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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 | |