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import os
import random
from PIL import Image
import cv2
import h5py
import numpy as np
import torch
from torch.utils.data import Dataset, DataLoader, ConcatDataset
import torchvision.transforms.functional as tvf
import kornia.augmentation as K
import os.path as osp
import matplotlib.pyplot as plt
import roma
from roma.utils import get_depth_tuple_transform_ops, get_tuple_transform_ops
from roma.utils.transforms import GeometricSequential
from tqdm import tqdm
class ScanNetScene:
def __init__(
self,
data_root,
scene_info,
ht=384,
wt=512,
min_overlap=0.0,
shake_t=0,
rot_prob=0.0,
use_horizontal_flip_aug=False,
) -> None:
self.scene_root = osp.join(data_root, "scans", "scans_train")
self.data_names = scene_info["name"]
self.overlaps = scene_info["score"]
# Only sample 10s
valid = (self.data_names[:, -2:] % 10).sum(axis=-1) == 0
self.overlaps = self.overlaps[valid]
self.data_names = self.data_names[valid]
if len(self.data_names) > 10000:
pairinds = np.random.choice(
np.arange(0, len(self.data_names)), 10000, replace=False
)
self.data_names = self.data_names[pairinds]
self.overlaps = self.overlaps[pairinds]
self.im_transform_ops = get_tuple_transform_ops(resize=(ht, wt), normalize=True)
self.depth_transform_ops = get_depth_tuple_transform_ops(
resize=(ht, wt), normalize=False
)
self.wt, self.ht = wt, ht
self.shake_t = shake_t
self.H_generator = GeometricSequential(K.RandomAffine(degrees=90, p=rot_prob))
self.use_horizontal_flip_aug = use_horizontal_flip_aug
def load_im(self, im_B, crop=None):
im = Image.open(im_B)
return im
def load_depth(self, depth_ref, crop=None):
depth = cv2.imread(str(depth_ref), cv2.IMREAD_UNCHANGED)
depth = depth / 1000
depth = torch.from_numpy(depth).float() # (h, w)
return depth
def __len__(self):
return len(self.data_names)
def scale_intrinsic(self, K, wi, hi):
sx, sy = self.wt / wi, self.ht / hi
sK = torch.tensor([[sx, 0, 0], [0, sy, 0], [0, 0, 1]])
return sK @ K
def horizontal_flip(self, im_A, im_B, depth_A, depth_B, K_A, K_B):
im_A = im_A.flip(-1)
im_B = im_B.flip(-1)
depth_A, depth_B = depth_A.flip(-1), depth_B.flip(-1)
flip_mat = torch.tensor([[-1, 0, self.wt], [0, 1, 0], [0, 0, 1.0]]).to(
K_A.device
)
K_A = flip_mat @ K_A
K_B = flip_mat @ K_B
return im_A, im_B, depth_A, depth_B, K_A, K_B
def read_scannet_pose(self, path):
"""Read ScanNet's Camera2World pose and transform it to World2Camera.
Returns:
pose_w2c (np.ndarray): (4, 4)
"""
cam2world = np.loadtxt(path, delimiter=" ")
world2cam = np.linalg.inv(cam2world)
return world2cam
def read_scannet_intrinsic(self, path):
"""Read ScanNet's intrinsic matrix and return the 3x3 matrix."""
intrinsic = np.loadtxt(path, delimiter=" ")
return torch.tensor(intrinsic[:-1, :-1], dtype=torch.float)
def __getitem__(self, pair_idx):
# read intrinsics of original size
data_name = self.data_names[pair_idx]
scene_name, scene_sub_name, stem_name_1, stem_name_2 = data_name
scene_name = f"scene{scene_name:04d}_{scene_sub_name:02d}"
# read the intrinsic of depthmap
K1 = K2 = self.read_scannet_intrinsic(
osp.join(self.scene_root, scene_name, "intrinsic", "intrinsic_color.txt")
) # the depth K is not the same, but doesnt really matter
# read and compute relative poses
T1 = self.read_scannet_pose(
osp.join(self.scene_root, scene_name, "pose", f"{stem_name_1}.txt")
)
T2 = self.read_scannet_pose(
osp.join(self.scene_root, scene_name, "pose", f"{stem_name_2}.txt")
)
T_1to2 = torch.tensor(np.matmul(T2, np.linalg.inv(T1)), dtype=torch.float)[
:4, :4
] # (4, 4)
# Load positive pair data
im_A_ref = os.path.join(
self.scene_root, scene_name, "color", f"{stem_name_1}.jpg"
)
im_B_ref = os.path.join(
self.scene_root, scene_name, "color", f"{stem_name_2}.jpg"
)
depth_A_ref = os.path.join(
self.scene_root, scene_name, "depth", f"{stem_name_1}.png"
)
depth_B_ref = os.path.join(
self.scene_root, scene_name, "depth", f"{stem_name_2}.png"
)
im_A = self.load_im(im_A_ref)
im_B = self.load_im(im_B_ref)
depth_A = self.load_depth(depth_A_ref)
depth_B = self.load_depth(depth_B_ref)
# Recompute camera intrinsic matrix due to the resize
K1 = self.scale_intrinsic(K1, im_A.width, im_A.height)
K2 = self.scale_intrinsic(K2, im_B.width, im_B.height)
# Process images
im_A, im_B = self.im_transform_ops((im_A, im_B))
depth_A, depth_B = self.depth_transform_ops(
(depth_A[None, None], depth_B[None, None])
)
if self.use_horizontal_flip_aug:
if np.random.rand() > 0.5:
im_A, im_B, depth_A, depth_B, K1, K2 = self.horizontal_flip(
im_A, im_B, depth_A, depth_B, K1, K2
)
data_dict = {
"im_A": im_A,
"im_B": im_B,
"im_A_depth": depth_A[0, 0],
"im_B_depth": depth_B[0, 0],
"K1": K1,
"K2": K2,
"T_1to2": T_1to2,
}
return data_dict
class ScanNetBuilder:
def __init__(self, data_root="data/scannet") -> None:
self.data_root = data_root
self.scene_info_root = os.path.join(data_root, "scannet_indices")
self.all_scenes = os.listdir(self.scene_info_root)
def build_scenes(self, split="train", min_overlap=0.0, **kwargs):
# Note: split doesn't matter here as we always use same scannet_train scenes
scene_names = self.all_scenes
scenes = []
for scene_name in tqdm(scene_names, disable=roma.RANK > 0):
scene_info = np.load(
os.path.join(self.scene_info_root, scene_name), allow_pickle=True
)
scenes.append(
ScanNetScene(
self.data_root, scene_info, min_overlap=min_overlap, **kwargs
)
)
return scenes
def weight_scenes(self, concat_dataset, alpha=0.5):
ns = []
for d in concat_dataset.datasets:
ns.append(len(d))
ws = torch.cat([torch.ones(n) / n**alpha for n in ns])
return ws
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