opencv在3.0之后就支持调用深度学习模型。OpenCV dnn模块目前支持Caffe、TensorFlow、Torch、PyTorch等深度学习框架。另外，新版本中使用预训练深度学习模型的API同时兼容C++和Python。
参照官方教程进行一个分类模型的调用caffe模型进行分类，Load Caffe framework models。注意版本，我的版本是3.4.1，选择对应版本。
image.png
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在opencv的解压文件下面有sample/dnn/所有的例子。这个例子来自sample/dnn//caffe_googlenet.cpp
3.也要下载 synset_words.txt.
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把上面三个文件放在与caffe_googlenet.cpp同级目录。
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新建工程把caffe_googlenet.cpp添加进去。
caffe_googlenet.cpp 代码
#include <opencv2/dnn.hpp>
#include <opencv2/imgproc.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/core/utils/trace.hpp>
using namespace cv;
using namespace cv::dnn;
#include <fstream>
#include <iostream>
#include <cstdlib>
using namespace std;
/* Find best class for the blob (i. e. class with maximal probability) */
static void getMaxClass(const Mat &probBlob, int *classId, double *classProb)
{
Mat probMat = probBlob.reshape(1, 1); //reshape the blob to 1x1000 matrix
Point classNumber;
minMaxLoc(probMat, NULL, classProb, NULL, &classNumber);
*classId = classNumber.x;
}
static std::vector<String> readClassNames(const char *filename)
{
std::vector<String> 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;
}
const char* params
= "{ help | false | Sample app for loading googlenet model }"
"{ proto | bvlc_googlenet.prototxt | model configuration }"
"{ model | bvlc_googlenet.caffemodel | model weights }"
"{ label | classification_classes_ILSVRC2012.txt | names of ILSVRC2012 classes }"
"{ image | space_shuttle.jpg | path to image file }"
"{ opencl | false | enable OpenCL }"
;
int main(int argc, char **argv)
{
CV_TRACE_FUNCTION();
CommandLineParser parser(argc, argv, params);
if (parser.get<bool>("help"))
{
parser.printMessage();
return 0;
}
String modelTxt = parser.get<string>("proto");
String modelBin = parser.get<string>("model");
String imageFile = parser.get<String>("image");
String classNameFile = parser.get<String>("label");
Net net;
try {
//! [Read and initialize network]
net = dnn::readNetFromCaffe(modelTxt, modelBin);
//! [Read and initialize network]
}
catch (const cv::Exception& e) {
std::cerr << "Exception: " << e.what() << std::endl;
//! [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]
}
if (parser.get<bool>("opencl"))
{
net.setPreferableTarget(DNN_TARGET_OPENCL);
}
//! [Prepare blob]
Mat img = imread(imageFile);
if (img.empty())
{
std::cerr << "Can't read image from the file: " << imageFile << std::endl;
exit(-1);
}
//GoogLeNet accepts only 224x224 BGR-images
Mat inputBlob = blobFromImage(img, 1.0f, Size(224, 224),
Scalar(104, 117, 123), false); //Convert Mat to batch of images
//! [Prepare blob]
net.setInput(inputBlob, "data"); //set the network input
Mat prob = net.forward("prob"); //compute output
cv::TickMeter t;
for (int i = 0; i < 10; i++)
{
CV_TRACE_REGION("forward");
//! [Set input blob]
net.setInput(inputBlob, "data"); //set the network input
//! [Set input blob]
t.start();
//! [Make forward pass]
prob = net.forward("prob"); //compute output
//! [Make forward pass]
t.stop();
}
//! [Gather output]
int classId;
double classProb;
getMaxClass(prob, &classId, &classProb);//find the best class
//! [Gather output]
//! [Print results]
std::vector<String> classNames = readClassNames(classNameFile.c_str());
std::cout << "Best class: #" << classId << " '" << classNames.at(classId) << "'" << std::endl;
std::cout << "Probability: " << classProb * 100 << "%" << std::endl;
//! [Print results]
std::cout << "Time: " << (double)t.getTimeMilli() / t.getCounter() << " ms (average from " << t.getCounter() << " iterations)" << std::endl;
// input samething
int a;
std::cin >> a;
return 0;
} //main
按照对应的版本使用例子，一般不会出错。
这里有几个比较重要的参数，需改对应的参数就可进行不同图片的分类了。
const char* params
= "{ help | false | Sample app for loading googlenet model }"
"{ proto | bvlc_googlenet.prototxt | model configuration }"
"{ model | bvlc_googlenet.caffemodel | model weights }"
"{ label | synset_words.txt | names of ILSVRC2012 classes }"
"{ image | space_shuttle.jpg | path to image file }"
"{ opencl | false | enable OpenCL }"
proto: caffe的配置文件
model：caffe的参数文件
image：图片
opencl：是否使用opencl进行加速
result
Attempting to upgrade input file specified using deprecated V1LayerParameter: bvlc_googlenet.caffemodel
Successfully upgraded file specified using deprecated V1LayerParameter
[ INFO:0] Initialize OpenCL runtime...
Net Outputs(1):
prob
Best class: #812 'shuttle'
Probability: 99.993%
Time: 1492.05 ms (average from 10 iterations)
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