func (phi *MaxPool) Apply(x *rimg64.Multi) (*rimg64.Multi, error) {
if phi.Field.X <= 0 || phi.Field.Y <= 0 {
err := fmt.Errorf("invalid field size: %v", phi.Field)
return nil, err
}
if phi.Stride <= 0 {
err := fmt.Errorf("invalid stride: %d", phi.Stride)
return nil, err
}
size := image.Pt(
ceilDiv(x.Width-phi.Field.X+1, phi.Stride),
ceilDiv(x.Height-phi.Field.Y+1, phi.Stride),
)
y := rimg64.NewMulti(size.X, size.Y, x.Channels)
for i := 0; i < y.Width; i++ {
for j := 0; j < y.Height; j++ {
for k := 0; k < x.Channels; k++ {
// Position in original image.
p := image.Pt(i, j).Mul(phi.Stride)
max := math.Inf(-1)
for u := 0; u < phi.Field.X; u++ {
for v := 0; v < phi.Field.Y; v++ {
q := p.Add(image.Pt(u, v))
max = math.Max(max, x.At(q.X, q.Y, k))
}
}
y.Set(i, j, k, max)
}
}
}
return y, nil
}
// Assumes that f is no smaller than x.
func copyRealToChannel(f *rimg64.Multi, p int, x *fftw.Array2) {
for u := 0; u < f.Width; u++ {
for v := 0; v < f.Height; v++ {
f.Set(u, v, p, real(x.At(u, v)))
}
}
}
// EvalFunc evaluates a function on every window in an image.
// If the input image is M x N and the window size is m x n,
// then the output is (M-m+1) x (N-n+1).
// If the window size is larger than the image size in either dimension,
// a nil image is returned with no error.
func EvalFunc(im *rimg64.Multi, size image.Point, f ScoreFunc) (*rimg64.Image, error) {
if im.Width < size.X || im.Height < size.Y {
return nil, nil
}
r := rimg64.New(im.Width-size.X+1, im.Height-size.Y+1)
x := rimg64.NewMulti(size.X, size.Y, im.Channels)
for i := 0; i < r.Width; i++ {
for j := 0; j < r.Height; j++ {
// Copy window into x.
for u := 0; u < size.X; u++ {
for v := 0; v < size.Y; v++ {
for p := 0; p < im.Channels; p++ {
x.Set(u, v, p, im.At(i+u, j+v, p))
}
}
}
y, err := f(x)
if err != nil {
return nil, err
}
r.Set(i, j, y)
}
}
return r, nil
}
func (phi SumPool) Apply(x *rimg64.Multi) (*rimg64.Multi, error) {
if phi.Field.X <= 0 || phi.Field.Y <= 0 {
err := fmt.Errorf("invalid field size: %v", phi.Field)
return nil, err
}
if phi.Stride <= 0 {
err := fmt.Errorf("invalid stride: %d", phi.Stride)
return nil, err
}
size := image.Pt(
ceilDiv(x.Width-phi.Field.X+1, phi.Stride),
ceilDiv(x.Height-phi.Field.Y+1, phi.Stride),
)
y := rimg64.NewMulti(size.X, size.Y, x.Channels)
for i := 0; i < y.Width; i++ {
for j := 0; j < y.Height; j++ {
for k := 0; k < x.Channels; k++ {
// Position in original image.
p := image.Pt(i, j).Mul(phi.Stride)
var t float64
for u := p.X; u < p.X+phi.Field.X; u++ {
for v := p.Y; v < p.Y+phi.Field.Y; v++ {
t += x.At(u, v, k)
}
}
y.Set(i, j, k, t)
}
}
}
return y, nil
}
// CorrMultiBankStrideBLAS computes the strided correlation of
// a multi-channel image with a bank of multi-channel filters.
