# -*- coding: utf-8 -*-
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is authorized to grant you that right.
# Any use of the computer program without a valid license is prohibited and
# liable to prosecution.
#
# Copyright©2019 Max-Planck-Gesellschaft zur Förderung
# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
# for Intelligent Systems. All rights reserved.
#
# Contact: ps-license@tuebingen.mpg.de
import numpy as np
import cv2
import pymeshlab
import torch
import torchvision
import trimesh
from pytorch3d.io import load_obj
from termcolor import colored
from scipy.spatial import cKDTree
from pytorch3d.structures import Meshes
import torch.nn.functional as F
import os
from lib.pymaf.utils.imutils import uncrop
from lib.common.render_utils import Pytorch3dRasterizer, face_vertices
from pytorch3d.renderer.mesh import rasterize_meshes
from PIL import Image, ImageFont, ImageDraw
from kaolin.ops.mesh import check_sign
from kaolin.metrics.trianglemesh import point_to_mesh_distance
from pytorch3d.loss import (
mesh_laplacian_smoothing,
mesh_normal_consistency
)
from huggingface_hub import hf_hub_download, hf_hub_url, cached_download
def rot6d_to_rotmat(x):
"""Convert 6D rotation representation to 3x3 rotation matrix.
Based on Zhou et al., "On the Continuity of Rotation Representations in Neural Networks", CVPR 2019
Input:
(B,6) Batch of 6-D rotation representations
Output:
(B,3,3) Batch of corresponding rotation matrices
"""
x = x.view(-1, 3, 2)
a1 = x[:, :, 0]
a2 = x[:, :, 1]
b1 = F.normalize(a1)
b2 = F.normalize(a2 - torch.einsum("bi,bi->b", b1, a2).unsqueeze(-1) * b1)
b3 = torch.cross(b1, b2)
return torch.stack((b1, b2, b3), dim=-1)
def tensor2variable(tensor, device):
# [1,23,3,3]
return torch.tensor(tensor, device=device, requires_grad=True)
def normal_loss(vec1, vec2):
# vec1_mask = vec1.sum(dim=1) != 0.0
# vec2_mask = vec2.sum(dim=1) != 0.0
# union_mask = vec1_mask * vec2_mask
vec_sim = torch.nn.CosineSimilarity(dim=1, eps=1e-6)(vec1, vec2)
# vec_diff = ((vec_sim-1.0)**2)[union_mask].mean()
vec_diff = ((vec_sim-1.0)**2).mean()
return vec_diff
class GMoF(torch.nn.Module):
def __init__(self, rho=1):
super(GMoF, self).__init__()
self.rho = rho
def extra_repr(self):
return 'rho = {}'.format(self.rho)
def forward(self, residual):
dist = torch.div(residual, residual + self.rho ** 2)
return self.rho ** 2 * dist
def mesh_edge_loss(meshes, target_length: float = 0.0):
"""
Computes mesh edge length regularization loss averaged across all meshes
in a batch. Each mesh contributes equally to the final loss, regardless of
the number of edges per mesh in the batch by weighting each mesh with the
inverse number of edges. For example, if mesh 3 (out of N) has only E=4
edges, then the loss for each edge in mesh 3 should be multiplied by 1/E to
contribute to the final loss.
Args:
meshes: Meshes object with a batch of meshes.
target_length: Resting value for the edge length.
Returns:
loss: Average loss across the batch. Returns 0 if meshes contains
no meshes or all empty meshes.
