first commit

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+.idea/
+/.idea
+*.iml
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
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+.scrapy
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+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
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+# Jupyter Notebook
+.ipynb_checkpoints
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+profile_default/
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+
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+.python-version
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+celerybeat-schedule
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+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
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+/site
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+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# JPG PNG
+*.jpg
+*.png

Module/SideWindowFilter.py

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+#!/usr/bin/python3
+# -*- coding: utf-8 -*-
+"""
+@Time    : 2019-05-23 16:32
+@Author  : Wang Xin
+@Email   : [email protected]
+@File    : SideWindowFilter.py
+"""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class SideWindowFilter(nn.Module):
+
+    def __init__(self, radius, iteration):
+        super(SideWindowFilter, self).__init__()
+        self.radius = radius
+        self.iteration = iteration
+        self.kernel_size = 2 * self.radius + 1
+        self.filter = nn.Parameter(torch.Tensor(1, 1, self.kernel_size, self.kernel_size))
+
+    def forward(self, im):
+        b, c, h, w = im.size()
+
+        d = torch.zeros(b, 8, h, w, dtype=torch.float)
+        res = im.clone()
+
+        L, R, U, D = [self.filter.clone() for _ in range(4)]
+
+        L[:, :, :, self.radius + 1:] = 0
+        R[:, :, :, 0: self.radius] = 0
+        U[:, :, self.radius + 1:, :] = 0
+        D[:, :, 0: self.radius, :] = 0
+
+        NW, NE, SW, SE = U.clone(), U.clone(), D.clone(), D.clone()
+
+        L, R, U, D = L / ((self.radius + 1) * self.kernel_size), R / ((self.radius + 1) * self.kernel_size), \
+                     U / ((self.radius + 1) * self.kernel_size), D / ((self.radius + 1) * self.kernel_size)
+
+        NW[:, :, :, self.radius + 1:] = 0
+        NE[:, :, :, 0: self.radius] = 0
+        SW[:, :, :, self.radius + 1:] = 0
+        SE[:, :, :, 0: self.radius] = 0
+
+        NW, NE, SW, SE = NW / ((self.radius + 1) ** 2), NE / ((self.radius + 1) ** 2), \
+                         SW / ((self.radius + 1) ** 2), SE / ((self.radius + 1) ** 2)
+
+        print('L:', L)
+        print('R:', R)
+        print('U:', U)
+        print('D:', D)
+        print('NW:', NW)
+        print('NE:', NE)
+        print('SW:', SW)
+        print('SE:', SE)
+
+        for ch in range(c):
+            im_ch = im[:, ch, ::].clone().view(b, 1, h, w)
+            # print('im size in each channel:', im_ch.size())
+
+            for i in range(self.iteration):
+                # print('###', (F.conv2d(input=im_ch, weight=L, padding=(self.radius, self.radius)) / sum_L -
+                # im_ch).size(), d[:, 0,::].size())
+                d[:, 0, ::] = F.conv2d(input=im_ch, weight=L, padding=(self.radius, self.radius)) - im_ch
+                d[:, 1, ::] = F.conv2d(input=im_ch, weight=R, padding=(self.radius, self.radius)) - im_ch
+                d[:, 2, ::] = F.conv2d(input=im_ch, weight=U, padding=(self.radius, self.radius)) - im_ch
+                d[:, 3, ::] = F.conv2d(input=im_ch, weight=D, padding=(self.radius, self.radius)) - im_ch
+                d[:, 4, ::] = F.conv2d(input=im_ch, weight=NW, padding=(self.radius, self.radius)) - im_ch
+                d[:, 5, ::] = F.conv2d(input=im_ch, weight=NE, padding=(self.radius, self.radius)) - im_ch
+                d[:, 6, ::] = F.conv2d(input=im_ch, weight=SW, padding=(self.radius, self.radius)) - im_ch
+                d[:, 7, ::] = F.conv2d(input=im_ch, weight=SE, padding=(self.radius, self.radius)) - im_ch
+
+                d_abs = torch.abs(d)
+                print('im_ch', im_ch)
+                print('dm = ', d_abs.shape, d_abs)
+                mask_min = torch.argmin(d_abs, dim=1, keepdim=True)
+                print('mask min = ', mask_min.shape, mask_min)
+                dm = torch.gather(input=d, dim=1, index=mask_min)
+                im_ch = dm + im_ch
+
+            res[:, ch, ::] = im_ch
+        return res
+
+
+class Net(nn.Module):
+
+    def __init__(self):
+        super(Net, self).__init__()
+        self.filter = SideWindowFilter(radius=1, iteration=1)
+
+    def forward(self, x):
+        return self.filter(x)
+
+
+if __name__ == '__main__':
+    s = SideWindowFilter(radius=1, iteration=1)
+    from PIL import Image
+
+    import cv2
+    img = cv2.imread('../lena.png', flags=0)
+    img = torch.tensor(img, dtype=torch.float)
+    print('img ori = ', img)
+
+    print(len(img.size()))
+    if len(img.size()) == 2:
+        h, w = img.size()
+        img = img.view(-1, 1, h, w)
+    else:
+        c, h, w = img.size()
+        img = img.view(-1, c, h, w)
+    print('img = ', img.shape)
+
+    model = Net()
+    res = model(img)
+    res.mean().backward()
+
+    print('res = ', res.shape, res)
+    import numpy as np
+    if res.size(1) == 3:
+        img_res = np.transpose(np.squeeze(res.data.numpy()), (1, 2, 0))
+    else:
+        img_res = np.squeeze(res.data.numpy())
+
+    # print(img_res.shape, img_res)
+    img_res = img_res
+    img_res = img_res.astype(np.uint8)
+    print('img res:', img_res)
+    img_res = Image.fromarray(img_res)  # numpy to image
+    img_res.show()

