将图像视为一个函数来建模
训练一个网络,通过将图像视为从像素位置 (x, y) 到颜色值 (r, g, b) 的函数来再现图像.
将图像转换为联系归一化的像素位置与 RGB 值的规则列表.
In[1]:=
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"], {{0, 426}, {640, 0}}, {0, 255},
ColorFunction->RGBColor],
BoxForm`ImageTag["Byte", ColorSpace -> "RGB", Interleaving -> True],
Selectable->False],
DefaultBaseStyle->"ImageGraphics",
ImageSize->{320.42578125, Automatic},
ImageSizeRaw->{640, 426},
PlotRange->{{0, 640}, {0, 426}}]\);
In[2]:=
dims = Reverse@ImageDimensions[img];
rules = Flatten@
MapIndexed[(2 (#2 - 1.)/(dims - 1) - 1.) -> #1 &,
ImageData@img, {2}];
In[3]:=
Take[rules, 5]
Out[3]=
In[4]:=
Length[rules]
Out[4]=
创建一个根据像素位置来预测颜色的网络.
In[5]:=
chain = NetChain[{100, Ramp, 100, Ramp, 50, Tanh, 3}, "Input" -> 2]
Out[5]=
训练这个网络.
In[6]:=
trained =
NetTrain[chain, rules, MaxTrainingRounds -> 200, BatchSize -> 2048];
Out[7]=
用该网络预测整个原始图像.
In[8]:=
{h, v} = Range[-1, 1, 2./#] & /@ dims;
coords = Tuples[{h, v}];
Image[Partition[trained@coords, Length[v]]]
Out[8]=
图示组合起来以产生最终图像的特征映射.
In[9]:=
featureNet = Take[trained, 4]
{h, v} = Range[-1, 1, 60./#] & /@ dims;
coords = Tuples[{h, v}];
features =
Image[Partition[featureNet@coords, Length[v]], Magnification -> 2];
Multicolumn[ColorSeparate[ImageAdjust@features],
Spacings -> {0.2, 0.2}]
Out[9]=