File size: 10,408 Bytes
82ea528 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 |
from abc import ABC, abstractmethod
from collections import OrderedDict
import numpy as np
import math
from scipy.spatial.transform import Rotation as R
import torch
from kiui.cam import orbit_camera
#{Key: [elevation, azimuth], ...}
ORBITPOSE_PRESET_DICT = OrderedDict([
("Custom", [[0.0, 90.0, 0.0, 0.0, -90.0, 0.0], [-90.0, 0.0, 180.0, 90.0, 0.0, 0.0]]),
("CRM(6)", [[0.0, 90.0, 0.0, 0.0, -90.0, 0.0], [-90.0, 0.0, 180.0, 90.0, 0.0, 0.0]]),
("Wonder3D(6)", [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 45.0, 90.0, 180.0, -90.0, -45.0]]),
("Zero123Plus(6)", [[-20.0, 10.0, -20.0, 10.0, -20.0, 10.0], [30.0, 90.0, 150.0, -150.0, -90.0, -30.0]]),
("Era3D(6)", [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 45.0, 90.0, 180.0, -90.0, -45.0]]),
("MVDream(4)", [[0.0, 0.0, 0.0, 0.0], [0.0, 90.0, 180.0, -90.0]]),
("Unique3D(4)", [[0.0, 0.0, 0.0, 0.0], [0.0, 90.0, 180.0, -90.0]]),
("CharacterGen(4)", [[0.0, 0.0, 0.0, 0.0], [-90.0, 180.0, 90.0, 0.0]]),
])
ELEVATION_MIN = -89.999
ELEVATION_MAX = 89.999
AZIMUTH_MIN = -180.0
AZIMUTH_MAX = 180.0
def dot(x, y):
if isinstance(x, np.ndarray):
return np.sum(x * y, -1, keepdims=True)
else:
return torch.sum(x * y, -1, keepdim=True)
def length(x, eps=1e-20):
if isinstance(x, np.ndarray):
return np.sqrt(np.maximum(np.sum(x * x, axis=-1, keepdims=True), eps))
else:
return torch.sqrt(torch.clamp(dot(x, x), min=eps))
def safe_normalize(x, eps=1e-20):
return x / length(x, eps)
def look_at(campos, target, opengl=True):
# campos: [N, 3], camera/eye position
# target: [N, 3], object to look at
# return: [N, 3, 3], rotation matrix
if not opengl:
# camera forward aligns with -z
forward_vector = safe_normalize(target - campos)
up_vector = np.array([0, 1, 0], dtype=np.float32)
right_vector = safe_normalize(np.cross(forward_vector, up_vector))
up_vector = safe_normalize(np.cross(right_vector, forward_vector))
else:
# camera forward aligns with +z
forward_vector = safe_normalize(campos - target)
up_vector = np.array([0, 1, 0], dtype=np.float32)
right_vector = safe_normalize(np.cross(up_vector, forward_vector))
up_vector = safe_normalize(np.cross(forward_vector, right_vector))
R = np.stack([right_vector, up_vector, forward_vector], axis=1)
return R
def get_look_at_camera_pose(target, target_to_cam_offset, look_distance=0.1, opengl=True):
"""
Calculate the pose (cam2world) matrix from target position the camera suppose to look at and offset vector from target to camera
Args:
target (NDArray[float32], shape: 3): the target position the camera suppose to look at
target_to_cam_dir (NDArray[float32], shape: 3): offset direction from target to camera
look_distance (float, optional): length of offset vector from target to camera.
Returns:
NDArray[float32]: shape: (4, 4), pose (cam2world) matrix
"""
norm=np.linalg.norm(target_to_cam_offset)
if norm==0:
norm=np.finfo(np.float32).eps
target_to_cam_offset = look_distance * target_to_cam_offset / norm
campos = target_to_cam_offset + target # [3]
T = np.eye(4, dtype=np.float32)
T[:3, :3] = look_at(campos, target, opengl)
T[:3, 3] = campos
return T
class OrbitCamera:
def __init__(self, W, H, r=2, fovy=60, near=0.01, far=100):
self.W = W
self.H = H
self.radius = r # camera distance from center
self.fovy = np.deg2rad(fovy) # deg 2 rad
self.near = near
self.far = far
self.center = np.array([0, 0, 0], dtype=np.float32) # look at this point
self.rot = R.from_matrix(np.eye(3))
self.up = np.array([0, 1, 0], dtype=np.float32) # need to be normalized!
@property
def fovx(self):
return 2 * np.arctan(np.tan(self.fovy / 2) * self.W / self.H)
@property
def campos(self):
return self.pose[:3, 3]
# pose (c2w)
@property
def pose(self):
# first move camera to radius
res = np.eye(4, dtype=np.float32)
res[2, 3] = self.radius # opengl convention...
