Golang Image.Size方法代码示例

// CorrBankStrideFFT computes the strided correlation of
// an image with a bank of filters.
// 	h_p[u, v] = (f corr g_p)[stride*u, stride*v]
func CorrBankStrideFFT(f *rimg64.Image, g *Bank, stride int) (*rimg64.Multi, error) {
	out := ValidSizeStride(f.Size(), g.Size(), stride)
	if out.X <= 0 || out.Y <= 0 {
		return nil, nil
	}
	// Compute strided convolution as the sum over
	// a stride x stride grid of small convolutions.
	grid := image.Pt(stride, stride)
	// But do not divide into a larger grid than the size of the filter.
	// If the filter is smaller than the stride,
	// then some pixels in the image will not affect the output.
	grid.X = min(grid.X, g.Width)
	grid.Y = min(grid.Y, g.Height)
	// Determine the size of the sub-sampled filter.
	gsub := image.Pt(ceilDiv(g.Width, grid.X), ceilDiv(g.Height, grid.Y))
	// The sub-sampled size of the image should be such that
	// the output size is attained.
	fsub := image.Pt(out.X+gsub.X-1, out.Y+gsub.Y-1)

	// Determine optimal size for FFT.
	work, _ := FFT2Size(fsub)
	// Cache FFT of image for convolving with multiple filters.
	// Re-use plan for multiple convolutions too.
	fhat := fftw.NewArray2(work.X, work.Y)
	ffwd := fftw.NewPlan2(fhat, fhat, fftw.Forward, fftw.Estimate)
	defer ffwd.Destroy()
	// FFT for current filter.
	ghat := fftw.NewArray2(work.X, work.Y)
	gfwd := fftw.NewPlan2(ghat, ghat, fftw.Forward, fftw.Estimate)
	defer gfwd.Destroy()
	// Allocate one array per output channel.
	hhat := make([]*fftw.Array2, len(g.Filters))
	for k := range hhat {
		hhat[k] = fftw.NewArray2(work.X, work.Y)
	}
	// Normalization factor.
	alpha := complex(1/float64(work.X*work.Y), 0)
	// Add the convolutions over channels and strides.
	for i := 0; i < grid.X; i++ {
		for j := 0; j < grid.Y; j++ {
			// Take transform of downsampled image given offset (i, j).
			copyStrideTo(fhat, f, stride, image.Pt(i, j))
			ffwd.Execute()
			// Take transform of each downsampled channel given offset (i, j).
			for q := range hhat {
				copyStrideTo(ghat, g.Filters[q], stride, image.Pt(i, j))
				gfwd.Execute()
				addMul(hhat[q], ghat, fhat)
			}
		}
	}
	// Take the inverse transform of each channel.
	h := rimg64.NewMulti(out.X, out.Y, len(g.Filters))
	for q := range hhat {
		scale(alpha, hhat[q])
		fftw.IFFT2To(hhat[q], hhat[q])
		copyRealToChannel(h, q, hhat[q])
	}
	return h, nil
}

// CorrBankFFT computes the correlation of an image with a bank of filters.
// 	h_p[u, v] = (f corr g_p)[u, v]
func CorrBankFFT(f *rimg64.Image, g *Bank) (*rimg64.Multi, error) {
	out := ValidSize(f.Size(), g.Size())
	if out.X <= 0 || out.Y <= 0 {
		return nil, nil
	}
	// Determine optimal size for FFT.
	work, _ := FFT2Size(f.Size())
	// Re-use FFT of image.
	fhat := fftw.NewArray2(work.X, work.Y)
	copyImageTo(fhat, f)
	fftw.FFT2To(fhat, fhat)
	// Transform of each filter.
	curr := fftw.NewArray2(work.X, work.Y)
	fwd := fftw.NewPlan2(curr, curr, fftw.Forward, fftw.Estimate)
	defer fwd.Destroy()
	bwd := fftw.NewPlan2(curr, curr, fftw.Backward, fftw.Estimate)
	defer bwd.Destroy()

	h := rimg64.NewMulti(out.X, out.Y, len(g.Filters))
	alpha := complex(1/float64(work.X*work.Y), 0)
	// For each output channel.
	for p, gp := range g.Filters {
		// Take FFT.
		copyImageTo(curr, gp)
		fwd.Execute()
		// h_p[x] = (G_p corr F)[x]
		// H_p[x] = conj(G_p[x]) F[x]
		scaleMul(curr, alpha, curr, fhat)
		bwd.Execute()
		copyRealToChannel(h, p, curr)
	}
	return h, nil
}

