表情识别 [传统方法 VS 深度学习方法]

数据准备：

采用 CK+ 的数据集作为训练集和验证集

深度学习流程

1.CNN 提取特征

2.FC 基于特征进行分类。

3.SoftMaxwithLoss 进行 loss 定义

建立网络如下图所示：

1.引入了inception 模块来增加网络的宽度。

2.pool3使用 ave pooling 来减少 FC层的使用，可以有效减少参数。

3.训练迭代50000次后，准确率在 97% 左右。

传统实现流程

1.使用HOG提取图片特征。

2.使用 SVM 对特征进行分类。

3.训练后查看测试集准确率（准确率 99% ，可能是因为数据不多的原因，所以验证集准确率比较高【训练集：1600张，验证集：130张】）。

代码中包含【1.使用hog特征， 2.使用lbp特征 +PCA降维 3.使用旋转不变性的lbp特征】

代码实现：

1.util.h#pragma once
#include <iostream>
#include <opencv2/opencv.hpp>namespace glasssix
{struct Label{std::string fileName;int label;};const int HOG_FEATURE = 0;const int SIFT_FEATURE = 1;const int LBP_FEATURE = 2;class DataPrepare{public:DataPrepare() {};~DataPrepare() {};int getImageHogFeature(cv::Mat &img, std::vector<float> & descriptors, cv::Size & size);int getImageSiftFeature(cv::Mat &img, std::vector<float> & descriptors, cv::Size & size);int getImageLBPFeature(cv::Mat &img, std::vector<float> & descriptors, cv::Size & size, int radias = 1);void PCA_Reduce(cv::Mat & input_train, cv::Mat & input_test, cv::Mat & output_train, cv::Mat & output_test);void readDirFile(std::string dir, std::vector<Label> & data);void getData(std::string dir, cv::Mat & data, cv::Mat & label, int flage = HOG_FEATURE);void SVM_Train(cv::Mat & data, cv::Mat &label, std::string save_path = "svm.xml");void SVM_Predict(std::string mode, cv::Mat & data, cv::Mat &label);void libSVM_Train(cv::Mat & data, cv::Mat &label, std::string save_path = "libsvm.xml");void libSVM_Predict(std::string mode, cv::Mat & data, cv::Mat &label);

