Upload VALERIE22.py
Browse files- VALERIE22.py +231 -0
VALERIE22.py
ADDED
@@ -0,0 +1,231 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# coding=utf-8
|
2 |
+
# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
|
3 |
+
#
|
4 |
+
# Licensed under the Apache License, Version 2.0 (the "License");
|
5 |
+
# you may not use this file except in compliance with the License.
|
6 |
+
# You may obtain a copy of the License at
|
7 |
+
#
|
8 |
+
# http://www.apache.org/licenses/LICENSE-2.0
|
9 |
+
#
|
10 |
+
# Unless required by applicable law or agreed to in writing, software
|
11 |
+
# distributed under the License is distributed on an "AS IS" BASIS,
|
12 |
+
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
13 |
+
# See the License for the specific language governing permissions and
|
14 |
+
# limitations under the License.
|
15 |
+
"""VALERIE22 dataset"""
|
16 |
+
|
17 |
+
import os
|
18 |
+
import json
|
19 |
+
import glob
|
20 |
+
|
21 |
+
import datasets
|
22 |
+
|
23 |
+
|
24 |
+
_HOMEPAGE = "https://huggingface.co/datasets/Intel/VALERIE22"
|
25 |
+
|
26 |
+
_LICENSE = "Creative Commons — CC0 1.0 Universal"
|
27 |
+
|
28 |
+
_CITATION = """\
|
29 |
+
tba
|
30 |
+
"""
|
31 |
+
|
32 |
+
_DESCRIPTION = """\
|
33 |
+
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.
|
34 |
+
"""
|
35 |
+
|
36 |
+
_REPO = "https://huggingface.co/datasets/Intel/VALERIE22/resolve/main"
|
37 |
+
|
38 |
+
_SEQUENCES = {
|
39 |
+
"train": ["intel_results_sequence_0050.zip", "intel_results_sequence_0052.zip", "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"],
|
40 |
+
"validation":["intel_results_sequence_0062_part1.zip", "intel_results_sequence_0062_part2.zip"],
|
41 |
+
"test":["intel_results_sequence_0062_part1.zip", "intel_results_sequence_0062_part2.zip"]
|
42 |
+
}
|
43 |
+
|
44 |
+
_URLS = {
|
45 |
+
"train": [f"{_REPO}/data/{sequence}" for sequence in _SEQUENCES["train"]],
|
46 |
+
"validation": [f"{_REPO}/data/{sequence}" for sequence in _SEQUENCES["validation"]],
|
47 |
+
"test": [f"{_REPO}/data/{sequence}" for sequence in _SEQUENCES["test"]]
|
48 |
+
}
|
49 |
+
|
50 |
+
class VALERIE22(datasets.GeneratorBasedBuilder):
|
51 |
+
"""VALERIE22 dataset."""
|
52 |
+
|
53 |
+
VERSION = datasets.Version("1.0.0")
|
54 |
+
|
55 |
+
def _info(self):
|
56 |
+
return datasets.DatasetInfo(
|
57 |
+
description=_DESCRIPTION,
|
58 |
+
features=datasets.Features(
|
59 |
+
{
|
60 |
+
"image": datasets.Image(),
|
61 |
+
"image_distorted": datasets.Image(),
|
62 |
+
"persons_png": datasets.Sequence(
|
63 |
+
{
|
64 |
+
"bbox": datasets.Sequence(datasets.Value("float32"), length=4),
|
65 |
+
"bbox_vis": datasets.Sequence(datasets.Value("float32"), length=4),
|
66 |
+
"occlusion": datasets.Value("float32"),
|
67 |
+
"distance": datasets.Value("float32"),
|
68 |
+
"v_x": datasets.Value("float32"),
|
69 |
+
"v_y": datasets.Value("float32"),
|
70 |
+
"truncated": datasets.Value("bool"),
|
71 |
+
"total_pixels_object": datasets.Value("float32"),
|
72 |
+
"total_visible_pixels_object": datasets.Value("float32"),
|
73 |
+
"contrast_rgb_full": datasets.Value("float32"),
|
74 |
+
"contrast_edge": datasets.Value("float32"),
|
75 |
+
"contrast_rgb": datasets.Value("float32"),
|
76 |
+
"luminance": datasets.Value("float32"),
|
77 |
+
"perceived_lightness": datasets.Value("float32"),
|
78 |
+
"3dbbox": datasets.Sequence(datasets.Value("float32"), length=6) # 3center, 3 size
|
79 |
+
}
|
80 |
+
),
|
81 |
+
"persons_png_distorted": datasets.Sequence(
|
82 |
+
{
|
83 |
+
"bbox": datasets.Sequence(datasets.Value("float32"), length=4),