"""
if meshes.isempty():
return torch.tensor(
[0.0], dtype=torch.float32, device=meshes.device, requires_grad=True
)
N = len(meshes)
edges_packed = meshes.edges_packed() # (sum(E_n), 3)
verts_packed = meshes.verts_packed() # (sum(V_n), 3)
edge_to_mesh_idx = meshes.edges_packed_to_mesh_idx() # (sum(E_n), )
num_edges_per_mesh = meshes.num_edges_per_mesh() # N
# Determine the weight for each edge based on the number of edges in the
# mesh it corresponds to.
# TODO (nikhilar) Find a faster way of computing the weights for each edge
# as this is currently a bottleneck for meshes with a large number of faces.
weights = num_edges_per_mesh.gather(0, edge_to_mesh_idx)
weights = 1.0 / weights.float()
verts_edges = verts_packed[edges_packed]
v0, v1 = verts_edges.unbind(1)
loss = ((v0 - v1).norm(dim=1, p=2) - target_length) ** 2.0
loss_vertex = loss * weights
# loss_outlier = torch.topk(loss, 100)[0].mean()
# loss_all = (loss_vertex.sum() + loss_outlier.mean()) / N
loss_all = loss_vertex.sum() / N
return loss_all
def remesh(obj_path, perc, device):
ms = pymeshlab.MeshSet()
ms.load_new_mesh(obj_path)
ms.laplacian_smooth()
ms.remeshing_isotropic_explicit_remeshing(
targetlen=pymeshlab.Percentage(perc), adaptive=True)
ms.save_current_mesh(obj_path.replace("recon", "remesh"))
polished_mesh = trimesh.load_mesh(obj_path.replace("recon", "remesh"))
verts_pr = torch.tensor(polished_mesh.vertices).float().unsqueeze(0).to(device)
faces_pr = torch.tensor(polished_mesh.faces).long().unsqueeze(0).to(device)
return verts_pr, faces_pr
def possion(mesh, obj_path):
mesh.export(obj_path)
ms = pymeshlab.MeshSet()
ms.load_new_mesh(obj_path)
ms.surface_reconstruction_screened_poisson(depth=10)
ms.set_current_mesh(1)
ms.save_current_mesh(obj_path)
return trimesh.load(obj_path)
def get_mask(tensor, dim):
mask = torch.abs(tensor).sum(dim=dim, keepdims=True) > 0.0
mask = mask.type_as(tensor)
return mask
def blend_rgb_norm(rgb, norm, mask):
# [0,0,0] or [127,127,127] should be marked as mask
final = rgb * (1-mask) + norm * (mask)
return final.astype(np.uint8)
def unwrap(image, data):
img_uncrop = uncrop(np.array(Image.fromarray(image).resize(data['uncrop_param']['box_shape'][:2])),
data['uncrop_param']['center'],
data['uncrop_param']['scale'],
data['uncrop_param']['crop_shape'])
img_orig = cv2.warpAffine(img_uncrop,
np.linalg.inv(data['uncrop_param']['M'])[:2, :],
data['uncrop_param']['ori_shape'][::-1][1:],
flags=cv2.INTER_CUBIC)
return img_orig
# Losses to smooth / regularize the mesh shape
def update_mesh_shape_prior_losses(mesh, losses):
# and (b) the edge length of the predicted mesh
losses["edge"]['value'] = mesh_edge_loss(mesh)
# mesh normal consistency
losses["nc"]['value'] = mesh_normal_consistency(mesh)
# mesh laplacian smoothing
losses["laplacian"]['value'] = mesh_laplacian_smoothing(
mesh, method="uniform")
def rename(old_dict, old_name, new_name):
new_dict = {}
for key, value in zip(old_dict.keys(), old_dict.values()):
new_key = key if key != old_name else new_name
new_dict[new_key] = old_dict[key]
return new_dict
def load_checkpoint(model, cfg):
model_dict = model.state_dict()
main_dict = {}
normal_dict = {}
device = torch.device(f"cuda:{cfg['test_gpus'][0]}")
main_dict = torch.load(cached_download(cfg.resume_path, use_auth_token=os.environ['ICON']),
map_location=device)['state_dict']
main_dict = {
k: v
for k, v in main_dict.items()
if k in model_dict and v.shape == model_dict[k].shape and (
'reconEngine' not in k) and ("normal_filter" not in k) and (
'voxelization' not in k)
}
