Peter M. Groen / openbr

Commit 7e7832fb823a4b838be95099fb50daa73d71a98c

Authored by Josh Klontz
1 parent d2d7f162

removed onlinerod

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Showing 1 changed file with 0 additions and 125 deletions
openbr/plugins/cluster/onlinerod.cpp deleted
View file @d2d7f16
1 -#include <openbr/plugins/openbr_internal.h>  
2 -#include <openbr/core/cluster.h>  
3 -#include <openbr/core/opencvutils.h>  
4 -  
5 -using namespace cv;  
6 -  
7 -namespace br  
8 -{  
9 -  
10 -/*!  
11 - * \brief Constructors clusters based on the Rank-Order distance in an online, incremental manner  
12 - * \author Charles Otto \cite caotto  
13 - * \author Jordan Cheney \cite JordanCheney  
14 - * \br_property br::Distance* distance Distance to compute the similarity score between templates. Default is L2.  
15 - * \br_property int kNN Maximum number of nearest neighbors to keep for each template. Default is 20.  
16 - * \br_property float aggression Clustering aggresiveness. A higher value will result in larger clusters. Default is 10.  
17 - * \br_property bool incremental If true, compute the clusters as each template is processed otherwise compute the templates at the end. Default is false.  
18 - * \br_property QString evalOutput Path to store cluster informtation. Optional. Default is an empty string.  
19 - * \br_paper Zhu et al.  
20 - * "A Rank-Order Distance based Clustering Algorithm for Face Tagging"  
21 - * CVPR 2011  
22 - */  
23 -class OnlineRODTransform : public TimeVaryingTransform  
24 -{  
25 - Q_OBJECT  
26 - Q_PROPERTY(br::Distance *distance READ get_distance WRITE set_distance RESET reset_distance STORED true)  
27 - Q_PROPERTY(int kNN READ get_kNN WRITE set_kNN RESET reset_kNN STORED false)  
28 - Q_PROPERTY(float aggression READ get_aggression WRITE set_aggression RESET reset_aggression STORED false)  
29 - Q_PROPERTY(bool incremental READ get_incremental WRITE set_incremental RESET reset_incremental STORED false)  
30 - Q_PROPERTY(QString evalOutput READ get_evalOutput WRITE set_evalOutput RESET reset_evalOutput STORED false)  
31 -  
32 - BR_PROPERTY(br::Distance*, distance, Distance::make(".Dist(L2)",this))  
33 - BR_PROPERTY(int, kNN, 20)  
34 - BR_PROPERTY(float, aggression, 10)  
35 - BR_PROPERTY(bool, incremental, false)  
36 - BR_PROPERTY(QString, evalOutput, "")  
37 -  
38 - TemplateList templates;  
39 - Neighborhood neighborhood;  
40 -  
41 -public:  
42 - OnlineRODTransform() : TimeVaryingTransform(false, false) {}  
43 -  
44 -private:  
45 - void projectUpdate(const TemplateList &src, TemplateList &dst)  
46 - {  
47 - // update current graph  
48 - foreach(const Template &t, src) {  
49 - QList<float> scores = distance->compare(templates, t);  
50 -  
51 - // attempt to udpate each existing point's (sorted) k-NN list with these results.  
52 - Neighbors currentN;  
53 - for (int i=0; i < scores.size(); i++) {  
54 - currentN.append(Neighbor(i, scores[i]));  
55 - Neighbors target = neighborhood[i];  
56 -  
57 - // should we insert the new neighbor into the current target's list?  
58 - if (target.size() < kNN || scores[i] > target.last().second) {  
59 - // insert into the sorted nearest neighbor list  
60 - Neighbor temp(scores.size(), scores[i]);  
61 - br::Neighbors::iterator res = qLowerBound(target.begin(), target.end(), temp, compareNeighbors);  
62 - target.insert(res, temp);  
63 -  
64 - if (target.size() > kNN)  
65 - target.removeLast();  
66 -  
67 - neighborhood[i] = target;  
68 - }  
69 - }  
70 -  
71 - // add a new row, consisting of the top neighbors of the newest point  
72 - int actuallyKeep = std::min(kNN, currentN.size());  
73 - std::partial_sort(currentN.begin(), currentN.begin()+actuallyKeep, currentN.end(), compareNeighbors);  
74 -  
75 - Neighbors selected = currentN.mid(0, actuallyKeep);  
76 - neighborhood.append(selected);  
77 - templates.append(t);  
78 - }  
79 -  
80 - if (incremental)  
81 - identifyClusters(dst);  
82 - }  
83 -  
84 - void finalize(TemplateList &output)  
85 - {  
86 - if (!templates.empty()) {  
87 - identifyClusters(output);  
88 - templates.clear();  
89 - neighborhood.clear();  
90 - }  
91 - }  
92 -  
93 - void identifyClusters(TemplateList &dst)  
94 - {  
95 - Clusters clusters = ClusterGraph(neighborhood, aggression, evalOutput);  
96 - if (Globals->verbose)  
97 - qDebug("Built %d clusters from %d templates", clusters.size(), templates.size());  
98 -  
99 - for (int i = 0; i < clusters.size(); i++) {  
100 - // Calculate the centroid of each cluster  
101 - Mat center = Mat::zeros(templates[0].m().rows, templates[0].m().cols, CV_32F);  
102 - foreach (int t, clusters[i]) {  
103 - Mat converted; templates[t].m().convertTo(converted, CV_32F);  
104 - center += converted;  
105 - }  
106 - center /= clusters[i].size();  
107 -  
108 - // Calculate the Euclidean distance from the center to use as the cluster confidence  
109 - foreach (int t, clusters[i]) {  
110 - templates[t].file.set("Cluster", i);  
111 - Mat c; templates[t].m().convertTo(c, CV_32F);  
112 - Mat p; pow(c - center, 2, p);  
113 - templates[t].file.set("ClusterConfidence", sqrt(sum(p)[0]));  
114 - }  
115 - }  
116 - dst.append(templates);  
117 - }  
118 -};  
119 -  
120 -BR_REGISTER(Transform, OnlineRODTransform)  
121 -  
122 -} // namespace br  
123 -  
124 -#include "onlinerod.moc"  
125 -