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# coding=utf-8
# Copyright 2020 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.
"""VALERIE22 dataset"""
import os
import json
import glob
import datasets
_HOMEPAGE = "https://huggingface.co/datasets/Intel/VALERIE22"
_LICENSE = "Creative Commons — CC0 1.0 Universal"
_CITATION = """\
tba
"""
_DESCRIPTION = """\
The VALERIE22 dataset was generated with the VALERIE procedural tools pipeline providing a photorealistic sensor simulation rendered from automatically synthesized scenes. The dataset provides a uniquely rich set of metadata, allowing extraction of specific scene and semantic features (like pixel-accurate occlusion rates, positions in the scene and distance + angle to the camera). This enables a multitude of possible tests on the data and we hope to stimulate research on understanding performance of DNNs.
"""
_REPO = "https://huggingface.co/datasets/Intel/VALERIE22/resolve/main"
_SEQUENCES = {
"train": ["intel_results_sequence_0057.zip", "intel_results_sequence_0058.zip", "intel_results_sequence_0059.zip", "intel_results_sequence_0060.zip", "intel_results_sequence_0062_part1.zip", "intel_results_sequence_0062_part2.zip"],
"validation":["intel_results_sequence_0062_part1.zip", "intel_results_sequence_0062_part2.zip"],
"test":["intel_results_sequence_0062_part1.zip", "intel_results_sequence_0062_part2.zip"]
}
_URLS = {
"train": [f"{_REPO}/data/{sequence}" for sequence in _SEQUENCES["train"]],
"validation": [f"{_REPO}/data/{sequence}" for sequence in _SEQUENCES["validation"]],
"test": [f"{_REPO}/data/{sequence}" for sequence in _SEQUENCES["test"]]
}
class VALERIE22(datasets.GeneratorBasedBuilder):
"""VALERIE22 dataset."""
VERSION = datasets.Version("1.0.0")
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"image": datasets.Image(),
"image_distorted": datasets.Image(),
"persons_png": datasets.Sequence(
{
"bbox": datasets.Sequence(datasets.Value("float32"), length=4),
"bbox_vis": datasets.Sequence(datasets.Value("float32"), length=4),
"occlusion": datasets.Value("float32"),
"distance": datasets.Value("float32"),
"v_x": datasets.Value("float32"),
"v_y": datasets.Value("float32"),
"truncated": datasets.Value("bool"),
"total_pixels_object": datasets.Value("float32"),
"total_visible_pixels_object": datasets.Value("float32"),
"contrast_rgb_full": datasets.Value("float32"),
"contrast_edge": datasets.Value("float32"),
"contrast_rgb": datasets.Value("float32"),
"luminance": datasets.Value("float32"),
"perceived_lightness": datasets.Value("float32"),
"3dbbox": datasets.Sequence(datasets.Value("float32"), length=6) # 3center, 3 size
}
),
"persons_png_distorted": datasets.Sequence(
{
"bbox": datasets.Sequence(datasets.Value("float32"), length=4),
"bbox_vis": datasets.Sequence(datasets.Value("float32"), length=4),
"occlusion": datasets.Value("float32"),
"distance": datasets.Value("float32"),
"v_x": datasets.Value("float32"),
"v_y": datasets.Value("float32"),
"truncated": datasets.Value("bool"),
"total_pixels_object": datasets.Value("float32"),
"total_visible_pixels_object": datasets.Value("float32"),
"contrast_rgb_full": datasets.Value("float32"),
"contrast_edge": datasets.Value("float32"),
"contrast_rgb": datasets.Value("float32"),
"luminance": datasets.Value("float32"),
"perceived_lightness": datasets.Value("float32"),
"3dbbox": datasets.Sequence(datasets.Value("float32"), length=6) # 3center, 3 size
}
),
"semantic_group_segmentation": datasets.Image(),
"semantic_instance_segmentation": datasets.Image()
}
),
supervised_keys=None,
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={
"split": "train",
"data_dirs": data_dir["train"],
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={
"split": "test",
"data_dirs": data_dir["test"],
},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={
"split": "validation",
"data_dirs": data_dir["validation"],
},
),
]
def _generate_examples(self, split, data_dirs):
sequence_dirs = []
for data_dir, sequence in zip(data_dirs, _SEQUENCES[split]):
sequence = sequence.replace(".zip","")
if "_part1" in sequence:
sequence = sequence.replace("_part1","")
if "_part2" in sequence:
sequence_0062_part2_dir = os.path.join(data_dir, sequence.replace("_part2","_b"))
continue
sequence_dirs.append(os.path.join(data_dir, sequence))
idx = 0
for sequence_dir in sequence_dirs:
for filename in glob.glob(os.path.join(os.path.join(sequence_dir, "sensor/camera/left/png"), "*.png")):
# image_file_path
image_file_path = filename
# image_distorted_file_path
if "_0062" in sequence_dir:
image_distorted_file_path = os.path.join(sequence_0062_part2_dir, "sensor/camera/left/png_distorted/", os.path.basename(filename))
else:
image_distorted_file_path = filename.replace("/png/", "/png_distorted/")