print(colored(f"Resume MLP weights from {cfg.resume_path}", 'green'))
normal_dict = torch.load(cached_download(cfg.normal_path, use_auth_token=os.environ['ICON']),
map_location=device)['state_dict']
for key in normal_dict.keys():
normal_dict = rename(normal_dict, key,
key.replace("netG", "netG.normal_filter"))
normal_dict = {
k: v
for k, v in normal_dict.items()
if k in model_dict and v.shape == model_dict[k].shape
}
print(colored(f"Resume normal model from {cfg.normal_path}", 'green'))
model_dict.update(main_dict)
model_dict.update(normal_dict)
model.load_state_dict(model_dict)
model.netG = model.netG.to(device)
model.reconEngine = model.reconEngine.to(device)
model.netG.training = False
model.netG.eval()
del main_dict
del normal_dict
del model_dict
return model
def read_smpl_constants(folder):
"""Load smpl vertex code"""
smpl_vtx_std = np.loadtxt(cached_download(os.path.join(folder, 'vertices.txt'), use_auth_token=os.environ['ICON']))
min_x = np.min(smpl_vtx_std[:, 0])
max_x = np.max(smpl_vtx_std[:, 0])
min_y = np.min(smpl_vtx_std[:, 1])
max_y = np.max(smpl_vtx_std[:, 1])
min_z = np.min(smpl_vtx_std[:, 2])
max_z = np.max(smpl_vtx_std[:, 2])
smpl_vtx_std[:, 0] = (smpl_vtx_std[:, 0] - min_x) / (max_x - min_x)
smpl_vtx_std[:, 1] = (smpl_vtx_std[:, 1] - min_y) / (max_y - min_y)
smpl_vtx_std[:, 2] = (smpl_vtx_std[:, 2] - min_z) / (max_z - min_z)
smpl_vertex_code = np.float32(np.copy(smpl_vtx_std))
"""Load smpl faces & tetrahedrons"""
smpl_faces = np.loadtxt(cached_download(os.path.join(folder, 'faces.txt'), use_auth_token=os.environ['ICON']),
dtype=np.int32) - 1
smpl_face_code = (smpl_vertex_code[smpl_faces[:, 0]] +
smpl_vertex_code[smpl_faces[:, 1]] +
smpl_vertex_code[smpl_faces[:, 2]]) / 3.0
smpl_tetras = np.loadtxt(cached_download(os.path.join(folder, 'tetrahedrons.txt'), use_auth_token=os.environ['ICON']),
dtype=np.int32) - 1
return smpl_vertex_code, smpl_face_code, smpl_faces, smpl_tetras
def feat_select(feat, select):
# feat [B, featx2, N]
# select [B, 1, N]
# return [B, feat, N]
dim = feat.shape[1] // 2
idx = torch.tile((1-select), (1, dim, 1))*dim + \
torch.arange(0, dim).unsqueeze(0).unsqueeze(2).type_as(select)
feat_select = torch.gather(feat, 1, idx.long())
return feat_select
def get_visibility(xy, z, faces):
"""get the visibility of vertices
Args:
xy (torch.tensor): [N,2]
z (torch.tensor): [N,1]
faces (torch.tensor): [N,3]
size (int): resolution of rendered image
"""
xyz = torch.cat((xy, -z), dim=1)
xyz = (xyz + 1.0) / 2.0
faces = faces.long()
rasterizer = Pytorch3dRasterizer(image_size=2**12)
meshes_screen = Meshes(verts=xyz[None, ...], faces=faces[None, ...])
raster_settings = rasterizer.raster_settings
pix_to_face, zbuf, bary_coords, dists = rasterize_meshes(
meshes_screen,
image_size=raster_settings.image_size,
blur_radius=raster_settings.blur_radius,
faces_per_pixel=raster_settings.faces_per_pixel,
bin_size=raster_settings.bin_size,
max_faces_per_bin=raster_settings.max_faces_per_bin,
perspective_correct=raster_settings.perspective_correct,
cull_backfaces=raster_settings.cull_backfaces,
)
vis_vertices_id = torch.unique(faces[torch.unique(pix_to_face), :])
vis_mask = torch.zeros(size=(z.shape[0], 1))
vis_mask[vis_vertices_id] = 1.0
# print("------------------------\n")
# print(f"keep points : {vis_mask.sum()/len(vis_mask)}")
return vis_mask
def barycentric_coordinates_of_projection(points, vertices):
''' https://github.com/MPI-IS/mesh/blob/master/mesh/geometry/barycentric_coordinates_of_projection.py
'''
"""Given a point, gives projected coords of that point to a triangle
in barycentric coordinates.