// CorrBankBLAS computes the correlation of an image with a bank of filters.
// 	h_p[u, v] = (f corr g_p)[u, v]
func CorrBankBLAS(f *rimg64.Image, g *Bank) (*rimg64.Multi, error) {
	out := ValidSize(f.Size(), g.Size())
	if out.X <= 0 || out.Y <= 0 {
		return nil, nil
	}
	// Express as dense matrix multiplication.
	//   h_p[u, v] = (f corr g_q)[u, v]
	//   Y(h) = A(f) X(g)
	// If the number of output channels is k, then
	//   A is (M-m+1)(N-n+1) x mn and
	//   X is mn x k, so that
	//   Y is (M-m+1)(N-n+1) x k.

	h := rimg64.NewMulti(out.X, out.Y, len(g.Filters))
	m, n, k := g.Width, g.Height, len(g.Filters)
	a := blas.NewMat(out.X*out.Y, m*n)
	{
		var r int
		for u := 0; u < h.Width; u++ {
			for v := 0; v < h.Height; v++ {
				var s int
				for i := 0; i < g.Width; i++ {
					for j := 0; j < g.Height; j++ {
						a.Set(r, s, f.At(i+u, j+v))
						s++
					}
				}
				r++
			}
		}
	}
	x := blas.NewMat(m*n, k)
	{
		var r int
		for i := 0; i < g.Width; i++ {
			for j := 0; j < g.Height; j++ {
				for p := 0; p < h.Channels; p++ {
					x.Set(r, p, g.Filters[p].At(i, j))
				}
				r++
			}
		}
	}
	y := blas.MatMul(1, a, x)
	{
		var r int
		for u := 0; u < h.Width; u++ {
			for v := 0; v < h.Height; v++ {
				for p := 0; p < h.Channels; p++ {
					h.Set(u, v, p, y.At(r, p))
				}
				r++
			}
		}
	}
	return h, nil
}

// CorrBankStrideBLAS computes the strided correlation of
// an image with a bank of filters.
// 	h_p[u, v] = (f corr g_p)[stride*u, stride*v]
func CorrBankStrideBLAS(f *rimg64.Image, g *Bank, stride int) (*rimg64.Multi, error) {
	out := ValidSizeStride(f.Size(), g.Size(), stride)
	if out.X <= 0 || out.Y <= 0 {
		return nil, nil
	}
	h := rimg64.NewMulti(out.X, out.Y, len(g.Filters))
	// Size of filters.
	m, n := g.Width, g.Height
	// Express as dense matrix multiplication.
	//   h_p[u, v] = sum_q (f_q corr g_pq)[u, v]
	//   h = A(f) X(g)
	// where A is whk by mnk
	// with w = ceil[(M-m+1)/stride],
	//      h = ceil[(N-n+1)/stride].
	a := blas.NewMat(h.Width*h.Height, m*n)
	{
		var r int
		for u := 0; u < h.Width; u++ {
			for v := 0; v < h.Height; v++ {
				var s int
				for i := 0; i < g.Width; i++ {
					for j := 0; j < g.Height; j++ {
						a.Set(r, s, f.At(stride*u+i, stride*v+j))
						s++
					}
				}
				r++
			}
		}
	}
	x := blas.NewMat(m*n, h.Channels)
	{
		var r int
		for i := 0; i < g.Width; i++ {
			for j := 0; j < g.Height; j++ {
				for p := 0; p < h.Channels; p++ {
					x.Set(r, p, g.Filters[p].At(i, j))
				}
				r++
			}
		}
	}
	y := blas.MatMul(1, a, x)
	{
		var r int
		for u := 0; u < h.Width; u++ {
			for v := 0; v < h.Height; v++ {
				for p := 0; p < h.Channels; p++ {
					h.Set(u, v, p, y.At(r, p))
				}
				r++
			}
		}
	}
	return h, nil
}

// Decimate takes every r-th sample starting at (0, 0).
func Decimate(f *rimg64.Image, r int) *rimg64.Image {
	out := ceilDivPt(f.Size(), r)
	g := rimg64.New(out.X, out.Y)
	for i := 0; i < g.Width; i++ {
		for j := 0; j < g.Height; j++ {
			g.Set(i, j, f.At(r*i, r*j))
		}
	}
	return g
}

