path.join(folder, 'mesh_normals'), exist_ok=True)
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F = np.load('smpl/smpl_faces_orig.npy')
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# F_skirt = np.load('smpl/smpl_faces_skirt.npy')
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for frame in tqdm(frames):
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id, frame_nr = frame.split('.')[:2]
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verts = np.load(os.path.join(folder, 'mesh_posed', f'{id}.{frame_nr}.npz'))['mesh_posed']
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normals = calculate_normals(verts[None,:,:], F)[0]
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np.savez_compressed(os.path.join(folder, 'mesh_normals', f'{id}.{frame_nr}.npz'), mesh_normals=normals)
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# normals = readPC2(os.path.join(out_folder, 'mesh_normals', f'{id}_{garment}.pc16'), True)['V']
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# colors = np.clip(np.abs(normals), 0, 1)
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# verts = (verts[:, reg_data['F'], :] * reg_data['W'][None, :, :, None]).sum(axis=2)
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# plot as scatter plot with plotly
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# import plotly.graph_objects as go
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# frame = 5
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# fig = go.Figure(data=[go.Scatter3d(
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# x=verts[:,0],
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# y=verts[:,2],
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# z=verts[:,1],
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# mode='markers',
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# marker=dict(
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# size=1,
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# color=normals[:,:],
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# colorscale='Viridis',
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# opacity=0.8
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# )
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# )])
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# moved_verts = verts + 0.05 * normals
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# # Create the scatter plot for the moved vertices
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# fig.add_trace(go.Scatter3d(
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# x=moved_verts[:,0],
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# y=moved_verts[:,2],
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# z=moved_verts[:,1],
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# mode='markers',
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# marker=dict(
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# size=1,
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# color='red',
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# opacity=0.8
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# )
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# ))
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# fig.show()
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# # create a plotly mesh plot for verts and faces
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# fig = go.Figure(data=[go.Mesh3d(x=verts[frame,:,0], y=verts[frame,:,2], z=verts[frame,:,1], i=faces[:,0], j=faces[:,1], k=faces[:,2], color='lightpink', opacity=1)])
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# fig.show()
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pass
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def verify_registration_ans_mask(folder, out_folder):
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ids = sorted(os.listdir(folder))
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for id in ids:
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garment_dir = os.path.join(folder, id)
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if not os.path.isdir(garment_dir):
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continue
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garments = [f.replace('.obj','') for f in os.listdir(garment_dir) if f.endswith('.obj')]
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for garment in garments:
