需要使用caffe 的matlab 接口測試分類,所以需要將之前的均值文件轉換成.mat
caffe 根目錄下,matlab/+caffe 目錄下有io.m, 裏面寫好了一個fuction read_mean() .如下所示。
調用方法, 直接在caffe 的根目錄下, 進入matlab,命令行,
>>addpath('./matlab')
>>mean_file = 'path/to/*.binaryproto'
>>image_mean = caffe.io.read_mean(mean_file);
>>save 'path/to/save/*.mat' image_mean
之後,在你保存的地方就可以看到生成的mat 均值文件
function im_data = load_image(im_file)
% im_data = load_image(im_file)
% load an image from disk into Caffe-supported data format
% switch channels from RGB to BGR, make width the fastest dimension
% and convert to single
% returns im_data in W x H x C. For colored images, C = 3 in BGR
% channels, and for grayscale images, C = 1
CHECK(ischar(im_file), 'im_file must be a string');
CHECK_FILE_EXIST(im_file);
im_data = imread(im_file);
% permute channels from RGB to BGR for colored images
if size(im_data, 3) == 3
im_data = im_data(:, :, [3, 2, 1]);
end
% 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);
end
function mean_data = read_mean(mean_proto_file)
% mean_data = read_mean(mean_proto_file)
% read image mean data from binaryproto file
% returns mean_data in W x H x C with BGR channels
CHECK(ischar(mean_proto_file), 'mean_proto_file must be a string');
CHECK_FILE_EXIST(mean_proto_file);
mean_data = caffe_('read_mean', mean_proto_file);
end
function write_mean(mean_data, mean_proto_file)
% write_mean(mean_data, mean_proto_file)
% write image mean data to binaryproto file
% mean_data should be W x H x C with BGR channels
CHECK(ischar(mean_proto_file), 'mean_proto_file must be a string');
CHECK(isa(mean_data, 'single'), 'mean_data must be a SINGLE matrix');
caffe_('write_mean', mean_data, mean_proto_file);
end
end
end