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Diffstat (limited to 'Godeps/_workspace/src/code.google.com/p/graphics-go/graphics/detect/detect.go')
-rw-r--r-- | Godeps/_workspace/src/code.google.com/p/graphics-go/graphics/detect/detect.go | 133 |
1 files changed, 0 insertions, 133 deletions
diff --git a/Godeps/_workspace/src/code.google.com/p/graphics-go/graphics/detect/detect.go b/Godeps/_workspace/src/code.google.com/p/graphics-go/graphics/detect/detect.go deleted file mode 100644 index dde941cbe..000000000 --- a/Godeps/_workspace/src/code.google.com/p/graphics-go/graphics/detect/detect.go +++ /dev/null @@ -1,133 +0,0 @@ -// 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 -} |