将mnist训练的caffemodel生成动态链接库DLL

在项目程序中经常看到动态链接库，非常好奇，想自己实现一下，于是乎尝试一波。就因为这种好奇，每天都被bug所困扰。。。

在windows环境下搭建caffe无果，转投Ubuntu。。。

用的caffe--example--mnist中的文件，新建文件夹的话注意改路径，下面为train.sh

#!/usr/bin/env sh
set -e
/home/fish/caffe/build/tools/caffe train --solver=/home/fish/STUDY/lenet_solver.prototxt

 训练好后把lenet_train_test.prototxt和训练好的模型lenet_iter_10000.caffemodel拿出来。

根据文章https://blog.csdn.net/sushiqian/article/details/78555891，修改模型文件。若图片为白底黑字，bitwise_not一下。

#include
#include
#include
using namespace std;
using namespace cv;
using namespace cv::dnn;
/* 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);
Point classNumber;
minMaxLoc(probMat, NULL, classProb, NULL, &classNumber);
*classId = classNumber.x;
}
int main(int argc, char* argv[])
{
string modelTxt = "C:\\Users\\ATWER\\Desktop\\lenet_train_test.prototxt";
string modelBin = "C:\\Users\\ATWER\\Desktop\\lenet_iter_10000.caffemodel";
string imgFileName = "C:\\Users\\ATWER\\Desktop\\9.png";
//read image
Mat imgSrc = imread(imgFileName);
if (imgSrc.empty()) {
cout <<"Failed to read image " < exit(-1);
}
Mat img;
cvtColor(imgSrc, img, COLOR_BGR2GRAY);
//LeNet accepts 28*28 gray image
resize(img, img, Size(28, 28));
bitwise_not(img, img);
img /= 255;
//transfer image(1*28*28) to blob data with 4 dimensions(1*1*28*28)
Mat inputBlob = dnn::blobFromImage(img);
dnn::Net net;
try {
net = dnn::readNetFromCaffe(modelTxt, modelBin);
}
catch (cv::Exception& ee) {
cerr <<"Exception: " < if (net.empty()) {
cout <<"Can't load the network by using the flowing files:" < cout <<"modelTxt: " < cout <<"modelBin: " < }
}
Mat pred;
net.setInput(inputBlob, "data");//set the network input, "data" is the name of the input layer
pred = net.forward("prob");//compute output, "prob" is the name of the output layer
cout < cout <<"Best Class: " < cout <<"Probability: " <}

参考https://blog.csdn.net/qq_30139555/article/details/103621955

class.h

#include
#include
#include
using namespace std;
using namespace cv;
using namespace cv::dnn;
extern "C" _declspec(dllexport) void Classfication(char* imgpath, char* result);

在此处卡的最久，原本我写的是Classfication(string imgpath, string result)，生成dll时没问题，调用时总是System.AccessViolationException: 尝试读取或写入受保护的内存。后来发现要写成指针的形式。

class.cpp

#include
#include
#include
#include "class.h"
using namespace std;
using namespace cv;
using namespace cv::dnn;
/* 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);
Point classNumber;
minMaxLoc(probMat, NULL, classProb, NULL, &classNumber);
*classId = classNumber.x;
}
void Classfication(char* imgpath, char* result)
{
string res = "";
string modelTxt = "C:\\Users\\ATWER\\Desktop\\lenet_train_test.prototxt";
string modelBin = "C:\\Users\\ATWER\\Desktop\\lenet_iter_10000.caffemodel";
//string imgFileName = "C:\\Users\\ATWER\\Desktop\\9.png";
string imgFileName = imgpath;
//read image
Mat imgSrc = imread(imgFileName);
if (imgSrc.empty()) {
cout <<"Failed to read image " < exit(-1);
}
Mat img;
cvtColor(imgSrc, img, COLOR_BGR2GRAY);
//LeNet accepts 28*28 gray image
resize(img, img, Size(28, 28));
bitwise_not(img, img);
img /= 255;
//transfer image(1*28*28) to blob data with 4 dimensions(1*1*28*28)
Mat inputBlob = dnn::blobFromImage(img);
dnn::Net net;
try {
net = dnn::readNetFromCaffe(modelTxt, modelBin);
}
catch (cv::Exception& ee) {
cerr <<"Exception: " < if (net.empty()) {
cout <<"Can't load the network by using the flowing files:" < cout <<"modelTxt: " < cout <<"modelBin: " < }
}
Mat pred;
net.setInput(inputBlob, "data");//set the network input, "data" is the name of the input layer
pred = net.forward("prob");//compute output, "prob" is the name of the output layer
int classId;
　　 double classProb;
　　 getMaxClass(pred, &classId, &classProb);
res += to_string(classId);
res += '|';
res += to_string(classProb);
strcpy_s(result, 15, res.c_str());
}
4. 调用动态链接库

根据数据的长度申请非托管空间参考：https://blog.csdn.net/xiaoyong_net/article/details/50178021

文中说：“一定要加1,否则后面是乱码，原因未找到 ”，应该是打印字符串时会打印到“\n”为止，没有遇到\n会一直打印下去。.Length方法没有计算"\n"，+1的空间用于存放“\n”。

using System;
using System.Runtime.InteropServices;
namespace Test
{
class Program
{
[DllImport("E:/c++project/caffedll/x64/Debug/caffedll.dll", EntryPoint = "Classfication")]
unsafe private static extern void Classfication(IntPtr imgpath, IntPtr result);
private static IntPtr mallocIntptr(string strData)
{
//先将字符串转化成字节方式
Byte[] btData = System.Text.Encoding.Default.GetBytes(strData);
//申请非拖管空间
IntPtr m_ptr = Marshal.AllocHGlobal(btData.Length);
//给非拖管空间清０
Byte[] btZero = new Byte[btData.Length + 1]; //一定要加1,否则后面是乱码，原因未找到
Marshal.Copy(btZero, 0, m_ptr, btZero.Length);
//给指针指向的空间赋值
Marshal.Copy(btData, 0, m_ptr, btData.Length);
return m_ptr;
}
private static IntPtr mallocIntptr(int length)
{
//申请非拖管空间
IntPtr m_ptr = Marshal.AllocHGlobal(length);
//给非拖管空间清０
Byte[] btZero = new Byte[length];
Marshal.Copy(btZero, 0, m_ptr, btZero.Length);
//给指针指向的空间赋值
Marshal.Copy(btZero, 0, m_ptr, length);
return m_ptr;
}
static void Main(string[] args)
{
string s = "C:\\Users\\ATWER\\Desktop\\9.png";
IntPtr ptrFileName;
IntPtr res;
//根据数据的长度申请非托管空间
ptrFileName = mallocIntptr(s);
res = mallocIntptr(50);
Classfication(ptrFileName, res);
string result = Marshal.PtrToStringAnsi(res);
string[] a = result.Split('|');
Console.WriteLine("class:"+a[0]+"\n"+"score:"+a[1]);
Marshal.FreeHGlobal(res);
}
}
}