dnn: fix build warnings

modules/dnn/include/opencv2/dnn/shape_utils.hpp

@@ -60,7 +60,7 @@ inline std::ostream &operator<< (std::ostream &s, cv::Range &r)
 struct _Range : public cv::Range
 {
     _Range(const Range &r) : cv::Range(r) {}
-    _Range(int start, int size = 1) : cv::Range(start, start + size) {}
+    _Range(int start_, int size_ = 1) : cv::Range(start_, start_ + size_) {}
 };
 
 static inline Mat slice(const Mat &m, const _Range &r0)
@@ -148,13 +148,13 @@ static inline MatShape shape(int a0, int a1=-1, int a2=-1, int a3=-1)
 static inline int total(const MatShape& shape, int start = -1, int end = -1)
 {
     if (start == -1) start = 0;
-    if (end == -1) end = shape.size();
+    if (end == -1) end = (int)shape.size();
 
     if (shape.empty())
         return 0;
 
     int elems = 1;
-    CV_Assert(start < shape.size() && end <= shape.size() &&
+    CV_Assert(start < (int)shape.size() && end <= (int)shape.size() &&
               start <= end);
     for(int i = start; i < end; i++)
     {
@@ -187,7 +187,7 @@ inline int clamp(int ax, int dims)
 
 inline int clamp(int ax, const MatShape& shape)
 {
-    return clamp(ax, shape.size());
+    return clamp(ax, (int)shape.size());
 }
 
 }

samples/dnn/caffe_googlenet.cpp

@@ -50,7 +50,7 @@ using namespace cv::dnn;
 using namespace std;
 
 /* Find best class for the blob (i. e. class with maximal probability) */
-void getMaxClass(const Mat &probBlob, int *classId, double *classProb)
+static void getMaxClass(const Mat &probBlob, int *classId, double *classProb)
 {
     Mat probMat = probBlob.reshape(1, 1); //reshape the blob to 1x1000 matrix
     Point classNumber;
@@ -59,7 +59,7 @@ void getMaxClass(const Mat &probBlob, int *classId, double *classProb)
     *classId = classNumber.x;
 }
 
-std::vector<String> readClassNames(const char *filename = "synset_words.txt")
+static std::vector<String> readClassNames(const char *filename = "synset_words.txt")
 {
     std::vector<String> classNames;
 

samples/dnn/fcn_semsegm.cpp

@@ -33,9 +33,9 @@ static vector<cv::Vec3b> readColors(const string &filename = "pascal-classes.txt
             string name; ss >> name;
             int temp;
             cv::Vec3b color;
-            ss >> temp; color[0] = temp;
-            ss >> temp; color[1] = temp;
-            ss >> temp; color[2] = temp;
+            ss >> temp; color[0] = (uchar)temp;
+            ss >> temp; color[1] = (uchar)temp;
+            ss >> temp; color[2] = (uchar)temp;
             colors.push_back(color);
         }
     }
@@ -64,7 +64,7 @@ static void colorizeSegmentation(const Mat &score, const vector<cv::Vec3b> &colo
                 if (ptrScore[col] > ptrMaxVal[col])
                 {
                     ptrMaxVal[col] = ptrScore[col];
-                    ptrMaxCl[col] = ch;
+                    ptrMaxCl[col] = (uchar)ch;
                 }
             }
         }

samples/dnn/squeezenet_halide.cpp

@@ -19,7 +19,7 @@ using namespace cv::dnn;
 #include <cstdlib>
 
 /* Find best class for the blob (i. e. class with maximal probability) */
-void getMaxClass(const Mat &probBlob, int *classId, double *classProb)
+static void getMaxClass(const Mat &probBlob, int *classId, double *classProb)
 {
     Mat probMat = probBlob.reshape(1, 1); //reshape the blob to 1x1000 matrix
     Point classNumber;
@@ -28,7 +28,7 @@ void getMaxClass(const Mat &probBlob, int *classId, double *classProb)
     *classId = classNumber.x;
 }
 
