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完成基本功能

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完成基本功能c

Co-Authored-By: Chris Kong <609027949@qq.com>
This commit is contained in:
ChrisKong
2019-12-30 16:05:49 +08:00
parent 05d26d4b0f
commit 52d50c4544
8 changed files with 1228 additions and 1013 deletions
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1869
src/facenet.cpp
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13
src/facenet.h
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@@ -14,7 +14,7 @@ public:
~facenet();
void run(Mat &image, mydataFmt *o, int count = 1);
void run(Mat &image, vector<mydataFmt> &o, int count = 1);
private:
void Stem(Mat &image, pBox *output);
@@ -35,20 +35,9 @@ private:
void fully_connect(pBox *input, pBox *output, string filepath = "");
void conv_merge(pBox *output, pBox *c1 = 0, pBox *c2 = 0, pBox *c3 = 0, pBox *c4 = 0);
void conv_mergeInit(pBox *output, pBox *c1 = 0, pBox *c2 = 0, pBox *c3 = 0, pBox *c4 = 0);
void mulandaddInit(const pBox *inpbox, const pBox *temppbox, pBox *outpBox, float scale);
void mulandadd(const pBox *inpbox, const pBox *temppbox, pBox *outpBox, float scale = 1);
void Flatten(pBox *input, pBox *output);
void printData(pBox *output);
};
#endif //MAIN_FACENET_H

229
src/network.cpp
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@@ -45,44 +45,57 @@ void image2Matrix(const Mat &image, const struct pBox *pbox, int num) {
return;
}
mydataFmt *p = pbox->pdata;
double sqr, stddev_adj;
int size;
mydataFmt mymean, mystddev;
// prewhiten
if (num != 0) {
MeanAndDev(image, mymean, mystddev);
cout << mymean << "----" << mystddev << endl;
size = image.cols * image.rows * image.channels();
sqr = sqrt(double(size));
if (mystddev >= 1.0 / sqr) {
stddev_adj = mystddev;
} else {
stddev_adj = 1.0 / sqr;
}
}
for (int rowI = 0; rowI < image.rows; rowI++) {
for (int colK = 0; colK < image.cols; colK++) {
if (num == 0) {
*p = (image.at<Vec3b>(rowI, colK)[0] - 127.5) * 0.0078125;
*p = (image.at<Vec3b>(rowI, colK)[2] - 127.5) * 0.0078125;
*(p + image.rows * image.cols) = (image.at<Vec3b>(rowI, colK)[1] - 127.5) * 0.0078125;
*(p + 2 * image.rows * image.cols) = (image.at<Vec3b>(rowI, colK)[2] - 127.5) * 0.0078125;
*(p + 2 * image.rows * image.cols) = (image.at<Vec3b>(rowI, colK)[0] - 127.5) * 0.0078125;
p++;
} else {
double mean, stddev, sqr, stddev_adj;
int size;
Mat temp_m, temp_sd;
meanStdDev(image, temp_m, temp_sd);
mean = temp_m.at<double>(0, 0);
stddev = temp_sd.at<double>(0, 0);
size = image.cols * image.rows * image.channels();
sqr = sqrt(double(size));
if (stddev >= 1.0 / sqr) {
stddev_adj = stddev;
} else {
stddev_adj = 1.0 / sqr;
}
// cout << mean << "|" << stddev << "|" << size << "|" << stddev_adj << "|" << endl;
for (int i = 0; i < image.rows; i++) {
for (int j = 0; j < image.cols; j++) {
image.at<uchar>(i, j);
*p = (image.at<Vec3b>(i, j)[0] - mean) / stddev_adj;
*(p + image.rows * image.cols) = (image.at<Vec3b>(i, j)[1] - mean) / stddev_adj;
*(p + 2 * image.rows * image.cols) = (image.at<Vec3b>(i, j)[2] - mean) / stddev_adj;
