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| # MIT License | |
| # Copyright (c) 2022 Intelligent Systems Lab Org | |
| # Permission is hereby granted, free of charge, to any person obtaining a copy | |
| # of this software and associated documentation files (the "Software"), to deal | |
| # in the Software without restriction, including without limitation the rights | |
| # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | |
| # copies of the Software, and to permit persons to whom the Software is | |
| # furnished to do so, subject to the following conditions: | |
| # The above copyright notice and this permission notice shall be included in all | |
| # copies or substantial portions of the Software. | |
| # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR | |
| # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, | |
| # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE | |
| # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER | |
| # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, | |
| # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE | |
| # SOFTWARE. | |
| # File author: Shariq Farooq Bhat | |
| import os | |
| import numpy as np | |
| import torch | |
| from PIL import Image | |
| from torch.utils.data import DataLoader, Dataset | |
| from torchvision import transforms as T | |
| class iBims(Dataset): | |
| def __init__(self, config): | |
| root_folder = config.ibims_root | |
| with open(os.path.join(root_folder, "imagelist.txt"), 'r') as f: | |
| imglist = f.read().split() | |
| samples = [] | |
| for basename in imglist: | |
| img_path = os.path.join(root_folder, 'rgb', basename + ".png") | |
| depth_path = os.path.join(root_folder, 'depth', basename + ".png") | |
| valid_mask_path = os.path.join( | |
| root_folder, 'mask_invalid', basename+".png") | |
| transp_mask_path = os.path.join( | |
| root_folder, 'mask_transp', basename+".png") | |
| samples.append( | |
| (img_path, depth_path, valid_mask_path, transp_mask_path)) | |
| self.samples = samples | |
| # self.normalize = T.Normalize( | |
| # mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) | |
| self.normalize = lambda x : x | |
| def __getitem__(self, idx): | |
| img_path, depth_path, valid_mask_path, transp_mask_path = self.samples[idx] | |
| img = np.asarray(Image.open(img_path), dtype=np.float32) / 255.0 | |
| depth = np.asarray(Image.open(depth_path), | |
| dtype=np.uint16).astype('float')*50.0/65535 | |
| mask_valid = np.asarray(Image.open(valid_mask_path)) | |
| mask_transp = np.asarray(Image.open(transp_mask_path)) | |
| # depth = depth * mask_valid * mask_transp | |
| depth = np.where(mask_valid * mask_transp, depth, -1) | |
| img = torch.from_numpy(img).permute(2, 0, 1) | |
| img = self.normalize(img) | |
| depth = torch.from_numpy(depth).unsqueeze(0) | |
| return dict(image=img, depth=depth, image_path=img_path, depth_path=depth_path, dataset='ibims') | |
| def __len__(self): | |
| return len(self.samples) | |
| def get_ibims_loader(config, batch_size=1, **kwargs): | |
| dataloader = DataLoader(iBims(config), batch_size=batch_size, **kwargs) | |
| return dataloader | |