added matcaffe_demo for the VGG models (RGB input)

matlab/caffe/matcaffe_demo_vgg.m

@@ -0,0 +1,109 @@
+function [scores, maxlabel] = matcaffe_demo_vgg(im, use_gpu, model_def_file, model_file, mean_file)
+% scores = matcaffe_demo(im, use_gpu)
+%
+% Demo of the matlab wrapper using the ILSVRC network.
+%
+% 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
+%
+% 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 = matcaffe_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:
+%   % convert from uint8 to single
+%   im = single(im);
+%   % reshape to a fixed size (e.g., 227x227)
+%   im = imresize(im, [IMAGE_DIM IMAGE_DIM], 'bilinear');
+%   % permute from RGB to BGR and subtract the data mean (already in BGR)
+%   im = im(:,:,[3 2 1]) - data_mean;
+%   % flip width and height to make width the fastest dimension
+%   im = permute(im, [2 1 3]);
+
+% If you have multiple images, cat them with cat(4, ...)
+
+% The actual forward function. It takes in a cell array of 4-D arrays as
+% input and outputs a cell array. 
+
+
+% init caffe network (spews logging info)
+matcaffe_init(use_gpu, model_def_file, model_file);
+
+% prepare oversampled input
+% input_data is Height x Width x Channel x Num
+tic;
+input_data = {prepare_image(im, mean_file)};
+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_file)
+% ------------------------------------------------------------------------
+IMAGE_DIM = 256;
+CROPPED_DIM = 224;
+
+d = load(mean_file);
+IMAGE_MEAN = d.image_mean;
+
+% 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
+
+% 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, :)-IMAGE_MEAN, [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,:)-IMAGE_MEAN, ...
+        [2 1 3]);
+images(:,:,:,10) = images(end:-1:1, :, :, curr);