|
|
""" |
|
|
https://github.com/google/dynibar/blob/main/ibrnet/data_loaders/llff_data_utils.py |
|
|
""" |
|
|
|
|
|
import numpy as np |
|
|
import cv2 |
|
|
|
|
|
|
|
|
def render_stabilization_path(poses, k_size=45): |
|
|
"""Rendering stablizaed camera path.""" |
|
|
|
|
|
|
|
|
num_frames = poses.shape[0] |
|
|
output_poses = [] |
|
|
|
|
|
input_poses = [] |
|
|
|
|
|
for i in range(num_frames): |
|
|
input_poses.append( |
|
|
np.concatenate( |
|
|
[poses[i, :3, 0:1], poses[i, :3, 1:2], poses[i, :3, 3:4]], axis=-1 |
|
|
) |
|
|
) |
|
|
|
|
|
input_poses = np.array(input_poses) |
|
|
|
|
|
gaussian_kernel = cv2.getGaussianKernel(ksize=k_size, sigma=-1) |
|
|
output_r1 = cv2.filter2D(input_poses[:, :, 0], -1, gaussian_kernel) |
|
|
output_r2 = cv2.filter2D(input_poses[:, :, 1], -1, gaussian_kernel) |
|
|
|
|
|
output_r1 = output_r1 / np.linalg.norm(output_r1, axis=-1, keepdims=True) |
|
|
output_r2 = output_r2 / np.linalg.norm(output_r2, axis=-1, keepdims=True) |
|
|
|
|
|
output_t = cv2.filter2D(input_poses[:, :, 2], -1, gaussian_kernel) |
|
|
|
|
|
for i in range(num_frames): |
|
|
output_r3 = np.cross(output_r1[i], output_r2[i]) |
|
|
|
|
|
render_pose = np.concatenate( |
|
|
[ |
|
|
output_r1[i, :, None], |
|
|
output_r2[i, :, None], |
|
|
output_r3[:, None], |
|
|
output_t[i, :, None], |
|
|
], |
|
|
axis=-1, |
|
|
) |
|
|
|
|
|
output_poses.append(render_pose[:3, :]) |
|
|
|
|
|
return output_poses |
|
|
|
