Peter M. Groen / openbr

Commit 23bd6de45a37ef8b25a3f6b9d3edf469dd952312

Authored by Jordan Cheney
1 parent a231cbed

Smarter use of pointers

Inline Side-by-side
Showing 9 changed files with 93 additions and 32 deletions
openbr/core/boost.cpp
  View file @23bd6de
... ... @@ -144,7 +144,7 @@ void FeatureEvaluator::init(Representation *_representation, int _maxSampleCount
144 144 representation = _representation;
145 145  
146 146 int dx, dy;
147   - Size windowSize = representation->windowSize(dx, dy);
  147 + Size windowSize = representation->windowSize(&dx, &dy);
148 148 data.create((int)_maxSampleCount, Size(windowSize.width + dx, windowSize.height + dy).area(), CV_32SC1);
149 149 cls.create( (int)_maxSampleCount, 1, CV_32FC1 );
150 150 }
... ... @@ -154,7 +154,7 @@ void FeatureEvaluator::setImage(const Mat &img, uchar clsLabel, int idx)
154 154 cls.ptr<float>(idx)[0] = clsLabel;
155 155  
156 156 int dx, dy;
157   - Size windowSize = representation->windowSize(dx, dy);
  157 + Size windowSize = representation->windowSize(&dx, &dy);
158 158 Mat integralImg(Size(windowSize.width + dx, windowSize.height + dy), data.type(), data.ptr<int>(idx));
159 159 representation->preprocess(img, integralImg);
160 160 }
... ...
openbr/core/cascade.cpp
  View file @23bd6de
... ... @@ -232,7 +232,7 @@ void _CascadeClassifier::detectMultiScale(const Mat&amp; image, vector&lt;Rect&gt;&amp; object
232 232  
233 233 for (double factor = 1; ; factor *= scaleFactor) {
234 234 int dx, dy;
235   - Size originalWindowSize = representation->windowSize(dx, dy);
  235 + Size originalWindowSize = representation->windowSize(&dx, &dy);
236 236  
237 237 Size windowSize(cvRound(originalWindowSize.width*factor), cvRound(originalWindowSize.height*factor) );
238 238 Size scaledImageSize(cvRound(image.cols/factor ), cvRound(image.rows/factor));
... ...
openbr/openbr_plugin.h
  View file @23bd6de
... ... @@ -1407,7 +1407,7 @@ public:
1407 1407 // and returned.
1408 1408 virtual cv::Mat evaluate(const cv::Mat &image, const QList<int> &indices = QList<int>()) const = 0;
1409 1409  
1410   - virtual cv::Size windowSize(int &dx, int &dy) const = 0; // dx and dy should indicate the change to the original window size after preprocessing
  1410 + virtual cv::Size windowSize(int *dx = NULL, int *dy = NULL) const = 0; // dx and dy should indicate the change to the original window size after preprocessing
1411 1411 virtual int numFeatures() const = 0;
1412 1412 virtual int maxCatCount() const = 0;
1413 1413 };
... ... @@ -1422,11 +1422,11 @@ public:
1422 1422 static Classifier *make(QString str, QObject *parent); /*!< \brief Make a classifier from a string. */
1423 1423  
1424 1424 virtual void train(const QList<cv::Mat> &images, const QList<float> &labels) = 0;
1425   - virtual float classify(const cv::Mat &image, float &confidence) const = 0;
  1425 + virtual float classify(const cv::Mat &image, float *confidence = NULL) const = 0;
1426 1426  
1427 1427 // Slots for representation
1428 1428 virtual cv::Mat preprocess(const cv::Mat &image) const = 0;
1429   - virtual cv::Size windowSize(int &dx, int &dy) const = 0;
  1429 + virtual cv::Size windowSize(int *dx = NULL, int *dy = NULL) const = 0;
1430 1430  
1431 1431 // OpenCV compatibility
1432 1432 virtual int numFeatures() const = 0;
... ...