See
**Heidrich**, Computing the Barycentric Coordinates of a Projected Point, JGT 05
at http://www.cs.ubc.ca/~heidrich/Papers/JGT.05.pdf
:param p: point to project. [B, 3]
:param v0: first vertex of triangles. [B, 3]
:returns: barycentric coordinates of ``p``'s projection in triangle defined by ``q``, ``u``, ``v``
vectorized so ``p``, ``q``, ``u``, ``v`` can all be ``3xN``
"""
#(p, q, u, v)
v0, v1, v2 = vertices[:, 0], vertices[:, 1], vertices[:, 2]
p = points
q = v0
u = v1 - v0
v = v2 - v0
n = torch.cross(u, v)
s = torch.sum(n * n, dim=1)
# If the triangle edges are collinear, cross-product is zero,
# which makes "s" 0, which gives us divide by zero. So we
# make the arbitrary choice to set s to epsv (=numpy.spacing(1)),
# the closest thing to zero
s[s == 0] = 1e-6
oneOver4ASquared = 1.0 / s
w = p - q
b2 = torch.sum(torch.cross(u, w) * n, dim=1) * oneOver4ASquared
b1 = torch.sum(torch.cross(w, v) * n, dim=1) * oneOver4ASquared
weights = torch.stack((1 - b1 - b2, b1, b2), dim=-1)
# check barycenric weights
# p_n = v0*weights[:,0:1] + v1*weights[:,1:2] + v2*weights[:,2:3]
return weights
def cal_sdf_batch(verts, faces, cmaps, vis, points):
# verts [B, N_vert, 3]
# faces [B, N_face, 3]
# triangles [B, N_face, 3, 3]
# points [B, N_point, 3]
# cmaps [B, N_vert, 3]
Bsize = points.shape[0]
normals = Meshes(verts, faces).verts_normals_padded()
triangles = face_vertices(verts, faces)
normals = face_vertices(normals, faces)
cmaps = face_vertices(cmaps, faces)
vis = face_vertices(vis, faces)
residues, pts_ind, _ = point_to_mesh_distance(points, triangles)
closest_triangles = torch.gather(
triangles, 1, pts_ind[:, :, None, None].expand(-1, -1, 3, 3)).view(-1, 3, 3)
closest_normals = torch.gather(
normals, 1, pts_ind[:, :, None, None].expand(-1, -1, 3, 3)).view(-1, 3, 3)
closest_cmaps = torch.gather(
cmaps, 1, pts_ind[:, :, None, None].expand(-1, -1, 3, 3)).view(-1, 3, 3)
closest_vis = torch.gather(
vis, 1, pts_ind[:, :, None, None].expand(-1, -1, 3, 1)).view(-1, 3, 1)
bary_weights = barycentric_coordinates_of_projection(
points.view(-1, 3), closest_triangles)
pts_cmap = (closest_cmaps*bary_weights[:, :, None]).sum(1).unsqueeze(0).clamp_(min=0.0, max=1.0)
pts_vis = (closest_vis*bary_weights[:,
:, None]).sum(1).unsqueeze(0).ge(1e-1)
pts_norm = (closest_normals*bary_weights[:, :, None]).sum(
1).unsqueeze(0) * torch.tensor([-1.0, 1.0, -1.0]).type_as(normals)
pts_norm = F.normalize(pts_norm, dim=2)
pts_dist = torch.sqrt(residues) / torch.sqrt(torch.tensor(3))
pts_signs = 2.0 * (check_sign(verts, faces[0], points).float() - 0.5)
pts_sdf = (pts_dist * pts_signs).unsqueeze(-1)
return pts_sdf.view(Bsize, -1, 1), pts_norm.view(Bsize, -1, 3), pts_cmap.view(Bsize, -1, 3), pts_vis.view(Bsize, -1, 1)
def orthogonal(points, calibrations, transforms=None):
'''
Compute the orthogonal projections of 3D points into the image plane by given projection matrix
:param points: [B, 3, N] Tensor of 3D points
:param calibrations: [B, 3, 4] Tensor of projection matrix
:param transforms: [B, 2, 3] Tensor of image transform matrix
:return: xyz: [B, 3, N] Tensor of xyz coordinates in the image plane
'''
rot = calibrations[:, :3, :3]