     void adaboostTrain(cv::Mat & data, cv::Mat &label, std::string save_path = "adaboost.xml");void adaboostPredict(std::string mode, cv::Mat & data, cv::Mat &label);};
}2.util.cpp#include "util.h"
#include <opencv2/opencv.hpp>
#include <opencv2/xfeatures2d.hpp>
#include <opencv2/ml/ml.hpp>
#include <iostream>
#include <fstream>#include "../../../../SoftWare/libsvm-3.22/svm.h"
using namespace std;
using namespace cv;
using namespace glasssix;using namespace ml;const int SIFT_NUM = 30;
string str_label[] = { "中性", "愤怒", "蔑视", "厌恶", "恐惧", "高兴", "悲伤", "惊讶" };
template <class Type>
Type stringToNum(const string& str)
{istringstream iss(str);Type num;iss >> num;return num;
}
void  splitString(const string& s, vector<string>& v, const string& c)
{string::size_type pos1, pos2;pos2 = s.find(c);pos1 = 0;while (string::npos != pos2){v.push_back(s.substr(pos1, pos2 - pos1));pos1 = pos2 + c.size();pos2 = s.find(c, pos1);}if (pos1 != s.length()) {v.push_back(s.substr(pos1));}
}int DataPrepare::getImageHogFeature(Mat &img, vector<float> & descriptors, Size & size)
{if (img.data == NULL){cout << "No exist" << endl;return -1;}resize(img, img, size);HOGDescriptor *hog = new HOGDescriptor(size, Size(16, 16), Size(4, 4), Size(8, 8), 9);hog->compute(img, descriptors, Size(1, 1), Size(0, 0));cout << "descriptors size is :" << descriptors.size() << endl;return 0;
}
void lbp_circle(Mat& src, Mat &dst, int radius, int neighbors)
{for (int n = 0; n < neighbors; n++){// 采样点的计算float x = static_cast<float>(-radius * sin(2.0*CV_PI*n / static_cast<float>(neighbors)));float y = static_cast<float>(radius * cos(2.0*CV_PI*n / static_cast<float>(neighbors)));// 上取整和下取整的值int fx = static_cast<int>(floor(x));int fy = static_cast<int>(floor(y));int cx = static_cast<int>(ceil(x));int cy = static_cast<int>(ceil(y));// 小数部分float ty = y - fy;float tx = x - fx;// 设置插值权重float w1 = (1 - tx) * (1 - ty);float w2 = tx * (1 - ty);float w3 = (1 - tx) * ty;float w4 = tx * ty;// 循环处理图像数据for (int i = radius; i < src.rows - radius; i++){for (int j = radius; j < src.cols - radius; j++){// 计算插值float t = static_cast<float>(w1*src.at<uchar>(i + fy, j + fx) + w2*src.at<uchar>(i + fy, j + cx) + w3*src.at<uchar>(i + cy, j + fx) + w4*src.at<uchar>(i + cy, j + cx));// 进行编码  当t>=src(i,j)的时候取1，并进行相应的移位    避免 精度 损失：std::abs(t - src.at<uchar>(i, j)) < std::numeric_limits<float>::epsilon())dst.at<uchar>(i - radius, j - radius) += ((t > src.at<uchar>(i, j)) || (std::abs(t - src.at<uchar>(i, j)) < std::numeric_limits<float>::epsilon())) << n;}}}
}
//旋转不变 Mapping  range = 2^neighbors
void rotation_invariant_mapping(Mat & src, int range, int neighbors, int *Mapping)
{int newMax, rm, r;int *tmpMap;newMax = 0;tmpMap = (int *)malloc(sizeof(int)*range);memset(tmpMap, -1, sizeof(int)*range);for (int i = 0; i < range; i++){rm = i;r = i;for (int j = 0; j < neighbors - 1; j++){//将r向左循环移动一位,当r超过num_sp位时，舍弃  r = r << 1;if (r > range - 1){r = r - (range - 1);}if (r < rm){rm = r;}}if (tmpMap[rm] < 0){tmpMap[rm] = newMax;newMax++;}Mapping[i] = tmpMap[rm];}for (int i = 0; i < src.cols; i++){for (int j = 0; j < src.rows; j++){src.at<uchar>(i, j) = Mapping[src.at<uchar>(i, j)];}}free(tmpMap);
}
int calc_sum(int r)
{int res_sum;res_sum = 0;while (r){res_sum = res_sum + r % 2;r /= 2;}return res_sum;
}
//计算旋转不变 + 等价LBP特征
void rotation_uniform_invariant_mapping(Mat & src, int range, int num_sp, int *Mapping)
{int numt, i, j, tem_xor;numt = 0;tem_xor = 0;for (i = 0; i < range; i++){j = i << 1;if (j > range - 1){j = j - (range - 1);}tem_xor = i ^ j;    // 异或  numt = calc_sum(tem_xor);//计算异或结果中1的个数，即跳变个数  if (numt <= 2){Mapping[i] = calc_sum(i);}else {Mapping[i] = num_sp + 1;}}for (int i = 0; i < src.cols; i++){for (int j = 0; j < src.rows; j++){src.at<uchar>(i, j) = Mapping[src.at<uchar>(i, j)];}}
}
void lbp_normal(Mat& src, Mat &dst)
{// 循环处理图像数据for (int i = 1; i < src.rows - 1; i++){for (int j = 1; j < src.cols - 1; j++){uchar tt = 0;int tt1 = 0;uchar u = src.at<uchar>(i, j);if (src.at<uchar>(i - 1, j - 1) > u) { tt += 1 << tt1; }tt1++;if (src.at<uchar>(i - 1, j) > u) { tt += 1 << tt1; }tt1++;if (src.at<uchar>(i - 1, j + 1) > u) { tt += 1 << tt1; }tt1++;if (src.at<uchar>(i, j + 1) > u) { tt += 1 << tt1; }tt1++;if (src.at<uchar>(i + 1, j + 1) > u) { tt += 1 << tt1; }tt1++;if (src.at<uchar>(i + 1, j) > u) { tt += 1 << tt1; }tt1++;if (src.at<uchar>(i + 1, j - 1) > u) { tt += 1 << tt1; }tt1++;if (src.at<uchar>(i - 1, j) > u) { tt += 1 << tt1; }tt1++;dst.at<uchar>(i - 1, j - 1) = tt;}}
}
void lbp_to_feature(Mat & src, vector<float> &descriptors, int num)
{int rows = src.rows;int cols = src.cols;Rect block = Rect(0, 0, rows / 3, cols / 3);Mat roi;int fea[256] = { 0 };for (int k = 0; k < 3; k++){for (int n = 0; n < 3; n++){block.x = k * rows / 3;block.y = n * cols / 3;roi = src(block);fea[256] = { 0 };for (int i = 0; i < roi.rows; i++){for (int j = 0; j < roi.cols; j++){fea[roi.at<uchar>(i, j)]++;}}for (int i = 0; i < num; i++){descriptors.push_back(fea[i] / (16 * 16.0));}}}}
int DataPrepare::getImageLBPFeature(Mat &img, vector<float> & descriptors, Size & size, int radias)
{if (img.data == NULL){cout << "No exist" << endl;return -1;}resize(img, img, Size(size.width + radias * 2, size.height + radias * 2));Mat dst = Mat(img.rows - 2 * radias, img.cols - 2 * radias, CV_8UC1, Scalar(0));;//lbp_normal(img, dst);int neighbors = 8;int range = pow(2, neighbors);lbp_circle(img, dst, radias, neighbors);//int * map = (int *)malloc(sizeof(int)* range);rotation_invariant_mapping(dst, range, neighbors, map);//rotation_uniform_invariant_mapping(dst, range, neighbors, map);lbp_to_feature(dst, descriptors, 256);//cout << "descriptors is :" << descriptors.size() << "  "<< descriptors[0] << endl;return 0;
}int DataPrepare::getImageSiftFeature(cv::Mat &img, std::vector<float> & descriptors, cv::Size & size)
{if (img.data == NULL){cout << "No exist" << endl;return -1;}resize(img, img, size);Ptr<Feature2D> sift = xfeatures2d::SIFT::create(SIFT_NUM, 3, 0.01, 80);std::vector<KeyPoint> keypointsa;keypointsa.clear();Mat a;//特征点描述//sift->detectAndCompute(src, mask, keypointsa, a);//得到特征点和特征点描述sift->detectAndCompute(img, Mat(), keypointsa, a);cout << "a length is :" << a.rows << "  " << a.cols << "  " << a.channels() << endl;a.reshape(1, SIFT_NUM * 128);//cout << "a length is :" << a.rows << "  " << a.cols << "  " << a.channels() << endl;cout << "descriptors shape is :" << descriptors.size() << endl;