// cout << (image.at<Vec3b>(i, j)[0] - mean) / stddev_adj << endl;
// return;
}
}
// brg2rgb
*(p + 0 * image.rows * image.cols) = (image.at<Vec3b>(rowI, colK)[2] - mymean) / stddev_adj;
*(p + 1 * image.rows * image.cols) = (image.at<Vec3b>(rowI, colK)[1] - mymean) / stddev_adj;
*(p + 2 * image.rows * image.cols) = (image.at<Vec3b>(rowI, colK)[0] - mymean) / stddev_adj;
p++;
}
}
}
}
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++) {
meansum += image.at<Vec3b>(rowI, colK)[0] + image.at<Vec3b>(rowI, colK)[1] + image.at<Vec3b>(rowI, colK)[2];
}
}
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);
}
}
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) {
if (pad < -1) {
cout << "the data needn't to pad,please check you network!" << endl;
@@ -156,12 +169,6 @@ void convolutionInit(const Weight *weight, pBox *pbox, pBox *outpBox) {
}
void convolution(const Weight *weight, const pBox *pbox, pBox *outpBox) {
// if (weight->pad != 0) {
// pBox *padpbox = new pBox;
// featurePadInit(outpBox, padpbox, weight->pad, weight->padw, weight->padh);
// featurePad(outpBox, padpbox, weight->pad, weight->padw, weight->padh);
// *outpBox = *padpbox;
// }
int ckh, ckw, ckd, stride, cknum, ckpad, imginputh, imginputw, imginputd, Nh, Nw;
mydataFmt *ck, *imginput;
// float *output = outpBox->pdata;
@@ -206,7 +213,6 @@ void convolution(const Weight *weight, const pBox *pbox, pBox *outpBox) {
}
}
}
// cout << "output->pdata:" << (outpBox->pdata[10]) << endl;
}
void maxPoolingInit(const pBox *pbox, pBox *Matrix, int kernelSize, int stride, int flag) {
@@ -315,8 +321,12 @@ void avePooling(const pBox *pbox, pBox *Matrix, int kernelSize, int stride) {
}
}
void prelu(struct pBox *pbox, mydataFmt *pbias, mydataFmt *prelu_gmma) {
/**
* 激活函数 没有系数
* @param pbox
* @param pbias
*/
void relu(struct pBox *pbox, mydataFmt *pbias) {
if (pbox->pdata == NULL) {
cout << "the Relu feature is NULL!!" << endl;
return;
@@ -327,17 +337,15 @@ void prelu(struct pBox *pbox, mydataFmt *pbias, mydataFmt *prelu_gmma) {
}
mydataFmt *op = pbox->pdata;
mydataFmt *pb = pbias;
mydataFmt *pg = prelu_gmma;
long dis = pbox->width * pbox->height;
for (int channel = 0; channel < pbox->channel; channel++) {
for (int col = 0; col < dis; col++) {
*op = *op + *pb;
*op = (*op > 0) ? (*op) : ((*op) * (*pg));
*op = (*op > 0) ? (*op) : ((*op) * 0);
op++;
}
pb++;
pg++;
}
}
@@ -364,36 +372,30 @@ void fullconnect(const Weight *weight, const pBox *pbox, pBox *outpBox) {
// row no trans A's row A'col
//cblas_sgemv(CblasRowMajor, CblasNoTrans, weight->selfChannel, weight->lastChannel, 1, weight->pdata, weight->lastChannel, pbox->pdata, 1, 0, outpBox->pdata, 1);
vectorXmatrix(pbox->pdata, weight->pdata,
pbox->width * pbox->height * pbox->channel,
weight->lastChannel, weight->selfChannel,
outpBox->pdata);
}
void vectorXmatrix(mydataFmt *matrix, mydataFmt *v, int size, int v_w, int v_h, mydataFmt *p) {
void vectorXmatrix(mydataFmt *matrix, mydataFmt *v, int v_w, int v_h, mydataFmt *p) {
for (int i = 0; i < v_h; i++) {
p[i] = 0;
for (int j = 0; j < v_w; j++) {
p[i] += matrix[j] * v[i * v_w + j];
// cout << p[i] << endl;
}
