Peter M. Groen / openbr

Commit 140268451d944f241d54fc77d503347c9ea4f5ae

Authored by Josh Klontz
1 parent cff9e644

more plugin development

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Showing 3 changed files with 65 additions and 31 deletions
sdk/plugins/algorithms.cpp
  View file @1402684
... ... @@ -32,7 +32,6 @@ class AlgorithmsInitializer : public Initializer
32 32 {
33 33 // Face
34 34 Globals->abbreviations.insert("FaceRecognition", "FaceDetection!<FaceRecognitionRegistration>!<FaceRecognitionExtraction>+<FaceRecognitionEmbedding>+<FaceRecognitionQuantization>:MatchProbability(ByteL1)");
35   - Globals->abbreviations.insert("FaceRecognitionNoTraining", "Open+Cascade(FrontalFace)+ASEFEyes+Affine(256,256,0.25,0.35)+(RG+MAdd(0.5))/(Cvt(Gray)+Gradient+Bin(0,360,8,true))+Merge+Integral+IntegralSampler+CvtFloat:L2");
36 35 Globals->abbreviations.insert("GenderClassification", "FaceDetection!<FaceClassificationRegistration>!<FaceClassificationExtraction>+<GenderClassifier>+Discard");
37 36 Globals->abbreviations.insert("AgeRegression", "FaceDetection!<FaceClassificationRegistration>!<FaceClassificationExtraction>+<AgeRegressor>+Discard");
38 37 Globals->abbreviations.insert("FaceQuality", "Open!Cascade(FrontalFace)+ASEFEyes+Affine(64,64,0.25,0.35)+ImageQuality+Cvt(Gray)+DFFS+Discard");
... ... @@ -43,7 +42,7 @@ class AlgorithmsInitializer : public Initializer
43 42 Globals->abbreviations.insert("OpenBR", "FaceRecognition");
44 43 Globals->abbreviations.insert("GenderEstimation", "GenderClassification");
45 44 Globals->abbreviations.insert("AgeEstimation", "AgeRegression");
46   - Globals->abbreviations.insert("ImageRetrieval", "Open+(RG+MAdd(0.5))/(Cvt(Gray)+Gradient+Bin(0,360,8,true))+Merge+Integral+IntegralSampler+CvtFloat+WordWise(RowWisePCA(8)+RowWiseMeanCenter+Binarize,RowWisePCA)+Sentence:SentenceSimilarity");
  45 + Globals->abbreviations.insert("FaceRecognitionHoG", "Open+Cvt(Gray)+Cascade(FrontalFace)+ASEFEyes+Affine(64,64,0.25,0.35)+Gradient+Bin(0,360,8,true)+Merge+Integral+IntegralSampler+ProductQuantization:ProductQuantization");
47 46  
48 47 // Generic Image Processing
49 48 Globals->abbreviations.insert("SIFT", "Open+KeyPointDetector(SIFT)+KeyPointDescriptor(SIFT):KeyPointMatcher(BruteForce)");
... ... @@ -51,6 +50,7 @@ class AlgorithmsInitializer : public Initializer
51 50 Globals->abbreviations.insert("SmallSIFT", "Open+LimitSize(512)+KeyPointDetector(SIFT)+KeyPointDescriptor(SIFT):KeyPointMatcher(BruteForce)");
52 51 Globals->abbreviations.insert("SmallSURF", "Open+LimitSize(512)+KeyPointDetector(SURF)+KeyPointDescriptor(SURF):KeyPointMatcher(BruteForce)");
53 52 Globals->abbreviations.insert("ColorHist", "Open+LimitSize(512)!EnsureChannels(3)+SplitChannels+Hist(256,0,8)+Cat+Normalize(L1):L2");
  53 + Globals->abbreviations.insert("ImageRetrieval", "Open+Cvt(Gray)+Cascade(FrontalFace)+ASEFEyes+Affine(88,88,0.25,0.35)+Gradient+Bin(0,360,8,true)+Merge+Integral+IntegralSampler+WordWise(RowWisePCA(8)+RowWiseMeanCenter+Binarize,RowWisePCA)+Sentence:SentenceSimilarity");
54 54  
55 55 // Hash
56 56 Globals->abbreviations.insert("FileName", "Name+Identity:Identical");
... ...
