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Zero
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from typing import List
from heapq import heappush, heappop, heapify
from dataclasses import dataclass
from abc import ABC, abstractmethod
import numpy as np
from numpy import ndarray
from typing import Dict, Tuple
from .asset import Asset
from .spec import ConfigSpec
@dataclass
class SamplerConfig(ConfigSpec):
'''
Config to handle bones re-ordering.
'''
# which sampler to use
method: str
# how many samples in total
num_samples: int
# how many vertex samples
vertex_samples: int
# kwargs
kwargs: Dict[str, Dict]
@classmethod
def parse(cls, config) -> 'SamplerConfig':
cls.check_keys(config)
return SamplerConfig(
method=config.method,
num_samples=config.get('num_samples', 0),
vertex_samples=config.get('vertex_samples', 0),
kwargs=config.get('kwargs', {}),
)
@dataclass
class SamplerResult():
# sampled vertices
vertices: ndarray
# sampled normals
normals: ndarray
# sampled vertex groups
vertex_groups: Dict[str, ndarray]
class Sampler(ABC):
'''
Abstract class for samplers.
'''
def _sample_barycentric(
self,
vertex_group: ndarray,
faces: ndarray,
face_index: ndarray,
random_lengths: ndarray,
):
v_origins = vertex_group[faces[face_index, 0]]
v_vectors = vertex_group[faces[face_index, 1:]]
v_vectors -= v_origins[:, np.newaxis, :]
sample_vector = (v_vectors * random_lengths).sum(axis=1)
v_samples = sample_vector + v_origins
return v_samples
@abstractmethod
def __init__(self, config: SamplerConfig):
pass
@abstractmethod
def sample(
self,
asset: Asset,
) -> SamplerResult:
'''
Return sampled vertices, sampled normals and vertex groups.
'''
pass
class SamplerOrigin(Sampler):
def __init__(self, config: SamplerConfig):
super().__init__(config)
self.num_samples = config.num_samples
self.vertex_samples = config.vertex_samples
def sample(
self,
asset: Asset,
) -> SamplerResult:
perm = np.random.permutation(asset.vertices.shape[0])
if asset.vertices.shape[0] < self.num_samples:
m = self.num_samples - asset.vertices.shape[0]
perm = np.concatenate([perm, np.random.randint(0, asset.vertices.shape[0], (m,))])
perm = perm[:self.num_samples]
n_v = asset.vertices[perm]
n_n = asset.vertex_normals[perm]
n_vg = {name: v[perm] for name, v in asset.vertex_groups.items()}
return SamplerResult(
vertices=n_v,
normals=n_n,
vertex_groups=n_vg,
)
class SamplerMix(Sampler):
def __init__(self, config: SamplerConfig):
super().__init__(config)
self.num_samples = config.num_samples
self.vertex_samples = config.vertex_samples
assert self.num_samples >= self.vertex_samples, 'num_samples should >= vertex_samples'
@property
def mesh_preserve(self):
return self.num_samples==-1
def sample(
self,
asset: Asset,
) -> SamplerResult:
# 1. sample vertices
num_samples = self.num_samples
perm = np.random.permutation(asset.vertices.shape[0])
vertex_samples = min(self.vertex_samples, asset.vertices.shape[0])
num_samples -= vertex_samples
perm = perm[:vertex_samples]
n_vertex = asset.vertices[perm]
n_normal = asset.vertex_normals[perm]
n_v = {name: v[perm] for name, v in asset.vertex_groups.items()}
# 2. sample surface
perm = np.random.permutation(num_samples)
vertex_samples, face_index, random_lengths = sample_surface(
num_samples=num_samples,
vertices=asset.vertices,
faces=asset.faces,
return_weight=True,
)
vertex_samples = np.concatenate([n_vertex, vertex_samples], axis=0)
normal_samples = np.concatenate([n_normal, asset.face_normals[face_index]], axis=0)
vertex_group_samples = {}
for n, v in asset.vertex_groups.items():
g = self._sample_barycentric(
vertex_group=v,
faces=asset.faces,
face_index=face_index,
random_lengths=random_lengths,
)
vertex_group_samples[n] = np.concatenate([n_v[n], g], axis=0)
return SamplerResult(
vertices=vertex_samples,
normals=normal_samples,
vertex_groups=vertex_group_samples,
)
def sample_surface(
num_samples: int,
vertices: ndarray,
faces: ndarray,
return_weight: bool=False,
):
'''
Randomly pick samples according to face area.
See sample_surface: https://github.com/mikedh/trimesh/blob/main/trimesh/sample.py
'''
# get face area
offset_0 = vertices[faces[:, 1]] - vertices[faces[:, 0]]
offset_1 = vertices[faces[:, 2]] - vertices[faces[:, 0]]
face_weight = np.cross(offset_0, offset_1, axis=-1)
face_weight = (face_weight * face_weight).sum(axis=1)
weight_cum = np.cumsum(face_weight, axis=0)
face_pick = np.random.rand(num_samples) * weight_cum[-1]
face_index = np.searchsorted(weight_cum, face_pick)
# pull triangles into the form of an origin + 2 vectors
tri_origins = vertices[faces[:, 0]]
tri_vectors = vertices[faces[:, 1:]]
tri_vectors -= np.tile(tri_origins, (1, 2)).reshape((-1, 2, 3))
# pull the vectors for the faces we are going to sample from
tri_origins = tri_origins[face_index]
tri_vectors = tri_vectors[face_index]
# randomly generate two 0-1 scalar components to multiply edge vectors b
random_lengths = np.random.rand(len(tri_vectors), 2, 1)
random_test = random_lengths.sum(axis=1).reshape(-1) > 1.0
random_lengths[random_test] -= 1.0
random_lengths = np.abs(random_lengths)
sample_vector = (tri_vectors * random_lengths).sum(axis=1)
vertex_samples = sample_vector + tri_origins
if not return_weight:
return vertex_samples
return vertex_samples, face_index, random_lengths
def get_sampler(config: SamplerConfig) -> Sampler:
method = config.method
if method=='origin':
sampler = SamplerOrigin(config)
elif method=='mix':
sampler = SamplerMix(config)
else:
raise ValueError(f"sampler method {method} not supported")
return sampler |