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vertices = readPC2(os.path.join(out_folder, 'mesh_posed_no_orient', f'{id}_{garment}.pc16'), True)['V']
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mask = np.load(os.path.join(out_folder, 'mesh_mask', f'{id}_{garment}.npy'))
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if mask.sum() == vertices.shape[1]:
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print(id, garment)
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pass
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def create_mesh_unposed_no_shape(folder, folder_reg, out_folder, start=0, end=1):
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ids = sorted(os.listdir(folder))[start:end]
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os.makedirs(os.path.join(out_folder, 'mesh_unposed_no_shape'), exist_ok=True)
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F = np.load('smpl/smpl_faces.npy')
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# F_skirt = np.load('smpl/smpl_faces_skirt.npy')
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for id in tqdm(ids):
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garment_dir = os.path.join(folder, id)
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if not os.path.isdir(garment_dir):
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continue
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garments = [f.replace('.obj','') for f in os.listdir(garment_dir) if f.endswith('.obj')]
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# info = sio.loadmat(os.path.join(folder, id, 'info.mat'))
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pose = np.load(os.path.join(out_folder, 'pose', f'{id}.npy'))
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shape = np.tile(np.load(os.path.join(out_folder, 'shape', f'{id}.npy'))[None,:], [pose.shape[0],1])
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pose = torch.tensor(pose, dtype=torch.float32, device='cuda')
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shape = torch.tensor(shape, dtype=torch.float32, device='cuda')
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smpl = SMPL(path='smpl', device='cuda', batch_size=pose.shape[0])
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for garment in garments:
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if os.path.exists(os.path.join(out_folder, f'{id}_{garment}.pc16')):
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continue
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mesh_static = np.load(os.path.join(out_folder, 'mesh_static', f'{id}_{garment}.npy'))
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mesh_posed = readPC2(os.path.join(out_folder, 'mesh_posed_no_orient', f'{id}_{garment}.pc16'), True)['V']
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mask = np.load(os.path.join(out_folder, 'mesh_mask', f'{id}_{garment}.npy'))
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# mask_idx = np.where(mask)[0]
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pose[:,:3] = 0
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# pose = pose[:10]
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# shape = shape[:10]
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# mesh_posed = mesh_posed[:10]
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unposed = np.zeros((14475, 3), dtype=np.float32)
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unposed[mask] = mesh_static
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unposed = torch.tensor(np.tile(unposed[None,:,:], [mesh_posed.shape[0],1,1]), dtype=torch.float32, device='cuda')
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unposed.requires_grad = True
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mesh_posed = torch.tensor(mesh_posed, dtype=torch.float32, device='cuda')
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optimizer = torch.optim.Adam([unposed], lr=0.01)
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# verts_1 = unposed[0].cpu().detach().numpy()[mask]
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# _, faces = _maskFaces(np.zeros((14475, 3)), F, mask)
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# fig = go.Figure(data=[go.Mesh3d(x=verts_1[:,0], y=verts_1[:,2], z=verts_1[:,1], i=faces[:,0], j=faces[:,1], k=faces[:,2], color='lightpink', opacity=1)])
|
|
# fig.update_layout(scene = dict(
|
|
# xaxis = dict(range=[-1,1],),
|
|
