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

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);