added a Matlab demo with mean BGR pixel subtraction instead of the me…

…an image subtraction

matlab/caffe/matcaffe_demo_vgg_mean_pix.m

@@ -0,0 +1,102 @@
+function scores = matcaffe_demo_vgg_mean_pix(im, use_gpu, model_def_file, model_file)
+% scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file)
+%
+% Demo of the matlab wrapper based on the networks used for the "VGG" entry
+% in the ILSVRC-2014 competition and described in the tech. report 
+% "Very Deep Convolutional Networks for Large-Scale Image Recognition"
+% http://arxiv.org/abs/1409.1556/
+%
+% INPUT
+%   im - color image as uint8 HxWx3
+%   use_gpu - 1 to use the GPU, 0 to use the CPU
+%   model_def_file - network configuration (.prototxt file)
+%   model_file - network weights (.caffemodel file)
+%
+% OUTPUT
+%   scores   1000-dimensional ILSVRC score vector
+%
+% EXAMPLE USAGE
+%  model_def_file = 'zoo/deploy.prototxt';
+%  model_file = 'zoo/model.caffemodel';
+%  use_gpu = true;
+%  im = imread('../../examples/images/cat.jpg');
+%  scores = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file);
+% 
+% NOTES
+%  mean pixel subtraction is used instead of the mean image subtraction
+%
+% PREREQUISITES
+%  You may need to do the following before you start matlab:
+%   $ export LD_LIBRARY_PATH=/opt/intel/mkl/lib/intel64:/usr/local/cuda/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
+
+% init caffe network (spews logging info)
+matcaffe_init(use_gpu, model_def_file, model_file);
+
+% mean BGR pixel
+mean_pix = [103.939, 116.779, 123.68];
+
+% prepare oversampled input
+% input_data is Height x Width x Channel x Num
+tic;
+input_data = {prepare_image(im, mean_pix)};
+toc;
+
+% do forward pass to get scores
+% scores are now Width x Height x Channels x Num
+tic;
+scores = caffe('forward', input_data);
+toc;
+
+scores = scores{1};
+% size(scores)
+scores = squeeze(scores);
+% scores = mean(scores,2);
+
+% [~,maxlabel] = max(scores);
+
+% ------------------------------------------------------------------------
+function images = prepare_image(im, mean_pix)
+% ------------------------------------------------------------------------
+IMAGE_DIM = 256;
+CROPPED_DIM = 224;
+
+% resize to fixed input size
+im = single(im);
+
+if size(im, 1) < size(im, 2)
+    im = imresize(im, [IMAGE_DIM NaN]);
+else
+    im = imresize(im, [NaN IMAGE_DIM]);
+end
+
+% RGB -> BGR
+im = im(:, :, [3 2 1]);
+
+% oversample (4 corners, center, and their x-axis flips)
+images = zeros(CROPPED_DIM, CROPPED_DIM, 3, 10, 'single');
+
+indices_y = [0 size(im,1)-CROPPED_DIM] + 1;
+indices_x = [0 size(im,2)-CROPPED_DIM] + 1;
+center_y = floor(indices_y(2) / 2)+1;
+center_x = floor(indices_x(2) / 2)+1;
+
+curr = 1;
+for i = indices_y
+  for j = indices_x
+    images(:, :, :, curr) = ...
+        permute(im(i:i+CROPPED_DIM-1, j:j+CROPPED_DIM-1, :), [2 1 3]);
+    images(:, :, :, curr+5) = images(end:-1:1, :, :, curr);
+    curr = curr + 1;
+  end
+end
+images(:,:,:,5) = ...
+    permute(im(center_y:center_y+CROPPED_DIM-1,center_x:center_x+CROPPED_DIM-1,:), ...
+        [2 1 3]);
+images(:,:,:,10) = images(end:-1:1, :, :, curr);
+
+% mean BGR pixel subtraction
+for c = 1:3
+    images(:, :, c, :) = images(:, :, c, :) - mean_pix(c);
+end