Warning fixes

modules/xobjdetect/src/detector.cpp

@@ -60,12 +60,12 @@ static vector<Mat> sample_patches(
         const string& path,
         int n_rows,
         int n_cols,
-        int n_patches)
+        size_t n_patches)
 {
     vector<String> filenames;
     glob(path, filenames);
     vector<Mat> patches;
-    int patch_count = 0;
+    size_t patch_count = 0;
     for (size_t i = 0; i < filenames.size(); ++i) {
         Mat img = imread(filenames[i], CV_LOAD_IMAGE_GRAYSCALE);
         for (int row = 0; row + n_rows < img.rows; row += n_rows) {
@@ -116,7 +116,7 @@ void WBDetector::train(
     const int stage_neg = 5000;
     const int max_per_image = 25;
 
-    const float scales_arr[] = {.3, .4, .5, .6, .7, .8, .9, 1};
+    const float scales_arr[] = {.3f, .4f, .5f, .6f, .7f, .8f, .9f, 1.0f};
     const vector<float> scales(scales_arr,
             scales_arr + sizeof(scales_arr) / sizeof(*scales_arr));
 
@@ -129,8 +129,8 @@ void WBDetector::train(
 
         cerr << "compute features" << endl;
 
-        pos_data = Mat1b(n_features, pos_imgs.size());
-        neg_data = Mat1b(n_features, neg_imgs.size());
+        pos_data = Mat1b(n_features, (int)pos_imgs.size());
+        neg_data = Mat1b(n_features, (int)neg_imgs.size());
 
         for (size_t k = 0; k < pos_imgs.size(); ++k) {
             eval->setImage(pos_imgs[k], +1, 0, boost.get_feature_indices());
@@ -142,7 +142,7 @@ void WBDetector::train(
         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);
+                neg_data.at<uchar>(j, (int)k) = (*eval)(j);
             }
         }
 
@@ -201,7 +201,7 @@ void WBDetector::detect(
     bboxes.clear();
     confidences.clear();
     vector<float> scales;
-    for (float scale = 0.2f; scale < 1.2f; scale *= 1.1) {
+    for (float scale = 0.2f; scale < 1.2f; scale *= 1.1f) {
         scales.push_back(scale);
     }
     Ptr<CvFeatureParams> params = CvFeatureParams::create();

modules/xobjdetect/src/features.cpp

@@ -114,7 +114,7 @@ void CvFeatureEvaluator::init(const CvFeatureParams *_featureParams,
     generateFeatures();
 }
 
-void CvFeatureEvaluator::setImage(const Mat &, uchar clsLabel, int idx, const std::vector<int>& feature_ind)
+void CvFeatureEvaluator::setImage(const Mat &, uchar clsLabel, int idx, const std::vector<int>&)
 {
     //CV_Assert(img.cols == winSize.width);
     //CV_Assert(img.rows == winSize.height);

modules/xobjdetect/src/waldboost.cpp

@@ -84,7 +84,7 @@ static void compute_min_step(const Mat &data_pos, const Mat &data_neg, size_t n_
     max(reduced_pos, reduced_neg, data_max);
     data_max += 0.01;
 
-    data_step = (data_max - data_min) / (n_bins - 1);
+    data_step = (data_max - data_min) / (double)(n_bins - 1);
 }
 
 static void quantize_data(Mat &data, Mat1f &data_min, Mat1f &data_step)
@@ -132,15 +132,15 @@ void WaldBoost::detect(Ptr<CvFeatureEvaluator> eval,
         float scale = scales[i];
         resize(img, resized_img, Size(), scale, scale);
         eval->setImage(resized_img, 0, 0, feature_indices_);
-        int n_rows = 24 / scale;
-        int n_cols = 24 / scale;
+        int n_rows = (int)(24 / scale);
+        int n_cols = (int)(24 / scale);
         for (int r = 0; r + 24 < resized_img.rows; r += step) {
             for (int c = 0; c + 24 < resized_img.cols; c += step) {
                 //eval->setImage(resized_img(Rect(c, r, 24, 24)), 0, 0);
                 eval->setWindow(Point(c, r));
                 if (predict(eval, &h) == +1) {
-                    int row = r / scale;
-                    int col = c / scale;
+                    int row = (int)(r / scale);
+                    int col = (int)(c / scale);
                     bboxes.push_back(Rect(col, row, n_cols, n_rows));
                     confidences.push_back(h);
                 }
@@ -164,14 +164,14 @@ void WaldBoost::detect(Ptr<CvFeatureEvaluator> eval,
         float scale = scales[i];
         resize(img, resized_img, Size(), scale, scale);
         eval->setImage(resized_img, 0, 0, feature_indices_);
-        int n_rows = 24 / scale;
-        int n_cols = 24 / scale;
+        int n_rows = (int)(24 / scale);
+        int n_cols = (int)(24 / scale);
         for (int r = 0; r + 24 < resized_img.rows; r += step) {
             for (int c = 0; c + 24 < resized_img.cols; c += step) {
                 eval->setWindow(Point(c, r));
                 if (predict(eval, &h) == +1) {
-                    int row = r / scale;
-                    int col = c / scale;
+                    int row = (int)(r / scale);
+                    int col = (int)(c / scale);
                     bboxes.push_back(Rect(col, row, n_cols, n_rows));
                     confidences.push_back(h);
                 }
@@ -233,7 +233,7 @@ void WaldBoost::fit(Mat& data_pos, Mat& data_neg)
             compute_cdf(data_pos.row(feat_i), pos_weights, pos_cdf);
             compute_cdf(data_neg.row(feat_i), neg_weights, neg_cdf);
 
-            float neg_total = sum(neg_weights)[0];
+            float neg_total = (float)sum(neg_weights)[0];
             Mat1f err_direct = pos_cdf + neg_total - neg_cdf;
             Mat1f err_backward = 1.0f - err_direct;
 
@@ -265,7 +265,7 @@ void WaldBoost::fit(Mat& data_pos, Mat& data_neg)
         }
 
 
-        float alpha = .5f * log((1 - min_err) / min_err);
+        float alpha = .5f * (float)log((1 - min_err) / min_err);
         alphas_.push_back(alpha);
         feature_indices_.push_back(min_feature_ind);
         thresholds_.push_back(min_threshold);