import csv | |
import json | |
import os | |
from PIL import Image | |
import pandas as pd | |
from huggingface_hub import hf_hub_download, snapshot_download | |
import datasets | |
import cv2 | |
# _CITATION = """\ | |
# @InProceedings{huggingface:dataset, | |
# title = {A great new dataset}, | |
# author={huggingface, Inc. | |
# }, | |
# year={2020} | |
# } | |
# """ | |
_DESCRIPTION = """\ | |
Image Quality Assessment Dataset consisting of 25 reference images, 17 different distortions and 4 intensities per distortion. | |
In total there are 1700 (reference, distortion, MOS) tuples. | |
""" | |
# _HOMEPAGE = "" | |
# _LICENSE = "" | |
# TODO: Add link to the official dataset URLs here | |
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files. | |
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) | |
# _URLS = { | |
# "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip", | |
# "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip", | |
# } | |
# _REPO = "https://huggingface.co/datasets/frgfm/imagenette/resolve/main/metadata" # Stolen from imagenette.py | |
_REPO = "https://huggingface.co/datasets/Jorgvt/TID2008/resolve/main" | |
class TID2008(datasets.GeneratorBasedBuilder): | |
"""TID2008 Image Quality Dataset""" | |
VERSION = datasets.Version("1.0.0") | |
def _info(self): | |
# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset | |
features = datasets.Features( | |
{ | |
"reference": datasets.Image(), | |
"distorted": datasets.Image(), | |
"mos": datasets.Value("float") | |
} | |
) | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=features, | |
# supervised_keys=("reference", "distorted", "mos"), | |
# homepage=_HOMEPAGE, | |
# license=_LICENSE, | |
# citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
data_path = dl_manager.download("image_pairs_mos.csv") | |
data = pd.read_csv(data_path, index_col=0) | |
# kk = dl_manager.download("distorted_images") | |
# print(kk) | |
root_path = "/".join(data_path.split("/")[:-1]) | |
reference_path = dl_manager.download("reference_images") | |
distorted_path = dl_manager.download("distorted_images") | |
data["Reference"] = data["Reference"].apply(lambda x: os.path.join(reference_path, x)) | |
data["Distorted"] = data["Distorted"].apply(lambda x: os.path.join(distorted_path, x)) | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
gen_kwargs={ | |
"reference": data["Reference"], | |
"distorted": data["Distorted"], | |
"mos": data["MOS"], | |
"split": "train", | |
}, | |
) | |
] | |
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators` | |
def _generate_examples(self, reference, distorted, mos, split): | |
for key, (ref, dist, m) in enumerate(zip(reference, distorted, mos)): | |
yield key, { | |
"reference": ref, | |
"distorted": dist, | |
"mos": m, | |
} | |
# with open(filepath, encoding="utf-8") as f: | |
# for key, row in enumerate(f): | |
# data = json.loads(row) | |
# if self.config.name == "first_domain": | |
# # Yields examples as (key, example) tuples | |
# yield key, { | |
# "sentence": data["sentence"], | |
# "option1": data["option1"], | |
# "answer": "" if split == "test" else data["answer"], | |
# } | |
# else: | |
# yield key, { | |
# "sentence": data["sentence"], | |
# "option2": data["option2"], | |
# "second_domain_answer": "" if split == "test" else data["second_domain_answer"], | |
# } |