biwi_kinect_head_pose / biwi_kinect_head_pose.py
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Add Biwi Kinect Head Pose dataset. (#3903)
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# Copyright 2022 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Biwi Kinect Head Pose Database."""
import glob
import os
import datasets
_CITATION = """\
@article{fanelli_IJCV,
author = {Fanelli, Gabriele and Dantone, Matthias and Gall, Juergen and Fossati, Andrea and Van Gool, Luc},
title = {Random Forests for Real Time 3D Face Analysis},
journal = {Int. J. Comput. Vision},
year = {2013},
month = {February},
volume = {101},
number = {3},
pages = {437--458}
}
"""
_DESCRIPTION = """\
The Biwi Kinect Head Pose Database is acquired with the Microsoft Kinect sensor, a structured IR light device.It contains 15K images of 20 people with 6 females and 14 males where 4 people were recorded twice.
"""
_HOMEPAGE = "https://icu.ee.ethz.ch/research/datsets.html"
_LICENSE = "This database is made available for non-commercial use such as university research and education."
_URLS = {
"kinect_head_pose_db": "https://data.vision.ee.ethz.ch/cvl/gfanelli/kinect_head_pose_db.tgz",
}
_sequence_to_subject_map = {
"01": "F01",
"02": "F02",
"03": "F03",
"04": "F04",
"05": "F05",
"06": "F06",
"07": "M01",
"08": "M02",
"09": "M03",
"10": "M04",
"11": "M05",
"12": "M06",
"13": "M07",
"14": "M08",
"15": "F03",
"16": "M09",
"17": "M10",
"18": "F05",
"19": "M11",
"20": "M12",
"21": "F02",
"22": "M01",
"23": "M13",
"24": "M14",
}
class BiwiKinectHeadPose(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version("1.0.0")
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"sequence_number": datasets.Value("string"),
"subject_id": datasets.Value("string"),
"rgb": datasets.Sequence(datasets.Image()),
"rgb_cal": {
"intrisic_mat": datasets.Array2D(shape=(3, 3), dtype="float64"),
"extrinsic_mat": {
"rotation": datasets.Array2D(shape=(3, 3), dtype="float64"),
"translation": datasets.Sequence(datasets.Value("float64"), length=3),
},
},
"depth": datasets.Sequence(datasets.Value("string")),
"depth_cal": {
"intrisic_mat": datasets.Array2D(shape=(3, 3), dtype="float64"),
"extrinsic_mat": {
"rotation": datasets.Array2D(shape=(3, 3), dtype="float64"),
"translation": datasets.Sequence(datasets.Value("float64"), length=3),
},
},
"head_pose_gt": datasets.Sequence(
{
"center": datasets.Sequence(datasets.Value("float64"), length=3),
"rotation": datasets.Array2D(shape=(3, 3), dtype="float64"),
}
),
"head_template": datasets.Value("string"),
}
),
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
data_dir = dl_manager.download_and_extract(_URLS)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"dataset_path": os.path.join(data_dir["kinect_head_pose_db"], "hpdb"),
},
),
]
@staticmethod
def _get_calibration_information(cal_file_path):
with open(cal_file_path, "r", encoding="utf-8") as f:
cal_info = f.read().splitlines()
intrisic_mat = []
extrinsic_mat = []
for data in cal_info[:3]:
row = list(map(float, data.strip().split(" ")))
intrisic_mat.append(row)
for data in cal_info[6:9]:
row = list(map(float, data.strip().split(" ")))
extrinsic_mat.append(row)
translation = list(map(float, cal_info[10].strip().split(" ")))
return {
"intrisic_mat": intrisic_mat,
"extrinsic_mat": {
"rotation": extrinsic_mat,
"translation": translation,
},
}
@staticmethod
def _parse_head_pose_info(head_pose_file):
with open(head_pose_file, "r", encoding="utf-8") as f:
head_pose_info = f.read().splitlines()
rotation = []
for data in head_pose_info[:3]:
row = list(map(float, data.strip().split(" ")))
rotation.append(row)
center = list(map(float, head_pose_info[4].strip().split(" ")))
return {
"center": center,
"rotation": rotation,
}
@staticmethod
def _get_head_pose_information(path):
head_pose_files = sorted(glob.glob(os.path.join(path, "*_pose.txt")))
head_poses_info = []
for head_pose_file in head_pose_files:
head_pose = BiwiKinectHeadPose._parse_head_pose_info(head_pose_file)
head_poses_info.append(head_pose)
return head_poses_info
def _generate_examples(self, dataset_path):
idx = 0
folders = os.listdir(dataset_path)
for item in folders:
sequence_number = item
sequence_base_path = os.path.join(dataset_path, sequence_number)
if os.path.isdir(sequence_base_path):
rgb_files = sorted(glob.glob(os.path.join(sequence_base_path, "*.png")))
depth_files = sorted(glob.glob(os.path.join(sequence_base_path, "*.bin")))
head_template_path = os.path.join(dataset_path, sequence_number + ".obj")
rgb_cal = self._get_calibration_information(os.path.join(sequence_base_path, "rgb.cal"))
depth_cal = self._get_calibration_information(os.path.join(sequence_base_path, "depth.cal"))
head_pose_gt = self._get_head_pose_information(sequence_base_path)
yield idx, {
"sequence_number": sequence_number,
"subject_id": _sequence_to_subject_map[sequence_number],
"rgb": rgb_files,
"rgb_cal": rgb_cal,
"depth": depth_files,
"depth_cal": depth_cal,
"head_pose_gt": head_pose_gt,
"head_template": head_template_path,
}
idx += 1