Modélisez une image en tant que fonction
Entraînez un réseau pour reproduire une image par le traitement d'image en tant que fonction des positions de pixels de (x, y) et des valeurs de couleur (r, v, b).
Convertissez une image dans des listes de règles associant des positions de pixels normalisées avec les valeurs RVB.
In[1]:=

img = \!\(\*
GraphicsBox[
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"], {{0, 426}, {640, 0}}, {0, 255},
ColorFunction->RGBColor],
BoxForm`ImageTag["Byte", ColorSpace -> "RGB", Interleaving -> True],
Selectable->False],
DefaultBaseStyle->"ImageGraphics",
ImageSize->{320.42578125, Automatic},
ImageSizeRaw->{640, 426},
PlotRange->{{0, 640}, {0, 426}}]\);
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]=

Créez un réseau pour prédire la couleur en fonction de la position du pixel.
In[5]:=

chain = NetChain[{100, Ramp, 100, Ramp, 50, Tanh, 3}, "Input" -> 2]
Out[5]=

Entraînez le réseau.
In[6]:=

trained =
NetTrain[chain, rules, MaxTrainingRounds -> 200, BatchSize -> 2048];
Out[7]=

Utilisez le réseau pour prédire l'ensemble de l'image originale.
In[8]:=

{h, v} = Range[-1, 1, 2./#] & /@ dims;
coords = Tuples[{h, v}];
Image[Partition[trained@coords, Length[v]]]
Out[8]=

Visualisez les cartes de caractéristiques qui sont combinées pour produire l'image finale.
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]=
