Background images for training

[twehrbein]

Hi! Thanks for releasing the code!
I'm trying to reproduce the training and thus need to gather all the training and validation backgrounds. Following your description, I used the lsun repo to download and extract the backgrounds. However, now I'm struggling with 1) selecting the "correct" background images and 2) converting them to the right format and to the right location.
The provided script data/copy_lsun_images_to_train_files_dir.py doesn't work for me, since I guess the directory structure isn't correct after extracting the images. E.g. "bedroom_train_lmdb" extracts images to e.g. ./f/8/8/1/2/2/*.webp which isn't compatible with your script. Your script also only looks for .jpg files. Furthermore, I don't know how to select the mentioned 397582 training backgrounds, since e.g. "bedroom_train_lmdb" alone has over 3mio images. Would be grateful for any help!

[akashsengupta1997]

Hey!

That's odd, IIRC the script used to work with the dataset as extracted. I will take a look this weekend and get back to you.

[twehrbein]

Hey, any update?

[Fly-Pluche]

Hello, may I ask if there is any progress？

[noahcao]

Hey is there any update?

One way may work, you should change the function for exporting images as [see issue]

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')

then, you should extract the images with a --flat flag:

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