Datasets:
Size:
10K<n<100K
License:
File size: 2,208 Bytes
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import io
from PIL import Image
from datasets import GeneratorBasedBuilder, DatasetInfo, Features, SplitGenerator, Value, Array2D, Split
import datasets
import OpenEXR
import Imath
import numpy as np
import h5py
class RGBSemanticDepthDataset(GeneratorBasedBuilder):
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):
# 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'])
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)
print(left_rgb.shape, left_depth.shape, left_seg.shape)
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_1729368304.063240.tar"]})
return [
SplitGenerator(
name=Split.TRAIN,
gen_kwargs={
"archives": [dl_manager.iter_archive(archive) for archive in archives["train"]],
},
),
]
def _generate_examples(self, archives):
for archive in archives:
for path, file in archive:
print(path)
left_rgb, right_rgb, left_seg, left_depth, right_depth = self._h5_loader(file.read())
yield path, {
"left_rgb": left_rgb,
"right_rgb": right_rgb,
"left_seg": left_seg,
"left_depth": left_depth,
"right_depth": right_depth,
} |