trans = calibrations[:, :3, 3:4]
pts = torch.baddbmm(trans, rot, points) # [B, 3, N]
if transforms is not None:
scale = transforms[:2, :2]
shift = transforms[:2, 2:3]
pts[:, :2, :] = torch.baddbmm(shift, scale, pts[:, :2, :])
return pts
def projection(points, calib, format='numpy'):
if format == 'tensor':
return torch.mm(calib[:3, :3], points.T).T + calib[:3, 3]
else:
return np.matmul(calib[:3, :3], points.T).T + calib[:3, 3]
def load_calib(calib_path):
calib_data = np.loadtxt(calib_path, dtype=float)
extrinsic = calib_data[:4, :4]
intrinsic = calib_data[4:8, :4]
calib_mat = np.matmul(intrinsic, extrinsic)
calib_mat = torch.from_numpy(calib_mat).float()
return calib_mat
def load_obj_mesh_for_Hoppe(mesh_file):
vertex_data = []
face_data = []
if isinstance(mesh_file, str):
f = open(mesh_file, "r")
else:
f = mesh_file
for line in f:
if isinstance(line, bytes):
line = line.decode("utf-8")
if line.startswith('#'):
continue
values = line.split()
if not values:
continue
if values[0] == 'v':
v = list(map(float, values[1:4]))
vertex_data.append(v)
elif values[0] == 'f':
# quad mesh
if len(values) > 4:
f = list(map(lambda x: int(x.split('/')[0]), values[1:4]))
face_data.append(f)
f = list(
map(lambda x: int(x.split('/')[0]),
[values[3], values[4], values[1]]))
face_data.append(f)
# tri mesh
else:
f = list(map(lambda x: int(x.split('/')[0]), values[1:4]))
face_data.append(f)
vertices = np.array(vertex_data)
faces = np.array(face_data)
faces[faces > 0] -= 1
normals, _ = compute_normal(vertices, faces)
return vertices, normals, faces
def load_obj_mesh_with_color(mesh_file):
vertex_data = []
color_data = []
face_data = []
if isinstance(mesh_file, str):
f = open(mesh_file, "r")
else:
f = mesh_file
for line in f:
if isinstance(line, bytes):
line = line.decode("utf-8")
if line.startswith('#'):
continue
values = line.split()
if not values:
continue
if values[0] == 'v':
v = list(map(float, values[1:4]))
vertex_data.append(v)
c = list(map(float, values[4:7]))
color_data.append(c)
elif values[0] == 'f':
# quad mesh
if len(values) > 4:
f = list(map(lambda x: int(x.split('/')[0]), values[1:4]))
face_data.append(f)
f = list(
map(lambda x: int(x.split('/')[0]),
[values[3], values[4], values[1]]))
face_data.append(f)
# tri mesh
else:
f = list(map(lambda x: int(x.split('/')[0]), values[1:4]))
face_data.append(f)
vertices = np.array(vertex_data)
colors = np.array(color_data)
faces = np.array(face_data)
faces[faces > 0] -= 1
return vertices, colors, faces
def load_obj_mesh(mesh_file, with_normal=False, with_texture=False):
vertex_data = []
norm_data = []
uv_data = []
face_data = []
face_norm_data = []
face_uv_data = []
if isinstance(mesh_file, str):
f = open(mesh_file, "r")
else:
f = mesh_file
for line in f:
if isinstance(line, bytes):
line = line.decode("utf-8")
if line.startswith('#'):
continue
values = line.split()
if not values:
continue
if values[0] == 'v':
v = list(map(float, values[1:4]))
vertex_data.append(v)
elif values[0] == 'vn':
vn = list(map(float, values[1:4]))
norm_data.append(vn)
elif values[0] == 'vt':
vt = list(map(float, values[1:3]))
uv_data.append(vt)
elif values[0] == 'f':
# quad mesh
if len(values) > 4:
