# 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)