多种分类器训练检测

OCR （Optical Character Recognition，光学字符识别），我们这个练习就是对OCR英文字母进行识别。得到一张OCR图片后，提取出字符相关的ROI图像，并且大小归一化，整个图像的像素值序列可以直接作为特征。但直接将整个图像作为特征数据维度太高，计算量太大，所以也可以进行一些降维处理，减少输入的数据量。

处理过程一般这样：先对原图像进行裁剪，得到字符的ROI图像，二值化。然后将图像分块，统计每个小块中非0像素的个数，这样就形成了一个较小的矩阵，这矩阵就是新的特征了。opencv为我们提供了一些这样的数据，放在

\opencv\sources\samples\data\letter-recognition.data

这个文件里，打开看看：

每一行代表一个样本。第一列大写的字母，就是标注，随后的16列就是该字母的特征向量。这个文件中总共有20000行样本，共分类26类（26个字母）。

我们将这些数据读取出来后，分成两部分，第一部分16000个样本作为训练样本，训练出分类器后，对这16000个训练数据和余下的4000个数据分别进行测试，得到训练精度和测试精度。其中adaboost比较特殊一点，训练和测试样本各为10000.

完整代码为：

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#include "opencv2\opencv.hpp"
#include <iostream>
using namespace std;
using namespace cv;
using namespace cv::ml;


// 读取文件数据
bool read_num_class_data(const string& filename, int var_count, Mat* _data, Mat* _responses)
{
const int M = 1024;
char buf[M + 2];


Mat el_ptr(1, var_count, CV_32F);
int i;
vector<int> responses;


_data->release();
_responses->release();
FILE *f;
fopen_s(&f, filename.c_str(), "rt");
if (!f)
{
cout << "Could not read the database " << filename << endl;
return false;
}


for (;;)
{
char* ptr;
if (!fgets(buf, M, f) || !strchr(buf, ','))
break;
responses.push_back((int)buf[0]);
ptr = buf + 2;
for (i = 0; i < var_count; i++)
{
int n = 0;
sscanf_s(ptr, "%f%n", &el_ptr.at<float>(i), &n);
ptr += n + 1;
}
if (i < var_count)
break;
_data->push_back(el_ptr);
}
fclose(f);
Mat(responses).copyTo(*_responses);
return true;
}




//准备训练数据
Ptr<TrainData> prepare_train_data(const Mat& data, const Mat& responses, int ntrain_samples)
{
Mat sample_idx = Mat::zeros(1, data.rows, CV_8U);
Mat train_samples = sample_idx.colRange(0, ntrain_samples);
train_samples.setTo(Scalar::all(1));


int nvars = data.cols;
Mat var_type(nvars + 1, 1, CV_8U);
var_type.setTo(Scalar::all(VAR_ORDERED));
var_type.at<uchar>(nvars) = VAR_CATEGORICAL;


return TrainData::create(data, ROW_SAMPLE, responses,
noArray(), sample_idx, noArray(), var_type);
}


//设置迭代条件
inline TermCriteria TC(int iters, double eps)
{
return TermCriteria(TermCriteria::MAX_ITER + (eps > 0 ? TermCriteria::EPS : 0), iters, eps);
}


//分类预测
void test_and_save_classifier(const Ptr<StatModel>& model, const Mat& data, const Mat& responses,
int ntrain_samples, int rdelta)
{
int i, nsamples_all = data.rows;
double train_hr = 0, test_hr = 0;


// compute prediction error on train and test data
for (i = 0; i < nsamples_all; i++)
{
Mat sample = data.row(i);


float r = model->predict(sample);
r = std::abs(r + rdelta - responses.at<int>(i)) <= FLT_EPSILON ? 1.f : 0.f;


if (i < ntrain_samples)
train_hr += r;
else
test_hr += r;
}


test_hr /= nsamples_all - ntrain_samples;
train_hr = ntrain_samples > 0 ? train_hr / ntrain_samples : 1.;


printf("Recognition rate: train = %.1f%%, test = %.1f%%\n",
train_hr*100., test_hr*100.);
}


//随机树分类
bool build_rtrees_classifier(const string& data_filename)
{
Mat data;
Mat responses;
read_num_class_data(data_filename, 16, &data, &responses);


int nsamples_all = data.rows;
int ntrain_samples = (int)(nsamples_all*0.8);


