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| 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) | |
| 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) | |