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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
from dkm.utils import get_depth_tuple_transform_ops, get_tuple_transform_ops
from dkm.utils.transforms import GeometricSequential
from tqdm import tqdm
class ScanNetScene:
def __init__(self, data_root, scene_info, ht = 384, wt = 512, min_overlap=0., shake_t = 0, rot_prob=0.) -> 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))
def load_im(self, im_ref, crop=None):
im = Image.open(im_ref)
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 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 intrinsic[:-1, :-1]
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_src_ref = os.path.join(self.scene_root, scene_name, 'color', f'{stem_name_1}.jpg')
im_pos_ref = os.path.join(self.scene_root, scene_name, 'color', f'{stem_name_2}.jpg')
depth_src_ref = os.path.join(self.scene_root, scene_name, 'depth', f'{stem_name_1}.png')
depth_pos_ref = os.path.join(self.scene_root, scene_name, 'depth', f'{stem_name_2}.png')
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]))
data_dict = {'query': im_src,
'support': im_pos,
'query_depth': depth_src[0,0],
'support_depth': depth_pos[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., **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):
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=.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(ScanNetBuilder("data/scannet").build_scenes(split='train'))
mega_test[0] |