Peter M. Groen / openbr

Commit 3c99d0905921176e9251b6d10fd1561f2d93472b

Authored by Josh Klontz
1 parent 8f693acf

remove representations

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Showing 4 changed files with 0 additions and 511 deletions
openbr/plugins/representation/haar.cpp deleted
View file @8f693ac
1 -#include <opencv2/imgproc/imgproc.hpp>  
2 -  
3 -#include <openbr/plugins/openbr_internal.h>  
4 -#include <openbr/core/opencvutils.h>  
5 -  
6 -using namespace cv;  
7 -  
8 -namespace br  
9 -{  
10 -  
11 -#define CV_SUM_OFFSETS( p0, p1, p2, p3, rect, step ) \  
12 - /* (x, y) */ \  
13 - (p0) = (rect).x + (step) * (rect).y; \  
14 - /* (x + w, y) */ \  
15 - (p1) = (rect).x + (rect).width + (step) * (rect).y; \  
16 - /* (x + w, y) */ \  
17 - (p2) = (rect).x + (step) * ((rect).y + (rect).height); \  
18 - /* (x + w, y + h) */ \  
19 - (p3) = (rect).x + (rect).width + (step) * ((rect).y + (rect).height);  
20 -  
21 -/*!  
22 - * \brief An implementation of Haar Features for Viola-Jones cascade object detection  
23 - * \author Jordan Cheney \cite jcheney  
24 - * \br_property int winWidth The width of the input image. The total feature space is based on this and the winHeight  
25 - * \br_property int winHeight The height of the input image. The total feature space is based on this and the winWidth.  
26 - * \br_paper Paul Viola, Michael Jones  
27 - * Rapid Object Detection using a Boosted Cascade of Simple Features  
28 - * CVPR, 2001  
29 - * \br_link Rapid Object Detection using a Boosted Cascade of Simple Features https://www.cs.cmu.edu/~efros/courses/LBMV07/Papers/viola-cvpr-01.pdf  
30 - */  
31 -class HaarRepresentation : public Representation  
32 -{  
33 - Q_OBJECT  
34 -  
35 - Q_PROPERTY(int winWidth READ get_winWidth WRITE set_winWidth RESET reset_winWidth STORED false)  
36 - Q_PROPERTY(int winHeight READ get_winHeight WRITE set_winHeight RESET reset_winHeight STORED false)  
37 - BR_PROPERTY(int, winWidth, 24)  
38 - BR_PROPERTY(int, winHeight, 24)  
39 -  
40 - void init()  
41 - {  
42 - if (features.isEmpty()) {  
43 - // Pre-determine the size of features to avoid reallocations  
44 - int numFeatures = 0;  
45 - for (int x = 0; x < winWidth; x++)  
46 - for (int y = 0; y < winHeight; y++)  
47 - for (int dx = 1; dx <= winWidth; dx++)  
48 - for (int dy = 1; dy <= winHeight; dy++)  
49 - numFeatures += ((x+dx*2 <= winWidth) && (y+dy <= winHeight))  
50 - + ((x+dx <= winWidth) && (y+dy*2 <= winHeight))  
51 - + ((x+dx*3 <= winWidth) && (y+dy <= winHeight))  
52 - + ((x+dx <= winWidth) && (y+dy*3 <= winHeight))  
53 - + ((x+dx*2 <= winWidth) && (y+dy*2 <= winHeight));  
54 - features.reserve(numFeatures);  
55 -  
56 - const int offset = winWidth + 1;  
