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// Copyright 2011 The Graphics-Go Authors. All rights reserved.
// Use of this source code is governed by a BSD-style
// license that can be found in the LICENSE file.
package detect
import (
"image"
"math"
)
// Feature is a Haar-like feature.
type Feature struct {
Rect image.Rectangle
Weight float64
}
// Classifier is a set of features with a threshold.
type Classifier struct {
Feature []Feature
Threshold float64
Left float64
Right float64
}
// CascadeStage is a cascade of classifiers.
type CascadeStage struct {
Classifier []Classifier
Threshold float64
}
// Cascade is a degenerate tree of Haar-like classifiers.
type Cascade struct {
Stage []CascadeStage
Size image.Point
}
// Match returns true if the full image is classified as an object.
func (c *Cascade) Match(m image.Image) bool {
return c.classify(newWindow(m))
}
// Find returns a set of areas of m that match the feature cascade c.
func (c *Cascade) Find(m image.Image) []image.Rectangle {
// TODO(crawshaw): Consider de-duping strategies.
matches := []image.Rectangle{}
w := newWindow(m)
b := m.Bounds()
origScale := c.Size
for s := origScale; s.X < b.Dx() && s.Y < b.Dy(); s = s.Add(s.Div(10)) {
// translate region and classify
tx := image.Pt(s.X/10, 0)
ty := image.Pt(0, s.Y/10)
for r := image.Rect(0, 0, s.X, s.Y).Add(b.Min); r.In(b); r = r.Add(ty) {
for r1 := r; r1.In(b); r1 = r1.Add(tx) {
if c.classify(w.subWindow(r1)) {
matches = append(matches, r1)
}
}
}
}
return matches
}
type window struct {
mi *integral
miSq *integral
rect image.Rectangle
invArea float64
stdDev float64
}
func (w *window) init() {
w.invArea = 1 / float64(w.rect.Dx()*w.rect.Dy())
mean := float64(w.mi.sum(w.rect)) * w.invArea
vr := float64(w.miSq.sum(w.rect))*w.invArea - mean*mean
if vr < 0 {
vr = 1
}
w.stdDev = math.Sqrt(vr)
}
func newWindow(m image.Image) *window {
mi, miSq := newIntegrals(m)
res := &window{
mi: mi,
miSq: miSq,
rect: m.Bounds(),
}
res.init()
return res
}
func (w *window) subWindow(r image.Rectangle) *window {
res := &window{
mi: w.mi,
miSq: w.miSq,
rect: r,
}
res.init()
return res
}
func (c *Classifier) classify(w *window, pr *projector) float64 {
s := 0.0
for _, f := range c.Feature {
s += float64(w.mi.sum(pr.rect(f.Rect))) * f.Weight
}
s *= w.invArea // normalize to maintain scale invariance
if s < c.Threshold*w.stdDev {
return c.Left
}
return c.Right
}
func (s *CascadeStage) classify(w *window, pr *projector) bool {
sum := 0.0
for _, c := range s.Classifier {
sum += c.classify(w, pr)
}
return sum >= s.Threshold
}
func (c *Cascade) classify(w *window) bool {
pr := newProjector(w.rect, image.Rectangle{image.Pt(0, 0), c.Size})
for _, s := range c.Stage {
if !s.classify(w, pr) {
return false
}
}
return true
}
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