lineart_models/mangaline_preprocessor.py

'''
MIT License

Copyright (c) 2021 Miaomiao Li

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
'''
import os

import cv2
import numpy as np
import torch
import torch.nn as nn
from einops import rearrange

from .utils import load_file_from_url


class _bn_relu_conv(nn.Module):
    def __init__(self, in_filters, nb_filters, fw, fh, subsample=1):
        super(_bn_relu_conv, self).__init__()
        self.model = nn.Sequential(
            nn.BatchNorm2d(in_filters, eps=1e-3),
            nn.LeakyReLU(0.2),
            nn.Conv2d(
                in_filters,
                nb_filters,
                (fw, fh),
                stride=subsample,
                padding=(fw // 2, fh // 2),
                padding_mode="zeros",
            ),
        )

    def forward(self, x):
        return self.model(x)

        # the following are for debugs
        print(
            "****",
            np.max(x.cpu().numpy()),
            np.min(x.cpu().numpy()),
            np.mean(x.cpu().numpy()),
            np.std(x.cpu().numpy()),
            x.shape,
        )
        for i, layer in enumerate(self.model):
            if i != 2:
                x = layer(x)
            else:
                x = layer(x)
                # x = nn.functional.pad(x, (1, 1, 1, 1), mode='constant', value=0)
            print(
                "____",
                np.max(x.cpu().numpy()),
                np.min(x.cpu().numpy()),
                np.mean(x.cpu().numpy()),
                np.std(x.cpu().numpy()),
                x.shape,
            )
            print(x[0])
        return x


class _u_bn_relu_conv(nn.Module):
    def __init__(self, in_filters, nb_filters, fw, fh, subsample=1):
        super(_u_bn_relu_conv, self).__init__()
        self.model = nn.Sequential(
            nn.BatchNorm2d(in_filters, eps=1e-3),
            nn.LeakyReLU(0.2),
            nn.Conv2d(
                in_filters,
                nb_filters,
                (fw, fh),
                stride=subsample,
                padding=(fw // 2, fh // 2),
            ),
            nn.Upsample(scale_factor=2, mode="nearest"),
        )

    def forward(self, x):
        return self.model(x)


class _shortcut(nn.Module):
    def __init__(self, in_filters, nb_filters, subsample=1):
        super(_shortcut, self).__init__()
        self.process = False
        self.model = None
        if in_filters != nb_filters or subsample != 1:
            self.process = True
            self.model = nn.Sequential(
                nn.Conv2d(in_filters, nb_filters, (1, 1), stride=subsample)
            )

    def forward(self, x, y):
        # print(x.size(), y.size(), self.process)
        if self.process:
            y0 = self.model(x)
            # print("merge+", torch.max(y0+y), torch.min(y0+y),torch.mean(y0+y), torch.std(y0+y), y0.shape)
            return y0 + y
        else:
            # print("merge", torch.max(x+y), torch.min(x+y),torch.mean(x+y), torch.std(x+y), y.shape)
            return x + y


class _u_shortcut(nn.Module):
    def __init__(self, in_filters, nb_filters, subsample):
        super(_u_shortcut, self).__init__()
        self.process = False
        self.model = None
        if in_filters != nb_filters:
            self.process = True
            self.model = nn.Sequential(
                nn.Conv2d(
                    in_filters,
                    nb_filters,
                    (1, 1),
                    stride=subsample,
                    padding_mode="zeros",
                ),
                nn.Upsample(scale_factor=2, mode="nearest"),
            )

    def forward(self, x, y):
        if self.process:
            return self.model(x) + y
        else:
            return x + y


class basic_block(nn.Module):
    def __init__(self, in_filters, nb_filters, init_subsample=1):
        super(basic_block, self).__init__()
        self.conv1 = _bn_relu_conv(
            in_filters, nb_filters, 3, 3, subsample=init_subsample
        )
        self.residual = _bn_relu_conv(nb_filters, nb_filters, 3, 3)
        self.shortcut = _shortcut(in_filters, nb_filters, subsample=init_subsample)

    def forward(self, x):
        x1 = self.conv1(x)
        x2 = self.residual(x1)
        return self.shortcut(x, x2)


class _u_basic_block(nn.Module):
    def __init__(self, in_filters, nb_filters, init_subsample=1):
        super(_u_basic_block, self).__init__()
        self.conv1 = _u_bn_relu_conv(
            in_filters, nb_filters, 3, 3, subsample=init_subsample
        )
        self.residual = _bn_relu_conv(nb_filters, nb_filters, 3, 3)
        self.shortcut = _u_shortcut(in_filters, nb_filters, subsample=init_subsample)

    def forward(self, x):
        y = self.residual(self.conv1(x))
        return self.shortcut(x, y)


class _residual_block(nn.Module):
    def __init__(self, in_filters, nb_filters, repetitions, is_first_layer=False):
        super(_residual_block, self).__init__()
        layers = []
        for i in range(repetitions):
            init_subsample = 1
            if i == repetitions - 1 and not is_first_layer:
                init_subsample = 2
            if i == 0:
                l = basic_block(
                    in_filters=in_filters,
                    nb_filters=nb_filters,
                    init_subsample=init_subsample,
                )
            else:
                l = basic_block(
                    in_filters=nb_filters,
                    nb_filters=nb_filters,
                    init_subsample=init_subsample,
                )
            layers.append(l)

