Fixed more warnings. Updated glog_emulator.hpp:

modules/dnn/CMakeLists.txt

@@ -14,6 +14,7 @@ else()
   message(STATUS "PROTOBUF not found. Caffe import function will be disabled.")
   set(HAVE_PROTOBUF OFF)
   set(PROTOBUF_LIBRARIES "")
+  add_definitions(-DHAVE_PROTOBUF=0)
 endif()
 
 endmacro(_dnn_find_protobuf)
@@ -24,7 +25,7 @@ set(OPENCV_MODULE_IS_PART_OF_WORLD OFF)
 _dnn_find_protobuf()
 
 ocv_add_module(dnn opencv_imgproc opencv_core opencv_highgui WRAP python matlab)
-ocv_warnings_disable(CMAKE_CXX_FLAGS -Wno-shadow -Wno-parentheses)
+ocv_warnings_disable(CMAKE_CXX_FLAGS -Wno-shadow -Wno-parentheses -Wmaybe-uninitialized -Wsign-promo -Wmissing-declarations -Wmissing-prototypes)
 ocv_glob_module_sources(${PROTO_SRCS} ${PROTO_HDRS})
 ocv_source_group("Src\\protobuf" FILES ${PROTO_SRCS} ${PROTO_HDRS})
 ocv_module_include_directories(include src/caffe ${PROTOBUF_INCLUDE_DIR})

modules/dnn/README.md

@@ -0,0 +1,2 @@
+Deep Neural Network module
+==========================

modules/dnn/include/opencv2/dnn.hpp

@@ -5,7 +5,14 @@
 // We are free to change headers layout in dnn subfolder, so please include
 // this header for future compartibility
 
+
+/** @defgroup dnn Deep Neural Network module
+  @{
+    This module contain tools to load artifical neural network models and to make forward test passes.
+  @}
+*/
+
 #include <opencv2/dnn/dnn.hpp>
 
 
-#endif /* __OPENCV_DNN_HPP__ */
+#endif /* __OPENCV_DNN_HPP__ */

modules/dnn/samples/alexnet.cpp

@@ -53,6 +53,7 @@ int main(void)
     CV_Assert(!img.empty());
     cvtColor(img, img, COLOR_BGR2RGB);
     img.convertTo(img, CV_32F);
+    resize(img, img, Size(227, 227));
     subtract(img, cv::mean(img), img);
     Blob imgBlob(img);
 

modules/dnn/src/caffe/common.hpp

@@ -19,17 +19,6 @@
 #include <utility>  // pair
 #include <vector>
 
-//// Disable the copy and assignment operator for a class.
-//#define DISABLE_COPY_AND_ASSIGN(classname) \
-//private:\
-//  classname(const classname&);\
-//  classname& operator=(const classname&)
-//
-//// A simple macro to mark codes that are not implemented, so that when the code
-//// is executed we will see a fatal log.
-//#define NOT_IMPLEMENTED LOG(FATAL) << "Not Implemented Yet"
-//
-
 namespace caffe {
 
 // Common functions and classes from std that caffe often uses.

modules/dnn/src/caffe/glog_emulator.hpp

@@ -1,10 +1,11 @@
 #pragma once
 #include <stdlib.h>
 #include <iostream>
+#include <sstream>
 #include <opencv2/core.hpp>
 
 #define CHECK(cond)     cv::GLogWrapper(__FILE__, CV_Func, __LINE__, "CHECK", #cond, cond)
-#define CHECK_EQ(a, b)  cv::GLogWrapper(__FILE__, CV_Func, __LINE__, "CHECK", #a #b, ((a) == (b)))
+#define CHECK_EQ(a, b)  cv::GLogWrapper(__FILE__, CV_Func, __LINE__, "CHECK", #a"="#b, ((a) == (b)))
 #define LOG(TYPE)       cv::GLogWrapper(__FILE__, CV_Func, __LINE__, #TYPE)
 
 namespace cv
@@ -12,18 +13,10 @@ namespace cv
 
 class GLogWrapper
 {
-    const char *type, *cond_str, *file, *func;
+    std::stringstream stream;
+    const char *file, *func, *type, *cond_str;
     int line;
     bool cond_staus;
-    std::ostream &stream;
-
-    static std::ostream &selectStream(const char *type)
-    {
-        if (!strcmp(type, "INFO"))
-            return std::cout;
-        else
-            return std::cerr;
-    }
 
 public:
 
