483 lines
18 KiB
Python
483 lines
18 KiB
Python
import os
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import sys
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import random
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import numpy as np
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import plotly.graph_objects as go
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import torch
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from torch.nn.functional import mse_loss
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from pytorch3d.structures import Meshes
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from pytorch3d.loss import mesh_laplacian_smoothing, mesh_edge_loss, mesh_normal_consistency, chamfer_distance
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from pymeshfix import MeshFix
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# os.environ["CUDA_LAUNCH_BLOCKING"] = '1'
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sys.path.append(os.path.dirname(os.path.abspath(__file__)) + '/..')
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from data.cloth3d_io import (
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read_garment_mask,
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read_garment_template,
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read_posed_garment_frame,
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loadInfo,
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read_posed_body_frame,
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read_posed_body,
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read_posed_garment,
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read_curvatures,
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read_curvature_frame,
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writeOBJ,
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readOBJ,
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quads2tris
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)
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from smpl.smpl_torch import SMPL
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# from custom_utils.custom_utils import _maskFaces
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device = 'cuda'
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smpl = SMPL(device, 'smpl')
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import torch.nn.functional as F
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from pytorch3d.structures import Meshes
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def find_closest_vertices(mesh1: Meshes, mesh2: Meshes, vertex_indices: torch.Tensor) -> torch.Tensor:
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"""
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Given two PyTorch3D meshes and a set of vertex indices for mesh1,
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find the closest vertices from mesh2 to these vertices.
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Args:
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mesh1: A PyTorch3D Meshes object representing the first mesh.
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mesh2: A PyTorch3D Meshes object representing the second mesh.
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vertex_indices: A tensor of shape (N,) representing the indices of the vertices in mesh1.
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Returns:
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A tensor of shape (N,) representing the indices of the closest vertices in mesh2.
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"""
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# Extract the vertices of mesh1 and mesh2.
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vertices1 = mesh1.verts_packed()
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vertices2 = mesh2.verts_packed()
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# Extract the vertices of mesh1 that correspond to the given indices.
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vertices1_selected = vertices1[vertex_indices]
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# Compute the pairwise distances between the selected vertices of mesh1 and all vertices of mesh2.
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distances = F.pairwise_distance(vertices1_selected.unsqueeze(1), vertices2.unsqueeze(0), p=2)
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# Find the indices of the closest vertices in mesh2.
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closest_indices = torch.argmin(distances, dim=1)
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return closest_indices
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def _maskFaces(V, F, mask, return_unfixed=False):
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V_mask = mask
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F_mask = np.take(V_mask, F.reshape(-1), axis=0)
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F_mask = F_mask.reshape((-1, 3)).sum(axis=1) == 3
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V_ind = np.zeros(V_mask.shape)
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V_ind[V_mask] = np.arange(V_mask.sum())
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F_new = np.take(V_ind, F[F_mask, :].reshape(-1), axis=0).reshape((-1, 3))
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V_new = V[V_mask, :]
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if return_unfixed:
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return V_new, F_new, F[F_mask, :]
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else:
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return V_new, F_new
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def get_frames(folder, frames_file):
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frames = []
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with open(os.path.join(folder, frames_file), 'r') as f:
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for line in f:
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frames.append(line.strip())
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return frames
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def plot(mesh, faces):
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fig = go.Figure()
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fig.add_trace(go.Mesh3d(
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x=mesh[:,0],
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y=mesh[:,1],
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z=mesh[:,2],
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i = faces[:,0],
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j = faces[:,1],
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k = faces[:,2]
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),)
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fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=1, z=1),
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)
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helper = dict(xaxis = dict(nticks=4, range=[-1,1],),
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yaxis = dict(nticks=4, range=[-1,1],),
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zaxis = dict(nticks=4, range=[-1,1],),)
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fig.update_layout(scene = helper,
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)
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fig.show()
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def build_lookup_table(repeated):
