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import h5py
import numpy as np
from PIL import Image
import os
import torch
from torch.utils.data import Dataset
import time
from tqdm import tqdm
from lib.utils import preprocess_image
class MegaDepthDataset(Dataset):
def __init__(
self,
scene_list_path='megadepth_utils/train_scenes.txt',
scene_info_path='/local/dataset/megadepth/scene_info',
base_path='/local/dataset/megadepth',
train=True,
preprocessing=None,
min_overlap_ratio=.5,
max_overlap_ratio=1,
max_scale_ratio=np.inf,
pairs_per_scene=100,
image_size=256
):
self.scenes = []
with open(scene_list_path, 'r') as f:
lines = f.readlines()
for line in lines:
self.scenes.append(line.strip('\n'))
self.scene_info_path = scene_info_path
self.base_path = base_path
self.train = train
self.preprocessing = preprocessing
self.min_overlap_ratio = min_overlap_ratio
self.max_overlap_ratio = max_overlap_ratio
self.max_scale_ratio = max_scale_ratio
self.pairs_per_scene = pairs_per_scene
self.image_size = image_size
self.dataset = []
def build_dataset(self):
self.dataset = []
if not self.train:
np_random_state = np.random.get_state()
np.random.seed(42)
print('Building the validation dataset...')
else:
print('Building a new training dataset...')
for scene in tqdm(self.scenes, total=len(self.scenes)):
scene_info_path = os.path.join(
self.scene_info_path, '%s.npz' % scene
)
if not os.path.exists(scene_info_path):
continue
scene_info = np.load(scene_info_path, allow_pickle=True)
overlap_matrix = scene_info['overlap_matrix']
scale_ratio_matrix = scene_info['scale_ratio_matrix']
valid = np.logical_and(
np.logical_and(
overlap_matrix >= self.min_overlap_ratio,
overlap_matrix <= self.max_overlap_ratio
),
scale_ratio_matrix <= self.max_scale_ratio
)
pairs = np.vstack(np.where(valid))
try:
selected_ids = np.random.choice(
pairs.shape[1], self.pairs_per_scene
)
except:
continue
image_paths = scene_info['image_paths']
depth_paths = scene_info['depth_paths']
points3D_id_to_2D = scene_info['points3D_id_to_2D']
points3D_id_to_ndepth = scene_info['points3D_id_to_ndepth']
intrinsics = scene_info['intrinsics']
poses = scene_info['poses']
for pair_idx in selected_ids:
idx1 = pairs[0, pair_idx]
idx2 = pairs[1, pair_idx]
matches = np.array(list(
points3D_id_to_2D[idx1].keys() &
points3D_id_to_2D[idx2].keys()
))
# Scale filtering
matches_nd1 = np.array([points3D_id_to_ndepth[idx1][match] for match in matches])
matches_nd2 = np.array([points3D_id_to_ndepth[idx2][match] for match in matches])
scale_ratio = np.maximum(matches_nd1 / matches_nd2, matches_nd2 / matches_nd1)
matches = matches[np.where(scale_ratio <= self.max_scale_ratio)[0]]
point3D_id = np.random.choice(matches)
point2D1 = points3D_id_to_2D[idx1][point3D_id]
point2D2 = points3D_id_to_2D[idx2][point3D_id]
nd1 = points3D_id_to_ndepth[idx1][point3D_id]
nd2 = points3D_id_to_ndepth[idx2][point3D_id]
central_match = np.array([
point2D1[1], point2D1[0],
point2D2[1], point2D2[0]
])
self.dataset.append({
'image_path1': image_paths[idx1],
'depth_path1': depth_paths[idx1],
'intrinsics1': intrinsics[idx1],
'pose1': poses[idx1],
'image_path2': image_paths[idx2],
'depth_path2': depth_paths[idx2],
'intrinsics2': intrinsics[idx2],
'pose2': poses[idx2],
'central_match': central_match,
'scale_ratio': max(nd1 / nd2, nd2 / nd1)
})
np.random.shuffle(self.dataset)
if not self.train:
np.random.set_state(np_random_state)