// h_p[u, v] = sum_q (f_q corr g_pq)[stride*u, stride*v]
func CorrMultiBankStrideBLAS(f *rimg64.Multi, g *MultiBank, 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, k := g.Width, g.Height, g.Channels
// 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*k)
{
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++ {
for q := 0; q < g.Channels; q++ {
a.Set(r, s, f.At(stride*u+i, stride*v+j, q))
s++
}
}
}
r++
}
}
}
x := blas.NewMat(m*n*k, h.Channels)
{
var r int
for i := 0; i < g.Width; i++ {
for j := 0; j < g.Height; j++ {
for q := 0; q < g.Channels; q++ {
for p := 0; p < h.Channels; p++ {
x.Set(r, p, g.Filters[p].At(i, j, q))
}
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
}
// CorrMultiStrideFFT computes the correlation of
// a multi-channel image with a multi-channel filter.
// h[u, v] = sum_q (f_q corr g_q)[u, v]
func CorrMultiStrideFFT(f, g *rimg64.Multi, stride int) (*rimg64.Image, error) {
if err := errIfChannelsNotEq(f, g); err != nil {
panic(err)
}
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()
ghat := fftw.NewArray2(work.X, work.Y)
gfwd := fftw.NewPlan2(ghat, ghat, fftw.Forward, fftw.Estimate)
defer gfwd.Destroy()
// Normalization factor.
alpha := complex(1/float64(work.X*work.Y), 0)
// Add the convolutions over channels and strides.
hhat := fftw.NewArray2(work.X, work.Y)
for k := 0; k < f.Channels; k++ {
for i := 0; i < grid.X; i++ {
for j := 0; j < grid.Y; j++ {
// Copy each downsampled channel and take its transform.
copyChannelStrideTo(fhat, f, k, stride, image.Pt(i, j))
ffwd.Execute()
copyChannelStrideTo(ghat, g, k, stride, image.Pt(i, j))
gfwd.Execute()
addMul(hhat, ghat, 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
}
// FlipMulti mirrors a multi-channel image in x and y.
func FlipMulti(f *rimg64.Multi) *rimg64.Multi {
g := rimg64.NewMulti(f.Width, f.Height, f.Channels)
for i := 0; i < f.Width; i++ {
for j := 0; j < f.Height; j++ {
for k := 0; k < f.Channels; k++ {
g.Set(f.Width-1-i, f.Height-1-j, k, f.At(i, j, k))
}
}
}
return g
}
func (phi *Scale) Apply(x *rimg64.Multi) (*rimg64.Multi, error) {
y := rimg64.NewMulti(x.Width, x.Height, x.Channels)
for u := 0; u < x.Width; u++ {
for v := 0; v < x.Height; v++ {
for p := 0; p < x.Channels; p++ {
y.Set(u, v, p, float64(*phi)*x.At(u, v, p))
}
}
}
return y, nil
}
// Takes the sum over channels.
func squareMulti(f *rimg64.Multi) *rimg64.Image {
g := rimg64.New(f.Width, f.Height)
for i := 0; i < f.Width; i++ {
for j := 0; j < f.Height; j++ {
for k := 0; k < f.Channels; k++ {
g.Set(i, j, g.At(i, j)+sqr(f.At(i, j, k)))
}
}
}
return g
}
func (phi *SelectChannels) Apply(f *rimg64.Multi) (*rimg64.Multi, error) {
g := rimg64.NewMulti(f.Width, f.Height, len(phi.Set))
for u := 0; u < f.Width; u++ {
for v := 0; v < f.Height; v++ {
for i, p := range phi.Set {
g.Set(u, v, i, f.At(u, v, p))
}
}
}
return g, nil
}
func (phi *ChannelInterval) Apply(f *rimg64.Multi) (*rimg64.Multi, error) {
g := rimg64.NewMulti(f.Width, f.Height, phi.B-phi.A)
for u := 0; u < f.Width; u++ {
for v := 0; v < f.Height; v++ {
for p := phi.A; p < phi.B; p++ {
g.Set(u, v, p-phi.A, f.At(u, v, p))
}
}
}
return g, nil
}
func dot(x, y *rimg64.Multi) float64 {
var d float64
for i := 0; i < x.Width; i++ {
for j := 0; j < x.Height; j++ {
for k := 0; k < x.Channels; k++ {
d += x.At(i, j, k) * y.At(i, j, k)
}
}
}
return d
}
func (phi *PosPart) Apply(x *rimg64.Multi) (*rimg64.Multi, error) {
y := rimg64.NewMulti(x.Width, x.Height, x.Channels)
for i := 0; i < x.Width; i++ {
for j := 0; j < x.Height; j++ {
for k := 0; k < x.Channels; k++ {
y.Set(i, j, k, math.Max(0, x.At(i, j, k)))
}
}
}
return y, nil
}
// DecimateMulti takes every r-th sample starting at (0, 0).
