树莓派python手势识别控制_OpenCV——手势识别

#include "header.h"

intmain()

{const int sample_num = 10; //训练每类图片数量

const int class_num = 3; //训练类数3:石头剪刀布

const int image_cols = 30;const int image_rows = 30;stringName, Path;float trainingData[class_num * sample_num][image_cols * image_rows] = { { 0 } }; //每一行一个训练样本

float labels[class_num * sample_num][class_num] = { { 0 } }; //训练样本标签

cout<< "training Data.........\n";for (int i = 0; i < class_num; i++){int j = 0;

Path= getstring(i + 1) + "/" + "*.*";

HANDLE hFile;

LPCTSTR lp=StringToWchar(Path);

WIN32_FIND_DATA pNextInfo;

hFile= FindFirstFile(lp, &pNextInfo);if (hFile ==INVALID_HANDLE_VALUE){

cout<< "failed" <

exit(-1);//搜索失败

}

cout<< "folder name:" << i + 1 <

if (pNextInfo.cFileName[0] == '.')continue;

cout<< "file name" << WcharToChar(pNextInfo.cFileName) <

Mat srcImage= imread(getstring(i+1) + "/" +WcharToChar(pNextInfo.cFileName), CV_LOAD_IMAGE_GRAYSCALE);

Mat trainImage;//if (!srcImage.empty())cout << " done \n";//处理样本图像

resize(srcImage, trainImage, Size(image_cols, image_rows), (0, 0), (0, 0), CV_INTER_AREA);

Canny(trainImage, trainImage,150, 100, 3, false);for (int k = 0; k < image_rows * image_cols; k++){//cout << "矩阵 k-- " << k << " j--" << j << " i--" << i << endl;

trainingData[i*sample_num + j][k] = (float)trainImage.data[k];

}

j++;

}while (FindNextFile(hFile, &pNextInfo));

}//训练好的矩阵

Mat DataMat(class_num*sample_num, image_rows*image_cols, CV_32FC1, trainingData);

cout<< "DataMat done~" <

for (int i = 0; i < class_num ; i++){for (int j = 0; j < sample_num; j++){for (int k = 0; k < class_num; k++){if (k == i)labels[i*sample_num + j][k] = 1;else labels[i*sample_num + j][k] = 0;

}

}

}//标签矩阵

Mat labelsMat(class_num*sample_num, class_num, CV_32FC1, labels);

cout<< "labelsMat done~" <

CvANN_MLP bp;

CvANN_MLP_TrainParamsparams;params.train_method =CvANN_MLP_TrainParams::BACKPROP;params.bp_dw_scale = 0.001;params.bp_moment_scale = 0.1;//cvTermCriteria 迭代终止规则

params.term_crit = cvTermCriteria(CV_TERMCRIT_ITER | CV_TERMCRIT_EPS, 10000, 0.0001);//设置网络层数

Mat layerSizes = (Mat_(1, 4) << image_rows*image_cols, int(image_rows*image_cols / 2),int(image_rows*image_cols / 2), class_num);

bp.create(layerSizes, CvANN_MLP::SIGMOID_SYM,1.0, 1.0);

cout<< "training...." <

bp.train(DataMat, labelsMat, Mat(), Mat(),params);

bp.save("detect_gesture.xml");

cout<< "done" <

cout << "testing...." <

Mat test= imread("test.jpg");

Mat temp;

resize(test, temp, Size(image_cols, image_rows), (0, 0), (0, 0), CV_INTER_AREA);

Canny(temp, temp,150, 100, 3, false);

Mat_sample(1, image_rows*image_cols);for (int i = 0; i

sample.at(0, i) = (float)temp.data[i];

}

Mat result;

bp.predict(sample, result);float* p = result.ptr(0);float max = -1, min = 0;int index = 0;for (int i = 0; i

{

cout<< (float)(*(p + i)) << " ";if (i == class_num - 1)

cout

{

min=max;

max= (float)(*(p +i));

index=i;

}else{if (min < (float)(*(p +i)))

min= (float)(*(p +i));

}

}

cout<< "Your choice :" << choice[index] << endl << "识别率:"

<< (((max - min) * 100) > 100 ? 100 : ((max - min) * 100)) <

int computer = random(3);

cout<< "computer's choice :" << choice[computer] <

cout<< "You Lose T_T" << endl <

}else cout << "You Win o *￣▽￣*" << endl <

system("pause");

waitKey(100);return 0;

}