Ptr<RTrees> model;
Ptr<TrainData> tdata = prepare_train_data(data, responses, ntrain_samples);
model = RTrees::create();
model->setMaxDepth(10);
model->setMinSampleCount(10);
model->setRegressionAccuracy(0);
model->setUseSurrogates(false);
model->setMaxCategories(15);
model->setPriors(Mat());
model->setCalculateVarImportance(true);
model->setActiveVarCount(4);
model->setTermCriteria(TC(100, 0.01f));
model->train(tdata);
test_and_save_classifier(model, data, responses, ntrain_samples, 0);
cout << "Number of trees: " << model->getRoots().size() << endl;


// Print variable importance
Mat var_importance = model->getVarImportance();
if (!var_importance.empty())
{
double rt_imp_sum = sum(var_importance)[0];
printf("var#\timportance (in %%):\n");
int i, n = (int)var_importance.total();
for (i = 0; i < n; i++)
printf("%-2d\t%-4.1f\n", i, 100.f*var_importance.at<float>(i) / rt_imp_sum);
}


return true;
}


//adaboost分类
bool build_boost_classifier(const string& data_filename)
{
const int class_count = 26;
Mat data;
Mat responses;
Mat weak_responses;


read_num_class_data(data_filename, 16, &data, &responses);
int i, j, k;
Ptr<Boost> model;


int nsamples_all = data.rows;
int ntrain_samples = (int)(nsamples_all*0.5);
int var_count = data.cols;


Mat new_data(ntrain_samples*class_count, var_count + 1, CV_32F);
Mat new_responses(ntrain_samples*class_count, 1, CV_32S);


for (i = 0; i < ntrain_samples; i++)
{
const float* data_row = data.ptr<float>(i);
for (j = 0; j < class_count; j++)
{
float* new_data_row = (float*)new_data.ptr<float>(i*class_count + j);
memcpy(new_data_row, data_row, var_count * sizeof(data_row[0]));
new_data_row[var_count] = (float)j;
new_responses.at<int>(i*class_count + j) = responses.at<int>(i) == j + 'A';
}
}


Mat var_type(1, var_count + 2, CV_8U);
var_type.setTo(Scalar::all(VAR_ORDERED));
var_type.at<uchar>(var_count) = var_type.at<uchar>(var_count + 1) = VAR_CATEGORICAL;


Ptr<TrainData> tdata = TrainData::create(new_data, ROW_SAMPLE, new_responses,
noArray(), noArray(), noArray(), var_type);
vector<double> priors(2);
priors[0] = 1;
priors[1] = 26;


model = Boost::create();
model->setBoostType(Boost::GENTLE);
model->setWeakCount(100);
model->setWeightTrimRate(0.95);
model->setMaxDepth(5);
model->setUseSurrogates(false);
model->setPriors(Mat(priors));
model->train(tdata);
Mat temp_sample(1, var_count + 1, CV_32F);
float* tptr = temp_sample.ptr<float>();


// compute prediction error on train and test data
double train_hr = 0, test_hr = 0;
for (i = 0; i < nsamples_all; i++)
{
int best_class = 0;
double max_sum = -DBL_MAX;
const float* ptr = data.ptr<float>(i);
for (k = 0; k < var_count; k++)
tptr[k] = ptr[k];


for (j = 0; j < class_count; j++)
{
tptr[var_count] = (float)j;
float s = model->predict(temp_sample, noArray(), StatModel::RAW_OUTPUT);
if (max_sum < s)
{
max_sum = s;
best_class = j + 'A';
}
}


double r = std::abs(best_class - responses.at<int>(i)) < FLT_EPSILON ? 1 : 0;
if (i < ntrain_samples)
train_hr += r;
else
test_hr += r;
}


test_hr /= nsamples_all - ntrain_samples;
train_hr = ntrain_samples > 0 ? train_hr / ntrain_samples : 1.;
printf("Recognition rate: train = %.1f%%, test = %.1f%%\n",
train_hr*100., test_hr*100.);


cout << "Number of trees: " << model->getRoots().size() << endl;
return true;
}


//多层感知机分类（ANN）
bool build_mlp_classifier(const string& data_filename)
{
const int class_count = 26;
Mat data;
Mat responses;


read_num_class_data(data_filename, 16, &data, &responses);
Ptr<ANN_MLP> model;


int nsamples_all = data.rows;
int ntrain_samples = (int)(nsamples_all*0.8);
Mat train_data = data.rowRange(0, ntrain_samples);
Mat train_responses = Mat::zeros(ntrain_samples, class_count, CV_32F);