f = list(map(lambda x: int(x.split('/')[0]), values[1:4]))
face_data.append(f)
f = list(
map(lambda x: int(x.split('/')[0]),
[values[3], values[4], values[1]]))
face_data.append(f)
# tri mesh
else:
f = list(map(lambda x: int(x.split('/')[0]), values[1:4]))
face_data.append(f)
# deal with texture
if len(values[1].split('/')) >= 2:
# quad mesh
if len(values) > 4:
f = list(map(lambda x: int(x.split('/')[1]), values[1:4]))
face_uv_data.append(f)
f = list(
map(lambda x: int(x.split('/')[1]),
[values[3], values[4], values[1]]))
face_uv_data.append(f)
# tri mesh
elif len(values[1].split('/')[1]) != 0:
f = list(map(lambda x: int(x.split('/')[1]), values[1:4]))
face_uv_data.append(f)
# deal with normal
if len(values[1].split('/')) == 3:
# quad mesh
if len(values) > 4:
f = list(map(lambda x: int(x.split('/')[2]), values[1:4]))
face_norm_data.append(f)
f = list(
map(lambda x: int(x.split('/')[2]),
[values[3], values[4], values[1]]))
face_norm_data.append(f)
# tri mesh
elif len(values[1].split('/')[2]) != 0:
f = list(map(lambda x: int(x.split('/')[2]), values[1:4]))
face_norm_data.append(f)
vertices = np.array(vertex_data)
faces = np.array(face_data)
faces[faces > 0] -= 1
if with_texture and with_normal:
uvs = np.array(uv_data)
face_uvs = np.array(face_uv_data)
face_uvs[face_uvs > 0] -= 1
norms = np.array(norm_data)
if norms.shape[0] == 0:
norms, _ = compute_normal(vertices, faces)
face_normals = faces
else:
norms = normalize_v3(norms)
face_normals = np.array(face_norm_data)
face_normals[face_normals > 0] -= 1
return vertices, faces, norms, face_normals, uvs, face_uvs
if with_texture:
uvs = np.array(uv_data)
face_uvs = np.array(face_uv_data) - 1
return vertices, faces, uvs, face_uvs
if with_normal:
norms = np.array(norm_data)
norms = normalize_v3(norms)
face_normals = np.array(face_norm_data) - 1
return vertices, faces, norms, face_normals
return vertices, faces
def normalize_v3(arr):
''' Normalize a numpy array of 3 component vectors shape=(n,3) '''
lens = np.sqrt(arr[:, 0]**2 + arr[:, 1]**2 + arr[:, 2]**2)
eps = 0.00000001
lens[lens < eps] = eps
arr[:, 0] /= lens
arr[:, 1] /= lens
arr[:, 2] /= lens
return arr
def compute_normal(vertices, faces):
# Create a zeroed array with the same type and shape as our vertices i.e., per vertex normal
vert_norms = np.zeros(vertices.shape, dtype=vertices.dtype)
# Create an indexed view into the vertex array using the array of three indices for triangles
tris = vertices[faces]
# Calculate the normal for all the triangles, by taking the cross product of the vectors v1-v0, and v2-v0 in each triangle
face_norms = np.cross(tris[::, 1] - tris[::, 0], tris[::, 2] - tris[::, 0])
# n is now an array of normals per triangle. The length of each normal is dependent the vertices,
# we need to normalize these, so that our next step weights each normal equally.
normalize_v3(face_norms)
# now we have a normalized array of normals, one per triangle, i.e., per triangle normals.
# But instead of one per triangle (i.e., flat shading), we add to each vertex in that triangle,
# the triangles' normal. Multiple triangles would then contribute to every vertex, so we need to normalize again afterwards.
