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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 | |
| class ToTensor(object): | |
| def __init__(self, resize_shape): | |
| # self.normalize = transforms.Normalize( | |
| # mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) | |
| self.normalize = lambda x : x | |
| self.resize = transforms.Resize(resize_shape) | |
| def __call__(self, sample): | |
| image, depth = sample['image'], sample['depth'] | |
| image = self.to_tensor(image) | |
| image = self.normalize(image) | |
| depth = self.to_tensor(depth) | |
| image = self.resize(image) | |
| return {'image': image, 'depth': depth, 'dataset': "ddad"} | |
| def to_tensor(self, pic): | |
| if isinstance(pic, np.ndarray): | |
| img = torch.from_numpy(pic.transpose((2, 0, 1))) | |
| return img | |
| # # handle PIL Image | |
| if pic.mode == 'I': | |
| img = torch.from_numpy(np.array(pic, np.int32, copy=False)) | |
| elif pic.mode == 'I;16': | |
| img = torch.from_numpy(np.array(pic, np.int16, copy=False)) | |
| else: | |
| img = torch.ByteTensor( | |
| torch.ByteStorage.from_buffer(pic.tobytes())) | |
| # PIL image mode: 1, L, P, I, F, RGB, YCbCr, RGBA, CMYK | |
| if pic.mode == 'YCbCr': | |
| nchannel = 3 | |
| elif pic.mode == 'I;16': | |
| nchannel = 1 | |
| else: | |
| nchannel = len(pic.mode) | |
| img = img.view(pic.size[1], pic.size[0], nchannel) | |
| img = img.transpose(0, 1).transpose(0, 2).contiguous() | |
| if isinstance(img, torch.ByteTensor): | |
| return img.float() | |
| else: | |
| return img | |
| class DDAD(Dataset): | |
| def __init__(self, data_dir_root, resize_shape): | |
| import glob | |
| # image paths are of the form <data_dir_root>/{outleft, depthmap}/*.png | |
| self.image_files = glob.glob(os.path.join(data_dir_root, '*.png')) | |
| self.depth_files = [r.replace("_rgb.png", "_depth.npy") | |
| for r in self.image_files] | |
| self.transform = ToTensor(resize_shape) | |
| def __getitem__(self, idx): | |
| image_path = self.image_files[idx] | |
| depth_path = self.depth_files[idx] | |
| image = np.asarray(Image.open(image_path), dtype=np.float32) / 255.0 | |
| depth = np.load(depth_path) # meters | |
| # depth[depth > 8] = -1 | |
| depth = depth[..., None] | |
| sample = dict(image=image, depth=depth) | |
| sample = self.transform(sample) | |
| if idx == 0: | |
| print(sample["image"].shape) | |
| return sample | |
| def __len__(self): | |
| return len(self.image_files) | |
| def get_ddad_loader(data_dir_root, resize_shape, batch_size=1, **kwargs): | |
| dataset = DDAD(data_dir_root, resize_shape) | |
| return DataLoader(dataset, batch_size, **kwargs) | |