}void DataPrepare::readDirFile(std::string filePath, std::vector<Label> & data)
{vector<Label> result;ifstream fileA(filePath);if (!fileA){cout << "没有找到需要读取的  " << filePath << " 请将文件放到指定位置再次运行本程序。" << endl << "  按任意键以退出";}for (int i = 0; !fileA.eof(); i++){Label lb;string buf;getline(fileA, buf, '\n');if (buf == ""){cout << "buf is empty." << endl;continue;}vector<string> result;splitString(buf, result, " ");lb.fileName = result[0];lb.label = stringToNum<int>(result[1]);data.push_back(lb);}fileA.close();
}void DataPrepare::getData(std::string dir, cv::Mat & data, cv::Mat & label, int flage)
{std::vector<Label> vec_data;readDirFile(dir, vec_data);int length = vec_data.size();cout << "train image is :" << length << endl;int n = 0;int featureNum = 0;for (int i = 0; i < length; i++){Mat img = imread(vec_data[i].fileName, CV_LOAD_IMAGE_GRAYSCALE);std::vector<float> descriptors;if (flage == HOG_FEATURE){getImageHogFeature(img, descriptors, Size(48, 48));}else if (flage == SIFT_FEATURE){getImageSiftFeature(img, descriptors, Size(48, 48));}else if (flage == LBP_FEATURE){getImageLBPFeature(img, descriptors, Size(48, 48), 1);}if (i == 0){featureNum = descriptors.size();label = Mat::zeros(length, 1, CV_32SC1); //注意其中训练和自动训练的接口，还有labelMat一定要用CV_32SC1的类型data = Mat::zeros(length, descriptors.size(), CV_32FC1);}label.at<int>(i, 0) = vec_data[i].label;n = 0;for (std::vector<float>::iterator iter = descriptors.begin(); iter != descriptors.end(); iter++){data.at<float>(i, n) = *iter;n++;}}
}void DataPrepare::SVM_Train(cv::Mat & data, cv::Mat &label, string save_path)
{Ptr<SVM> svm = SVM::create();svm->setType(SVM::C_SVC);svm->setKernel(SVM::RBF);TermCriteria ctr = TermCriteria(CV_TERMCRIT_ITER + CV_TERMCRIT_EPS, 1000, FLT_EPSILON);svm->setTermCriteria(ctr);Ptr<TrainData> traindata = ml::TrainData::create(data, ROW_SAMPLE, label);double t0 = (double)cvGetTickCount();cout << "start train .." << endl;svm->trainAuto(traindata, 10);double t1 = (double)cvGetTickCount();cout << "train end . cost time is :" << ((t1 - t0) / ((double)cvGetTickFrequency() * 1000 * 1000)) / 60.0 << " minute" << endl;svm->save(save_path);//float response = svm->predict(sampleMat);//cout <<" response is :" <<response << endl;
}void DataPrepare::SVM_Predict(string mode, cv::Mat & data, cv::Mat &label)
{Ptr<SVM> model = Algorithm::load<SVM>(mode);int rows = data.rows;int cols = data.cols;Rect r;r.height = 1;r.width = cols;r.x = 0;Mat feature;int error = 0;double t0 = (double)cvGetTickCount();for (int i = 0; i < rows; i++){//float * feature = data.ptr<float>(i);r.y = i;feature = data(r);int result = model->predict(feature);if (result != label.at<int>(i, 0)){cout << "has a error   ..predict label is :" << result << "  really label is:" << label.at<int>(i, 0) << endl;cout << "predict is :" << str_label[result] << "   really label is:" << str_label[label.at<int>(i, 0)] << endl;error++;}}double t1 = (double)cvGetTickCount();cout << "average cost time is: " << ((t1 - t0) / ((double)cvGetTickFrequency() * 1000 * 1000)) * 1000.0 / rows << "ms" << endl;cout << "accuracy is : " << (float)(rows - error) / rows << endl;}void DataPrepare::PCA_Reduce(cv::Mat & input_train, cv::Mat & input_test, cv::Mat & output_train, cv::Mat & output_test)
{cout << "start pca" << endl;double t0 = (double)cvGetTickCount();PCA pca(input_train, Mat(), PCA::DATA_AS_ROW, 0.97);cout << "end pca" << endl;double t1 = (double)cvGetTickCount();cout << "cost time is: " << ((t1 - t0) / ((double)cvGetTickFrequency() * 1000 * 1000)) << "s" << endl;//cout << pca.eigenvalues << endl;//cout << pca.eigenvectors << endl;output_train = pca.project(input_train);output_test = pca.project(input_test);cout << " point size :" << output_train.rows << " " << output_train.cols << endl;
}svm_parameter param;
void init_param()
{param.svm_type = C_SVC;param.kernel_type = RBF;param.degree = 3;param.gamma = 0.1;param.coef0 = 0;param.nu = 0.5;param.cache_size = 1000;param.C = 20;param.eps = 1e-6;param.shrinking = 1;param.probability = 0;param.nr_weight = 0;param.weight_label = NULL;param.weight = NULL;
}
void DataPrepare::libSVM_Train(cv::Mat & data, cv::Mat &label, string save_path)
{init_param();int rows = data.rows;int cols = data.cols;svm_problem prob;prob.l = rows;svm_node *x_space = new svm_node[(cols + 1)*prob.l];//样本特征存储空间prob.x = new svm_node *[prob.l]; //每一个X指向一个样本cout << "size :" << sizeof(x_space) << endl;prob.y = new double[prob.l];//libsvm train data prepare.for (int i = 0; i < rows; i++){for (int j = 0; j < cols + 1; j++){if (j == cols){x_space[i*(cols + 1) + j].index = -1;prob.x[i] = &x_space[i * (cols + 1)];prob.y[i] = label.at<uchar>(i, 0);break;}x_space[i*(cols + 1) + j].index = j + 1;x_space[i*(cols + 1) + j].value = data.at<float>(i, j);}}cout << "start train svm." << endl;svm_model *model = svm_train(&prob, ¶m);cout << "save model" << endl;svm_save_model(save_path.c_str(), model);cout << "done!" << endl;delete[] x_space;delete[] prob.x;delete[] prob.y;
}
void DataPrepare::libSVM_Predict(std::string mode, cv::Mat & data, cv::Mat &label)
{svm_model* model = svm_load_model(mode.c_str());int test_cols = data.cols;int test_rows = data.rows;svm_node *test_space = new svm_node[test_cols + 1];//svm_problem prob_test;//libsvm test data prepare.int error = 0;double t0 = (double)cvGetTickCount();for (int i = 0; i < test_rows; i++){for (int j = 0; j < test_cols + 1; j++){if (j == test_cols){test_space[j].index = -1;break;}test_space[j].index = j + 1;test_space[j].value = data.at<float>(i, j);}int d = svm_predict(model, test_space);if (d != label.at<int>(i, 0)){cout << "predict is :" << d << " really is :" << label.at<int>(i, 0) << endl;error++;}}double t1 = (double)cvGetTickCount();cout << "average time is: " << ((t1 - t0) / ((double)cvGetTickFrequency() * 1000 * 1000))*1000.0 / test_rows << "ms" << endl;cout << "acurcy is :" << (float)(test_rows - error) / test_rows << endl;delete[] test_space;