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# iterate through the list and create a dict from each sub array in the list where each dict has one element of the sub array as key and the other elements as value as a list and repeat for each element
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lookup = {}
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for i in range(len(repeated)):
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for j in range(len(repeated[i])):
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if len(repeated[i]) == 1:
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continue
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lookup[repeated[i][j]] = [repeated[i][k] for k in range(len(repeated[i]))] # if k != j
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return lookup
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from data.cloth3d_io import readPC2Frame
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import scipy.io as sio
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import scipy
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import scipy.spatial as spatial
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def local_read_posed_garment_frame(folder, id, garment, frame, pose=None, deorient=False):
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pc16_path = os.path.join(folder, id, garment + '.pc16')
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verts = readPC2Frame(pc16_path, frame, True)
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map = sio.loadmat(os.path.join(folder, id, garment + '_map.mat'))['map'].reshape((-1))
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if deorient:
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R = spatial.transform.Rotation.from_euler('xyz', pose[:3]).inv()
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# R = spatial.transform.Rotation.from_euler([0,0,-zrot])
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verts = R.apply(verts)
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return verts, map
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def local_read_posed_body_frame(folder, id, frame, pose=None, deorient=False):
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pc16_path = os.path.join(folder, id, 'Body_posed.pc16')
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verts = readPC2Frame(pc16_path, frame, True)
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if deorient:
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R = spatial.transform.Rotation.from_euler('xyz', pose[:3]).inv()
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# R = spatial.transform.Rotation.from_euler([0,0,-zrot])
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verts = R.apply(verts)
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return verts
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def edge_loss_custom(x, y, edges):
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x_e = x[:,edges[:,0],:] - x[:,edges[:,1],:]
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x_e = torch.sum(x_e ** 2, -1)
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x_e = torch.sqrt(x_e + 1e-9)
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y_e = y[:,edges[:,0],:] - y[:,edges[:,1],:]
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y_e = torch.sum(y_e ** 2, -1)
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y_e = torch.sqrt(y_e + 1e-9)
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d_e = y_e - x_e
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loss = torch.mean(d_e ** 2, dim=-1)[0]
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return loss
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def crotch(mesh, body, crotch_idx, nr=75):
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crotch_middle_on_body = crotch_idx
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# create kdtree for body and mesh
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body_tree = spatial.cKDTree(body)
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mesh_tree = spatial.cKDTree(mesh)
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# find closest point on mesh from crotch middle on body along the z axis
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crotch_middle_on_mesh = find_closest_point_along_z(body[crotch_middle_on_body], mesh)
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crotch_body = body_tree.query(body[crotch_middle_on_body], k=nr)[1]
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crotch_mesh = mesh_tree.query(mesh[crotch_middle_on_mesh], k=nr)[1]
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body_verts = body[crotch_body]
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mesh_verts = mesh[crotch_mesh] - (mesh[crotch_middle_on_mesh] - body[crotch_middle_on_body])
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mesh_verts = spatial.cKDTree(mesh_verts)
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closest_mesh_verts = mesh_verts.query(body_verts, k=1)[1]
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crotch_mesh = crotch_mesh[closest_mesh_verts]
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return crotch_body, crotch_mesh
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def find_closest_point_along_z(point, verts, eps=0.01):
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min_dist = np.inf
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closest_point = None
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for i, v in enumerate(verts):
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dist = np.linalg.norm(point - v)
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if dist < min_dist and np.abs(point[0] - v[0]) < eps and np.abs(point[1] - v[1]) < eps:
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min_dist = dist
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closest_point = i
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return closest_point
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def mesh_edge_lengths(mesh: Meshes) -> torch.Tensor:
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"""
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Given a PyTorch3D Meshes object, compute the lengths of all edges in the mesh.
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Args:
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mesh: A PyTorch3D Meshes object representing the mesh.
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Returns:
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A tensor of shape (E,) representing the lengths of all edges in the mesh,
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where E is the total number of edges in the mesh.
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"""
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# Extract the vertices and faces of the mesh.
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vertices = mesh.verts_packed()
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faces = mesh.faces_packed()
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# Compute the pairwise distances between all pairs of vertices that form an edge.
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edge_indices = faces.view(-1, 3)
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edge_vertices = vertices[edge_indices]
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edge_lengths = torch.norm(edge_vertices[:, 1:] - edge_vertices[:, :-1], dim=2)
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return edge_lengths
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# try to optimize the already registered garment ??????????
