update code and src

update code and src

src/network.cpp

@@ -50,7 +50,7 @@ void image2Matrix(const Mat &image, const struct pBox *pbox, int num) {
     mydataFmt mymean, mystddev;
     // prewhiten
     if (num != 0) {
-        meanAndDev(image, &mymean, &mystddev);
+        meanAndDev(image, mymean, mystddev);
         cout << mymean << "----" << mystddev << endl;
         size = image.cols * image.rows * image.channels();
         sqr = sqrt(double(size));
@@ -78,7 +78,7 @@ void image2Matrix(const Mat &image, const struct pBox *pbox, int num) {
     }
 }
 
-void meanAndDev(const Mat &image, mydataFmt *p, mydataFmt *q) {
+void meanAndDev(const Mat &image, mydataFmt &p, mydataFmt &q) {
     mydataFmt meansum = 0, stdsum = 0;
     for (int rowI = 0; rowI < image.rows; rowI++) {
         for (int colK = 0; colK < image.cols; colK++) {
@@ -88,15 +88,15 @@ void meanAndDev(const Mat &image, mydataFmt *p, mydataFmt *q) {
 //            cout << int(image.at<Vec3b>(rowI, colK)[2]) << endl;
         }
     }
-    *p = meansum / (image.cols * image.rows * image.channels());
+    p = meansum / (image.cols * image.rows * image.channels());
     for (int rowI = 0; rowI < image.rows; rowI++) {
         for (int colK = 0; colK < image.cols; colK++) {
-            stdsum += pow((image.at<Vec3b>(rowI, colK)[0] - *p), 2) +
-                      pow((image.at<Vec3b>(rowI, colK)[1] - *p), 2) +
-                      pow((image.at<Vec3b>(rowI, colK)[2] - *p), 2);
+            stdsum += pow((image.at<Vec3b>(rowI, colK)[0] - p), 2) +
+                      pow((image.at<Vec3b>(rowI, colK)[1] - p), 2) +
+                      pow((image.at<Vec3b>(rowI, colK)[2] - p), 2);
         }
     }
-    *q = sqrt(stdsum / (image.cols * image.rows * image.channels()));
+    q = sqrt(stdsum / (image.cols * image.rows * image.channels()));
 }
 
 void featurePadInit(const pBox *pbox, pBox *outpBox, const int pad, const int padw, const int padh) {
@@ -682,10 +682,12 @@ void BatchNorm(struct pBox *pbox, struct BN *var, struct BN *mean, struct BN *be
     mydataFmt *vp = var->pdata;
     mydataFmt *mp = mean->pdata;
     mydataFmt *bp = beta->pdata;
-    double scale = 0.995;
-    double bias = 0.0010000000474974513;
+    int gamma = 1;
+    float epsilon = 0.001;
     long dis = pbox->width * pbox->height;
+    mydataFmt temp = 0;
     for (int channel = 0; channel < pbox->channel; channel++) {
+        temp = gamma / sqrt(((vp[channel]) + epsilon));
         for (int col = 0; col < dis; col++) {
 //            *pp = *pp + *vp;
 //            cout << ((*pp) / (sqrt(*vp + bias))) << endl;
@@ -693,12 +695,12 @@ void BatchNorm(struct pBox *pbox, struct BN *var, struct BN *mean, struct BN *be
 //            if (*pp == 0) {
 //                cout << *vp << "===" << *mp << "===" << *bp << endl;
 //            }
-            *pp = ((*pp) * (scale) / (sqrt(*vp + bias))) + ((*bp) - (((*pp) * (*mp) * (scale)) / (sqrt(*vp + bias))));
+            *pp = temp * (*pp) + ((bp[channel]) - temp * (mp[channel]));
 //            cout << *pp << endl;
             pp++;
         }
-        vp++;
-        mp++;
-        bp++;
+//        vp++;
+//        mp++;
+//        bp++;
     }
 }

src/network.h

@@ -63,7 +63,7 @@ void vectorXmatrix(mydataFmt *matrix, mydataFmt *v, int size, int v_w, int v_h,
 
 void convolution(const Weight *weight, const pBox *pbox, pBox *outpBox);
 
