style_transfer_folder.py

import argparse
import os

import cv2
import numpy as np
import torch

from model import Generator
from psp_encoder.psp_encoders import PSPEncoder
from utils import ten2cv, cv2ten
import glob
import random

seed = 0

random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)


if __name__ == '__main__':
    device = 'cpu'

    parser = argparse.ArgumentParser()

    parser.add_argument('--size', type=int, default=1024)

    parser.add_argument('--ckpt', type=str, default='', help='path to BlendGAN checkpoint')
    parser.add_argument('--psp_encoder_ckpt', type=str, default='', help='path to psp_encoder checkpoint')

    parser.add_argument('--style_img_path', type=str, default=None, help='path to style image')
    parser.add_argument('--input_img_path', type=str, default=None, help='path to input image')
    parser.add_argument('--add_weight_index', type=int, default=6)

    parser.add_argument('--channel_multiplier', type=int, default=2)
    parser.add_argument('--outdir', type=str, default="")

    args = parser.parse_args()



    args.latent = 512
    args.n_mlp = 8

    checkpoint = torch.load(args.ckpt)
    model_dict = checkpoint['g_ema']
    print('ckpt: ', args.ckpt)

    g_ema = Generator(
        args.size, args.latent, args.n_mlp, channel_multiplier=args.channel_multiplier
    ).to(device)
    g_ema.load_state_dict(model_dict)
    g_ema.eval()

    psp_encoder = PSPEncoder(args.psp_encoder_ckpt, output_size=args.size).to(device)
    psp_encoder.eval()

    num = 0

   
    print(num)
    num += 1

    img_in = cv2.imread(args.input_img_path)
    img_in_ten = cv2ten(img_in, device)
    img_in = cv2.resize(img_in, (args.size, args.size))

  
    img_style = cv2.imread(args.style_img_path)
    img_style_ten = cv2ten(img_style, device)
    img_style = cv2.resize(img_style, (args.size, args.size))

    with torch.no_grad():
        sample_style = g_ema.get_z_embed(img_style_ten)
        sample_in = psp_encoder(img_in_ten)
        img_out_ten, _ = g_ema([sample_in], z_embed=sample_style, add_weight_index=args.add_weight_index,
                               input_is_latent=True, return_latents=False, randomize_noise=False)
        img_out = ten2cv(img_out_ten)
    out = np.concatenate([img_in, img_style, img_out], axis=1)
    cv2.imwrite('out.jpg', out)

    print('Done!')