Spaces:
Running
on
Zero
Running
on
Zero
File size: 4,436 Bytes
18d2806 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 |
# Open Source Model Licensed under the Apache License Version 2.0
# and Other Licenses of the Third-Party Components therein:
# The below Model in this distribution may have been modified by THL A29 Limited
# ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited.
# Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved.
# The below software and/or models in this distribution may have been
# modified by THL A29 Limited ("Tencent Modifications").
# All Tencent Modifications are Copyright (C) THL A29 Limited.
# Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT
# except for the third-party components listed below.
# Hunyuan 3D does not impose any additional limitations beyond what is outlined
# in the repsective licenses of these third-party components.
# Users must comply with all terms and conditions of original licenses of these third-party
# components and must ensure that the usage of the third party components adheres to
# all relevant laws and regulations.
# For avoidance of doubts, Hunyuan 3D means the large language models and
# their software and algorithms, including trained model weights, parameters (including
# optimizer states), machine-learning model code, inference-enabling code, training-enabling code,
# fine-tuning enabling code and other elements of the foregoing made publicly available
# by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT.
import math
import numpy as np
import torch
def transform_pos(mtx, pos, keepdim=False):
t_mtx = torch.from_numpy(mtx).to(
pos.device) if isinstance(
mtx, np.ndarray) else mtx
if pos.shape[-1] == 3:
posw = torch.cat(
[pos, torch.ones([pos.shape[0], 1]).to(pos.device)], axis=1)
else:
posw = pos
if keepdim:
return torch.matmul(posw, t_mtx.t())[...]
else:
return torch.matmul(posw, t_mtx.t())[None, ...]
def get_mv_matrix(elev, azim, camera_distance, center=None):
elev = -elev
azim += 90
elev_rad = math.radians(elev)
azim_rad = math.radians(azim)
camera_position = np.array([camera_distance * math.cos(elev_rad) * math.cos(azim_rad),
camera_distance *
math.cos(elev_rad) * math.sin(azim_rad),
camera_distance * math.sin(elev_rad)])
if center is None:
center = np.array([0, 0, 0])
else:
center = np.array(center)
lookat = center - camera_position
lookat = lookat / np.linalg.norm(lookat)
up = np.array([0, 0, 1.0])
right = np.cross(lookat, up)
right = right / np.linalg.norm(right)
up = np.cross(right, lookat)
up = up / np.linalg.norm(up)
c2w = np.concatenate(
[np.stack([right, up, -lookat], axis=-1), camera_position[:, None]], axis=-1)
w2c = np.zeros((4, 4))
w2c[:3, :3] = np.transpose(c2w[:3, :3], (1, 0))
w2c[:3, 3:] = -np.matmul(np.transpose(c2w[:3, :3], (1, 0)), c2w[:3, 3:])
w2c[3, 3] = 1.0
return w2c.astype(np.float32)
def get_orthographic_projection_matrix(
left=-1, right=1, bottom=-1, top=1, near=0, far=2):
"""
计算正交投影矩阵。
参数:
left (float): 投影区域左侧边界。
right (float): 投影区域右侧边界。
bottom (float): 投影区域底部边界。
top (float): 投影区域顶部边界。
near (float): 投影区域近裁剪面距离。
far (float): 投影区域远裁剪面距离。
返回:
numpy.ndarray: 正交投影矩阵。
"""
ortho_matrix = np.eye(4, dtype=np.float32)
ortho_matrix[0, 0] = 2 / (right - left)
ortho_matrix[1, 1] = 2 / (top - bottom)
ortho_matrix[2, 2] = -2 / (far - near)
ortho_matrix[0, 3] = -(right + left) / (right - left)
ortho_matrix[1, 3] = -(top + bottom) / (top - bottom)
ortho_matrix[2, 3] = -(far + near) / (far - near)
return ortho_matrix
def get_perspective_projection_matrix(fovy, aspect_wh, near, far):
fovy_rad = math.radians(fovy)
return np.array([[1.0 / (math.tan(fovy_rad / 2.0) * aspect_wh), 0, 0, 0],
[0, 1.0 / math.tan(fovy_rad / 2.0), 0, 0],
[0, 0, -(far + near) / (far - near), -
2.0 * far * near / (far - near)],
[0, 0, -1, 0]]).astype(np.float32)
|