// CorrStrideBLAS computes the strided correlation of an image with a filter.
// 	h[u, v] = (f corr g)[stride*u, stride*v]
func CorrStrideBLAS(f, g *rimg64.Image, stride int) (*rimg64.Image, error) {
	out := ValidSizeStride(f.Size(), g.Size(), stride)
	if out.X <= 0 || out.Y <= 0 {
		return nil, nil
	}
	h := rimg64.New(out.X, out.Y)
	// Size of filters.
	m, n := g.Width, g.Height
	// Express as dense matrix multiplication.
	//   h[u, v] = (f corr g)[stride*u, stride*v]
	//   y(h) = A(f) x(g)
	// where A is wh by mn
	// with w = ceil[(M-m+1)/stride],
	//      h = ceil[(N-n+1)/stride].
	a := blas.NewMat(h.Width*h.Height, m*n)
	{
		var r int
		for u := 0; u < h.Width; u++ {
			for v := 0; v < h.Height; v++ {
				var s int
				for i := 0; i < g.Width; i++ {
					for j := 0; j < g.Height; j++ {
						a.Set(r, s, f.At(stride*u+i, stride*v+j))
						s++
					}
				}
				r++
			}
		}
	}
	x := blas.NewMat(m*n, 1)
	{
		var r int
		for i := 0; i < g.Width; i++ {
			for j := 0; j < g.Height; j++ {
				x.Set(r, 0, g.At(i, j))
				r++
			}
		}
	}
	y := blas.MatMul(1, a, x)
	{
		var r int
		for u := 0; u < h.Width; u++ {
			for v := 0; v < h.Height; v++ {
				h.Set(u, v, y.At(r, 0))
				r++
			}
		}
	}
	return h, nil
}

// CorrStrideFFT computes the strided correlation of an image with a filter.
// 	h[u, v] = (f corr g)[stride*u, stride*v]
func CorrStrideFFT(f, g *rimg64.Image, stride int) (*rimg64.Image, error) {
	out := ValidSizeStride(f.Size(), g.Size(), stride)
	if out.X <= 0 || out.Y <= 0 {
		return nil, nil
	}
	// Compute strided convolution as the sum over
	// a stride x stride grid of small convolutions.
	grid := image.Pt(stride, stride)
	// But do not divide into a larger grid than the size of the filter.
	// If the filter is smaller than the stride,
	// then some pixels in the image will not affect the output.
	grid.X = min(grid.X, g.Width)
	grid.Y = min(grid.Y, g.Height)
	// Determine the size of the sub-sampled filter.
	gsub := image.Pt(ceilDiv(g.Width, grid.X), ceilDiv(g.Height, grid.Y))
	// The sub-sampled size of the image should be such that
	// the output size is attained.
	fsub := image.Pt(out.X+gsub.X-1, out.Y+gsub.Y-1)

	// Determine optimal size for FFT.
	work, _ := FFT2Size(fsub)
	// Cache FFT of each channel of image for convolving with multiple filters.
	// Re-use plan for multiple convolutions too.
	fhat := fftw.NewArray2(work.X, work.Y)
	ffwd := fftw.NewPlan2(fhat, fhat, fftw.Forward, fftw.Estimate)
	defer ffwd.Destroy()
	// FFT for current filter.
	curr := fftw.NewArray2(work.X, work.Y)
	gfwd := fftw.NewPlan2(curr, curr, fftw.Forward, fftw.Estimate)
	defer gfwd.Destroy()
	// Normalization factor.
	alpha := complex(1/float64(work.X*work.Y), 0)
	// Add the convolutions over strides.
	hhat := fftw.NewArray2(work.X, work.Y)
	for i := 0; i < grid.X; i++ {
		for j := 0; j < grid.Y; j++ {
			// Copy each downsampled channel and take its transform.
			copyStrideTo(fhat, f, stride, image.Pt(i, j))
			ffwd.Execute()
			copyStrideTo(curr, g, stride, image.Pt(i, j))
			gfwd.Execute()
			addMul(hhat, curr, fhat)
		}
	}
	// Take the inverse transform.
	h := rimg64.New(out.X, out.Y)
	scale(alpha, hhat)
	fftw.IFFT2To(hhat, hhat)
	copyRealTo(h, hhat)
	return h, nil
}

// CorrStrideNaive computes the strided correlation of an image with a filter.
// 	h[u, v] = (f corr g)[stride*u, stride*v]
func CorrStrideNaive(f, g *rimg64.Image, stride int) (*rimg64.Image, error) {
	out := ValidSizeStride(f.Size(), g.Size(), stride)
	h := rimg64.New(out.X, out.Y)
	for i := 0; i < h.Width; i++ {
		for j := 0; j < h.Height; j++ {
			var total float64
			for u := 0; u < g.Width; u++ {
				for v := 0; v < g.Height; v++ {
					p := image.Pt(i, j).Mul(stride).Add(image.Pt(u, v))
					total += f.At(p.X, p.Y) * g.At(u, v)
				}
			}
			h.Set(i, j, total)
		}
	}
	return h, nil
}

// CorrNaive computes the correlation of an image with a filter.
// 	h[u, v] = (f corr g)[u, v]
func CorrNaive(f, g *rimg64.Image) (*rimg64.Image, error) {
	out := ValidSize(f.Size(), g.Size())
	if out.X <= 0 || out.Y <= 0 {
		return nil, nil
	}
	h := rimg64.New(out.X, out.Y)
	for i := 0; i < out.X; i++ {
		for j := 0; j < out.Y; j++ {
			var total float64
			for u := 0; u < g.Width; u++ {
				for v := 0; v < g.Height; v++ {
					total += f.At(i+u, j+v) * g.At(u, v)
				}
			}
			h.Set(i, j, total)
		}
	}
	return h, nil
}