openbr/plugins/classification/boostedforest.cpp
  View file @23bd6de
... ... @@ -129,9 +129,9 @@ class BoostedForestClassifier : public Classifier
129 129 }
130 130 }
131 131  
132   - float classify(const Mat &image, float &confidence) const
  132 + float classify(const Mat &image, float *confidence) const
133 133 {
134   - confidence = 0;
  134 + float sum = 0;
135 135 for (int i = 0; i < classifiers.size(); i++) {
136 136 Node *node = classifiers[i];
137 137  
... ... @@ -144,10 +144,12 @@ class BoostedForestClassifier : public Classifier
144 144 node = val <= node->threshold ? node->left : node->right;
145 145 }
146 146 }
147   - confidence += node->value;
  147 + sum += node->value;
148 148 }
149 149  
150   - return confidence < threshold - THRESHOLD_EPS ? 0.0f : 1.0f;
  150 + if (confidence)
  151 + *confidence = sum;
  152 + return sum < threshold - THRESHOLD_EPS ? 0.0f : 1.0f;
151 153 }
152 154  
153 155 int numFeatures() const
... ... @@ -167,7 +169,7 @@ class BoostedForestClassifier : public Classifier
167 169 return dst;
168 170 }
169 171  
170   - Size windowSize(int &dx, int &dy) const
  172 + Size windowSize(int *dx, int *dy) const
171 173 {
172 174 return representation->windowSize(dx, dy);
173 175 }
... ...
openbr/plugins/classification/cascade.cpp
  View file @23bd6de
... ... @@ -152,23 +152,18 @@ class CascadeClassifier : public Classifier
152 152 }
153 153 }
154 154  
155   - float classify(const Mat &image, float &confidence) const
  155 + float classify(const Mat &image, float *confidence) const
156 156 {
157   - if (stages.empty()) {
158   - confidence = 0.0f;
159   - return 1.0f;
160   - }
161   -
162   - for (int i = 0; i < stages.size(); i++) {
163   - float result = stages[i]->classify(image, confidence);
  157 + float stageConf = 0.0f;
  158 + foreach (const Classifier *stage, stages) {
  159 + float result = stage->classify(image, &stageConf);
164 160 if (result == 0.0f) {
165   - //confidence *= i;
166   - return i;
  161 + if (confidence)
  162 + *confidence += stageConf;
  163 + return 0.0f;
167 164 }
168 165 }
169   -
170   - //confidence *= stages.size();
171   - return stages.size();
  166 + return 1.0f;
172 167 }
173 168  
174 169 int numFeatures() const
... ... @@ -186,7 +181,7 @@ class CascadeClassifier : public Classifier
186 181 return stages.first()->preprocess(image);
187 182 }
188 183  
189   - Size windowSize(int &dx, int &dy) const
  184 + Size windowSize(int *dx, int *dy) const
190 185 {
191 186 return stages.first()->windowSize(dx, dy);
192 187 }
... ... @@ -217,14 +212,14 @@ private:
217 212 {
218 213 imgHandler.restart();
219 214  
220   - float confidence = 0.0f; // not used;
  215 + float confidence = 0.0f;
221 216  
222 217 while (images.size() < numPos) {
223 218 Mat pos(imgHandler.winSize, CV_8UC1);
224 219 if (!imgHandler.getPos(pos))
225 220 qFatal("Cannot get another positive sample!");
226 221  
227   - if (classify(pos, confidence) > 0.0f) {
  222 + if (classify(pos, &confidence) > 0.0f) {
228 223 printf("POS current samples: %d\r", images.size());
229 224 images.append(pos);
230 225 labels.append(1.0f);
... ... @@ -240,7 +235,7 @@ private:
240 235 if (!imgHandler.getNeg(neg))
241 236 qFatal("Cannot get another negative sample!");
242 237  
243   - if (classify(neg, confidence) > 0.0f) {
  238 + if (classify(neg, &confidence) > 0.0f) {
244 239 printf("NEG current samples: %d\r", images.size() - posCount);
245 240 images.append(neg);
246 241 labels.append(0.0f);
... ...