# yaxis = dict(range=[-1,1],),
|
|
# zaxis = dict(range=[-1,1],),),)
|
|
# fig.update_layout(scene_aspectmode='cube')
|
|
# fig.show()
|
|
|
|
# verts_2 = mesh_posed[0].cpu().detach().numpy()
|
|
# fig = go.Figure(data=[go.Mesh3d(x=verts_2[:,0], y=verts_2[:,2], z=verts_2[:,1], i=faces[:,0], j=faces[:,1], k=faces[:,2], color='lightpink', opacity=1)])
|
|
# fig.update_layout(scene = dict(
|
|
# xaxis = dict(range=[-1,1],),
|
|
# yaxis = dict(range=[-1,1],),
|
|
# zaxis = dict(range=[-1,1],),),)
|
|
# fig.update_layout(scene_aspectmode='cube')
|
|
# fig.show()
|
|
|
|
# zero_body = smpl(torch.zeros_like(pose), torch.zeros_like(shape))[0]
|
|
|
|
# verts_3 = smpl(pose, shape, zero_body)[0][0].cpu().detach().numpy()
|
|
# # verts_3 = zero_body[0].cpu().detach().numpy()
|
|
# faces_3 = F
|
|
# fig = go.Figure(data=[go.Mesh3d(x=verts_3[:,0], y=verts_3[:,2], z=verts_3[:,1], i=faces_3[:,0], j=faces_3[:,1], k=faces_3[:,2], color='lightpink', opacity=1)])
|
|
# fig.update_layout(scene = dict(
|
|
# xaxis = dict(range=[-1,1],),
|
|
# yaxis = dict(range=[-1,1],),
|
|
# zaxis = dict(range=[-1,1],),),)
|
|
# fig.update_layout(scene_aspectmode='cube')
|
|
# fig.show()
|
|
|
|
for i in range(5000):
|
|
optimizer.zero_grad()
|
|
|
|
pred = smpl(pose, shape, unposed)[0]
|
|
loss : torch.Tensor = ((pred[:,mask,:] - mesh_posed)**2).sum()
|
|
|
|
# Backward pass
|
|
loss.backward()
|
|
optimizer.step()
|
|
|
|
with torch.inference_mode():
|
|
euclidean_error = torch.sqrt(((pred[:,mask,:] - mesh_posed)**2).sum(-1)).mean()
|
|
|
|
# Print the loss
|
|
if i % 50 == 0:
|
|
print(f"it: {i} loss: {loss:.8f} euclidean_error: {euclidean_error:.8f}")
|
|
|
|
if euclidean_error < 0.0000001:
|
|
break
|
|
|
|
if euclidean_error < 0.0000005 and i > 2000:
|
|
break
|
|
|
|
if euclidean_error < 0.000001 and i > 4000:
|
|
break
|
|
|
|
unposed = unposed.detach().cpu().numpy()
|
|
|
|
gender = int(shape[0].cpu().numpy()[-1] > 0)
|
|
shape_offsets = torch.tensordot(shape[:,:10], smpl.shapedirs[gender], dims=[[1], [2]])
|
|
shape_offsets = shape_offsets.cpu().numpy()[:,smpl.to_keep,:]
|
|
|
|
unshaped = (unposed - shape_offsets)[:,mask,:]
|
|
|
|
writePC2(os.path.join(out_folder, 'mesh_unposed_no_shape', f'{id}_{garment}.pc16'), unshaped, True)
|
|
|
|
# verts = unposed[:,mask,:]
|
|
# verts = unshaped
|
|
# _, faces = _maskFaces(np.zeros((14475, 3)), F, mask)
|
|
# frame = 9
|
|
# fig = go.Figure(data=[go.Mesh3d(x=verts[frame,:,0], y=verts[frame,:,2], z=verts[frame,:,1], i=faces[:,0], j=faces[:,1], k=faces[:,2], color='lightpink', opacity=1)])
|
|
# fig.update_layout(scene = dict(
|
|
# xaxis = dict(range=[-1,1],),
|
|
# yaxis = dict(range=[-1,1],),
|
|
# zaxis = dict(range=[-1,1],),),)
|
|
# fig.update_layout(scene_aspectmode='cube')
|
|
# fig.show()
|
|
|
|
# verts = pred.detach().cpu().numpy()[:,mask,:]
|
|
# frame = 9
|
|
# fig = go.Figure(data=[go.Mesh3d(x=verts[frame,:,0], y=verts[frame,:,2], z=verts[frame,:,1], i=faces[:,0], j=faces[:,1], k=faces[:,2], color='lightpink', opacity=1)])
|
|
# fig.update_layout(scene = dict(
|
|
# xaxis = dict(range=[-1,1],),
|
|
# yaxis = dict(range=[-1,1],),
|
|
# zaxis = dict(range=[-1,1],),),)
|
|
# fig.update_layout(scene_aspectmode='cube')
|
|
# fig.show()
|
|
pass
|
|
|
|
|
|
F_coll = torch.tensor(np.load("smpl/smpl_faces.npy"), device='cuda')
|
|
F_coll_skirt = torch.tensor(np.load("smpl/smpl_faces_skirt.npy"), device='cuda')
|
|
F_norm = torch.tensor(np.tile(np.load("smpl/smpl_faces.npy")[None,:,:], [1,1,1]), device='cuda')
|
|
F_norm_skirt = torch.tensor(np.tile(np.load("smpl/smpl_faces_skirt.npy")[None,:,:], [1,1,1]), device='cuda')
|
|
|
|
from scipy.spatial import KDTree
|
|
from pytorch3d.structures import Meshes
|
|
from torch_cluster import knn
|
|
|
|
def _nearest_neighbour(V: torch.Tensor, B: torch.Tensor):
|
|
# flatten V and B along the batch dimension, create a batch_x and batch_y variable to restore the batch dimension after the nearest neighbour search using pytorch_geomtry knn function
|
|
batch_size = V.shape[0]
|
|
vertices_count = V.shape[1]
|
|
batch_x = torch.arange(batch_size, device=V.get_device()).repeat_interleave(vertices_count)
|
|
batch_y = torch.arange(batch_size, device=V.get_device()).repeat_interleave(14475)
|
|
V = V.reshape(-1, V.shape[-1])
|
|
B = B.reshape(-1, B.shape[-1])
|
|
idx = knn(B.to(V.dtype), V, k=1, batch_x=batch_y, batch_y=batch_x)[1]
|
|
idx = (idx - batch_x * vertices_count).reshape(batch_size, vertices_count)
|
|
return idx
|
|
|
|
|
|
