basic matlab detection functionality

matlab/README.md

+A basic demo in MATLAB.
+
+Detection is also implemented in MATLAB (though missing some bells and whistles
+compared to the Python version) via the fast_rcnn_im_detect() function.
+
+See fast_rcnn_demo.m for example usage.

matlab/fast_rcnn_demo.m

+% --------------------------------------------------------
+% Fast R-CNN
+% Copyright (c) 2015 Microsoft
+% Licensed under The MIT License [see LICENSE for details]
+% Written by Ross Girshick
+% --------------------------------------------------------
+
+function fast_rcnn_demo()
+% Fast R-CNN demo (in matlab).
+
+[folder, name, ext] = fileparts(mfilename('fullpath'));
+
+caffe_path = fullfile(folder, '..', 'caffe-fast-rcnn', 'matlab', 'caffe');
+addpath(caffe_path);
+
+use_gpu = true;
+% You can try other models here:
+def = fullfile(folder, '..', 'models', 'VGG16', 'test.prototxt');;
+net = fullfile(folder, '..', 'data', 'fast_rcnn_models', ...
+               'vgg16_fast_rcnn_iter_40000.caffemodel');
+model = fast_rcnn_load_net(def, net, use_gpu);
+
+car_ind = 7;
+sofa_ind = 18;
+tv_ind = 20;
+
+demo(model, '000004', [car_ind], {'car'});
+demo(model, '001551', [sofa_ind, tv_ind], {'sofa', 'tvmonitor'});
+fprintf('\n');
+
+% ------------------------------------------------------------------------
+function demo(model, im_id, cls_inds, cls_names)
+% ------------------------------------------------------------------------
+[folder, name, ext] = fileparts(mfilename('fullpath'));
+box_file = fullfile(folder, '..', 'data', 'demo', [im_id '_boxes.mat']);
+% Boxes were saved with 0-based indexing
+ld = load(box_file); boxes = single(ld.boxes) + 1; clear ld;
+im_file = fullfile(folder, '..', 'data', 'demo', [im_id '.jpg']);
+im = imread(im_file);
+dets = fast_rcnn_im_detect(model, im, boxes);
+
+THRESH = 0.8;
+for j = 1:length(cls_inds)
+  cls_ind = cls_inds(j);
+  cls_name = cls_names{j};
+  I = find(dets{cls_ind}(:, end) >= THRESH);
+  showboxes(im, dets{cls_ind}(I, :));
+  title(sprintf('%s detections with p(%s | box) >= %.3f', ...
+                cls_name, cls_name, THRESH))
+  fprintf('\n> Press any key to continue');
+  pause;
+end

