rec to image

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import sys
import mxnet as mx
from mxnet import ndarray as nd
import random
import argparse
import cv2
import time
import sklearn
import numpy as np


def main(args):
    print(args.include)
    include_datasets = args.include.split(',')
    print(include_datasets)
    rec_list = []
    for ds in include_datasets:
        path_imgrec = os.path.join(ds, 'train.rec')
        path_imgidx = os.path.join(ds, 'train.idx')
        imgrec = mx.recordio.MXIndexedRecordIO(path_imgidx, path_imgrec, 'r')  # pylint: disable=redefined-variable-type
        rec_list.append(imgrec)
    if not os.path.exists(args.output):
        os.makedirs(args.output)

    all_count = 0
    img_label = 0
    id_dir = args.output
    for ds_id in range(len(rec_list)):
        id_list = []
        imgrec = rec_list[ds_id]
        s = imgrec.read_idx(0)
        header, _ = mx.recordio.unpack(s)
        assert header.flag > 0
        print('header0 label', header.label)
        header0 = (int(header.label[0]), int(header.label[1]))
        seq_identity = range(int(header.label[0]), int(header.label[1]))
        pp = 0
        for identity in seq_identity:
            # id_dir = os.path.join(args.output, "%d" % (img_label))
            # os.makedirs(id_dir)
            pp += 1
            if pp % 10 == 0:
                print('processing id', pp)
            s = imgrec.read_idx(identity)
            header, _ = mx.recordio.unpack(s)
            imgid = 0
            count = 0
            for _idx in range(int(header.label[0]), int(header.label[1])): # int(header.label[0]), int(header.label[1])
                s = imgrec.read_idx(_idx)
                _header, _img = mx.recordio.unpack(s)
                _img = mx.image.imdecode(_img).asnumpy()[:, :, ::-1]  # to bgr
                image_path = os.path.join(id_dir, "%d_%d.jpg" % (img_label, count))
                cv2.imwrite(image_path, _img)
                imgid += 1
                count += 1
                all_count += 1
                if count == 20:
                    break
            if all_count >= 10000:
                break
            img_label += 1

if __name__ == '__main__':
    parser = argparse.ArgumentParser(description='do dataset merge')
    # general
    parser.add_argument('--include', default='D:/hiw_Work/MEC/Data/face_dataset/asdf', type=str, help='')
    parser.add_argument('--output', default='D:/hiw_Work/MEC/Data/face_dataset/asdf/train_3', type=str, help='')
    args = parser.parse_args()
    main(args)