def collision_loss(V: torch.Tensor, B: torch.Tensor, thr: float = .004):
|
|
F_norm_local = F_norm
|
|
mesh = Meshes(B, F_norm_local)
|
|
B_vn = torch.stack(mesh.verts_normals_list())
|
|
|
|
# compute correspondences
|
|
idx = _nearest_neighbour(V, B)
|
|
# compute loss
|
|
D = V - torch.stack([B[i,idx[i],:] for i in range(V.shape[0])])
|
|
b_vn = torch.stack([B_vn[i,idx[i],:] for i in range(V.shape[0])])
|
|
dot = torch.einsum('abc,abc->ab', D, b_vn)
|
|
thresholded = torch.minimum(dot - thr, torch.zeros_like(dot))
|
|
loss = torch.sum(thresholded ** 2, axis=-1)
|
|
|
|
return loss
|
|
|
|
|
|
def area_loss(pred, gt, faces):
|
|
"""
|
|
Calculates the area loss between predicted and ground truth meshes.
|
|
|
|
Args:
|
|
pred (torch.Tensor): Predicted mesh vertices of shape (batch_size, num_vertices, 3).
|
|
gt (torch.Tensor): Ground truth mesh vertices of shape (batch_size, num_vertices, 3).
|
|
faces (torch.Tensor): Mesh faces of shape (num_faces, 3).
|
|
|
|
Returns:
|
|
torch.Tensor: Area loss between predicted and ground truth meshes.
|
|
"""
|
|
# Calculate the area of each face for predicted and ground truth meshes
|
|
pred_faces = pred[:, faces, :]
|
|
gt_faces = gt[:, faces, :]
|
|
pred_edges1 = pred_faces[:, :, 1, :] - pred_faces[:, :, 0, :]
|
|
pred_edges2 = pred_faces[:, :, 2, :] - pred_faces[:, :, 0, :]
|
|
gt_edges1 = gt_faces[:, :, 1, :] - gt_faces[:, :, 0, :]
|
|
gt_edges2 = gt_faces[:, :, 2, :] - gt_faces[:, :, 0, :]
|
|
pred_cross = torch.cross(pred_edges1, pred_edges2, dim=2)
|
|
gt_cross = torch.cross(gt_edges1, gt_edges2, dim=2)
|
|
pred_area = 0.5 * torch.norm(pred_cross, dim=2)
|
|
gt_area = 0.5 * torch.norm(gt_cross, dim=2)
|
|
|
|
# Calculate the area loss
|
|
area_loss = torch.mean(torch.abs(pred_area - gt_area))
|
|
|
|
return area_loss
|
|
|
|
|
|
|
|
def create_mesh_unposed_no_shape_static(folder, folder_reg, out_folder, start=0, end=1):
|
|
ids = sorted(os.listdir(folder))[start:end]
|
|
os.makedirs(os.path.join(out_folder, 'mesh_static_unposed_no_shape'), exist_ok=True)
|
|
F = np.load('smpl/smpl_faces.npy')
|
|
# F_skirt = np.load('smpl/smpl_faces_skirt.npy')
|
|
|
|
ids = ['00005']
|
|
|
|
for id in tqdm(ids):
|
|
garment_dir = os.path.join(folder, id)
|
|
if not os.path.isdir(garment_dir):
|
|
continue
|
|
|
|
garments = [f.replace('.obj','') for f in os.listdir(garment_dir) if f.endswith('.obj')]
|
|
shape = np.load(os.path.join(out_folder, 'shape', f'{id}.npy'))[None,:]
|
|
|
|
shape = torch.tensor(shape, dtype=torch.float32, device='cuda')
|
|
|
|
smpl = SMPL(path='smpl', device='cuda', batch_size=1)
|
|
pose = np.zeros((24, 3), np.float32)
|
|
pose[1,2] = .15
|
|
pose[2,2] = -.15
|
|
pose = torch.tensor(pose.flatten()[None,:], dtype=torch.float32, device='cuda')
|
|
|
|
|
|
for garment in garments:
|
|
if os.path.exists(os.path.join(out_folder, f'{id}_{garment}.pc16')):
|
|
continue
|
|
|
|
mesh_static, faces, _, _ = readOBJ(os.path.join(folder, id, f'{garment}.obj'))
|
|
faces = quads2tris(faces)
|
|
mesh_static = np.matmul(mesh_static, np.array([[1,0,0],[0,0,-1],[0,1,0]]))
|
|
|
|
unposed = mesh_static
|
|
|
|
unposed = torch.tensor(unposed[None,:,:], dtype=torch.float32, device='cuda')
|
|
unposed.requires_grad = True
|
|
mesh_static = torch.tensor(mesh_static[None,:,:], dtype=torch.float32, device='cuda')
|
|
|
|
optimizer = torch.optim.Adam([unposed], lr=0.0001)
|
|
|
|
for i in range(10000):
|
|
optimizer.zero_grad()
|
|
|
|
body = smpl(pose, shape)[0]
|
|
c_loss = collision_loss(unposed, body)
|
|
a_loss = area_loss(unposed, mesh_static, faces)
|
|
loss : torch.Tensor = c_loss + a_loss
|
|
|
|
# Backward pass
|
|
loss.backward()
|
|
optimizer.step()
|
|
|
|
pose = pose / 1.0005
|
|
|
|
# Print the loss
|
|
if i % 50 == 0:
|
|
pose_sum = np.abs(pose.detach().cpu().numpy()).sum().item()
|
|
loss_str = loss.detach().cpu().numpy()[0]
|
|
c_loss_str = c_loss.detach().cpu().numpy()[0]
|
|
a_loss_str = a_loss.detach().cpu().numpy()
|
|
print(f"it: {i} loss: {loss_str:.10f} c_loss: {c_loss_str:.10f}, a_loss: {a_loss_str:.8f}, pose: {pose_sum:.8f}")
|
|
|
|
|
|
unposed = unposed.detach().cpu().numpy()
|
|
body = body.detach().cpu().numpy()
|
|
|
|
gender = int(shape[0].cpu().numpy()[-1] > 0)
|
|
shape_offsets = torch.tensordot(shape[:,:10], smpl.shapedirs[gender], dims=[[1], [2]])
|
|
shape_offsets = shape_offsets.cpu().numpy()[:,smpl.to_keep,:]
|
|
|
|
unshaped = (unposed - shape_offsets)
|
|
|
|
writePC2(os.path.join(out_folder, 'mesh_static_unposed_no_shape', f'{id}_{garment}.pc16'), unshaped, True)