# rotate
rot = np.eye(4, dtype=np.float32)
rot[:3, :3] = self.rot.as_matrix()
res = rot @ res
# translate
res[:3, 3] -= self.center
return res
# view (w2c)
@property
def view(self):
return np.linalg.inv(self.pose)
# projection (perspective)
@property
def perspective(self):
y = np.tan(self.fovy / 2)
aspect = self.W / self.H
return np.array(
[
[1 / (y * aspect), 0, 0, 0],
[0, -1 / y, 0, 0],
[
0,
0,
-(self.far + self.near) / (self.far - self.near),
-(2 * self.far * self.near) / (self.far - self.near),
],
[0, 0, -1, 0],
],
dtype=np.float32,
)
# intrinsics
@property
def intrinsics(self):
focal = self.H / (2 * np.tan(self.fovy / 2))
return np.array([focal, focal, self.W // 2, self.H // 2], dtype=np.float32)
@property
def mvp(self):
return self.perspective @ np.linalg.inv(self.pose) # [4, 4]
def orbit(self, dx, dy):
# rotate along camera up/side axis!
side = self.rot.as_matrix()[:3, 0]
rotvec_x = self.up * np.radians(-0.05 * dx)
rotvec_y = side * np.radians(-0.05 * dy)
self.rot = R.from_rotvec(rotvec_x) * R.from_rotvec(rotvec_y) * self.rot
def scale(self, delta):
self.radius *= 1.1 ** (-delta)
def pan(self, dx, dy, dz=0):
# pan in camera coordinate system (careful on the sensitivity!)
self.center += 0.0005 * self.rot.as_matrix()[:3, :3] @ np.array([-dx, -dy, dz])
def calculate_fovX(H, W, fovy):
return 2 * np.arctan(np.tan(fovy / 2) * W / H)
def get_projection_matrix(znear, zfar, fovX, fovY, z_sign=1.0):
tanHalfFovY = math.tan((fovY / 2))
tanHalfFovX = math.tan((fovX / 2))
P = torch.zeros(4, 4)
P[0, 0] = 1 / tanHalfFovX
P[1, 1] = 1 / tanHalfFovY
P[3, 2] = z_sign
P[2, 2] = z_sign * zfar / (zfar - znear)
P[2, 3] = -(zfar * znear) / (zfar - znear)
return P
class MiniCam:
def __init__(self, c2w, width, height, fovy, fovx, znear, zfar, projection_matrix=None):
# c2w (pose) should be in NeRF convention.
self.image_width = width
self.image_height = height
self.FoVy = fovy
self.FoVx = fovx
self.znear = znear
self.zfar = zfar
w2c = np.linalg.inv(c2w)
# rectify...
w2c[1:3, :3] *= -1
w2c[:3, 3] *= -1
self.world_view_transform = torch.tensor(w2c).transpose(0, 1).cuda()
self.projection_matrix = (
get_projection_matrix(
znear=self.znear, zfar=self.zfar, fovX=self.FoVx, fovY=self.FoVy
)
.transpose(0, 1)
.cuda()
) if projection_matrix is None else projection_matrix
self.full_proj_transform = self.world_view_transform @ self.projection_matrix
self.camera_center = -torch.tensor(c2w[:3, 3]).cuda()
class BaseCameraController(ABC):
def __init__(self, renderer, cam_size_W, cam_size_H, reference_orbit_camera_fovy, invert_bg_prob=1.0, static_bg=None, device='cuda'):
self.device = torch.device(device)
self.renderer = renderer
self.cam = OrbitCamera(cam_size_W, cam_size_H, fovy=reference_orbit_camera_fovy)
self.invert_bg_prob = invert_bg_prob
self.black_bg = torch.tensor([0, 0, 0], dtype=torch.float32, device=self.device)
self.white_bg = torch.tensor([1, 1, 1], dtype=torch.float32, device=self.device)
self.static_bg = None if static_bg is None else torch.tensor(static_bg, dtype=torch.float32, device=self.device)
self.post_init()
super().__init__()
def post_init(self):
# Calls after default initialize at the end of __init__()
pass
@abstractmethod
def get_render_result(self, render_pose, bg_color, **kwargs):
pass
def render_at_pose(self, cam_pose, **kwargs):
radius, elevation, azimuth, center_X, center_Y, center_Z = cam_pose
orbit_target = np.array([center_X, center_Y, center_Z], dtype=np.float32)
render_pose = orbit_camera(elevation, azimuth, radius, target=orbit_target)
if self.static_bg is None:
bg_color = self.white_bg if np.random.rand() > self.invert_bg_prob else self.black_bg
else:
bg_color = self.static_bg
return self.get_render_result(render_pose, bg_color, **kwargs)
def render_all_pose(self, all_cam_poses, **kwargs):
all_rendered_images, all_rendered_masks = [], []
extra_outputs = {}
for cam_pose in all_cam_poses:
out = self.render_at_pose(cam_pose, **kwargs)
image = out["image"] # [3, H, W] in [0, 1]
mask = out["alpha"] # [1, H, W] in [0, 1]
all_rendered_images.append(image)
all_rendered_masks.append(mask)
for k in out:
if k not in extra_outputs:
extra_outputs[k] = []
extra_outputs[k].append(out[k])
for k in extra_outputs:
extra_outputs[k] = torch.stack(extra_outputs[k], dim=0)
# [Number of Poses, 3, H, W], [Number of Poses, 1, H, W] both in [0, 1]
return torch.stack(all_rendered_images, dim=0), torch.stack(all_rendered_masks, dim=0), extra_outputs
def compose_orbit_camposes(orbit_radius, orbit_elevations, orbit_azimuths, orbit_center_x, orbit_center_y, orbit_center_z):
orbit_camposes = []
campose_num = len(orbit_radius)
for i in range(campose_num):
orbit_camposes.append([
orbit_radius[i],
np.clip(orbit_elevations[i], ELEVATION_MIN, ELEVATION_MAX),
np.clip(orbit_azimuths[i], AZIMUTH_MIN, AZIMUTH_MAX),
orbit_center_x[i], orbit_center_y[i], orbit_center_z[i]
])
return orbit_camposes |