win10+caffe-master + VS2013 + Matlab2016a 快速配置+matlab调试

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第一步

在github上下载Microsoft为Windows用户提供的Caffe for Windows分支,下载链接:https://github.com/microsoft/caffe。下载后解压到你要安装的目录,本人安装目录为E:\Caffe。然后复制Windows下CommonSettings.props.example,后缀改为CommonSettings.props,如下图:


win10+caffe-master + VS2013 + Matlab2016a 快速配置+matlab调试_第1张图片

第二步

我的电脑无GPU,修改复制过来的CommonSettings.props配置文件如下:CpuOnlyBuild:true;UseCuDNN:false


win10+caffe-master + VS2013 + Matlab2016a 快速配置+matlab调试_第2张图片

第53行修改MatlabDir为 E:\Program Files (x86)\MATLAB R2016a,即你的matlab安装路径;


win10+caffe-master + VS2013 + Matlab2016a 快速配置+matlab调试_第3张图片

第三步

下载Caffe依赖包NugetPackages,下载链接: https://pan.baidu.com/s/1jHWzike 密码: u3tb,下载解压放到与caffe-master并列文件夹即可,如图:


win10+caffe-master + VS2013 + Matlab2016a 快速配置+matlab调试_第4张图片

第四步

双击Windows下的caffe.sln,选中libcaffe项目,右键->属性->配置管理器->活动解决方案配置为Release,如下图:


win10+caffe-master + VS2013 + Matlab2016a 快速配置+matlab调试_第5张图片

然后右键->属性->生成解决方案;
为了使用caffe的matlab接口,需要添加matlab中的C++头文件mxGPUArray.h,:选中matcaffe项目,右键->属性->配置属性->C/C++->常规->附加包含目录,本人目录:.\MATLAB R2016a\toolbox\distcomp\gpu\extern\include\gpu,
读者照此修改为自己的matlab安装路径即可;最后编译caffe_.cpp,生成caffe_.mexw64,即表明编译成功:


win10+caffe-master + VS2013 + Matlab2016a 快速配置+matlab调试_第6张图片

最后,选中caffe.sln下的16个项目,右键->生成解决方案,如下图:


win10+caffe-master + VS2013 + Matlab2016a 快速配置+matlab调试_第7张图片

这一过程在本人电脑上十分钟左右,结果如下:


win10+caffe-master + VS2013 + Matlab2016a 快速配置+matlab调试_第8张图片

按照这个流程编译下来很少会出现无法解析外部符号问题,出现这种问题有很大可能是静态库没连接好,但是微软的所有库是自动下载的,除非没下载全。还有关于一些.h 头文件未找到,请自行核对此头文件的位置,然后再配置文件中随便找个IncludePath(此includePath必须在编译时候被使用),将路径加进去即可,同时也必须注意是否需要相关的lib文件。不过帮忙配置挺多电脑以后,基本没遇到太多问题。

第五步

运行caffe.cpp,直接双击打开caffe.cpp,然后ctrl+f5直接编译,运行后出现如下命令窗口说明编译成功:


win10+caffe-master + VS2013 + Matlab2016a 快速配置+matlab调试_第9张图片

第六步 运行matlab实例

配置好Caffe运行所需要的环境变量后(否则matcaffe会运行失败),将\caffe-master\Build\x64\Release添加进系统环境变量,否则matcaffe会运行失败;
在matlab中打开classification_demo.m和test.m(test.m为本人自己编写),


