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import numpy as np
import torch
import copy
from utils.commons.tensor_utils import convert_to_tensor, convert_to_np
from deep_3drecon.deep_3drecon_models.bfm import ParametricFaceModel
def _fix_intrinsics(intrinsics):
"""
intrinsics: [3,3], not batch-wise
"""
# unnormalized normalized
# [[ f_x, s=0, x_0] [[ f_x/size_x, s=0, x_0/size_x=0.5]
# [ 0, f_y, y_0] -> [ 0, f_y/size_y, y_0/size_y=0.5]
# [ 0, 0, 1 ]] [ 0, 0, 1 ]]
intrinsics = np.array(intrinsics).copy()
assert intrinsics.shape == (3, 3), intrinsics
intrinsics[0,0] = 2985.29/700
intrinsics[1,1] = 2985.29/700
intrinsics[0,2] = 1/2
intrinsics[1,2] = 1/2
assert intrinsics[0,1] == 0
assert intrinsics[2,2] == 1
assert intrinsics[1,0] == 0
assert intrinsics[2,0] == 0
assert intrinsics[2,1] == 0
return intrinsics
# Used in original submission
def _fix_pose_orig(pose):
"""
pose: [4,4], not batch-wise
"""
pose = np.array(pose).copy()
location = pose[:3, 3]
radius = np.linalg.norm(location)
pose[:3, 3] = pose[:3, 3]/radius * 2.7
return pose
def get_eg3d_convention_camera_pose_intrinsic(item):
"""
item: a dict during binarize
"""
if item['euler'].ndim == 1:
angle = convert_to_tensor(copy.copy(item['euler']))
trans = copy.deepcopy(item['trans'])
# handle the difference of euler axis between eg3d and ours
# see data_gen/process_ffhq_for_eg3d/transplant_eg3d_ckpt_into_our_convention.ipynb
# angle += torch.tensor([0, 3.1415926535, 3.1415926535], device=angle.device)
R = ParametricFaceModel.compute_rotation(angle.unsqueeze(0))[0].cpu().numpy()
trans[2] += -10
c = -np.dot(R, trans)
pose = np.eye(4)
pose[:3,:3] = R
c *= 0.27 # normalize camera radius
c[1] += 0.006 # additional offset used in submission
c[2] += 0.161 # additional offset used in submission
pose[0,3] = c[0]
pose[1,3] = c[1]
pose[2,3] = c[2]
focal = 2985.29 # = 1015*1024/224*(300/466.285),
# todo： 如果修改了fit 3dmm阶段的camera intrinsic，这里也要跟着改
pp = 512#112
w = 1024#224
h = 1024#224
K = np.eye(3)
K[0][0] = focal
K[1][1] = focal
K[0][2] = w/2.0
K[1][2] = h/2.0
convention_K = _fix_intrinsics(K)
Rot = np.eye(3)
Rot[0, 0] = 1
Rot[1, 1] = -1
Rot[2, 2] = -1
pose[:3, :3] = np.dot(pose[:3, :3], Rot) # permute axes
convention_pose = _fix_pose_orig(pose)
item['c2w'] = pose
item['convention_c2w'] = convention_pose
item['intrinsics'] = convention_K
return item
else:
num_samples = len(item['euler'])
eulers_all = convert_to_tensor(copy.deepcopy(item['euler'])) # [B, 3]
trans_all = copy.deepcopy(item['trans']) # [B, 3]
# handle the difference of euler axis between eg3d and ours
# see data_gen/process_ffhq_for_eg3d/transplant_eg3d_ckpt_into_our_convention.ipynb
# eulers_all += torch.tensor([0, 3.1415926535, 3.1415926535], device=eulers_all.device).unsqueeze(0).repeat([eulers_all.shape[0],1])
intrinsics = []
poses = []
convention_poses = []
for i in range(num_samples):
angle = eulers_all[i]
trans = trans_all[i]
R = ParametricFaceModel.compute_rotation(angle.unsqueeze(0))[0].cpu().numpy()
trans[2] += -10
c = -np.dot(R, trans)
pose = np.eye(4)
pose[:3,:3] = R
c *= 0.27 # normalize camera radius
c[1] += 0.006 # additional offset used in submission
c[2] += 0.161 # additional offset used in submission
pose[0,3] = c[0]
pose[1,3] = c[1]
pose[2,3] = c[2]
focal = 2985.29 # = 1015*1024/224*(300/466.285),
# todo： 如果修改了fit 3dmm阶段的camera intrinsic，这里也要跟着改
pp = 512#112
w = 1024#224
h = 1024#224
K = np.eye(3)
K[0][0] = focal
K[1][1] = focal
K[0][2] = w/2.0
K[1][2] = h/2.0
convention_K = _fix_intrinsics(K)
intrinsics.append(convention_K)
Rot = np.eye(3)
Rot[0, 0] = 1
Rot[1, 1] = -1
Rot[2, 2] = -1
pose[:3, :3] = np.dot(pose[:3, :3], Rot)
convention_pose = _fix_pose_orig(pose)
convention_poses.append(convention_pose)
poses.append(pose)
intrinsics = np.stack(intrinsics) # [B, 3, 3]
poses = np.stack(poses) # [B, 4, 4]
convention_poses = np.stack(convention_poses) # [B, 4, 4]
item['intrinsics'] = intrinsics
item['c2w'] = poses
item['convention_c2w'] = convention_poses
return item
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