import cv2 import torch import torch.nn as nn import torch.nn.functional as F import numpy as np from PIL import Image from lama_cleaner.helper import load_model from lama_cleaner.plugins.base_plugin import BasePlugin class REBNCONV(nn.Module): def __init__(self, in_ch=3, out_ch=3, dirate=1, stride=1): super(REBNCONV, self).__init__() self.conv_s1 = nn.Conv2d( in_ch, out_ch, 3, padding=1 * dirate, dilation=1 * dirate, stride=stride ) self.bn_s1 = nn.BatchNorm2d(out_ch) self.relu_s1 = nn.ReLU(inplace=True) def forward(self, x): hx = x xout = self.relu_s1(self.bn_s1(self.conv_s1(hx))) return xout ## upsample tensor 'src' to have the same spatial size with tensor 'tar' def _upsample_like(src, tar): src = F.interpolate(src, size=tar.shape[2:], mode="bilinear", align_corners=False) return src ### RSU-7 ### class RSU7(nn.Module): def __init__(self, in_ch=3, mid_ch=12, out_ch=3, img_size=512): super(RSU7, self).__init__() self.in_ch = in_ch self.mid_ch = mid_ch self.out_ch = out_ch self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1) ## 1 -> 1/2 self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1) self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True) self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1) self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True) self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1) self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True) self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1) self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True) self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=1) self.pool5 = nn.MaxPool2d(2, stride=2, ceil_mode=True) self.rebnconv6 = REBNCONV(mid_ch, mid_ch, dirate=1) self.rebnconv7 = REBNCONV(mid_ch, mid_ch, dirate=2) self.rebnconv6d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) self.rebnconv5d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1) def forward(self, x): b, c, h, w = x.shape hx = x hxin = self.rebnconvin(hx) hx1 = self.rebnconv1(hxin) hx = self.pool1(hx1) hx2 = self.rebnconv2(hx) hx = self.pool2(hx2) hx3 = self.rebnconv3(hx) hx = self.pool3(hx3) hx4 = self.rebnconv4(hx) hx = self.pool4(hx4) hx5 = self.rebnconv5(hx) hx = self.pool5(hx5) hx6 = self.rebnconv6(hx) hx7 = self.rebnconv7(hx6) hx6d = self.rebnconv6d(torch.cat((hx7, hx6), 1)) hx6dup = _upsample_like(hx6d, hx5) hx5d = self.rebnconv5d(torch.cat((hx6dup, hx5), 1)) hx5dup = _upsample_like(hx5d, hx4) hx4d = self.rebnconv4d(torch.cat((hx5dup, hx4), 1)) hx4dup = _upsample_like(hx4d, hx3) hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1)) hx3dup = _upsample_like(hx3d, hx2) hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1)) hx2dup = _upsample_like(hx2d, hx1) hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1)) return hx1d + hxin ### RSU-6 ### class RSU6(nn.Module): def __init__(self, in_ch=3, mid_ch=12, out_ch=3): super(RSU6, self).__init__() self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1) self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1) self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True) self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1) self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True) self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1) self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True) self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1) self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True) self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=1) self.rebnconv6 = REBNCONV(mid_ch, mid_ch, dirate=2) self.rebnconv5d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1) def forward(self, x): hx = x hxin = self.rebnconvin(hx) hx1 = self.rebnconv1(hxin) hx = self.pool1(hx1) hx2 = self.rebnconv2(hx) hx = self.pool2(hx2) hx3 = self.rebnconv3(hx) hx = self.pool3(hx3) hx4 = self.rebnconv4(hx) hx = self.pool4(hx4) hx5 = self.rebnconv5(hx) hx6 = self.rebnconv6(hx5) hx5d = self.rebnconv5d(torch.cat((hx6, hx5), 1)) hx5dup = _upsample_like(hx5d, hx4) hx4d = self.rebnconv4d(torch.cat((hx5dup, hx4), 1)) hx4dup = _upsample_like(hx4d, hx3) hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1)) hx3dup = _upsample_like(hx3d, hx2) hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1)) hx2dup = _upsample_like(hx2d, hx1) hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1)) return hx1d + hxin ### RSU-5 ### class RSU5(nn.Module): def __init__(self, in_ch=3, mid_ch=12, out_ch=3): super(RSU5, self).__init__() self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1) self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1) self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True) self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1) self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True) self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1) self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True) self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1) self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=2) self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1) def forward(self, x): hx = x hxin = self.rebnconvin(hx) hx1 = self.rebnconv1(hxin) hx = self.pool1(hx1) hx2 = self.rebnconv2(hx) hx = self.pool2(hx2) hx3 = self.rebnconv3(hx) hx = self.pool3(hx3) hx4 = self.rebnconv4(hx) hx5 = self.rebnconv5(hx4) hx4d = self.rebnconv4d(torch.cat((hx5, hx4), 1)) hx4dup = _upsample_like(hx4d, hx3) hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1)) hx3dup = _upsample_like(hx3d, hx2) hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1)) hx2dup = _upsample_like(hx2d, hx1) hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1)) return hx1d + hxin ### RSU-4 ### class RSU4(nn.Module): def __init__(self, in_ch=3, mid_ch=12, out_ch=3): super(RSU4, self).