def __len__(self):
return len(self.dataset)
def recover_pair(self, pair_metadata):
depth_path1 = os.path.join(
self.base_path, pair_metadata['depth_path1']
)
with h5py.File(depth_path1, 'r') as hdf5_file:
depth1 = np.array(hdf5_file['/depth'])
assert(np.min(depth1) >= 0)
image_path1 = os.path.join(
self.base_path, pair_metadata['image_path1']
)
image1 = Image.open(image_path1)
if image1.mode != 'RGB':
image1 = image1.convert('RGB')
image1 = np.array(image1)
assert(image1.shape[0] == depth1.shape[0] and image1.shape[1] == depth1.shape[1])
intrinsics1 = pair_metadata['intrinsics1']
pose1 = pair_metadata['pose1']
depth_path2 = os.path.join(
self.base_path, pair_metadata['depth_path2']
)
with h5py.File(depth_path2, 'r') as hdf5_file:
depth2 = np.array(hdf5_file['/depth'])
assert(np.min(depth2) >= 0)
image_path2 = os.path.join(
self.base_path, pair_metadata['image_path2']
)
image2 = Image.open(image_path2)
if image2.mode != 'RGB':
image2 = image2.convert('RGB')
image2 = np.array(image2)
assert(image2.shape[0] == depth2.shape[0] and image2.shape[1] == depth2.shape[1])
intrinsics2 = pair_metadata['intrinsics2']
pose2 = pair_metadata['pose2']
central_match = pair_metadata['central_match']
image1, bbox1, image2, bbox2 = self.crop(image1, image2, central_match)
depth1 = depth1[
bbox1[0] : bbox1[0] + self.image_size,
bbox1[1] : bbox1[1] + self.image_size
]
depth2 = depth2[
bbox2[0] : bbox2[0] + self.image_size,
bbox2[1] : bbox2[1] + self.image_size
]
return (
image1, depth1, intrinsics1, pose1, bbox1,
image2, depth2, intrinsics2, pose2, bbox2
)
def crop(self, image1, image2, central_match):
bbox1_i = max(int(central_match[0]) - self.image_size // 2, 0)
if bbox1_i + self.image_size >= image1.shape[0]:
bbox1_i = image1.shape[0] - self.image_size
bbox1_j = max(int(central_match[1]) - self.image_size // 2, 0)
if bbox1_j + self.image_size >= image1.shape[1]:
bbox1_j = image1.shape[1] - self.image_size
bbox2_i = max(int(central_match[2]) - self.image_size // 2, 0)
if bbox2_i + self.image_size >= image2.shape[0]:
bbox2_i = image2.shape[0] - self.image_size
bbox2_j = max(int(central_match[3]) - self.image_size // 2, 0)
if bbox2_j + self.image_size >= image2.shape[1]:
bbox2_j = image2.shape[1] - self.image_size
return (
image1[
bbox1_i : bbox1_i + self.image_size,
bbox1_j : bbox1_j + self.image_size
],
np.array([bbox1_i, bbox1_j]),
image2[
bbox2_i : bbox2_i + self.image_size,
bbox2_j : bbox2_j + self.image_size
],
np.array([bbox2_i, bbox2_j])
)
def __getitem__(self, idx):
(
image1, depth1, intrinsics1, pose1, bbox1,
image2, depth2, intrinsics2, pose2, bbox2
) = self.recover_pair(self.dataset[idx])
image1 = preprocess_image(image1, preprocessing=self.preprocessing)
image2 = preprocess_image(image2, preprocessing=self.preprocessing)
return {
'image1': torch.from_numpy(image1.astype(np.float32)),
'depth1': torch.from_numpy(depth1.astype(np.float32)),
'intrinsics1': torch.from_numpy(intrinsics1.astype(np.float32)),
'pose1': torch.from_numpy(pose1.astype(np.float32)),
'bbox1': torch.from_numpy(bbox1.astype(np.float32)),
'image2': torch.from_numpy(image2.astype(np.float32)),
'depth2': torch.from_numpy(depth2.astype(np.float32)),
'intrinsics2': torch.from_numpy(intrinsics2.astype(np.float32)),
'pose2': torch.from_numpy(pose2.astype(np.float32)),
'bbox2': torch.from_numpy(bbox2.astype(np.float32))
}
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