#persons_png
persons_png_path = filename.replace("sensor/camera/left/png/", "ground-truth/2d-bounding-box_json/")
#persons_distorted_png
persons_distorted_png_path = filename.replace("sensor/camera/left/png/", "ground-truth/2d-bounding-box_json_png_distorted/")
#semantic_group_segmentation_file_path
semantic_group_segmentation_file_path = filename.replace("sensor/camera/left/png/", "ground-truth/semantic-group-segmentation_png/")
# semantic_instance_segmentation_file_path
semantic_instance_segmentation_file_path = filename.replace("sensor/camera/left/png/", "ground-truth/semantic-instance-segmentation_png/")
# check if all gt files are available
if not (os.path.isfile(image_file_path) and os.path.isfile(image_distorted_file_path) and os.path.isfile(persons_png_path.replace(".png",".json")) and os.path.isfile(persons_distorted_png_path.replace(".png",".json")) and os.path.isfile(semantic_group_segmentation_file_path) and os.path.isfile(semantic_instance_segmentation_file_path)):
continue
with open(persons_png_path.replace(".png",".json"), 'r') as json_file:
bb_person_json = json.load(json_file)
with open(persons_distorted_png_path.replace(".png",".json"), 'r') as json_file:
bb_person_distorted_json = json.load(json_file)
threed_bb_person_path = filename.replace("sensor/camera/left/png/", "ground-truth/3d-bounding-box_json/")
with open(os.path.join(threed_bb_person_path.replace(".png",".json")), 'r') as json_file:
threed_bb_person_distorted_json = json.load(json_file)
persons_png = []
persons_png_distorted = []
for key in bb_person_json:
persons_png.append(
{
"bbox": [bb_person_json[key]["bb"]["c_x"], bb_person_json[key]["bb"]["c_y"], bb_person_json[key]["bb"]["w"], bb_person_json[key]["bb"]["h"]],
"bbox_vis": [bb_person_json[key]["bb_vis"]["c_x"], bb_person_json[key]["bb_vis"]["c_y"], bb_person_json[key]["bb_vis"]["w"], bb_person_json[key]["bb_vis"]["h"]],
"occlusion": bb_person_json[key]["occlusion"],
"distance": bb_person_json[key]["distance"],
"v_x": bb_person_json[key]["v_x"],
"v_y": bb_person_json[key]["v_y"],
"truncated": bb_person_json[key]["truncated"],
"total_pixels_object": bb_person_json[key]["total_pixels_object"],
"total_visible_pixels_object": bb_person_json[key]["total_visible_pixels_object"],
"contrast_rgb_full": bb_person_json[key]["contrast_rgb_full"],
"contrast_edge": bb_person_json[key]["contrast_edge"],
"contrast_rgb": bb_person_json[key]["contrast_rgb"],
"luminance": bb_person_json[key]["luminance"],
"perceived_lightness": bb_person_json[key]["perceived_lightness"],
"3dbbox": [threed_bb_person_distorted_json[key]["center"][0], threed_bb_person_distorted_json[key]["center"][1], threed_bb_person_distorted_json[key]["center"][2], threed_bb_person_distorted_json[key]["size"][0],
threed_bb_person_distorted_json[key]["size"][1], threed_bb_person_distorted_json[key]["size"][2]] # 3center, 3 size
}
)
persons_png_distorted.append(
{
"bbox": [bb_person_distorted_json[key]["bb"]["c_x"], bb_person_distorted_json[key]["bb"]["c_y"], bb_person_distorted_json[key]["bb"]["w"], bb_person_distorted_json[key]["bb"]["h"]],
"bbox_vis": [bb_person_distorted_json[key]["bb_vis"]["c_x"], bb_person_distorted_json[key]["bb_vis"]["c_y"], bb_person_distorted_json[key]["bb_vis"]["w"], bb_person_distorted_json[key]["bb_vis"]["h"]],
"occlusion": bb_person_distorted_json[key]["occlusion"],
"distance": bb_person_distorted_json[key]["distance"],
"v_x": bb_person_distorted_json[key]["v_x"],
"v_y": bb_person_distorted_json[key]["v_y"],
"truncated": bb_person_distorted_json[key]["truncated"],
"total_pixels_object": bb_person_distorted_json[key]["total_pixels_object"],
"total_visible_pixels_object": bb_person_distorted_json[key]["total_visible_pixels_object"],
"contrast_rgb_full": bb_person_distorted_json[key]["contrast_rgb_full"],
"contrast_edge": bb_person_distorted_json[key]["contrast_edge"],
"contrast_rgb": bb_person_distorted_json[key]["contrast_rgb"],
"luminance": bb_person_distorted_json[key]["luminance"],
"perceived_lightness": bb_person_distorted_json[key]["perceived_lightness"],
"3dbbox": [threed_bb_person_distorted_json[key]["center"][0], threed_bb_person_distorted_json[key]["center"][1], threed_bb_person_distorted_json[key]["center"][2], threed_bb_person_distorted_json[key]["size"][0],
threed_bb_person_distorted_json[key]["size"][1], threed_bb_person_distorted_json[key]["size"][2]] # 3center, 3 size
}
)
yield idx, {"image": image_file_path, "image_distorted": image_distorted_file_path, "persons_png": persons_png, "persons_png_distorted":persons_png_distorted, "semantic_group_segmentation": semantic_group_segmentation_file_path, "semantic_instance_segmentation": semantic_instance_segmentation_file_path}
idx += 1
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