-std::vector<std::string> readClassNames(const char *filename = "synset_words.txt")
+static std::vector<std::string> readClassNames(const char *filename = "synset_words.txt")
 {
     std::vector<std::string> classNames;
 

samples/dnn/ssd_object_detection.cpp

@@ -13,22 +13,22 @@ using namespace std;
 const size_t width = 300;
 const size_t height = 300;
 
-Mat getMean(const size_t& imageHeight, const size_t& imageWidth)
+static Mat getMean(const size_t& imageHeight, const size_t& imageWidth)
 {
     Mat mean;
 
     const int meanValues[3] = {104, 117, 123};
     vector<Mat> meanChannels;
-    for(size_t i = 0; i < 3; i++)
+    for(int i = 0; i < 3; i++)
     {
-        Mat channel(imageHeight, imageWidth, CV_32F, Scalar(meanValues[i]));
+        Mat channel((int)imageHeight, (int)imageWidth, CV_32F, Scalar(meanValues[i]));
         meanChannels.push_back(channel);
     }
     cv::merge(meanChannels, mean);
     return mean;
 }
 
-Mat preprocess(const Mat& frame)
+static Mat preprocess(const Mat& frame)
 {
     Mat preprocessed;
     frame.convertTo(preprocessed, CV_32F);
@@ -124,7 +124,7 @@ int main(int argc, char** argv)
 
         if(confidence > confidenceThreshold)
         {
-            size_t objectClass = detectionMat.at<float>(i, 1);
+            size_t objectClass = (size_t)(detectionMat.at<float>(i, 1));
 
             float xLeftBottom = detectionMat.at<float>(i, 3) * frame.cols;
             float yLeftBottom = detectionMat.at<float>(i, 4) * frame.rows;
@@ -139,9 +139,9 @@ int main(int argc, char** argv)
                       << " " << xRightTop
                       << " " << yRightTop << std::endl;
 
-            Rect object(xLeftBottom, yLeftBottom,
-                        xRightTop - xLeftBottom,
-                        yRightTop - yLeftBottom);
+            Rect object((int)xLeftBottom, (int)yLeftBottom,
+                        (int)(xRightTop - xLeftBottom),
+                        (int)(yRightTop - yLeftBottom));
 
             rectangle(frame, object, Scalar(0, 255, 0));
         }

samples/dnn/torch_enet.cpp

@@ -141,7 +141,7 @@ static void colorizeSegmentation(const Mat &score, Mat &segm, Mat &legend, vecto
                 if (ptrScore[col] > ptrMaxVal[col])
                 {
                     ptrMaxVal[col] = ptrScore[col];
-                    ptrMaxCl[col] = ch;
+                    ptrMaxCl[col] = (uchar)ch;
                 }
             }
         }
@@ -161,8 +161,8 @@ static void colorizeSegmentation(const Mat &score, Mat &segm, Mat &legend, vecto
     if (classNames.size() == colors.size())
     {
         int blockHeight = 30;
-        legend.create(blockHeight*classNames.size(), 200, CV_8UC3);
-        for(int i = 0; i < classNames.size(); i++)
+        legend.create(blockHeight*(int)classNames.size(), 200, CV_8UC3);
+        for(int i = 0; i < (int)classNames.size(); i++)
         {
             cv::Mat block = legend.rowRange(i*blockHeight, (i+1)*blockHeight);
             block = colors[i];
@@ -194,9 +194,9 @@ static vector<Vec3b> readColors(const String &filename, vector<String>& classNam
             string name; ss >> name;
             int temp;
             cv::Vec3b color;
-            ss >> temp; color[0] = temp;
-            ss >> temp; color[1] = temp;
-            ss >> temp; color[2] = temp;
+            ss >> temp; color[0] = (uchar)temp;
+            ss >> temp; color[1] = (uchar)temp;
+            ss >> temp; color[2] = (uchar)temp;
             classNames.push_back(name);
             colors.push_back(color);
         }