matlab/fast_rcnn_im_detect.m

+% --------------------------------------------------------
+% Fast R-CNN
+% Copyright (c) 2015 Microsoft
+% Licensed under The MIT License [see LICENSE for details]
+% Written by Ross Girshick
+% --------------------------------------------------------
+
+function dets = fast_rcnn_im_detect(model, im, boxes)
+% Perform detection a Fast R-CNN network given an image and
+% object proposals.
+
+if model.init_key ~= caffe('get_init_key')
+  error('You probably need call fast_rcnn_load_net() first.');
+end
+
+[im_batch, scales] = image_pyramid(im, model.pixel_means, false);
+
+[feat_pyra_boxes, feat_pyra_levels] = project_im_rois(boxes, scales);
+rois = cat(2, feat_pyra_levels, feat_pyra_boxes);
+% Adjust to 0-based indexing and make roi info the fastest dimension
+rois = rois - 1;
+rois = permute(rois, [2 1]);
+
+input_blobs = cell(2, 1);
+input_blobs{1} = im_batch;
+input_blobs{2} = rois;
+th = tic();
+blobs_out = caffe('forward', input_blobs);
+fprintf('fwd: %.3fs\n', toc(th));
+
+bbox_deltas = squeeze(blobs_out{1})';
+probs = squeeze(blobs_out{2})';
+
+num_classes = size(probs, 2);
+dets = cell(num_classes - 1, 1);
+NMS_THRESH = 0.3;
+% class index 1 is __background__, so we don't return it
+for j = 2:num_classes
+  cls_probs = probs(:, j);
+  cls_deltas = bbox_deltas(:, (1 + (j - 1) * 4):(j * 4));
+  pred_boxes = bbox_pred(boxes, cls_deltas);
+  cls_dets = [pred_boxes cls_probs];
+  keep = nms(cls_dets, NMS_THRESH);
+  cls_dets = cls_dets(keep, :);
+  dets{j - 1} = cls_dets;
+end
+
+% ------------------------------------------------------------------------
+function [batch, scales] = image_pyramid(im, pixel_means, multiscale)
+% ------------------------------------------------------------------------
+% Construct an image pyramid that's ready for feeding directly into caffe
+if ~multiscale
+  SCALES = [600];
+  MAX_SIZE = 1000;
+else
+  SCALES = [1200 864 688 576 480];
+  MAX_SIZE = 2000;
+end
+num_levels = length(SCALES);
+
+im = single(im);
+% Convert to BGR
+im = im(:, :, [3 2 1]);
+% Subtract mean (mean of the image mean--one mean per channel)
+im = bsxfun(@minus, im, pixel_means);
+
+im_orig = im;
+im_size = min([size(im_orig, 1) size(im_orig, 2)]);
+im_size_big = max([size(im_orig, 1) size(im_orig, 2)]);
+scale_factors = SCALES ./ im_size;
+
+max_size = [0 0 0];
+for i = 1:num_levels
+  if round(im_size_big * scale_factors(i)) > MAX_SIZE
+    scale_factors(i) = MAX_SIZE / im_size_big;
+  end
+  ims{i} = imresize(im_orig, scale_factors(i), 'bilinear', ...
+                    'antialiasing', false);
+  max_size = max(cat(1, max_size, size(ims{i})), [], 1);
+end
+
+batch = zeros(max_size(2), max_size(1), 3, num_levels, 'single');
+for i = 1:num_levels
+  im = ims{i};
+  im_sz = size(im);
+  im_sz = im_sz(1:2);
+  % Make width the fastest dimension (for caffe)
+  im = permute(im, [2 1 3]);
+  batch(1:im_sz(2), 1:im_sz(1), :, i) = im;
+end
+scales = scale_factors';
+
+% ------------------------------------------------------------------------
+function [boxes, levels] = project_im_rois(boxes, scales)
+% ------------------------------------------------------------------------
+widths = boxes(:,3) - boxes(:,1) + 1;
+heights = boxes(:,4) - boxes(:,2) + 1;
+
+areas = widths .* heights;
+scaled_areas = bsxfun(@times, areas, (scales.^2)');
+diff_areas = abs(scaled_areas - (224 * 224));
+[~, levels] = min(diff_areas, [], 2);
+
+boxes = boxes - 1;
+boxes = bsxfun(@times, boxes, scales(levels));
+boxes = boxes + 1;
+
+% ------------------------------------------------------------------------
+function pred_boxes = bbox_pred(boxes, bbox_deltas)
+% ------------------------------------------------------------------------
+if isempty(boxes)
+  pred_boxes = [];
+  return;
+end
+
+Y = bbox_deltas;
+
+% Read out predictions
+dst_ctr_x = Y(:, 1);
+dst_ctr_y = Y(:, 2);
+dst_scl_x = Y(:, 3);
+dst_scl_y = Y(:, 4);
+
+src_w = boxes(:, 3) - boxes(:, 1) + eps;
+src_h = boxes(:, 4) - boxes(:, 2) + eps;
+src_ctr_x = boxes(:, 1) + 0.5 * src_w;
+src_ctr_y = boxes(:, 2) + 0.5 * src_h;
+
+pred_ctr_x = (dst_ctr_x .* src_w) + src_ctr_x;
+pred_ctr_y = (dst_ctr_y .* src_h) + src_ctr_y;
+pred_w = exp(dst_scl_x) .* src_w;
+pred_h = exp(dst_scl_y) .* src_h;
+pred_boxes = [pred_ctr_x - 0.5 * pred_w, pred_ctr_y - 0.5 * pred_h, ...
+              pred_ctr_x + 0.5 * pred_w, pred_ctr_y + 0.5 * pred_h];

matlab/fast_rcnn_load_net.m

+% --------------------------------------------------------
+% Fast R-CNN
+% Copyright (c) 2015 Microsoft
+% Licensed under The MIT License [see LICENSE for details]
+% Written by Ross Girshick
+% --------------------------------------------------------
+
+function model = fast_rcnn_load_net(def, net, use_gpu)
+% Load a Fast R-CNN network.
+
+init_key = caffe('init', def, net, 'test');
+if exist('use_gpu', 'var') && ~use_gpu
+  caffe('set_mode_cpu');
+else
+  caffe('set_mode_gpu');
+end
+
+model.init_key = init_key;
+% model.stride is correct for the included models, but may not be correct
+% for other models!
+model.stride = 16;
+model.pixel_means = reshape([102.9801, 115.9465, 122.7717], [1 1 3]);