|
|
|
|
verts = unposed
|
|
# verts = unshaped
|
|
frame = 0
|
|
fig = go.Figure(data=[go.Mesh3d(x=verts[frame,:,0], y=verts[frame,:,2], z=verts[frame,:,1], i=faces[:,0], j=faces[:,1], k=faces[:,2], color='lightpink', opacity=1)])
|
|
# add body mesh
|
|
fig.add_trace(go.Mesh3d(x=body[frame,:,0], y=body[frame,:,2], z=body[frame,:,1], i=F[:,0], j=F[:,1], k=F[:,2], color='lightblue', opacity=1))
|
|
fig.update_layout(scene = dict(
|
|
xaxis = dict(range=[-1,1],),
|
|
yaxis = dict(range=[-1,1],),
|
|
zaxis = dict(range=[-1,1],),),)
|
|
fig.update_layout(scene_aspectmode='cube')
|
|
fig.show()
|
|
|
|
# verts = pred.detach().cpu().numpy()[:,mask,:]
|
|
# frame = 9
|
|
# fig = go.Figure(data=[go.Mesh3d(x=verts[frame,:,0], y=verts[frame,:,2], z=verts[frame,:,1], i=faces[:,0], j=faces[:,1], k=faces[:,2], color='lightpink', opacity=1)])
|
|
# fig.update_layout(scene = dict(
|
|
# xaxis = dict(range=[-1,1],),
|
|
# yaxis = dict(range=[-1,1],),
|
|
# zaxis = dict(range=[-1,1],),),)
|
|
# fig.update_layout(scene_aspectmode='cube')
|
|
# fig.show()
|
|
pass
|
|
|
|
|
|
def create_frames_file(folder, out_folder, split=0.9):
|
|
ids = os.listdir(folder)
|
|
|
|
frames_non_skirt = []
|
|
frames_skirt = []
|
|
ids_non_skirt = set()
|
|
ids_skirt = set()
|
|
|
|
for id in tqdm(ids):
|
|
garment_dir = os.path.join(folder, id)
|
|
if not os.path.isdir(garment_dir):
|
|
continue
|
|
|
|
info = sio.loadmat(os.path.join(folder, id, 'info.mat'))
|
|
garments = [f.replace('.obj','') for f in os.listdir(garment_dir) if f.endswith('.obj')]
|
|
frames = info['poses'].shape[1]
|
|
|
|
assert info['poses'].shape[0] == 72
|
|
|
|
for garment in garments:
|
|
for i in range(frames):
|
|
if garment == 'Skirt' or garment == 'Dress':
|
|
frames_skirt.append(Frame(id, i, garment))
|
|
ids_skirt.add(id)
|
|
else:
|
|
frames_non_skirt.append(Frame(id, i, garment))
|
|
ids_non_skirt.add(id)
|
|
|
|
nr_train_non_skirt = int(len(ids_non_skirt) * split)
|
|
nr_train_skirt = int(len(ids_skirt) * split)
|
|
|
|
ids_skirt = list(ids_skirt)
|
|
ids_non_skirt = list(ids_non_skirt)
|
|
|
|
with open(os.path.join(out_folder, 'frames_train.txt'), 'w') as f:
|
|
for frame in frames_non_skirt:
|
|
if frame.id in ids_non_skirt[:nr_train_non_skirt]:
|
|
f.write(f'{frame.id} {frame.frame_nr} {frame.garment}\n')
|
|
|
|
with open(os.path.join(out_folder, 'frames_train_skirt.txt'), 'w') as f:
|
|
for frame in frames_skirt:
|
|
if frame.id in ids_skirt[:nr_train_skirt]:
|
|
f.write(f'{frame.id} {frame.frame_nr} {frame.garment}\n')
|
|
|
|
with open(os.path.join(out_folder, 'frames_valid.txt'), 'w') as f:
|
|
for frame in frames_non_skirt:
|
|
if frame.id in ids_non_skirt[nr_train_non_skirt:]:
|
|
f.write(f'{frame.id} {frame.frame_nr} {frame.garment}\n')
|
|
|
|
with open(os.path.join(out_folder, 'frames_valid_skirt.txt'), 'w') as f:
|
|
for frame in frames_skirt:
|
|
if frame.id in ids_skirt[nr_train_skirt:]:
|
|
f.write(f'{frame.id} {frame.frame_nr} {frame.garment}\n')
|
|
|
|
|
|
def create_frames_file_test(folder, out_folder):
|
|
ids = os.listdir(folder)
|
|
|
|
frames_non_skirt = []
|
|
frames_skirt = []
|
|
ids_non_skirt = set()
|
|
ids_skirt = set()
|
|
|
|
for id in tqdm(ids):
|
|
garment_dir = os.path.join(folder, id)
|
|
if not os.path.isdir(garment_dir):
|
|
continue
|
|
|
|
info = sio.loadmat(os.path.join(folder, id, 'info.mat'))
|
|
garments = [f.replace('.obj','') for f in os.listdir(garment_dir) if f.endswith('.obj')]
|
|
frames = info['poses'].shape[1]
|
|
|
|
assert info['poses'].shape[0] == 72
|
|
|
|
for garment in garments:
|
|
for i in range(frames):
|
|
if garment == 'Skirt' or garment == 'Dress':
|
|
frames_skirt.append(Frame(id, i, garment))
|
|
ids_skirt.add(id)
|
|
else:
|
|
frames_non_skirt.append(Frame(id, i, garment))
|
|
ids_non_skirt.add(id)
|
|
|
|
ids_skirt = list(ids_skirt)
|
|
ids_non_skirt = list(ids_non_skirt)
|
|
|
|
with open(os.path.join(out_folder, 'frames_test.txt'), 'w') as f:
|
|
for frame in frames_non_skirt:
|
|
if frame.id in ids_non_skirt:
|
|
f.write(f'{frame.id} {frame.frame_nr} {frame.garment}\n')
|
|
|
|
with open(os.path.join(out_folder, 'frames_test_skirt.txt'), 'w') as f:
|
|
for frame in frames_skirt:
|
|
if frame.id in ids_skirt:
|
|
f.write(f'{frame.id} {frame.frame_nr} {frame.garment}\n')
|
|
|
|
|
|
|
|
def create_frames_file_partial(folder, out_folder, split=0.9, part=0.1):
|
|
ids = os.listdir(folder)
|
|
|
|
frames_non_skirt = []
|
|
frames_skirt = []
|
|
ids_non_skirt = set()
|
|