Module/__init__.py

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+#!/usr/bin/python3
+# -*- coding: utf-8 -*-
+"""
+@Time    : 2019-05-23 16:32
+@Author  : Wang Xin
+@Email   : [email protected]
+@File    : __init__.py.py
+"""

README.md

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+# SideWindowFilter

SideWindowFilter.m

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+function result=SideWindowBoxFilter(im, radius, iteration)
+%papers: 1) Sub-window Box Filter, Y.Gong, B.Liu, X.Hou, G.Qiu, VCIP2018, Dec.09, Taiwan
+%        2) Side Window Filtering, H.Yin, Y.Gong, G.Qiu. CVPR2019
+%implemented by Yuanhao Gong
+
+r = radius;
+k = ones(2*r+1,1)/(2*r+1); %separable kernel
+k_L=k; k_L(r+2:end)=0; k_L = k_L/sum(k_L); %half kernel
+k_R=flipud(k_L);
+m = size(im,1)+2*r; n = size(im,2)+2*r; total = m*n;
+[row, col]=ndgrid(1:m,1:n);
+offset = row + m*(col-1) - total;
+im = single(im);
+result = im;
+d = zeros(m,n,8,'single');
+
+for ch=1:size(im,3)
+    U = padarray(im(:,:,ch),[r,r],'replicate');
+    for i = 1:iteration
+        %all projection distances
+        d(:,:,1) = conv2(k_L, k_L, U,'same') - U;
+        d(:,:,2) = conv2(k_L, k_R, U,'same') - U;
+        d(:,:,3) = conv2(k_R, k_L, U,'same') - U;
+        d(:,:,4) = conv2(k_R, k_R, U,'same') - U;
+        d(:,:,5) = conv2(k_L, k, U,'same') - U;
+        d(:,:,6) = conv2(k_R, k, U,'same') - U;
+        d(:,:,7) = conv2(k, k_L, U,'same') - U;
+        d(:,:,8) = conv2(k, k_R, U,'same') - U;
+
+        %find the minimal signed distance
+        tmp = abs(d);
+        [~,ind] = min(tmp,[],3);
+        index = offset+total*ind;
+        dm = d(index); %signed minimal distance
+        %update
+        U = U + dm;
+    end
+    result(:,:,ch) = U(r+1:end-r,r+1:end-r);
+end