matlab/init_cnn_model.m

+function cnn = init_cnn_model(varargin)
+% cnn = init_cnn_model
+% Initialize a CNN with caffe
+%
+% Optional arguments
+% net_file   network binary file
+% def_file   network prototxt file
+% use_gpu    set to false to use CPU (default: true)
+% use_caffe  set to false to avoid using caffe (default: true)
+%            useful for running on the cluster (must use cached pyramids!)
+
+% ------------------------------------------------------------------------
+% Options
+ip = inputParser;
+
+% network binary file
+ip.addParamValue('net_file', ...
+    './data/caffe_nets/ilsvrc_2012_train_iter_310k', ...
+    @isstr);
+
+% network prototxt file
+ip.addParamValue('def_file', ...
+    './model-defs/pyramid_cnn_output_conv5_scales_7_plane_1713.prototxt', ...
+    @isstr);
+
+% Set use_gpu to false to use the CPU
+ip.addParamValue('use_gpu', true, @islogical);
+
+% Set use_caffe to false to avoid using caffe
+% (must be used in conjunction with cached features!)
+ip.addParamValue('use_caffe', true, @islogical);
+
+ip.parse(varargin{:});
+opts = ip.Results;
+% ------------------------------------------------------------------------
+
+cnn.binary_file = opts.net_file;
+cnn.definition_file = opts.def_file;
+cnn.init_key = -1;
+
+% load the ilsvrc image mean
+data_mean_file = 'ilsvrc_2012_mean.mat';
+assert(exist(data_mean_file, 'file') ~= 0);
+% input size business isn't likley necessary, but we're doing it
+% to be consistent with previous experiments
+ld = load(data_mean_file);
+mu = ld.image_mean; clear ld;
+input_size = 227;
+off = floor((size(mu,1) - input_size)/2)+1;
+%mu = mu(off:off+input_size-1, off:off+input_size-1, :);
+%mu = sum(sum(mu, 1), 2) / size(mu, 1) / size(mu, 2);
+cnn.mu = reshape([102.9801, 115.9465, 122.7717], [1 1 3]);
+
+if opts.use_caffe
+  cnn.init_key = ...
+      caffe('init', cnn.definition_file, cnn.binary_file);
+  caffe('set_phase_test');
+  if opts.use_gpu
+    caffe('set_mode_gpu');
+  else
+    caffe('set_mode_cpu');
+  end
+end

matlab/nms.m

+function pick = nms(boxes, overlap)
+% top = nms(boxes, overlap)
+% Non-maximum suppression. (FAST VERSION)
+% Greedily select high-scoring detections and skip detections
+% that are significantly covered by a previously selected
+% detection.
+%
+% NOTE: This is adapted from Pedro Felzenszwalb's version (nms.m),
+% but an inner loop has been eliminated to significantly speed it
+% up in the case of a large number of boxes
+
+% Copyright (C) 2011-12 by Tomasz Malisiewicz
+% All rights reserved.
+%
+% This file is part of the Exemplar-SVM library and is made
+% available under the terms of the MIT license (see COPYING file).
+% Project homepage: https://github.com/quantombone/exemplarsvm
+
+
+if isempty(boxes)
+  pick = [];
+  return;
+end
+
+x1 = boxes(:,1);
+y1 = boxes(:,2);
+x2 = boxes(:,3);
+y2 = boxes(:,4);
+s = boxes(:,end);
+
+area = (x2-x1+1) .* (y2-y1+1);
+[vals, I] = sort(s);
+
+pick = s*0;
+counter = 1;
+while ~isempty(I)
+  last = length(I);
+  i = I(last);
+  pick(counter) = i;
+  counter = counter + 1;
+
+  xx1 = max(x1(i), x1(I(1:last-1)));
+  yy1 = max(y1(i), y1(I(1:last-1)));
+  xx2 = min(x2(i), x2(I(1:last-1)));
+  yy2 = min(y2(i), y2(I(1:last-1)));
+
+  w = max(0.0, xx2-xx1+1);
+  h = max(0.0, yy2-yy1+1);
+
+  inter = w.*h;
+  o = inter ./ (area(i) + area(I(1:last-1)) - inter);
+
+  I = I(find(o<=overlap));
+end
+
+pick = pick(1:(counter-1));

matlab/showboxes.m

+% --------------------------------------------------------
+% Fast R-CNN
+% Copyright (c) 2015 Microsoft
+% Licensed under The MIT License [see LICENSE for details]
+% Written by Ross Girshick
+% --------------------------------------------------------
+
+function showboxes(im, boxes)
+
+image(im);
+axis image;
+axis off;
+set(gcf, 'Color', 'white');
+
+if ~isempty(boxes)
+  x1 = boxes(:, 1);
+  y1 = boxes(:, 2);
+  x2 = boxes(:, 3);
+  y2 = boxes(:, 4);
+  c = 'r';
+  s = '-';
+  line([x1 x1 x2 x2 x1]', [y1 y2 y2 y1 y1]', ...
+       'color', c, 'linewidth', 2, 'linestyle', s);
+  for i = 1:size(boxes, 1)
+    text(double(x1(i)), double(y1(i)) - 2, ...
+         sprintf('%.3f', boxes(i, end)), ...
+         'backgroundcolor', 'r', 'color', 'w');
+  end
+end