win10+caffe-master + VS2013 + Matlab2016a 快速配置+matlab调试_第10张图片

classification_demo.m

function [scores, maxlabel] = classification_demo(im, use_gpu)  
% [scores, maxlabel] = classification_demo(im, use_gpu)  
% 使用BVLC CaffeNet进行图像分类的示例  
% 重要:运行前,应首先从Model Zoo(http://caffe.berkeleyvision.org/model_zoo.html) 下载BVLC CaffeNet训练好的权值  
%  
% ****************************************************************************  
% For detailed documentation and usage on Caffe's Matlab interface, please  
% refer to Caffe Interface Tutorial at  
% http://caffe.berkeleyvision.org/tutorial/interfaces.html#matlab  
% ****************************************************************************  
%  
% input  
%   im       color image as uint8 HxWx3  
%   use_gpu  1 to use the GPU, 0 to use the CPU  
%  
% output  
%   scores   1000-dimensional ILSVRC score vector  
%   maxlabel the label of the highest score  
%  
% You may need to do the following before you start matlab:  
%  $ export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda-5.5/lib64  
%  $ export LD_PRELOAD=/usr/lib/x86_64-linux-gnu/libstdc++.so.6  
% Or the equivalent based on where things are installed on your system  
%  
% Usage:  
%  im = imread('../../examples/images/cat.jpg');  
%  scores = classification_demo(im, 1);  
%  [score, class] = max(scores);  
% Five things to be aware of:  
%   caffe uses row-major order  
%   matlab uses column-major order  
%   caffe uses BGR color channel order  
%   matlab uses RGB color channel order  
%   images need to have the data mean subtracted  

% Data coming in from matlab needs to be in the order  
%   [width, height, channels, images]  
% where width is the fastest dimension.  
% Here is the rough matlab for putting image data into the correct  
% format in W x H x C with BGR channels:  
%   % permute channels from RGB to BGR  
%   im_data = im(:, :, [3, 2, 1]);  
%   % flip width and height to make width the fastest dimension  
%   im_data = permute(im_data, [2, 1, 3]);  
%   % convert from uint8 to single  
%   im_data = single(im_data);  
%   % reshape to a fixed size (e.g., 227x227).  
%   im_data = imresize(im_data, [IMAGE_DIM IMAGE_DIM], 'bilinear');  
%   % subtract mean_data (already in W x H x C with BGR channels)  
%   im_data = im_data - mean_data;  

% If you have multiple images, cat them with cat(4, ...)  

% Add caffe/matlab to you Matlab search PATH to use matcaffe  
% if exist('../+caffe', 'dir')     
%   addpath('..');  
% else  
%   error('Please run this demo from caffe/matlab/demo');  
% end  


% Set caffe mode  
if exist('use_gpu', 'var') && use_gpu  
  caffe.set_mode_gpu();  
  gpu_id = 0;  % we will use the first gpu in this demo  
  caffe.set_device(gpu_id);  
else  
  caffe.set_mode_cpu();  
end  

% Initialize the network using BVLC CaffeNet for image classification  
% Weights (parameter) file needs to be downloaded from Model Zoo.  
model_dir = 'E:/Caffe/caffe-master/models/bvlc_reference_caffenet/';    % 模型所在目录  
net_model = [model_dir 'deploy.prototxt'];              % 模型描述文件,注意是deploy.prototxt,不包含data layers  
net_weights = [model_dir 'bvlc_reference_caffenet.caffemodel'];   % 模型权值文件,需要预先下载到这里  
phase = 'test'; % run with phase test (so that dropout isn't applied)   % 只进行分类,不做训练  
if ~exist(net_weights, 'file')  
  error('Please download CaffeNet from Model Zoo before you run this demo');  
end  

% Initialize a network  
net = caffe.Net(net_model, net_weights, phase);   % 初始化网络  

if nargin < 1  
  % For demo purposes we will use the cat image  
  fprintf('using caffe/examples/images/cat.jpg as input image\n');  
  im = imread('E:/Caffe/caffe-master/examples/images/cat.jpg');    % 获取输入图像  
end  
% prepare oversampled input  
% input_data is Height x Width x Channel x Num  
tic;  
input_data = {prepare_image(im)};         % 图像冗余处理  
toc;  

