opencv3.3调用 caffe mnist 模型进行手写数字的分类（该博主的博客有很多关于OpenCV DNN模块的文章）

转载链接：https://blog.csdn.net/hust_bochu_xuchao/article/details/78931972

上一篇博文，正确跑通了mnist数据集，得到了lenet_iter_5000.caffemodel  lenet_iter_5000.solverstate  lenet_iter_10000.caffemodel  lenet_iter_10000.solverstate 四个文件

一、按照下面步骤修改caffe-master\examples\mnist文件夹中的  lenet_train_test.prototxt 文件

1、去掉数据输入层，即将top 为 “data” 的layers 去掉。 即将下面的内容删掉   

layer {
name: "mnist"
type: "Data"
top: "data"
top: "label"
include {
phase: TRAIN
}
transform_param {
scale: 0.00390625
}
data_param {
source: "examples/mnist/mnist_train_lmdb"
batch_size: 64
backend: LMDB
}
}
layer {
name: "mnist"
type: "Data"
top: "data"
top: "label"
include {
phase: TEST
}
transform_param {
scale: 0.00390625
}
data_param {
source: "examples/mnist/mnist_test_lmdb"
batch_size: 100
backend: LMDB
}
}

2、重新建立输入，即添加下面的内容

input: "data"
input_shape {
dim: 1 # batchsize
dim: 1 # number of channels
dim: 28 # width
dim: 28 # height
}

3、去掉输出层，即将bottom 为 “label” 的layers 去掉，即将下面的内容删掉

layer {
name: "accuracy"
type: "Accuracy"
bottom: "ip2"
bottom: "label"
top: "accuracy"
include {
phase: TEST
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "ip2"
bottom: "label"
top: "loss"
}

4、重新建立输出，即添加下面的内容

layer {
name: "prob"
type: "Softmax"
bottom: "ip2"
top: "prob"
}

二、使用cv::dnn 里的API加载model, 输入图片，进行测试

#include <iostream>
#include <opencv2/opencv.hpp>
#include <opencv2/dnn.hpp>

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()
{
string modelTxt = "lenet_train_test.prototxt";
string modelBin = "lenet_iter_10000.caffemodel";
//read image
Mat img = imread( "4.bmp", 0);

Mat inputBlob = dnn::blobFromImage(img, 0.00390625f, Size( 28, 28), Scalar(), false); //Convert Mat to batch of images
dnn::Net net;
try{
net = dnn::readNetFromCaffe(modelTxt, modelBin);
}
catch (cv::Exception &ee){
cerr << "Exception: " << ee.what() << endl;
if (net.empty()){
cout << "Can't load the network by using the flowing files:" << endl;
cout << "modelTxt: " << modelTxt << endl;
cout << "modelBin: " << modelBin << endl; exit( -1);
}
}
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 << pred << endl; int classId; double classProb; getMaxClass(pred, &classId, &classProb);
cout << "Best Class: " << classId << endl;
cout << "Probability: " << classProb * 100 << "%" << endl;
}

三、参考博文

http://blog.csdn.net/sushiqian/article/details/78555891
https://www.cnblogs.com/messier/p/7997951.html#_caption_1

https://docs.opencv.org/master/d5/de7/tutorial_dnn_googlenet.html

工程代码及caffemodel的下载