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Browse files- Dockerfile +14 -0
- briarmbg.py +457 -0
- example_inference.py +34 -0
- flask_app.py +52 -0
- images/normal_image.png +0 -0
- images/rm_image.png +0 -0
- requirements.txt +10 -0
- utilities.py +25 -0
Dockerfile
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# read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
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# you will also find guides on how best to write your Dockerfile
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FROM python:3.9
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WORKDIR /code
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COPY ./requirements.txt /code/requirements.txt
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RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
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COPY . .
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CMD ["gunicorn", "-b", "0.0.0.0:7860", "flask_app:app"]
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briarmbg.py
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from huggingface_hub import PyTorchModelHubMixin
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class REBNCONV(nn.Module):
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def __init__(self,in_ch=3,out_ch=3,dirate=1,stride=1):
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super(REBNCONV,self).__init__()
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self.conv_s1 = nn.Conv2d(in_ch,out_ch,3,padding=1*dirate,dilation=1*dirate,stride=stride)
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self.bn_s1 = nn.BatchNorm2d(out_ch)
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self.relu_s1 = nn.ReLU(inplace=True)
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def forward(self,x):
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hx = x
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xout = self.relu_s1(self.bn_s1(self.conv_s1(hx)))
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return xout
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## upsample tensor 'src' to have the same spatial size with tensor 'tar'
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def _upsample_like(src,tar):
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src = F.interpolate(src,size=tar.shape[2:],mode='bilinear')
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return src
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### RSU-7 ###
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class RSU7(nn.Module):
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def __init__(self, in_ch=3, mid_ch=12, out_ch=3, img_size=512):
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super(RSU7,self).__init__()
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self.in_ch = in_ch
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self.mid_ch = mid_ch
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self.out_ch = out_ch
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self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1) ## 1 -> 1/2
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self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
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self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
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self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)
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self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
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self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)
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self.pool3 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
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self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=1)
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self.pool4 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
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self.rebnconv5 = REBNCONV(mid_ch,mid_ch,dirate=1)
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self.pool5 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
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self.rebnconv6 = REBNCONV(mid_ch,mid_ch,dirate=1)
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self.rebnconv7 = REBNCONV(mid_ch,mid_ch,dirate=2)
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self.rebnconv6d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
