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#!/usr/bin/env python3
# Copyright (C) 2024-present Naver Corporation. All rights reserved.
# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).
#
# --------------------------------------------------------
# Script to pre-process the arkitscenes dataset.
# Usage:
# python3 datasets_preprocess/preprocess_arkitscenes.py --arkitscenes_dir /path/to/arkitscenes --precomputed_pairs /path/to/arkitscenes_pairs
# --------------------------------------------------------
import os
import json
import os.path as osp
import decimal
import argparse
import math
from bisect import bisect_left
from PIL import Image
import numpy as np
import quaternion
from scipy import interpolate
import cv2
def get_parser():
parser = argparse.ArgumentParser()
parser.add_argument('--arkitscenes_dir', required=True)
parser.add_argument('--precomputed_pairs', required=True)
parser.add_argument('--output_dir', default='data/arkitscenes_processed')
return parser
def value_to_decimal(value, decimal_places):
decimal.getcontext().rounding = decimal.ROUND_HALF_UP # define rounding method
return decimal.Decimal(str(float(value))).quantize(decimal.Decimal('1e-{}'.format(decimal_places)))
def closest(value, sorted_list):
index = bisect_left(sorted_list, value)
if index == 0:
return sorted_list[0]
elif index == len(sorted_list):
return sorted_list[-1]
else:
value_before = sorted_list[index - 1]
value_after = sorted_list[index]
if value_after - value < value - value_before:
return value_after
else:
return value_before
def get_up_vectors(pose_device_to_world):
return np.matmul(pose_device_to_world, np.array([[0.0], [-1.0], [0.0], [0.0]]))
def get_right_vectors(pose_device_to_world):
return np.matmul(pose_device_to_world, np.array([[1.0], [0.0], [0.0], [0.0]]))
def read_traj(traj_path):
quaternions = []
poses = []
timestamps = []
poses_p_to_w = []
with open(traj_path) as f:
traj_lines = f.readlines()
for line in traj_lines:
tokens = line.split()
assert len(tokens) == 7
traj_timestamp = float(tokens[0])
timestamps_decimal_value = value_to_decimal(traj_timestamp, 3)
timestamps.append(float(timestamps_decimal_value)) # for spline interpolation
angle_axis = [float(tokens[1]), float(tokens[2]), float(tokens[3])]
r_w_to_p, _ = cv2.Rodrigues(np.asarray(angle_axis))
t_w_to_p = np.asarray([float(tokens[4]), float(tokens[5]), float(tokens[6])])
pose_w_to_p = np.eye(4)
pose_w_to_p[:3, :3] = r_w_to_p
pose_w_to_p[:3, 3] = t_w_to_p
pose_p_to_w = np.linalg.inv(pose_w_to_p)
r_p_to_w_as_quat = quaternion.from_rotation_matrix(pose_p_to_w[:3, :3])
t_p_to_w = pose_p_to_w[:3, 3]
poses_p_to_w.append(pose_p_to_w)
poses.append(t_p_to_w)
quaternions.append(r_p_to_w_as_quat)
return timestamps, poses, quaternions, poses_p_to_w
def main(rootdir, pairsdir, outdir):
os.makedirs(outdir, exist_ok=True)
subdirs = ['Test', 'Training']
for subdir in subdirs:
if not osp.isdir(osp.join(rootdir, subdir)):
continue
# STEP 1: list all scenes
outsubdir = osp.join(outdir, subdir)
os.makedirs(outsubdir, exist_ok=True)
listfile = osp.join(pairsdir, subdir, 'scene_list.json')
with open(listfile, 'r') as f:
scene_dirs = json.load(f)
valid_scenes = []
for scene_subdir in scene_dirs:
out_scene_subdir = osp.join(outsubdir, scene_subdir)
os.makedirs(out_scene_subdir, exist_ok=True)
scene_dir = osp.join(rootdir, subdir, scene_subdir)
depth_dir = osp.join(scene_dir, 'lowres_depth')
rgb_dir = osp.join(scene_dir, 'vga_wide')
intrinsics_dir = osp.join(scene_dir, 'vga_wide_intrinsics')
traj_path = osp.join(scene_dir, 'lowres_wide.traj')
# STEP 2: read selected_pairs.npz
selected_pairs_path = osp.join(pairsdir, subdir, scene_subdir, 'selected_pairs.npz')
selected_npz = np.load(selected_pairs_path)
selection, pairs = selected_npz['selection'], selected_npz['pairs']
selected_sky_direction_scene = str(selected_npz['sky_direction_scene'][0])
if len(selection) == 0 or len(pairs) == 0:
# not a valid scene
continue
valid_scenes.append(scene_subdir)
