利用公开肤色数据集训练了一个肤色分类器，OpenCV中有现成的方法，拿来即用。在实际项目中应用，自制数据样本会更有效。
Talk is cheap. Show you the code.
#include <opencv2/core/core.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/ml/ml.hpp>
#include <fstream>
#include <iostream>
#ifdef _DEBUG
#pragma comment(lib,"opencv_core249d.lib")
#pragma comment(lib,"opencv_highgui249d.lib")
#pragma comment(lib,"opencv_imgproc249d.lib")
#pragma comment(lib,"opencv_ml249d.lib")
#else
#pragma comment(lib,"opencv_core249.lib")
#pragma comment(lib,"opencv_highgui249.lib")
#pragma comment(lib,"opencv_imgproc249.lib")
#pragma comment(lib,"opencv_ml249.lib")
#endif
using namespace std;
using namespace cv;
// split the data & label
bool changeDataType(vector<Vec4i> source, Mat & data, Mat & labels )
{
int nsample = source.size();
Mat bgr_data = Mat(nsample,1,CV_8UC3);
Mat tmp_labels = Mat(nsample,1,CV_32F);
for (int i = 0; i < nsample; i++ )
{
// BGR
bgr_data.ptr<uchar>(i)[0] = source[i][0];
bgr_data.ptr<uchar>(i)[1] = source[i][1];
bgr_data.ptr<uchar>(i)[2] = source[i][2];
// label
tmp_labels.ptr<float>(i)[0] = source[i][3];
}
Mat ycrcb_data;
cvtColor(bgr_data,ycrcb_data,CV_BGR2YCrCb);
Mat tmp_ycrcb_data = ycrcb_data.reshape(1,nsample);
tmp_ycrcb_data.convertTo(data,CV_32F);
tmp_labels.copyTo(labels);
return true;
}
// select crcb channel
bool ycrcb2crcb(Mat& ycrcb, Mat& crcb )
{
assert(ycrcb.type() == CV_32F);
if(!crcb.empty())
crcb.release();
int n = ycrcb.rows;
crcb = Mat(n,2,CV_32F);
for (int i =0 ; i < n; i++)
{
crcb.ptr<float>(i)[0] = ycrcb.ptr<float>(i)[1];
crcb.ptr<float>(i)[1] = ycrcb.ptr<float>(i)[2];
}
return true;
}
int _tmain(int argc, _TCHAR* argv[])
{
//////////////////////////////////////////////////////////////////////////
// load train data
string datafile = "skin_dataset/Skin_NonSkin.txt";
ifstream fp(datafile);
if(!fp.is_open()) return -1;
vector<Vec4i> total_data_labels;
while(!fp.eof())
{
int R = 0 ,G = 0 ,B = 0 ;
int label = 0;
fp >> B>>G>>R>> label;
if(label != 1 && label != 2 )
continue;
total_data_labels.push_back(Vec4i(B,G,R,label));
}
int nsample = total_data_labels.size();
cout<<"Load "<<nsample<<" samples: "<<endl;
//////////////////////////////////////////////////////////////////////////
// split training data and test samples
random_shuffle(total_data_labels.begin(),total_data_labels.end());
int train_n = 0.9*nsample;
vector<Vec4i> train_data_labels,test_data_labels;
for (int i = 0 ; i < nsample; i++)
{
if(i < train_n)
train_data_labels.push_back(total_data_labels[i]);
else
test_data_labels.push_back(total_data_labels[i]);
}
cout<<" split: "<< train_n<<endl;
cout<<" train samples: "<<train_data_labels.size()<<" "<<test_data_labels.size()<<endl;
cout<<"======================"<<endl;
// change data type
Mat train_data,train_labels;
changeDataType(train_data_labels,train_data,train_labels);
Mat train_crcb_data;
ycrcb2crcb(train_data,train_crcb_data);
//////////////////////////////////////////////////////////////////////////
// BayesClassifier
cout<<"Bayes training: "<<endl;
NormalBayesClassifier *bayes = new NormalBayesClassifier();
bayes->train(train_crcb_data,train_labels,Mat(),Mat());
cout<<"======================"<<endl;
//////////////////////////////////////////////////////////////////////////
// test sample predict
cout<<"Test sample predict: "<<endl;
Mat test_data,test_labels;
changeDataType(test_data_labels,test_data,test_labels);
Mat test_crcb_data;
ycrcb2crcb(test_data,test_crcb_data);
Mat result(test_data.rows, test_data.cols, CV_32F);
bayes->predict(test_crcb_data,&result);
int err = 0;
for (int i = 0; i < test_data.rows; i++){
int trueRes = test_labels.ptr<float>(i)[0];
float predictRes = result.ptr<float>(i)[0];
if (fabs(trueRes - predictRes) >= FLT_EPSILON){
err++;
}
}
cout <<" error No.:"<< err << " precision: "<< 100-err*1.f / test_data.rows * 100 << "%
";
//////////////////////////////////////////////////////////////////////////
// test image
cout<<"Test natural image: "<<endl;
Mat source = imread("image/zdm.jpg",1);
if(source.empty())
return -1;
Mat ycrcb_image;
cvtColor(source,ycrcb_image,CV_BGR2YCrCb);
int width = source.cols;
int height = source.rows;
Mat ycrcb_image_test_matrix = ycrcb_image.reshape(1,width*height);
Mat ycrcb_image_test_matrix32f;
ycrcb_image_test_matrix.convertTo(ycrcb_image_test_matrix32f,CV_32F);
Mat crcb_image_test_matrix32f;
ycrcb2crcb(ycrcb_image_test_matrix32f,crcb_image_test_matrix32f);
double t1 = getTickCount();
Mat output(ycrcb_image_test_matrix32f.rows, ycrcb_image_test_matrix32f.cols, CV_32F);
bayes->predict(crcb_image_test_matrix32f,&output);
Mat mask = Mat(height,width,CV_8U);
for (int i = 0; i < height; i++)
for(int j = 0 ; j < width; j++)
{
float predictRes = output.ptr<float>(i*width + j)[0];
if(fabs(predictRes - 1) <= FLT_EPSILON)
mask.ptr<uchar>(i)[j] = 255;
else
mask.ptr<uchar>(i)[j] = 0;
}
double t2 = getTickCount();
cout<<"times: "<<(t2-t1)*1000/getTickFrequency()<<" ms"<<endl;
//////////////////////////////////////////////////////////////////////////
// dilate
Mat element = getStructuringElement(MORPH_CROSS,Size(5,5));
dilate( mask, mask, element );
erode(mask,mask,element);
Mat foot;
source.copyTo(foot,mask);
imwrite("zdm_crcb.jpg",mask);
namedWindow("foot",0);
imshow("foot",mask);
waitKey();
return 0;
}
>>>
Load 245057 samples:
split: 220551
train samples: 220551 24506
======================
Bayes training:
======================
Test sample predict:
error No.:103 precision: 99.5797%
Test natural image:
Input image size: 649824 3
times: 243.396 ms
测试结果：
source:
output:
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