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self.rebnconv5d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
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self.rebnconv4d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
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self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
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self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
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self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
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def forward(self,x):
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b, c, h, w = x.shape
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hx = x
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hxin = self.rebnconvin(hx)
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hx1 = self.rebnconv1(hxin)
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hx = self.pool1(hx1)
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hx2 = self.rebnconv2(hx)
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hx = self.pool2(hx2)
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hx3 = self.rebnconv3(hx)
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hx = self.pool3(hx3)
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hx4 = self.rebnconv4(hx)
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hx = self.pool4(hx4)
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hx5 = self.rebnconv5(hx)
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hx = self.pool5(hx5)
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hx6 = self.rebnconv6(hx)
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hx7 = self.rebnconv7(hx6)
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hx6d = self.rebnconv6d(torch.cat((hx7,hx6),1))
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hx6dup = _upsample_like(hx6d,hx5)
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hx5d = self.rebnconv5d(torch.cat((hx6dup,hx5),1))
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hx5dup = _upsample_like(hx5d,hx4)
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hx4d = self.rebnconv4d(torch.cat((hx5dup,hx4),1))
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hx4dup = _upsample_like(hx4d,hx3)
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hx3d = self.rebnconv3d(torch.cat((hx4dup,hx3),1))
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hx3dup = _upsample_like(hx3d,hx2)
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hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
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hx2dup = _upsample_like(hx2d,hx1)
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hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))
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return hx1d + hxin
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### RSU-6 ###
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class RSU6(nn.Module):
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def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
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super(RSU6,self).__init__()
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self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)
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self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
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self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
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self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)
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self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
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self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)
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self.pool3 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
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self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=1)
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self.pool4 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
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self.rebnconv5 = REBNCONV(mid_ch,mid_ch,dirate=1)
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self.rebnconv6 = REBNCONV(mid_ch,mid_ch,dirate=2)
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self.rebnconv5d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
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self.rebnconv4d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