func DecimateMulti(f *rimg64.Multi, r int) *rimg64.Multi {
out := ceilDivPt(f.Size(), r)
g := rimg64.NewMulti(out.X, out.Y, f.Channels)
for i := 0; i < g.Width; i++ {
for j := 0; j < g.Height; j++ {
for k := 0; k < g.Channels; k++ {
g.Set(i, j, k, f.At(r*i, r*j, k))
}
}
}
return g
}
// Assumes that f is no smaller than x.
// Pads with zeros.
func copyChannelTo(x *fftw.Array2, f *rimg64.Multi, p int) {
w, h := x.Dims()
for u := 0; u < w; u++ {
for v := 0; v < h; v++ {
if u < f.Width && v < f.Height {
x.Set(u, v, complex(f.At(u, v, p), 0))
} else {
x.Set(u, v, 0)
}
}
}
}
func (phi *IsPos) Apply(x *rimg64.Multi) (*rimg64.Multi, error) {
y := rimg64.NewMulti(x.Width, x.Height, x.Channels)
for i := 0; i < x.Width; i++ {
for j := 0; j < x.Height; j++ {
for k := 0; k < x.Channels; k++ {
if x.At(i, j, k) > 0 {
y.Set(i, j, k, 1)
}
}
}
}
return y, nil
}
func (phi *PosNegPart) Apply(x *rimg64.Multi) (*rimg64.Multi, error) {
channels := x.Channels * 2
y := rimg64.NewMulti(x.Width, x.Height, channels)
for i := 0; i < x.Width; i++ {
for j := 0; j < x.Height; j++ {
for k := 0; k < x.Channels; k++ {
pos, neg := posNegPart(x.At(i, j, k))
y.Set(i, j, 2*k, pos)
y.Set(i, j, 2*k+1, neg)
}
}
}
return y, nil
}
func (phi *AdjChanNorm) Apply(x *rimg64.Multi) (*rimg64.Multi, error) {
y := rimg64.NewMulti(x.Width, x.Height, x.Channels)
r := (phi.Num - 1) / 2
for i := 0; i < x.Width; i++ {
for j := 0; j < x.Height; j++ {
k := 0
// Range over which to compute sum.
a, b := k-r, k+r+1
// Take sum excluding leading element.
var t float64
for p := 0; p < min(b, x.Channels); p++ {
t += sqr(x.At(i, j, p))
}
for ; k < x.Channels; k++ {
a, b = k-r, k+r+1
// Set element.
norm := math.Pow(phi.K+phi.Alpha*t, phi.Beta)
y.Set(i, j, k, x.At(i, j, k)/norm)
// Subtract trailing element.
if a >= 0 {
t -= sqr(x.At(i, j, a))
}
// Add leading element.
if b < x.Channels {
t += sqr(x.At(i, j, b))
}
}
}
}
return y, nil
}
// dst[i, j] = src[i*stride + offset.X, j*stride + offset.Y],
// or zero if this is outside the boundary.
func copyChannelStrideTo(dst *fftw.Array2, src *rimg64.Multi, channel, stride int, offset image.Point) {
m, n := dst.Dims()
bnds := image.Rect(0, 0, src.Width, src.Height)
for i := 0; i < m; i++ {
for j := 0; j < n; j++ {
p := image.Pt(i, j).Mul(stride).Add(offset)
var val complex128
if p.In(bnds) {
val = complex(src.At(p.X, p.Y, channel), 0)
}
dst.Set(i, j, val)
}
}
}
func drawCell(feat *rimg64.Multi, i, j int, gc *draw2d.ImageGraphicContext, cell int) {
u := (float64(i) + 0.5) * float64(cell)
v := (float64(j) + 0.5) * float64(cell)
r := float64(cell) / 2
for k := 0; k < Orientations; k++ {
x := feat.At(i, j, k)
x = math.Max(x, 0)
x = math.Min(x, 1)
gc.SetStrokeColor(color.Gray{uint8(x*254 + 1)})
theta := (0.5 + float64(k)/float64(Orientations)) * math.Pi
drawOrientedLine(gc, u, v, theta, r)
}
}
func invNormMulti(f *rimg64.Multi) float64 {
var norm float64
for i := 0; i < f.Width; i++ {
for j := 0; j < f.Height; j++ {
for k := 0; k < f.Channels; k++ {
norm += sqr(f.At(i, j, k))
}
}
}
norm = math.Sqrt(norm) // This cannot be negative.