57 - int index = 0;  
58 - for (int x = 0; x < winWidth; x++) {  
59 - for (int y = 0; y < winHeight; y++) {  
60 - for (int dx = 1; dx <= winWidth; dx++) {  
61 - for (int dy = 1; dy <= winHeight; dy++) {  
62 - // haar_x2  
63 - if ((x+dx*2 <= winWidth) && (y+dy <= winHeight))  
64 - features[index++] = Feature(offset,  
65 - x, y, dx*2, dy, -1,  
66 - x+dx, y, dx , dy, +2);  
67 - // haar_y2  
68 - if ((x+dx <= winWidth) && (y+dy*2 <= winHeight))  
69 - features[index++] = Feature(offset,  
70 - x, y, dx, dy*2, -1,  
71 - x, y+dy, dx, dy, +2);  
72 - // haar_x3  
73 - if ((x+dx*3 <= winWidth) && (y+dy <= winHeight))  
74 - features[index++] = Feature(offset,  
75 - x, y, dx*3, dy, -1,  
76 - x+dx, y, dx , dy, +3);  
77 - // haar_y3  
78 - if ((x+dx <= winWidth) && (y+dy*3 <= winHeight))  
79 - features[index++] = Feature(offset,  
80 - x, y, dx, dy*3, -1,  
81 - x, y+dy, dx, dy, +3);  
82 - // x2_y2  
83 - if ((x+dx*2 <= winWidth) && (y+dy*2 <= winHeight))  
84 - features[index++] = Feature(offset,  
85 - x, y, dx*2, dy*2, -1,  
86 - x, y, dx, dy, +2,  
87 - x+dx, y+dy, dx, dy, +2);  
88 -  
89 -  
90 - }  
91 - }  
92 - }  
93 - }  
94 - }  
95 - }  
96 -  
97 - Template preprocess(const Template &src) const  
98 - {  
99 - Template dst;  
100 - integral(src, dst);  
101 - return dst;  
102 - }  
103 -  
104 - float evaluate(const Template &src, int idx) const  
105 - {  
106 - return features[idx].calc(src.m());  
107 - }  
108 -  
109 - Mat evaluate(const Template &src, const QList<int> &indices) const  
110 - {  
111 - int size = indices.empty() ? numFeatures() : indices.size();  
112 -  
113 - Mat result(1, size, CV_32FC1);  
114 - for (int i = 0; i < size; i++)  
115 - result.at<float>(i) = evaluate(src, indices.empty() ? i : indices[i]);  
116 - return result;  
117 - }  
118 -  
119 - int numFeatures() const  
120 - {  
121 - return features.size();  
122 - }  
123 -  
124 - Size windowSize(int *dx, int *dy) const  
125 - {  
126 - if (dx && dy)  
127 - *dx = *dy = 1;  
128 - return Size(winWidth, winHeight);  
129 - }  
130 -  
131 - int maxCatCount() const { return 0; }  
132 -  
133 - struct Feature  
134 - {  
135 - Feature();  
136 - Feature( int offset,  
137 - int x0, int y0, int w0, int h0, float wt0,  
138 - int x1, int y1, int w1, int h1, float wt1,  
139 - int x2 = 0, int y2 = 0, int w2 = 0, int h2 = 0, float wt2 = 0.0F );  
140 - float calc(const Mat &img) const;  
141 -  
142 - struct {  
143 - float weight;  
144 - int p0, p1, p2, p3;  
145 - } fastRect[3];  
146 - };  
147 -  
148 - QVector<Feature> features;  
149 -};  
150 -  
151 -BR_REGISTER(Representation, HaarRepresentation)  
152 -  
153 -HaarRepresentation::Feature::Feature()  
154 -{  