-void meanAndDev(const Mat &image, mydataFmt *p, mydataFmt *q);
+void meanAndDev(const Mat &image, mydataFmt &p, mydataFmt &q);
 
 void initBN(struct BN *var, struct BN *mean, struct BN *beta, int width);
 

src/pBox.h

@@ -9,7 +9,7 @@
 using namespace std;
 //#define mydataFmt double
 #define Num 128
-typedef double mydataFmt;
+typedef float mydataFmt;
 
 
 struct pBox : public cv::String {

src/pikaqiu.cpp

@@ -56,16 +56,16 @@ float compare(vector<mydataFmt> &lineArray0, vector<mydataFmt> &lineArray1) {
     return result;
 }
 
-void run_mtcnn(Mat &image, vector<Rect> &vecRect) {
-    vector<Point> vecPoint;
+void run_mtcnn(Mat &image, vector<Rect> &vecRect, vector<Point> &vecPoint) {
+//    vector<Point> vecPoint;
     mtcnn find(image.rows, image.cols);
     find.findFace(image, vecRect, vecPoint);
-    for (int i = 0; i < vecRect.size(); ++i) {
-        rectangle(image, vecPoint[7 * i + 0], vecPoint[7 * i + 1], Scalar(0, 0, 255), 2, 8, 0);
-        for (int num = 0; num < 5; num++)
-            circle(image, vecPoint[7 * i + num + 2], 2, Scalar(0, 255, 255),
-                   -1);
-    }
+//    for (int i = 0; i < vecRect.size(); ++i) {
+//        rectangle(image, vecPoint[7 * i + 0], vecPoint[7 * i + 1], Scalar(0, 0, 255), 2, 8, 0);
+//        for (int num = 0; num < 5; num++)
+//            circle(image, vecPoint[7 * i + num + 2], 2, Scalar(0, 255, 255),
+//                   -1);
+//    }
 }
 
 void run_facenet(Mat &image, vector<Rect> &vecRect, int csv_num = 0) {
@@ -84,37 +84,66 @@ void run_facenet(Mat &image, vector<Rect> &vecRect, int csv_num = 0) {
             load_emb_csv(csv_num, o);
             for (int j = 0; j < o.size(); ++j) {
                 float result = compare(n, o[j]);
+                cout << "-------------------" << endl;
                 cout << result << endl;
-                if (result < 0.85)
+                if (result < 0.45)
                     cout << "it's me" << endl;
                 else
                     cout << "unknow" << endl;
+                cout << j << endl;
             }
         } else {
+            imwrite("../emb_img/" + to_string(i) + ".jpg", fourthImage);
             write_emb_csv(n, i);
         }
     }
 }
 
+float test_compare(vector<mydataFmt> &lineArray0, vector<mydataFmt> &lineArray1) {
+    mydataFmt sum = 0;
+    for (int i = 0; i < Num; ++i) {
+//        cout << lineArray0[i] << "===" << lineArray1[i] << endl;
+        mydataFmt sub = lineArray0[i] - lineArray1[i];
+        mydataFmt square = pow(sub, 2);
+        sum += square;
+    }
+    mydataFmt result = sqrt(sum);
+    return result;
+}
+
+void test_facenet(Mat &image, vector<Rect> &vecRect, vector<mydataFmt> &n) {
+    Mat fourthImage;
+    resize(image(vecRect[0]), fourthImage, Size(160, 160), 0, 0, cv::INTER_LINEAR);
+    facenet ggg;
+//        mydataFmt *o = new mydataFmt[Num];
+//    vector<mydataFmt> n;
+    vector<vector<mydataFmt>> o;
+    ggg.run(fourthImage, n, 0);
+}
+
 void run() {
     int b = 0;
     if (b == 0) {
 
-        //        Mat image = imread("../40.jpg");
-//        Mat image = imread("../3.jpeg");
-//        Mat image = imread("../xiena.jpg");
-//        Mat image = imread("../kongweiye.jpg");
+//                Mat image = imread("../40.jpg");
+        Mat image = imread("../3.jpeg");
+//        Mat image = imread("../4.jpeg");
+//        Mat image = imread("../xiena.jpeg");
+//        Mat image = imread("../hejiong.jpeg");
+//        Mat image = imread("../libingbing.jpeg");
+//        Mat image = imread("../zhangjie.jpg");
+//        Mat image = imread("../Kong_Weiye.jpg");
 //        Mat image = imread("../kkk.jpg");
-        Mat image = imread("../20.png");
+//        Mat image = imread("../20.png");
 //        Mat image = imread("../emb_img/0.jpg");
 