|
84 |
+
"bbox_vis": datasets.Sequence(datasets.Value("float32"), length=4),
|
85 |
+
"occlusion": datasets.Value("float32"),
|
86 |
+
"distance": datasets.Value("float32"),
|
87 |
+
"v_x": datasets.Value("float32"),
|
88 |
+
"v_y": datasets.Value("float32"),
|
89 |
+
"truncated": datasets.Value("bool"),
|
90 |
+
"total_pixels_object": datasets.Value("float32"),
|
91 |
+
"total_visible_pixels_object": datasets.Value("float32"),
|
92 |
+
"contrast_rgb_full": datasets.Value("float32"),
|
93 |
+
"contrast_edge": datasets.Value("float32"),
|
94 |
+
"contrast_rgb": datasets.Value("float32"),
|
95 |
+
"luminance": datasets.Value("float32"),
|
96 |
+
"perceived_lightness": datasets.Value("float32"),
|
97 |
+
"3dbbox": datasets.Sequence(datasets.Value("float32"), length=6) # 3center, 3 size
|
98 |
+
}
|
99 |
+
),
|
100 |
+
"semantic_group_segmentation": datasets.Image(),
|
101 |
+
"semantic_instance_segmentation": datasets.Image()
|
102 |
+
}
|
103 |
+
),
|
104 |
+
supervised_keys=None,
|
105 |
+
homepage=_HOMEPAGE,
|
106 |
+
license=_LICENSE,
|
107 |
+
citation=_CITATION,
|
108 |
+
)
|
109 |
+
|
110 |
+
def _split_generators(self, dl_manager):
|
111 |
+
data_dir = dl_manager.download_and_extract(_URLS)
|
112 |
+
return [
|
113 |
+
datasets.SplitGenerator(
|
114 |
+
name=datasets.Split.TRAIN,
|
115 |
+
gen_kwargs={
|
116 |
+
"split": "train",
|
117 |
+
"data_dirs": data_dir["train"],
|
118 |
+
},
|
119 |
+
),
|
120 |
+
datasets.SplitGenerator(
|
121 |
+
name=datasets.Split.TEST,
|
122 |
+
gen_kwargs={
|
123 |
+
"split": "test",
|
124 |
+
"data_dirs": data_dir["test"],
|
125 |
+
},
|
126 |
+
),
|
127 |
+
datasets.SplitGenerator(
|
128 |
+
name=datasets.Split.VALIDATION,
|
129 |
+
gen_kwargs={
|
130 |
+
"split": "validation",
|
131 |
+
"data_dirs": data_dir["validation"],
|
132 |
+
},
|
133 |
+
),
|
134 |
+
]
|
135 |
+
|
136 |
+
def _generate_examples(self, split, data_dirs):
|
137 |
+
sequence_dirs = []
|
138 |
+
for data_dir, sequence in zip(data_dirs, _SEQUENCES[split]):
|
139 |
+
sequence = sequence.replace(".zip","")
|
140 |
+
if "_part1" in sequence:
|
141 |
+
sequence = sequence.replace("_part1","")
|
142 |
+
if "_part2" in sequence:
|
143 |
+
sequence_0062_part2_dir = os.path.join(data_dir, sequence.replace("_part2","_b"))
|
144 |
+
continue
|
145 |
+
sequence_dirs.append(os.path.join(data_dir, sequence))
|
146 |
+
|
147 |
+
idx = 0
|
148 |
+
for sequence_dir in sequence_dirs:
|
149 |
+
for filename in glob.glob(os.path.join(os.path.join(sequence_dir, "sensor/camera/left/png"), "*.png")):
|
150 |
+
# image_file_path
|
151 |
+
image_file_path = filename
|
152 |
+
|
153 |
+
# image_distorted_file_path
|
154 |
+
if "_0062" in sequence_dir:
|
155 |
+
image_distorted_file_path = os.path.join(sequence_0062_part2_dir, "sensor/camera/left/png_distorted/", os.path.basename(filename))
|
156 |
+
else:
|
157 |
+
image_distorted_file_path = filename.replace("/png/", "/png_distorted/")
|
158 |
+
|
159 |
+
#persons_png
|
160 |
+
persons_png_path = filename.replace("sensor/camera/left/png/", "ground-truth/2d-bounding-box_json/")
|
161 |
+
|
162 |
+
#persons_distorted_png
|
163 |
+
persons_distorted_png_path = filename.replace("sensor/camera/left/png/", "ground-truth/2d-bounding-box_json_png_distorted/")
|
164 |
+
|
165 |
+
#semantic_group_segmentation_file_path
|
166 |
+
semantic_group_segmentation_file_path = filename.replace("sensor/camera/left/png/", "ground-truth/semantic-group-segmentation_png/")
|
167 |
+
|
168 |
+
# semantic_instance_segmentation_file_path
|
169 |
