test.py

import os

import argparse

import cv2
import numpy as np
import torch
from torch.utils.data import Dataset, DataLoader
from tqdm.asyncio import tqdm

from data_loader import create_training_datasets
from train import AnimeSegmentation, net_names
from inference import get_mask
import warnings


# warnings.filterwarnings("ignore")


def main(opt):
    train_dataset, _ = create_training_datasets(opt.data_dir, opt.fg_dir, opt.bg_dir, opt.img_dir,
                                                opt.mask_dir, opt.fg_ext, opt.bg_ext, opt.img_ext,
                                                opt.mask_ext, 1, opt.img_size)
    salobj_dataloader = DataLoader(train_dataset, batch_size=1, shuffle=True, num_workers=2)

    device = torch.device(opt.device)

    model = AnimeSegmentation.try_load(opt.net, opt.ckpt, img_size=opt.img_size)
    model.eval()
    model.to(device)

    if not os.path.exists(opt.out):
        os.mkdir(opt.out)

    for i, data in enumerate(tqdm(salobj_dataloader)):
        image, label = data["image"][0], data["label"][0]
        image = image.permute(1, 2, 0).numpy() * 255
        label = label.permute(1, 2, 0).numpy() * 255
        mask = get_mask(model, image, use_amp=not opt.fp32, s=opt.img_size)
        image = np.concatenate((image, mask.repeat(3, 2) * 255, label.repeat(3, 2)), axis=1).astype(np.uint8)
        image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
        cv2.imwrite(f'{opt.out}/{i:06d}.jpg', image)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    # model args
    parser.add_argument('--net', type=str, default='isnet_is',
                        choices=net_names,
                        help='net name')
    parser.add_argument('--ckpt', type=str, default='saved_models/isnetis.ckpt',
                        help='resume training from ckpt')
    parser.add_argument('--out', type=str, default='out',
                        help='output dir')
    parser.add_argument('--img-size', type=int, default=1024,
                        help='input image size')

    # dataset args
    parser.add_argument('--data-dir', type=str, default='../../dataset/anime-seg',
                        help='root dir of dataset')
    parser.add_argument('--fg-dir', type=str, default='fg',
                        help='relative dir of foreground')
    parser.add_argument('--bg-dir', type=str, default='bg',
                        help='relative dir of background')
    parser.add_argument('--img-dir', type=str, default='imgs',
                        help='relative dir of images')
    parser.add_argument('--mask-dir', type=str, default='masks',
                        help='relative dir of masks')
    parser.add_argument('--fg-ext', type=str, default='.png',
                        help='extension name of foreground')
    parser.add_argument('--bg-ext', type=str, default='.jpg',
                        help='extension name of background')
    parser.add_argument('--img-ext', type=str, default='.jpg',
                        help='extension name of images')
    parser.add_argument('--mask-ext', type=str, default='.jpg',
                        help='extension name of masks')

    parser.add_argument('--device', type=str, default='cuda:0',
                        help='cpu or cuda:0')
    parser.add_argument('--fp32', action='store_true', default=False,
                        help='disable mix precision')

    opt = parser.parse_args()
    print(opt)

    main(opt)