@@ -31,9 +24,8 @@ public:
                 const char *_type,
                 const char *_cond_str = NULL, bool _cond_status = true
                ) :
-               stream(selectStream(_type)),
-               file(_file), func(_func), line(_line),
-               type(_type), cond_str(_cond_str), cond_staus(_cond_status) {}
+               file(_file), func(_func), type(_type), cond_str(_cond_str),
+               line(_line), cond_staus(_cond_status) {}
 
     template<typename T>
     GLogWrapper &operator<<(const T &v)
@@ -47,13 +39,16 @@ public:
     {
         if (cond_str && !cond_staus)
         {
-            cv::error(cv::Error::StsAssert, cond_str, func, file, line);
+            cv::error(cv::Error::StsError, "FAILED: " + String(cond_str) + "." + stream.str(), func, file, line);
+        }
+        else if (!cond_str && strcmp(type, "CHECK"))
+        {
+            if (!strcmp(type, "INFO"))
+                std::cout << stream.str();
+            else
+                std::cerr << stream.str();
         }
-        //else if (!cond_str && strcmp(type, "INFO"))
-        //{
-        //    cv::error(cv::Error::StsAssert, type, func, file, line);
-        //}
     }
 };
 
-}
+}

modules/dnn/src/caffe/util/io.cpp

@@ -1,4 +1,5 @@
-#ifdef HAVE_PROTOBUF
+#if HAVE_PROTOBUF
+#include "io.hpp"
 #include <google/protobuf/io/coded_stream.h>
 #include <google/protobuf/io/zero_copy_stream_impl.h>
 #include <google/protobuf/text_format.h>
@@ -29,7 +30,7 @@ using google::protobuf::Message;
 
 bool ReadProtoFromTextFile(const char* filename, Message* proto) {
     std::ifstream fs(filename, std::ifstream::in);
-    CV_Assert(fs.is_open());
+    CHECK(fs.is_open()) << "Can't open \"" << filename << "\"";
     IstreamInputStream input(&fs);
     bool success = google::protobuf::TextFormat::Parse(&input, proto);
     fs.close();
@@ -47,7 +48,7 @@ bool ReadProtoFromTextFile(const char* filename, Message* proto) {
 //
 bool ReadProtoFromBinaryFile(const char* filename, Message* proto) {
     std::ifstream fs(filename, std::ifstream::in | std::ifstream::binary);
-    CV_Assert(fs.is_open());
+    CHECK(fs.is_open()) << "Can't open \"" << filename << "\"";
     ZeroCopyInputStream* raw_input = new IstreamInputStream(&fs);
     CodedInputStream* coded_input = new CodedInputStream(raw_input);
     coded_input->SetTotalBytesLimit(kProtoReadBytesLimit, 536870912);
@@ -66,4 +67,4 @@ bool ReadProtoFromBinaryFile(const char* filename, Message* proto) {
 //}
 
 }  // namespace caffe
-#endif
+#endif

modules/dnn/src/caffe/util/io.hpp

@@ -1,5 +1,6 @@
 #ifndef CAFFE_UTIL_IO_H_
 #define CAFFE_UTIL_IO_H_
+#if HAVE_PROTOBUF
 
 //instead of GLOG
 #include "../glog_emulator.hpp"
@@ -141,4 +142,5 @@ void CVMatToDatum(const cv::Mat& cv_img, Datum* datum);
 