if norm == 0 {
return 0
}
return 1 / norm
}
func (phi *AddConst) Apply(x *rimg64.Multi) (*rimg64.Multi, error) {
if x.Channels != len(*phi) {
err := fmt.Errorf("channels: image has %d, filter bank has %d", x.Channels, len(*phi))
return nil, err
}
y := rimg64.NewMulti(x.Width, x.Height, x.Channels)
for u := 0; u < x.Width; u++ {
for v := 0; v < x.Height; v++ {
for p := 0; p < x.Channels; p++ {
y.Set(u, v, p, x.At(u, v, p)+(*phi)[p])
}
}
}
return y, nil
}
func formatImageCSV(im *rimg64.Multi) [][]string {
var rows [][]string
for u := 0; u < im.Width; u++ {
for v := 0; v < im.Height; v++ {
for w := 0; w < im.Channels; w++ {
r := make([]string, 4)
r[0] = strconv.FormatInt(int64(u), 10)
r[1] = strconv.FormatInt(int64(v), 10)
r[2] = strconv.FormatInt(int64(w), 10)
r[3] = strconv.FormatFloat(im.At(u, v, w), 'g', -1, 64)
rows = append(rows, r)
}
}
}
return rows
}
// Returns gradient with greatest magnitude across all channels.
// 1 <= x <= width-2, 1 <= y <= height-2
func maxGrad(f *rimg64.Multi, x, y int) (point, float64) {
var (
grad point
max float64
)
for d := 0; d < f.Channels; d++ {
p := point{
f.At(x+1, y, d) - f.At(x-1, y, d),
f.At(x, y+1, d) - f.At(x, y-1, d),
}
v := p.X*p.X + p.Y*p.Y
if v > max {
grad, max = p, v
}
}
return grad, max
}
// CorrMultiStrideBLAS computes the strided correlation of
// a multi-channel image with a multi-channel filter.
// h[u, v] = sum_q (f_q corr g_q)[stride*u, stride*v]
func CorrMultiStrideBLAS(f, g *rimg64.Multi, 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, k := g.Width, g.Height, g.Channels
// Express as dense matrix multiplication.
// h[u, v] = sum_q (f_q corr g_q)[stride*u, stride*v]
// y(h) = A(f) x(g)
// where A is wh by mnk
// with w = ceil[(M-m+1)/stride],
// h = ceil[(N-n+1)/stride].
a := blas.NewMat(h.Width*h.Height, m*n*k)
{
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++ {
for q := 0; q < g.Channels; q++ {
a.Set(r, s, f.At(stride*u+i, stride*v+j, q))
s++
}
}
}
r++
}
}
}
x := blas.NewMat(m*n*k, 1)
{
var r int
for i := 0; i < g.Width; i++ {
for j := 0; j < g.Height; j++ {
for q := 0; q < g.Channels; q++ {
x.Set(r, 0, g.At(i, j, q))
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
}
func (phi *ConvEach) Apply(x *rimg64.Multi) (*rimg64.Multi, error) {
channels := x.Channels * len(phi.Filters.Filters)
field := image.Pt(phi.Filters.Width, phi.Filters.Height)
size := slide.ValidSize(x.Size(), field)
y := rimg64.NewMulti(size.X, size.Y, channels)
var n int
for i := 0; i < x.Channels; i++ {
// Convolve each channel of the input with the bank.
yi, err := slide.CorrBankBLAS(x.Channel(i), phi.Filters)
if err != nil {
return nil, err
}
for j := 0; j < yi.Channels; j++ {
// Copy the channels into the output.
y.SetChannel(n, yi.Channel(j))
n++
}
}
return y, nil
}
// CorrMultiBankFFT computes the correlation of
// a multi-channel image with a bank of multi-channel filters.
