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

版权声明：本文为stu_why原创文章，未经博主允许不得转载。stu_why博客地址：http://blog.csdn.net/zpp1994

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：

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



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

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：

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：



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：



最后，选中caffe.sln下的16个项目，右键->生成解决方案，如下图：



这一过程在本人电脑上十分钟左右，结果如下：



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:

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），

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：



设置路径后，运行test.m,结果如下：



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。