Datasets:
File size: 3,412 Bytes
a4fc994 d7fb0e1 a4fc994 d7fb0e1 a4fc994 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 |
# coding=utf-8
# Copyright 2021 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.
"""Dataset class for Food-101 dataset."""
import datasets
from datasets.tasks import ImageClassification
import json
import requests
_HOMEPAGE = "https://huggingface.co/datasets/emanuelebezzecchi/trailerShotScale"
_DESCRIPTION = (
"Shot scale has five categories: "
"0) extreme close-up shot (ECS) shows even smaller parts such as the image of an eye or a mouth."
"1) close-up shot (CS) concentrates on a relatively small object, showing the face of the hand of a person;"
"2) medium shot (MS) contains a figure from the knees or waist up;"
"3) full shot (FS) barely includes the human body in full;"
"4) long shot (LS) is taken from a long distance, sometimes as far as a quarter of a mile away;"
)
_CITATION = """\
@inproceedings{rao2020unified,
title={A Unified Framework for Shot Type Classification Based on Subject Centric Lens},
author={Rao, Anyi and Wang, Jiaze and Xu, Linning and Jiang, Xuekun and Huang, Qingqiu and Zhou, Bolei and Lin, Dahua},
booktitle = {The European Conference on Computer Vision (ECCV)},
year={2020}
}
"""
_LICENSE = """\
LICENSE AGREEMENT
=================
"""
_NAMES = ["ECS","CS","MS","FS","LS"]
_JSON_DIR = "https://huggingface.co/datasets/emanuelebezzecchi/trailerShotScale/resolve/main/data.json"
_URL = "https://huggingface.co/datasets/emanuelebezzecchi/trailerShotScale/resolve/main/images.tar.gz"
data = json.loads(requests.get(_JSON_DIR).content)
imgLabels = data['labels']
class trailerShotScale(datasets.GeneratorBasedBuilder):
"""trailerShotScale 10% of data, 5 images per folder respect to original"""
def _info(self):
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=datasets.Features(
{
"image": datasets.Image(),
"label": datasets.ClassLabel(names=_NAMES),
}
),
supervised_keys=("image", "label"),
homepage=_HOMEPAGE,
citation=_CITATION,
license=_LICENSE,
task_templates=[ImageClassification(image_column="image", label_column="label")],
)
def _split_generators(self, dl_manager):
path = dl_manager.download(_URL)
image_iters = dl_manager.iter_archive(path)
return [datasets.SplitGenerator(datasets.Split.TRAIN,gen_kwargs={"images":image_iters,})]
def _generate_examples(self, images):
"""Generate images and labels for splits."""
idx = 0
#Iterate through images
for filepath,image in images:
yield idx, {
"image":{"path":filepath, "bytes":image.read()},
"label":imgLabels[idx]
}
idx += 1
|