【caffe】OpenCV Load caffe model

最新推荐文章于 2021-05-06 18:19:32 发布

原创最新推荐文章于 2021-05-06 18:19:32 发布 · 6k 阅读

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#deep learning #cmake #caffe #opencv #dnn

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本文介绍如何利用OpenCV和Caffe框架实现图像分类任务，详细展示了从加载预训练模型到图像预处理、分类预测的完整流程，并解决了过程中遇到的兼容性问题。

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上一篇，我们介绍了opencv_contrib中的模块在windows下的编译，也提到了其中的dnn模块可以读取caffe的训练模型用于目标检测，这里我们具体介绍一下如何使用dnn读取caffe模型并进行目标分类。

代码如下:(代码主要来自参考[2]和[3]):

#include <opencv2/dnn.hpp>  
#include <opencv2/imgproc.hpp>  
#include <opencv2/highgui.hpp>  

#include <fstream>  
#include <iostream>  
#include <cstdlib>  

/* Find best class for the blob (i. e. class with maximal probability) */
void getMaxClass(cv::dnn::Blob &probBlob, int *classId, double *classProb)
{
	cv::Mat probMat = probBlob.matRefConst().reshape(1, 1); //reshape the blob to 1x1000 matrix  
	cv::Point classNumber;

	cv::minMaxLoc(probMat, NULL, classProb, NULL, &classNumber);
	*classId = classNumber.x;
}

std::vector<cv::String> readClassNames(const char *filename = "synset_words.txt")
{
	std::vector<cv::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;
}

int main(int argc, char **argv)
{
	void cv::dnn::initModule();

	cv::String modelTxt = "bvlc_googlenet.prototxt";
	cv::String modelBin = "bvlc_googlenet.caffemodel";
	cv::String imageFile = "space_shuttle.jpg";

	cv::dnn::Net net = cv::dnn::readNetFromCaffe(modelTxt, modelBin);

	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);
	}

	//! [Prepare blob]  
	cv::Mat img = cv::imread(imageFile, cv::IMREAD_COLOR);
	if (img.empty())
	{
		std::cerr << "Can't read image from the file: " << imageFile << std::endl;
		exit(-1);
	}

	cv::resize(img, img, cv::Size(224, 224));        
	cv::dnn::Blob inputBlob = cv::dnn::Blob(img);   //Convert Mat to dnn::Blob image batch  
	//! [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]  
	cv::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<cv::String> classNames = readClassNames();
	std::cout << "Best class: #" << classId << " '" << classNames.at(classId) << "'" << std::endl;
	std::cout << "Probability: " << classProb * 100 << "%" << std::endl;

	//! [Print results]  

	return 0;
} //main

代码详解 :
1、首先需要下载GoogLeNet模型及分类相关文件，可以从官网下载（或复制粘贴）： bvlc_googlenet.prototxt、bvlc_googlenet.caffemodel以及synset_words.txt.也可以直接下载我长传的打包好的资源（包括了2中的图片）

2、下载待检测图片文件，如下：

Buran space shuttle

3、读取.protxt文件和.caffemodel文件：

cv::dnn::Net net = cv::dnn::readNetFromCaffe(modelTxt, modelBin);

4、检查网络是否读取成功：

	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);
	}

5、读取图片并将其转换成GoogleNet可以读取的blob：

	cv::Mat img = cv::imread(imageFile, cv::IMREAD_COLOR);
	if (img.empty())
	{
		std::cerr << "Can't read image from the file: " << imageFile << std::endl;
		exit(-1);
	}

	cv::resize(img, img, cv::Size(224, 224));        
	cv::dnn::Blob inputBlob = cv::dnn::Blob(img);   //Convert Mat to dnn::Blob image batch

6、将blob传递给网络：

	net.setBlob(".data", inputBlob);        //set the network input

7、前向传递：

	net.forward();                          //compute output

8、分类：

	getMaxClass(prob, &classId, &classProb);//find the best class

9、打印分类结果：

	std::vector<cv::String> classNames = readClassNames();
	std::cout << "Best class: #" << classId << " '" << classNames.at(classId) << "'" << std::endl;
	std::cout << "Probability: " << classProb * 100 << "%" << std::endl;

运行,报错如下: