一、参考文档

我的前两篇博客中，分别介绍了Ubuntu16.04下Opencv3.4.0的编译以及Tensorflow C++ API的编译。
这篇文章就来讲一下，如何联调编译以及写对应的Cmakelist.txt文件。

参考链接：

https://blog.csdn.net/qq_30534935/article/details/96023523
https://blog.csdn.net/qq_30534935/article/details/96353857

二、代码编写

来源于：

https://blog.csdn.net/flyconley/article/details/96436323

内容如下，侵删：

#include "tensorflow/core/public/session.h"
#include "tensorflow/cc/ops/standard_ops.h"
#include "tensorflow/core/protobuf/meta_graph.pb.h"

#include <opencv2/highgui/highgui.hpp>
#include <opencv2/opencv.hpp>

using namespace std;
using namespace tensorflow;

GraphDef CreateGraphDef()
{
	Scope root = Scope::NewRootScope();

	auto X = ops::Placeholder(root.WithOpName("x"), DT_FLOAT,
		ops::Placeholder::Shape({ -1, 2 }));
	auto A = ops::Const(root, { { 3.f, 2.f },{ -1.f, 0.f } });
	auto Y = ops::MatMul(root.WithOpName("y"), A, X,
		ops::MatMul::TransposeB(true));

	GraphDef def;
	TF_CHECK_OK(root.ToGraphDef(&def));

	return def;
}


void CVMat_to_Tensor(cv::Mat img, Tensor* output_tensor, int input_rows, int input_cols)
{
	img.convertTo(img, CV_32FC1);
	float *p = output_tensor->flat<float>().data();
	cv::Mat tempMat(input_rows, input_cols, CV_32FC1, p);
	img.convertTo(tempMat, CV_32FC1);
}

int main()
{

	Session* session;
	Status status = NewSession(SessionOptions(), &session);
	if (!status.ok()) {
		std::cout << status.ToString() << std::endl;
	}
	else {
		std::cout << "Session created successfully" << std::endl;
	}

	GraphDef graph_def;
	status = ReadBinaryProto(Env::Default(), "chi.pb", &graph_def);
	if (!status.ok()) {
		std::cout << status.ToString() << std::endl;
	}
	else {
		std::cout << "Load graph protobuf successfully" << std::endl;
	}

	status = session->Create(graph_def);
	if (!status.ok()) {
		std::cout << status.ToString() << std::endl;
	}
	else {
		std::cout << "Add graph to session successfully" << std::endl;
	}

	cv::Mat img = cv::imread("char_33.jpg", 0);
	if (!img.empty()) {
		std::cout << "open the image successfully" << std::endl;
	}

	cv::Mat resize_image;
	int length = img.cols * 32 / float(img.rows);
	cv::resize(img, resize_image, cv::Size(length, 32));
	cv::transpose(resize_image, resize_image);
	cv::imwrite("test.jpg", resize_image);


	Tensor input_tensor(DT_FLOAT, TensorShape({ 1,length,32,1 }));
	CVMat_to_Tensor(resize_image, &input_tensor, length, 32);
	cout << input_tensor.DebugString() << endl;

	Tensor seq_len(DT_INT32, TensorShape({ 1 }));
	int *s = seq_len.flat<int>().data();
	std::vector<int> mydata;
	mydata.push_back (length / 4 - 1);
	copy_n(mydata.begin(), 1, s);      //复制mydata到dst

	cout << seq_len.DebugString() << endl;

	cout << endl << "<-------------Running the model with test_image--------------->" << endl;
	//前向运行，输出结果一定是一个tensor的vector
	vector<tensorflow::Tensor> outputs;
	string output_node = "output";
	Status status_run = session->Run({ { "input", input_tensor },{"seq_len", seq_len} }, { output_node }, {}, &outputs);

	clock_t start, ends;
	start = clock();

	//Tensor input_tensor(DT_FLOAT, TensorShape({ 1,length,32,1 }));
	CVMat_to_Tensor(resize_image, &input_tensor, length, 32);
	status_run = session->Run({ { "input", input_tensor },{ "seq_len", seq_len } }, { output_node }, {}, &outputs);

	ends = clock();
	std::cout << ends - start <<"ms"<< std::endl;

	if (!status_run.ok()) {
		cout << "ERROR: RUN failed..." << std::endl;
		cout << status_run.ToString() << "\n";
		return -1;
	}
	
	std::cout << outputs[0].DebugString() << std::endl;

	outputs[0].flat<tensorflow::int64>().data()[0] ;

	for (int i = 0; i < outputs[0].shape().dim_size(1); ++i)
	{
		std::cout << outputs[0].flat<tensorflow::int64>().data()[i] << " ";
	}
    cout  << endl;
}

三、Cmakelist.txt编写

将参考文档的整合一起即可：

cmake_minimum_required(VERSION 2.8)
project(tftest)
find_package(OpenCV REQUIRED)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -g -std=c++11 -W")

include_directories(
   ./lib
   /home/zhangru/tensorflow
   /home/zhangru/tensorflow/bazel-genfiles
   /home/zhangru/tensorflow/bazel-bin/tensorflow
   /home/zhangru/tensorflow/tensorflow/contrib/makefile/downloads/nsync/public
   /home/zhangru/tensorflow/tensorflow/contrib/makefile/gen/protobuf/include
   /usr/local/include/eigen3
   ) 

add_executable(tftest tfcpptest.cpp)
target_link_libraries(tftest tensorflow_cc tensorflow_framework ${OpenCV_LIBS})

这里请注意这些文件需要放到对应的路径：

usr/local/include/opencv
usr/local/include/opencv2
usr/local/lib/libopecv.so(大约51个）
usr/local/share/OpenCV
home/zhangru/tesorflow ..
usr/local/include/eigen3 ..
usr/local/include/google..

四、程序测试

依次在命令行输入以下代码：

sudo cmake ..
sudo make
sudo ./tftest