# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp

from .api_wrappers import COCO
from .builder import DATASETS
from .coco import CocoDataset

# images exist in annotations but not in image folder.
objv2_ignore_list = [
    osp.join('patch16', 'objects365_v2_00908726.jpg'),
    osp.join('patch6', 'objects365_v1_00320532.jpg'),
    osp.join('patch6', 'objects365_v1_00320534.jpg'),
]


@DATASETS.register_module()
class Objects365V1Dataset(CocoDataset):
    """Objects365 v1 dataset for detection."""
    CLASSES = (
        'person', 'sneakers', 'chair', 'hat', 'lamp', 'bottle',
        'cabinet/shelf', 'cup', 'car', 'glasses', 'picture/frame', 'desk',
        'handbag', 'street lights', 'book', 'plate', 'helmet', 'leather shoes',
        'pillow', 'glove', 'potted plant', 'bracelet', 'flower', 'tv',
        'storage box', 'vase', 'bench', 'wine glass', 'boots', 'bowl',
        'dining table', 'umbrella', 'boat', 'flag', 'speaker', 'trash bin/can',
        'stool', 'backpack', 'couch', 'belt', 'carpet', 'basket',
        'towel/napkin', 'slippers', 'barrel/bucket', 'coffee table', 'suv',
        'toy', 'tie', 'bed', 'traffic light', 'pen/pencil', 'microphone',
        'sandals', 'canned', 'necklace', 'mirror', 'faucet', 'bicycle',
        'bread', 'high heels', 'ring', 'van', 'watch', 'sink', 'horse', 'fish',
        'apple', 'camera', 'candle', 'teddy bear', 'cake', 'motorcycle',
        'wild bird', 'laptop', 'knife', 'traffic sign', 'cell phone', 'paddle',
        'truck', 'cow', 'power outlet', 'clock', 'drum', 'fork', 'bus',
        'hanger', 'nightstand', 'pot/pan', 'sheep', 'guitar', 'traffic cone',
        'tea pot', 'keyboard', 'tripod', 'hockey', 'fan', 'dog', 'spoon',
        'blackboard/whiteboard', 'balloon', 'air conditioner', 'cymbal',
        'mouse', 'telephone', 'pickup truck', 'orange', 'banana', 'airplane',
        'luggage', 'skis', 'soccer', 'trolley', 'oven', 'remote',
        'baseball glove', 'paper towel', 'refrigerator', 'train', 'tomato',
        'machinery vehicle', 'tent', 'shampoo/shower gel', 'head phone',
        'lantern', 'donut', 'cleaning products', 'sailboat', 'tangerine',
        'pizza', 'kite', 'computer box', 'elephant', 'toiletries', 'gas stove',
        'broccoli', 'toilet', 'stroller', 'shovel', 'baseball bat',
        'microwave', 'skateboard', 'surfboard', 'surveillance camera', 'gun',
        'life saver', 'cat', 'lemon', 'liquid soap', 'zebra', 'duck',
        'sports car', 'giraffe', 'pumpkin', 'piano', 'stop sign', 'radiator',
        'converter', 'tissue ', 'carrot', 'washing machine', 'vent', 'cookies',
        'cutting/chopping board', 'tennis racket', 'candy',
        'skating and skiing shoes', 'scissors', 'folder', 'baseball',
        'strawberry', 'bow tie', 'pigeon', 'pepper', 'coffee machine',
        'bathtub', 'snowboard', 'suitcase', 'grapes', 'ladder', 'pear',
        'american football', 'basketball', 'potato', 'paint brush', 'printer',
        'billiards', 'fire hydrant', 'goose', 'projector', 'sausage',
        'fire extinguisher', 'extension cord', 'facial mask', 'tennis ball',
        'chopsticks', 'electronic stove and gas stove', 'pie', 'frisbee',
        'kettle', 'hamburger', 'golf club', 'cucumber', 'clutch', 'blender',
        'tong', 'slide', 'hot dog', 'toothbrush', 'facial cleanser', 'mango',
        'deer', 'egg', 'violin', 'marker', 'ship', 'chicken', 'onion',
        'ice cream', 'tape', 'wheelchair', 'plum', 'bar soap', 'scale',
        'watermelon', 'cabbage', 'router/modem', 'golf ball', 'pine apple',
        'crane', 'fire truck', 'peach', 'cello', 'notepaper', 'tricycle',
        'toaster', 'helicopter', 'green beans', 'brush', 'carriage', 'cigar',
        'earphone', 'penguin', 'hurdle', 'swing', 'radio', 'CD',
        'parking meter', 'swan', 'garlic', 'french fries', 'horn', 'avocado',
        'saxophone', 'trumpet', 'sandwich', 'cue', 'kiwi fruit', 'bear',
        'fishing rod', 'cherry', 'tablet', 'green vegetables', 'nuts', 'corn',
        'key', 'screwdriver', 'globe', 'broom', 'pliers', 'volleyball',
        'hammer', 'eggplant', 'trophy', 'dates', 'board eraser', 'rice',
        'tape measure/ruler', 'dumbbell', 'hamimelon', 'stapler', 'camel',
        'lettuce', 'goldfish', 'meat balls', 'medal', 'toothpaste', 'antelope',
        'shrimp', 'rickshaw', 'trombone', 'pomegranate', 'coconut',
        'jellyfish', 'mushroom', 'calculator', 'treadmill', 'butterfly',
        'egg tart', 'cheese', 'pig', 'pomelo', 'race car', 'rice cooker',
        'tuba', 'crosswalk sign', 'papaya', 'hair drier', 'green onion',
        'chips', 'dolphin', 'sushi', 'urinal', 'donkey', 'electric drill',
        'spring rolls', 'tortoise/turtle', 'parrot', 'flute', 'measuring cup',
        'shark', 'steak', 'poker card', 'binoculars', 'llama', 'radish',
        'noodles', 'yak', 'mop', 'crab', 'microscope', 'barbell', 'bread/bun',
        'baozi', 'lion', 'red cabbage', 'polar bear', 'lighter', 'seal',
        'mangosteen', 'comb', 'eraser', 'pitaya', 'scallop', 'pencil case',
        'saw', 'table tennis paddle', 'okra', 'starfish', 'eagle', 'monkey',
        'durian', 'game board', 'rabbit', 'french horn', 'ambulance',
        'asparagus', 'hoverboard', 'pasta', 'target', 'hotair balloon',
        'chainsaw', 'lobster', 'iron', 'flashlight')

