Cmake编译OpenCV3.1源码+运行自带的dnn分类识别
深度学习作为今年来一个新兴的研究方向，如今真的是不要太火；而OpenCV作为一个久负盛名的开源视觉处理库，也一直被用在卷积神经网络的开源工具——caffe中，用来处理图像。而OpenCV再进入3.0时代以后，也顺应民意加入了DNN模块，能够与caffe无缝对接！
1、要想在OpenCV中运行dnn，首先得编译OpenCV3以上版本的源码，因为dnn模块封装在OpenCV的contrib库中，本博客选用的是OpenCV-3.1，采用的配置是VS2013加OpenCV-3.1，需要下载contrib库，然后在cmake中，
在“开始”菜单中点击“CMake (cmake-gui)”，打开CMake程序，此时将弹出编译设置界面。如下图所示
然后点击configure，选择与你对应的vs版本，然后很快就会配置完成；别急，接下来这步很重要，如下图，找到OPENCV_EXTRA_MODULES_PATH，然后再后面添加contrib的路径，一直到contrib（例如：F:/OpenCV/opencv-3.1.0/opencv_contrib-master/opencv_contrib-master/modules）,然后再次点击Configure，完成后点击Generate，就可以生成vs工程了。注意：在这过程中可能会出现一个ocl_text的错误，在搜索框找到text，然后取消后面的对勾，再次Generate就可以了。还有一个需要注意的地方：这Configure的时候选择对应的平台VS2012对应的是Win32，而VS2012 X64对应的是X64，这个和你最后生成的sln有影响，意味着每次只能生成X86的OpenCV或者是X64的版本，而不能在OpenCV.sln中通过切换同时生成两个！
2、编译源码，打开OPENCV.sln，然后rebuild所有项目，等待，大概30分钟左右，可能因每个人电脑性能不同，时间会不同，然后编译生成，右击install，仅重新生成install，然后就编译完成了。这中间可能会有的dll无法生成，因为不影响我的工作，就没再管了！如下图，源码编译的工作就完成了！
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3、接下来就是使用了，其实很简单，新建一个控制台工程，然后配置OpenCV。将
opencv_contrib\modules\dnn\samples目录下的4个文件拷贝至项目文件夹内覆盖。至于model文件bvlc_googlenet.caffemodel需要单独下载，即训练好的分类器模型。
4、生成成功后，就可以按F5运行了，但是可能会报一个OpenCL的错误，这里，我卡了好久，后来在于在OpenCV论坛的一个评论中搜到解决方法，因为是intel的显卡和cuda有冲突，需要关闭OpenCL。这里我把整个程序贴上，读者只需要复制到工程中就可以运行了！
/**M///////////////////////////////////////////////////////////////////////////////////////
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//M*/
#include
#include
#include
using namespace cv;
using namespace cv::dnn;
#include
#include
#include
#include
using namespace std;
/* Find best class for the blob (i. e. class with maximal probability) */
void getMaxClass(dnn::Blob &probBlob, int *classId, double *classProb)
{
Mat probMat = probBlob.matRefConst().reshape(1, 1); //reshape the blob to 1x1000 matrix
Point classNumber;
minMaxLoc(probMat, NULL, classProb, NULL, &classNumber);
*classId = classNumber.x;
}
std::vector readClassNames(const char *filename = "synset_words.txt")
{
std::vector classNames;
std::ifstream fp(filename);
if (!fp.is_open())
{
std::cerr << "File with classes labels not found: " << filename << std::endl;
exit(-1);
}
std::string name;
while (!fp.eof())
{
std::getline(fp, name);
if (name.length())
classNames.push_back( name.substr(name.find(' ')+1) );
}
fp.close();
return classNames;
}
int main(int argc, char **argv)
{
cv::dnn::initModule(); //Required if OpenCV is built as static libs
ocl::setUseOpenCL(false);//关闭OpenCL，就不会出错了
String modelTxt = "bvlc_googlenet.prototxt";
String modelBin = "bvlc_googlenet.caffemodel";
String imageFile = (argc > 1) ? argv[1] : "space_shuttle.jpg";
//! [Read and initialize network]
Net net = dnn::readNetFromCaffe(modelTxt, modelBin);
//! [Read and initialize network]
//! [Check that network was read successfully]
if (net.empty())
{
std::cerr << "Can't load network by using the following files: " << std::endl;
std::cerr << "prototxt: " << modelTxt << std::endl;
std::cerr << "caffemodel: " << modelBin << std::endl;
std::cerr << "bvlc_googlenet.caffemodel can be downloaded here:" << std::endl;
std::cerr << "http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel" << std::endl;
exit(-1);
}
//! [Check that network was read successfully]
//! [Prepare blob]
Mat img = imread(imageFile);
if (img.empty())
{
std::cerr << "Can't read image from the file: " << imageFile << std::endl;
exit(-1);
}
resize(img, img, Size(224, 224)); //GoogLeNet accepts only 224x224 RGB-images
dnn::Blob inputBlob = dnn::Blob::fromImages(img); //Convert Mat to dnn::Blob batch of images
//! [Prepare blob]
//! [Set input blob]
net.setBlob(".data", inputBlob); //set the network input
//! [Set input blob]
//! [Make forward pass]
net.forward(); // //compute output
//! [Make forward pass]
//! [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
//! [Gather output]
//! [Print results]
std::vector classNames = readClassNames();
std::cout << "Best class: #" << classId << " '" << classNames.at(classId) << "'" << std::endl;
std::cout << "Probability: " << classProb * 100 << "%" << std::endl;
//! [Print results]
return 0;
} //main