sdk/plugins/integral.cpp
  View file @1402684
... ... @@ -38,9 +38,9 @@ class IntegralSamplerTransform : public UntrainableTransform
38 38 Q_PROPERTY(float scaleFactor READ get_scaleFactor WRITE set_scaleFactor RESET reset_scaleFactor STORED false)
39 39 Q_PROPERTY(float stepFactor READ get_stepFactor WRITE set_stepFactor RESET reset_stepFactor STORED false)
40 40 Q_PROPERTY(int minSize READ get_minSize WRITE set_minSize RESET reset_minSize STORED false)
41   - BR_PROPERTY(int, scales, 4)
  41 + BR_PROPERTY(int, scales, 5)
42 42 BR_PROPERTY(float, scaleFactor, 1.5)
43   - BR_PROPERTY(float, stepFactor, 0.25)
  43 + BR_PROPERTY(float, stepFactor, 0.75)
44 44 BR_PROPERTY(int, minSize, 8)
45 45  
46 46 void project(const Template &src, Template &dst) const
... ... @@ -53,21 +53,23 @@ class IntegralSamplerTransform : public UntrainableTransform
53 53 const int rowStep = channels * m.cols;
54 54  
55 55 int descriptors = 0;
56   - int currentSize = min(m.rows, m.cols)-1;
  56 + float idealSize = min(m.rows, m.cols)-1;
57 57 for (int scale=0; scale<scales; scale++) {
58   - descriptors += (1+(m.rows-currentSize)/int(currentSize*stepFactor)) *
59   - (1+(m.cols-currentSize)/int(currentSize*stepFactor));
60   - currentSize /= scaleFactor;
61   - if (currentSize < minSize) break;
  58 + const int currentSize(idealSize);
  59 + descriptors += (1+(m.rows-currentSize-1)/int(idealSize*stepFactor)) *
  60 + (1+(m.cols-currentSize-1)/int(idealSize*stepFactor));
  61 + idealSize /= scaleFactor;
  62 + if (idealSize < minSize) break;
62 63 }
63 64 Mat n(descriptors, channels, CV_32FC1);
64 65  
65 66 const qint32 *dataIn = (qint32*)m.data;
66 67 float *dataOut = (float*)n.data;
67   - currentSize = min(m.rows, m.cols)-1;
  68 + idealSize = min(m.rows, m.cols)-1;
68 69 int index = 0;
69 70 for (int scale=0; scale<scales; scale++) {
70   - const int currentStep = currentSize * stepFactor;
  71 + const int currentSize(idealSize);
  72 + const int currentStep(idealSize*stepFactor);
71 73 for (int i=currentSize; i<m.rows; i+=currentStep) {
72 74 for (int j=currentSize; j<m.cols; j+=currentStep) {
73 75 InputDescriptor a(dataIn+((i-currentSize)*rowStep+(j-currentSize)*channels), channels, 1);
... ... @@ -79,8 +81,8 @@ class IntegralSamplerTransform : public UntrainableTransform
79 81 index++;
80 82 }
81 83 }
82   - currentSize /= scaleFactor;
83   - if (currentSize < minSize) break;
  84 + idealSize /= scaleFactor;
  85 + if (idealSize < minSize) break;
84 86 }
85 87  
86 88 if (descriptors != index)
... ...
sdk/plugins/quantize.cpp
  View file @1402684
... ... @@ -109,58 +109,90 @@ class PackTransform : public UntrainableTransform
109 109  
110 110 BR_REGISTER(Transform, PackTransform)
111 111  
112   -QVector<Mat> BayesianProductQuantizationLUTs;
  112 +QVector<Mat> ProductQuantizationLUTs;
  113 +
  114 +/*!
  115 + * \ingroup distances
  116 + * \brief Distance in a product quantized space \cite jegou11
  117 + * \author Josh Klontz
  118 + */
  119 +class ProductQuantizationDistance : public Distance
  120 +{
  121 + Q_OBJECT
  122 + Q_PROPERTY(bool bayesian READ get_bayesian WRITE set_bayesian RESET reset_bayesian STORED false)
  123 + BR_PROPERTY(bool, bayesian, false)
  124 +
  125 + float compare(const Template &a, const Template &b) const
  126 + {
  127 + float distance = 0;
  128 + for (int i=0; i<a.size(); i++) {
  129 + const int elements = a[i].total();
  130 + const uchar *aData = a[i].data;
  131 + const uchar *bData = b[i].data;
  132 + const float *lut = (const float*)ProductQuantizationLUTs[i].data;
  133 + for (int j=0; j<elements; j++)
  134 + distance += lut[i*256*256 + aData[j]*256+bData[j]];
  135 + }
  136 + if (!bayesian) distance = -log(distance+1);
  137 + return distance;
  138 + }
  139 +};
  140 +
  141 +BR_REGISTER(Distance, ProductQuantizationDistance)
113 142  
114 143 /*!
