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import os
import random
from PIL import Image
import h5py
import numpy as np
import torch
from torch.utils.data import Dataset, DataLoader, ConcatDataset
from dkm.utils import get_depth_tuple_transform_ops, get_tuple_transform_ops
import torchvision.transforms.functional as tvf
from dkm.utils.transforms import GeometricSequential
import kornia.augmentation as K
class MegadepthScene:
def __init__(
self,
data_root,
scene_info,
ht=384,
wt=512,
min_overlap=0.0,
shake_t=0,
rot_prob=0.0,
normalize=True,
) -> None:
self.data_root = data_root
self.image_paths = scene_info["image_paths"]
self.depth_paths = scene_info["depth_paths"]
self.intrinsics = scene_info["intrinsics"]
self.poses = scene_info["poses"]
self.pairs = scene_info["pairs"]
self.overlaps = scene_info["overlaps"]
threshold = self.overlaps > min_overlap
self.pairs = self.pairs[threshold]
self.overlaps = self.overlaps[threshold]
if len(self.pairs) > 100000:
pairinds = np.random.choice(
np.arange(0, len(self.pairs)), 100000, replace=False
)
self.pairs = self.pairs[pairinds]
self.overlaps = self.overlaps[pairinds]
# counts, bins = np.histogram(self.overlaps,20)
# print(counts)
self.im_transform_ops = get_tuple_transform_ops(
resize=(ht, wt), normalize=normalize
)
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))
def load_im(self, im_ref, crop=None):
im = Image.open(im_ref)
return im
def load_depth(self, depth_ref, crop=None):
depth = np.array(h5py.File(depth_ref, "r")["depth"])
return torch.from_numpy(depth)
def __len__(self):
return len(self.pairs)
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 rand_shake(self, *things):
t = np.random.choice(range(-self.shake_t, self.shake_t + 1), size=2)
return [
tvf.affine(thing, angle=0.0, translate=list(t), scale=1.0, shear=[0.0, 0.0])
for thing in things
], t
def __getitem__(self, pair_idx):
# read intrinsics of original size
idx1, idx2 = self.pairs[pair_idx]
K1 = torch.tensor(self.intrinsics[idx1].copy(), dtype=torch.float).reshape(3, 3)
K2 = torch.tensor(self.intrinsics[idx2].copy(), dtype=torch.float).reshape(3, 3)
# read and compute relative poses
T1 = self.poses[idx1]
T2 = self.poses[idx2]
T_1to2 = torch.tensor(np.matmul(T2, np.linalg.inv(T1)), dtype=torch.float)[
:4, :4
] # (4, 4)
# Load positive pair data
im1, im2 = self.image_paths[idx1], self.image_paths[idx2]
depth1, depth2 = self.depth_paths[idx1], self.depth_paths[idx2]
im_src_ref = os.path.join(self.data_root, im1)
im_pos_ref = os.path.join(self.data_root, im2)
depth_src_ref = os.path.join(self.data_root, depth1)
depth_pos_ref = os.path.join(self.data_root, depth2)
# return torch.randn((1000,1000))
im_src = self.load_im(im_src_ref)
im_pos = self.load_im(im_pos_ref)
depth_src = self.load_depth(depth_src_ref)
depth_pos = self.load_depth(depth_pos_ref)
# Recompute camera intrinsic matrix due to the resize
K1 = self.scale_intrinsic(K1, im_src.width, im_src.height)
K2 = self.scale_intrinsic(K2, im_pos.width, im_pos.height)
# Process images
im_src, im_pos = self.im_transform_ops((im_src, im_pos))
depth_src, depth_pos = self.depth_transform_ops(
(depth_src[None, None], depth_pos[None, None])
)
[im_src, im_pos, depth_src, depth_pos], t = self.rand_shake(
im_src, im_pos, depth_src, depth_pos
)
im_src, Hq = self.H_generator(im_src[None])
depth_src = self.H_generator.apply_transform(depth_src, Hq)
K1[:2, 2] += t
K2[:2, 2] += t
K1 = Hq[0] @ K1
data_dict = {
"query": im_src[0],
"query_identifier": self.image_paths[idx1].split("/")[-1].split(".jpg")[0],
"support": im_pos,
"support_identifier": self.image_paths[idx2]
.split("/")[-1]
.split(".jpg")[0],
"query_depth": depth_src[0, 0],
"support_depth": depth_pos[0, 0],
"K1": K1,
"K2": K2,
"T_1to2": T_1to2,
}
return data_dict
class MegadepthBuilder:
def __init__(self, data_root="data/megadepth") -> None:
self.data_root = data_root
self.scene_info_root = os.path.join(data_root, "prep_scene_info")
self.all_scenes = os.listdir(self.scene_info_root)
self.test_scenes = ["0017.npy", "0004.npy", "0048.npy", "0013.npy"]
self.test_scenes_loftr = ["0015.npy", "0022.npy"]
def build_scenes(self, split="train", min_overlap=0.0, **kwargs):
if split == "train":
scene_names = set(self.all_scenes) - set(self.test_scenes)
elif split == "train_loftr":
scene_names = set(self.all_scenes) - set(self.test_scenes_loftr)
elif split == "test":
scene_names = self.test_scenes
elif split == "test_loftr":
scene_names = self.test_scenes_loftr
else:
raise ValueError(f"Split {split} not available")
scenes = []
for scene_name in scene_names:
scene_info = np.load(
os.path.join(self.scene_info_root, scene_name), allow_pickle=True
).item()
scenes.append(
MegadepthScene(
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
if __name__ == "__main__":
mega_test = ConcatDataset(MegadepthBuilder().build_scenes(split="train"))
mega_test[0]
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