155 - fastRect[0].p0 = fastRect[1].p0 = fastRect[2].p0 = 0;  
156 - fastRect[0].p1 = fastRect[1].p1 = fastRect[2].p1 = 0;  
157 - fastRect[0].p2 = fastRect[1].p2 = fastRect[2].p2 = 0;  
158 - fastRect[0].p3 = fastRect[1].p3 = fastRect[2].p3 = 0;  
159 - fastRect[0].weight = fastRect[1].weight = fastRect[2].weight = 0;  
160 -}  
161 -  
162 -HaarRepresentation::Feature::Feature(int offset,  
163 - int x0, int y0, int w0, int h0, float wt0,  
164 - int x1, int y1, int w1, int h1, float wt1,  
165 - int x2, int y2, int w2, int h2, float wt2)  
166 -{  
167 - CV_SUM_OFFSETS(fastRect[0].p0, fastRect[0].p1, fastRect[0].p2, fastRect[0].p3, Rect(x0, y0, w0, h0), offset)  
168 - CV_SUM_OFFSETS(fastRect[1].p0, fastRect[1].p1, fastRect[1].p2, fastRect[1].p3, Rect(x1, y1, w1, h1), offset)  
169 - CV_SUM_OFFSETS(fastRect[2].p0, fastRect[2].p1, fastRect[2].p2, fastRect[2].p3, Rect(x2, y2, w2, h2), offset)  
170 - fastRect[0].weight = wt0;  
171 - fastRect[1].weight = wt1;  
172 - fastRect[2].weight = wt2;  
173 -}  
174 -  
175 -inline float HaarRepresentation::Feature::calc(const Mat &img) const  
176 -{  
177 - const int* ptr = img.ptr<int>();  
178 - float ret = fastRect[0].weight * (ptr[fastRect[0].p0] - ptr[fastRect[0].p1] - ptr[fastRect[0].p2] + ptr[fastRect[0].p3]) +  
179 - fastRect[1].weight * (ptr[fastRect[1].p0] - ptr[fastRect[1].p1] - ptr[fastRect[1].p2] + ptr[fastRect[1].p3]);  
180 - if (fastRect[2].weight != 0.0f)  
181 - ret += fastRect[2].weight * (ptr[fastRect[2].p0] - ptr[fastRect[2].p1] - ptr[fastRect[2].p2] + ptr[fastRect[2].p3]);  
182 - return ret;  
183 -}  
184 -  
185 -} // namespace br  
186 -  
187 -#include "representation/haar.moc"  
188 -  
openbr/plugins/representation/mblbp.cpp deleted
View file @8f693ac
1 -#include <opencv2/imgproc/imgproc.hpp>  
2 -  
3 -#include <openbr/plugins/openbr_internal.h>  
4 -#include <openbr/core/opencvutils.h>  
5 -  
6 -using namespace cv;  
7 -  
8 -namespace br  
9 -{  
10 -  
11 -#define CV_SUM_OFFSETS( p0, p1, p2, p3, rect, step ) \  
12 - /* (x, y) */ \  
13 - (p0) = (rect).x + (step) * (rect).y; \  
14 - /* (x + w, y) */ \  
15 - (p1) = (rect).x + (rect).width + (step) * (rect).y; \  
16 - /* (x + w, y) */ \  
17 - (p2) = (rect).x + (step) * ((rect).y + (rect).height); \  
18 - /* (x + w, y + h) */ \  
19 - (p3) = (rect).x + (rect).width + (step) * ((rect).y + (rect).height);  
20 -  
21 -/*!  
22 - * \brief An implementation of MBLBP as an OpenBR Representation  
23 - * \author Jordan Cheney \cite jcheney  
24 - * \br_property int winWidth The width of the input image. The total feature space is based on this and the winHeight  
25 - * \br_property int winHeight The height of the input image. The total feature space is based on this and the winWidth.  
