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import datasets
import os
from PIL import Image
_KITTI2012_URL = "https://s3.eu-central-1.amazonaws.com/avg-kitti/data_stereo_flow.zip"
class KITTIStereo2012(datasets.GeneratorBasedBuilder):
"""KITTI Stereo 2012 dataset with stereo grayscale/color images and disparity ground truth."""
VERSION = datasets.Version("1.0.0")
def _info(self):
return datasets.DatasetInfo(
description=(
"KITTI Stereo 2012 dataset: includes stereo grayscale and color images captured at two timepoints,"
" along with disparity ground truth and calibration matrices. Designed for stereo vision and flow tasks."
),
features=datasets.Features(
{
"ImageGray_t0": datasets.Sequence(datasets.Image()),
"ImageGray_t1": datasets.Sequence(datasets.Image()),
"ImageColor_t0": datasets.Sequence(datasets.Image()),
"ImageColor_t1": datasets.Sequence(datasets.Image()),
"calib": {
"P0": datasets.Sequence(datasets.Value("float32")),
"P1": datasets.Sequence(datasets.Value("float32")),
"P2": datasets.Sequence(datasets.Value("float32")),
"P3": datasets.Sequence(datasets.Value("float32")),
},
"disp_noc": datasets.Image(),
"disp_occ": datasets.Image(),
"disp_refl_noc": datasets.Image(),
"disp_refl_occ": datasets.Image(),
"flow_noc": datasets.Image(),
"flow_occ": datasets.Image(),
}
),
supervised_keys=("ImageGray_t0", "disp_noc"),
homepage="http://www.cvlibs.net/datasets/kitti/eval_stereo_flow.php?benchmark=stereo",
license="CC BY-NC-SA 3.0",
citation="""@inproceedings{Geiger2012CVPR,
author = {Andreas Geiger and Philip Lenz and Raquel Urtasun},
title = {Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite},
booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2012}
}""",
)
def _split_generators(self, dl_manager):
archive_path = dl_manager.download_and_extract(_KITTI2012_URL)
train_path = os.path.join(archive_path, "training")
test_path = os.path.join(archive_path, "testing")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"base_path": train_path, "split": "training"},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"base_path": test_path, "split": "testing"},
),
]
def _generate_examples(self, base_path, split):
image_0_path = os.path.join(base_path, "image_0")
image_1_path = os.path.join(base_path, "image_1")
color_0_path = os.path.join(base_path, "colored_0")
color_1_path = os.path.join(base_path, "colored_1")
calib_path = os.path.join(base_path, "calib")
if split == "training":
disp_path = os.path.join(base_path, "disp_noc")
disp_path_occ = os.path.join(base_path, "disp_occ")
disp_path_refl_noc = os.path.join(base_path, "disp_refl_noc")
disp_path_refl_occ = os.path.join(base_path, "disp_refl_occ")
flow_noc_path = os.path.join(base_path, "flow_noc")
flow_occ_path = os.path.join(base_path, "flow_occ")
files = sorted(os.listdir(image_0_path))
ids = sorted(set(f.split("_")[0] for f in files))
for id_ in ids:
example = {
"ImageGray_t0": [
Image.open(os.path.join(image_0_path, f"{id_}_10.png")),
Image.open(os.path.join(image_1_path, f"{id_}_10.png")),
],
"ImageGray_t1": [
Image.open(os.path.join(image_0_path, f"{id_}_11.png")),
Image.open(os.path.join(image_1_path, f"{id_}_11.png")),
],
"ImageColor_t0": [
Image.open(os.path.join(color_0_path, f"{id_}_10.png")),
Image.open(os.path.join(color_1_path, f"{id_}_10.png")),
],
"ImageColor_t1": [
Image.open(os.path.join(color_0_path, f"{id_}_11.png")),
Image.open(os.path.join(color_1_path, f"{id_}_11.png")),
],
"calib": {
"P0": [],
"P1": [],
"P2": [],
"P3": [],
},
"disp_noc": Image.open(os.path.join(disp_path, f"{id_}_10.png")) if split == "training" else None,
"disp_occ": Image.open(os.path.join(disp_path_occ, f"{id_}_10.png")) if split == "training" else None,
"disp_refl_noc": Image.open(os.path.join(disp_path_refl_noc, f"{id_}_10.png")) if split == "training" else None,
"disp_refl_occ": Image.open(os.path.join(disp_path_refl_occ, f"{id_}_10.png")) if split == "training" else None,
"flow_noc": Image.open(os.path.join(flow_noc_path, f"{id_}_10.png")) if split == "training" else None,
"flow_occ": Image.open(os.path.join(flow_occ_path, f"{id_}_10.png")) if split == "training" else None,
}
calib_file = os.path.join(calib_path, f"{id_}.txt")
with open(calib_file, "r") as f:
lines = f.readlines()
for line in lines:
key, value = line.strip().split(":", 1)
if key in ["P0", "P1", "P2", "P3"]:
example["calib"][key] = [float(x) for x in value.strip().split()]
yield id_, example |