}void adaboostTrain(cv::Mat & data, cv::Mat &label, std::string save_path = "adaboost.xml");void adaboostPredict(std::string mode, cv::Mat & data, cv::Mat &label);};
}2.util.cpp#include "util.h"
#include <opencv2/opencv.hpp>
#include <opencv2/xfeatures2d.hpp>
#include <opencv2/ml/ml.hpp>
#include <iostream>
#include <fstream>#include "../../../../SoftWare/libsvm-3.22/svm.h"
using namespace std;
using namespace cv;
using namespace glasssix;using namespace ml;const int SIFT_NUM = 30;
string str_label[] = { "中性", "愤怒", "蔑视", "厌恶", "恐惧", "高兴", "悲伤", "惊讶" };
template <class Type>
Type stringToNum(const string& str)
{istringstream iss(str);Type num;iss >> num;return num;
}
void  splitString(const string& s, vector<string>& v, const string& c)
{string::size_type pos1, pos2;pos2 = s.find(c);pos1 = 0;while (string::npos != pos2){v.push_back(s.substr(pos1, pos2 - pos1));pos1 = pos2 + c.size();pos2 = s.find(c, pos1);}if (pos1 != s.length()) {v.push_back(s.substr(pos1));}
}int DataPrepare::getImageHogFeature(Mat &img, vector<float> & descriptors, Size & size)
{if (img.data == NULL){cout << "No exist" << endl;return -1;}resize(img, img, size);HOGDescriptor *hog = new HOGDescriptor(size, Size(16, 16), Size(4, 4), Size(8, 8), 9);hog->compute(img, descriptors, Size(1, 1), Size(0, 0));cout << "descriptors size is :" << descriptors.size() << endl;return 0;
}
void lbp_circle(Mat& src, Mat &dst, int radius, int neighbors)
{for (int n = 0; n < neighbors; n++){// 采样点的计算float x = static_cast<float>(-radius * sin(2.0*CV_PI*n / static_cast<float>(neighbors)));float y = static_cast<float>(radius * cos(2.0*CV_PI*n / static_cast<float>(neighbors)));// 上取整和下取整的值int fx = static_cast<int>(floor(x));int fy = static_cast<int>(floor(y));int cx = static_cast<int>(ceil(x));int cy = static_cast<int>(ceil(y));// 小数部分float ty = y - fy;float tx = x - fx;// 设置插值权重float w1 = (1 - tx) * (1 - ty);float w2 = tx * (1 - ty);float w3 = (1 - tx) * ty;float w4 = tx * ty;// 循环处理图像数据for (int i = radius; i < src.rows - radius; i++){for (int j = radius; j < src.cols - radius; j++){// 计算插值float t = static_cast<float>(w1*src.at<uchar>(i + fy, j + fx) + w2*src.at<uchar>(i + fy, j + cx) + w3*src.at<uchar>(i + cy, j + fx) + w4*src.at<uchar>(i + cy, j + cx));// 进行编码  当t>=src(i,j)的时候取1，并进行相应的移位    避免 精度 损失：std::abs(t - src.at<uchar>(i, j)) < std::numeric_limits<float>::epsilon())dst.at<uchar>(i - radius, j - radius) += ((t > src.at<uchar>(i, j)) || (std::abs(t - src.at<uchar>(i, j)) < std::numeric_limits<float>::epsilon())) << n;}}}
}
//旋转不变 Mapping  range = 2^neighbors
void rotation_invariant_mapping(Mat & src, int range, int neighbors, int *Mapping)
{int newMax, rm, r;int *tmpMap;newMax = 0;tmpMap = (int *)malloc(sizeof(int)*range);memset(tmpMap, -1, sizeof(int)*range);for (int i = 0; i < range; i++){rm = i;r = i;for (int j = 0; j < neighbors - 1; j++){//将r向左循环移动一位,当r超过num_sp位时，舍弃  r = r << 1;if (r > range - 1){r = r - (range - 1);}if (r < rm){rm = r;}}if (tmpMap[rm] < 0){tmpMap[rm] = newMax;newMax++;}Mapping[i] = tmpMap[rm];}for (int i = 0; i < src.cols; i++){for (int j = 0; j < src.rows; j++){src.at<uchar>(i, j) = Mapping[src.at<uchar>(i, j)];}}free(tmpMap);
}
int calc_sum(int r)
{int res_sum;res_sum = 0;while (r){res_sum = res_sum + r % 2;r /= 2;}return res_sum;
}
//计算旋转不变 + 等价LBP特征
void rotation_uniform_invariant_mapping(Mat & src, int range, int num_sp, int *Mapping)
{int numt, i, j, tem_xor;numt = 0;tem_xor = 0;for (i = 0; i < range; i++){j = i << 1;if (j > range - 1){j = j - (range - 1);}tem_xor = i ^ j;    // 异或  numt = calc_sum(tem_xor);//计算异或结果中1的个数，即跳变个数  if (numt <= 2){Mapping[i] = calc_sum(i);}else {Mapping[i] = num_sp + 1;}}for (int i = 0; i < src.cols; i++){for (int j = 0; j < src.rows; j++){src.at<uchar>(i, j) = Mapping[src.at<uchar>(i, j)];}}
}
void lbp_normal(Mat& src, Mat &dst)
{// 循环处理图像数据for (int i = 1; i < src.rows - 1; i++){for (int j = 1; j < src.cols - 1; j++){uchar tt = 0;int tt1 = 0;uchar u = src.at<uchar>(i, j);if (src.at<uchar>(i - 1, j - 1) > u) { tt += 1 << tt1; }tt1++;if (src.at<uchar>(i - 1, j) > u) { tt += 1 << tt1; }tt1++;if (src.at<uchar>(i - 1, j + 1) > u) { tt += 1 << tt1; }tt1++;if (src.at<uchar>(i, j + 1) > u) { tt += 1 << tt1; }tt1++;if (src.at<uchar>(i + 1, j + 1) > u) { tt += 1 << tt1; }tt1++;if (src.at<uchar>(i + 1, j) > u) { tt += 1 << tt1; }tt1++;if (src.at<uchar>(i + 1, j - 1) > u) { tt += 1 << tt1; }tt1++;if (src.at<uchar>(i - 1, j) > u) { tt += 1 << tt1; }tt1++;dst.at<uchar>(i - 1, j - 1) = tt;}}
}
void lbp_to_feature(Mat & src, vector<float> &descriptors, int num)
{int rows = src.rows;int cols = src.cols;Rect block = Rect(0, 0, rows / 3, cols / 3);Mat roi;int fea[256] = { 0 };for (int k = 0; k < 3; k++){for (int n = 0; n < 3; n++){block.x = k * rows / 3;block.y = n * cols / 3;roi = src(block);fea[256] = { 0 };for (int i = 0; i < roi.rows; i++){for (int j = 0; j < roi.cols; j++){fea[roi.at<uchar>(i, j)]++;}}for (int i = 0; i < num; i++){descriptors.push_back(fea[i] / (16 * 16.0));}}}}
int DataPrepare::getImageLBPFeature(Mat &img, vector<float> & descriptors, Size & size, int radias)
{if (img.data == NULL){cout << "No exist" << endl;return -1;}resize(img, img, Size(size.width + radias * 2, size.height + radias * 2));Mat dst = Mat(img.rows - 2 * radias, img.cols - 2 * radias, CV_8UC1, Scalar(0));;//lbp_normal(img, dst);int neighbors = 8;int range = pow(2, neighbors);lbp_circle(img, dst, radias, neighbors);//int * map = (int *)malloc(sizeof(int)* range);rotation_invariant_mapping(dst, range, neighbors, map);//rotation_uniform_invariant_mapping(dst, range, neighbors, map);lbp_to_feature(dst, descriptors, 256);//cout << "descriptors is :" << descriptors.size() << "  "<< descriptors[0] << endl;return 0;
}int DataPrepare::getImageSiftFeature(cv::Mat &img, std::vector<float> & descriptors, cv::Size & size)
{if (img.data == NULL){cout << "No exist" << endl;return -1;}resize(img, img, size);Ptr<Feature2D> sift = xfeatures2d::SIFT::create(SIFT_NUM, 3, 0.01, 80);std::vector<KeyPoint> keypointsa;keypointsa.clear();Mat a;//特征点描述//sift->detectAndCompute(src, mask, keypointsa, a);//得到特征点和特征点描述sift->detectAndCompute(img, Mat(), keypointsa, a);cout << "a length is :" << a.rows << "  " << a.cols << "  " << a.channels() << endl;a.reshape(1, SIFT_NUM * 128);//cout << "a length is :" << a.rows << "  " << a.cols << "  " << a.channels() << endl;cout << "descriptors shape is :" << descriptors.size() << endl;