ids_skirt = set()
|
|
|
|
for id in tqdm(ids):
|
|
garment_dir = os.path.join(folder, id)
|
|
if not os.path.isdir(garment_dir):
|
|
continue
|
|
|
|
info = sio.loadmat(os.path.join(folder, id, 'info.mat'))
|
|
garments = [f.replace('.obj','') for f in os.listdir(garment_dir) if f.endswith('.obj')]
|
|
frames = info['poses'].shape[1]
|
|
|
|
assert info['poses'].shape[0] == 72
|
|
|
|
for garment in garments:
|
|
for i in range(frames):
|
|
if garment == 'Skirt' or garment == 'Dress':
|
|
frames_skirt.append(Frame(id, i, garment))
|
|
ids_skirt.add(id)
|
|
else:
|
|
frames_non_skirt.append(Frame(id, i, garment))
|
|
ids_non_skirt.add(id)
|
|
|
|
nr_train_non_skirt = int(len(ids_non_skirt) * split * part)
|
|
nr_train_skirt = int(len(ids_skirt) * split * part)
|
|
|
|
ids_skirt = list(ids_skirt)
|
|
ids_non_skirt = list(ids_non_skirt)
|
|
|
|
with open(os.path.join(out_folder, f'frames_train_{int(part*100)}p.txt'), 'w') as f:
|
|
for frame in frames_non_skirt:
|
|
if frame.id in ids_non_skirt[:nr_train_non_skirt]:
|
|
f.write(f'{frame.id} {frame.frame_nr} {frame.garment}\n')
|
|
|
|
with open(os.path.join(out_folder, f'frames_train_skirt_{int(part*100)}p.txt'), 'w') as f:
|
|
for frame in frames_skirt:
|
|
if frame.id in ids_skirt[:nr_train_skirt]:
|
|
f.write(f'{frame.id} {frame.frame_nr} {frame.garment}\n')
|
|
|
|
|
|
def check_irregular_poses(folder, folder_reg, out_folder):
|
|
for i in range(100, 200, 5):
|
|
frame: Frame = Frame('01512', i, 'Dress')
|
|
mesh_posed = readPC2Frame(os.path.join(out_folder, 'mesh_posed_no_orient', f'{frame.id}_{frame.garment}.pc16'), frame.frame_nr, True)
|
|
# mesh_static = np.load(f'{out_folder}/mesh_static/{frame.id}_{frame.garment}.npy')
|
|
|
|
mesh_mask = np.load(f'{out_folder}/mesh_mask/{frame.id}_{frame.garment}.npy')
|
|
mesh_body_posed = readPC2(os.path.join(out_folder, 'mesh_body_posed_no_orient', f'{frame.id}.pc16'), True)['V']
|
|
mesh_body_posed = mesh_body_posed[::2][frame.frame_nr]
|
|
|
|
F = np.load('smpl/smpl_faces_skirt.npy')
|
|
_, faces = _maskFaces(np.zeros((14475, 3)), F, mesh_mask)
|
|
|
|
fig = go.Figure(data=[go.Mesh3d(x=mesh_posed[:,0], y=mesh_posed[:,2], z=mesh_posed[:,1], i=faces[:,0], j=faces[:,1], k=faces[:,2], color='lightpink', opacity=1)])
|
|
fig.add_trace(go.Mesh3d(x=mesh_body_posed[:,0], y=mesh_body_posed[:,2], z=mesh_body_posed[:,1], i=F[:,0], j=F[:,1], k=F[:,2], color='lightblue', opacity=1))
|
|
fig.update_layout(scene = dict(
|
|
xaxis = dict(range=[-1,1],),
|
|
yaxis = dict(range=[-1,1],),
|
|
zaxis = dict(range=[-1,1],),),)
|
|
fig.update_layout(scene_aspectmode='cube')
|
|
fig.show()
|
|
pass
|
|
|
|
|
|
def calculate_average(folder, out_folder, frames_file='frames_train_skirt.txt'):
|
|
from custom_utils.custom_utils import UV2Mesh
|
|
import plotly.express as ex
|
|
import plotly.graph_objects as go
|
|
|
|
frames = []
|
|
with open(os.path.join(out_folder, frames_file), 'r') as f:
|
|
for line in f:
|
|
line = line.strip()
|
|
id, frame_nr, garment = line.split(' ')
|
|
frames.append(Frame(id, int(frame_nr), garment))
|
|
|
|
uv_sum = np.zeros((512, 256, 3), dtype=np.float32)
|
|
mask_sum = np.zeros((512, 256), dtype=np.float32)
|
|
|
|
for frame in tqdm(frames):
|
|
uv = np.load(os.path.join(out_folder, 'uv_unposed_no_shape', f'{frame.id}_{frame.frame_nr}_{frame.garment}.npz'))['uv_unposed']
|
|
mask = np.load(os.path.join(out_folder, 'uv_mask', f'{frame.id}_{frame.garment}.npz'))['uv_mask']
|
|
uv_sum += uv
|
|
mask_sum += mask
|
|
|
|
mask_sum[mask_sum == 0] = 1
|
|
avg_garment = uv_sum / mask_sum[:, :, None]
|
|
|
|
avg_mesh = UV2Mesh()(torch.from_numpy(avg_garment.transpose(2,0,1))[None,:,:,:]).numpy()[0]
|
|
F = np.load('smpl/smpl_faces.npy')
|
|
|
|
fig = go.Figure(data=[go.Mesh3d(x=avg_mesh[:,0], y=avg_mesh[:,2], z=avg_mesh[:,1], i=F[:,0], j=F[:,1], k=F[:,2], color='lightpink', opacity=1)])
|
|
fig.update_layout(scene = dict(
|
|
xaxis = dict(range=[-1,1],),
|
|
yaxis = dict(range=[-1,1],),
|
|
zaxis = dict(range=[-1,1],),),)
|
|
fig.update_layout(scene_aspectmode='cube')
|
|
fig.show()
|
|
|
|
np.save('custom_data/avg/train_skirt_no_shape_avg_uv.npy', avg_garment)
|
|
|
|
|
|
def remove_irregular_poses(folder, folder_reg, out_folder):
|
|
ids = sorted(os.listdir(folder))
|
|
|
|
for id in tqdm(ids):
|
|
|
|
garment_dir = os.path.join(folder, id)
|
|
if not os.path.isdir(garment_dir):
|
|
continue
|
|
|
|
garments = [f.replace('.obj','') for f in os.listdir(garment_dir) if f.endswith('.obj')]
|
|