    PALETTE = None

    def load_annotations(self, ann_file):
        """Load annotation from COCO style annotation file.

        Args:
            ann_file (str): Path of annotation file.

        Returns:
            list[dict]: Annotation info from COCO api.
        """

        self.coco = COCO(ann_file)
        # 'categories' list in objects365_train.json and objects365_val.
        # json is inconsistent, need sorted list(or dict) before get cat_ids.
        cats = self.coco.cats
        sorted_cats = {i: cats[i] for i in sorted(cats)}
        self.coco.cats = sorted_cats
        categories = self.coco.dataset['categories']
        sorted_categories = sorted(categories, key=lambda i: i['id'])
        self.coco.dataset['categories'] = sorted_categories
        # The order of returned `cat_ids` will not
        # change with the order of the CLASSES
        self.cat_ids = self.coco.get_cat_ids(cat_names=self.CLASSES)

        self.cat2label = {cat_id: i for i, cat_id in enumerate(self.cat_ids)}
        self.img_ids = self.coco.get_img_ids()
        data_infos = []
        total_ann_ids = []
        for i in self.img_ids:
            info = self.coco.load_imgs([i])[0]
            info['filename'] = info['file_name']
            data_infos.append(info)
            ann_ids = self.coco.get_ann_ids(img_ids=[i])
            total_ann_ids.extend(ann_ids)
        assert len(set(total_ann_ids)) == len(
            total_ann_ids), f"Annotation ids in '{ann_file}' are not unique!"
        return data_infos


@DATASETS.register_module()
class Objects365V2Dataset(CocoDataset):
    """Objects365 v2 dataset for detection."""

