验证码之字符分割&GUI显示

验证码思路流程：
1. 读取验证码

2.分割验证码
这里的验证码是那些背景只是一些噪点，没有很明显的横杆那种。
从图中（28*88）可知，4个字符从中间往两边看分布式均匀的，故只需将字符极左和极右找出，然后往两边各外扩使其像素达到18*4=72即可。
a)首先将三个通道RGB数据获取，二值化，然后将三个二值化图像相与，便得到第四个图

b)得到二维码的四个字的二值图像后，下面确定字符极左极右坐标

对每一列求和，数值分布如图所示
求其众数，那么众数开始上升之处便是左边第一个字符之处，由此找到了左边第一个字符的起始位置（极左），同理可得极右点；由此然后往两边各外扩使其像素达到18*4=72。再平均分割成四个字符即可。

3.识别验证码字符
首先要用到上篇文章训练好的模型_iter_1000.caffemodel、以及均值文件mean.binaryproto和由train_val.prototxt修改得到的网络框架deploy.prototxt（注不含网络层的初始条件，因其权值都已训练好，且最后层也要变动，但在数据层需要加入输入数据的格式）
content in train_val.prototxt

name: "deploy"input: "data"input_shape {  dim: 1 # batchsize  dim: 3 # number of colour channels - rgb  dim: 28 # width  dim: 28 # height}layer {  name: "conv1"  type: "Convolution"  bottom: "data"  top: "conv1"    convolution_param {    num_output: 32    pad: 2    kernel_size: 5    stride: 1    }}layer {  name: "pool1"  type: "Pooling"  bottom: "conv1"  top: "pool1"  pooling_param {    pool: MAX    kernel_size: 3    stride: 2  }}layer {  name: "relu1"  type: "ReLU"  bottom: "pool1"  top: "pool1"}layer {  name: "conv2"  type: "Convolution"  bottom: "pool1"  top: "conv2"    convolution_param {    num_output: 32    pad: 2    kernel_size: 5    stride: 1      }}layer {  name: "relu2"  type: "ReLU"  bottom: "conv2"  top: "conv2"}layer {  name: "pool2"  type: "Pooling"  bottom: "conv2"  top: "pool2"  pooling_param {    pool: AVE    kernel_size: 3    stride: 2  }}layer {  name: "conv3"  type: "Convolution"  bottom: "pool2"  top: "conv3"   convolution_param {    num_output: 64    pad: 2    kernel_size: 5    stride: 1      }}layer {  name: "relu3"  type: "ReLU"  bottom: "conv3"  top: "conv3"}layer {  name: "pool3"  type: "Pooling"  bottom: "conv3"  top: "pool3"  pooling_param {    pool: AVE    kernel_size: 3    stride: 2  }}layer {  name: "ip1"  type: "InnerProduct"  bottom: "pool3"  top: "ip1"    inner_product_param {    num_output: 64     }}layer {  name: "ip2"  type: "InnerProduct"  bottom: "ip1"  top: "ip2"    inner_product_param {    num_output: 10     }}layer {  name: "prob"  type: "Softmax"  bottom: "ip2"  top: "loss"}