+
semantic_instance_segmentation_file_path = filename.replace("sensor/camera/left/png/", "ground-truth/semantic-instance-segmentation_png/")
|
170 |
+
|
171 |
+
# check if all gt files are available
|
172 |
+
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)):
|
173 |
+
continue
|
174 |
+
|
175 |
+
with open(persons_png_path.replace(".png",".json"), 'r') as json_file:
|
176 |
+
bb_person_json = json.load(json_file)
|
177 |
+
|
178 |
+
with open(persons_distorted_png_path.replace(".png",".json"), 'r') as json_file:
|
179 |
+
bb_person_distorted_json = json.load(json_file)
|
180 |
+
|
181 |
+
threed_bb_person_path = filename.replace("sensor/camera/left/png/", "ground-truth/3d-bounding-box_json/")
|
182 |
+
with open(os.path.join(threed_bb_person_path.replace(".png",".json")), 'r') as json_file:
|
183 |
+
threed_bb_person_distorted_json = json.load(json_file)
|
184 |
+
|
185 |
+
persons_png = []
|
186 |
+
persons_png_distorted = []
|
187 |
+
for key in bb_person_json:
|
188 |
+
persons_png.append(
|
189 |
+
{
|
190 |
+
"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"]],
|
191 |
+
"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"]],
|
192 |
+
"occlusion": bb_person_json[key]["occlusion"],
|
193 |
+
"distance": bb_person_json[key]["distance"],
|
194 |
+
"v_x": bb_person_json[key]["v_x"],
|
195 |
+
"v_y": bb_person_json[key]["v_y"],
|
196 |
+
"truncated": bb_person_json[key]["truncated"],
|
197 |
+
"total_pixels_object": bb_person_json[key]["total_pixels_object"],
|
198 |
+
"total_visible_pixels_object": bb_person_json[key]["total_visible_pixels_object"],
|
199 |
+
"contrast_rgb_full": bb_person_json[key]["contrast_rgb_full"],
|
200 |
+
"contrast_edge": bb_person_json[key]["contrast_edge"],
|
201 |
+
"contrast_rgb": bb_person_json[key]["contrast_rgb"],
|
202 |
+
"luminance": bb_person_json[key]["luminance"],
|
203 |
+
"perceived_lightness": bb_person_json[key]["perceived_lightness"],
|
204 |
+
"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],
|
205 |
+
threed_bb_person_distorted_json[key]["size"][1], threed_bb_person_distorted_json[key]["size"][2]] # 3center, 3 size
|
206 |
+
}
|
207 |
+
)
|
208 |
+
|
209 |
+
persons_png_distorted.append(
|
210 |
+
{
|
211 |
+
"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"]],
|
212 |
+
"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"]],
|
213 |
+
"occlusion": bb_person_distorted_json[key]["occlusion"],
|
214 |
+
"distance": bb_person_distorted_json[key]["distance"],
|
215 |
+
"v_x": bb_person_distorted_json[key]["v_x"],
|
216 |
+
"v_y": bb_person_distorted_json[key]["v_y"],
|
217 |
+
"truncated": bb_person_distorted_json[key]["truncated"],
|
218 |
+
"total_pixels_object": bb_person_distorted_json[key]["total_pixels_object"],
|
219 |
+
"total_visible_pixels_object": bb_person_distorted_json[key]["total_visible_pixels_object"],
|
220 |
+
"contrast_rgb_full": bb_person_distorted_json[key]["contrast_rgb_full"],
|
221 |
+
"contrast_edge": bb_person_distorted_json[key]["contrast_edge"],
|
222 |
+
"contrast_rgb": bb_person_distorted_json[key]["contrast_rgb"],
|
223 |
+
"luminance": bb_person_distorted_json[key]["luminance"],
|
224 |
+
"perceived_lightness": bb_person_distorted_json[key]["perceived_lightness"],
|
225 |
+
"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],
|
226 |
+
threed_bb_person_distorted_json[key]["size"][1], threed_bb_person_distorted_json[key]["size"][2]] # 3center, 3 size
|
227 |
+
}
|
228 |
+
)
|
229 |
+
|
230 |
+
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}
|
231 |
+
idx += 1
|