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def register(folder, frame):
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id, frame_nr, garment = frame.split()
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frame_nr = int(frame_nr)
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info = loadInfo(os.path.join(folder, id, 'info.mat'))
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raw_mask = read_garment_mask(folder, id, garment, return_raw=True)
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# convert it to indices
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mask = raw_mask > 0
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mesh_posed, map = local_read_posed_garment_frame(folder, id, garment, frame_nr, pose=info['poses'][:,frame_nr], deorient=True)
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body_posed = local_read_posed_body_frame(folder, id, frame_nr, pose=info['poses'][:,frame_nr], deorient=True)
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template, faces, _, _ = readOBJ(os.path.join(folder, id, garment + '.obj'))
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faces = quads2tris(faces)
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boundaries = raw_mask == 2
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registered_boundaries = torch.tensor(np.where(boundaries[mask])[0]).to(device)
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# invert map and use it to invert boudaries
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# map_inv = np.zeros(map.max()+1, dtype=np.int32)
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# map_inv[map] = np.arange(len(map))
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# boundaries = boundaries[map_inv]
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# # get the boundary indices
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# mesh1_boundaries = torch.tensor(np.where(boundaries)[0]).to(device)
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# # pose = torch.zeros(1, 72).to('cuda')
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# shape = torch.zeros(1, 11).to(device)
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# shape[:, :10] = torch.tensor(info['shape']).to(device)
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# shape[:, -1] = torch.tensor(info['gender']).to(device)
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# # for star pose
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# # pose[:, 5] = 3.1415/4
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# # pose[:,8] = -3.1415/4
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# STAR_POSE = np.zeros((24, 3), np.float32)
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# STAR_POSE[1,2] = .15
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# STAR_POSE[2,2] = -.15
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# pose = torch.tensor(STAR_POSE.flatten()[None,:]).to(device)
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# star_body = smpl(pose, shape, remove=True)[0][0].cpu().numpy() - smpl(pose, shape, remove=True)[4][0].cpu().numpy()
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# # rotate star body with 90 degrees around x axis
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# R = spatial.transform.Rotation.from_euler('x', np.pi/2)
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# star_body = R.apply(star_body).astype(np.float32)
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is_skirt = (garment == 'Dress' or garment == 'Skirt')
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skirt_mod = '_skirt' if is_skirt else ''
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F = np.load(f'smpl/smpl_faces{skirt_mod}.npy').astype(np.int32)
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##################### mask body #####################
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new_crotch = 4729
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# star_body = star_body[mask]
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new_crotch = np.zeros((14475), bool)
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new_crotch[4729] = True
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new_crotch = new_crotch[mask]
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new_crotch = np.where(new_crotch)[0][0]
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_, F = _maskFaces(np.zeros((14475, 3)), F, mask)
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registered = template[map][mask]
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# plot(template, faces)
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# v = mesh_posed[mask,:]
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##################### boundaries #####################
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# meshfix = MeshFix(template, faces)
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# holes = meshfix.extract_holes()
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# new_border = []
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# for border in np.array(holes.points):
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# # find the index of the vertex in the original mesh
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# ind = np.argwhere(np.all(template == border, axis=1))
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# new_border.extend(ind.ravel())
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# mesh1_boundaries = torch.tensor(new_border).to(device)
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# np.save('temp/body_boundaries.npy', new_border)
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template_boundaries = torch.tensor(np.load('temp/mesh1_boundaries.npy')).to(device)
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# meshfix = MeshFix(template, faces)
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# holes = meshfix.extract_holes()
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# new_border = []