# The cool part, we can actually add the normals through an indexed view of our (zeroed) per vertex normal array
vert_norms[faces[:, 0]] += face_norms
vert_norms[faces[:, 1]] += face_norms
vert_norms[faces[:, 2]] += face_norms
normalize_v3(vert_norms)
return vert_norms, face_norms
def save_obj_mesh(mesh_path, verts, faces):
file = open(mesh_path, 'w')
for v in verts:
file.write('v %.4f %.4f %.4f\n' % (v[0], v[1], v[2]))
for f in faces:
f_plus = f + 1
file.write('f %d %d %d\n' % (f_plus[0], f_plus[1], f_plus[2]))
file.close()
def save_obj_mesh_with_color(mesh_path, verts, faces, colors):
file = open(mesh_path, 'w')
for idx, v in enumerate(verts):
c = colors[idx]
file.write('v %.4f %.4f %.4f %.4f %.4f %.4f\n' %
(v[0], v[1], v[2], c[0], c[1], c[2]))
for f in faces:
f_plus = f + 1
file.write('f %d %d %d\n' % (f_plus[0], f_plus[1], f_plus[2]))
file.close()
def calculate_mIoU(outputs, labels):
SMOOTH = 1e-6
outputs = outputs.int()
labels = labels.int()
intersection = (
outputs
& labels).float().sum() # Will be zero if Truth=0 or Prediction=0
union = (outputs | labels).float().sum() # Will be zzero if both are 0
iou = (intersection + SMOOTH) / (union + SMOOTH
) # We smooth our devision to avoid 0/0
thresholded = torch.clamp(
20 * (iou - 0.5), 0,
10).ceil() / 10 # This is equal to comparing with thresolds
return thresholded.mean().detach().cpu().numpy(
) # Or thresholded.mean() if you are interested in average across the batch
def mask_filter(mask, number=1000):
"""only keep {number} True items within a mask
Args:
mask (bool array): [N, ]
number (int, optional): total True item. Defaults to 1000.
"""
true_ids = np.where(mask)[0]
keep_ids = np.random.choice(true_ids, size=number)
filter_mask = np.isin(np.arange(len(mask)), keep_ids)
return filter_mask
def query_mesh(path):
verts, faces_idx, _ = load_obj(path)
return verts, faces_idx.verts_idx
def add_alpha(colors, alpha=0.7):
colors_pad = np.pad(colors, ((0, 0), (0, 1)),
mode='constant',
constant_values=alpha)
return colors_pad
def get_optim_grid_image(per_loop_lst, loss=None, nrow=4, type='smpl'):
font_path = os.path.join(os.path.dirname(__file__), "tbfo.ttf")
font = ImageFont.truetype(font_path, 30)
grid_img = torchvision.utils.make_grid(torch.cat(per_loop_lst, dim=0),
nrow=nrow)
grid_img = Image.fromarray(
((grid_img.permute(1, 2, 0).detach().cpu().numpy() + 1.0) * 0.5 *
255.0).astype(np.uint8))
# add text
draw = ImageDraw.Draw(grid_img)
grid_size = 512
if loss is not None:
draw.text((10, 5), f"error: {loss:.3f}", (255, 0, 0), font=font)
if type == 'smpl':
for col_id, col_txt in enumerate(
['image', 'smpl-norm(render)', 'cloth-norm(pred)', 'diff-norm', 'diff-mask']):
draw.text((10+(col_id*grid_size), 5),
col_txt, (255, 0, 0), font=font)
elif type == 'cloth':
for col_id, col_txt in enumerate(
['image', 'cloth-norm(recon)', 'cloth-norm(pred)', 'diff-norm']):
draw.text((10+(col_id*grid_size), 5),
col_txt, (255, 0, 0), font=font)
for col_id, col_txt in enumerate(
['0', '90', '180', '270']):
draw.text((10+(col_id*grid_size), grid_size*2+5),
col_txt, (255, 0, 0), font=font)
else:
