played around with weighing the HoG vectors based on magnitude, didn't see much of an impact

Josh Klontz
1 parent 4e02e315
Showing 3 changed files with 44 additions and 9 deletions
openbr/plugins/hist.cpp
openbr/plugins/integral.cpp
scripts/integralFaceRecognition-PCSO.sh
@@ -86,13 +86,36 @@ class BinTransform : public UntrainableTransform
     void project(const Template &src, Template &dst) const
     {
         const double floor = ((src.m().depth() == CV_32F) || (src.m().depth() == CV_64F)) ? -0.5 : 0;
-        src.m().convertTo(dst, bins > 256 ? CV_16U : CV_8U, bins/(max-min), floor);
+
+        Mat weights, vals;
+        const int channels = src.m().channels();
+        if (channels == 1) {
+            vals = src;
+        } else if (channels == 2) {
+            // If there are two channels, the first is channel is assumed to be a weight vector
+            // and the second channel contains the vectors we would like to bin.
+            vector<Mat> mv;
+            cv::split(src, mv);
+            weights = mv[0];
+            weights.convertTo(weights, CV_32F);
+            vals = mv[1];
+        } else {
+            qFatal("Invalid channel count: %d", channels);
+        }
+
+        vals.convertTo(dst, bins > 256 ? CV_16U : CV_8U, bins/(max-min), floor);
         if (!split) return;
-        Mat input = dst;
         QList<Mat> outputs; outputs.reserve(bins);
-        for (int i=0; i<bins; i++)
-            outputs.append(input == i); // Note: Matrix elements are 0 or 255
+        for (int i=0; i<bins; i++) {
+            Mat output = (dst == i);
+            if (weights.data) {
+                output.convertTo(output, CV_32F);
+                multiply(output, weights, output);
+                output.convertTo(output, CV_8U);
+            }
+            outputs.append(output);
+        }
         dst.clear(); dst.append(outputs);
     }
 };
@@ -212,6 +212,9 @@ class RecursiveIntegralSamplerTransform : public Transform
     void train(const TemplateList &src)
     {
+        if (src.first().m().depth() != CV_32S)
+            qFatal("Expected CV_32S depth!");
+
         if (subTransform != NULL) {
             TemplateList subSrc; subSrc.reserve(src.size());
             foreach (const Template &t, src)
@@ -287,13 +290,19 @@ private:
     void project(const Template &src, Template &dst) const
     {
         Mat dx, dy, magnitude, angle;
-        Sobel(src, dx, CV_32F, 1, 0);
-        Sobel(src, dy, CV_32F, 0, 1);
+        Sobel(src, dx, CV_32F, 1, 0, CV_SCHARR);
+        Sobel(src, dy, CV_32F, 0, 1, CV_SCHARR);
         cartToPolar(dx, dy, magnitude, angle, true);
-        if ((channel == Magnitude) || (channel == MagnitudeAndAngle))
-            dst.append(magnitude);
+        vector<Mat> mv;
+        if ((channel == Magnitude) || (channel == MagnitudeAndAngle)) {
+            const float theoreticalMaxMagnitude = sqrt(2*pow(float(2*(3+10+3)*255), 2.f));
+            mv.push_back(magnitude / theoreticalMaxMagnitude);
+        }
         if ((channel == Angle) || (channel == MagnitudeAndAngle))
-            dst.append(angle);
+            mv.push_back(angle);
+        Mat result;
+        merge(mv, result);
+        dst.append(result);
     }
 };
+#!/bin/sh
+
+br -algorithm "{PP5Register+Affine(128,128,0.25,0.35)}+Cvt(Gray)+Gradient+Bin(0,360,9,true)+Merge+Integral+CvtFloat+RecursiveIntegralSampler(4,2,8,LDA(.98)+Normalize(L1))+Cat+PCA(768)+Normalize(L1)+Quantize:UCharL1" -path ~/data/PCSO -train ../data/PCSO/sigset/PCSO_2x1k_train.xml -compare ../data/PCSO/sigset/PCSO_2x1k_test.xml . scores.mtx -eval scores.mtx scores.csv
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