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self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
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self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
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self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
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def forward(self,x):
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hx = x
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hxin = self.rebnconvin(hx)
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hx1 = self.rebnconv1(hxin)
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hx = self.pool1(hx1)
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hx2 = self.rebnconv2(hx)
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hx = self.pool2(hx2)
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hx3 = self.rebnconv3(hx)
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hx = self.pool3(hx3)
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hx4 = self.rebnconv4(hx)
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hx = self.pool4(hx4)
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hx5 = self.rebnconv5(hx)
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hx6 = self.rebnconv6(hx5)
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hx5d = self.rebnconv5d(torch.cat((hx6,hx5),1))
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hx5dup = _upsample_like(hx5d,hx4)
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hx4d = self.rebnconv4d(torch.cat((hx5dup,hx4),1))
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169 |
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hx4dup = _upsample_like(hx4d,hx3)
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170 |
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hx3d = self.rebnconv3d(torch.cat((hx4dup,hx3),1))
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hx3dup = _upsample_like(hx3d,hx2)
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hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
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hx2dup = _upsample_like(hx2d,hx1)
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hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))
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return hx1d + hxin
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181 |
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### RSU-5 ###
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class RSU5(nn.Module):
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def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
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super(RSU5,self).__init__()
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self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)
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self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
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self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
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self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)
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self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
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self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)
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self.pool3 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
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self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=1)
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self.rebnconv5 = REBNCONV(mid_ch,mid_ch,dirate=2)
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self.rebnconv4d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
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self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
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self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
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self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
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207 |
+
def forward(self,x):
|
208 |
+
|
209 |
+
hx = x
|
210 |
+
|
211 |
+
hxin = self.rebnconvin(hx)
|
212 |
+
|
213 |
+
hx1 = self.rebnconv1(hxin)
|
214 |
+
hx = self.pool1(hx1)
|
215 |
+
|
216 |
+
hx2 = self.rebnconv2(hx)
|
217 |
+
hx = self.pool2(hx2)
|
218 |
+
|
219 |
+
hx3 = self.rebnconv3(hx)
|
220 |
+
hx = self.pool3(hx3)
|
221 |
+
|
222 |
+
hx4 = self.rebnconv4(hx)
|
223 |
+
|
224 |
+