 }  // namespace caffe
 
+#endif
 #endif   // CAFFE_UTIL_IO_H_

modules/dnn/src/caffe/util/upgrade_proto.cpp

@@ -1,4 +1,4 @@
-#ifdef HAVE_PROTOBUF
+#if HAVE_PROTOBUF
 #include <google/protobuf/io/coded_stream.h>
 #include <google/protobuf/io/zero_copy_stream_impl.h>
 #include <google/protobuf/text_format.h>
@@ -939,4 +939,4 @@ void ReadNetParamsFromBinaryFileOrDie(const string& param_file,
 }
 
 }  // namespace caffe
-#endif
+#endif

modules/dnn/src/caffe/util/upgrade_proto.hpp

@@ -1,5 +1,6 @@
 #ifndef CAFFE_UTIL_UPGRADE_PROTO_H_
 #define CAFFE_UTIL_UPGRADE_PROTO_H_
+#if HAVE_PROTOBUF
 
 #include <string>
 #include "caffe/common.hpp"
@@ -61,4 +62,5 @@ void ReadNetParamsFromBinaryFileOrDie(const string& param_file,
 
 }  // namespace caffe
 
+#endif
 #endif   // CAFFE_UTIL_UPGRADE_PROTO_H_

modules/dnn/src/dnn.cpp

@@ -335,7 +335,7 @@ struct Net::Impl
         }
 
         std::cout << "\nNet Outputs(" << netOutputs.size() << "):\n";
-        for (int i = 0; i < netOutputs.size(); i++)
+        for (size_t i = 0; i < netOutputs.size(); i++)
             std::cout << layers[netOutputs[i]].name << std::endl;
     }
 
@@ -459,10 +459,10 @@ int Net::addLayer(const String &name, const String &type, LayerParams &params)
     return id;
 }
 
-void Net::connect(BlobId input, BlobId output)
-{
+//void Net::connect(BlobId input, BlobId output)
+//{
 
-}
+//}
 
 void Net::setOutputNames(LayerId layer, const std::vector<String> &outputNames)
 {
@@ -605,4 +605,4 @@ Ptr<Layer> LayerRegister::createLayerInstance(const String &_type, LayerParams&
 }
 
 }
-}
+}

modules/dnn/src/layers/convolution_layer.cpp

@@ -50,7 +50,7 @@ namespace dnn
         if (bias)
         {
             Blob &biasBlob = learnedParams[1];
-            CV_Assert(biasBlob.total() == numOutput);
+            CV_Assert(biasBlob.total() == (size_t)numOutput);
         }
     }
 
@@ -159,4 +159,4 @@ namespace dnn
         outW = (inW + 2 * padW - kernelW) / strideW + 1;
     }
 }
-}
+}

modules/dnn/src/layers/fully_connected_layer.cpp

@@ -30,7 +30,7 @@ namespace dnn
         bias = params.get<bool>("bias_term", true);
 
         CV_Assert(params.learnedBlobs.size() >= 1);
-        CV_Assert(!bias || (params.learnedBlobs.size() >= 2 && params.learnedBlobs[1].total() == numOutputs));
+        CV_Assert(!bias || (params.learnedBlobs.size() >= 2 && (int)params.learnedBlobs[1].total() == numOutputs));
 
         learnedParams.resize(bias ? 2 : 1);
         learnedParams[0] = params.learnedBlobs[0];
@@ -49,7 +49,7 @@ namespace dnn
         inW = inputs[0]->cols();
         inSize = inC * inH * inW;
 
-        CV_Assert(inSize * numOutputs == learnedParams[0].total());
+        CV_Assert((size_t)inSize * (size_t)numOutputs == learnedParams[0].total());
 
         outputs.resize(inputs.size());
         for (size_t i = 0; i < inputs.size(); i++)
@@ -84,4 +84,4 @@ namespace dnn
         }
     }
 }
-}
+}