| 80 | |
| 81 | |
| 82 | class DDAD(Dataset): |
| 83 | def __init__(self, data_dir_root, resize_shape): |
| 84 | import glob |
| 85 | |
| 86 | # image paths are of the form <data_dir_root>/{outleft, depthmap}/*.png |
| 87 | self.image_files = glob.glob(os.path.join(data_dir_root, '*.png')) |
| 88 | self.depth_files = [r.replace("_rgb.png", "_depth.npy") |
| 89 | for r in self.image_files] |
| 90 | self.transform = ToTensor(resize_shape) |
| 91 | |
| 92 | def __getitem__(self, idx): |
| 93 | |
| 94 | image_path = self.image_files[idx] |
| 95 | depth_path = self.depth_files[idx] |
| 96 | |
| 97 | image = np.asarray(Image.open(image_path), dtype=np.float32) / 255.0 |
| 98 | depth = np.load(depth_path) # meters |
| 99 | |
| 100 | # depth[depth > 8] = -1 |
| 101 | depth = depth[..., None] |
| 102 | |
| 103 | sample = dict(image=image, depth=depth) |
| 104 | sample = self.transform(sample) |
| 105 | |
| 106 | if idx == 0: |
| 107 | print(sample["image"].shape) |
| 108 | |
| 109 | return sample |
| 110 | |
| 111 | def __len__(self): |
| 112 | return len(self.image_files) |
| 113 | |
| 114 | |
| 115 | def get_ddad_loader(data_dir_root, resize_shape, batch_size=1, **kwargs): |