本文的思路主要來源於[1]，模型和參數來源於[2]，這裏只是在Caffe的框架下用C++實現了，效果還可以，目前打算做個三維點雲顯示，做好了再上傳吧．

這裏先貼代碼吧，最近比較忙，就先不講具體的，大家先看吧，有時間了再寫思路吧

#include <vector>
#include <string.h>
#include <caffe/caffe.hpp>
#include <opencv2/core/core.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/highgui/highgui.hpp>

using
 
 namespace caffe;
using namespace std;

int main(){ 
  
  /*Load the network*/
  string proto = "scripts/deploy_resnet50by2_pool.prototxt";
  Phase phase = TEST;
  Caffe::set_mode(Caffe::GPU);
  Caffe::SetDevice(0);
  boost::shared_ptr< Net<float> > net(new caffe::Net<float>(proto, phase));
  
 
string model = "model/train_iter_40000.caffemodel";
  net->CopyTrainedLayersFrom(model);
  
  CHECK_EQ(net->num_inputs(), 1) << "Network should have exactly one input.";
  CHECK_EQ(net->num_outputs(), 1) << "Network should have exactly one output.";
  
  /*Convert image to input blob. Blob: num, channel, height, width
 
*/
  Blob<float>* input_blob = net->input_blobs()[0];
  int input_channel = input_blob->channels();
  int input_height = input_blob->height();
  int input_width = input_blob->width();
  cout << "The size of input image should be " << input_width << "*" << input_height << endl;
  
  vector<cv::Mat> input_channels;  //The value of input image‘s each channel  
  float* input_data = input_blob->mutable_cpu_data();
  for(int i=0;i<input_channel;i++){
    cv::Mat channel(input_height, input_width, CV_32FC1, input_data);
    input_channels.push_back(channel);
    input_data += input_width*input_height;
  }
  
  string imgPath = "images/1.png";
  cv::Mat image = cv::imread(imgPath);
  
  cv::Size input_size = cv::Size(input_width, input_height);
  cv::Mat image_resized;  
  cv::resize(image, image_resized, input_size);
  cv::imshow("image", image_resized);
  cv::waitKey(0);
  
  cv::Mat image_float;
  image_resized.convertTo(image_float, CV_32FC3);
  cv::Mat image_normalized;
  cv::Mat mean(input_height, input_width, CV_32FC3, cv::Scalar(104,117,123));
  cv::subtract(image_float, mean, image_normalized);
  cv::split(image_normalized, input_channels);
  
  net->Forward();
  
  /*Convert output blob to image*/
  Blob<float>* output_blob = net->output_blobs()[0];
  int output_height = output_blob->height();
  int output_width = output_blob->width();
  float *output_data = output_blob->mutable_cpu_data();
  cv::Mat depth(output_height, output_width, CV_32FC1, output_data);
  cout << depth.at<float>(5,5);
  
  cv::Mat depth_uint8;
  depth.convertTo(depth_uint8, CV_8UC1);
  
  cv::imshow("depth", depth_uint8);
  cv::waitKey(0);
  
  /*Generate 3D image with rgb image and estimated depth*/
  
  
  return 0;
}

下面是我的CmakeList.txt，代碼中需要的模型．參數，和圖片都是包含在[2]裏面的．

cmake_minimum_required( VERSION 2.8 )
project( resTest )

add_executable( resTest resTest.cpp )
set( CMAKE_CXX_FLAGS "-std=c++11" )

include_directories( /home/ai/Programs/Caffe/include 
  /home/ai/Programs/Caffe/.build_release/src
  /usr/local/cuda/include
  /usr/local/include
  /usr/include
  
  )
target_link_libraries( resTest
  /home/ai/Programs/Caffe/.build_release/lib/libcaffe.so 
  /usr/lib/x86_64-linux-gnu/libopencv_highgui.so
  /usr/lib/x86_64-linux-gnu/libopencv_imgproc.so
  /usr/lib/x86_64-linux-gnu/libopencv_core.so
  /usr/lib/x86_64-linux-gnu/libboost_system.so  
  /usr/lib/x86_64-linux-gnu/libglog.so 
  )

參考文獻

[1]http://blog.csdn.net/jiongnima/article/details/70197866

[2]https://github.com/Ravi-Garg/Unsupervised_Depth_Estimation

單目深度估計－基於Caffe