Modele uma imagem como uma função
Treine uma rede para reproduzir uma imagem tratando a imagem como uma função de posição de pixels (x, y) a valores de cor (r, g, b).
Converta uma imagem em listas de regras associando posições de pixel normalizados com valores RGB.
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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]=
Crie uma rede para prever a cor baseada na posição do pixel.
In[5]:=
chain = NetChain[{100, Ramp, 100, Ramp, 50, Tanh, 3}, "Input" -> 2]
Out[5]=
Treine a rede.
In[6]:=
trained =
NetTrain[chain, rules, MaxTrainingRounds -> 200, BatchSize -> 2048];
Out[7]=
Use a rede para prever imagem original completa.
In[8]:=
{h, v} = Range[-1, 1, 2./#] & /@ dims;
coords = Tuples[{h, v}];
Image[Partition[trained@coords, Length[v]]]
Out[8]=
Visualize os mapas de características que são combinados para produzir a imagem final.
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]=