function script_faster_rcnn_demo()
close all; clc; clear mex; clear is_valid_handle; % to clear init_key
run(fullfile(fileparts(fileparts(mfilename('fullpath'))), 'startup'));
%% -------------------- CONFIG --------------------
opts.caffe_version = 'caffe_faster_rcnn';
opts.gpu_id = auto_select_gpu;
active_caffe_mex(opts.gpu_id, opts.caffe_version); opts.per_nms_topN = 6000;
opts.nms_overlap_thres = 0.7;
opts.after_nms_topN = 300;
opts.use_gpu = true; opts.test_scales = 600; %% -------------------- INIT_MODEL --------------------
% model_dir = fullfile(pwd, 'output', 'faster_rcnn_final', 'faster_rcnn_VOC0712_vgg_16layers'); %% VGG-16
model_dir = fullfile(pwd, 'output', 'faster_rcnn_final', 'faster_rcnn_VOC0712_ZF'); %% ZF
proposal_detection_model = load_proposal_detection_model(model_dir); proposal_detection_model.conf_proposal.test_scales = opts.test_scales;
proposal_detection_model.conf_detection.test_scales = opts.test_scales;
if opts.use_gpu
proposal_detection_model.conf_proposal.image_means = gpuArray(proposal_detection_model.conf_proposal.image_means);
proposal_detection_model.conf_detection.image_means = gpuArray(proposal_detection_model.conf_detection.image_means);
end % caffe.init_log(fullfile(pwd, 'caffe_log'));
% proposal net
rpn_net = caffe.Net(proposal_detection_model.proposal_net_def, 'test');
rpn_net.copy_from(proposal_detection_model.proposal_net);
% fast rcnn net
fast_rcnn_net = caffe.Net(proposal_detection_model.detection_net_def, 'test');
fast_rcnn_net.copy_from(proposal_detection_model.detection_net); % set gpu/cpu
if opts.use_gpu
caffe.set_mode_gpu();
else
caffe.set_mode_cpu();
end %% -------------------- WARM UP --------------------
% the first run will be slower; use an empty image to warm up for j = 1:2 % we warm up 2 times
im = uint8(ones(375, 500, 3)*128);
if opts.use_gpu
im = gpuArray(im);
end
[boxes, scores] = proposal_im_detect(proposal_detection_model.conf_proposal, rpn_net, im);
aboxes = boxes_filter([boxes, scores], opts.per_nms_topN, opts.nms_overlap_thres, opts.after_nms_topN, opts.use_gpu);
if proposal_detection_model.is_share_feature
[boxes, scores] = fast_rcnn_conv_feat_detect(proposal_detection_model.conf_detection, fast_rcnn_net, im, ...
rpn_net.blobs(proposal_detection_model.last_shared_output_blob_name), ...
aboxes(:, 1:4), opts.after_nms_topN);
else
[boxes, scores] = fast_rcnn_im_detect(proposal_detection_model.conf_detection, fast_rcnn_net, im, ...
aboxes(:, 1:4), opts.after_nms_topN);
end
end %% -------------------- TESTING --------------------
% im_names = {'001763.jpg', '004545.jpg', '000542.jpg', '000456.jpg', '001150.jpg'};
% these images can be downloaded with fetch_faster_rcnn_final_model.m
image_path = '/media/wangxiao/Elements/image_segment_backup/ImagesData223/Pedestrian/172.19.199.223/';
file1 = dir(image_path); for ii = 3:size(file1, 1)
new_path1 = [image_path, file1(ii).name, '/'];
file2 = dir(new_path1);
for jj = 3:size(file2, 1)
new_path2 = [new_path1, file2(jj).name, '/'];
file3 = dir(new_path2);
for kk = 3:size(file3, 1)
im = imread([new_path2, file3(kk).name]);
running_time = [];
% for j = 1:length(im_names)
% im = imread(fullfile(pwd, im_names{j})); if opts.use_gpu
im = gpuArray(im);
end % test proposal
th = tic();
[boxes, scores] = proposal_im_detect(proposal_detection_model.conf_proposal, rpn_net, im);
t_proposal = toc(th);
th = tic();
aboxes = boxes_filter([boxes, scores], opts.per_nms_topN, opts.nms_overlap_thres, opts.after_nms_topN, opts.use_gpu);
t_nms = toc(th); % test detection
th = tic();
if proposal_detection_model.is_share_feature
[boxes, scores] = fast_rcnn_conv_feat_detect(proposal_detection_model.conf_detection, fast_rcnn_net, im, ...
rpn_net.blobs(proposal_detection_model.last_shared_output_blob_name), ...
aboxes(:, 1:4), opts.after_nms_topN);
else
[boxes, scores] = fast_rcnn_im_detect(proposal_detection_model.conf_detection, fast_rcnn_net, im, ...
aboxes(:, 1:4), opts.after_nms_topN);
end
t_detection = toc(th); % fprintf('%s (%dx%d): time %.3fs (resize+conv+proposal: %.3fs, nms+regionwise: %.3fs)\n', im_names{j}, ...