// 1. unroll the responses
cout << "Unrolling the responses...\n";
for (int i = 0; i < ntrain_samples; i++)
{
int cls_label = responses.at<int>(i) - 'A';
train_responses.at<float>(i, cls_label) = 1.f;
}


// 2. train classifier
int layer_sz[] = { data.cols, 100, 100, class_count };
int nlayers = (int)(sizeof(layer_sz) / sizeof(layer_sz[0]));
Mat layer_sizes(1, nlayers, CV_32S, layer_sz);


#if 1
int method = ANN_MLP::BACKPROP;
double method_param = 0.001;
int max_iter = 300;
#else
int method = ANN_MLP::RPROP;
double method_param = 0.1;
int max_iter = 1000;
#endif


Ptr<TrainData> tdata = TrainData::create(train_data, ROW_SAMPLE, train_responses);
model = ANN_MLP::create();
model->setLayerSizes(layer_sizes);
model->setActivationFunction(ANN_MLP::SIGMOID_SYM, 0, 0);
model->setTermCriteria(TC(max_iter, 0));
model->setTrainMethod(method, method_param);
model->train(tdata);
return true;
}


//K最近邻分类
bool build_knearest_classifier(const string& data_filename, int K)
{
Mat data;
Mat responses;
read_num_class_data(data_filename, 16, &data, &responses);
int nsamples_all = data.rows;
int ntrain_samples = (int)(nsamples_all*0.8);


Ptr<TrainData> tdata = prepare_train_data(data, responses, ntrain_samples);
Ptr<KNearest> model = KNearest::create();
model->setDefaultK(K);
model->setIsClassifier(true);
model->train(tdata);


test_and_save_classifier(model, data, responses, ntrain_samples, 0);
return true;
}


//贝叶斯分类
bool build_nbayes_classifier(const string& data_filename)
{
Mat data;
Mat responses;
read_num_class_data(data_filename, 16, &data, &responses);


int nsamples_all = data.rows;
int ntrain_samples = (int)(nsamples_all*0.8);


Ptr<NormalBayesClassifier> model;
Ptr<TrainData> tdata = prepare_train_data(data, responses, ntrain_samples);
model = NormalBayesClassifier::create();
model->train(tdata);


test_and_save_classifier(model, data, responses, ntrain_samples, 0);
return true;
}




//svm分类
bool build_svm_classifier(const string& data_filename)
{
Mat data;
Mat responses;
read_num_class_data(data_filename, 16, &data, &responses);


int nsamples_all = data.rows;
int ntrain_samples = (int)(nsamples_all*0.8);


Ptr<SVM> model;
Ptr<TrainData> tdata = prepare_train_data(data, responses, ntrain_samples);
model = SVM::create();
model->setType(SVM::C_SVC);
model->setKernel(SVM::LINEAR);
model->setC(1);
model->train(tdata);


test_and_save_classifier(model, data, responses, ntrain_samples, 0);
return true;
}


int main()
{
string data_filename = "letter-recognition.data";  //字母数据


cout << "svm分类：" << endl;
build_svm_classifier(data_filename);


cout << "贝叶斯分类：" << endl;
build_nbayes_classifier(data_filename);


cout << "K最近邻分类：" << endl;
build_knearest_classifier(data_filename, 10);


cout << "随机树分类：" << endl;
build_rtrees_classifier(data_filename);


//cout << "adaboost分类：" << endl;
//build_boost_classifier(data_filename);


//cout << "ANN（多层感知机)分类：" << endl;
//build_mlp_classifier(data_filename);
}

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由于adaboost分类和 ann分类速度非常慢，因此我在main函数里把这两个分类注释掉了，大家有兴趣和时间可以测试一下。

结果：

从结果显示来看，测试的四种分类算法中，KNN（最近邻）分类精度是最高的。所以说，对ocr进行识别，还是用knn最好。