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
-}