513 lines
15 KiB
Python
513 lines
15 KiB
Python
# 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
|