Datasets:
Size:
10K<n<100K
License:
File size: 4,277 Bytes
5a009ef 09039e6 04a1354 de764b8 6052bc2 de764b8 09039e6 5a009ef 09039e6 c2447c9 09039e6 5a009ef acc9038 5a009ef 4973c42 acc9038 5a009ef 25b2942 de764b8 5a009ef e1be4b7 de764b8 c2447c9 be55668 c2447c9 e1be4b7 de764b8 5a009ef c2447c9 e1be4b7 abd471b c2447c9 c33d213 13f81de e1be4b7 13f81de d638aaf 13f81de |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 |
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
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,
} |