"""

def parse_intrinsics(filepath, trgt_sidelength, invert_y=False):

# 解析相机内参

with open(filepath, 'r') as file:

f, cx, cy = list(map(float, file.readline().split()))[:3]

grid_barycenter = np.array(list(map(float, file.readline().split())))

near_plane = float(file.readline())

scale = float(file.readline())

height, width = map(float, file.readline().split())

try:

world2cam_poses = int(file.readline())

except ValueError:

world2cam_poses = None

if world2cam_poses is None:

world2cam_poses = False

world2cam_poses = bool(world2cam_poses)

print(cx,cy,f,height,width)

cx = cx / width * trgt_sidelength

cy = cy / height * trgt_sidelength

f = trgt_sidelength / height * f

fx = f

if invert_y:

fy = -f

else:

fy = f

# 构造内参矩阵

full_intrinsic = np.array([[fx, 0., cx, 0.],

[0., fy, cy, 0],

[0., 0, 1, 0],

[0, 0, 0, 1]])

return full_intrinsic, grid_barycenter, scale, near_plane, world2cam_poses

def load_pose(filename):

# 加载相机外参

assert os.path.isfile(filename)

nums = open(filename).read().split()

return np.array([float(x) for x in nums]).reshape([4,4]).astype(np.float32)

H = 512

W = 512

deepvoxels_base = '{}/train/{}/'.format(basedir, scene)

full_intrinsic, grid_barycenter, scale, near_plane, world2cam_poses = parse_intrinsics(os.path.join(deepvoxels_base, 'intrinsics.txt'), H)

print(full_intrinsic, grid_barycenter, scale, near_plane, world2cam_poses)

focal = full_intrinsic[0,0]

print(H, W, focal)

def dir2poses(posedir):

poses = np.stack([load_pose(os.path.join(posedir, f)) for f in sorted(os.listdir(posedir)) if f.endswith('txt')], 0)

transf = np.array([

[1,0,0,0],

[0,-1,0,0],

[0,0,-1,0],

[0,0,0,1.],

])

poses = poses @ transf

poses = poses[:,:3,:4].astype(np.float32)

return poses

posedir = os.path.join(deepvoxels_base, 'pose')

poses = dir2poses(posedir)

testposes = dir2poses('{}/test/{}/pose'.format(basedir, scene))

testposes = testposes[::testskip]

valposes = dir2poses('{}/validation/{}/pose'.format(basedir, scene))

valposes = valposes[::testskip]

# 加载train图像

imgfiles = [f for f in sorted(os.listdir(os.path.join(deepvoxels_base, 'rgb'))) if f.endswith('png')]

imgs = np.stack([imageio.imread(os.path.join(deepvoxels_base, 'rgb', f))/255. for f in imgfiles], 0).astype(np.float32)

# 加载test图像

testimgd = '{}/test/{}/rgb'.format(basedir, scene)

imgfiles = [f for f in sorted(os.listdir(testimgd)) if f.endswith('png')]

testimgs = np.stack([imageio.imread(os.path.join(testimgd, f))/255. for f in imgfiles[::testskip]], 0).astype(np.float32)

# 加载validation图像

valimgd = '{}/validation/{}/rgb'.format(basedir, scene)

imgfiles = [f for f in sorted(os.listdir(valimgd)) if f.endswith('png')]

valimgs = np.stack([imageio.imread(os.path.join(valimgd, f))/255. for f in imgfiles[::testskip]], 0).astype(np.float32)

# 合并加载图像信息、位姿、数量标记等

all_imgs = [imgs, valimgs, testimgs]

counts = [0] + [x.shape[0] for x in all_imgs]

counts = np.cumsum(counts)

i_split = [np.arange(counts[i], counts[i+1]) for i in range(3)]

imgs = np.concatenate(all_imgs, 0)

poses = np.concatenate([poses, valposes, testposes], 0)

render_poses = testposes

print(poses.shape, imgs.shape)