-
-
         clock_t start;
         start = clock();
         vector<Rect> vecRect;
-        run_mtcnn(image, vecRect);
-        run_facenet(image, vecRect,13);// 第三个参数csv数量，如果为0，则是保存emb到csv功能
+        vector<Point> vecPoint;
+        run_mtcnn(image, vecRect, vecPoint);
+
+        run_facenet(image, vecRect, 13);// 第三个参数csv数量，如果为0，则是保存emb到csv功能
 
         imshow("result", image);
         imwrite("../result.jpg", image);
@@ -123,24 +152,6 @@ void run() {
         cout << "time is " << (double) start / CLOCKS_PER_SEC * 1000 << "ms" << endl;
         waitKey(5000);
         image.release();
-    } else if (b == 1) {
-        Mat image = imread("../10.jpg");
-//        Mat image = imread("../emb_img/0.jpg");
-//        Mat image = imread("../20.png");
-        Mat Image;
-        resize(image, Image, Size(160, 160), 0, 0, cv::INTER_LINEAR);
-        facenet ggg;
-        mydataFmt *o = new mydataFmt[Num];
-//        ggg.run(Image, o, 0);
-//        imshow("result", Image);
-        imwrite("../result.jpg", Image);
-
-        for (int i = 0; i < Num; ++i) {
-            cout << o[i] << endl;
-        }
-
-        waitKey(0);
-        image.release();
     } else {
         Mat image;
         VideoCapture cap(0);
@@ -171,9 +182,56 @@ void run() {
     }
 }
 
+void test() {
+    Mat image0 = imread("../kkk.jpg");
+    Mat image1 = imread("../hejiong0.jpeg");
+
+    clock_t start;
+    start = clock();
+    vector<Rect> vecRect0, vecRect1;
+    vector<mydataFmt> n0, n1;
+    vector<Point> vecPoint0, vecPoint1;
+    run_mtcnn(image0, vecRect0, vecPoint0);
+    test_facenet(image0, vecRect0, n0);
+    run_mtcnn(image1, vecRect1, vecPoint1);
+    test_facenet(image1, vecRect1, n1);
+
+    for (int i = 0; i < vecRect0.size(); ++i) {
+        rectangle(image0, vecPoint0[7 * i + 0], vecPoint0[7 * i + 1], Scalar(0, 0, 255), 2, 8, 0);
+        for (int num = 0; num < 5; num++)
+            circle(image0, vecPoint0[7 * i + num + 2], 2, Scalar(0, 255, 255),
+                   -1);
+    }
+    for (int i = 0; i < vecRect1.size(); ++i) {
+        rectangle(image1, vecPoint1[7 * i + 0], vecPoint1[7 * i + 1], Scalar(0, 0, 255), 2, 8, 0);
+        for (int num = 0; num < 5; num++)
+            circle(image1, vecPoint1[7 * i + num + 2], 2, Scalar(0, 255, 255),
+                   -1);
+    }
+
+    float result = test_compare(n0, n1);
+    cout << "-------------------" << endl;
+    cout << result << endl;
+    if (result < 0.45)
+        cout << "可能是同一个人" << endl;
+    else
+        cout << "很可能不是同一个人" << endl;
+
+    imshow("result0", image0);
+    imwrite("../result0.jpg", image0);
+    imshow("result1", image1);
+    imwrite("../result1.jpg", image1);
+    start = clock() - start;
+    //    cout<<"time is  "<<start/10e3<<endl;
+    cout << "time is " << (double) start / CLOCKS_PER_SEC * 1000 << "ms" << endl;
+    waitKey(0);
+    image0.release();
+    image1.release();
+}
+
 int main() {
     for (int i = 0; i < 1; ++i) {
-        run();
+        test();
         cout << "==============================" << endl;
     }
 }