# STEP 3: parse the scene and export the list of valid (K, pose, rgb, depth) and convert images
scene_metadata_path = osp.join(out_scene_subdir, 'scene_metadata.npz')
if osp.isfile(scene_metadata_path):
continue
else:
print(f'parsing {scene_subdir}')
# loads traj
timestamps, poses, quaternions, poses_cam_to_world = read_traj(traj_path)
poses = np.array(poses)
quaternions = np.array(quaternions, dtype=np.quaternion)
quaternions = quaternion.unflip_rotors(quaternions)
timestamps = np.array(timestamps)
selected_images = [(basename, basename.split(".png")[0].split("_")[1]) for basename in selection]
timestamps_selected = [float(frame_id) for _, frame_id in selected_images]
sky_direction_scene, trajectories, intrinsics, images = convert_scene_metadata(scene_subdir,
intrinsics_dir,
timestamps,
quaternions,
poses,
poses_cam_to_world,
selected_images,
timestamps_selected)
assert selected_sky_direction_scene == sky_direction_scene
os.makedirs(os.path.join(out_scene_subdir, 'vga_wide'), exist_ok=True)
os.makedirs(os.path.join(out_scene_subdir, 'lowres_depth'), exist_ok=True)
assert isinstance(sky_direction_scene, str)
for basename in images:
img_out = os.path.join(out_scene_subdir, 'vga_wide', basename.replace('.png', '.jpg'))
depth_out = os.path.join(out_scene_subdir, 'lowres_depth', basename)
if osp.isfile(img_out) and osp.isfile(depth_out):
continue
vga_wide_path = osp.join(rgb_dir, basename)
depth_path = osp.join(depth_dir, basename)
img = Image.open(vga_wide_path)
depth = cv2.imread(depth_path, cv2.IMREAD_UNCHANGED)
# rotate the image
if sky_direction_scene == 'RIGHT':
try:
img = img.transpose(Image.Transpose.ROTATE_90)
except Exception:
img = img.transpose(Image.ROTATE_90)
depth = cv2.rotate(depth, cv2.ROTATE_90_COUNTERCLOCKWISE)
elif sky_direction_scene == 'LEFT':
try:
img = img.transpose(Image.Transpose.ROTATE_270)
except Exception:
img = img.transpose(Image.ROTATE_270)
depth = cv2.rotate(depth, cv2.ROTATE_90_CLOCKWISE)
elif sky_direction_scene == 'DOWN':
try:
img = img.transpose(Image.Transpose.ROTATE_180)
except Exception:
img = img.transpose(Image.ROTATE_180)
depth = cv2.rotate(depth, cv2.ROTATE_180)
W, H = img.size
if not osp.isfile(img_out):
img.save(img_out)
depth = cv2.resize(depth, (W, H), interpolation=cv2.INTER_NEAREST_EXACT)
if not osp.isfile(depth_out): # avoid destroying the base dataset when you mess up the paths
cv2.imwrite(depth_out, depth)
# save at the end
np.savez(scene_metadata_path,
trajectories=trajectories,
intrinsics=intrinsics,
images=images,
pairs=pairs)
outlistfile = osp.join(outsubdir, 'scene_list.json')
with open(outlistfile, 'w') as f:
json.dump(valid_scenes, f)
# STEP 5: concat all scene_metadata.npz into a single file
scene_data = {}
for scene_subdir in valid_scenes:
scene_metadata_path = osp.join(outsubdir, scene_subdir, 'scene_metadata.npz')
with np.load(scene_metadata_path) as data:
trajectories = data['trajectories']
intrinsics = data['intrinsics']
images = data['images']
pairs = data['pairs']
scene_data[scene_subdir] = {'trajectories': trajectories,
'intrinsics': intrinsics,
'images': images,
'pairs': pairs}
offset = 0
counts = []
scenes = []
sceneids = []
images = []
intrinsics = []
trajectories = []
pairs = []
for scene_idx, (scene_subdir, data) in enumerate(scene_data.items()):
num_imgs = data['images'].shape[0]
img_pairs = data['pairs']
scenes.append(scene_subdir)
sceneids.extend([scene_idx] * num_imgs)
images.append(data['images'])
K = np.expand_dims(np.eye(3), 0).repeat(num_imgs, 0)
K[:, 0, 0] = [fx for _, _, fx, _, _, _ in data['intrinsics']]
K[:, 1, 1] = [fy for _, _, _, fy, _, _ in data['intrinsics']]
K[:, 0, 2] = [hw for _, _, _, _, hw, _ in data['intrinsics']]
K[:, 1, 2] = [hh for _, _, _, _, _, hh in data['intrinsics']]
intrinsics.append(K)
trajectories.append(data['trajectories'])
# offset pairs
img_pairs[:, 0:2] += offset
pairs.append(img_pairs)
counts.append(offset)
offset += num_imgs
images = np.concatenate(images, axis=0)
intrinsics = np.concatenate(intrinsics, axis=0)