26 - * \br_paper Shengcai Liao, Xiangxin Zhu, Zhen Lei, Lun Zhang, Stan Z. Li  
27 - * Learning Multi-scale Block Local Binary Patterns for Face Recognition  
28 - * ICB, 2007  
29 - * \br_link Learning Multi-scale Block Local Binary Patterns for Face Recognition http://www.cbsr.ia.ac.cn/users/lzhang/papers/ICB07/ICB07_Liao.pdf  
30 - */  
31 -class MBLBPRepresentation : public Representation  
32 -{  
33 - Q_OBJECT  
34 -  
35 - Q_PROPERTY(int winWidth READ get_winWidth WRITE set_winWidth RESET reset_winWidth STORED false)  
36 - Q_PROPERTY(int winHeight READ get_winHeight WRITE set_winHeight RESET reset_winHeight STORED false)  
37 - BR_PROPERTY(int, winWidth, 24)  
38 - BR_PROPERTY(int, winHeight, 24)  
39 -  
40 - void init()  
41 - {  
42 - if (features.isEmpty()) {  
43 - int offset = winWidth + 1;  
44 - for (int x = 0; x < winWidth; x++ )  
45 - for (int y = 0; y < winHeight; y++ )  
46 - for (int w = 1; w <= winWidth / 3; w++ )  
47 - for (int h = 1; h <= winHeight / 3; h++ )  
48 - if ((x+3*w <= winWidth) && (y+3*h <= winHeight) )  
49 - features.append(Feature(offset, x, y, w, h ) );  
50 - }  
51 - }  
52 -  
53 - Template preprocess(const Template &src) const  
54 - {  
55 - Template dst;  
56 - integral(src, dst);  
57 - return dst;  
58 - }  
59 -  
60 - float evaluate(const Template &src, int idx) const  
61 - {  
62 - return (float)features[idx].calc(src.m());  
63 - }  
64 -  
65 - Mat evaluate(const Template &src, const QList<int> &indices) const  
66 - {  
67 - int size = indices.empty() ? numFeatures() : indices.size();  
68 -  
69 - Mat result(1, size, CV_32FC1);  
70 - for (int i = 0; i < size; i++)  
71 - result.at<float>(i) = evaluate(src, indices.empty() ? i : indices[i]);  
72 - return result;  
73 - }  
74 -  
75 - Size windowSize(int *dx, int *dy) const  
76 - {  
77 - if (dx && dy)  
78 - *dx = *dy = 1;  
79 - return Size(winWidth, winHeight);  
80 - }  
81 -  
82 - int numFeatures() const { return features.size(); }  
83 - int maxCatCount() const { return 256; }  
84 -  
85 - struct Feature  
86 - {  
87 - Feature() { rect = Rect(0, 0, 0, 0); }  
88 - Feature( int offset, int x, int y, int _block_w, int _block_h );  
89 - uchar calc(const Mat &img) const;  
90 -  
91 - Rect rect;  
92 - int p[16];  
93 - };  
94 - QList<Feature> features;  
95 -};  
96 -  
97 -BR_REGISTER(Representation, MBLBPRepresentation)  
98 -  
99 -static inline void calcOffset(int &p0, int &p1, int &p2, int &p3, Rect rect, int offset)  
100 -{  
101 - /* (x, y) */  
102 - p0 = rect.x + offset * rect.y;  
103 - /* (x + w, y) */  
104 - p1 = rect.x + rect.width + offset * rect.y;  
105 - /* (x + w, y) */  
106 - p2 = rect.x + offset * (rect.y + rect.height);  
107 - /* (x + w, y + h) */  
108 - p3 = rect.x + rect.width + offset * (rect.y + rect.height);  
109 -}  
110 -  
111 -MBLBPRepresentation::Feature::Feature( int offset, int x, int y, int _blockWidth, int _blockHeight )  
112 -{  
113 - Rect tr = rect = Rect(x, y, _blockWidth, _blockHeight);  
114 - calcOffset(p[0], p[1], p[4], p[5], tr, offset);  
115 - tr.x += 2*rect.width;  
116 - calcOffset(p[2], p[3], p[6], p[7], tr, offset);  
117 - tr.y +=2*rect.height;  
118 - calcOffset(p[10], p[11], p[14], p[15], tr, offset);  
119 - tr.x -= 2*rect.width;  
120 - calcOffset(p[8], p[9], p[12], p[13], tr, offset);  
121 -}  
122 -  
123 -inline uchar MBLBPRepresentation::Feature::calc(const Mat &img) const  
