func main() {
// input files
files := []string{"01.png", "02.png", "03.png"}
// load images and make 100x100 thumbnails of them
var images []image.Image
for _, file := range files {
img, err := imaging.Open(file)
if err != nil {
panic(err)
}
images = append(images, img)
}
n := neuro.NewNetwork([]int{3, 3, 3})
for y := 0; y < images[0].Bounds().Dy(); y++ {
for x := 0; x < images[0].Bounds().Dx(); x++ {
imgR, imgG, imgB, _ := images[0].At(x, y).RGBA()
r := float64(imgR) / 65535
g := float64(imgG) / 65535
b := float64(imgB) / 65535
n.FeedForward([]float64{r, g, b})
imgR2, imgG2, imgB2, _ := images[1].At(x, y).RGBA()
r2 := float64(imgR2) / 65535
g2 := float64(imgG2) / 65535
b2 := float64(imgB2) / 65535
n.BackPropagation([]float64{r2, g2, b2})
}
}
m := image.NewRGBA(image.Rect(0, 0, 128, 128))
for y := 0; y < images[2].Bounds().Dy(); y++ {
for x := 0; x < images[2].Bounds().Dx(); x++ {
imgR, imgG, imgB, _ := images[2].At(x, y).RGBA()
r := float64(imgR) / 65535
g := float64(imgG) / 65535
b := float64(imgB) / 65535
n.FeedForward([]float64{r, g, b})
results := n.Results()
c := color.RGBA{uint8(results[0] * 255), uint8(results[1] * 255), uint8(results[2] * 255), 255}
m.Set(x, y, c)
}
}
dst := imaging.New(256, 256, color.NRGBA{0, 0, 0, 0})
dst = imaging.Paste(dst, images[0], image.Pt(0, 0))
dst = imaging.Paste(dst, images[1], image.Pt(128, 0))
dst = imaging.Paste(dst, images[2], image.Pt(0, 128))
dst = imaging.Paste(dst, m, image.Pt(128, 128))
// save the combined image to file
err := imaging.Save(dst, "dst.jpg")
if err != nil {
panic(err)
}
}
func NewImgNet(w, h int) *ImgNet {
topology := []int{w * h * 3, 9, w * h * 3}
i := &ImgNet{
w: w,
h: h,
net: neuro.NewNetwork(topology),
}
return i
}