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# Copyright 2022 Daniel van Strien.
#
# 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.
"""Card Display Detection"""
import collections
import json
import os
from typing import Any, Dict, List
import pandas as pd
import datasets
_CITATION = """Connor Hoehn"""
_DESCRIPTION = "This dataset comprises of card display images from the public domain"
_HOMEPAGE = "https://www.connorhoehn.com"
_LICENSE = "Public Domain Mark 1.0"
_DATASET_URL = "https://www.connorhoehn.com/object_detection_dataset_v2.zip"
_CATEGORIES = ["boxed","grid","spread","stack"]
class CardDisplayDetectorConfig(datasets.BuilderConfig):
"""BuilderConfig for card display dataset."""
def __init__(self, name, **kwargs):
super(CardDisplayDetectorConfig, self).__init__(
version=datasets.Version("1.0.0"),
name=name,
description="Card Display Detector",
**kwargs,
)
class CardDisplayDetector(datasets.GeneratorBasedBuilder):
"""Card Display dataset."""
BUILDER_CONFIGS = [
CardDisplayDetectorConfig("display-detection"),
]
def _info(self):
features = datasets.Features(
{
"image_id": datasets.Value("int64"),
"image": datasets.Image(),
"width": datasets.Value("int32"),
"height": datasets.Value("int32"),
}
)
object_dict = {
"category_id": datasets.ClassLabel(names=_CATEGORIES),
"image_id": datasets.Value("string"),
"id": datasets.Value("int64"),
"area": datasets.Value("int64"),
"bbox": datasets.Sequence(datasets.Value("float32"), length=4),
"iscrowd": datasets.Value("bool"),
}
features["objects"] = [object_dict]
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
dataset_zip = dl_manager.download_and_extract(_DATASET_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# COCO -> x.json, images/
gen_kwargs={
"annotations_file": os.path.join(dataset_zip, "result.json"),
# Annotator indicated there was a folder named 1 that doesn't exist
"image_dir": os.path.join(dataset_zip),
},
)
]
# Return dictionary of unique image_ids that have multiple nested annotations
def _get_image_id_to_annotations_mapping(self, annotations: List[Dict]) -> Dict[int, List[Dict[Any, Any]]]:
"""
A helper function to build a mapping from image ids to annotations.
"""
image_id_to_annotations = collections.defaultdict(list)
for annotation in annotations:
image_id_to_annotations[annotation["image_id"]].append(annotation)
return image_id_to_annotations
def _generate_examples(self, annotations_file, image_dir):
def _image_info_to_example(image_info, image_dir):
# from the annotation file
image = image_info["file_name"]
return {
"image_id": image_info["id"],
"image": os.path.join(image_dir, image),
"width": image_info["width"],
"height": image_info["height"],
}
with open(annotations_file, encoding="utf8") as annotation_json:
annotation_data = json.load(annotation_json)
images = annotation_data["images"]
annotations = annotation_data["annotations"]
# dictionary of image_ids with all related annotations (bbox)
image_id_to_annotations = self._get_image_id_to_annotations_mapping(
annotations
)
if self.config.name == "display-detection":
# yield image_id, features
for image_id, image_info in enumerate(images):
#image_info -> (w,h,id,filename)
image_details = _image_info_to_example(image_info, image_dir)
# Get images unit id
annotations = image_id_to_annotations[image_info["id"]]
objects = []
# Add the annotation information to the image details
for annotation in annotations:
del annotation['segmentation']
del annotation['ignore']
objects.append(annotation)
# nested dictionary
image_details["objects"] = objects
yield (image_id, image_details) |