// h_p[u, v] = sum_q (f_q corr g_pq)[u, v]
func CorrMultiBankFFT(f *rimg64.Multi, g *MultiBank) (*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())
// Cache FFT of each channel of image.
fhat := make([]*fftw.Array2, f.Channels)
for i := range fhat {
fhat[i] = fftw.NewArray2(work.X, work.Y)
copyChannelTo(fhat[i], f, i)
fftw.FFT2To(fhat[i], fhat[i])
}
curr := fftw.NewArray2(work.X, work.Y)
fwd := fftw.NewPlan2(curr, curr, fftw.Forward, fftw.Estimate)
defer fwd.Destroy()
sum := fftw.NewArray2(work.X, work.Y)
bwd := fftw.NewPlan2(sum, sum, 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 {
zero(sum)
// For each input channel.
for q := 0; q < f.Channels; q++ {
// Take FFT of this input channel.
copyChannelTo(curr, gp, q)
fwd.Execute()
// h_p[x] = (G_qp corr F_p)[x]
// H_p[x] = conj(G_qp[x]) F_p[x]
addScaleMul(sum, alpha, curr, fhat[q])
}
bwd.Execute()
copyRealToChannel(h, p, sum)
}
return h, nil
}
// Flattens 31 (or 27) channels down to 9 for visualization.
func compress(src *rimg64.Multi, weights WeightSet) *rimg64.Multi {
dst := rimg64.NewMulti(src.Width, src.Height, 9)
for i := 0; i < 27; i++ {
for x := 0; x < src.Width; x++ {
for y := 0; y < src.Height; y++ {
v := src.At(x, y, i)
switch weights {
default:
case Pos:
v = math.Max(0, v)
case Neg:
v = math.Min(0, v)
case Abs:
v = math.Abs(v)
}
dst.Set(x, y, i%9, dst.At(x, y, i%9)+v)
}
}
}
return dst
}
// CorrMultiBankFFT computes the correlation of
// a multi-channel image with a multi-channel filter.
// h[u, v] = sum_p (f_p corr g_p)[u, v]
func CorrMultiFFT(f, g *rimg64.Multi) (*rimg64.Image, error) {
if err := errIfChannelsNotEq(f, g); err != nil {
panic(err)
}
out := ValidSize(f.Size(), g.Size())
if out.Eq(image.ZP) {
return nil, nil
}
work, _ := FFT2Size(f.Size())
fhat := fftw.NewArray2(work.X, work.Y)
ghat := fftw.NewArray2(work.X, work.Y)
ffwd := fftw.NewPlan2(fhat, fhat, fftw.Forward, fftw.Estimate)
defer ffwd.Destroy()
gfwd := fftw.NewPlan2(ghat, ghat, fftw.Forward, fftw.Estimate)
defer gfwd.Destroy()
hhat := fftw.NewArray2(work.X, work.Y)
for p := 0; p < f.Channels; p++ {
// Take transform of each channel.
copyChannelTo(fhat, f, p)
ffwd.Execute()
copyChannelTo(ghat, g, p)
gfwd.Execute()
addMul(hhat, ghat, fhat)
}
n := float64(work.X * work.Y)
scale(complex(1/n, 0), hhat)
fftw.IFFT2To(hhat, hhat)
h := rimg64.New(out.X, out.Y)
copyRealTo(h, hhat)
return h, nil
}
// CorrMultiBankStrideNaive computes the strided correlation of
// a multi-channel image with a bank of multi-channel filters.
// h_p[u, v] = sum_q (f_q corr g_pq)[stride*u, stride*v]
func CorrMultiBankStrideNaive(f *rimg64.Multi, g *MultiBank, 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++ {
for i := 0; i < g.Width; i++ {
for j := 0; j < g.Height; j++ {
for q := 0; q < g.Channels; q++ {
val := f.At(stride*u+i, stride*v+j, q) * g.Filters[p].At(i, j, q)
h.Set(u, v, p, h.At(u, v, p)+val)
}
}
}
}
}
}
return h, nil
}