info = loadInfo(os.path.join(folder, id, 'info.mat'))
|
|
|
|
for garment in garments:
|
|
|
|
verts_count = readPC2(os.path.join(garment_dir, garment + '.pc16'), True)['V'].shape[0]
|
|
if info['poses'].shape[1] != verts_count:
|
|
shutil.rmtree(os.path.join(folder, id))
|
|
if os.path.exists(os.path.join(out_folder, 'mesh_static', f'{id}_{garment}.pc16')):
|
|
os.remove(os.path.join(out_folder, 'mesh_static', f'{id}_{garment}.pc16'))
|
|
if os.path.exists(os.path.join(out_folder, 'mesh_posed_no_orient', f'{id}_{garment}.pc16')):
|
|
os.remove(os.path.join(out_folder, 'mesh_posed_no_orient', f'{id}_{garment}.pc16'))
|
|
if os.path.exists(os.path.join(out_folder, 'mesh_body_posed_no_orient', f'{id}.pc16')):
|
|
os.remove(os.path.join(out_folder, 'mesh_body_posed_no_orient', f'{id}.pc16'))
|
|
if os.path.exists(os.path.join(out_folder, 'mesh_mask', f'{id}_{garment}.npy')):
|
|
os.remove(os.path.join(out_folder, 'mesh_mask', f'{id}_{garment}.npy'))
|
|
# os.remove(os.path.join(out_folder, 'mesh_unposed_no_shape', f'{id}_{garment}.npy'))
|
|
|
|
if len(garments) == 2:
|
|
if os.path.exists(os.path.join(out_folder, 'mesh_static', f'{id}_{garments[1]}.pc16')):
|
|
os.remove(os.path.join(out_folder, 'mesh_static', f'{id}_{garments[1]}.pc16'))
|
|
if os.path.exists(os.path.join(out_folder, 'mesh_posed_no_orient', f'{id}_{garments[1]}.pc16')):
|
|
os.remove(os.path.join(out_folder, 'mesh_posed_no_orient', f'{id}_{garments[1]}.pc16'))
|
|
if os.path.exists(os.path.join(out_folder, 'mesh_mask', f'{id}_{garments[1]}.pc16')):
|
|
os.remove(os.path.join(out_folder, 'mesh_mask', f'{id}_{garments[1]}.npy'))
|
|
break
|
|
# os.remove(os.path.join(out_folder, 'mesh_unposed_no_shape', f'{id}_{garments[1]}.npy'))
|
|
|
|
|
|
def double_check_irregular_poses(folder, folder_reg, out_folder):
|
|
# ids = sorted(os.listdir(folder))
|
|
|
|
# for id in tqdm(ids):
|
|
|
|
# garment_dir = os.path.join(folder, id)
|
|
# if not os.path.isdir(garment_dir):
|
|
# continue
|
|
|
|
# garments = [f.replace('.obj','') for f in os.listdir(garment_dir) if f.endswith('.obj')]
|
|
# info = loadInfo(os.path.join(folder, id, 'info.mat'))
|
|
|
|
# for garment in garments:
|
|
# body_count = readPC2(os.path.join(out_folder, 'mesh_body_posed_no_orient', f'{id}.pc16'), True)['V'].shape[0]
|
|
# verts_count = readPC2(os.path.join(out_folder, 'mesh_posed_no_orient', f'{id}_{garment}.pc16'), True)['V'].shape[0]
|
|
|
|
# assert body_count == verts_count
|
|
|
|
frames = sorted(os.listdir(out_folder + '/mesh_posed_no_orient'))
|
|
frames = [(id.split('_')[0], id.split('_')[1].split('.')[0]) for id in frames]
|
|
|
|
for frame in tqdm(frames):
|
|
id = frame[0]
|
|
garment_dir = os.path.join(folder, id)
|
|
if not os.path.isdir(garment_dir):
|
|
continue
|
|
|
|
garment = frame[1]
|
|
body_count = readPC2(os.path.join(out_folder, 'mesh_body_posed_no_orient', f'{id}.pc16'), True)['V'].shape[0]
|
|
verts_count = readPC2(os.path.join(out_folder, 'mesh_posed_no_orient', f'{id}_{garment}.pc16'), True)['V'].shape[0]
|
|
|
|
assert body_count == verts_count
|
|
|
|
|
|
def get_ids():
|
|
ids = []
|
|
with open(os.path.join('/data-local/127/cloth3d_processed', 'frames_train.txt'), 'r') as f:
|
|
for line in f:
|
|
ids.append(line.split()[0])
|
|
|
|
with open(os.path.join('/data-local/127/cloth3d_processed', 'frames_train_skirt.txt'), 'r') as f:
|
|
for line in f:
|
|
ids.append(line.split()[0])
|
|
|
|
ids = sorted(list(set(ids)))
|
|
|
|
with open(os.path.join('/data-local/127/cloth3d_processed', 'frames_train_ids.txt'), 'w') as f:
|
|
for id in ids:
|
|
f.write(f'{id}\n')
|
|
|
|
ids = []
|
|
with open(os.path.join('/data-local/127_2/cloth3d_processed_test', 'frames_test.txt'), 'r') as f:
|
|
for line in f:
|
|
ids.append(line.split()[0])
|
|
|
|
with open(os.path.join('/data-local/127_2/cloth3d_processed_test', 'frames_test_skirt.txt'), 'r') as f:
|
|
for line in f:
|
|
ids.append(line.split()[0])
|
|
|
|
ids = sorted(list(set(ids)))
|
|
|
|
with open(os.path.join('/data-local/127_2/cloth3d_processed_test', 'frames_test_ids.txt'), 'w') as f:
|
|
for id in ids:
|
|
f.write(f'{id}\n')
|
|
|
|
pass
|
|
|
|
|
|
def generate_fixed_masks(folder, out_folder):
|
|
ids = sorted(os.listdir(folder))
|
|
os.makedirs(os.path.join(out_folder, 'mesh_fixed'), exist_ok=True)
|
|
F = np.load('smpl/smpl_faces.npy')
|
|
F_skirt = np.load('smpl/smpl_faces_skirt.npy')
|
|
|
|
import random
|
|
# import trimesh
|
|
import pyvista as pv
|
|
random.shuffle(ids)
|
|
|
|
|
|
for id in tqdm(ids):