}void DataPrepare::readDirFile(std::string filePath, std::vector<Label> & data)
{vector<Label> result;ifstream fileA(filePath);if (!fileA){cout << "没有找到需要读取的  " << filePath << " 请将文件放到指定位置再次运行本程序。" << endl << "  按任意键以退出";}for (int i = 0; !fileA.eof(); i++){Label lb;string buf;getline(fileA, buf, '\n');if (buf == ""){cout << "buf is empty." << endl;continue;}vector<string> result;splitString(buf, result, " ");lb.fileName = result[0];lb.label = stringToNum<int>(result[1]);data.push_back(lb);}fileA.close();
}void DataPrepare::getData(std::string dir, cv::Mat & data, cv::Mat & label, int flage)
{std::vector<Label> vec_data;readDirFile(dir, vec_data);int length = vec_data.size();cout << "train image is :" << length << endl;int n = 0;int featureNum = 0;for (int i = 0; i < length; i++){Mat img = imread(vec_data[i].fileName, CV_LOAD_IMAGE_GRAYSCALE);std::vector<float> descriptors;if (flage == HOG_FEATURE){getImageHogFeature(img, descriptors, Size(48, 48));}else if (flage == SIFT_FEATURE){getImageSiftFeature(img, descriptors, Size(48, 48));}else if (flage == LBP_FEATURE){getImageLBPFeature(img, descriptors, Size(48, 48), 1);}if (i == 0){featureNum = descriptors.size();label = Mat::zeros(length, 1, CV_32SC1); //注意其中训练和自动训练的接口，还有labelMat一定要用CV_32SC1的类型data = Mat::zeros(length, descriptors.size(), CV_32FC1);}label.at<int>(i, 0) = vec_data[i].label;n = 0;for (std::vector<float>::iterator iter = descriptors.begin(); iter != descriptors.end(); iter++){data.at<float>(i, n) = *iter;n++;}}
}void DataPrepare::SVM_Train(cv::Mat & data, cv::Mat &label, string save_path)
{Ptr<SVM> svm = SVM::create();svm->setType(SVM::C_SVC);svm->setKernel(SVM::RBF);TermCriteria ctr = TermCriteria(CV_TERMCRIT_ITER + CV_TERMCRIT_EPS, 1000, FLT_EPSILON);svm->setTermCriteria(ctr);Ptr<TrainData> traindata = ml::TrainData::create(data, ROW_SAMPLE, label);double t0 = (double)cvGetTickCount();cout << "start train .." << endl;svm->trainAuto(traindata, 10);double t1 = (double)cvGetTickCount();cout << "train end . cost time is :" << ((t1 - t0) / ((double)cvGetTickFrequency() * 1000 * 1000)) / 60.0 << " minute" << endl;svm->save(save_path);//float response = svm->predict(sampleMat);//cout <<" response is :" <<response << endl;
}void DataPrepare::SVM_Predict(string mode, cv::Mat & data, cv::Mat &label)
{Ptr<SVM> model = Algorithm::load<SVM>(mode);int rows = data.rows;int cols = data.cols;Rect r;r.height = 1;r.width = cols;r.x = 0;Mat feature;int error = 0;double t0 = (double)cvGetTickCount();for (int i = 0; i < rows; i++){//float * feature = data.ptr<float>(i);r.y = i;feature = data(r);int result = model->predict(feature);if (result != label.at<int>(i, 0)){cout << "has a error   ..predict label is :" << result << "  really label is:" << label.at<int>(i, 0) << endl;cout << "predict is :" << str_label[result] << "   really label is:" << str_label[label.at<int>(i, 0)] << endl;error++;}}double t1 = (double)cvGetTickCount();cout << "average cost time is: " << ((t1 - t0) / ((double)cvGetTickFrequency() * 1000 * 1000)) * 1000.0 / rows << "ms" << endl;cout << "accuracy is : " << (float)(rows - error) / rows << endl;}void DataPrepare::PCA_Reduce(cv::Mat & input_train, cv::Mat & input_test, cv::Mat & output_train, cv::Mat & output_test)
{cout << "start pca" << endl;double t0 = (double)cvGetTickCount();PCA pca(input_train, Mat(), PCA::DATA_AS_ROW, 0.97);cout << "end pca" << endl;double t1 = (double)cvGetTickCount();cout << "cost time is: " << ((t1 - t0) / ((double)cvGetTickFrequency() * 1000 * 1000)) << "s" << endl;//cout << pca.eigenvalues << endl;//cout << pca.eigenvectors << endl;output_train = pca.project(input_train);output_test = pca.project(input_test);cout << " point size :" << output_train.rows << " " << output_train.cols << endl;
}svm_parameter param;
void init_param()
{param.svm_type = C_SVC;param.kernel_type = RBF;param.degree = 3;param.gamma = 0.1;param.coef0 = 0;param.nu = 0.5;param.cache_size = 1000;param.C = 20;param.eps = 1e-6;param.shrinking = 1;param.probability = 0;param.nr_weight = 0;param.weight_label = NULL;param.weight = NULL;
}
void DataPrepare::libSVM_Train(cv::Mat & data, cv::Mat &label, string save_path)
{init_param();int rows = data.rows;int cols = data.cols;svm_problem prob;prob.l = rows;svm_node *x_space = new svm_node[(cols + 1)*prob.l];//样本特征存储空间prob.x = new svm_node *[prob.l]; //每一个X指向一个样本cout << "size :" << sizeof(x_space) << endl;prob.y = new double[prob.l];//libsvm train data prepare.for (int i = 0; i < rows; i++){for (int j = 0; j < cols + 1; j++){if (j == cols){x_space[i*(cols + 1) + j].index = -1;prob.x[i] = &x_space[i * (cols + 1)];prob.y[i] = label.at<uchar>(i, 0);break;}x_space[i*(cols + 1) + j].index = j + 1;x_space[i*(cols + 1) + j].value = data.at<float>(i, j);}}cout << "start train svm." << endl;svm_model *model = svm_train(&prob, ¶m);cout << "save model" << endl;svm_save_model(save_path.c_str(), model);cout << "done!" << endl;delete[] x_space;delete[] prob.x;delete[] prob.y;
}
void DataPrepare::libSVM_Predict(std::string mode, cv::Mat & data, cv::Mat &label)
{svm_model* model = svm_load_model(mode.c_str());int test_cols = data.cols;int test_rows = data.rows;svm_node *test_space = new svm_node[test_cols + 1];//svm_problem prob_test;//libsvm test data prepare.int error = 0;double t0 = (double)cvGetTickCount();for (int i = 0; i < test_rows; i++){for (int j = 0; j < test_cols + 1; j++){if (j == test_cols){test_space[j].index = -1;break;}test_space[j].index = j + 1;test_space[j].value = data.at<float>(i, j);}int d = svm_predict(model, test_space);if (d != label.at<int>(i, 0)){cout << "predict is :" << d << " really is :" << label.at<int>(i, 0) << endl;error++;}}double t1 = (double)cvGetTickCount();cout << "average time is: " << ((t1 - t0) / ((double)cvGetTickFrequency() * 1000 * 1000))*1000.0 / test_rows << "ms" << endl;cout << "acurcy is :" << (float)(test_rows - error) / test_rows << endl;delete[] test_space;
}

