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 | |
| import time | |
| import os | |
| import hashlib | |
| from pathlib import Path | |
| class CustomConfig(datasets.BuilderConfig): | |
| def __init__(self, extract, remove_tar, chunk, **kwargs): | |
| super().__init__(**kwargs) | |
| self.dataset_type = kwargs.pop("name", "all") | |
| self.extract = kwargs.pop("extract", extract) | |
| self.remove_tar = kwargs.pop("remove_tar", remove_tar) | |
| self.chunk = kwargs.pop("chunk", chunk) | |
| _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="download and extract the dataset to h5 pairs (all tar files automatically removed by default, 160GB)", | |
| extract=True, remove_tar=True, chunk=1), | |
| CustomConfig(name="sample", version="1.0.0", description="load both segmentation and depth (for 1 tar file, 870MB)", | |
| extract=False, remove_tar=False, chunk=1), | |
| CustomConfig(name="depth", version="1.0.0", description="only load depth (sample)", | |
| extract=False, remove_tar=False, chunk=1), | |
| CustomConfig(name="seg", version="1.0.0", description="only load segmentation (sample)", | |
| extract=False, remove_tar=False, chunk=1), | |
| ] # 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, fileobj, type_dataset): | |
| # Reference: https://github.com/dwofk/fast-depth/blob/master/dataloaders/dataloader.py#L8-L13 | |
| file_bytes = fileobj.read() | |
| file_bytes = io.BytesIO(file_bytes) | |
| with h5py.File(file_bytes, "r") as h5f: | |
| 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': | |
| seg_left = h5f['seg_left'][:] | |
| left_semantic = seg_left[:,:,2] | |
| left_instance = seg_left[:,:,0] + 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'][:]) | |
| seg_left = h5f['seg_left'][:] | |
| left_semantic = seg_left[:,:,2] | |
| left_instance = seg_left[:,:,0] + 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): | |
| img = Image.open(io.BytesIO(bytes_stream)) | |
| return img | |
| def _split_generators(self, dl_manager): | |
| if 'full' == self.config.dataset_type: | |
| dataset_names = self._get_dataset_filenames() | |
| if self.config.extract: | |
| dataset_chunk_list = [dataset_names[i:i+self.config.chunk] for i in range(0, len(dataset_names), self.config.chunk)] | |
| for dataset_chunk in dataset_chunk_list: | |
| print(self.config.chunk) | |
| print(dataset_chunk) | |
| archives = dl_manager.download({"train":dataset_chunk, | |
| "val":dataset_chunk}) | |
| for archive, tar_file_name in zip(archives["train"], dataset_chunk): | |
| extracted_dir = dl_manager.extract(archive) | |
| print(f"\tExtracted {archive} to {extracted_dir}") | |
| if self.config.remove_tar and os.path.exists(archive): | |
| os.remove(archive) | |
| print(f"\tDeleted tar file {archive}") | |
| blob_folder = '/'.join(archive.replace("snapshots","blobs").split('/')[:-3]) | |
| if self.config.remove_tar and os.path.exists(blob_folder): | |
| for filename in os.listdir(blob_folder): | |
| filepath = os.path.join(blob_folder, filename) | |
| os.remove(filepath) | |
| print(f"\tDeleted tar file {blob_folder}") | |
| print("All extracted. exiting") | |
| exit() | |
| archives = dl_manager.download({"train":self._get_dataset_filenames(), | |
| "val":self._get_dataset_filenames()}) | |
| else: | |
| archives = dl_manager.download({"train":self._get_dataset_filenames()[0:2], | |
| "val":self._get_dataset_filenames()[0:2]}) | |
| split_metadata = dl_manager.download(_metadata_urls) | |
| train_archives = [dl_manager.iter_archive(archive) for archive in archives["train"]] | |
| val_archives = [dl_manager.iter_archive(archive) for archive in archives["val"]] | |
| return [ | |
| SplitGenerator( | |
| name=Split.TRAIN, | |
| gen_kwargs={ | |
| "archives": train_archives, | |
| "split_txt": split_metadata["train"] | |
| }, | |
| ), | |
| SplitGenerator( | |
| name=Split.VALIDATION, | |
| gen_kwargs={ | |
| "archives": val_archives, | |
| "split_txt": split_metadata["val"] | |
| }, | |
| ), | |
| ] | |
| def _generate_examples(self, archives, split_txt): | |
| with open(split_txt, encoding="utf-8") as split_f: | |
| all_splits = set(split_f.read().split('\n')) | |
| for archive in archives: | |
| for path, file in archive: | |
| archive_start_time = time.time() | |
| if path.split('/')[-1][:-3] not in all_splits: | |
| # skip the image pairs not in train.txt or val.txt | |
| continue | |
| left_rgb, right_rgb, left_semantic, left_instance, left_depth, right_depth = self._h5_loader(file, 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 |