openbr/plugins/imgproc/slidingwindow.cpp
  View file @23bd6de
... ... @@ -100,7 +100,7 @@ class SlidingWindowTransform : public MetaTransform
100 100  
101 101 for (double factor = 1; ; factor *= scaleFactor) {
102 102 int dx, dy;
103   - Size originalWindowSize = classifier->windowSize(dx, dy);
  103 + Size originalWindowSize = classifier->windowSize(&dx, &dy);
104 104  
105 105 Size windowSize(cvRound(originalWindowSize.width*factor), cvRound(originalWindowSize.height*factor) );
106 106 Size scaledImageSize(cvRound(m.cols/factor ), cvRound(m.rows/factor));
... ... @@ -124,7 +124,7 @@ class SlidingWindowTransform : public MetaTransform
124 124 Mat window = repImage(Rect(Point(x, y), Size(originalWindowSize.width + dx, originalWindowSize.height + dy))).clone();
125 125  
126 126 float gypWeight;
127   - int result = classifier->classify(window, gypWeight);
  127 + int result = classifier->classify(window, &gypWeight);
128 128  
129 129 if (12 - result < 4) {
130 130 rects.push_back(Rect(cvRound(x*factor), cvRound(y*factor), windowSize.width, windowSize.height));
... ...
openbr/plugins/representation/haar.cpp
  View file @23bd6de
... ... @@ -89,7 +89,14 @@ class HaarRepresentation : public Representation
89 89 }
90 90  
91 91 int numFeatures() const { return features.size(); }
92   - Size windowSize(int &dx, int &dy) const { dx = dy = 1; return Size(winWidth, winHeight); }
  92 +
  93 + Size windowSize(int *dx, int *dy) const
  94 + {
  95 + if (dx && dy)
  96 + *dx = *dy = 1;
  97 + return Size(winWidth, winHeight);
  98 + }
  99 +
93 100 int maxCatCount() const { return 0; }
94 101  
95 102 struct Feature
... ...
openbr/plugins/representation/mblbp.cpp
  View file @23bd6de
... ... @@ -58,7 +58,13 @@ class MBLBPRepresentation : public Representation
58 58 return result;
59 59 }
60 60  
61   - Size windowSize(int &dx, int &dy) const { dx = dy = 1; return Size(winWidth, winHeight); }
  61 + Size windowSize(int *dx, int *dy) const
  62 + {
  63 + if (dx && dy)
  64 + *dx = *dy = 1;
  65 + return Size(winWidth, winHeight);
  66 + }
  67 +
62 68 int numFeatures() const { return features.size(); }
63 69 int maxCatCount() const { return 256; }
64 70  
... ...
openbr/plugins/representation/npd.cpp 0 → 100644
  View file @23bd6de
  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 + for (int p1 = 0; p1 < (winWidth * winHeight); p1++)
  20 + for (int p2 = p1; p2 < (winWidth * winHeight); p2++)
  21 + features.append(Feature(p1, p2));
  22 + }
  23 +
  24 + float evaluate(const Mat &image, int idx) const
  25 + {
  26 + return features[idx].calc(image);
  27 + }
  28 +
  29 + Size windowSize(int *dx, int *dy) const
  30 + {
  31 + if (dx && dy)
  32 + *dx = *dy = 0;
  33 + return Size(winWidth, winHeight);
  34 + }
  35 +
  36 + struct Feature
  37 + {
  38 + Feature() {}
  39 + Feature(int p1, int p2) { p[0] = p1; p[1] = p2; }
  40 + float calc(const Mat &image) const;
  41 +
  42 + int p[2];
  43 + };
  44 + QList<Feature> features;
  45 +};
  46 +
  47 +BR_REGISTER(Representation, NPDRepresentation)
  48 +
  49 +} // namespace br
  50 +
  51 +#include "representation/npd.moc"
... ...