Datasets:
biwi_kinect_head_pose

	# 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