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# for border in np.array(holes.points):
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# # find the index of the vertex in the original mesh
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# ind = np.argwhere(np.all(template == border, axis=1))
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# new_border.extend(ind.ravel())
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# mesh1_boundaries = torch.tensor(new_border).to(device)
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# np.save('temp/body_boundaries.npy', new_border)
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# mesh1_boundaries = torch.tensor(np.load('temp/mesh1_boundaries.npy')).to(device)
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##################### crotch #####################
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# crotch_body, crotch_mesh = crotch(template, star_body, crotch_idx=new_crotch, nr=50)
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# export star_body as obj
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# writeOBJ('temp/star_body.obj', star_body, F)
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template = torch.tensor(template).to(device)
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template_orig = template.clone()
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faces = torch.tensor(faces).to(device)
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registered = torch.tensor(registered).to(device)
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F = torch.tensor(F).to(device)
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# verts = template.cpu().numpy()
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# star_body = star_body.cpu().numpy()
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# faces = faces.detach().cpu().numpy()
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# F = F.detach().cpu().numpy()
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# fig = go.Figure(data=[
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# go.Mesh3d(
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# x=verts[:,0], y=verts[:,1], z=verts[:,2],
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# i=faces[:,0], j=faces[:,1], k=faces[:,2],
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# color='lightpink', opacity=0.50),
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# go.Mesh3d(
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# x=star_body[:,0], y=star_body[:,1], z=star_body[:,2],
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# i=F[:,0], j=F[:,1], k=F[:,2],
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# color='lightblue', opacity=0.50),
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# go.Scatter3d(
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# x=verts[crotch_mesh,0], y=verts[crotch_mesh,1], z=verts[crotch_mesh,2],
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# mode='markers', marker=dict(size=2, color='red')),
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# go.Scatter3d(
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# x=star_body[crotch_body,0], y=star_body[crotch_body,1], z=star_body[crotch_body,2],
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# mode='markers', marker=dict(size=2, color='blue')),
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# ])
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# # make it cuve and limit to -1 and -1
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# fig.update_layout(scene = dict(
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# xaxis = dict(range=[-1,1],),
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# yaxis = dict(range=[-1,1],),
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# zaxis = dict(range=[-1,1],),),)
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# fig.show()
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# Define the meshes to optimize
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mesh1 = Meshes(verts=registered[None,:,:], faces=F[None,:,:])
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mesh2 = Meshes(verts=template[None,:,:], faces=faces[None,:,:])
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mesh1_boundaries = registered_boundaries
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mesh2_boundaries = template_boundaries
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# mesh2_boundaries = find_closest_vertices(mesh1, mesh2, mesh1_boundaries)
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avg_edge_length = mesh_edge_lengths(mesh1).mean()
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# Define the optimizer and learning rate
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mesh1.verts_packed().requires_grad = True
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optimizer = torch.optim.Adam([mesh1.verts_packed()], lr=0.0001)
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# optimizer = torch.optim.SGD([mesh1.verts_packed()], lr=0.001)
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# Define the loss functions
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def laplacian_smoothing_loss(mesh):
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return mesh_laplacian_smoothing(mesh, method="uniform") # cotcurv uniform cot
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def edge_loss(mesh):
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return mesh_edge_loss(mesh, target_length=avg_edge_length)
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def normal_consistency_loss(mesh):
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return mesh_normal_consistency(mesh)
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# Optimization loop
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for i in range(10000):
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optimizer.zero_grad()
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# find boundaries with the find holes function?
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# Forward pass
