Fix MS compiler warnings

modules/xobjdetect/include/opencv2/xobjdetect.hpp

@@ -109,7 +109,7 @@ struct CV_EXPORTS WaldBoostParams
     int weak_count;
     float alpha;
 
-    WaldBoostParams(): weak_count(100), alpha(0.01)
+    WaldBoostParams(): weak_count(100), alpha(0.01f)
     {}
 };
 

modules/xobjdetect/src/acffeature.cpp

@@ -83,7 +83,7 @@ void ACFFeatureEvaluator::setChannels(cv::InputArrayOfArrays channels)
                 int sum = 0;
                 for( int cell_row = row; cell_row < row + 4; ++cell_row )
                     for( int cell_col = col; cell_col < col + 4; ++cell_col )
-                        sum += channel.at<float>(cell_row, cell_col);
+                        sum += (int)channel.at<float>(cell_row, cell_col);
 
                 acf_channel(row / 4, col / 4) = sum;
             }
@@ -99,8 +99,8 @@ void ACFFeatureEvaluator::setPosition(Size position)
 
 void ACFFeatureEvaluator::evaluateAll(OutputArray feature_values) const
 {
-    Mat_<int> feature_vals(1, features_.size());
-    for( size_t i = 0; i < features_.size(); ++i )
+    Mat_<int> feature_vals(1, (int)features_.size());
+    for( int i = 0; i < (int)features_.size(); ++i )
     {
         feature_vals(0, i) = evaluate(i);
     }
@@ -146,13 +146,13 @@ void computeChannels(cv::InputArray image, cv::OutputArrayOfArrays channels_)
     magnitude(row_der, col_der, grad);
 
     Mat_<Vec6f> hist(grad.rows, grad.cols);
-    const float to_deg = 180 / 3.1415926;
+    const float to_deg = 180 / 3.1415926f;
     for (int row = 0; row < grad.rows; ++row) {
         for (int col = 0; col < grad.cols; ++col) {
             float angle = atan2(row_der(row, col), col_der(row, col)) * to_deg;
             if (angle < 0)
                 angle += 180;
-            int ind = angle / 30;
+            int ind = (int)(angle / 30);
             hist(row, col)[ind] = grad(row, col);
         }
     }

modules/xobjdetect/src/icfdetector.cpp

@@ -68,7 +68,7 @@ void ICFDetector::train(const vector<string>& image_filenames,
 
     vector<Mat> samples; /* positive samples + negative samples */
     Mat sample, resized_sample;
-    size_t pos_count = 0;
+    int pos_count = 0;
     for( size_t i = 0; i < image_filenames.size(); ++i, ++pos_count )
     {
         Mat img = imread(String(image_filenames[i].c_str()));
@@ -92,7 +92,7 @@ void ICFDetector::train(const vector<string>& image_filenames,
     for( size_t i = 0; i < image_filenames.size(); ++i )
     {
         Mat img = imread(String(image_filenames[i].c_str()));
-        for( size_t j = 0; j < pos_count / image_filenames.size() + 1; )
+        for( int j = 0; j < pos_count / image_filenames.size() + 1; )
         {
             Rect r;
             r.x = rng.uniform(0, img.cols);
@@ -112,19 +112,19 @@ void ICFDetector::train(const vector<string>& image_filenames,
     }
 
     Mat_<int> labels(1, pos_count + neg_count);
-    for( size_t i = 0; i < pos_count; ++i)
+    for( int i = 0; i < pos_count; ++i)
         labels(0, i) = 1;
-    for( size_t i = pos_count; i < pos_count + neg_count; ++i )
+    for( int i = pos_count; i < pos_count + neg_count; ++i )
         labels(0, i) = -1;
 
     vector<Point3i> features = generateFeatures(model_size);
     ACFFeatureEvaluator feature_evaluator(features);
 
-    Mat_<int> data(features.size(), samples.size());
+    Mat_<int> data((int)features.size(), (int)samples.size());
     Mat_<int> feature_col;
 
     vector<Mat> channels;
-    for( size_t i = 0; i < samples.size(); ++i )
+    for( int i = 0; i < (int)samples.size(); ++i )
     {
         computeChannels(samples[i], channels);
         feature_evaluator.setChannels(channels);
@@ -135,7 +135,7 @@ void ICFDetector::train(const vector<string>& image_filenames,
 
     WaldBoostParams wparams;
     wparams.weak_count = params.weak_count;
-    wparams.alpha = 0.001;
+    wparams.alpha = 0.001f;
 
     WaldBoost waldboost(wparams);
     waldboost.train(data, labels);

modules/xobjdetect/src/stump.cpp

@@ -81,7 +81,7 @@ int Stump::train(const Mat& data, const Mat& labels, const Mat& weights)
             sorted_labels.at<int>(row, col) =
                 labels.at<int>(0, indices.at<int>(row, col));
             sorted_weights.at<float>(row, col) =
-                weights.at<float>(0, indices.at<float>(row, col));
+                weights.at<float>(0, indices.at<int>(row, col));
         }
     }
 
@@ -124,7 +124,7 @@ int Stump::train(const Mat& data, const Mat& labels, const Mat& weights)
     float neg_total_weight = neg_cum_weights.at<float>(0, weights.cols - 1);
 
 
-    float eps = 1. / 4 * labels.cols;
+    float eps = 1.0f / 4 * labels.cols;
 
     /* Compute minimal error */
     float min_err = FLT_MAX;
@@ -147,8 +147,8 @@ int Stump::train(const Mat& data, const Mat& labels, const Mat& weights)
             float neg_right = neg_cum_weights.at<float>(row, col);
             float neg_wrong = neg_total_weight - neg_right;
 
-            h_pos = .5 * log((pos_right + eps) / (pos_wrong + eps));
-            h_neg = .5 * log((neg_wrong + eps) / (neg_right + eps));
+            h_pos = (float)(.5 * log((pos_right + eps) / (pos_wrong + eps)));
+            h_neg = (float)(.5 * log((neg_wrong + eps) / (neg_right + eps)));
 
             err = sqrt(pos_right * neg_wrong) + sqrt(pos_wrong * neg_right);
 

modules/xobjdetect/src/waldboost.cpp

@@ -69,8 +69,8 @@ vector<int> WaldBoost::train(const Mat& data, const Mat& labels)
     }
 
     Mat_<float> weights(labels.rows, labels.cols);
-    float pos_weight = 1. / (2 * pos_count);
-    float neg_weight = 1. / (2 * neg_count);
+    float pos_weight = 1.0f / (2 * pos_count);
+    float neg_weight = 1.0f / (2 * neg_count);
     for( int col = 0; col < weights.cols; ++col )
     {
         if( labels.at<int>(0, col) == +1 )
@@ -101,7 +101,7 @@ vector<int> WaldBoost::train(const Mat& data, const Mat& labels)
         }
 
         // Normalize weights
-        float z = sum(weights)[0];
+        float z = (float)sum(weights)[0];
         for( int col = 0; col < weights.cols; ++col)
         {
             weights.at<float>(0, col) /= z;