// CorrBankNaive computes the correlation of an image with a bank of filters.
// 	h_p[u, v] = (f corr g_p)[u, v]
func CorrBankNaive(f *rimg64.Image, g *Bank) (*rimg64.Multi, error) {
	out := ValidSize(f.Size(), g.Size())
	if out.X <= 0 || out.Y <= 0 {
		return nil, nil
	}
	h := rimg64.NewMulti(out.X, out.Y, len(g.Filters))
	for u := 0; u < h.Width; u++ {
		for v := 0; v < h.Height; v++ {
			for p := 0; p < h.Channels; p++ {
				var total float64
				for i := 0; i < g.Width; i++ {
					for j := 0; j < g.Height; j++ {
						total += f.At(i+u, j+v) * g.Filters[p].At(i, j)
					}
				}
				h.Set(u, v, p, total)
			}
		}
	}
	return h, nil
}

// CorrBankStrideNaive computes the strided correlation of
// an image with a bank of filters.
// 	h_p[u, v] = (f corr g_p)[stride*u, stride*v]
func CorrBankStrideNaive(f *rimg64.Image, g *Bank, stride int) (*rimg64.Multi, error) {
	out := ValidSizeStride(f.Size(), g.Size(), stride)
	if out.X <= 0 || out.Y <= 0 {
		return nil, nil
	}
	h := rimg64.NewMulti(out.X, out.Y, len(g.Filters))
	for u := 0; u < h.Width; u++ {
		for v := 0; v < h.Height; v++ {
			for p := 0; p < h.Channels; p++ {
				var sum float64
				for i := 0; i < g.Width; i++ {
					for j := 0; j < g.Height; j++ {
						sum += f.At(stride*u+i, stride*v+j) * g.Filters[p].At(i, j)
					}
				}
				h.Set(u, v, p, sum)
			}
		}
	}
	return h, nil
}

func errIfNotEqImage(f, g *rimg64.Image, eps float64) error {
	if !f.Size().Eq(g.Size()) {
		return fmt.Errorf("different size: %v, %v", f.Size(), g.Size())
	}
	for i := 0; i < f.Width; i++ {
		for j := 0; j < f.Height; j++ {
			a, b := f.At(i, j), g.At(i, j)
			if math.Abs(a-b) > eps*math.Max(math.Abs(a), math.Abs(b)) {
				return fmt.Errorf("different at x %d, y %d: %g, %g", i, j, a, b)
			}
		}
	}
	return nil
}

// CorrAuto computes the correlation of an image with a filter.
// 	h[u, v] = (f corr g)[u, v]
// Automatically selects between naive and Fourier-domain convolution.
func CorrAuto(f, g *rimg64.Image) (*rimg64.Image, error) {
	// Size of output.
	size := ValidSize(f.Size(), g.Size())
	// Return empty image if that's the result.
	if size.Eq(image.ZP) {
		return nil, nil
	}
	// Need to compute one inner product per output element.
	naiveMuls := size.X * size.Y * g.Width * g.Height
	// Optimal FFT size and number of multiplications.
	_, fftMuls := FFT2Size(f.Size())
	// Need to perform two forward and one inverse transform.
	fftMuls *= 3
	// Switch implementation based on image size.
	if fftMuls < naiveMuls {
		return CorrFFT(f, g)
	}
	return CorrNaive(f, g)
}

// CorrFFT computes the correlation of an image with a filter.
// 	h[u, v] = (f corr g)[u, v]
func CorrFFT(f, g *rimg64.Image) (*rimg64.Image, error) {
	out := ValidSize(f.Size(), g.Size())
	if out.X <= 0 || out.Y <= 0 {
		return nil, nil
	}
	// Determine optimal size for FFT.
	work, _ := FFT2Size(f.Size())
	fhat := fftw.NewArray2(work.X, work.Y)
	ghat := fftw.NewArray2(work.X, work.Y)
	// Take forward transforms.
	copyImageTo(fhat, f)
	fftw.FFT2To(fhat, fhat)
	copyImageTo(ghat, g)
	fftw.FFT2To(ghat, ghat)
	// Scale such that convolution theorem holds.
	n := float64(work.X * work.Y)
	scaleMul(fhat, complex(1/n, 0), ghat, fhat)
	// Take inverse transform.
	h := rimg64.New(out.X, out.Y)
	fftw.IFFT2To(fhat, fhat)
	copyRealTo(h, fhat)
	return h, nil
}