hx5 = self.rebnconv5(hx4)
|
225 |
+
|
226 |
+
hx4d = self.rebnconv4d(torch.cat((hx5,hx4),1))
|
227 |
+
hx4dup = _upsample_like(hx4d,hx3)
|
228 |
+
|
229 |
+
hx3d = self.rebnconv3d(torch.cat((hx4dup,hx3),1))
|
230 |
+
hx3dup = _upsample_like(hx3d,hx2)
|
231 |
+
|
232 |
+
hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
|
233 |
+
hx2dup = _upsample_like(hx2d,hx1)
|
234 |
+
|
235 |
+
hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))
|
236 |
+
|
237 |
+
return hx1d + hxin
|
238 |
+
|
239 |
+
### RSU-4 ###
|
240 |
+
class RSU4(nn.Module):
|
241 |
+
|
242 |
+
def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
|
243 |
+
super(RSU4,self).__init__()
|
244 |
+
|
245 |
+
self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)
|
246 |
+
|
247 |
+
self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
|
248 |
+
self.pool1 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
|
249 |
+
|
250 |
+
self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=1)
|
251 |
+
self.pool2 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
|
252 |
+
|
253 |
+
self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=1)
|
254 |
+
|
255 |
+
self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=2)
|
256 |
+
|
257 |
+
self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
|
258 |
+
self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=1)
|
259 |
+
self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
|
260 |
+
|
261 |
+
def forward(self,x):
|
262 |
+
|
263 |
+
hx = x
|
264 |
+
|
265 |
+
hxin = self.rebnconvin(hx)
|
266 |
+
|
267 |
+
hx1 = self.rebnconv1(hxin)
|
268 |
+
hx = self.pool1(hx1)
|
269 |
+
|
270 |
+
hx2 = self.rebnconv2(hx)
|
271 |
+
hx = self.pool2(hx2)
|
272 |
+
|
273 |
+
hx3 = self.rebnconv3(hx)
|
274 |
+
|
275 |
+
hx4 = self.rebnconv4(hx3)
|
276 |
+
|
277 |
+
hx3d = self.rebnconv3d(torch.cat((hx4,hx3),1))
|
278 |
+
hx3dup = _upsample_like(hx3d,hx2)
|
279 |
+
|
280 |
+
hx2d = self.rebnconv2d(torch.cat((hx3dup,hx2),1))
|
281 |
+
hx2dup = _upsample_like(hx2d,hx1)
|
282 |
+
|
283 |
+
hx1d = self.rebnconv1d(torch.cat((hx2dup,hx1),1))
|
284 |
+
|
285 |
+
return hx1d + hxin
|
286 |
+
|
287 |
+
### RSU-4F ###
|
288 |
+
class RSU4F(nn.Module):
|
289 |
+
|
290 |
+
def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
|
291 |
+
super(RSU4F,self).__init__()
|
292 |
+
|
293 |
+
self.rebnconvin = REBNCONV(in_ch,out_ch,dirate=1)
|
294 |
+
|
295 |
+
self.rebnconv1 = REBNCONV(out_ch,mid_ch,dirate=1)
|
296 |
+
self.rebnconv2 = REBNCONV(mid_ch,mid_ch,dirate=2)
|
297 |
+
self.rebnconv3 = REBNCONV(mid_ch,mid_ch,dirate=4)
|
298 |
+
|
299 |
+
self.rebnconv4 = REBNCONV(mid_ch,mid_ch,dirate=8)
|
300 |
+
|
301 |
+
self.rebnconv3d = REBNCONV(mid_ch*2,mid_ch,dirate=4)
|
302 |
+
self.rebnconv2d = REBNCONV(mid_ch*2,mid_ch,dirate=2)
|
303 |
+
self.rebnconv1d = REBNCONV(mid_ch*2,out_ch,dirate=1)
|
304 |
+
|
305 |
+
def forward(self,x):
|
306 |
+
|
307 |
+
hx = x
|
308 |
+
|
309 |
+
hxin = self.rebnconvin(hx)
|
310 |
+
|
311 |
+
hx1 = self.rebnconv1(hxin)
|
312 |
+
hx2 = self.rebnconv2(hx1)
|
313 |
+
hx3 = self.rebnconv3(hx2)
|
314 |
+
|
315 |
+
hx4 = self.rebnconv4(hx3)
|
316 |
+
|
317 |
+
hx3d = self.rebnconv3d(torch.cat((hx4,hx3),1))
|
318 |
+
hx2d = self.rebnconv2d(torch.cat((hx3d,hx2),1))
|
319 |
+
hx1d = self.rebnconv1d(torch.cat((hx2d,hx1),1))
|
320 |
+
|
321 |
+
return hx1d + hxin
|
322 |
+
|
323 |
+
|
324 |
+
class myrebnconv(nn.Module):
|
325 |
+
def __init__(self, in_ch=3,
|
326 |
+
out_ch=1,
|
327 |
+
kernel_size=3,
|
328 |
+
stride=1,
|
329 |
+
padding=1,
|
330 |
+
dilation=1,
|
331 |
+
groups=1):
|
332 |
+
super(myrebnconv,self).__init__()
|
333 |
+
|
334 |
+
self.conv = nn.Conv2d(in_ch,
|
335 |
+
out_ch,
|
336 |
+
kernel_size=kernel_size,
|
337 |
+
stride=stride,
|
338 |
+
padding=padding,
|
339 |
+
dilation=dilation,
|
340 |
+
groups=groups)
|
341 |
+
self.bn = nn.BatchNorm2d(out_ch)
|
342 |
+
self.rl = nn.ReLU(inplace=True)
|
343 |
+
|
344 |
+
def forward(self,x):
|
345 |
+
return self.rl(self.bn(self.conv(x)))
|
346 |
+
|
347 |
+
|
348 |
+
class BriaRMBG(nn.Module, PyTorchModelHubMixin):
|
349 |
+
|
350 |
+
def __init__(self,config:dict={"in_ch":3,"out_ch":1}):
|
351 |
+
super(BriaRMBG,self).__init__()
|
352 |
+
in_ch=config["in_ch"]
|
353 |
+
out_ch=config["out_ch"]
|
354 |
+
self.conv_in = nn.Conv2d(in_ch,64,3,stride=2,padding=1)
|
355 |
+
self.pool_in = nn.MaxPool2d(2,stride=2,ceil_mode=True)
|
356 |
+
|
357 |
+
self.stage1 = RSU7(64,32,64)
|
358 |
+
self.pool12 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
|
359 |
+
|
360 |
+
self.stage2 = RSU6(64,32,128)
|
361 |
+
self.pool23 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
|
362 |
+
|
363 |
+
self.stage3 = RSU5(128,64,256)
