288 lines
10 KiB
Python
288 lines
10 KiB
Python
from abc import ABC, abstractmethod
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from collections import OrderedDict
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import numpy as np
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import math
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from scipy.spatial.transform import Rotation as R
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import torch
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from kiui.cam import orbit_camera
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#{Key: [elevation, azimuth], ...}
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ORBITPOSE_PRESET_DICT = OrderedDict([
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("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]]),
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("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]]),
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("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]]),
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("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]]),
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("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]]),
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("MVDream(4)", [[0.0, 0.0, 0.0, 0.0], [0.0, 90.0, 180.0, -90.0]]),
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("Unique3D(4)", [[0.0, 0.0, 0.0, 0.0], [0.0, 90.0, 180.0, -90.0]]),
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("CharacterGen(4)", [[0.0, 0.0, 0.0, 0.0], [-90.0, 180.0, 90.0, 0.0]]),
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])
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ELEVATION_MIN = -89.999
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ELEVATION_MAX = 89.999
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AZIMUTH_MIN = -180.0
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AZIMUTH_MAX = 180.0
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def dot(x, y):
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if isinstance(x, np.ndarray):
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return np.sum(x * y, -1, keepdims=True)
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else:
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return torch.sum(x * y, -1, keepdim=True)
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def length(x, eps=1e-20):
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if isinstance(x, np.ndarray):
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return np.sqrt(np.maximum(np.sum(x * x, axis=-1, keepdims=True), eps))
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else:
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return torch.sqrt(torch.clamp(dot(x, x), min=eps))
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def safe_normalize(x, eps=1e-20):
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return x / length(x, eps)
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def look_at(campos, target, opengl=True):
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# campos: [N, 3], camera/eye position
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# target: [N, 3], object to look at
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# return: [N, 3, 3], rotation matrix
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if not opengl:
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# camera forward aligns with -z
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forward_vector = safe_normalize(target - campos)
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up_vector = np.array([0, 1, 0], dtype=np.float32)
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right_vector = safe_normalize(np.cross(forward_vector, up_vector))
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up_vector = safe_normalize(np.cross(right_vector, forward_vector))
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else:
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# camera forward aligns with +z
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forward_vector = safe_normalize(campos - target)
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up_vector = np.array([0, 1, 0], dtype=np.float32)
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right_vector = safe_normalize(np.cross(up_vector, forward_vector))
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up_vector = safe_normalize(np.cross(forward_vector, right_vector))
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R = np.stack([right_vector, up_vector, forward_vector], axis=1)
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return R
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def get_look_at_camera_pose(target, target_to_cam_offset, look_distance=0.1, opengl=True):
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"""
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Calculate the pose (cam2world) matrix from target position the camera suppose to look at and offset vector from target to camera
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Args:
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target (NDArray[float32], shape: 3): the target position the camera suppose to look at
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target_to_cam_dir (NDArray[float32], shape: 3): offset direction from target to camera
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look_distance (float, optional): length of offset vector from target to camera.
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Returns:
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NDArray[float32]: shape: (4, 4), pose (cam2world) matrix
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"""
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norm=np.linalg.norm(target_to_cam_offset)
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if norm==0:
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norm=np.finfo(np.float32).eps
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target_to_cam_offset = look_distance * target_to_cam_offset / norm
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campos = target_to_cam_offset + target # [3]
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T = np.eye(4, dtype=np.float32)
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T[:3, :3] = look_at(campos, target, opengl)
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T[:3, 3] = campos
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return T
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class OrbitCamera:
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def __init__(self, W, H, r=2, fovy=60, near=0.01, far=100):
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self.W = W
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self.H = H
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self.radius = r # camera distance from center
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self.fovy = np.deg2rad(fovy) # deg 2 rad
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self.near = near
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self.far = far
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self.center = np.array([0, 0, 0], dtype=np.float32) # look at this point
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self.rot = R.from_matrix(np.eye(3))
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self.up = np.array([0, 1, 0], dtype=np.float32) # need to be normalized!
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@property
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def fovx(self):
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return 2 * np.arctan(np.tan(self.fovy / 2) * self.W / self.H)
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@property
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def campos(self):
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return self.pose[:3, 3]
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# pose (c2w)
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@property
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def pose(self):
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# first move camera to radius
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res = np.eye(4, dtype=np.float32)
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res[2, 3] = self.radius # opengl convention...
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# rotate
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rot = np.eye(4, dtype=np.float32)
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rot[:3, :3] = self.rot.as_matrix()
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res = rot @ res
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# translate
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res[:3, 3] -= self.center
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return res
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# view (w2c)
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@property
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def view(self):
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return np.linalg.inv(self.pose)
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# projection (perspective)
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@property
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def perspective(self):
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y = np.tan(self.fovy / 2)
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aspect = self.W / self.H
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return np.array(
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[
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[1 / (y * aspect), 0, 0, 0],
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[0, -1 / y, 0, 0],
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[
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0,
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0,
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-(self.far + self.near) / (self.far - self.near),
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-(2 * self.far * self.near) / (self.far - self.near),
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],
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[0, 0, -1, 0],
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],
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dtype=np.float32,
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)
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# intrinsics
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@property
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def intrinsics(self):
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focal = self.H / (2 * np.tan(self.fovy / 2))
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return np.array([focal, focal, self.W // 2, self.H // 2], dtype=np.float32)
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@property
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def mvp(self):
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return self.perspective @ np.linalg.inv(self.pose) # [4, 4]
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def orbit(self, dx, dy):
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# rotate along camera up/side axis!
