OpenCV调用caffe版本MobilenetSSD

转载地址
前言

当前，在目标检测领域，基于深度学习的目标检测方法在准确度上碾压传统的方法。基于深度学习的目标检测先后出现了RCNN,FastRCNN,FasterRCNN, 端到端目标检测方法YOLO,YOLO-9000,YOLO-v3, MobileNet-SSD,以及Mask-RCNN等。MobileNet是一种轻量级的网络，本文基于MobileNet-SSD+opencv实现目标检测。

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开发环境

• windows10 x64
• IntellJ IDEA
• opencv3.4.2
• Visual Studio 2017(测试C++版本MobileNet-SSD)

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MobileNet-SSD简介

MobileNet-SSD caffe

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opencv调用MobileNet-SSD

C++版本MobileNet-SSD的运行

目前MobileNet有基于caffe框架训练好的，caffe本身就是C++实现的，因此网上的大部分opencv调用MobileNet都是C++代码。本人先采用vs+opencv3.4.1成功测试之后，再用Java代码进行移植。

Visual Stuido 2017配置opencv过程就不赘述了
MobileNet-SSD训练好的caffe模型在上面的MobileNet-SSD caffe链接下载

#include
#include
#include
using namespace std;
using namespace cv;
using namespace cv::dnn;
class Object
{
public:
    Object();
    Object(int index, float confidence, String name, Rect rect);
    ~Object();
public:
    int index;
    String name;
    float confidence;
    Rect rect;
private:
};
Object::Object() {
}
Object::Object(int index,float confidence,String name,Rect rect) {
    this->index = index;
    this->confidence = confidence;
    this->name = name;
    this->rect = rect;
}
Object::~Object() {
}
//----------------------------全局常量----------------------------------
//配置好protxt文件，网络结构描述文件
//配置好caffemodel文件，训练好的网络权重
const String PROTOTX_FILE ="MobileNetSSD\\MobileNetSSD_deploy.prototxt";
const String CAFFE_MODEL_FILE = "MobileNet-SSD\\MobileNetSSD_deploy.caffemodel";
const String classNames[] = { "background", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair",
"cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor" };
const float CONF_THRESH = 0.7f;
int main() {
    //------------------------实例化网络----------------------------
    Net mobileNetSSD = readNetFromCaffe(PROTOTX_FILE, CAFFE_MODEL_FILE);
    if (mobileNetSSD.empty()) {
        cerr << "加载网络失败！" << endl;
        return -1;
    }
    TickMeter t;
    //----------------------设置网络输入-----------------------
    Mat srcImg = imread("D:\\小可爱\\Java学习\\day-6\\代码\\opencv调用MobileNet-SSD\\MobileNet-test.jpg");
    //将二维图像转换为CNN输入的张量Tensor,作为网络的输入
    mobileNetSSD.setInput(blobFromImage(srcImg, 1.0 / 127.5, Size(300, 300), Scalar(127.5, 127.5, 127.5), true, false));
    t.start();
    //--------------------CNN网络前向计算----------------------
    Mat netOut = mobileNetSSD.forward();
    t.stop();
    cout << "检测时间=" << t.getTimeMilli() << "ms" << endl;
    //----------------------解析计算结果-----------------------
    vector