115 144 * \ingroup transforms
116   - * \brief A bayesian extension to product quantization \cite jegou11
  145 + * \brief Product quantization \cite jegou11
117 146 * \author Josh Klontz \cite jklontz
118 147 */
119   -class BayesianProductQuantizationTransform : public Transform
  148 +class ProductQuantizationTransform : public Transform
120 149 {
121 150 Q_OBJECT
122 151 Q_PROPERTY(int n READ get_n WRITE set_n RESET reset_n STORED false)
123   - BR_PROPERTY(int, n, 3)
  152 + Q_PROPERTY(bool bayesian READ get_bayesian WRITE set_bayesian RESET reset_bayesian STORED false)
  153 + BR_PROPERTY(int, n, 2)
  154 + BR_PROPERTY(bool, bayesian, false)
124 155  
125 156 int index;
126 157 QList<Mat> centers;
127 158  
128 159 public:
129   - BayesianProductQuantizationTransform()
  160 + ProductQuantizationTransform()
130 161 {
131   - index = BayesianProductQuantizationLUTs.size();
132   - BayesianProductQuantizationLUTs.append(Mat());
  162 + index = ProductQuantizationLUTs.size();
  163 + ProductQuantizationLUTs.append(Mat());
133 164 }
134 165  
135 166 private:
136 167 void train(const TemplateList &src)
137 168 {
138   - Mat data = OpenCVUtils::toMatByRow(src.data());
  169 + Mat data = OpenCVUtils::toMat(src.data());
  170 + qDebug() << data.rows << data.cols;
139 171 if (data.cols % n != 0) qFatal("Expected dimensionality to be divisible by n.");
140 172  
141   - Mat &lut = BayesianProductQuantizationLUTs[index];
  173 + Mat &lut = ProductQuantizationLUTs[index];
142 174 lut = Mat(data.cols/n, 256*256, CV_32FC1);
143 175  
144   - for (int i=0; i<data.cols/n; i++) {
  176 + for (int i=0; i<lut.rows; i++) {
145 177 Mat labels, center;
146 178 kmeans(data.colRange(i*n,(i+1)*n), 256, labels, TermCriteria(TermCriteria::MAX_ITER, 10, 0), 3, KMEANS_PP_CENTERS, center);
147   -
148 179 for (int j=0; j<256; j++)
149 180 for (int k=0; k<256; k++)
150   - lut.at<float>(i,j*256+k) = norm(center.row(k), center.row(k), NORM_L2);
  181 + lut.at<float>(i,j*256+k) = norm(center.row(j), center.row(k), NORM_L2);
151 182 centers.append(center);
152 183 }
153 184 }
154 185  
155 186 void project(const Template &src, Template &dst) const
156 187 {
  188 + Mat m = src.m().reshape(1, 1);
157 189 dst = Mat(1, src.m().cols/n, CV_8UC1);
158   - for (int i=0; i<src.m().cols/n; i++) {
  190 + for (int i=0; i<m.cols/n; i++) {
159 191 uchar bestIndex = -1;
160 192 double bestDistance = std::numeric_limits<double>::max();
161   - Mat m = src.m().colRange(i*n, (i+1)*n);
  193 + Mat m_i = m.colRange(i*n, (i+1)*n);
162 194 for (uchar i=0; i<256; i++) {
163   - double distance = norm(m, centers[index].row(i), NORM_L2);
  195 + double distance = norm(m_i, centers[index].row(i), NORM_L2);
164 196 if (distance < bestDistance) {
165 197 bestDistance = distance;
166 198 bestIndex = i;
... ... @@ -172,16 +204,16 @@ private:
172 204  
173 205 void store(QDataStream &stream) const
174 206 {
175   - stream << centers << BayesianProductQuantizationLUTs[index];
  207 + stream << centers << ProductQuantizationLUTs[index];
176 208 }
177 209  
178 210 void load(QDataStream &stream)
179 211 {
180   - stream >> centers >> BayesianProductQuantizationLUTs[index];
  212 + stream >> centers >> ProductQuantizationLUTs[index];
181 213 }
182 214 };
183 215  
184   -BR_REGISTER(Transform, BayesianProductQuantizationTransform)
  216 +BR_REGISTER(Transform, ProductQuantizationTransform)
185 217  
186 218 } // namespace br
187 219  
... ...