ubuntu用QT调用pb

在Ubuntu上编译好tensorflow，并且将其移植到另一台ubuntu系统。

现在需要在移植的系统上调用tensorflow训练好的pb模型。

1.安装eigen库

对于已经建立好软链接的tensorflow文件夹，需要进行Eigen库的安装。打开./tensorflow/tensorflow/contrib/makefile/downloads/eigen文件夹，在该文件夹下打开终端，依次输入

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mkdir build
cd build
cmake ..
make
sudo make install

安装完毕后，在usr/local/include目录下会出现eigen3文件夹

2.protobuf的安装

由于在服务器使用的是protobuf-3.5进行编译的，因此在本机上要用protobuf还原环境。

下载protobuf-3.5.0源码，解压，进入文件夹

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sudo apt-get install automake libtool
./autogen.sh
./configure
make
sudo make install
sudo ldconfig
protoc --version  # 查看protobuf版本

3.使用qtcreator读取pb文件

（1）将“tensorflow/bazel-genfiles/tensorflow/”中的cc和core文件夹中的内容copy到“tensorflow/tensorflow/”中，选择合并覆盖

（2）进入tensorflow/bazel-bin/tensorflow 文件夹，会有一个编译生成的libtensorflow_cc.so文件。若是通过bazel build –config=opt //tensorflow:libtensorflow_cc.so指令进行编译的，将libtensorflow_cc.so和libtensorflow_framework.so两个文件复制到/usr/local/lib文件夹下：

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sudo cp libtensorflow_cc.so /usr/local/lib/
sudo cp libtensorflow_framework.so /usr/local/lib/

若是通过bazel build –config=monolithic // tensorflow:libtensorflow_cc.so编译的，只需将libtensorflow_cc.so文件复制到/usr/local/lib文件夹下：

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sudo cp libtensorflow_cc.so /usr/local/lib

（3）配置opencv，见博文

（4）配置.pro文件，将libtensorflow_cc.so包含进去

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TEMPLATE = app

QT += qml quick
CONFIG += c++11

SOURCES += main.cpp \
    tools.cpp

RESOURCES += qml.qrc

# Additional import path used to resolve QML modules in Qt Creator's code model
QML_IMPORT_PATH =

# Additional import path used to resolve QML modules just for Qt Quick Designer
QML_DESIGNER_IMPORT_PATH =

# The following define makes your compiler emit warnings if you use
# any feature of Qt which as been marked deprecated (the exact warnings
# depend on your compiler). Please consult the documentation of the
# deprecated API in order to know how to port your code away from it.
DEFINES += QT_DEPRECATED_WARNINGS

# You can also make your code fail to compile if you use deprecated APIs.
# In order to do so, uncomment the following line.
# You can also select to disable deprecated APIs only up to a certain version of Qt.
#DEFINES += QT_DISABLE_DEPRECATED_BEFORE=0x060000    # disables all the APIs deprecated before Qt 6.0.0

# Default rules for deployment.
qnx: target.path = /tmp/$${TARGET}/bin
else: unix:!android: target.path = /opt/$${TARGET}/bin
!isEmpty(target.path): INSTALLS += target
INCLUDEPATH += /usr/local/include/opencv2
INCLUDEPATH += /usr/local/include/opencv
INCLUDEPATH += /usr/local/include
INCLUDEPATH += /home/mumu/Downloads/tf_c/tensorflow-1.8.0
INCLUDEPATH += /usr/local/include/eigen3
#INCLUDEPATH += /home/mumu/Downloads/tf_c/tensorflow/bazel-genfiles

LIBS += /usr/local/lib/libtensorflow_cc.so
LIBS += /usr/local/lib/libopencv_core.so.3.4
LIBS += /usr/local/lib/libopencv_highgui.so.3.4
LIBS += /usr/local/lib/libopencv_core.so.3.4
LIBS += /usr/local/lib/libopencv_highgui.so.3.4
LIBS += /usr/local/lib/libopencv_calib3d.so.3.4
LIBS += /usr/local/lib/libopencv_features2d.so.3.4
LIBS += /usr/local/lib/libopencv_flann.so.3.4
LIBS += /usr/local/lib/libopencv_imgcodecs.so.3.4
LIBS += /usr/local/lib/libopencv_imgproc.so.3.4
LIBS += /usr/local/lib/libopencv_ml.so.3.4
LIBS += /usr/local/lib/libopencv_objdetect.so.3.4
LIBS += /usr/local/lib/libopencv_video.so.3.4
LIBS += /usr/local/lib/libopencv_shape.so
LIBS += /usr/local/lib/libopencv_videoio.so

HEADERS += \
    tools.h

（5）加载pb模型

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#include <QGuiApplication>
#include <QQmlApplicationEngine>