4.Matlab调用模型
classification_demo改编而来

function [scores, maxlabel] = classification_vic(im, use_gpu)% [scores, maxlabel] = classification_demo(im, use_gpu)%% Image classification demo using BVLC CaffeNet.%% IMPORTANT: before you run this demo, you should download BVLC CaffeNet% from Model Zoo (http://caffe.berkeleyvision.org/model_zoo.html)%% ****************************************************************************% For detailed documentation and usage on Caffe's Matlab interface, please% refer to Caffe Interface Tutorial at% http://caffe.berkeleyvision.org/tutorial/interfaces.html#matlab% ****************************************************************************%% 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%   maxlabel the label of the highest score%% 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 = classification_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 in W x H x C with BGR channels:%   % permute channels from RGB to BGR%   im_data = im(:, :, [3, 2, 1]);%   % flip width and height to make width the fastest dimension%   im_data = permute(im_data, [2, 1, 3]);%   % convert from uint8 to single%   im_data = single(im_data);%   % reshape to a fixed size (e.g., 227x227).%   im_data = imresize(im_data, [IMAGE_DIM IMAGE_DIM], 'bilinear');%   % subtract mean_data (already in W x H x C with BGR channels)%   im_data = im_data - mean_data;% If you have multiple images, cat them with cat(4, ...)% Add caffe/matlab to you Matlab search PATH to use matcaffe% if exist('../+caffe', 'dir')%    addpath('../../Build/x64/Release/matcaffe');% else%   error('Please run this demo from caffe/matlab/demo');% end% Set caffe modeif exist('use_gpu', 'var') && use_gpu  caffe.set_mode_gpu();  gpu_id = 0;  % we will use the first gpu in this demo  caffe.set_device(gpu_id);else  caffe.set_mode_cpu();end% Initialize the network using BVLC CaffeNet for image classification% Weights (parameter) file needs to be downloaded from Model Zoo.model_dir = 'Datatest/';net_model = [model_dir 'deploy.prototxt'];net_weights = [model_dir '_iter_1000.caffemodel'];phase = 'test'; % run with phase test (so that dropout isn't applied)if ~exist(net_weights, 'file')  error('Please download CaffeNet from Model Zoo before you run this demo');end% Initialize a networknet = caffe.Net(net_model, net_weights, phase);if nargin < 1  % For demo purposes we will use the cat image  fprintf('using test/00E714_E.bmp as input image\n');  im = imread('test/00E714_E.bmp');end% prepare oversampled input% input_data is Height x Width x Channel x Numtic;  input_data = {prepare_image(im)};toc;% do forward pass to get scores% scores are now Channels x Num, where Channels == 1000tic;% The net forward function. It takes in a cell array of N-D arrays% (where N == 4 here) containing data of input blob(s) and outputs a cell% array containing data from output blob(s)scores = net.forward(input_data);toc;scores = scores{1};scores = mean(scores, 2);  % take average scores over 10 crops[~, maxlabel] = max(scores);% call caffe.reset_all() to reset caffecaffe.reset_all();% ------------------------------------------------------------------------function crops_data = prepare_image(im)% ------------------------------------------------------------------------% caffe/matlab/+caffe/imagenet/ilsvrc_2012_mean.mat contains mean_data that% is already in W x H x C with BGR channels% d = load('../+caffe/imagenet/ilsvrc_2012_mean.mat');% mean_data = d.mean_data;mean_data =caffe.io.read_mean('Datatest/mean.binaryproto');%CROPPED_DIM = 227;% Convert an image returned by Matlab's imread to im_data in caffe's data% format: W x H x C with BGR channelsim_data = im(:, :, [3, 2, 1]);  % permute channels from RGB to BGRim_data = permute(im_data, [2, 1, 3]);  % flip width and heightim_data = single(im_data);  % convert from uint8 to single%im_data = imresize(im_data, [IMAGE_DIM IMAGE_DIM], 'bilinear');  % resize im_dataim_data = im_data - mean_data;  % subtract mean_data (already in W x H x C, BGR)crops_data =im_data ;% % oversample (4 corners, center, and their x-axis flips)% crops_data = zeros(CROPPED_DIM, CROPPED_DIM, 3, 10, 'single');% indices = [0 IMAGE_DIM-CROPPED_DIM] + 1;% n = 1;% for i = indices%   for j = indices%     crops_data(:, :, :, n) = im_data(i:i+CROPPED_DIM-1, j:j+CROPPED_DIM-1, :);%     crops_data(:, :, :, n+5) = crops_data(end:-1:1, :, :, n);%     n = n + 1;%   end% end% center = floor(indices(2) / 2) + 1;% crops_data(:,:,:,5) = ...%   im_data(center:center+CROPPED_DIM-1,center:center+CROPPED_DIM-1,:);% crops_data(:,:,:,10) = crops_data(end:-1:1, :, :, 5);

5.GUI显示
结果显示如下

6.源码可从我的Github上Clone下来