tensorflowC++接口调用目标检测pb模型代码

#include
#include "tensorflow/cc/ops/const_op.h"
#include "tensorflow/cc/ops/image_ops.h"
#include "tensorflow/cc/ops/standard_ops.h"
#include "tensorflow/core/framework/graph.pb.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/graph/default_device.h"
#include "tensorflow/core/graph/graph_def_builder.h"
#include "tensorflow/core/lib/core/errors.h"
#include "tensorflow/core/lib/core/stringpiece.h"
#include "tensorflow/core/lib/core/threadpool.h"
#include "tensorflow/core/lib/io/path.h"
#include "tensorflow/core/lib/strings/stringprintf.h"
#include "tensorflow/core/platform/env.h"
#include "tensorflow/core/platform/init_main.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/platform/types.h"
#include "tensorflow/core/public/session.h"
#include "tensorflow/core/util/command_line_flags.h"
#include
#include
#include
#include
#include
using namespace std;
using namespace cv;
using namespace tensorflow;

// 定义一个函数讲OpenCV的Mat数据转化为tensor，python里面只要对cv2.read读进来的矩阵进行np.reshape之后，
// 数据类型就成了一个tensor，即tensor与矩阵一样，然后就可以输入到网络的入口了，但是C++版本，我们网络开放的入口
// 也需要将输入图片转化成一个tensor，所以如果用OpenCV读取图片的话，就是一个Mat，然后就要考虑怎么将Mat转化为
// Tensor了
void CVMat_to_Tensor(Mat img,Tensor* output_tensor,int input_rows,int input_cols)
{
//imshow("input image",img);
//图像进行resize处理
resize(img,img,cv::Size(input_cols,input_rows));
//imshow("resized image",img);
//归一化
img.convertTo(img,CV_8UC3); // CV_32FC3
//img=1-img/255;
//创建一个指向tensor的内容的指针
uint8 *p = output_tensor->flat().data();
//创建一个Mat，与tensor的指针绑定,改变这个Mat的值，就相当于改变tensor的值
cv::Mat tempMat(input_rows, input_cols, CV_8UC3, p);
img.convertTo(tempMat,CV_8UC3);
// waitKey(0);

}
int main()
{
/*--------------------------------配置关键信息------------------------------*/
string model_path="../model/coco.pb";
string image_path="../test.jpg";
int input_height = 1000;
int input_width = 1000;
string input_tensor_name="image_tensor";
vector<string> out_put_nodes; //注意，在object detection中输出的三个节点名称为以下三个
out_put_nodes.push_back("detection_scores"); //detection_scores detection_classes detection_boxes
out_put_nodes.push_back("detection_classes");
out_put_nodes.push_back("detection_boxes");
/*--------------------------------创建session------------------------------*/
Session* session;
Status status = NewSession(SessionOptions(), &session);//创建新会话Session
/*--------------------------------从pb文件中读取模型--------------------------------*/
GraphDef graphdef; //Graph Definition for current model

Status status_load = ReadBinaryProto(Env::Default(), model_path, &graphdef); //从pb文件中读取图模型;
if (!status_load.ok()) {
cout <<"ERROR: Loading model failed..." < std::endl;
cout <"\n";
return -1;
}
Status status_create = session->Create(graphdef); //将模型导入会话Session中;
if (!status_create.ok()) {
cout <<"ERROR: Creating graph in session failed..." < std::endl;
return -1;
}
cout <<"<----Successfully created session and load graph.------->"<< endl;
/*---------------------------------载入测试图片-------------------------------------*/
cout<"<------------loading test_image-------------->"<<endl;
Mat img;
img = imread(image_path);
cvtColor(img, img, CV_BGR2RGB);
if(img.empty())
{
cout<<"can‘t open the image!!!!!!!"<<endl;
return -1;
}
//创建一个tensor作为输入网络的接口
Tensor resized_tensor(DT_UINT8, TensorShape({1,input_height,input_width,3})); //DT_FLOAT
//将Opencv的Mat格式的图片存入tensor
CVMat_to_Tensor(img,&resized_tensor,input_height,input_width);
cout <endl;
/*-----------------------------------用网络进行测试-----------------------------------------*/
cout<"<-------------Running the model with test_image--------------->"<<endl;
//前向运行，输出结果一定是一个tensor的vector
vector outputs;
Status status_run = session->Run({{input_tensor_name, resized_tensor}}, {out_put_nodes}, {}, &outputs);
if (!status_run.ok()) {
cout <<"ERROR: RUN failed..." << std::endl;
cout <"\n";
return -1;
}
//把输出值给提取出
cout <<"Output tensor size:" <//3
for (int i = 0; i )
{
cout <// [1, 50], [1, 50], [1, 50, 4]
}
cvtColor(img, img, CV_RGB2BGR); // opencv读入的是BGR格式输入网络前转为RGB
resize(img,img,cv::Size(1000,1000)); // 模型输入图像大小
int pre_num = outputs[0].dim_size(1); // 50 模型预测的目标数量
auto tmap_pro = outputs[0].tensor<float, 2>(); //第一个是score输出shape为[1,50]
auto tmap_clas = outputs[1].tensor<float, 2>(); //第二个是class输出shape为[1,50]
auto tmap_coor = outputs[2].tensor<float, 3>(); //第三个是coordinate输出shape为[1,50,4]
float probability = 0.5; //自己设定的score阈值
for (int pre_i = 0; pre_i )
{
if (tmap_pro(0, pre_i) < probability)
{
break;
}
cout <<"Class ID: " <0, pre_i) << endl;
cout <<"Probability: " <0, pre_i) << endl;
string id = to_string(int(tmap_clas(0, pre_i)));
int xmin = int(tmap_coor(0, pre_i, 1) * input_width);
int ymin = int(tmap_coor(0, pre_i, 0) * input_height);
int xmax = int(tmap_coor(0, pre_i, 3) * input_width);
int ymax = int(tmap_coor(0, pre_i, 2) * input_height);
cout <<"Xmin is: " < endl;
cout <<"Ymin is: " < endl;
cout <<"Xmax is: " < endl;
cout <<"Ymax is: " < endl;
rectangle(img, cvPoint(xmin, ymin), cvPoint(xmax, ymax), Scalar(255, 0, 0), 1, 1, 0);
putText(img, id, cvPoint(xmin, ymin), FONT_HERSHEY_COMPLEX, 1.0, Scalar(255,0,0), 1);
}
imshow("1", img);
cvWaitKey(0);
return 0;
}

cmake_minimum_required(VERSION 3.0.0)
project(tensorflow_cpp)
set(CMAKE_CXX_STANDARD 11)
find_package(OpenCV 3.0 QUIET)
if(NOT OpenCV_FOUND)
find_package(OpenCV 2.4.3 QUIET)
if(NOT OpenCV_FOUND)
message(FATAL_ERROR "OpenCV > 2.4.3 not found.")
endif()
endif()
set(TENSORFLOW_INCLUDES
/usr/local/include/tf/
/usr/local/include/tf/bazel-genfiles
/usr/local/include/tf/tensorflow/
/usr/local/include/tf/tensorflow/third_party)
set(TENSORFLOW_LIBS
/usr/local/lib/libtensorflow_cc.so
/usr/local/lib//libtensorflow_framework.so)

include_directories(
${TENSORFLOW_INCLUDES}
${PROJECT_SOURCE_DIR}/third_party/eigen3
)
add_executable(predict predict.cpp)
target_link_libraries(predict
${TENSORFLOW_LIBS}
${OpenCV_LIBS}
)