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utils.py
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utils.py
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import os
import yaml
import trimesh
import torch
import matplotlib.cm
import torch_geometric.transforms
import networkx as nx
import numpy as np
from collections import Counter
from torch_geometric.data import Data
from torch_geometric.utils import get_laplacian
def get_config(config):
with open(config, 'r') as stream:
return yaml.safe_load(stream)
def prepare_sub_folder(output_directory):
checkpoint_directory = os.path.join(output_directory, 'checkpoints')
if not os.path.exists(checkpoint_directory):
print(f"Creating directory: {checkpoint_directory}")
os.makedirs(checkpoint_directory)
return checkpoint_directory
def load_template(mesh_path):
mesh = trimesh.load_mesh(mesh_path, 'ply', process=False)
feat_and_cont = extract_feature_and_contour_from_colour(mesh)
mesh_verts = torch.tensor(mesh.vertices, dtype=torch.float,
requires_grad=False)
face = torch.from_numpy(mesh.faces).t().to(torch.long).contiguous()
mesh_colors = torch.tensor(mesh.visual.vertex_colors,
dtype=torch.float, requires_grad=False)
data = Data(pos=mesh_verts, face=face, colors=mesh_colors,
feat_and_cont=feat_and_cont)
data = torch_geometric.transforms.FaceToEdge(False)(data)
data.laplacian = torch.sparse_coo_tensor(
*get_laplacian(data.edge_index, normalization='rw'))
return data
def extract_feature_and_contour_from_colour(colored):
# assuming that the feature is colored in red and its contour in black
if isinstance(colored, torch_geometric.data.Data):
assert hasattr(colored, 'colors')
colored_trimesh = torch_geometric.utils.to_trimesh(colored)
colors = colored.colors.to(torch.long).numpy()
elif isinstance(colored, trimesh.Trimesh):
colored_trimesh = colored
colors = colored_trimesh.visual.vertex_colors
else:
raise NotImplementedError
graph = nx.from_edgelist(colored_trimesh.edges_unique)
one_rings_indices = [list(graph[i].keys()) for i in range(len(colors))]
features = {}
for index, (v_col, i_ring) in enumerate(zip(colors, one_rings_indices)):
if str(v_col) not in features:
features[str(v_col)] = {'feature': [], 'contour': []}
if is_contour(colors, index, i_ring):
features[str(v_col)]['contour'].append(index)
else:
features[str(v_col)]['feature'].append(index)
# certain vertices on the contour have interpolated colours ->
# assign them to adjacent region
elem_to_remove = []
for key, feat in features.items():
if len(feat['feature']) < 3:
elem_to_remove.append(key)
for idx in feat['feature']:
counts = Counter([str(colors[ri])
for ri in one_rings_indices[idx]])
most_common = counts.most_common(1)[0][0]
if most_common == key:
break
features[most_common]['feature'].append(idx)
features[most_common]['contour'].append(idx)
for e in elem_to_remove:
features.pop(e, None)
# with b map
# 0=eyes, 1=ears, 2=sides, 3=neck, 4=back, 5=mouth, 6=forehead,
# 7=cheeks 8=cheekbones, 9=forehead, 10=jaw, 11=nose
# key = list(features.keys())[11]
# feature_idx = features[key]['feature']
# contour_idx = features[key]['contour']
# find surroundings
# all_distances = self.compute_minimum_distances(
# colored.vertices, colored.vertices[contour_idx]
# )
# max_distance = max(all_distances)
# all_distances[feature_idx] = max_distance
# all_distances[contour_idx] = max_distance
# threshold = 0.005
# surrounding_idx = np.squeeze(np.argwhere(all_distances < threshold))
# colored.visual.vertex_colors[surrounding_idx] = [0, 0, 0, 255]
# colored.show()
return features
def is_contour(colors, center_index, ring_indices):
center_color = colors[center_index]
ring_colors = [colors[ri] for ri in ring_indices]
for r in ring_colors:
if not np.array_equal(center_color, r):
return True
return False
def to_torch_sparse(spmat):
return torch.sparse_coo_tensor(
torch.LongTensor([spmat.tocoo().row, spmat.tocoo().col]),
torch.FloatTensor(spmat.tocoo().data), torch.Size(spmat.tocoo().shape))
def batch_mm(sparse, matrix_batch):
"""
:param sparse: Sparse matrix, size (m, n).
:param matrix_batch: Batched dense matrices, size (b, n, k).
:return: The batched matrix-matrix product, size (b, m, k).
"""
batch_size = matrix_batch.shape[0]
# Stack the vector batch into columns (b, n, k) -> (n, b, k) -> (n, b*k)
matrix = matrix_batch.transpose(0, 1).reshape(sparse.shape[1], -1)
# And then reverse the reshaping.
return sparse.mm(matrix).reshape(sparse.shape[0],
batch_size, -1).transpose(1, 0)
def errors_to_colors(values, min_value=None, max_value=None, cmap=None):
device = values.device
min_value = values.min() if min_value is None else min_value
max_value = values.max() if max_value is None else max_value
if min_value != max_value:
values = (values - min_value) / (max_value - min_value)
cmapper = matplotlib.cm.get_cmap(cmap)
values = cmapper(values.cpu().detach().numpy(), bytes=True)
return torch.tensor(values[:, :, :3]).to(device)
def get_model_list(dirname, key):
if os.path.exists(dirname) is False:
return None
gen_models = [os.path.join(dirname, f) for f in os.listdir(dirname) if
os.path.isfile(
os.path.join(dirname, f)) and key in f and ".pt" in f]
if gen_models is None:
return None
gen_models.sort()
last_model_name = gen_models[-1]
return last_model_name