# Copyright (c) OpenMMLab. All rights reserved.
import argparse
import os.path as osp
import sys
import cv2
import numpy as np
from typing import Tuple
from mmengine.config import Config, DictAction
from mmengine.dataset import Compose
from mmengine.utils import ProgressBar
from mmengine.visualization import Visualizer
from mmyolo.utils import register_all_modules
from mmyolo.registry import DATASETS, VISUALIZERS

def parse_args():
    parser = argparse.ArgumentParser(description='Browse a dataset')
    parser.add_argument('--config',default="",help='train config file path')
    parser.add_argument(
        '--output-dir',
        '-o',
        default='o',
        type=str,
        help='If there is no display interface, you can save it.')
    parser.add_argument('--not-show', default=False, action='store_true')
    parser.add_argument(
        '--phase',
        '-p',
        default='train',
        type=str,
        choices=['train', 'test', 'val'],
        help='phase of dataset to visualize, accept "train" "test" and "val".'
        ' Defaults to "train".')
    parser.add_argument(
        '--show-number',
        '-n',
        type=int,
        default=sys.maxsize,
        help='number of images selected to visualize, must bigger than 0. if '
        'the number is bigger than length of dataset, show all the images in '
        'dataset; default "sys.maxsize", show all images in dataset')
    parser.add_argument(
        '--show-interval',
        '-i',
        type=float,
        default=2,
        help='the interval of show (s)')
    parser.add_argument(
        '--mode',
        '-m',
        default='transformer',
        type=str,
        choices=['original', 'transformed', 'concat', 'pipeline'],
        help='display mode; display original pictures or transformed pictures'
        ' or comparison pictures. "original" means show images load from disk'
        '; "transformed" means to show images after transformed; "concat" '
        'means show images stitched by "original" and "output" images. '
        '"pipeline" means show all the intermediate images. '
        'Defaults to "transformed".')
    parser.add_argument(
        '--channel-order',
        '-c',
        default='BGR',
        choices=['BGR', 'RGB'],
        help='The channel order of the showing images, could be "BGR" '
        'or "RGB", Defaults to "BGR".')
    parser.add_argument(
        '--cfg-options',
        nargs='+',
        action=DictAction,
        help='override some settings in the used config, the key-value pair '
        'in xxx=yyy format will be merged into config file. If the value to '
        'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
        'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
        'Note that the quotation marks are necessary and that no white space '
        'is allowed.')
    parser.add_argument(
        '--key',
        '-k',
        default="print",
        choices=['None','print'],
        help='The key order for printing the detail of the change in key after the transform..')
    args = parser.parse_args()
    return args

def _get_adaptive_scale(img_shape: Tuple[int, int],
                        min_scale: float = 0.3,
                        max_scale: float = 3.0) -> float:
    """Get adaptive scale according to image shape.
    The target scale depends on the the short edge length of the image. If the
    short edge length equals 224, the output is 1.0. And output linear scales
    according the short edge length.
    You can also specify the minimum scale and the maximum scale to limit
 the
    linear scale.
    Args:
        img_shape (Tuple[int, int]): The shape of the canvas image.
        min_size (int): The minimum scale. Defaults to 0.3.
        max_size (int): The maximum scale. Defaults to 3.0.
    Returns:
        int: The adaptive scale.
    """
    short_edge_length = min(img_shape)
    scale = short_edge_length / 224.
    return min(max(scale, min_scale), max_scale)


def make_grid(imgs, names):
    """Concat list of pictures into a single big picture, align height here."""
    vis = Visualizer()
    ori_shapes = [img.shape[:2] for img in imgs]
    max_height = int(max(img.shape[0] for img in imgs) * 1.1)
    min_width = min(img.shape[1] for img in imgs)
    horizontal_gap = min_width // 10
    img_scale = _get_adaptive_scale((max_height, min_width))

