# 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.
"""ODOR dataset."""


import collections
import json
import os
import pandas as pd

import datasets


_CITATION = """\

"""

_DESCRIPTION = """\
Real-world applications of computer vision in the humanities require algorithms to be robust against artistic abstraction, peripheral objects, and subtle differences between fine-grained target classes. Existing datasets provide instance-level
annotations on artworks but are generally biased towards the image centre and limited with regard to detailed object classes. The proposed ODOR dataset fills this gap, offering 38,116 object-level annotations across 4,712 images, spanning an extensive set of 139 fine-grained categories. Conducting a statistical analysis, we showcase challenging dataset properties, such as a detailed set of categories, dense and overlapping objects, and spatial distribution over the whole image canvas. Furthermore, we provide an extensive baseline analysis for object detection models and highlight the challenging properties of the dataset through a set of secondary studies. Inspiring further research on artwork object detection and broader visual cultural heritage studies, the dataset challenges researchers to explore the intersection of object recognition and smell perception.
"""

_HOMEPAGE = "https://zenodo.org/record/8398464"

_LICENSE = "Unknown"

_URL = "https://zenodo.org/record/8398464/files/odor-dataset.zip?download=1"

_CATEGORIES = ['ant', 'camel', 'jewellery', 'frog', 'physalis', 'celery', 'cauliflower', 'pepper', 'ranunculus', 'chess flower', 'cigarette', 'matthiola', 'cabbage', 'earring', 'dandelion', 'neroli', 'dragonfly', 'hyacinth', 'reptile/amphibia', 'apricot', 'snake', 'lizard', 'asparagus', 'spring onion', 'snowflake', 'moth', 'poppy', 'columbine', 'rabbit', 'geranium', 'crab', 'radish', 'big cat', 'jan steen jug', 'monkey', 'snail', 'bellflower', 'lilac', 'pot', 'peony', 'coffeepot', 'hazelnut', 'censer', 'artichoke', 'dahlia', 'sniffing', 'fly', 'deer', 'caterpillar', 'garlic', 'blackberry', 'chalice', 'lobster', 'necklace', 'bug', 'insect', 'prawn', 'bracelet', 'carrot', 'cornflower', 'pumpkin', 'orange', 'walnut', 'cat', 'daisy', 'forget-me-not', 'carafe', 'match', 'beer stein', 'tobacco-box', 'violet', 'pomander', 'bottle', 'candle', 'heliotrope', 'wine bottle', 'strawberry', 'pomegranate', 'whale', 'lily of the valley', 'iris', 'tobacco', 'olive', 'tobacco-packaging', 'meat', 'daffodil', 'melon', 'fire', 'petunia', 'mushroom', 'teapot', 'ring', 'pig', 'ashtray', 'cheese', 'onion', 'cup', 'nut', 'fig', 'drinking vessel', 'donkey', 'holding the nose', 'lily', 'smoke', 'bread', 'currant', 'glass without stem', 'anemone', 'mammal', 'chimney', 'smoking equipment', 'bivalve', 'butterfly', 'gloves', 'lemon', 'horse', 'plum', 'jasmine', 'pear', 'glass with stem', 'vegetable', 'carnation', 'jug', 'goat', 'fish', 'apple', 'tulip', 'cherry', 'cow', 'animal corpse', 'dog', 'fruit', 'bird', 'rose', 'peach', 'sheep', 'pipe', 'grapes', 'flower']


class ODOR(datasets.GeneratorBasedBuilder):
    """ODOR dataset."""

    VERSION = datasets.Version("1.0.0")

    def _info(self):
        features = datasets.Features(
            {
                "image_id": datasets.Value("int64"),
                "image": datasets.Image(),
                "width": datasets.Value("int32"),
                "height": datasets.Value("int32"),
                "objects": datasets.Sequence(
                    {
                        "id": datasets.Value("int64"),
                        "area": datasets.Value("int64"),
                        "bbox": datasets.Sequence(datasets.Value("float32"), length=4),
                        "category": datasets.ClassLabel(names=_CATEGORIES),
                    }
                ),
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        # df_meta = pd.read_csv('meta/meta.csv')
        # dl_manager.download(df_meta['Image Credits'].values)
        dl_manager.download_and_extract(_URL)
        
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "annotation_file_path": "annotations/train.json",
                    "metadata_file_path": "meta/meta_train.csv"
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "annotation_file_path": "annotations/test.json",
                    "metadata_file_path": "meta/meta_test.csv"
                },
            ),
        ]

    def _generate_examples(self, annotation_file_path, metadata_file_path):
        # load metadata 
        # meta_df = pd.read_csv(metadata_file_path)

        # files = download_images(meta_df) 


        def process_annot(annot, category_id_to_category):
            return {
                "id": annot["id"],
                "area": annot["area"],
                "bbox": annot["bbox"],
                "category": category_id_to_category[annot["category_id"]],
            }

        image_id_to_image = {}
        idx = 0
        # This loop relies on the ordering of the files in the archive:
        # Annotation files come first, then the images.
        for path, f in files:
            file_name = os.path.basename(path)
            if path == annotation_file_path:
                annotations = json.load(f)
                category_id_to_category = {category["id"]: category["name"] for category in annotations["categories"]}
                image_id_to_annotations = collections.defaultdict(list)
                for annot in annotations["annotations"]:
                    image_id_to_annotations[annot["image_id"]].append(annot)
                image_id_to_image = {annot["file_name"]: annot for annot in annotations["images"]}
            elif file_name in image_id_to_image:
                image = image_id_to_image[file_name]
                objects = [
                    process_annot(annot, category_id_to_category) for annot in image_id_to_annotations[image["id"]]
                ]
                yield idx, {
                    "image_id": image["id"],
                    "image": {"path": path, "bytes": f.read()},
                    "width": image["width"],
                    "height": image["height"],
                    "objects": objects,
                }
                idx += 1