| import torch
|
| from einops import rearrange
|
| import numpy as np
|
| import json
|
|
|
| class Camera(object):
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| def __init__(self, c2w):
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| c2w_mat = np.array(c2w).reshape(4, 4)
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| self.c2w_mat = c2w_mat
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| self.w2c_mat = np.linalg.inv(c2w_mat)
|
|
|
|
|
|
|
| def parse_matrix(matrix_str):
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| rows = matrix_str.strip().split('] [')
|
| matrix = []
|
| for row in rows:
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| row = row.replace('[', '').replace(']', '')
|
| matrix.append(list(map(float, row.split())))
|
| return np.array(matrix)
|
|
|
|
|
| def get_relative_pose(cam_params):
|
| abs_w2cs = [cam_param.w2c_mat for cam_param in cam_params]
|
| abs_c2ws = [cam_param.c2w_mat for cam_param in cam_params]
|
|
|
| cam_to_origin = 0
|
| target_cam_c2w = np.array([
|
| [1, 0, 0, 0],
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| [0, 1, 0, -cam_to_origin],
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| [0, 0, 1, 0],
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| [0, 0, 0, 1]
|
| ])
|
| abs2rel = target_cam_c2w @ abs_w2cs[0]
|
| ret_poses = [target_cam_c2w, ] + [abs2rel @ abs_c2w for abs_c2w in abs_c2ws[1:]]
|
| ret_poses = np.array(ret_poses, dtype=np.float32)
|
| return ret_poses
|
|
|
|
|
| def get_camera_embedding(cam_type, num_frames=81):
|
|
|
|
|
| tgt_camera_path = "models/wan/camera_extrinsics.json"
|
| with open(tgt_camera_path, 'r') as file:
|
| cam_data = json.load(file)
|
|
|
| cam_idx = list(range(num_frames))[::4]
|
| traj = [parse_matrix(cam_data[f"frame{idx}"][f"cam{int(cam_type):02d}"]) for idx in cam_idx]
|
| traj = np.stack(traj).transpose(0, 2, 1)
|
| c2ws = []
|
| for c2w in traj:
|
| c2w = c2w[:, [1, 2, 0, 3]]
|
| c2w[:3, 1] *= -1.
|
| c2w[:3, 3] /= 100
|
| c2ws.append(c2w)
|
| tgt_cam_params = [Camera(cam_param) for cam_param in c2ws]
|
| relative_poses = []
|
| for i in range(len(tgt_cam_params)):
|
| relative_pose = get_relative_pose([tgt_cam_params[0], tgt_cam_params[i]])
|
| relative_poses.append(torch.as_tensor(relative_pose)[:,:3,:][1])
|
| pose_embedding = torch.stack(relative_poses, dim=0)
|
| pose_embedding = rearrange(pose_embedding, 'b c d -> b (c d)')
|
| return pose_embedding
|
|
|