    texts = []
    text_positions = []
    start_x = 0
    for i, img in enumerate(imgs):
        pad_height = (max_height - img.shape[0]) // 2
        pad_width = horizontal_gap // 2
        # make border
        imgs[i] = cv2.copyMakeBorder(
            img,
            pad_height,
            max_height - img.shape[0] - pad_height + int(img_scale * 30 * 2),
            pad_width,
            pad_width,
            cv2.BORDER_CONSTANT,
            value=(255, 255, 255))
        texts.append(f'{"execution: "}{i}\n{names[i]}\n{ori_shapes[i]}')
        text_positions.append(
            [start_x + img.shape[1] // 2 + pad_width, max_height])
        start_x += img.shape[1] + horizontal_gap

    display_img = np.concatenate(imgs, axis=1)
    vis.set_image(display_img)
    img_scale = _get_adaptive_scale(display_img.shape[:2])
    vis.draw_texts(
        texts,
        positions=np.array(text_positions),
        font_sizes=img_scale * 13,
        colors='black',
        horizontal_alignments='center',
        font_families='monospace')
    return vis.get_image()


class InspectCompose(Compose):
    """Compose multiple transforms sequentially.
    And record "img" field of all results in one list.
    """

    def __init__(self, transforms, intermediate_imgs):
        super().__init__(transforms=transforms)
        self.intermediate_imgs = intermediate_imgs

    def __call__(self, data):
        if 'img' in data:
            self.intermediate_imgs.append({
                'name': 'original',
                'img': data['img'].copy()
            })

        for t in self.transforms:
            data = t(data)
            if data is None:
                return None
            if 'img' in data:
                self.intermediate_imgs.append({
                    'name': t.__class__.__name__,
                    'img': data['img'].copy()
                })
        return data


def main():
    args = parse_args()
    cfg = Config.fromfile(args.config)
    if args.cfg_options is not None:
        cfg.merge_from_dict(args.cfg_options)

    # register all modules in mmcls into the registries
    register_all_modules()

    dataset_cfg = cfg.get(args.phase + '_dataloader').get('dataset')
    dataset = DATASETS.build(dataset_cfg)

    if args.key == "print":
        for i in dataset.pipeline.transforms:
            print(i)

    intermediate_imgs = []
    dataset.pipeline = InspectCompose(dataset.pipeline.transforms,intermediate_imgs)

    # init visualizer
    visualizer = VISUALIZERS.build(cfg.visualizer)
    visualizer.dataset_meta = dataset.metainfo

    # init visualization image number
    display_number = min(args.show_number, len(dataset))
    progress_bar = ProgressBar(display_number)

    for i, item in zip(range(display_number), dataset):
        if args.mode == 'original':
            image = intermediate_imgs[0]['img']
        elif args.mode == 'transformed':
            image = intermediate_imgs[-1]['img']
        elif args.mode == 'concat':
            ori_image = intermediate_imgs[0]['img']
            trans_image = intermediate_imgs[-1]['img']
            image = make_grid([ori_image, trans_image],
                              ['original', 'transformed'])
        else:
            image = make_grid([result['img'] for result in intermediate_imgs],
                              [result['name'] for result in intermediate_imgs],
                              )

        intermediate_imgs.clear()
        data_sample = item['data_samples'].numpy()

        # get filename from dataset or just use index as filename
        if hasattr(item['data_samples'], 'img_path'):
            filename = osp.basename(item['data_samples'].img_path)
        else:
            # some dataset have not image path
            filename = f'{i}.jpg'

        out_file = osp.join(args.output_dir,
                            filename) if args.output_dir is not None else None
        visualizer.add_datasample(
            filename,
            image if args.channel_order == 'RGB' else image[..., ::-1],
            data_sample,
            draw_gt=False,
            show=not args.not_show,
            wait_time=args.show_interval,
            out_file=out_file)
        progress_bar.update()


if __name__ == '__main__':
    main()