|
364 |
+
self.pool34 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
|
365 |
+
|
366 |
+
self.stage4 = RSU4(256,128,512)
|
367 |
+
self.pool45 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
|
368 |
+
|
369 |
+
self.stage5 = RSU4F(512,256,512)
|
370 |
+
self.pool56 = nn.MaxPool2d(2,stride=2,ceil_mode=True)
|
371 |
+
|
372 |
+
self.stage6 = RSU4F(512,256,512)
|
373 |
+
|
374 |
+
# decoder
|
375 |
+
self.stage5d = RSU4F(1024,256,512)
|
376 |
+
self.stage4d = RSU4(1024,128,256)
|
377 |
+
self.stage3d = RSU5(512,64,128)
|
378 |
+
self.stage2d = RSU6(256,32,64)
|
379 |
+
self.stage1d = RSU7(128,16,64)
|
380 |
+
|
381 |
+
self.side1 = nn.Conv2d(64,out_ch,3,padding=1)
|
382 |
+
self.side2 = nn.Conv2d(64,out_ch,3,padding=1)
|
383 |
+
self.side3 = nn.Conv2d(128,out_ch,3,padding=1)
|
384 |
+
self.side4 = nn.Conv2d(256,out_ch,3,padding=1)
|
385 |
+
self.side5 = nn.Conv2d(512,out_ch,3,padding=1)
|
386 |
+
self.side6 = nn.Conv2d(512,out_ch,3,padding=1)
|
387 |
+
|
388 |
+
# self.outconv = nn.Conv2d(6*out_ch,out_ch,1)
|
389 |
+
|
390 |
+
def forward(self,x):
|
391 |
+
|
392 |
+
hx = x
|
393 |
+
|
394 |
+
hxin = self.conv_in(hx)
|
395 |
+
#hx = self.pool_in(hxin)
|
396 |
+
|
397 |
+
#stage 1
|
398 |
+
hx1 = self.stage1(hxin)
|
399 |
+
hx = self.pool12(hx1)
|
400 |
+
|
401 |
+
#stage 2
|
402 |
+
hx2 = self.stage2(hx)
|
403 |
+
hx = self.pool23(hx2)
|
404 |
+
|
405 |
+
#stage 3
|
406 |
+
hx3 = self.stage3(hx)
|
407 |
+
hx = self.pool34(hx3)
|
408 |
+
|
409 |
+
#stage 4
|
410 |
+
hx4 = self.stage4(hx)
|
411 |
+
hx = self.pool45(hx4)
|
412 |
+
|
413 |
+
#stage 5
|
414 |
+
hx5 = self.stage5(hx)
|
415 |
+
hx = self.pool56(hx5)
|
416 |
+
|
417 |
+
#stage 6
|
418 |
+
hx6 = self.stage6(hx)
|
419 |
+
hx6up = _upsample_like(hx6,hx5)
|
420 |
+
|
421 |
+
#-------------------- decoder --------------------
|
422 |
+
hx5d = self.stage5d(torch.cat((hx6up,hx5),1))
|
423 |
+
hx5dup = _upsample_like(hx5d,hx4)
|
424 |
+
|
425 |
+
hx4d = self.stage4d(torch.cat((hx5dup,hx4),1))
|
426 |
+
hx4dup = _upsample_like(hx4d,hx3)
|
427 |
+
|
428 |
+
hx3d = self.stage3d(torch.cat((hx4dup,hx3),1))
|
429 |
+
hx3dup = _upsample_like(hx3d,hx2)
|
430 |
+
|
431 |
+
hx2d = self.stage2d(torch.cat((hx3dup,hx2),1))
|
432 |
+
hx2dup = _upsample_like(hx2d,hx1)
|
433 |
+
|
434 |
+
hx1d = self.stage1d(torch.cat((hx2dup,hx1),1))
|
435 |
+
|
436 |
+
|
437 |
+
#side output
|
438 |
+
d1 = self.side1(hx1d)
|
439 |
+
d1 = _upsample_like(d1,x)
|
440 |
+
|
441 |
+
d2 = self.side2(hx2d)
|
442 |
+
d2 = _upsample_like(d2,x)
|
443 |
+
|
444 |
+
d3 = self.side3(hx3d)
|
445 |
+
d3 = _upsample_like(d3,x)
|
446 |
+
|
447 |
+
d4 = self.side4(hx4d)
|
448 |
+
d4 = _upsample_like(d4,x)
|
449 |
+
|
450 |
+
d5 = self.side5(hx5d)
|
451 |
+
d5 = _upsample_like(d5,x)
|
452 |
+
|
453 |
+
d6 = self.side6(hx6)
|
454 |
+
d6 = _upsample_like(d6,x)
|
455 |
+
|
456 |
+
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]
|
457 |
+
|
example_inference.py
ADDED
@@ -0,0 +1,34 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from skimage import io
|
2 |
+
import torch, os
|
3 |
+
from PIL import Image
|
4 |
+
from briarmbg import BriaRMBG
|
5 |
+
from utilities import preprocess_image, postprocess_image
|
6 |
+
from huggingface_hub import hf_hub_download
|
7 |
+
|
8 |
+
def example_inference(im_path, transprent_bg=False):
|
9 |
+
|
10 |
+
net = BriaRMBG()
|
11 |
+
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
12 |
+
net = BriaRMBG.from_pretrained("briaai/RMBG-1.4")
|
13 |
+
net.to(device)
|
14 |
+
net.eval()
|
15 |
+
|
16 |
+
# prepare input
|
17 |
+
model_input_size = [1024,1024]
|
18 |
+
orig_im = io.imread(im_path)
|
19 |
+
orig_im_size = orig_im.shape[0:2]
|
20 |
+
image = preprocess_image(orig_im, model_input_size).to(device)
|
21 |
+
|
22 |
+
# inference
|
23 |
+
result=net(image)
|
24 |
+
|
25 |
+
# post process
|
26 |
+
result_image = postprocess_image(result[0][0], orig_im_size)
|
27 |
+
bgColor = (0,0,0, 0) if transprent_bg else (255,255,255, 255)
|
28 |
+
# save result
|
29 |
+
pil_im = Image.fromarray(result_image)
|
30 |
+
no_bg_image = Image.new("RGBA", pil_im.size, bgColor)
|
31 |
+
orig_image = Image.open(im_path)
|
32 |
+
no_bg_image.paste(orig_image, mask=pil_im)
|
33 |
+
no_bg_image.save("images/rm_image.png")
|
34 |
+
return 'rm_image.png'
|
flask_app.py
ADDED
@@ -0,0 +1,52 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
|
2 |
+
import os
|
3 |
+
from flask import Flask, flash, request, redirect, url_for, session
|
4 |
+
from flask_session import Session
|
5 |
+
from werkzeug.utils import secure_filename
|
6 |
+
from gevent.pywsgi import WSGIServer
|
7 |
+
from example_inference import example_inference
|
8 |
+
