#!/usr/bin/env python3
import math
import os
import os.path as osp
from argparse import ArgumentParser
from functools import partial

import mmcv
from mmocr.utils.fileio import list_to_file
from PIL import Image


def parse_args():
    parser = ArgumentParser(
        description='Generate training and validation set '
        'of OpenVINO annotations for Open '
        'Images by cropping box image.'
    )
    parser.add_argument('root_path', help='Root dir containing images and annotations')
    parser.add_argument('n_proc', default=1, type=int, help='Number of processes to run')
    args = parser.parse_args()
    return args


def process_img(args, src_image_root, dst_image_root):
    # Dirty hack for multiprocessing
    img_idx, img_info, anns = args
    src_img = Image.open(osp.join(src_image_root, img_info['file_name']))
    labels = []
    for ann_idx, ann in enumerate(anns):
        attrs = ann['attributes']
        text_label = attrs['transcription']

        # Ignore illegible or non-English words
        if not attrs['legible'] or attrs['language'] != 'english':
            continue

        x, y, w, h = ann['bbox']
        x, y = max(0, math.floor(x)), max(0, math.floor(y))
        w, h = math.ceil(w), math.ceil(h)
        dst_img = src_img.crop((x, y, x + w, y + h))
        dst_img_name = f'img_{img_idx}_{ann_idx}.jpg'
        dst_img_path = osp.join(dst_image_root, dst_img_name)
        # Preserve JPEG quality
        dst_img.save(dst_img_path, qtables=src_img.quantization)
        labels.append(f'{osp.basename(dst_image_root)}/{dst_img_name}' f' {text_label}')
    src_img.close()
    return labels


def convert_openimages(root_path, dst_image_path, dst_label_filename, annotation_filename, img_start_idx=0, nproc=1):
    annotation_path = osp.join(root_path, annotation_filename)
    if not osp.exists(annotation_path):
        raise Exception(f'{annotation_path} not exists, please check and try again.')
    src_image_root = root_path

    # outputs
    dst_label_file = osp.join(root_path, dst_label_filename)
    dst_image_root = osp.join(root_path, dst_image_path)
    os.makedirs(dst_image_root, exist_ok=True)

    annotation = mmcv.load(annotation_path)

    process_img_with_path = partial(process_img, src_image_root=src_image_root, dst_image_root=dst_image_root)
    tasks = []
    anns = {}
    for ann in annotation['annotations']:
        anns.setdefault(ann['image_id'], []).append(ann)
    for img_idx, img_info in enumerate(annotation['images']):
        tasks.append((img_idx + img_start_idx, img_info, anns[img_info['id']]))
    labels_list = mmcv.track_parallel_progress(process_img_with_path, tasks, keep_order=True, nproc=nproc)
    final_labels = []
    for label_list in labels_list:
        final_labels += label_list
    list_to_file(dst_label_file, final_labels)
    return len(annotation['images'])


def main():
    args = parse_args()
    root_path = args.root_path
    print('Processing training set...')
    num_train_imgs = 0
    for s in '125f':
        num_train_imgs = convert_openimages(
            root_path=root_path,
            dst_image_path=f'image_{s}',
            dst_label_filename=f'train_{s}_label.txt',
            annotation_filename=f'text_spotting_openimages_v5_train_{s}.json',
            img_start_idx=num_train_imgs,
            nproc=args.n_proc,
        )
    print('Processing validation set...')
    convert_openimages(
        root_path=root_path,
        dst_image_path='image_val',
        dst_label_filename='val_label.txt',
        annotation_filename='text_spotting_openimages_v5_validation.json',
        img_start_idx=num_train_imgs,
        nproc=args.n_proc,
    )
    print('Finish')


if __name__ == '__main__':
    main()