Fix warnings

modules/xobjdetect/src/detector.cpp

@@ -107,8 +107,8 @@ void WBDetector::train(
     int n_features;
     Mat pos_data, neg_data;
 
-    Ptr<CvFeatureEvaluator> eval = CvFeatureEvaluator::create(CvFeatureParams::LBP);
-    eval->init(CvFeatureParams::create(CvFeatureParams::LBP), 1, Size(24, 24));
+    Ptr<CvFeatureEvaluator> eval = CvFeatureEvaluator::create();
+    eval->init(CvFeatureParams::create(), 1, Size(24, 24));
     n_features = eval->getNumFeatures();
 
     const int stages[] = {32, 64, 128, 256, 512, 1024, 2048, 4096};
@@ -135,14 +135,14 @@ void WBDetector::train(
         for (size_t k = 0; k < pos_imgs.size(); ++k) {
             eval->setImage(pos_imgs[k], +1, 0, boost.get_feature_indices());
             for (int j = 0; j < n_features; ++j) {
-                pos_data.at<uchar>(j, k) = (*eval)(j, 0);
+                pos_data.at<uchar>(j, k) = (*eval)(j);
             }
         }
 
         for (size_t k = 0; k < neg_imgs.size(); ++k) {
             eval->setImage(neg_imgs[k], 0, 0, boost.get_feature_indices());
             for (int j = 0; j < n_features; ++j) {
-                neg_data.at<uchar>(j, k) = (*eval)(j, 0);
+                neg_data.at<uchar>(j, k) = (*eval)(j);
             }
         }
 
@@ -204,8 +204,8 @@ void WBDetector::detect(
     for (float scale = 0.2f; scale < 1.2f; scale *= 1.1) {
         scales.push_back(scale);
     }
-    Ptr<CvFeatureParams> params = CvFeatureParams::create(CvFeatureParams::LBP);
-    Ptr<CvFeatureEvaluator> eval = CvFeatureEvaluator::create(CvFeatureParams::LBP);
+    Ptr<CvFeatureParams> params = CvFeatureParams::create();
+    Ptr<CvFeatureEvaluator> eval = CvFeatureEvaluator::create();
     eval->init(params, 1, Size(24, 24));
     boost.detect(eval, img, scales, bboxes, confidences);
     assert(confidences.size() == bboxes.size());

modules/xobjdetect/src/features.cpp

@@ -96,7 +96,7 @@ bool CvFeatureParams::read( const FileNode &node )
     return ( maxCatCount >= 0 && featSize >= 1 );
 }
 
-Ptr<CvFeatureParams> CvFeatureParams::create( int featureType )
+Ptr<CvFeatureParams> CvFeatureParams::create()
 {
     return Ptr<CvFeatureParams>(new CvLBPFeatureParams);
 }
@@ -114,7 +114,7 @@ void CvFeatureEvaluator::init(const CvFeatureParams *_featureParams,
     generateFeatures();
 }
 
-void CvFeatureEvaluator::setImage(const Mat &img, uchar clsLabel, int idx, const std::vector<int>& feature_ind)
+void CvFeatureEvaluator::setImage(const Mat &, uchar clsLabel, int idx, const std::vector<int>& feature_ind)
 {
     //CV_Assert(img.cols == winSize.width);
     //CV_Assert(img.rows == winSize.height);
@@ -122,7 +122,7 @@ void CvFeatureEvaluator::setImage(const Mat &img, uchar clsLabel, int idx, const
     cls.ptr<float>(idx)[0] = clsLabel;
 }
 
-Ptr<CvFeatureEvaluator> CvFeatureEvaluator::create(int type)
+Ptr<CvFeatureEvaluator> CvFeatureEvaluator::create()
 {
     return Ptr<CvFeatureEvaluator>(new CvLBPEvaluator);
 }

modules/xobjdetect/src/lbpfeatures.h

@@ -64,8 +64,8 @@ public:
     virtual void setImage(const cv::Mat& img, uchar clsLabel, int idx, const std::vector<int> &feature_ind);
     virtual void setWindow(const cv::Point& p)
     { cur_sum = sum.rowRange(p.y, p.y + winSize.height).colRange(p.x, p.x + winSize.width); }
-    virtual float operator()(int featureIdx, int sampleIdx)
-    { return (float)features[featureIdx].calc( cur_sum, offset_, sampleIdx); }
+    virtual float operator()(int featureIdx)
+    { return (float)features[featureIdx].calc( cur_sum ); }
     virtual void writeFeatures( cv::FileStorage &fs, const cv::Mat& featureMap ) const;
 protected:
     virtual void generateFeatures();
@@ -75,7 +75,7 @@ protected:
     public:
         Feature();
         Feature( int offset, int x, int y, int _block_w, int _block_h  );
-        uchar calc( const cv::Mat& _sum, int offset, size_t y );
+        uchar calc( const cv::Mat& _sum );
         void write( cv::FileStorage &fs ) const;
 
         cv::Rect rect;
@@ -90,7 +90,7 @@ protected:
     int offset_;
 };
 
-inline uchar CvLBPEvaluator::Feature::calc(const cv::Mat &_sum, int offset, size_t y)
+inline uchar CvLBPEvaluator::Feature::calc(const cv::Mat &_sum)
 {
     const int* psum = _sum.ptr<int>();
 

modules/xobjdetect/src/traincascade_features.h

@@ -110,7 +110,7 @@ public:
     virtual void init( const CvFeatureParams& fp );
     virtual void write( cv::FileStorage &fs ) const;
     virtual bool read( const cv::FileNode &node );
-    static cv::Ptr<CvFeatureParams> create( int featureType );
+    static cv::Ptr<CvFeatureParams> create();
     int maxCatCount; // 0 in case of numerical features
     int featSize; // 1 in case of simple features (HAAR, LBP) and N_BINS(9)*N_CELLS(4) in case of Dalal's HOG features
 };
@@ -124,8 +124,8 @@ public:
     virtual void setImage(const cv::Mat& img, uchar clsLabel, int idx, const std::vector<int> &feature_ind);
     virtual void setWindow(const cv::Point& p) = 0;
     virtual void writeFeatures( cv::FileStorage &fs, const cv::Mat& featureMap ) const = 0;
-    virtual float operator()(int featureIdx, int sampleIdx) = 0;
-    static cv::Ptr<CvFeatureEvaluator> create(int type);
+    virtual float operator()(int featureIdx) = 0;
+    static cv::Ptr<CvFeatureEvaluator> create();
 
     int getNumFeatures() const { return numFeatures; }
     int getMaxCatCount() const { return featureParams->maxCatCount; }

modules/xobjdetect/src/waldboost.cpp

@@ -317,7 +317,7 @@ int WaldBoost::predict(Ptr<CvFeatureEvaluator> eval, float *h) const
     assert(feature_indices_.size() == size_t(weak_count_));
     float res = 0;
     for (int i = 0; i < weak_count_; ++i) {
-        float val = (*eval)(feature_indices_[i], 0);
+        float val = (*eval)(feature_indices_[i]);
         int label = polarities_[i] * (val - thresholds_[i]) > 0 ? +1: -1;
         res += alphas_[i] * label;
         if (res < THR) {

modules/xobjdetect/tools/waldboost_detector/waldboost_detector.cpp

@@ -30,4 +30,3 @@ int main(int argc, char **argv)
         imwrite(argv[4], img);
     }
 }
-