void DataPrepare::adaboostTrain(cv::Mat & data, cv::Mat &label, std::string save_path)
{   Ptr<Boost> boost = Boost::create();int i, j, k;Ptr<Boost> model;int class_count = 8;int ntrain_samples = data.rows;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, 0) = label.at<int>(i, 0) == j;}}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] = class_count;model = Boost::create();model->setBoostType(Boost::GENTLE);model->setWeakCount(100);model->setWeightTrimRate(0.95);model->setMaxDepth(5);model->setUseSurrogates(false);model->setPriors(Mat(priors));double t0 = (double)cvGetTickCount();cout << "start train .." << endl;model->train(tdata);double t1 = (double)cvGetTickCount();cout << "train end . cost time is :" << ((t1 - t0) / ((double)cvGetTickFrequency() * 1000 * 1000)) / 60.0 << " minute" << endl;model->save(save_path);Mat temp_sample(1, var_count + 1, CV_32F);float* tptr = temp_sample.ptr<float>();// compute prediction error on train and test datadouble train_hr = 0, test_hr = 0;for (i = 0; i < ntrain_samples; 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;}}double r = std::abs(best_class - label.at<int>(i)) < FLT_EPSILON ? 1 : 0;if (i < ntrain_samples)train_hr += r;elsetest_hr += r;}test_hr /= ntrain_samples - 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;
}
void DataPrepare::adaboostPredict(std::string mode, cv::Mat & data, cv::Mat &label)
{Ptr<Boost> model = Algorithm::load<Boost>(mode);int rows = data.rows;int cols = data.cols;int class_count = 8;double t0 = (double)cvGetTickCount();Mat temp_sample(1, cols + 1, CV_32F);int i, j, k;float* tptr = temp_sample.ptr<float>();// compute prediction error on train and test datadouble test_hr = 0;for (i = 0; i < rows; i++){int best_class = 0;double max_sum = -DBL_MAX;const float* ptr = data.ptr<float>(i);for (k = 0; k < cols; k++)tptr[k] = ptr[k];for (j = 0; j < class_count; j++){tptr[cols] = (float)j;float s = model->predict(temp_sample, noArray(), StatModel::RAW_OUTPUT);if (max_sum < s){max_sum = s;best_class = j;}}double r = std::abs(best_class - label.at<int>(i)) < FLT_EPSILON ? 1 : 0;test_hr += r;}test_hr = test_hr / rows;//train_hr = ntrain_samples > 0 ? train_hr / ntrain_samples : 1.;printf("Recognition rate:test = %.1f%%\n", test_hr*100.);double t1 = (double)cvGetTickCount();cout << "average cost time is: " << ((t1 - t0) / ((double)cvGetTickFrequency() * 1000 * 1000)) * 1000.0 / rows << "ms" << endl;
}    Ptr<Boost> boost = Boost::create();int i, j, k;Ptr<Boost> model;int class_count = 8;int ntrain_samples = data.rows;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, 0) = label.at<int>(i, 0) == j;}}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] = class_count;model = Boost::create();model->setBoostType(Boost::GENTLE);model->setWeakCount(100);model->setWeightTrimRate(0.95);model->setMaxDepth(5);model->setUseSurrogates(false);model->setPriors(Mat(priors));double t0 = (double)cvGetTickCount();cout << "start train .." << endl;model->train(tdata);double t1 = (double)cvGetTickCount();cout << "train end . cost time is :" << ((t1 - t0) / ((double)cvGetTickFrequency() * 1000 * 1000)) / 60.0 << " minute" << endl;model->save(save_path);Mat temp_sample(1, var_count + 1, CV_32F);float* tptr = temp_sample.ptr<float>();// compute prediction error on train and test datadouble train_hr = 0, test_hr = 0;for (i = 0; i < ntrain_samples; 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;}}double r = std::abs(best_class - label.at<int>(i)) < FLT_EPSILON ? 1 : 0;if (i < ntrain_samples)train_hr += r;elsetest_hr += r;}test_hr /= ntrain_samples - 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;
}
void DataPrepare::adaboostPredict(std::string mode, cv::Mat & data, cv::Mat &label)
{Ptr<Boost> model = Algorithm::load<Boost>(mode);int rows = data.rows;int cols = data.cols;int class_count = 8;double t0 = (double)cvGetTickCount();Mat temp_sample(1, cols + 1, CV_32F);int i, j, k;float* tptr = temp_sample.ptr<float>();// compute prediction error on train and test datadouble test_hr = 0;for (i = 0; i < rows; i++){int best_class = 0;double max_sum = -DBL_MAX;const float* ptr = data.ptr<float>(i);for (k = 0; k < cols; k++)tptr[k] = ptr[k];for (j = 0; j < class_count; j++){tptr[cols] = (float)j;float s = model->predict(temp_sample, noArray(), StatModel::RAW_OUTPUT);if (max_sum < s){max_sum = s;best_class = j;}}double r = std::abs(best_class - label.at<int>(i)) < FLT_EPSILON ? 1 : 0;test_hr += r;}test_hr = test_hr / rows;//train_hr = ntrain_samples > 0 ? train_hr / ntrain_samples : 1.;printf("Recognition rate:test = %.1f%%\n", test_hr*100.);double t1 = (double)cvGetTickCount();cout << "average cost time is: " << ((t1 - t0) / ((double)cvGetTickFrequency() * 1000 * 1000)) * 1000.0 / rows << "ms" << endl;
}

