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| # copy from: https://huggingface.co/spaces/briaai/BRIA-RMBG-1.4/blob/main/briarmbg.py | |
| import cv2 | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from PIL import Image | |
| import numpy as np | |
| from torchvision.transforms.functional import normalize | |
| 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") | |
| 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 myrebnconv(nn.Module): | |
| def __init__( | |
| self, | |
| in_ch=3, | |
| out_ch=1, | |
| kernel_size=3, | |
| stride=1, | |
| padding=1, | |
| dilation=1, | |
| groups=1, | |
| ): | |
| super(myrebnconv, self).__init__() | |
| self.conv = nn.Conv2d( | |
| in_ch, | |
| out_ch, | |
| kernel_size=kernel_size, | |
| stride=stride, | |
| padding=padding, | |
| dilation=dilation, | |
| groups=groups, | |
| ) | |
| self.bn = nn.BatchNorm2d(out_ch) | |
| self.rl = nn.ReLU(inplace=True) | |
| def forward(self, x): | |
| return self.rl(self.bn(self.conv(x))) | |
| class BriaRMBG(nn.Module): | |
| def __init__(self, in_ch=3, out_ch=1): | |
| super(BriaRMBG, 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) | |
| self.side2 = nn.Conv2d(64, out_ch, 3, padding=1) | |
| self.side3 = nn.Conv2d(128, out_ch, 3, padding=1) | |
| self.side4 = nn.Conv2d(256, out_ch, 3, padding=1) | |
| self.side5 = nn.Conv2d(512, out_ch, 3, padding=1) | |
| self.side6 = nn.Conv2d(512, out_ch, 3, padding=1) | |
| # self.outconv = nn.Conv2d(6*out_ch,out_ch,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) | |
| d2 = self.side2(hx2d) | |
| d2 = _upsample_like(d2, x) | |
| d3 = self.side3(hx3d) | |
| d3 = _upsample_like(d3, x) | |
| d4 = self.side4(hx4d) | |
| d4 = _upsample_like(d4, x) | |
| d5 = self.side5(hx5d) | |
| d5 = _upsample_like(d5, x) | |
| d6 = self.side6(hx6) | |
| d6 = _upsample_like(d6, x) | |
| return [ | |
| F.sigmoid(d1), | |
| F.sigmoid(d2), | |
| F.sigmoid(d3), | |
| F.sigmoid(d4), | |
| F.sigmoid(d5), | |
| F.sigmoid(d6), | |
| ], [hx1d, hx2d, hx3d, hx4d, hx5d, hx6] | |
| def resize_image(image): | |
| image = image.convert("RGB") | |
| model_input_size = (1024, 1024) | |
| image = image.resize(model_input_size, Image.BILINEAR) | |
| return image | |
| def create_briarmbg_session(): | |
| from huggingface_hub import hf_hub_download | |
| net = BriaRMBG() | |
| model_path = hf_hub_download("briaai/RMBG-1.4", "model.pth") | |
| net.load_state_dict(torch.load(model_path, map_location="cpu")) | |
| net.eval() | |
| return net | |
| def briarmbg_process(bgr_np_image, session, only_mask=False): | |
| # prepare input | |
| orig_bgr_image = Image.fromarray(bgr_np_image) | |
| w, h = orig_im_size = orig_bgr_image.size | |
| image = resize_image(orig_bgr_image) | |
| im_np = np.array(image) | |
| im_tensor = torch.tensor(im_np, dtype=torch.float32).permute(2, 0, 1) | |
| im_tensor = torch.unsqueeze(im_tensor, 0) | |
| im_tensor = torch.divide(im_tensor, 255.0) | |
| im_tensor = normalize(im_tensor, [0.5, 0.5, 0.5], [1.0, 1.0, 1.0]) | |
| # inference | |
| result = session(im_tensor) | |
| # post process | |
| result = torch.squeeze(F.interpolate(result[0][0], size=(h, w), mode="bilinear"), 0) | |
| ma = torch.max(result) | |
| mi = torch.min(result) | |
| result = (result - mi) / (ma - mi) | |
| # image to pil | |
| im_array = (result * 255).cpu().data.numpy().astype(np.uint8) | |
| mask = np.squeeze(im_array) | |
| if only_mask: | |
| return mask | |
| pil_im = Image.fromarray(mask) | |
| # paste the mask on the original image | |
| new_im = Image.new("RGBA", pil_im.size, (0, 0, 0, 0)) | |
| new_im.paste(orig_bgr_image, mask=pil_im) | |
| rgba_np_img = np.asarray(new_im) | |
| return rgba_np_img | |