#include"tools.h"

int main()
{
    tensorflow::string graph = "/home/mumu/Documents/SUN/pb_test/model/test_model_n.pb";
    tensorflow::string input_layer0 = "rgb_input:0";
    tensorflow::string input_layer1 = "hsv_input:0";
    tensorflow::string output_layer = "output/Softmax:0";
    tensorflow::string file = "/home/mumu/Documents/SUN/pb_test/photo/3.png";

    std::vector<cv::Mat> image_mat0;
    std::vector<cv::Mat> image_mat1;
    process_Image(file,image_mat0,image_mat1);

    std::unique_ptr<tensorflow::Session> session;
    tensorflow::Status load_graph_status = LoadGraph(graph,&session);
    if(!load_graph_status.ok())
    {
        LOG(ERROR)<<load_graph_status;
        return -1;
    }

    tensorflow::Tensor input_tensor0 = convertMatToTensor(image_mat0);
    tensorflow::Tensor input_tensor1 = convertMatToTensor(image_mat1);

    std::vector<tensorflow::Tensor> outputs;
    tensorflow::Status run_status = session->Run({{input_layer0,input_tensor0},{input_layer1,input_tensor1}},{output_layer},{},&outputs);
    if(!run_status.ok())
    {
        LOG(ERROR)<<"Running model failed"<<run_status;
        return -1;
    }

    float* feature = new float[2];
    auto outputs_map = outputs[0].flat<float>();
    for(int i = 0;i<2;i++){
        feature[i] = outputs_map(i);
    }

    std::cout<<feature[0]<<"    "<<feature[1]<< std::endl;

    system("pause");
    return 0;
}

其中tools.h包含如下函数：

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#ifndef TOOLS_H
#define TOOLS_H

#include <highgui.hpp>
#include <imgproc.hpp>
#include <fstream>
#include <vector>
#include <utility>
#include <iostream>
#include <string>

#include"tensorflow/core/public/session.h"

tensorflow::Status LoadGraph(const tensorflow::string& graph_file_name,std::unique_ptr<tensorflow::Session>* session);
tensorflow::Tensor convertMatToTensor(std::vector<cv::Mat> images);
void process_Image(tensorflow::string file,std::vector<cv::Mat> &image_mat0,std::vector<cv::Mat> &image_mat1);

#endif // TOOLS_H

具体实现在tools.cpp中：

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#include "tools.h"

tensorflow::Status LoadGraph(const tensorflow::string& graph_file_name,std::unique_ptr<tensorflow::Session>* session)
{
    tensorflow::GraphDef graph_def;
    tensorflow::Status load_graph_status = ReadBinaryProto(tensorflow::Env::Default(),graph_file_name,&graph_def);
    if(!load_graph_status.ok())
    {
        return tensorflow::errors::NotFound("Failed to load compute graph ");
    }
    session->reset(tensorflow::NewSession(tensorflow::SessionOptions()));
    tensorflow::Status session_create_status = (*session)->Create(graph_def);
    if(!session_create_status.ok())
    {
        return session_create_status;
    }
    return tensorflow::Status::OK();
}

tensorflow::Tensor convertMatToTensor(std::vector<cv::Mat> images)
{

    int width = images[0].cols;
    int height = images[0].cols;
    int channel = images[0].channels();
    tensorflow::Tensor input_tensor(tensorflow::DT_FLOAT,tensorflow::TensorShape({(int64)images.size(),height,width,channel}));
    auto input_tensor_map = input_tensor.tensor<float,4>();

    const float* source_row;
    const float* source_pixel;
    const float* source_value;

    for(int i= 0;i<images.size();i++){
        for(int y=0;y<height;y++){
            source_row = (float*)images[i].data + (y*width*channel);
            for(int x = 0;x<width;x++){
                source_pixel = source_row + (x*channel);
                for(int c = 0;c<channel;c++){
                    source_value = source_pixel + c;
                    input_tensor_map(i,y,x,c) = *source_value;
                }
            }
        }
    }

    return input_tensor;
}

void process_Image(tensorflow::string file,std::vector<cv::Mat> &image_mat0,std::vector<cv::Mat> &image_mat1)
{
    cv::Mat image0;
    cv::Mat image1;
    cv::Mat image = cv::imread(file);//读取图片到mat矩阵中

    cv::resize(image,image,cv::Size(224,224));//resize(InputArray src, OutputArray dst, Size dsize)

    cv::Mat image_input0(image.rows,image.cols,CV_32F);//创建两个float型Mat图像
    cv::Mat image_input1(image.rows,image.cols,CV_32F);

    cv::cvtColor(image,image0,CV_BGR2RGB);//将image(BGR)转为image0(RGB)
    cv::cvtColor(image,image1,CV_BGR2HSV);//将image(BGR)转为image1(HSV)

    image0.convertTo(image_input0,CV_32F,1/255.0);//归一化
    image1.convertTo(image_input1,CV_32F,1/255.0);

    image_mat0.push_back(image_input0);
    image_mat1.push_back(image_input1);
}

运行main函数，最终得到结果

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    3、在根目录_config.yml里添加配置： 

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    pages: false
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      text: false
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