    CLASSES = (
        'Person', 'Sneakers', 'Chair', 'Other Shoes', 'Hat', 'Car', 'Lamp',
        'Glasses', 'Bottle', 'Desk', 'Cup', 'Street Lights', 'Cabinet/shelf',
        'Handbag/Satchel', 'Bracelet', 'Plate', 'Picture/Frame', 'Helmet',
        'Book', 'Gloves', 'Storage box', 'Boat', 'Leather Shoes', 'Flower',
        'Bench', 'Potted Plant', 'Bowl/Basin', 'Flag', 'Pillow', 'Boots',
        'Vase', 'Microphone', 'Necklace', 'Ring', 'SUV', 'Wine Glass', 'Belt',
        'Moniter/TV', 'Backpack', 'Umbrella', 'Traffic Light', 'Speaker',
        'Watch', 'Tie', 'Trash bin Can', 'Slippers', 'Bicycle', 'Stool',
        'Barrel/bucket', 'Van', 'Couch', 'Sandals', 'Bakset', 'Drum',
        'Pen/Pencil', 'Bus', 'Wild Bird', 'High Heels', 'Motorcycle', 'Guitar',
        'Carpet', 'Cell Phone', 'Bread', 'Camera', 'Canned', 'Truck',
        'Traffic cone', 'Cymbal', 'Lifesaver', 'Towel', 'Stuffed Toy',
        'Candle', 'Sailboat', 'Laptop', 'Awning', 'Bed', 'Faucet', 'Tent',
        'Horse', 'Mirror', 'Power outlet', 'Sink', 'Apple', 'Air Conditioner',
        'Knife', 'Hockey Stick', 'Paddle', 'Pickup Truck', 'Fork',
        'Traffic Sign', 'Ballon', 'Tripod', 'Dog', 'Spoon',
        'Clock', 'Pot', 'Cow', 'Cake', 'Dinning Table', 'Sheep', 'Hanger',
        'Blackboard/Whiteboard', 'Napkin', 'Other Fish', 'Orange/Tangerine',
        'Toiletry', 'Keyboard', 'Tomato', 'Lantern',
        'Machinery Vehicle', 'Fan', 'Green Vegetables', 'Banana',
        'Baseball Glove', 'Airplane', 'Mouse', 'Train', 'Pumpkin', 'Soccer',
        'Skiboard', 'Luggage', 'Nightstand', 'Tea pot', 'Telephone', 'Trolley',
        'Head Phone', 'Sports Car', 'Stop Sign', 'Dessert', 'Scooter',
        'Stroller', 'Crane', 'Remote', 'Refrigerator', 'Oven', 'Lemon', 'Duck',
        'Baseball Bat', 'Surveillance Camera', 'Cat', 'Jug', 'Broccoli',
        'Piano', 'Pizza', 'Elephant', 'Skateboard', 'Surfboard', 'Gun',
        'Skating and Skiing shoes', 'Gas stove', 'Donut', 'Bow Tie', 'Carrot',
        'Toilet', 'Kite', 'Strawberry', 'Other Balls', 'Shovel', 'Pepper',
        'Computer Box', 'Toilet Paper', 'Cleaning Products', 'Chopsticks',
        'Microwave', 'Pigeon', 'Baseball', 'Cutting/chopping Board',
        'Coffee Table', 'Side Table', 'Scissors', 'Marker', 'Pie', 'Ladder',
        'Snowboard', 'Cookies', 'Radiator', 'Fire Hydrant', 'Basketball',
        'Zebra', 'Grape', 'Giraffe', 'Potato', 'Sausage', 'Tricycle', 'Violin',
        'Egg', 'Fire Extinguisher', 'Candy', 'Fire Truck', 'Billards',
        'Converter', 'Bathtub', 'Wheelchair', 'Golf Club', 'Briefcase',
        'Cucumber', 'Cigar/Cigarette ', 'Paint Brush', 'Pear', 'Heavy Truck',
        'Hamburger', 'Extractor', 'Extention Cord', 'Tong', 'Tennis Racket',
        'Folder', 'American Football', 'earphone', 'Mask', 'Kettle', 'Tennis',
        'Ship', 'Swing', 'Coffee Machine', 'Slide', 'Carriage', 'Onion',
        'Green beans', 'Projector', 'Frisbee',
        'Washing Machine/Drying Machine', 'Chicken', 'Printer', 'Watermelon',
        'Saxophone', 'Tissue', 'Toothbrush', 'Ice cream', 'Hotair ballon',
