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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 romatch | |
| from romatch.utils import get_depth_tuple_transform_ops, get_tuple_transform_ops | |
| from romatch.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.,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.]]).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., **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 = romatch.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=.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 | |