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# 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.
"""TODO: Add a description here."""
import datasets
import evaluate
# TODO: Add BibTeX citation
from ocr_evaluation.evaluate.metrics import evaluate_by_symbols, evaluate_by_words, evaluate_by_word_groups
from ocr_evaluation.ocr.fiftyone import FiftyOneOcr
_CITATION = """\
@InProceedings{huggingface:module,
title = {Iliauni ICC OCR Evaluation},
authors={},
year={2022}
}
"""
# TODO: Add description of the module here
_DESCRIPTION = """\
Better OCR evaluation metric that enables to evaluate OCR results in various ways. It is robust in a way that
it matches the words using their bounding boxes instead of using plain edit distance matching between two texts.
Elaborate more on this later.
"""
# TODO: Add description of the arguments of the module here
_KWARGS_DESCRIPTION = """
Calculates how good are predictions given some references, using certain scores
Args:
predictions: list of OCR detections in FiftyOne dataset format.
references: list of OCR detections in FiftyOne dataset format.
Returns:
evaluation_results: list of dictionaries containing multiple metrics
Examples:
Examples should be written in doctest format, and should illustrate how
to use the function.
>>> dataset = load_dataset("anz2/iliauni_icc_georgian_ocr", use_auth_token="<auth token here>")
>>> sample = dataset['test'][0]
>>> ocr_evaluator = evaluate.load("anz2/iliauniiccocrevaluation")
>>> results = ocr_evaluator._compute(predictions=[sample], references=[sample])
>>> print(results[0].keys())
dict_keys(['accuracy', 'precision', 'recall', 'f1', 'levenstein_distances_stats', 'levenstein_similarities_stats', 'iou_stats', 'edit_operations_stats'])
"""
@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION)
class IliauniIccOCREvaluation(evaluate.Metric):
"""TODO: Short description of my evaluation module."""
def _info(self):
# TODO: Specifies the evaluate.EvaluationModuleInfo object
return evaluate.MetricInfo(
# This is the description that will appear on the modules page.
module_type="metric",
description=_DESCRIPTION,
citation=_CITATION,
inputs_description=_KWARGS_DESCRIPTION,
# This defines the format of each prediction and reference
features=datasets.Features(
{
"predictions": datasets.Sequence(
feature=datasets.Features(
{
"id": datasets.Value("string"),
"filepath": datasets.Value("string"),
"tags": datasets.Sequence(datasets.Value("string")),
"metadata": datasets.Features(
{
"size_bytes": datasets.Value("int32"),
"mime_type": datasets.Value("string"),
"width": datasets.Value("int32"),
"height": datasets.Value("int32"),
"num_channels": datasets.Value("int32"),
"author": datasets.Value("string"),
"category": datasets.Value("string"),
"document_name": datasets.Value("string"),
"source": datasets.Value("string"),
"year": datasets.Value("int32")
}
),
"_media_type": datasets.Value("string"),
"_rand": datasets.Value("string"),
"detections": datasets.Features(
{
"detections": datasets.Sequence(
datasets.Features(
{
"id": datasets.Value("string"),
"attributes": datasets.Sequence(datasets.Value("string")),
"tags": datasets.Value("string"),
"label": datasets.Value("string"),
"bounding_box": datasets.Sequence(datasets.Value("float32")),
"confidence": datasets.Value("float32"),
"index": datasets.Value("int32"),
"page": datasets.Value("int32"),
"block": datasets.Value("int32"),
"paragraph": datasets.Value("int32"),
"word": datasets.Value("int32"),
"text": datasets.Value("string"),
}
)
)
}
),
"image": datasets.Image()
}
)
),
"references": datasets.Sequence(
feature=datasets.Features(
{
"id": datasets.Value("string"),
"filepath": datasets.Value("string"),
"tags": datasets.Sequence(datasets.Value("string")),
"metadata": datasets.Features(
{
"size_bytes": datasets.Value("int32"),
"mime_type": datasets.Value("string"),
"width": datasets.Value("int32"),
"height": datasets.Value("int32"),
"num_channels": datasets.Value("int32"),
"author": datasets.Value("string"),
"category": datasets.Value("string"),
"document_name": datasets.Value("string"),
"source": datasets.Value("string"),
"year": datasets.Value("int32")
}
),
"_media_type": datasets.Value("string"),
"_rand": datasets.Value("string"),
"detections": datasets.Features(
{
"detections": datasets.Sequence(
datasets.Features(
{
"id": datasets.Value("string"),
"attributes": datasets.Sequence(datasets.Value("string")),
"tags": datasets.Value("string"),
"label": datasets.Value("string"),
"bounding_box": datasets.Sequence(datasets.Value("float32")),
"confidence": datasets.Value("float32"),
"index": datasets.Value("int32"),
"page": datasets.Value("int32"),
"block": datasets.Value("int32"),
"paragraph": datasets.Value("int32"),
"word": datasets.Value("int32"),
"text": datasets.Value("string"),
}
)
)
}
),
"image": datasets.Image()
}
)
)
}
),
# Homepage of the module for documentation
homepage="",
# Additional links to the codebase or references
codebase_urls=["https://github.com/IliaUni-ICC/ocr_evaluation"],
reference_urls=[]
)
def _download_and_prepare(self, dl_manager):
"""Optional: download external resources useful to compute the scores"""
# TODO: Download external resources if needed
pass
def _compute(self, *, predictions=None, references=None, **kwargs):
"""Returns the scores"""
eval_method = "word"
if kwargs.get("eval_method", "word") in ["symbol", "word", "word_group"]:
eval_method = kwargs["eval_method"]
assert len(predictions) == len(references)
results = []
for prediction, reference in zip(predictions, references):
prediction_df = FiftyOneOcr(data=prediction).get_detections(convert_bbox=True)
reference_df = FiftyOneOcr(data=reference).get_detections(convert_bbox=True)
if eval_method == "symbol":
result = evaluate_by_symbols(prediction_df, reference_df, pref1="Pred_", pref2="Tar_")
elif eval_method == "word":
result = evaluate_by_words(prediction_df, reference_df, pref1="Pred_", pref2="Tar_")
elif eval_method == "word_group":
result = evaluate_by_word_groups(prediction_df, reference_df, pref1="Pred_", pref2="Tar_")
else:
result = {}
results.append(result)
return results
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