        'Cello', 'French Fries', 'Scale', 'Trophy', 'Cabbage', 'Hot dog',
        'Blender', 'Peach', 'Rice', 'Wallet/Purse', 'Volleyball', 'Deer',
        'Goose', 'Tape', 'Tablet', 'Cosmetics', 'Trumpet', 'Pineapple',
        'Golf Ball', 'Ambulance', 'Parking meter', 'Mango', 'Key', 'Hurdle',
        'Fishing Rod', 'Medal', 'Flute', 'Brush', 'Penguin', 'Megaphone',
        'Corn', 'Lettuce', 'Garlic', 'Swan', 'Helicopter', 'Green Onion',
        'Sandwich', 'Nuts', 'Speed Limit Sign', 'Induction Cooker', 'Broom',
        'Trombone', 'Plum', 'Rickshaw', 'Goldfish', 'Kiwi fruit',
        'Router/modem', 'Poker Card', 'Toaster', 'Shrimp', 'Sushi', 'Cheese',
        'Notepaper', 'Cherry', 'Pliers', 'CD', 'Pasta', 'Hammer', 'Cue',
        'Avocado', 'Hamimelon', 'Flask', 'Mushroon', 'Screwdriver', 'Soap',
        'Recorder', 'Bear', 'Eggplant', 'Board Eraser', 'Coconut',
        'Tape Measur/ Ruler', 'Pig', 'Showerhead', 'Globe', 'Chips', 'Steak',
        'Crosswalk Sign', 'Stapler', 'Campel', 'Formula 1 ', 'Pomegranate',
        'Dishwasher', 'Crab', 'Hoverboard', 'Meat ball', 'Rice Cooker', 'Tuba',
        'Calculator', 'Papaya', 'Antelope', 'Parrot', 'Seal', 'Buttefly',
        'Dumbbell', 'Donkey', 'Lion', 'Urinal', 'Dolphin', 'Electric Drill',
        'Hair Dryer', 'Egg tart', 'Jellyfish', 'Treadmill', 'Lighter',
        'Grapefruit', 'Game board', 'Mop', 'Radish', 'Baozi', 'Target',
        'French', 'Spring Rolls', 'Monkey', 'Rabbit', 'Pencil Case', 'Yak',
        'Red Cabbage', 'Binoculars', 'Asparagus', 'Barbell', 'Scallop',
        'Noddles', 'Comb', 'Dumpling', 'Oyster', 'Table Teniis paddle',
        'Cosmetics Brush/Eyeliner Pencil', 'Chainsaw', 'Eraser', 'Lobster',
        'Durian', 'Okra', 'Lipstick', 'Cosmetics Mirror', 'Curling',
        'Table Tennis ')

    def load_annotations(self, ann_file):
        """Load annotation from COCO style annotation file.

        Args:
            ann_file (str): Path of annotation file.

        Returns:
            list[dict]: Annotation info from COCO api.
        """

        self.coco = COCO(ann_file)
        # The order of returned `cat_ids` will not
        # change with the order of the CLASSES
        self.cat_ids = self.coco.get_cat_ids(cat_names=self.CLASSES)

        self.cat2label = {cat_id: i for i, cat_id in enumerate(self.cat_ids)}
        self.img_ids = self.coco.get_img_ids()
        data_infos = []
        total_ann_ids = []
        for i in self.img_ids:
            info = self.coco.load_imgs([i])[0]
            file_name = osp.join(
                osp.split(osp.split(info['file_name'])[0])[-1],
                osp.split(info['file_name'])[-1])
            info['file_name'] = file_name
            if info['file_name'] in objv2_ignore_list:
                continue
            info['filename'] = info['file_name']
            data_infos.append(info)
            ann_ids = self.coco.get_ann_ids(img_ids=[i])
            total_ann_ids.extend(ann_ids)
        assert len(set(total_ann_ids)) == len(
            total_ann_ids), f"Annotation ids in '{ann_file}' are not unique!"
        return data_infos