3.main.cpp#include <iostream>
#include <opencv2/core/core.hpp>
#include <opencv2/xfeatures2d.hpp>
#include <opencv2/opencv.hpp>#include "util/util.h"#include "../../../SoftWare/libsvm-3.22/svm.h"using namespace cv;
using namespace std;
using namespace glasssix;string base = "C:\\WorkSpace\\Word\\emotion_reconginzed\\data_ck\\";
string train_text = base + "really_path_train.txt";
string test_text = base + "really_path_test.txt";
string mode = "svm.xml";
string svm_mode = "modelFileName.xml";int main(int argc, char** argv)
{DataPrepare dp;/*//1. HOG + SVMstd::vector<Label> data;Mat train_data, train_label;Mat test_data, test_label;dp.getData(train_text, train_data, train_label);dp.getData(test_text, test_data, test_label);dp.SVM_Train(train_data, train_label, mode);dp.SVM_Predict(mode, test_data, test_label);*/

        /*// HOG + adabooststd::vector<Label> data;Mat train_data, train_label;Mat test_data, test_label;dp.getData(train_text, train_data, train_label);dp.getData(test_text, test_data, test_label);//dp.adaboostTrain(train_data, train_label, ada_mode);dp.adaboostPredict(ada_mode, test_data, test_label);*///2. HOG + libsvmstd::vector<Label> data;Mat train_data, train_label;Mat test_data, test_label;dp.getData(train_text, train_data, train_label);dp.getData(test_text, test_data, test_label);dp.libSVM_Train(train_data, train_label, svm_mode);dp.libSVM_Predict(svm_mode, test_data, test_label);/*//3. LBP + PCA + SVMstd::vector<Label> data;Mat train_data, train_label;Mat test_data, test_label;dp.getData(train_text, train_data, train_label, LBP_FEATURE);dp.getData(test_text, test_data, test_label, LBP_FEATURE);Mat output_train, output_test;dp.PCA_Reduce(train_data, test_data, output_train, output_test);dp.SVM_Train(output_train, train_label, mode);dp.SVM_Predict(mode, output_test, test_label);*/system("PAUSE");return 0;
}
 std::vector<Label> data;Mat train_data, train_label;Mat test_data, test_label;dp.getData(train_text, train_data, train_label);dp.getData(test_text, test_data, test_label);//dp.adaboostTrain(train_data, train_label, ada_mode);dp.adaboostPredict(ada_mode, test_data, test_label);*///2. HOG + libsvmstd::vector<Label> data;Mat train_data, train_label;Mat test_data, test_label;dp.getData(train_text, train_data, train_label);dp.getData(test_text, test_data, test_label);dp.libSVM_Train(train_data, train_label, svm_mode);dp.libSVM_Predict(svm_mode, test_data, test_label);/*//3. LBP + PCA + SVMstd::vector<Label> data;Mat train_data, train_label;Mat test_data, test_label;dp.getData(train_text, train_data, train_label, LBP_FEATURE);dp.getData(test_text, test_data, test_label, LBP_FEATURE);Mat output_train, output_test;dp.PCA_Reduce(train_data, test_data, output_train, output_test);dp.SVM_Train(output_train, train_label, mode);dp.SVM_Predict(mode, output_test, test_label);*/system("PAUSE");return 0;
}

