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Correct poses

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Matt Tancik 2022-06-28 10:45:54 -07:00 committed by Matthew Tancik
parent f71c2c21f2
commit d160fa87cb
1 changed files with 18 additions and 14 deletions
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32
pyrad/data/format/mipnerf_360.py
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@ -31,11 +31,13 @@ def load_mipnerf_360_data(
split: str = "train",
val_skip: int = 8,
auto_scale: bool = True,
aabb_scale: float = 1.0,
aabb_scale: float = 5.0,
) -> DatasetInputs:
"""Processes mipnerf 360 data.
Example data can be downloaded from https://jonbarron.info/mipnerf360/.
TODO: code currently assumes images were previously downscaled.
Args:
data_directory (str): Location of data
downscale_factor (int, optional): How much to downscale images. Must be a factor of 2. Defaults to 1.
@ -65,19 +67,9 @@ def load_mipnerf_360_data(
image_filenames = sorted(image_filenames)
num_images = len(image_filenames)
poses_bounds = np.load(os.path.join(basedir, "poses_bounds.npy"))
poses = poses_bounds[:, :-2].reshape([-1, 3, 5]).astype(np.float32)
bounds = poses_bounds[:, -2:].transpose([1, 0])
# Scale factor used in mipnerf
if auto_scale:
scale_factor = 1 / (np.min(poses_bounds) * 0.75)
poses[:, :3, 3] *= scale_factor
bounds *= scale_factor
# Center poses
center = poses[:, :3, 3].mean(0)
poses[:, :3, :3] -= center
poses_data = np.load(os.path.join(basedir, "poses_bounds.npy"))
poses = poses_data[:, :-2].reshape([-1, 3, 5]).astype(np.float32)
bounds = poses_data[:, -2:].transpose([1, 0])
if num_images != poses.shape[0]:
raise RuntimeError(f"Different number of images ({num_images}), and poses ({poses.shape[0]})")
@ -95,6 +87,18 @@ def load_mipnerf_360_data(
poses[:, :2, 4] = np.array([image_height, image_width])
poses[:, 2, 4] = poses[:, 2, 4] * 1.0 / downscale_factor
# Reorder pose to match our convention
poses = np.concatenate([poses[:, :, 1:2], -poses[:, :, 0:1], poses[:, :, 2:]], axis=-1)
# Scale factor used in mipnerf
if auto_scale:
scale_factor = 1 / (np.min(bounds) * 0.75)
poses[:, :3, 3] *= scale_factor
bounds *= scale_factor
# Center poses
poses[:, :3, 3] = poses[:, :3, 3] - np.mean(poses[:, :3, :], axis=0)[:, 3]
focal_length = poses[0, -1, -1]
cx = image_width / 2.0