from flask import send_from_directory
|
9 |
+
|
10 |
+
|
11 |
+
UPLOAD_FOLDER = 'images'
|
12 |
+
ALLOWED_EXTENSIONS = {'png', 'jpg', 'jpeg'}
|
13 |
+
|
14 |
+
app = Flask(__name__)
|
15 |
+
app.config["SESSION_PERMANENT"] = False
|
16 |
+
app.config["SESSION_TYPE"] = "filesystem"
|
17 |
+
app.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER
|
18 |
+
|
19 |
+
Session(app)
|
20 |
+
|
21 |
+
def allowed_file(filename):
|
22 |
+
return '.' in filename and \
|
23 |
+
filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS
|
24 |
+
|
25 |
+
@app.route('/rm-bg/<transparent>', methods=['GET', 'POST'])
|
26 |
+
def upload_file(transparent):
|
27 |
+
print(transparent)
|
28 |
+
transparent = True if transparent == "true" else False
|
29 |
+
print(transparent)
|
30 |
+
if request.method == 'POST':
|
31 |
+
if 'file' not in request.files:
|
32 |
+
flash('No file part')
|
33 |
+
return {"status": "Failed", "message": "Please Provide file name(file)."}
|
34 |
+
file = request.files['file']
|
35 |
+
if file.filename == '':
|
36 |
+
flash('No selected file')
|
37 |
+
return {"status": "Failed", "message": "Filename Not Found."}
|
38 |
+
if file and allowed_file(file.filename):
|
39 |
+
filename = secure_filename('normal_image.png')
|
40 |
+
file.save(os.path.join(app.config['UPLOAD_FOLDER'], filename))
|
41 |
+
im_path = f"{os.path.dirname(os.path.abspath(__file__))}/images/{filename}"
|
42 |
+
rm_image_path = example_inference(im_path, transparent)
|
43 |
+
return send_from_directory(app.config["UPLOAD_FOLDER"], rm_image_path)
|
44 |
+
return {
|
45 |
+
"message": "Get Request not allowed"
|
46 |
+
}
|
47 |
+
|
48 |
+
if __name__ == '__main__':
|
49 |
+
# http_server = WSGIServer(('', 8000), app)
|
50 |
+
# http_server.serve_forever()
|
51 |
+
app.debug = True
|
52 |
+
app.run(port=8000)
|
images/normal_image.png
ADDED
![]() |
images/rm_image.png
ADDED
![]() |
requirements.txt
ADDED
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
torch
|
2 |
+
torchvision
|
3 |
+
pillow
|
4 |
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numpy
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typing
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scikit-image
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huggingface_hub
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8 |
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flask
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Flask-Session
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10 |
+
gunicorn
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utilities.py
ADDED
@@ -0,0 +1,25 @@
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import torch
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import torch.nn.functional as F
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from torchvision.transforms.functional import normalize
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import numpy as np
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def preprocess_image(im: np.ndarray, model_input_size: list) -> torch.Tensor:
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if len(im.shape) < 3:
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im = im[:, :, np.newaxis]
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# orig_im_size=im.shape[0:2]
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im_tensor = torch.tensor(im, dtype=torch.float32).permute(2,0,1)
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im_tensor = F.interpolate(torch.unsqueeze(im_tensor,0), size=model_input_size, mode='bilinear').type(torch.uint8)
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image = torch.divide(im_tensor,255.0)
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image = normalize(image,[0.5,0.5,0.5],[1.0,1.0,1.0])
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return image
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def postprocess_image(result: torch.Tensor, im_size: list)-> np.ndarray:
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result = torch.squeeze(F.interpolate(result, size=im_size, mode='bilinear') ,0)
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ma = torch.max(result)
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mi = torch.min(result)
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result = (result-mi)/(ma-mi)
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im_array = (result*255).permute(1,2,0).cpu().data.numpy().astype(np.uint8)
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im_array = np.squeeze(im_array)
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return im_array
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