import gravdataset
import os
from gravdataset.features import Features, Sequence, Value
from pycocotools.coco import COCO

_DESCRIPTION = 'COCO dataset for detection and instance segmentation task.'

_URLS = {
    'COCO2014': {
        'train_prefix': 'train2014',
        'train_meta': 'annotations/instances_train2014.json',
        'val_prefix': 'val2014',
        'val_meta': 'annotations/instances_val2014.json'
    },
    'COCO2017': {
        'train_prefix': 'train2017',
        'train_meta': 'annotations/instances_train2017.json',
        'val_prefix': 'val2017',
        'val_meta': 'annotations/instances_val2017.json'
    },
}

_CLASSES = ('person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
            'train', 'truck', 'boat', 'traffic light', 'fire hydrant',
            'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog',
            'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe',
            'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
            'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat',
            'baseball glove', 'skateboard', 'surfboard', 'tennis racket',
            'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl',
            'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot',
            'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
            'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop',
            'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven',
            'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase',
            'scissors', 'teddy bear', 'hair drier', 'toothbrush')


class Coco(gravdataset.GeneratorBasedBuilder):
    """COCO dataset for detection and instance segmentation task."""

    VERSION = gravdataset.Version('0.1.0')

    BUILDER_CONFIGS = [
        gravdataset.BuilderConfig(
            name='COCO2014',
            version=VERSION,
            description='COCO2014 dataset for det and segm'),
        gravdataset.BuilderConfig(
            name='COCO2017',
            version=VERSION,
            description='COCO2017 dataset for det and segm'),
    ]
    # It's not mandatory to have a default configuration.
    # Just use one if it make sense.
    DEFAULT_CONFIG_NAME = 'train'

    def _info(self):
        return gravdataset.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            meta_info=dict(classes=_CLASSES),
            features=Features({
                'img_info': {
                    'filename': Value('string'),
                    'height': Value('int32'),
                    'width': Value('int32'),
                },
                'ann_info': {
                    'bboxes': Sequence(Sequence(Value('float64'))),
                    'labels': Sequence(Value('int64')),
                    'masks': Sequence(Sequence(Sequence(Value('float64')))),
                    'bboxes_ignore': Sequence(Sequence(Value('float64'))),
                    'label_ignore': Sequence(Value('int64')),
                    'masks_ignore': Sequence(
                        {
                            'counts': Sequence(Value('int64')),
                            'size': Sequence(Value('int64'))
                        }
                    ),
                    'seg_map': Value('string')
                }
            }))

    def _split_generators(self, dl_manager):
        train_prefix = _URLS[self.config.name]['train_prefix']
        train_meta = _URLS[self.config.name]['train_meta']
        val_prefix = _URLS[self.config.name]['val_prefix']
        val_meta = _URLS[self.config.name]['val_meta']
        train_meta = dl_manager.download(train_meta)
        val_meta = dl_manager.download(val_meta)
        return [
            gravdataset.SplitGenerator(
                name='train',
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    'img_prefix': train_prefix,
                    'ann_file': train_meta
                }),
            gravdataset.SplitGenerator(
                name='val',
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    'img_prefix': val_prefix,
                    'ann_file': val_meta
                }),
        ]

    def _generate_examples(self, img_prefix, ann_file):
        """Parser coco format annotation file."""
        coco = COCO(ann_file)
        cat_ids = coco.getCatIds(_CLASSES)
        cat2label = {cat_id: i for i, cat_id in enumerate(cat_ids)}
        img_ids = coco.getImgIds()
        index = 0
        for i in img_ids:
            sample = dict(img_info=dict())
            info = coco.loadImgs([i])[0]
            sample['img_info']['filename'] = os.path.join(
                img_prefix, info['file_name'])
            sample['img_info']['height'] = info['height']
            sample['img_info']['width'] = info['width']
            ann_ids = coco.getAnnIds([i])
            ann_info = coco.loadAnns(ann_ids)
            gt_bboxes = []
            gt_labels = []
            gt_bboxes_ignore = []
            gt_label_ignore = []
            gt_masks_ann = []
            gt_masks_ignore = []
            for i, ann in enumerate(ann_info):
                if ann.get('ignore', False):
                    continue
                x1, y1, w, h = ann['bbox']
                inter_w = max(0, min(x1 + w, info['width']) - max(x1, 0))
                inter_h = max(0, min(y1 + h, info['height']) - max(y1, 0))
                if inter_w * inter_h == 0:
                    continue
                if ann['area'] <= 0 or w < 1 or h < 1:
                    continue
                if ann['category_id'] not in cat_ids:
                    continue
                bbox = [x1, y1, x1 + w, y1 + h]
                if ann.get('iscrowd', False):
                    gt_bboxes_ignore.append(bbox)
                    gt_label_ignore.append(cat2label[ann['category_id']])
                    gt_masks_ignore.append(ann.get('segmentation', None))
                else:
                    gt_bboxes.append(bbox)
                    gt_labels.append(cat2label[ann['category_id']])
                    gt_masks_ann.append(ann.get('segmentation', None))

            seg_map = sample['img_info']['filename'].rsplit('.', 1)[0] + '.png'

            sample['ann_info'] = dict(
                bboxes=gt_bboxes,
                labels=gt_labels,
                bboxes_ignore=gt_bboxes_ignore,
                label_ignore=gt_label_ignore,
                masks=gt_masks_ann,
                masks_ignore=gt_masks_ignore,
                seg_map=seg_map)
            yield index, sample
            index += 1