|
|
garment_dir = os.path.join(folder, id)
|
|
if not os.path.isdir(garment_dir):
|
|
continue
|
|
|
|
garments = [f.replace('.obj','') for f in os.listdir(garment_dir) if f.endswith('.obj')]
|
|
|
|
for garment in garments:
|
|
if os.path.exists(os.path.join(out_folder, f'{id}_{garment}.pc16')):
|
|
continue
|
|
|
|
is_skirt = garment in ['Dress', 'Skirt']
|
|
|
|
mesh_static = np.load(os.path.join(out_folder, 'mesh_static', f'{id}_{garment}.npy'))
|
|
mask = np.load(os.path.join(out_folder, 'mesh_mask', f'{id}_{garment}.npy'))
|
|
_, faces = _maskFaces(np.zeros((14475, 3)), F_skirt if is_skirt else F, mask)
|
|
_, new_mask = remove_long_edges(mesh_static, faces.astype(np.int32), 0.2 if is_skirt else 0.1)
|
|
new_mask_full = np.zeros((14475), dtype=bool)
|
|
new_mask_full[mask] = new_mask
|
|
_, new_faces = _maskFaces(np.zeros((14475, 3)), F_skirt if is_skirt else F, new_mask_full)
|
|
|
|
mesh_new = mesh_static[new_mask]
|
|
|
|
# fig = go.Figure(data=[go.Mesh3d(x=mesh_new[:,0], y=mesh_new[:,1], z=mesh_new[:,2], i=new_faces[:,0], j=new_faces[:,1], k=new_faces[:,2])])
|
|
# fig.update_layout(scene_aspectmode='cube')
|
|
# fig.update_layout(scene=dict(xaxis=dict(range=[-1,1]), yaxis=dict(range=[-1,1]), zaxis=dict(range=[-1,1])))
|
|
# fig.show()
|
|
|
|
flattened_faces = np.asarray([np.concatenate((np.array([3]), f)) for f in new_faces.astype(np.int32)]).flatten()
|
|
m = pv.PolyData(mesh_new, flattened_faces)
|
|
|
|
m.fill_holes(0.03, inplace=True, progress_bar=False)
|
|
|
|
# m = trimesh.Trimesh(vertices=mesh_new, faces=new_faces, process=False, validate=False)
|
|
# trimesh.repair.fill_holes(m)
|
|
|
|
mesh_new = np.asarray(m.points)
|
|
new_faces = np.asarray(m.faces).reshape([-1, 4])[:, 1:]
|
|
|
|
m = tm.Trimesh(vertices=mesh_new, faces=new_faces, process=False, validate=False)
|
|
tm.repair.fix_winding(m)
|
|
|
|
mesh_new = np.asarray(m.vertices)
|
|
new_faces = np.asarray(m.faces)
|
|
|
|
# fig = go.Figure(data=[go.Mesh3d(x=mesh_new[:,0], y=mesh_new[:,1], z=mesh_new[:,2], i=new_faces[:,0], j=new_faces[:,1], k=new_faces[:,2])])
|
|
# fig.update_layout(scene_aspectmode='cube')
|
|
# fig.update_layout(scene=dict(xaxis=dict(range=[-1,1]), yaxis=dict(range=[-1,1]), zaxis=dict(range=[-1,1])))
|
|
# fig.show()
|
|
|
|
# fig = go.Figure(data=[go.Mesh3d(x=mesh_static[:,0], y=mesh_static[:,1], z=mesh_static[:,2], i=faces[:,0], j=faces[:,1], k=faces[:,2])])
|
|
# fig.update_layout(scene_aspectmode='cube')
|
|
# fig.update_layout(scene=dict(xaxis=dict(range=[-1,1]), yaxis=dict(range=[-1,1]), zaxis=dict(range=[-1,1])))
|
|
# fig.show()
|
|
|
|
# print((~new_mask).sum())
|
|
|
|
np.savez_compressed(os.path.join(out_folder, 'mesh_fixed', f'{id}_{garment}.npz'), mask=new_mask_full, mas_convert=new_mask, faces=new_faces)
|
|
|
|
pass
|
|
|
|
|
|
|
|
|
|
|
|
if __name__ == '__main__':
|
|
|
|
|
|
folder = '/data-net/dataset/cloth3d/test'
|
|
folder_reg = '/data-local/127/cloth3d_registration/test'
|
|
out_folder = '/data-local/127_2/cloth3d_processed_test'
|
|
|
|
# folder = '/data-net/dataset/cloth3d/train'
|
|
# folder_reg = '/data-local/127/cloth3d_registration/train'
|
|
# out_folder = '/data-local/127/cloth3d_processed'
|
|
|
|
smpl = SMPL(path='smpl', device='cuda')
|
|
|
|
# clean_dataset(folder)
|
|
# generate_frames_file(folder, 'frames_all.txt')
|
|
# split_data(folder, 0.9)
|
|
# create_extras_file(folder)
|
|
|
|
# check_irregular_poses(folder, folder_reg, out_folder)
|
|
# remove_irregular_poses(folder, folder_reg, out_folder)
|
|
# double_check_irregular_poses(folder, folder_reg, out_folder)
|
|
|
|
create_mesh_posed_no_orient_and_mask(folder, folder_reg, out_folder)
|
|
# verify_registration_ans_mask(folder, out_folder)
|
|
# create_pose_shape(folder, out_folder)
|
|
# create_mesh_static(folder, out_folder)
|
|
# create_mesh_body_posed_no_orient(folder, out_folder)
|
|
# create_mesh_normals(folder, out_folder)
|
|
# create_mesh_unposed_no_shape(folder, folder_reg, out_folder, start=args.start, end=args.end)
|
|
# create_mesh_unposed_no_shape_static(folder, folder_reg, out_folder, start=args.start, end=args.end)
|
|
# calculate_average(folder, out_folder)
|
|
|
|
# create_frames_file(folder, out_folder)
|
|
# get_ids()
|
|
# create_frames_file_test(folder, out_folder)
|
|
# create_frames_file_partial(folder, out_folder, part=0.5)
|
|
|
|
# folder = '/data-local/127_2/cape_processed/test/female'
|
|
# create_mesh_normals_cape(folder, None)
|
|
|
|
# generate_fixed_masks(folder, out_folder) |