Changed Caffe googlenet tutorial

modules/dnn/samples/caffe_googlenet.cpp

@@ -1,4 +1,4 @@
-/*M///////////////////////////////////////////////////////////////////////////////////////
+/**M///////////////////////////////////////////////////////////////////////////////////////
 //
 //  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
 //
@@ -38,14 +38,12 @@
 // the use of this software, even if advised of the possibility of such damage.
 //
 //M*/
-
 #include <opencv2/dnn.hpp>
 #include <opencv2/imgproc.hpp>
 #include <opencv2/highgui.hpp>
 using namespace cv;
 using namespace cv::dnn;
 
-#include <algorithm>
 #include <fstream>
 #include <iostream>
 #include <cstdlib>
@@ -68,8 +66,7 @@ std::vector<String> readClassNames(const char *filename = "synset_words.txt")
     std::ifstream fp(filename);
     if (!fp.is_open())
     {
-        std::cerr << "File with classes labels not found" << std::endl;
-        std::cerr << "Check it: " << filename << std::endl;
+        std::cerr << "File with classes labels not found: " << filename << std::endl;
         exit(-1);
     }
 
@@ -89,19 +86,19 @@ int main(int argc, char **argv)
 {
     String modelTxt = "bvlc_googlenet.prototxt";
     String modelBin = "bvlc_googlenet.caffemodel";
+    String imageFile = (argc > 1) ? argv[1] : "space_shuttle.jpg";
 
-    //! [importer_creation]
+    //! [Create the importer of Caffe model]
     Ptr<dnn::Importer> importer;
-    try //Try to import Caffe GoogleNet model
+    try                                     //Try to import Caffe GoogleNet model
     {
         importer = dnn::createCaffeImporter(modelTxt, modelBin);
     }
-    catch (const cv::Exception &err) //importer can throw errors, we will catch them
+    catch (const cv::Exception &err)        //Importer can throw errors, we will catch them
     {
         std::cerr << err.msg << std::endl;
-        importer = Ptr<Importer>(); //NULL
     }
-    //! [importer_creation]
+    //! [Create the importer of Caffe model]
 
     if (!importer)
     {
@@ -113,52 +110,45 @@ int main(int argc, char **argv)
         exit(-1);
     }
 
-    //! [network_initialization]
+    //! [Initialize network]
     dnn::Net net;
     importer->populateNet(net);
+    importer.release();                     //We don't need importer anymore
+    //! [Initialize network]
 
-    delete importer;
-    //! [network_initialization]
-
-
-    String imagefile = (argc > 1) ? argv[1] : "space_shuttle.jpg";
-
-    //! [input_blob_preparation]
-    Mat img = imread(imagefile);
+    //! [Prepare blob]
+    Mat img = imread(imageFile);
     if (img.empty())
     {
-        std::cerr << "Can't read image from the file: " << imagefile << std::endl;
+        std::cerr << "Can't read image from the file: " << imageFile << std::endl;
         exit(-1);
     }
 
-    //GoogLeNet accepts only 224x224 RGB-images
-    cvtColor(img, img, COLOR_BGR2RGB);
-    resize(img, img, Size(224, 224));
-
-    dnn::Blob inputBlob = dnn::Blob(img);
-    //! [input_blob_preparation]
+    resize(img, img, Size(224, 224));       //GoogLeNet accepts only 224x224 RGB-images
+    dnn::Blob inputBlob = dnn::Blob(img);   //Convert Mat to dnn::Blob image batch
+    //! [Prepare blob]
 
-    //! [setup_blob]
-    net.setBlob(".data", inputBlob);            //set the network input
-    //! [setup_blob]
+    //! [Set input blob]
+    net.setBlob(".data", inputBlob);        //set the network input
+    //! [Set input blob]
 
-    //! [make_forward]
-    net.forward();                              //compute output
-    //! [make_forward]
+    //! [Make forward pass]
+    net.forward();                          //compute output
+    //! [Make forward pass]
 
-    //! [get_output]
-    dnn::Blob prob = net.getBlob("prob");       //gather output of "prob" layer
+    //! [Gather output]
+    dnn::Blob prob = net.getBlob("prob");   //gather output of "prob" layer
 
     int classId;
     double classProb;
-    getMaxClass(prob, &classId, &classProb);    //find the best class
-    //! [get_output]
+    getMaxClass(prob, &classId, &classProb);//find the best class
+    //! [Gather output]
 
-    //! [print_info]
+    //! [Print results]
     std::vector<String> classNames = readClassNames();
     std::cout << "Best class: #" << classId << " '" << classNames.at(classId) << "'" << std::endl;
     std::cout << "Probability: " << classProb * 100 << "%" << std::endl;
-    //! [print_info]
+    //! [Print results]
 
     return 0;
-}
+} //main

modules/dnn/tutorials/tutorial_dnn_googlenet.markdown

@@ -15,6 +15,8 @@ Source Code
 
 We will be using snippets from the example application, that can be downloaded [here](https://github.com/ludv1x/opencv_contrib/blob/master/modules/dnn/samples/caffe_googlenet.cpp).
 
+@include dnn/samples/caffe_googlenet.cpp
+
 Explanation
 -----------
 
@@ -28,13 +30,13 @@ Explanation
    Put these files into working dir of this program example.
 
 -# Create the importer of Caffe models
-   @snippet dnn/samples/caffe_googlenet.cpp importer_creation
+   @snippet dnn/samples/caffe_googlenet.cpp Create the importer of Caffe model
 
 -# Create the network and initialize its by using the created importer
-   @snippet dnn/samples/caffe_googlenet.cpp network_initialization
+   @snippet dnn/samples/caffe_googlenet.cpp Initialize network
 
 -# Read input image and convert to the blob, acceptable by GoogleNet
-   @snippet dnn/samples/caffe_googlenet.cpp input_blob_preparation
+   @snippet dnn/samples/caffe_googlenet.cpp Prepare blob
    Firstly, we resize the image and change its channel sequence order.
 
    Now image is actually a 3-dimensional array with 224x224x3 shape.
@@ -42,22 +44,22 @@ Explanation
    Next, we convert the image to 4-dimensional blob (so-called batch) with 1x2x224x224 shape by using special @ref cv::dnn::Blob constructor.
 
 -# Pass the blob to the network
-   @snippet dnn/samples/caffe_googlenet.cpp setup_blob
+   @snippet dnn/samples/caffe_googlenet.cpp Set input blob
    In bvlc_googlenet.prototxt the network input blob named as "data", therefore this blob labeled as ".data" in opencv_dnn API.
 
    Other blobs labeled as "name_of_layer.name_of_layer_output".
 
 -# Make forward pass
-   @snippet dnn/samples/caffe_googlenet.cpp make_forward
+   @snippet dnn/samples/caffe_googlenet.cpp Make forward pass
    During the forward pass output of each network layer is computed, but in this example we need output from "prob" layer only.
 
 -# Determine the best class
-   @snippet dnn/samples/caffe_googlenet.cpp get_output
+   @snippet dnn/samples/caffe_googlenet.cpp Gather output
    We put the output of "prob" layer, which contain probabilities for each of 1000 ILSVRC2012 image classes, to the `prob` blob.
    And find the index of element with maximal value in this one. This index correspond to the class of the image.
 
 -# Print results
-   @snippet dnn/samples/caffe_googlenet.cpp print_info
+   @snippet dnn/samples/caffe_googlenet.cpp Print results
    For our image we get:
 > Best class: #812 'space shuttle'
 >