Source code for tensorbay.opendataset.ImageEmotion.loader

#!/usr/bin/env python3
#
# Copyright 2021 Graviti. Licensed under MIT License.
#
# pylint: disable=invalid-name

"""Dataloaders of ImageEmotionAbstract dataset and ImageEmotionArtPhoto dataset."""

import csv
import os

from tensorbay.dataset import Data, Dataset
from tensorbay.label import Classification
from tensorbay.opendataset._utility import glob

DATASET_NAME_ABSTRACT = "ImageEmotionAbstract"
DATASET_NAME_ARTPHOTO = "ImageEmotionArtPhoto"


[docs]def ImageEmotionAbstract(path: str) -> Dataset: """`Image Emotion-abstract <https://www.imageemotion.org/>`_ dataset. The file structure should be like:: <path> ABSTRACT_groundTruth.csv abstract_xxxx.jpg ... Arguments: path: The root directory of the dataset. Returns: Loaded :class:`~tensorbay.dataset.dataset.Dataset` instance. """ root_path = os.path.abspath(os.path.expanduser(path)) dataset = Dataset(DATASET_NAME_ABSTRACT) dataset.load_catalog(os.path.join(os.path.dirname(__file__), "catalog_abstract.json")) segment = dataset.create_segment() csv_path = os.path.join(root_path, "ABSTRACT_groundTruth.csv") with open(csv_path, encoding="utf-8") as fp: reader = csv.DictReader(fp) reader.fieldnames = [ field.strip("'") for field in reader.fieldnames # type:ignore[union-attr] ] for row in reader: image_path = os.path.join(root_path, row.pop("").strip("'")) data = Data(image_path) values = {key: int(value) for key, value in row.items()} data.label.classification = Classification(attributes=values) segment.append(data) return dataset
[docs]def ImageEmotionArtphoto(path: str) -> Dataset: """`Image Emotion-art Photo <https://www.imageemotion.org/>`_ dataset. The file structure should be like:: <path> <filename>.jpg ... Arguments: path: The root directory of the dataset. Returns: Loaded :class:`~tensorbay.dataset.dataset.Dataset` instance. """ root_path = os.path.abspath(os.path.expanduser(path)) dataset = Dataset(DATASET_NAME_ARTPHOTO) dataset.load_catalog(os.path.join(os.path.dirname(__file__), "catalog_artphoto.json")) segment = dataset.create_segment() image_paths = glob(os.path.join(root_path, "*.jpg")) for image_path in image_paths: image_category = os.path.basename(image_path).split("_", 1)[0] data = Data(image_path) data.label.classification = Classification(category=image_category) segment.append(data) return dataset