import io from PIL import Image from datasets import GeneratorBasedBuilder, DatasetInfo, Features, SplitGenerator, Value, Array2D, Split import datasets import numpy as np import h5py 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="all", version="1.0.0", description="load both segmentation and depth"), CustomConfig(name="depth", version="1.0.0", description="only load depth"), CustomConfig(name="seg", version="1.0.0", description="only load segmentation"), ] # Configs initialization BUILDER_CONFIG_CLASS = CustomConfig def _info(self): return DatasetInfo( features=Features({ "left_rgb": datasets.Image(), "right_rgb": datasets.Image(), "left_seg": 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)), left_depth, right_depth elif type_dataset == 'seg': left_seg = h5f['seg_left'][:] return left_rgb, np.zeros((1,1)), left_seg, np.zeros((1,1)), np.zeros((1,1)) else: right_rgb = self._read_jpg(h5f['rgb_right'][:]) left_seg = h5f['seg_left'][:] left_depth = h5f['depth_left'][:].astype(np.float32) right_depth = h5f['depth_right'][:].astype(np.float32) return left_rgb, right_rgb, left_seg, left_depth, right_depth def _read_jpg(self, bytes_stream): return Image.open(io.BytesIO(bytes_stream)) def _split_generators(self, dl_manager): archives = dl_manager.download({"train":["data/images_1730238419.175364.tar"], "val":["data/images_1730238419.175364.tar"]}) 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_seg, left_depth, right_depth = self._h5_loader(file.read(), self.config.dataset_type) yield path, { "left_rgb": left_rgb, "right_rgb": right_rgb, "left_seg": left_seg, "left_depth": left_depth, "right_depth": right_depth, }