__init__() self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1) self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1) self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True) self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1) self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True) self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1) self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=2) self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1) self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1) def forward(self, x): hx = x hxin = self.rebnconvin(hx) hx1 = self.rebnconv1(hxin) hx = self.pool1(hx1) hx2 = self.rebnconv2(hx) hx = self.pool2(hx2) hx3 = self.rebnconv3(hx) hx4 = self.rebnconv4(hx3) hx3d = self.rebnconv3d(torch.cat((hx4, hx3), 1)) hx3dup = _upsample_like(hx3d, hx2) hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1)) hx2dup = _upsample_like(hx2d, hx1) hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1)) return hx1d + hxin ### RSU-4F ### class RSU4F(nn.Module): def __init__(self, in_ch=3, mid_ch=12, out_ch=3): super(RSU4F, self).__init__() self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1) self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1) self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=2) self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=4) self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=8) self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=4) self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=2) self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1) def forward(self, x): hx = x hxin = self.rebnconvin(hx) hx1 = self.rebnconv1(hxin) hx2 = self.rebnconv2(hx1) hx3 = self.rebnconv3(hx2) hx4 = self.rebnconv4(hx3) hx3d = self.rebnconv3d(torch.cat((hx4, hx3), 1)) hx2d = self.rebnconv2d(torch.cat((hx3d, hx2), 1)) hx1d = self.rebnconv1d(torch.cat((hx2d, hx1), 1)) return hx1d + hxin class ISNetDIS(nn.Module): def __init__(self, in_ch=3, out_ch=1): super(ISNetDIS, self).__init__() self.conv_in = nn.Conv2d(in_ch, 64, 3, stride=2, padding=1) self.pool_in = nn.MaxPool2d(2, stride=2, ceil_mode=True) self.stage1 = RSU7(64, 32, 64) self.pool12 = nn.MaxPool2d(2, stride=2, ceil_mode=True) self.stage2 = RSU6(64, 32, 128) self.pool23 = nn.MaxPool2d(2, stride=2, ceil_mode=True) self.stage3 = RSU5(128, 64, 256) self.pool34 = nn.MaxPool2d(2, stride=2, ceil_mode=True) self.stage4 = RSU4(256, 128, 512) self.pool45 = nn.MaxPool2d(2, stride=2, ceil_mode=True) self.stage5 = RSU4F(512, 256, 512) self.pool56 = nn.MaxPool2d(2, stride=2, ceil_mode=True) self.stage6 = RSU4F(512, 256, 512) # decoder self.stage5d = RSU4F(1024, 256, 512) self.stage4d = RSU4(1024, 128, 256) self.stage3d = RSU5(512, 64, 128) self.stage2d = RSU6(256, 32, 64) self.stage1d = RSU7(128, 16, 64) self.side1 = nn.Conv2d(64, out_ch, 3, padding=1) def forward(self, x): hx = x hxin = self.conv_in(hx) hx = self.pool_in(hxin) # stage 1 hx1 = self.stage1(hxin) hx = self.pool12(hx1) # stage 2 hx2 = self.stage2(hx) hx = self.pool23(hx2) # stage 3 hx3 = self.stage3(hx) hx = self.pool34(hx3) # stage 4 hx4 = self.stage4(hx) hx = self.pool45(hx4) # stage 5 hx5 = self.stage5(hx) hx = self.pool56(hx5) # stage 6 hx6 = self.stage6(hx) hx6up = _upsample_like(hx6, hx5) # -------------------- decoder -------------------- hx5d = self.stage5d(torch.cat((hx6up, hx5), 1)) hx5dup = _upsample_like(hx5d, hx4) hx4d = self.stage4d(torch.cat((hx5dup, hx4), 1)) hx4dup = _upsample_like(hx4d, hx3) hx3d = self.stage3d(torch.cat((hx4dup, hx3), 1)) hx3dup = _upsample_like(hx3d, hx2) hx2d = self.stage2d(torch.cat((hx3dup, hx2), 1)) hx2dup = _upsample_like(hx2d, hx1) hx1d = self.stage1d(torch.cat((hx2dup, hx1), 1)) # side output d1 = self.side1(hx1d) d1 = _upsample_like(d1, x) return d1.sigmoid() # 从小到大 ANIME_SEG_MODELS = { "url": "https://github.com/Sanster/models/releases/download/isnetis/isnetis.pth", "md5": "5f25479076b73074730ab8de9e8f2051", } class AnimeSeg(BasePlugin): # Model from: https://github.com/SkyTNT/anime-segmentation name = "AnimeSeg" def __init__(self): super().__init__() self.model = load_model( ISNetDIS(), ANIME_SEG_MODELS["url"], "cpu", ANIME_SEG_MODELS["md5"], ) def __call__(self, rgb_np_img, files, form): return self.forward(rgb_np_img) @torch.no_grad() def forward(self, rgb_np_img): s = 1024 h0, w0 = h, w = rgb_np_img.shape[0], rgb_np_img.shape[1] if h > w: h, w = s, int(s * w / h) else: h, w = int(s * h / w), s ph, pw = s - h, s - w tmpImg = np.zeros([s, s, 3], dtype=np.float32) tmpImg[ph // 2 : ph // 2 + h, pw // 2 : pw // 2 + w] = ( cv2.resize(rgb_np_img, (w, h)) / 255 ) tmpImg = tmpImg.transpose((2, 0, 1)) tmpImg = torch.from_numpy(tmpImg).unsqueeze(0).type(torch.FloatTensor) mask = self.model(tmpImg) mask = mask[0, :, ph // 2 : ph // 2 + h, pw // 2 : pw // 2 + w] mask = cv2.resize(mask.cpu().numpy().transpose((1, 2, 0)), (w0, h0)) mask = Image.fromarray((mask * 255).astype("uint8"), mode="L") empty = Image.new("RGBA", (w0, h0), 0) img = Image.fromarray(rgb_np_img) cutout = Image.composite(img, empty, mask) return np.asarray(cutout)