% size(im, 2), size(im, 1), t_proposal + t_nms + t_detection, t_proposal, t_nms+t_detection);
% running_time(end+1) = t_proposal + t_nms + t_detection; % visualize
classes = proposal_detection_model.classes;
boxes_cell = cell(length(classes), 1);
thres = 0.6;
for i = 1:length(boxes_cell)
boxes_cell{i} = [boxes(:, (1+(i-1)*4):(i*4)), scores(:, i)];
boxes_cell{i} = boxes_cell{i}(nms(boxes_cell{i}, 0.3), :); I = boxes_cell{i}(:, 5) >= thres;
boxes_cell{i} = boxes_cell{i}(I, :);
end
figure(j); [location, label, score] = output(im, boxes_cell, classes, 'voc');
if (score==0)
continue;
else
disp(file3(kk).name);
save(['./mat results/' file3(kk).name '.mat' ], 'location', 'label', 'score', 'im');
end showboxes(im, boxes_cell, classes, 'voc');
pause(0.1);
end
end
end % fprintf('mean time: %.3fs\n', mean(running_time)); caffe.reset_all();
clear mex; end -------------------------------------------------------
--------------------------------------------------------- function proposal_detection_model = load_proposal_detection_model(model_dir)
ld = load(fullfile(model_dir, 'model'));
proposal_detection_model = ld.proposal_detection_model;
clear ld; proposal_detection_model.proposal_net_def ...
= fullfile(model_dir, proposal_detection_model.proposal_net_def);
proposal_detection_model.proposal_net ...
= fullfile(model_dir, proposal_detection_model.proposal_net);
proposal_detection_model.detection_net_def ...
= fullfile(model_dir, proposal_detection_model.detection_net_def);
proposal_detection_model.detection_net ...
= fullfile(model_dir, proposal_detection_model.detection_net); end function aboxes = boxes_filter(aboxes, per_nms_topN, nms_overlap_thres, after_nms_topN, use_gpu)
% to speed up nms
if per_nms_topN > 0
aboxes = aboxes(1:min(length(aboxes), per_nms_topN), :);
end
% do nms
if nms_overlap_thres > 0 && nms_overlap_thres < 1
aboxes = aboxes(nms(aboxes, nms_overlap_thres, use_gpu), :);
end
if after_nms_topN > 0
aboxes = aboxes(1:min(length(aboxes), after_nms_topN), :);
end
end
matlab命令窗口，显示： 刚开始都是正常的，如下：
fast_rcnn startup done
GPU 1: free memory 3824902144
Use GPU 1
Warning: Specified caffe folder (/home/wangxiao/Downloads/faster_rcnn-master/experiments/external/caffe/matlab/caffe_faster_rcnn)
is not exist, change to default one (/home/wangxiao/Downloads/faster_rcnn-master/experiments/external/caffe/matlab)
> In active_caffe_mex at 19
In ori_demo at 7
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Index exceeds matrix dimensions.
Error in ori_demo (line 114)
boxes_cell{i} = [boxes(:, (1+(i-1)*4):(i*4)), scores(:, i)];
但是，跑着跑着就出问题了，索引超过矩阵维度是什么鬼？？只好硬着头皮一句一句的调试了，fuck 。。。。
但是，搞不定啊。。只知道了一个：非极大值抑制（NMS）
为什么会出错呢？？？ 之前跑的好好地啊。。。提示超过矩阵范围的那一句是作者自己写的。。。怎么会错呢？怎么会错？怎么会？怎么？怎？ ？
不搞了，先把原始的图像，整理成一个文件夹在做处理：
clc; close all; path = '/media/wangxiao/Elements/image_segment_backup/';
savePath = '/media/wangxiao/Elements/wang xiao/additional_data/';
camera = dir(path); txt_path = '/media/wangxiao/Elements/wang xiao/';
txt_file = fopen([txt_path, 'log_file.txt'], 'a'); for i = 3:length(camera)
disp(['camera ', num2str(i-2), '---']);
fprintf(txt_file, '%s \n ', num2str(i-2));
path2 = [path, camera(i).name, '/'];
file1 = dir(path2);
for ii = 5:size(file1, 1)
disp([' ', file1(ii).name, '---']);
fprintf(txt_file, '%s \n', file1(ii).name);
new_path = [path2, file1(ii).name, '/'];
file2 = dir(new_path);
for j = 3:size(file2, 1)
disp([' ', file2(j).name, '---']);
fprintf(txt_file, '%s \n', file2(j).name);
new_path2 = [new_path, file2(j).name, '/'];
file3 = dir(new_path2);
for k = 3:size(file3, 1)
disp([' ', file3(k).name, '---']);
fprintf(txt_file, '%s \n', file3(k).name);
new_path3 = [new_path2, file3(k).name , '/'];
file4 = dir(new_path3);
for r = 3:size(file4, 1)
disp([' ', file4(r).name, '---']);
fprintf(txt_file, '%s \n', file4(r).name);
new_path4 = [new_path3, file4(r).name, '/'];
file5 = dir(new_path4);
for w = 3:size(file5, 1)
if (imread([new_path4, file5(w).name]))
im = imread([new_path4, file5(w).name]);
imshow(im);
imwrite(im, [savePath, file5(w).name]);
else
continue; end
end
end
end
end
end
end fclose(txt_file);
找到原因了，妈的，原来是因为，输入图像的大小不一致导致的，奇怪了，只要加一句： im = imresize(im, [127 127]); 将输入的图像统一resize成 固定的大小，即可。。。简单 粗暴 但是，不解其惑 。。。。
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