ChrisKong/MTCNN-FaceNet-light
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update code and src

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update code and src
This commit is contained in:
ChrisKong
2019-12-27 18:06:31 +08:00
parent e4056d9c2e
commit cf6be359ba
19 changed files with 112 additions and 52 deletions
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28
src/network.cpp
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@@ -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++;
}
}