Background images for training about humaniflow HOT 4 OPEN

def export_images(db_path, out_dir, flat=True, limit=-1):
    print('Exporting', db_path, 'to', out_dir)
    env = lmdb.open(db_path, map_size=1099511627776,
                    max_readers=100, readonly=True)
    count = 0
    with env.begin(write=False) as txn:
        cursor = txn.cursor()
        for key, val in cursor:
            if not flat:
                image_out_dir = join(out_dir, '/'.join(key[:6].decode()))
            else:
                image_out_dir = out_dir
            if not exists(image_out_dir):
                os.makedirs(image_out_dir)
            print('Current key:', key)
            image_out_path = join(image_out_dir, key.decode() + '.jpg')
            img = cv2.imdecode(
                numpy.fromstring(val, dtype=numpy.uint8), 1)
            cv2.imwrite(image_out_path, img)
            count += 1
            if count == limit:
                break
            if count % 1000 == 0:
                print('Finished', count, 'images')

python3 data.py export *_val_lmdb --out_dir val
python3 data.py export *_train_lmdb --out_dir train