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mesh1_out = Meshes(verts=[mesh1.verts_packed()], faces=[mesh1.faces_packed()])
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ch_loss, _ = chamfer_distance(torch.stack(mesh1_out.verts_list()), torch.stack(mesh2.verts_list()))
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bound_ch_loss, _ = chamfer_distance(torch.stack(mesh1_out.verts_list())[:,mesh1_boundaries,:], torch.stack(mesh2.verts_list())[:,mesh2_boundaries,:])
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# crotch_ch_loss, _ = chamfer_distance(torch.stack(mesh1_out.verts_list())[:,crotch_mesh,:], torch.stack(mesh2.verts_list())[:,crotch_body,:],)
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# crotch_ch_loss = torch.mean((torch.stack(mesh1_out.verts_list())[:,crotch_mesh,:] - torch.stack(mesh2.verts_list())[:,crotch_body,:])**2, dim=2).mean()
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loss: torch.Tensor = ch_loss * 3 + (bound_ch_loss) * 20
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# loss: torch.Tensor = ch_loss + ch_norm_loss + (bound_ch_loss + bound_ch_norm_loss) * 1
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lap_loss = laplacian_smoothing_loss(mesh1_out)
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e_loss = edge_loss(mesh1_out)
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# loss += edge_loss_custom(torch.stack(mesh1_out.verts_list()), template_orig[None,:,:], mesh1.edges_packed()) * 100
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norm_loss = normal_consistency_loss(mesh1_out)
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loss += lap_loss * 0.05 + e_loss * 100 + norm_loss * 0.1
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# Backward pass
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loss.backward()
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optimizer.step()
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# Print the loss
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if i % 10 == 0:
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print(f"it: {i} : loss: {loss:.5f} chamfer: {ch_loss:.6f} bound_chamfer: {bound_ch_loss:.6f} laplacian: {lap_loss:.6f} edge: {e_loss:.6f} normal: {norm_loss:.5f}")
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# create a plotly plot with the body and the mesh1_out
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registered = mesh1_out.verts_packed().detach().cpu().numpy()
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template = mesh2.verts_packed().detach().cpu().numpy()
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faces = faces.detach().cpu().numpy()
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F = F.detach().cpu().numpy()
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# faces, F = F, faces
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# verts, star_body = star_body, verts
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mesh1_boundaries, mesh2_boundaries = mesh1_boundaries.detach().cpu().numpy(), mesh2_boundaries.detach().cpu().numpy()
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# mesh2_boundaries = mesh1_boundaries.detach().cpu().numpy()
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fig = go.Figure(data=[
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go.Mesh3d(
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x=template[:,0], y=template[:,1], z=template[:,2],
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i=faces[:,0], j=faces[:,1], k=faces[:,2],
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color='lightblue', opacity=0.80),
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go.Mesh3d(
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x=registered[:,0], y=registered[:,1], z=registered[:,2],
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i=F[:,0], j=F[:,1], k=F[:,2],
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color='lightpink', opacity=0.50),
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go.Scatter3d(
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x=registered[mesh1_boundaries,0], y=registered[mesh1_boundaries,1], z=registered[mesh1_boundaries,2],
|
|
mode='markers', marker=dict(size=2, color='red')),
|
|
go.Scatter3d(
|
|
x=template[mesh2_boundaries,0], y=template[mesh2_boundaries,1], z=template[mesh2_boundaries,2],
|
|
mode='markers', marker=dict(size=2, color='blue')),
|
|
# go.Scatter3d(
|
|
# x=verts[crotch_mesh,0], y=verts[crotch_mesh,1], z=verts[crotch_mesh,2],
|
|
# mode='markers', marker=dict(size=2, color='red')),
|
|
# go.Scatter3d(
|
|
# x=star_body[crotch_body,0], y=star_body[crotch_body,1], z=star_body[crotch_body,2],
|
|
# mode='markers', marker=dict(size=2, color='blue')),
|
|
])
|
|
# make it cuve and limit to -1 and -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()
|
|
|
|
# create a plotly wireframe plot of registered
|
|
fig = go.Figure()
|
|
for edge in mesh1_out.edges_packed().detach().cpu().numpy():
|
|
fig.add_trace(go.Scatter3d(
|
|
x=registered[edge,0], y=registered[edge,1], z=registered[edge,2],
|
|
mode='lines', line=dict(color='red', width=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
|
|
|
|
if __name__ == "__main__":
|
|
folder = '/data-net/code/cloth3d/train'
|
|
# folder = r'D:\Work\code\cloth3d\train'
|
|
# frames = [f'00761_{i}' for i in range(300)]
|
|
# folder = r'Z:\Work\Huni\Barcelona\Dataset\cloth3d\train'
|
|
frames = get_frames(folder, 'frames_all.txt')
|
|
root = '/'.join(folder.split('/')[:-1])
|
|
type = folder.split('/')[-1]
|
|
|
|
use_no_orient = False
|
|
no_shape = False
|
|
is_skirt = False
|
|
|
|
skirt_modifier = '_skirt' if is_skirt else ''
|
|
orient_mod = '_no_orient' if use_no_orient else ''
|
|
shape_mod = '_no_shape' if no_shape else ''
|
|
|
|
# mask = np.load('/data-net/dataset/00761/mesh_mask/00761.npy')
|
|
# faces = np.load('/data-net/dataset/00761/faces/00761.npy')
|
|
|
|
for frame in frames:
|
|
frame = random.choice(frames)
|
|
# frame = '00761 0 Tshirt'
|
|
frame = '01893 256 Jumpsuit'
|
|
|
|
|
|
register(folder, frame)
|
|
break
|
|
pass
|