        self.model = nn.Sequential(*layers)

    def forward(self, x):
        return self.model(x)


class _upsampling_residual_block(nn.Module):
    def __init__(self, in_filters, nb_filters, repetitions):
        super(_upsampling_residual_block, self).__init__()
        layers = []
        for i in range(repetitions):
            l = None
            if i == 0:
                l = _u_basic_block(
                    in_filters=in_filters, nb_filters=nb_filters
                )  # (input)
            else:
                l = basic_block(in_filters=nb_filters, nb_filters=nb_filters)  # (input)
            layers.append(l)

        self.model = nn.Sequential(*layers)

    def forward(self, x):
        return self.model(x)


class res_skip(nn.Module):
    def __init__(self):
        super(res_skip, self).__init__()
        self.block0 = _residual_block(
            in_filters=1, nb_filters=24, repetitions=2, is_first_layer=True
        )  # (input)
        self.block1 = _residual_block(
            in_filters=24, nb_filters=48, repetitions=3
        )  # (block0)
        self.block2 = _residual_block(
            in_filters=48, nb_filters=96, repetitions=5
        )  # (block1)
        self.block3 = _residual_block(
            in_filters=96, nb_filters=192, repetitions=7
        )  # (block2)
        self.block4 = _residual_block(
            in_filters=192, nb_filters=384, repetitions=12
        )  # (block3)

        self.block5 = _upsampling_residual_block(
            in_filters=384, nb_filters=192, repetitions=7
        )  # (block4)
        self.res1 = _shortcut(
            in_filters=192, nb_filters=192
        )  # (block3, block5, subsample=(1,1))

        self.block6 = _upsampling_residual_block(
            in_filters=192, nb_filters=96, repetitions=5
        )  # (res1)
        self.res2 = _shortcut(
            in_filters=96, nb_filters=96
        )  # (block2, block6, subsample=(1,1))

        self.block7 = _upsampling_residual_block(
            in_filters=96, nb_filters=48, repetitions=3
        )  # (res2)
        self.res3 = _shortcut(
            in_filters=48, nb_filters=48
        )  # (block1, block7, subsample=(1,1))

        self.block8 = _upsampling_residual_block(
            in_filters=48, nb_filters=24, repetitions=2
        )  # (res3)
        self.res4 = _shortcut(
            in_filters=24, nb_filters=24
        )  # (block0,block8, subsample=(1,1))

        self.block9 = _residual_block(
            in_filters=24, nb_filters=16, repetitions=2, is_first_layer=True
        )  # (res4)
        self.conv15 = _bn_relu_conv(
            in_filters=16, nb_filters=1, fh=1, fw=1, subsample=1
        )  # (block7)

    def forward(self, x):
        x0 = self.block0(x)
        x1 = self.block1(x0)
        x2 = self.block2(x1)
        x3 = self.block3(x2)
        x4 = self.block4(x3)

        x5 = self.block5(x4)
        res1 = self.res1(x3, x5)

        x6 = self.block6(res1)
        res2 = self.res2(x2, x6)

        x7 = self.block7(res2)
        res3 = self.res3(x1, x7)

        x8 = self.block8(res3)
        res4 = self.res4(x0, x8)

        x9 = self.block9(res4)
        y = self.conv15(x9)

        return y


class MangaLineExtraction:
    def __init__(self, device=None, model_dir=None):
        self.model = None
        self.device = device
        MangaLineExtraction.model_dir = model_dir

    def load_model(self):
        remote_model_path = (
            "https://huggingface.co/lllyasviel/Annotators/resolve/main/erika.pth"
        )
        modelpath = os.path.join(self.model_dir, "erika.pth")
        if not os.path.exists(modelpath):
            load_file_from_url(remote_model_path, model_dir=self.model_dir)
        # norm_layer = functools.partial(nn.InstanceNorm2d, affine=False, track_running_stats=False)
        net = res_skip()
        ckpt = torch.load(modelpath)
        for key in list(ckpt.keys()):
            if "module." in key:
                ckpt[key.replace("module.", "")] = ckpt[key]
                del ckpt[key]
        net.load_state_dict(ckpt)
        net.eval()
        self.model = net.to(self.device)

    def unload_model(self):
        if self.model is not None:
            self.model.cpu()

    def __call__(self, input_image):
        if self.model is None:
            self.load_model()
        self.model.to(self.device)
        # if width or height is not divisible by 16, pad the image
        h, w = input_image.shape[:2]
        # get adjusted pixel amount to max 1280x1280
        total_pixels = h * w
        if total_pixels > 1280 * 1280:
            ratio = (1280 * 1280) / total_pixels
            ratio = ratio**0.5
            h = int(h * ratio)
            w = int(w * ratio)
        divisible = 16
        h = h + (divisible - h % divisible) % divisible
        w = w + (divisible - w % divisible) % divisible
        input_image = cv2.resize(input_image, (w, h))
        img = cv2.cvtColor(input_image, cv2.COLOR_RGB2GRAY)
        img = np.ascontiguousarray(img.copy()).copy()
        with torch.no_grad():
            image_feed = torch.from_numpy(img).float().to(self.device)
            image_feed = rearrange(image_feed, "h w -> 1 1 h w")
            line = self.model(image_feed).cpu().numpy()[0, 0]
            # line = 255 - line
            return line.clip(0, 255).astype(np.uint8)