(
self,
index: Optional[int] = None,
sequence_name: Optional[str] = None,
ids: Union[List[int], np.ndarray, None] = None,
)
| 142 | return self.get_data(index=index, ids=ids) |
| 143 | |
| 144 | def get_data( |
| 145 | self, |
| 146 | index: Optional[int] = None, |
| 147 | sequence_name: Optional[str] = None, |
| 148 | ids: Union[List[int], np.ndarray, None] = None, |
| 149 | ): |
| 150 | if sequence_name is None: |
| 151 | if index is None: |
| 152 | raise ValueError("Please specify either index or sequence_name") |
| 153 | sequence_name: str = self.sequence_list[index] |
| 154 | seq_len: int = self.metadata[sequence_name] |
| 155 | |
| 156 | if ids is None: |
| 157 | ids = np.arange(seq_len).tolist() |
| 158 | elif isinstance(ids, np.ndarray): |
| 159 | assert ids.ndim == 1, f"ids should be a 1D array, but got {ids.ndim}D" |
| 160 | ids = ids.tolist() |
| 161 | |
| 162 | image_path = osp.join(self.DTU_DIR, sequence_name, "images") |
| 163 | depth_path = osp.join(self.DTU_DIR, sequence_name, "depths") |
| 164 | mask_path = osp.join(self.DTU_DIR, sequence_name, "binary_masks") |
| 165 | cam_path = osp.join(self.DTU_DIR, sequence_name, "cams") |
| 166 | |
| 167 | image_paths: list = [""] * len(ids) |
| 168 | images: list = [0] * len(ids) |
| 169 | depths: list = [0] * len(ids) |
| 170 | extrinsics: np.ndarray = np.zeros((len(ids), 3, 4)) |
| 171 | intrinsics: np.ndarray = np.zeros((len(ids), 3, 3)) |
| 172 | |
| 173 | for id_index, id in enumerate(ids): |
| 174 | impath = osp.join(image_path, f"{id:08d}.jpg") |
| 175 | depthpath = osp.join(depth_path, f"{id:08d}.npy") |
| 176 | campath = osp.join(cam_path, f"{id:08d}_cam.txt") |
| 177 | maskpath = osp.join(mask_path, f"{id:08d}.png") |
| 178 | |
| 179 | rgb_image: Image.Image = Image.open(impath) |
| 180 | depthmap: np.ndarray = np.load(depthpath) |
| 181 | rgb_image: Image.Image = resize_image(rgb_image, (depthmap.shape[1], depthmap.shape[0])) |
| 182 | |
| 183 | depthmap = np.nan_to_num(depthmap.astype(np.float32), 0.0) |
| 184 | |
| 185 | mask_pil = Image.open(maskpath) |
| 186 | |
| 187 | # 如果图像本身是单通道，保持原样 |
| 188 | if mask_pil.mode in ['L', '1']: # L:灰度, 1:二值 |
| 189 | mask = np.array(mask_pil) / 255.0 |
| 190 | else: |
| 191 | # 如果是多通道，转换为灰度 |
| 192 | mask = np.array(mask_pil.convert('L')) / 255.0 |
| 193 | mask = mask.astype(np.float32) |
| 194 | |
| 195 | mask[mask > 0.5] = 1.0 |
| 196 | mask[mask < 0.5] = 0.0 |
| 197 | |
| 198 | mask = cv2.resize( |
| 199 | mask, |
| 200 | (depthmap.shape[1], depthmap.shape[0]), |
| 201 | interpolation=cv2.INTER_NEAREST, |
no test coverage detected