表情识别 [传统方法 VS 深度学习方法]相关推荐

论文阅读：（2020版）A Survey on Deep Learning for Named Entity Recognition 命名实体识别中的深度学习方法
A Survey on Deep Learning for Named Entity Recognition 命名实体识别中的深度学习方法目录 A Survey on Deep Learning f ...
全面涵盖传统方法和深度学习方法：3D人脸识别的十年发展及未来趋势
©作者 | 机器之心编辑部来源 | 机器之心人脸识别是机器学习社区研究最多的课题之一,以 3D 人脸识别为代表的相关 ML 技术十年来都有哪些进展?这篇文章给出了答案. 近年来,人脸识别的研究已经 ...
【蜂口 | AI人工智能】表情识别——龙鹏深度学习与人脸图像应用连载（七）...
[文章首发于蜂口知道公众号,内容来源于蜂口小程序,欢迎关注了解~] 作者:龙鹏,前360AI研究员算法工程师,现任陌陌深度学习实验室高级算法工程师. 本次继续给大家带来的是表情识别这个问题. 我们将 ...
指纹识别综述(10): 深度学习方法
指纹识别综述系列 1.导论:2.指纹传感器:3.特征提取:4.匹配:5.分类与检索:6.现场指纹识别: 7.指纹合成: 8.唯一性: 9.指纹系统安全:10.深度学习方法:11.专利. 本文会不定期更 ...
Image Matting传统方法和深度学习方法回顾
Image Matting课题回顾 Introduction 简单图像的Image Matting--证件照换背景复杂图像的Image Matting 1. Deep Image Matting 2 ...
单应性Homography梳理，概念解释，传统方法，深度学习方法
Homography 这篇博客比较清晰准确的介绍了关于刚性变换,仿射变换,透视投影变换的理解单应性变换的条件和表示用 [无镜头畸变] 的相机从不同位置拍摄 [同一平面物体] 的图像之间存在单应 ...
Micro-expression recognition with supervised contrastive learning基于对比监督学习的微表情识别
Micro-expression recognition with supervised contrastive learning基于对比监督学习的微表情识别--2022 PRL 文章链接:https ...
表情识别综述论文《Deep Facial Expression Recognition: A Survey》中文翻译
本篇博客为论文<Deep Facial Expression Recognition: A Survey>的中文翻译,如有翻译错误请见谅,同时希望您能为我提出改正建议,谢谢! 论文链接:h ...
python人脸识别框很小_人脸识别：从传统方法到深度学习
人脸识别:从传统方法到深度学习这开始于上世纪七十年代,人脸识别成为了计算机视觉领域和生物识别领域最具有研究型的话题之一.传统方法依赖于手工制作模型特征,通过深度神经网络训练大量的数据集的方法也在最近 ...
温故而知新，6位顶级CV科学家聚首：计算机视觉中的深度学习方法vs传统方法...
来源:AI科技评论作者:Mr Bear 编辑:青暮 2021 年 10 月 13 日,来自麻省理工学院.加州大学伯克利分校.伊利诺伊大学香槟分校.华盛顿大学.帝国理工学院的六名顶级人工智能科学家.计 ...

表情识别 [传统方法 VS 深度学习方法]

数据准备：

深度学习流程

传统实现流程

表情识别 [传统方法 VS 深度学习方法]相关推荐

最新文章

热门文章

表情识别 [传统方法 VS 深度学习方法]

数据准备：

深度学习 流程

传统实现流程

表情识别 [传统方法 VS 深度学习方法]相关推荐

最新文章

热门文章

深度学习流程