Neural_Style_Transfer.py

import os
import copy
import torch
import random
import argparse
import numpy as np
from PIL import Image
from pathlib import Path
from tqdm.auto import tqdm
from lib.style_transfer_utils import (
    tensor2pil,
    load_style_transfer_model,
    run_style_transfer,
    style_content_image_loader,
)


def parse_arguments():
    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--content-imgs", type=str, help="Path to the content images.", required=True
    )
    parser.add_argument(
        "--style-imgs", type=str, help="Path to the style images.", required=True
    )
    parser.add_argument(
        "--save-folder",
        type=str,
        help="Path to the save the generated images.",
        required=True,
    )
    parser.add_argument(
        "--vgg", type=str, help="Path to the pretrained VGG model.", required=True
    )

    parser.add_argument("--cuda", action="store_true", help="use cuda.")
    parser.add_argument(
        "--ext", type=str, default="stl", help="extension for generated image."
    )
    parser.add_argument(
        "--min-step", type=int, default=100, help="minimum iteration steps"
    )
    parser.add_argument(
        "--max-step", type=int, default=200, help="maximum iteration steps"
    )
    parser.add_argument(
        "--style-weight", type=float, default=100000, help="weight for style loss"
    )
    parser.add_argument(
        "--content-weight", type=float, default=2, help="weight for content loss"
    )

    return parser.parse_args()


def transfer_style(
    cnn_path,
    cimg,
    simg,
    min_step=100,
    max_step=200,
    style_weight=100000,
    content_weight=2,
    device="cpu",
):
    cnn = load_style_transfer_model(pretrained=cnn_path)

    content_img, style_img = style_content_image_loader(cimg, simg)
    input_img = copy.deepcopy(content_img).to(device, torch.float)

    output = run_style_transfer(
        cnn,
        content_img,
        style_img,
        input_img,
        num_steps=random.randint(min_step, max_step),
        style_weight=style_weight,
        content_weight=content_weight,
        device=device,
    )
    return tensor2pil(output[0].detach().cpu())


def main():
    args = parse_arguments()
    if args.cuda and torch.cuda.is_available():
        device = torch.device("cuda:0")
    else:
        device = torch.device("cpu")

    content_images = sorted(Path(args.content_imgs).glob("*"))
    # with open(Path(args.content_imgs), "r") as f:
    #     lines = f.read()
    # content_images = lines.split("\n")
    # content_images = [Path("./content_images") / f for f in content_images]
    style_images = sorted(Path(args.style_imgs).glob("*"))

    save_folder = Path(args.save_folder)
    if not os.path.exists(args.save_folder):
        print(f"Creating {args.save_folder}")
        os.makedirs(str(save_folder))

    for i, cimg in enumerate(content_images):
        name, extension = cimg.name.split(".")
        simg = style_images[i % len(style_images)]

        output_img = transfer_style(
            cnn_path=args.vgg,
            cimg=cimg,
            simg=simg,
            min_step=args.min_step,
            max_step=args.max_step,
            style_weight=args.style_weight,
            content_weight=args.content_weight,
            device=device,
        )
        output_img.save(save_folder / f"{name}.{extension}")


if __name__ == "__main__":
    main()