Datasets:
Size:
10K<n<100K
License:
| import io | |
| from PIL import Image | |
| from datasets import GeneratorBasedBuilder, DatasetInfo, Features, SplitGenerator, Value, Array2D, Split | |
| import datasets | |
| import numpy as np | |
| import h5py | |
| from huggingface_hub import HfFileSystem | |
| class CustomConfig(datasets.BuilderConfig): | |
| def __init__(self, **kwargs): | |
| super(CustomConfig, self).__init__(**kwargs) | |
| self.dataset_type = kwargs.pop("name", "all") | |
| _metadata_urls = { | |
| "train":"https://huggingface.co/datasets/XingjianLi/tomatotest/resolve/main/train.txt", | |
| "val":"https://huggingface.co/datasets/XingjianLi/tomatotest/resolve/main/val.txt" | |
| } | |
| class RGBSemanticDepthDataset(GeneratorBasedBuilder): | |
| BUILDER_CONFIGS = [ | |
| CustomConfig(name="full", version="1.0.0", description="load both segmentation and depth (for all tar files, 160GB)"), | |
| CustomConfig(name="sample", version="1.0.0", description="load both segmentation and depth (for 1 tar file, 870MB)"), | |
| CustomConfig(name="depth", version="1.0.0", description="only load depth (sample)"), | |
| CustomConfig(name="seg", version="1.0.0", description="only load segmentation (sample)"), | |
| ] # Configs initialization | |
| BUILDER_CONFIG_CLASS = CustomConfig | |
| def _info(self): | |
| return DatasetInfo( | |
| features=Features({ | |
| "left_rgb": datasets.Image(), | |
| "right_rgb": datasets.Image(), | |
| "left_semantic": datasets.Image(), | |
| "left_instance": datasets.Image(), | |
| "left_depth": datasets.Image(), | |
| "right_depth": datasets.Image(), | |
| }) | |
| ) | |
| def _h5_loader(self, bytes_stream, type_dataset): | |
| # Reference: https://github.com/dwofk/fast-depth/blob/master/dataloaders/dataloader.py#L8-L13 | |
| f = io.BytesIO(bytes_stream) | |
| h5f = h5py.File(f, "r") | |
| left_rgb = self._read_jpg(h5f['rgb_left'][:]) | |
| if type_dataset == 'depth': | |
| right_rgb = self._read_jpg(h5f['rgb_right'][:]) | |
| left_depth = h5f['depth_left'][:].astype(np.float32) | |
| right_depth = h5f['depth_right'][:].astype(np.float32) | |
| return left_rgb, right_rgb, np.zeros((1,1)), np.zeros((1,1)), left_depth, right_depth | |
| elif type_dataset == 'seg': | |
| left_semantic = h5f['seg_left'][:][:,:,2] | |
| left_instance = h5f['seg_left'][:][:,:,0] + h5f['seg_left'][:][:,:,1] * 256 | |
| return left_rgb, np.zeros((1,1)), left_semantic, left_instance, np.zeros((1,1)), np.zeros((1,1)) | |
| else: | |
| right_rgb = self._read_jpg(h5f['rgb_right'][:]) | |
| left_semantic = h5f['seg_left'][:][:,:,2] | |
| left_instance = h5f['seg_left'][:][:,:,0] + h5f['seg_left'][:][:,:,1] * 256 | |
| left_depth = h5f['depth_left'][:].astype(np.float32) | |
| right_depth = h5f['depth_right'][:].astype(np.float32) | |
| return left_rgb, right_rgb, left_semantic, left_instance, left_depth, right_depth | |
| def _read_jpg(self, bytes_stream): | |
| return Image.open(io.BytesIO(bytes_stream)) | |
| def _split_generators(self, dl_manager): | |
| if 'full' == self.config.dataset_type: | |
| archives = dl_manager.download({"train":self._get_dataset_filenames(), | |
| "val":self._get_dataset_filenames()}) | |
| else: | |
| archives = dl_manager.download({"train":[self._get_dataset_filenames()[0]], | |
| "val":[self._get_dataset_filenames()[0]]}) | |
| split_metadata = dl_manager.download(_metadata_urls) | |
| return [ | |
| SplitGenerator( | |
| name=Split.TRAIN, | |
| gen_kwargs={ | |
| "archives": [dl_manager.iter_archive(archive) for archive in archives["train"]], | |
| "split_txt": split_metadata["train"] | |
| }, | |
| ), | |
| SplitGenerator( | |
| name=Split.VALIDATION, | |
| gen_kwargs={ | |
| "archives": [dl_manager.iter_archive(archive) for archive in archives["val"]], | |
| "split_txt": split_metadata["val"] | |
| }, | |
| ), | |
| ] | |
| def _generate_examples(self, archives, split_txt): | |
| #print(split_txt, archives) | |
| with open(split_txt, encoding="utf-8") as split_f: | |
| all_splits = split_f.read().split('\n') | |
| #print(len(all_splits)) | |
| for archive in archives: | |
| #print(archive) | |
| for path, file in archive: | |
| if path.split('/')[-1][:-3] not in all_splits: | |
| #print(path.split('/')[-1][:-3], all_splits[0]) | |
| continue | |
| #print("added") | |
| left_rgb, right_rgb, left_semantic, left_instance, left_depth, right_depth = self._h5_loader(file.read(), self.config.dataset_type) | |
| yield path, { | |
| "left_rgb": left_rgb, | |
| "right_rgb": right_rgb, | |
| "left_semantic": left_semantic, | |
| "left_instance": left_instance, | |
| "left_depth": left_depth, | |
| "right_depth": right_depth, | |
| } | |
| def _get_dataset_filenames(self): | |
| fs = HfFileSystem() | |
| all_files = fs.ls("datasets/xingjianli/tomatotest/data") | |
| filenames = sorted(['/'.join(f['name'].split('/')[-2:]) for f in all_files]) | |
| return filenames |