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import imageio
import numpy as np
import torch
from PIL import Image
from scipy.interpolate import UnivariateSpline
from scipy.interpolate import interp1d
def load_video(video_path):
reader = imageio.get_reader(video_path)
total_frames = reader.count_frames()
frames = []
for i in range(total_frames):
frame = reader.get_data(i)
frames.append(Image.fromarray(frame))
reader.close()
return frames
def points_padding(points):
padding = torch.ones_like(points)[..., 0:1]
points = torch.cat([points, padding], dim=-1)
return points
def np_points_padding(points):
padding = np.ones_like(points)[..., 0:1]
points = np.concatenate([points, padding], axis=-1)
return points
def txt_interpolation(input_list, n, mode='smooth'):
x = np.linspace(0, 1, len(input_list))
if mode == 'smooth':
f = UnivariateSpline(x, input_list, k=3)
elif mode == 'linear':
f = interp1d(x, input_list)
else:
raise KeyError(f"Invalid txt interpolation mode: {mode}")
xnew = np.linspace(0, 1, n)
ynew = f(xnew)
return ynew
def traj_map(traj_type):
# pre-defined trajectories
if traj_type == "free1": # Zoom out and rotate to the upper left
cam_traj = "free"
x_offset = 0.0
y_offset = 0.0
z_offset = 0.0
d_theta = -15.0
d_phi = 45.0
d_r = 1.6
elif traj_type == "free2": # Rotate to the right horizontally
cam_traj = "free"
x_offset = -0.05
y_offset = 0.0
z_offset = 0.0
d_theta = 0.0
d_phi = -60.0
d_r = 1.0
elif traj_type == "free3": # Move back to the left
cam_traj = "free"
x_offset = -0.25
y_offset = 0.0
z_offset = 0.0
d_theta = 0.0
d_phi = 0.0
d_r = 1.7
elif traj_type == "free4": # Rotate and approach to the upper right
cam_traj = "free"
x_offset = 0.0
y_offset = 0.0
z_offset = 0.0
d_theta = -15.0
d_phi = -60.0
d_r = 0.75
elif traj_type == "free5": # Large-angle camera movement to the upper right
cam_traj = "free"
x_offset = 0.0
y_offset = 0.0
z_offset = 0.0
d_theta = -15.0
d_phi = -120.0
d_r = 1.6
elif traj_type == "swing1": # Swing shot 1
cam_traj = "swing1"
x_offset = 0.0
y_offset = 0.0
z_offset = 0.0
d_theta = 0.0
d_phi = 0.0
d_r = 1.0
elif traj_type == "swing2": # Swing shot 2
cam_traj = "swing2"
x_offset = 0.0
y_offset = 0.0
z_offset = 0.0
d_theta = 0.0
d_phi = 0.0
d_r = 1.0
elif traj_type == "orbit": # 360-degree counterclockwise rotation
cam_traj = "free"
x_offset = 0.0
y_offset = 0.0
z_offset = 0.0
d_theta = 0.0
d_phi = -360.0
d_r = 1.0
else:
raise NotImplementedError
return cam_traj, x_offset, y_offset, z_offset, d_theta, d_phi, d_r
def set_initial_camera(start_elevation, radius):
c2w_0 = torch.tensor([[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, 1, -radius],
[0, 0, 0, 1]], dtype=torch.float32)
elevation_rad = np.deg2rad(start_elevation)
R_elevation = torch.tensor([[1, 0, 0, 0],
[0, np.cos(-elevation_rad), -np.sin(-elevation_rad), 0],
[0, np.sin(-elevation_rad), np.cos(-elevation_rad), 0],
