How do you take the FFT of an image in Google Go?
The Go DSP library (github.com/mjibson/go-dsp/fft) has a function for a 2D FFT with the following signature:
func FFT2Real(x [][]float64) [][]complex128
How do I convert an image from the standard go image types to float64? Is this the right approach?
Here is a link to the documentation.
如何在Google Go中对图像进行FFT？
Go DSP库（github.com/mjibson/go-dsp/fft）具有具有以下签名的2D FFT函数：
func FFT2Real（x [] [] float64）[] [] complex128
如何将图像从标准go图像类型转换为float64 ？ 这是正确的方法吗？
这是文档的链接< /a>。
You have two options, both involve copying the pixels. You can either use the methods provided by the Image interface, namely At(x,y) or you can assert the image to one of the image types provided by the image packet and access the Pix attribute directly.
Since you will most likely be using a Gray image, you could easily assert your image to type *image.Gray and access the pixels directly but for the sake of abstraction I did not in my example:
微信扫一扫inImage, _, err := image.Decode(inFile)
// error checking
bounds := inImage.Bounds()
realPixels := make([][]float64, bounds.Dy())
for y := 0; y < bounds.Dy(); y++ {
realPixels[y] = make([]float64, bounds.Dx())
for x := 0; x < bounds.Dx(); x++ {
r, _, _, _ := inImage.At(x, y).RGBA()
realPixels[y][x] = float64(r)
}
}
This way you read all the pixels of your image inImage and store them as float64 values in a two-dimensional slice, ready to be processed by fft.FFT2Real:
// apply discrete fourier transform on realPixels.
coeffs := fft.FFT2Real(realPixels)
// use inverse fourier transform to transform fft
// values back to the original image.
coeffs = fft.IFFT2(coeffs)
// write everything to a new image
outImage := image.NewGray(bounds)
for y := 0; y < bounds.Dy(); y++ {
for x := 0; x < bounds.Dx(); x++ {
px := uint8(cmplx.Abs(coeffs[y][x]))
outImage.SetGray(x, y, color.Gray{px})
}
}
err = png.Encode(outFile, outImage)
In the code above I applied FFT on the pixels stored in realPixels and then, to see whether it worked, used inverse FFT on the result. The expected result is the original image.
A full example can be found here.