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side = self.rot.as_matrix()[:3, 0]
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rotvec_x = self.up * np.radians(-0.05 * dx)
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rotvec_y = side * np.radians(-0.05 * dy)
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self.rot = R.from_rotvec(rotvec_x) * R.from_rotvec(rotvec_y) * self.rot
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def scale(self, delta):
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self.radius *= 1.1 ** (-delta)
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def pan(self, dx, dy, dz=0):
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# pan in camera coordinate system (careful on the sensitivity!)
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self.center += 0.0005 * self.rot.as_matrix()[:3, :3] @ np.array([-dx, -dy, dz])
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def calculate_fovX(H, W, fovy):
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return 2 * np.arctan(np.tan(fovy / 2) * W / H)
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def get_projection_matrix(znear, zfar, fovX, fovY, z_sign=1.0):
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tanHalfFovY = math.tan((fovY / 2))
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tanHalfFovX = math.tan((fovX / 2))
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P = torch.zeros(4, 4)
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P[0, 0] = 1 / tanHalfFovX
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P[1, 1] = 1 / tanHalfFovY
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P[3, 2] = z_sign
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P[2, 2] = z_sign * zfar / (zfar - znear)
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P[2, 3] = -(zfar * znear) / (zfar - znear)
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return P
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class MiniCam:
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def __init__(self, c2w, width, height, fovy, fovx, znear, zfar, projection_matrix=None):
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# c2w (pose) should be in NeRF convention.
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self.image_width = width
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self.image_height = height
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self.FoVy = fovy
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self.FoVx = fovx
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self.znear = znear
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self.zfar = zfar
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w2c = np.linalg.inv(c2w)
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# rectify...
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w2c[1:3, :3] *= -1
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w2c[:3, 3] *= -1
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self.world_view_transform = torch.tensor(w2c).transpose(0, 1).cuda()
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self.projection_matrix = (
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get_projection_matrix(
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znear=self.znear, zfar=self.zfar, fovX=self.FoVx, fovY=self.FoVy
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)
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.transpose(0, 1)
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.cuda()
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) if projection_matrix is None else projection_matrix
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self.full_proj_transform = self.world_view_transform @ self.projection_matrix
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self.camera_center = -torch.tensor(c2w[:3, 3]).cuda()
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class BaseCameraController(ABC):
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def __init__(self, renderer, cam_size_W, cam_size_H, reference_orbit_camera_fovy, invert_bg_prob=1.0, static_bg=None, device='cuda'):
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self.device = torch.device(device)
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self.renderer = renderer
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self.cam = OrbitCamera(cam_size_W, cam_size_H, fovy=reference_orbit_camera_fovy)
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self.invert_bg_prob = invert_bg_prob
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self.black_bg = torch.tensor([0, 0, 0], dtype=torch.float32, device=self.device)
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self.white_bg = torch.tensor([1, 1, 1], dtype=torch.float32, device=self.device)
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self.static_bg = None if static_bg is None else torch.tensor(static_bg, dtype=torch.float32, device=self.device)
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self.post_init()
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super().__init__()
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def post_init(self):
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# Calls after default initialize at the end of __init__()
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pass
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@abstractmethod
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def get_render_result(self, render_pose, bg_color, **kwargs):
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pass
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def render_at_pose(self, cam_pose, **kwargs):
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radius, elevation, azimuth, center_X, center_Y, center_Z = cam_pose
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orbit_target = np.array([center_X, center_Y, center_Z], dtype=np.float32)
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render_pose = orbit_camera(elevation, azimuth, radius, target=orbit_target)
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if self.static_bg is None:
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bg_color = self.white_bg if np.random.rand() > self.invert_bg_prob else self.black_bg
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else:
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bg_color = self.static_bg
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return self.get_render_result(render_pose, bg_color, **kwargs)
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def render_all_pose(self, all_cam_poses, **kwargs):
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all_rendered_images, all_rendered_masks = [], []
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extra_outputs = {}
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for cam_pose in all_cam_poses:
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out = self.render_at_pose(cam_pose, **kwargs)
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image = out["image"] # [3, H, W] in [0, 1]
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mask = out["alpha"] # [1, H, W] in [0, 1]
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all_rendered_images.append(image)
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all_rendered_masks.append(mask)
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for k in out:
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if k not in extra_outputs:
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extra_outputs[k] = []
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extra_outputs[k].append(out[k])
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for k in extra_outputs:
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extra_outputs[k] = torch.stack(extra_outputs[k], dim=0)
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# [Number of Poses, 3, H, W], [Number of Poses, 1, H, W] both in [0, 1]
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return torch.stack(all_rendered_images, dim=0), torch.stack(all_rendered_masks, dim=0), extra_outputs
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def compose_orbit_camposes(orbit_radius, orbit_elevations, orbit_azimuths, orbit_center_x, orbit_center_y, orbit_center_z):
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orbit_camposes = []
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campose_num = len(orbit_radius)
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for i in range(campose_num):
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orbit_camposes.append([
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orbit_radius[i],
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np.clip(orbit_elevations[i], ELEVATION_MIN, ELEVATION_MAX),
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np.clip(orbit_azimuths[i], AZIMUTH_MIN, AZIMUTH_MAX),
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orbit_center_x[i], orbit_center_y[i], orbit_center_z[i]
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])
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return orbit_camposes |