print(f"{type} should be 'smpl' or 'cloth'")
grid_img = grid_img.resize((grid_img.size[0], grid_img.size[1]),
Image.ANTIALIAS)
return grid_img
def clean_mesh(verts, faces):
device = verts.device
mesh_lst = trimesh.Trimesh(verts.detach().cpu().numpy(),
faces.detach().cpu().numpy())
mesh_lst = mesh_lst.split(only_watertight=False)
comp_num = [mesh.vertices.shape[0] for mesh in mesh_lst]
mesh_clean = mesh_lst[comp_num.index(max(comp_num))]
final_verts = torch.as_tensor(mesh_clean.vertices).float().to(device)
final_faces = torch.as_tensor(mesh_clean.faces).int().to(device)
return final_verts, final_faces
def merge_mesh(verts_A, faces_A, verts_B, faces_B, color=False):
sep_mesh = trimesh.Trimesh(np.concatenate([verts_A, verts_B], axis=0),
np.concatenate(
[faces_A, faces_B + faces_A.max() + 1],
axis=0),
maintain_order=True,
process=False)
if color:
colors = np.ones_like(sep_mesh.vertices)
colors[:verts_A.shape[0]] *= np.array([255.0, 0.0, 0.0])
colors[verts_A.shape[0]:] *= np.array([0.0, 255.0, 0.0])
sep_mesh.visual.vertex_colors = colors
# union_mesh = trimesh.boolean.union([trimesh.Trimesh(verts_A, faces_A),
# trimesh.Trimesh(verts_B, faces_B)], engine='blender')
return sep_mesh
def mesh_move(mesh_lst, step, scale=1.0):
trans = np.array([1.0, 0.0, 0.0]) * step
resize_matrix = trimesh.transformations.scale_and_translate(
scale=(scale), translate=trans)
results = []
for mesh in mesh_lst:
mesh.apply_transform(resize_matrix)
results.append(mesh)
return results
class SMPLX():
def __init__(self):
REPO_ID = "Yuliang/SMPL"
self.smpl_verts_path = hf_hub_download(REPO_ID, filename='smpl_data/smpl_verts.npy', use_auth_token=os.environ['ICON'])
self.smplx_verts_path = hf_hub_download(REPO_ID, filename='smpl_data/smplx_verts.npy', use_auth_token=os.environ['ICON'])
self.faces_path = hf_hub_download(REPO_ID, filename='smpl_data/smplx_faces.npy', use_auth_token=os.environ['ICON'])
self.cmap_vert_path = hf_hub_download(REPO_ID, filename='smpl_data/smplx_cmap.npy', use_auth_token=os.environ['ICON'])
self.faces = np.load(self.faces_path)
self.verts = np.load(self.smplx_verts_path)
self.smpl_verts = np.load(self.smpl_verts_path)
self.model_dir = hf_hub_url(REPO_ID, filename='models')
self.tedra_dir = hf_hub_url(REPO_ID, filename='tedra_data')
def get_smpl_mat(self, vert_ids):
mat = torch.as_tensor(np.load(self.cmap_vert_path)).float()
return mat[vert_ids, :]
def smpl2smplx(self, vert_ids=None):
"""convert vert_ids in smpl to vert_ids in smplx
Args:
vert_ids ([int.array]): [n, knn_num]
"""
smplx_tree = cKDTree(self.verts, leafsize=1)
_, ind = smplx_tree.query(self.smpl_verts, k=1) # ind: [smpl_num, 1]
if vert_ids is not None:
smplx_vert_ids = ind[vert_ids]
else:
smplx_vert_ids = ind
return smplx_vert_ids
def smplx2smpl(self, vert_ids=None):
"""convert vert_ids in smplx to vert_ids in smpl
Args:
vert_ids ([int.array]): [n, knn_num]
"""
smpl_tree = cKDTree(self.smpl_verts, leafsize=1)
_, ind = smpl_tree.query(self.verts, k=1) # ind: [smplx_num, 1]
if vert_ids is not None:
smpl_vert_ids = ind[vert_ids]
else:
smpl_vert_ids = ind
return smpl_vert_ids