Windows下用Matlab加载caffemodel做图像分类

1.编译caffe的matlab接口

用到了happynear提供的caffe-windows-master，编译caffe和matlab接口的过程看这里。编译好之后，caffe-windows-master\matlab\+caffe\private内的文件如下：



如果嫌麻烦，可以直接跳过1，下载我编译好的matlab接口，以及happynear提供的第三方库的dll(在bin文件夹)：



下载地址：
matlab接口；
bin文件夹；
注意：记得将该bin文件夹加入系环境变量；

2.修改matlab\demo\classification_demo.m

将bin文件夹加入环境变量后，修改matlab\demo\classification_demo.m，可以参考我修改的代码：

function classification_demo()
close all;
clc;
clear mex;
clear is_valid_handle;
caffe.reset_all();
if isdir('log')
rmdir('log','s');
end
net_model =  'deploy.prototxt';
net_weights = 'Net_iter_150000.caffemodel';
label_file = 'car_words.txt';
im_path = 'car\';
phase = 'test'; % run with phase test (so that dropout isn't applied)
use_gpu=true;
gpu_id=0;
if exist('../+caffe', 'dir')
addpath('..')
else
error('error');
end
% Set caffe mode

if  use_gpu
caffe.set_mode_gpu();
caffe.set_device(gpu_id);
else
caffe.set_mode_cpu();
end

if ~exist(net_weights, 'file')
error('模型文件不存在!');
end

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

Files = dir(im_path);
im_names = cell(1,length(Files)-2);
for j = 3:length(Files)
im_names{j-2} = Files(j).name;
end

for j = 1:length(im_names)
im = imread([im_path, im_names{j}]);

tic;
input_data = {prepare_image(im)};
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);
toc;

scores = scores{1};
scores = mean(scores, 2);  % take average scores over 10 crops

[maxscores, maxlabel] = max(scores);

fid=fopen(label_file,'r','n','UTF-8');
for ii = 1:maxlabel
tline=fgetl(fid);
end
fclose(fid);

label=tline;
label=strcat(label,':',num2str(maxscores));
imshow(im);
text(50,50,label,'fontsize',20,'color','r');
pause();

% call caffe.reset_all() to reset caffe
if j == length(im_names)
caffe.reset_all();
end
end
% ------------------------------------------------------------------------
function crops_data = prepare_image(im)
d = load('+caffe/imagenet/ilsvrc_2012_mean.mat');
mean_data = d.mean_data;
IMAGE_DIM = 256;
CROPPED_DIM = 224;

% 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);

其中，net_model是模型定义prototxt文件；net_weights是训练的到的caffemodel；label_file是标签文件。

CROPPED_DIM = 224;

修改成你的模型输入大小。

(因为不知道怎么用matlab读取binaryproto文件，所以这里均值文件用的是ilsvrc2012的均值文件。之前试过用c++读取binaryproto文件并保存为jpg，但是感觉效果不是很好。)

原来它是测试一张图片的，这里改为测试整个文件夹的图片；

3.结果