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import os
from PIL import Image
import h5py
import numpy as np
import torch
import torchvision.transforms.functional as tvf
import kornia.augmentation as K
from roma.utils import get_depth_tuple_transform_ops, get_tuple_transform_ops
import roma
from roma.utils import *
import math
class MegadepthScene:
def __init__(
self,
data_root,
scene_info,
ht=384,
wt=512,
min_overlap=0.0,
max_overlap=1.0,
shake_t=0,
rot_prob=0.0,
normalize=True,
max_num_pairs = 100_000,
scene_name = None,
use_horizontal_flip_aug = False,
use_single_horizontal_flip_aug = False,
colorjiggle_params = None,
random_eraser = None,
use_randaug = False,
randaug_params = None,
randomize_size = False,
) -> None:
self.data_root = data_root
self.scene_name = os.path.splitext(scene_name)[0]+f"_{min_overlap}_{max_overlap}"
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.overlaps < max_overlap)
self.pairs = self.pairs[threshold]
self.overlaps = self.overlaps[threshold]
if len(self.pairs) > max_num_pairs:
pairinds = np.random.choice(
np.arange(0, len(self.pairs)), max_num_pairs, replace=False
)
self.pairs = self.pairs[pairinds]
self.overlaps = self.overlaps[pairinds]
if randomize_size:
area = ht * wt
s = int(16 * (math.sqrt(area)//16))
sizes = ((ht,wt), (s,s), (wt,ht))
choice = roma.RANK % 3
ht, wt = sizes[choice]
# counts, bins = np.histogram(self.overlaps,20)
# print(counts)
self.im_transform_ops = get_tuple_transform_ops(
resize=(ht, wt), normalize=normalize, colorjiggle_params = colorjiggle_params,
)
self.depth_transform_ops = get_depth_tuple_transform_ops(
resize=(ht, wt)
)
self.wt, self.ht = wt, ht
self.shake_t = shake_t
self.random_eraser = random_eraser
if use_horizontal_flip_aug and use_single_horizontal_flip_aug:
raise ValueError("Can't both flip both images and only flip one")
self.use_horizontal_flip_aug = use_horizontal_flip_aug
self.use_single_horizontal_flip_aug = use_single_horizontal_flip_aug
self.use_randaug = use_randaug
def load_im(self, im_path):
im = Image.open(im_path)
return im
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.]]).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 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
im_A, im_B = self.image_paths[idx1], self.image_paths[idx2]
depth1, depth2 = self.depth_paths[idx1], self.depth_paths[idx2]
im_A_ref = os.path.join(self.data_root, im_A)
im_B_ref = os.path.join(self.data_root, im_B)
depth_A_ref = os.path.join(self.data_root, depth1)
depth_B_ref = os.path.join(self.data_root, depth2)
im_A = self.load_im(im_A_ref)
im_B = self.load_im(im_B_ref)
K1 = self.scale_intrinsic(K1, im_A.width, im_A.height)
K2 = self.scale_intrinsic(K2, im_B.width, im_B.height)
if self.use_randaug:
im_A, im_B = self.rand_augment(im_A, im_B)
depth_A = self.load_depth(depth_A_ref)
depth_B = self.load_depth(depth_B_ref)
# 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])
)
[im_A, im_B, depth_A, depth_B], t = self.rand_shake(im_A, im_B, depth_A, depth_B)
K1[:2, 2] += t
K2[:2, 2] += t
im_A, im_B = im_A[None], im_B[None]
if self.random_eraser is not None:
im_A, depth_A = self.random_eraser(im_A, depth_A)
im_B, depth_B = self.random_eraser(im_B, depth_B)
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)
if self.use_single_horizontal_flip_aug:
if np.random.rand() > 0.5:
im_B, depth_B, K2 = self.single_horizontal_flip(im_B, depth_B, K2)
if roma.DEBUG_MODE:
tensor_to_pil(im_A[0], unnormalize=True).save(
f"vis/im_A.jpg")
tensor_to_pil(im_B[0], unnormalize=True).save(
f"vis/im_B.jpg")
data_dict = {
"im_A": im_A[0],
"im_A_identifier": self.image_paths[idx1].split("/")[-1].split(".jpg")[0],
"im_B": im_B[0],
"im_B_identifier": self.image_paths[idx2].split("/")[-1].split(".jpg")[0],
"im_A_depth": depth_A[0, 0],
"im_B_depth": depth_B[0, 0],
"K1": K1,
"K2": K2,
"T_1to2": T_1to2,
"im_A_path": im_A_ref,
"im_B_path": im_B_ref,
}
return data_dict
class MegadepthBuilder:
def __init__(self, data_root="data/megadepth", loftr_ignore=True, imc21_ignore = True) -> 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"]
# LoFTR did the D2-net preprocessing differently than we did and got more ignore scenes, can optionially ignore those
self.loftr_ignore_scenes = set(['0121.npy', '0133.npy', '0168.npy', '0178.npy', '0229.npy', '0349.npy', '0412.npy', '0430.npy', '0443.npy', '1001.npy', '5014.npy', '5015.npy', '5016.npy'])
self.imc21_scenes = set(['0008.npy', '0019.npy', '0021.npy', '0024.npy', '0025.npy', '0032.npy', '0063.npy', '1589.npy'])
self.test_scenes_loftr = ["0015.npy", "0022.npy"]
self.loftr_ignore = loftr_ignore
self.imc21_ignore = imc21_ignore
def build_scenes(self, split="train", min_overlap=0.0, scene_names = None, **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
elif split == "custom":
scene_names = scene_names
else:
raise ValueError(f"Split {split} not available")
scenes = []
for scene_name in scene_names:
if self.loftr_ignore and scene_name in self.loftr_ignore_scenes:
continue
if self.imc21_ignore and scene_name in self.imc21_scenes:
continue
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,scene_name = scene_name, **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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