% do forward pass to get scores  
% scores are now Channels x Num, where Channels == 1000  
tic;  
% The net forward function. It takes in a cell array of N-D arrays  
% (where N == 4 here) containing data of input blob(s) and outputs a cell  
% array containing data from output blob(s)  
scores = net.forward(input_data);      %  分类,得到scores  
toc;  

scores = scores{1};  
scores = mean(scores, 2);  % 取所有分类结果的平均值  

[~, maxlabel] = max(scores);  % 找到最大概率对应的标签号  

% call caffe.reset_all() to reset caffe  
caffe.reset_all();  

% ------------------------------------------------------------------------  
function crops_data = prepare_image(im)  
% ------------------------------------------------------------------------  
% caffe/matlab/+caffe/imagenet/ilsvrc_2012_mean.mat contains mean_data that  
% is already in W x H x C with BGR channels  
d = load('E:/Caffe/caffe-master/matlab/+caffe/imagenet/ilsvrc_2012_mean.mat');  
mean_data = d.mean_data;  
IMAGE_DIM = 256;  
CROPPED_DIM = 227;  

% Convert an image returned by Matlab's imread to im_data in caffe's data  
% format: W x H x C with BGR channels  
im_data = im(:, :, [3, 2, 1]);  % permute channels from RGB to BGR  
im_data = permute(im_data, [2, 1, 3]);  % flip width and height  
im_data = single(im_data);  % convert from uint8 to single  
im_data = imresize(im_data, [IMAGE_DIM IMAGE_DIM], 'bilinear');  % resize im_data  
im_data = im_data - mean_data;  % subtract mean_data (already in W x H x C, BGR)  

% oversample (4 corners, center, and their x-axis flips)  
crops_data = zeros(CROPPED_DIM, CROPPED_DIM, 3, 10, 'single');  
indices = [0 IMAGE_DIM-CROPPED_DIM] + 1;  
n = 1;  
for i = indices  
  for j = indices  
    crops_data(:, :, :, n) = im_data(i:i+CROPPED_DIM-1, j:j+CROPPED_DIM-1, :);  
    crops_data(:, :, :, n+5) = crops_data(end:-1:1, :, :, n);  
    n = n + 1;  
  end  
end  
center = floor(indices(2) / 2) + 1;  
crops_data(:,:,:,5) = ...  
  im_data(center:center+CROPPED_DIM-1,center:center+CROPPED_DIM-1,:);  
crops_data(:,:,:,10) = crops_data(end:-1:1, :, :, 5);  

test.m

clear
clc  

im = imread('E:/Caffe/caffe-master/examples/images/cat.jpg');%读取图片  

[scores, maxlabel] = classification_demo(im, 0);%获取得分第二个参数0为CPU,1为GPU  
maxlabel;%查看最大标签是谁  
figure;
x=[1:1000];
plot(x,scores);%画出得分情况  
axis([1, 1000, -0.1, 0.5]);%坐标轴范围  
grid on %有网格  
hold on
plot(maxlabel,max(scores),'r*');
jieguo=strcat(num2str(maxlabel),',');
jieguo=strcat(jieguo,num2str(max(scores)));
text(im2double(maxlabel)+20,im2double(max(scores)),jieguo);
% [maxv,maxl]=findpeaks(im2double(scores),'minpeakdistance',1);
% plot(maxl,maxv,'*','color','R');    %绘制最大值点


fid = fopen('E:/Caffe/caffe-master/data/ilsvrc12/synset_words.txt', 'r');  
i=0;  
while ~feof(fid)  
    i=i+1;  
    lin = fgetl(fid);  

    lin = strtrim(lin);  
    if(i==maxlabel)  
        fprintf('the label of %d is %s\n',i,lin)  
        break  
    end  
end  
figure;imshow(im);%显示图片  
str=strcat('分类结果:',lin);
title(str);

设置路径如下:


win10+caffe-master + VS2013 + Matlab2016a 快速配置+matlab调试_第11张图片

设置路径后,运行test.m,结果如下:


win10+caffe-master + VS2013 + Matlab2016a 快速配置+matlab调试_第12张图片

说明最大分类概率的标签号为282,对应的是n02123045 tabby, tabby cat,即这张图片被分到第282类的概率为0.29848。

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