124 -{  
125 - const int* ptr = img.ptr<int>();  
126 - int cval = ptr[p[5]] - ptr[p[6]] - ptr[p[9]] + ptr[p[10]];  
127 -  
128 - return (uchar)((ptr[p[0]] - ptr[p[1]] - ptr[p[4]] + ptr[p[5]] >= cval ? 128 : 0) | // 0  
129 - (ptr[p[1]] - ptr[p[2]] - ptr[p[5]] + ptr[p[6]] >= cval ? 64 : 0) | // 1  
130 - (ptr[p[2]] - ptr[p[3]] - ptr[p[6]] + ptr[p[7]] >= cval ? 32 : 0) | // 2  
131 - (ptr[p[6]] - ptr[p[7]] - ptr[p[10]] + ptr[p[11]] >= cval ? 16 : 0) | // 5  
132 - (ptr[p[10]] - ptr[p[11]] - ptr[p[14]] + ptr[p[15]] >= cval ? 8 : 0) | // 8  
133 - (ptr[p[9]] - ptr[p[10]] - ptr[p[13]] + ptr[p[14]] >= cval ? 4 : 0) | // 7  
134 - (ptr[p[8]] - ptr[p[9]] - ptr[p[12]] + ptr[p[13]] >= cval ? 2 : 0) | // 6  
135 - (ptr[p[4]] - ptr[p[5]] - ptr[p[8]] + ptr[p[9]] >= cval ? 1 : 0)); // 3  
136 -}  
137 -  
138 -} // namespace br  
139 -  
140 -#include "representation/mblbp.moc"  
openbr/plugins/representation/npd.cpp deleted
View file @8f693ac
1 -#include <openbr/plugins/openbr_internal.h>  
2 -  
3 -using namespace cv;  
4 -  
5 -namespace br  
6 -{  
7 -  
8 -class NPDRepresentation : public Representation  
9 -{  
10 - Q_OBJECT  
11 -  
12 - Q_PROPERTY(int winWidth READ get_winWidth WRITE set_winWidth RESET reset_winWidth STORED false)  
13 - Q_PROPERTY(int winHeight READ get_winHeight WRITE set_winHeight RESET reset_winHeight STORED false)  
14 - BR_PROPERTY(int, winWidth, 24)  
15 - BR_PROPERTY(int, winHeight, 24)  
16 -  
17 - void init()  
18 - {  
19 - if (features.isEmpty()) {  
20 - for (int p1 = 0; p1 < (winWidth * winHeight); p1++)  
21 - for (int p2 = p1; p2 < (winWidth * winHeight); p2++)  
22 - features.append(Feature(p1, p2));  
23 - }  
24 - }  
25 -  
26 - float evaluate(const Template &src, int idx) const  
27 - {  
28 - return features[idx].calc(src.m());  
29 - }  
30 -  
31 - Mat evaluate(const Template &src, const QList<int> &indices) const  
32 - {  
33 - int size = indices.empty() ? numFeatures() : indices.size();  
34 -  
35 - Mat result(1, size, CV_32FC1);  
36 - for (int i = 0; i < size; i++)  
37 - result.at<float>(i) = evaluate(src, indices.empty() ? i : indices[i]);  
38 - return result;  
39 - }  
40 -  
41 - Size windowSize(int *dx, int *dy) const  
42 - {  
43 - if (dx && dy)  
44 - *dx = *dy = 0;  
45 - return Size(winWidth, winHeight);  
46 - }  
47 -  
48 - int numFeatures() const { return features.size(); }  
49 - int maxCatCount() const { return 0; }  
50 -  
51 - struct Feature  
52 - {  
53 - Feature() {}  
54 - Feature(int p0, int p1) : p0(p0), p1(p1) {}  
55 - float calc(const Mat &image) const;  
56 -  
57 - int p0, p1;  
58 - };  
59 - QList<Feature> features;  
60 -};  
61 -  
62 -BR_REGISTER(Representation, NPDRepresentation)  
63 -  
64 -inline float NPDRepresentation::Feature::calc(const Mat &image) const  
65 -{  
66 - const uchar *ptr = image.ptr();  
67 - int v1 = (int)ptr[p0], v2 = (int)ptr[p1];  
68 - return (v1 + v2) == 0 ? 0 : (1.0 * (v1 - v2)) / (v1 + v2);  
69 -}  
70 -  
71 -} // namespace br  
72 -  
73 -#include "representation/npd.moc"  
openbr/plugins/representation/random.cpp deleted
View file @8f693ac
1 -#include <opencv2/imgproc/imgproc.hpp>  
2 -#include <opencv2/highgui/highgui.hpp>  
3 -  
4 -#include <openbr/plugins/openbr_internal.h>  
5 -#include <openbr/core/opencvutils.h>  
6 -#include <openbr/core/common.h>  
7 -  
8 -using namespace cv;  
9 -  
10 -namespace br  
11 -{  
12 -  
13 -/*!  