[0, 0, 0, 1]], dtype=torch.float32)
c2w_0 = R_elevation @ c2w_0
w2c_0 = c2w_0.inverse()
return w2c_0, c2w_0
def build_cameras(cam_traj, w2c_0, c2w_0, intrinsic, nframe, focal_length,
d_theta, d_phi, d_r, radius, x_offset, y_offset, z_offset):
# build camera viewpoints according to d_theta，d_phi, d_r
# return: w2cs:[V,4,4], c2ws:[V,4,4], intrinsic:[V,3,3]
if intrinsic.ndim == 2:
intrinsic = intrinsic[None].repeat(nframe, 1, 1)
c2ws = [c2w_0]
w2cs = [w2c_0]
d_thetas, d_phis, d_rs = [], [], []
x_offsets, y_offsets, z_offsets = [], [], []
if cam_traj == "free":
for i in range(nframe - 1):
coef = (i + 1) / (nframe - 1)
d_thetas.append(d_theta * coef)
d_phis.append(d_phi * coef)
d_rs.append(coef * d_r + (1 - coef) * 1.0)
x_offsets.append(radius * x_offset * ((i + 1) / nframe))
y_offsets.append(radius * y_offset * ((i + 1) / nframe))
z_offsets.append(radius * z_offset * ((i + 1) / nframe))
elif cam_traj == "swing1":
phis__ = [0, -5, -25, -30, -20, -8, 0]
thetas__ = [0, -8, -12, -20, -17, -12, -5, -2, 1, 5, 3, 1, 0]
rs__ = [0, 0.2]
d_phis = txt_interpolation(phis__, nframe, mode='smooth')
d_phis[0] = phis__[0]
d_phis[-1] = phis__[-1]
d_thetas = txt_interpolation(thetas__, nframe, mode='smooth')
d_thetas[0] = thetas__[0]
d_thetas[-1] = thetas__[-1]
d_rs = txt_interpolation(rs__, nframe, mode='linear')
d_rs = 1.0 + d_rs
elif cam_traj == "swing2":
phis__ = [0, 5, 25, 30, 20, 10, 0]
thetas__ = [0, -5, -14, -11, 0, 1, 5, 3, 0]
rs__ = [0, -0.03, -0.1, -0.2, -0.17, -0.1, 0]
d_phis = txt_interpolation(phis__, nframe, mode='smooth')
d_phis[0] = phis__[0]
d_phis[-1] = phis__[-1]
d_thetas = txt_interpolation(thetas__, nframe, mode='smooth')
d_thetas[0] = thetas__[0]
d_thetas[-1] = thetas__[-1]
d_rs = txt_interpolation(rs__, nframe, mode='smooth')
d_rs = 1.0 + d_rs
else:
raise NotImplementedError("Unknown trajectory type...")
for i in range(nframe - 1):
d_theta_rad = np.deg2rad(d_thetas[i])
R_theta = torch.tensor([[1, 0, 0, 0],
[0, np.cos(d_theta_rad), -np.sin(d_theta_rad), 0],
[0, np.sin(d_theta_rad), np.cos(d_theta_rad), 0],
[0, 0, 0, 1]], dtype=torch.float32)
d_phi_rad = np.deg2rad(d_phis[i])
R_phi = torch.tensor([[np.cos(d_phi_rad), 0, np.sin(d_phi_rad), 0],
[0, 1, 0, 0],
[-np.sin(d_phi_rad), 0, np.cos(d_phi_rad), 0],
[0, 0, 0, 1]], dtype=torch.float32)
c2w_1 = R_phi @ R_theta @ c2w_0
if i < len(x_offsets) and i < len(y_offsets) and i < len(z_offsets):
c2w_1[:3, -1] += torch.tensor([x_offsets[i], y_offsets[i], z_offsets[i]])
c2w_1[:3, -1] *= d_rs[i]
w2c_1 = c2w_1.inverse()
c2ws.append(c2w_1)
w2cs.append(w2c_1)
intrinsic[i + 1, :2, :2] = intrinsic[i + 1, :2, :2] * focal_length * ((i + 1) / nframe) + \
intrinsic[i + 1, :2, :2] * ((nframe - (i + 1)) / nframe)
w2cs = torch.stack(w2cs, dim=0)
c2ws = torch.stack(c2ws, dim=0)
return w2cs, c2ws, intrinsic
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