trajectories = np.concatenate(trajectories, axis=0)
pairs = np.concatenate(pairs, axis=0)
np.savez(osp.join(outsubdir, 'all_metadata.npz'),
counts=counts,
scenes=scenes,
sceneids=sceneids,
images=images,
intrinsics=intrinsics,
trajectories=trajectories,
pairs=pairs)
def convert_scene_metadata(scene_subdir, intrinsics_dir,
timestamps, quaternions, poses, poses_cam_to_world,
selected_images, timestamps_selected):
# find scene orientation
sky_direction_scene, rotated_to_cam = find_scene_orientation(poses_cam_to_world)
# find/compute pose for selected timestamps
# most images have a valid timestamp / exact pose associated
timestamps_selected = np.array(timestamps_selected)
spline = interpolate.interp1d(timestamps, poses, kind='linear', axis=0)
interpolated_rotations = quaternion.squad(quaternions, timestamps, timestamps_selected)
interpolated_positions = spline(timestamps_selected)
trajectories = []
intrinsics = []
images = []
for i, (basename, frame_id) in enumerate(selected_images):
intrinsic_fn = osp.join(intrinsics_dir, f"{scene_subdir}_{frame_id}.pincam")
if not osp.exists(intrinsic_fn):
intrinsic_fn = osp.join(intrinsics_dir, f"{scene_subdir}_{float(frame_id) - 0.001:.3f}.pincam")
if not osp.exists(intrinsic_fn):
intrinsic_fn = osp.join(intrinsics_dir, f"{scene_subdir}_{float(frame_id) + 0.001:.3f}.pincam")
assert osp.exists(intrinsic_fn)
w, h, fx, fy, hw, hh = np.loadtxt(intrinsic_fn) # PINHOLE
pose = np.eye(4)
pose[:3, :3] = quaternion.as_rotation_matrix(interpolated_rotations[i])
pose[:3, 3] = interpolated_positions[i]
images.append(basename)
if sky_direction_scene == 'RIGHT' or sky_direction_scene == 'LEFT':
intrinsics.append([h, w, fy, fx, hh, hw]) # swapped intrinsics
else:
intrinsics.append([w, h, fx, fy, hw, hh])
trajectories.append(pose @ rotated_to_cam) # pose_cam_to_world @ rotated_to_cam = rotated(cam) to world
return sky_direction_scene, trajectories, intrinsics, images
def find_scene_orientation(poses_cam_to_world):
if len(poses_cam_to_world) > 0:
up_vector = sum(get_up_vectors(p) for p in poses_cam_to_world) / len(poses_cam_to_world)
right_vector = sum(get_right_vectors(p) for p in poses_cam_to_world) / len(poses_cam_to_world)
up_world = np.array([[0.0], [0.0], [1.0], [0.0]])
else:
up_vector = np.array([[0.0], [-1.0], [0.0], [0.0]])
right_vector = np.array([[1.0], [0.0], [0.0], [0.0]])
up_world = np.array([[0.0], [0.0], [1.0], [0.0]])
# value between 0, 180
device_up_to_world_up_angle = np.arccos(np.clip(np.dot(np.transpose(up_world),
up_vector), -1.0, 1.0)).item() * 180.0 / np.pi
device_right_to_world_up_angle = np.arccos(np.clip(np.dot(np.transpose(up_world),
right_vector), -1.0, 1.0)).item() * 180.0 / np.pi
up_closest_to_90 = abs(device_up_to_world_up_angle - 90.0) < abs(device_right_to_world_up_angle - 90.0)
if up_closest_to_90:
assert abs(device_up_to_world_up_angle - 90.0) < 45.0
# LEFT
if device_right_to_world_up_angle > 90.0:
sky_direction_scene = 'LEFT'
cam_to_rotated_q = quaternion.from_rotation_vector([0.0, 0.0, math.pi / 2.0])
else:
# note that in metadata.csv RIGHT does not exist, but again it's not accurate...
# well, turns out there are scenes oriented like this
# for example Training/41124801
sky_direction_scene = 'RIGHT'
cam_to_rotated_q = quaternion.from_rotation_vector([0.0, 0.0, -math.pi / 2.0])
else:
# right is close to 90
assert abs(device_right_to_world_up_angle - 90.0) < 45.0
if device_up_to_world_up_angle > 90.0:
sky_direction_scene = 'DOWN'
cam_to_rotated_q = quaternion.from_rotation_vector([0.0, 0.0, math.pi])
else:
sky_direction_scene = 'UP'
cam_to_rotated_q = quaternion.quaternion(1, 0, 0, 0)
cam_to_rotated = np.eye(4)
cam_to_rotated[:3, :3] = quaternion.as_rotation_matrix(cam_to_rotated_q)
rotated_to_cam = np.linalg.inv(cam_to_rotated)
return sky_direction_scene, rotated_to_cam
if __name__ == '__main__':
parser = get_parser()
args = parser.parse_args()
main(args.arkitscenes_dir, args.precomputed_pairs, args.output_dir)
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