14 - * \ingroup representations  
15 - * \brief A meta Representation that creates a smaller, random feature space from the full feature space of a given representation.  
16 - * \author Scott Klum \cite sklum  
17 - * \br_property Representation* representation The representation from which to create the random feature space  
18 - * \br_property int count The size of the random feature space  
19 - */  
20 -class RandomRepresentation : public Representation  
21 -{  
22 - Q_OBJECT  
23 -  
24 - Q_PROPERTY(br::Representation* representation READ get_representation WRITE set_representation RESET reset_representation STORED false)  
25 - Q_PROPERTY(int count READ get_count WRITE set_count RESET reset_count STORED false)  
26 - BR_PROPERTY(br::Representation*, representation, NULL)  
27 - BR_PROPERTY(int, count, 20000)  
28 -  
29 - QList<int> features;  
30 -  
31 - void train(const TemplateList &data)  
32 - {  
33 - representation->train(data);  
34 -  
35 - const int nFeatures = representation->numFeatures();  
36 -  
37 - if (Globals->verbose)  
38 - qDebug() << "Randomly sampling from" << nFeatures << "features.";  
39 -  
40 - features = Common::RandSample(count,nFeatures,0,true);  
41 - }  
42 -  
43 - Template preprocess(const Template &src) const  
44 - {  
45 - return representation->preprocess(src);  
46 - }  
47 -  
48 - float evaluate(const Template &src, int idx) const  
49 - {  
50 - return representation->evaluate(src, features[idx]);  
51 - }  
52 -  
53 - Mat evaluate(const Template &src, const QList<int> &indices) const  
54 - {  
55 - QList<int> newIndices;  
56 - if (indices.empty())  
57 - newIndices = features;  
58 - else  
59 - for (int i = 0; i < indices.size(); i++)  
60 - newIndices.append(features[indices[i]]);  
61 -  
62 - return representation->evaluate(src, newIndices);  
63 - }  
64 -  
65 - int numFeatures() const  
66 - {  
67 - return features.size();  
68 - }  
69 -  
70 - int numChannels() const  
71 - {  
72 - return representation->numChannels();  
73 - }  
74 -  
75 - Size windowSize(int *dx, int *dy) const  
76 - {  
77 - return representation->windowSize(dx,dy);  
78 - }  
79 -  
80 - int maxCatCount() const  
81 - {  
82 - return representation->maxCatCount();  
83 - }  
84 -  
85 - void load(QDataStream &stream)  
86 - {  
87 - representation->load(stream);  
88 -  
89 - int numFeatures; stream >> numFeatures;  
90 - for (int i=0; i<numFeatures; i++) {  
91 - int feature; stream >> feature;  
92 - features.append(feature);  
93 - }  
94 - }  
95 -  
96 - void store(QDataStream &stream) const  
97 - {  
98 - representation->store(stream);  
99 -  
100 - stream << features.size();  
101 - for (int i=0; i<features.size(); i++)  
102 - stream << features[i];  
103 - }  
104 -};  
105 -  
106 -BR_REGISTER(Representation, RandomRepresentation)  
107 -  
108 -} // namespace br  
109 -  
110 -#include "representation/random.moc"