// cout << p[i] << endl;
// p[i] = -0.0735729;
// cout << "...." << endl;
// break;
}
// cout << "...." << endl;
}
void readData(string filename, long dataNumber[], mydataFmt *pTeam[], int length) {
ifstream in(filename.data());
string line;
long temp = dataNumber[0];
if (in) {
int i = 0;
int count = 0;
int pos = 0;
while (getline(in, line)) {
try {
if (i < dataNumber[count]) {
if (i < temp) {
line.erase(0, 1);
pos = line.find(']');
line.erase(pos, 1);
@@ -401,12 +403,14 @@ void readData(string filename, long dataNumber[], mydataFmt *pTeam[], int length
if (pos != -1) {
line.erase(pos, 1);
}
*(pTeam[count])++ = atof(line.data());
if (dataNumber[count] != 0) {
*(pTeam[count])++ = atof(line.data());
}
} else {
count++;
if ((length != 0) && (count == length))
break;
dataNumber[count] += dataNumber[count - 1];
temp += dataNumber[count];
line.erase(0, 1);
pos = line.find(']');
line.erase(pos, 1);
@@ -414,7 +418,9 @@ void readData(string filename, long dataNumber[], mydataFmt *pTeam[], int length
if (pos != -1) {
line.erase(pos, 1);
}
*(pTeam[count])++ = atof(line.data());
if (dataNumber[count] != 0) {
*(pTeam[count])++ = atof(line.data());
}
}
i++;
}
@@ -429,19 +435,15 @@ void readData(string filename, long dataNumber[], mydataFmt *pTeam[], int length
}
// w sc lc ks s p kw kh
long initConvAndFc(struct Weight *weight, int schannel, int lchannel, int kersize,
long ConvAndFcInit(struct Weight *weight, int schannel, int lchannel, int kersize,
int stride, int pad, int w, int h, int padw, int padh) {
weight->selfChannel = schannel;
weight->lastChannel = lchannel;
weight->kernelSize = kersize;
// if (kersize == 0) {
weight->h = h;
weight->w = w;
// }
// if (pad == -1) {
weight->padh = padh;
weight->padw = padw;
// }
weight->stride = stride;
weight->pad = pad;
weight->pbias = (mydataFmt *) malloc(schannel * sizeof(mydataFmt));
@@ -459,9 +461,114 @@ long initConvAndFc(struct Weight *weight, int schannel, int lchannel, int kersiz
return byteLenght;
}
void initpRelu(struct pRelu *prelu, int width) {
prelu->width = width;
prelu->pdata = (mydataFmt *) malloc(width * sizeof(mydataFmt));
if (prelu->pdata == NULL)cout << "prelu apply for memory failed!!!!";
memset(prelu->pdata, 0, width * sizeof(mydataFmt));
void conv_mergeInit(pBox *output, pBox *c1, pBox *c2, pBox *c3, pBox *c4) {
output->channel = 0;
output->height = c1->height;
output->width = c1->width;
if (c1 != 0) {
output->channel = c1->channel;
if (c2 != 0) {
output->channel += c2->channel;
if (c3 != 0) {
output->channel += c3->channel;
if (c4 != 0) {
output->channel += c4->channel;
}
}
}
}
output->pdata = (mydataFmt *) malloc(output->width * output->height * output->channel * sizeof(mydataFmt));
if (output->pdata == NULL)cout << "the conv_mergeInit is failed!!" << endl;
memset(output->pdata, 0, output->width * output->height * output->channel * sizeof(mydataFmt));
}
void conv_merge(pBox *output, pBox *c1, pBox *c2, pBox *c3, pBox *c4) {
// cout << "output->channel:" << output->channel << endl;
if (c1 != 0) {
long count1 = c1->height * c1->width * c1->channel;
//output->pdata = c1->pdata;
for (long i = 0; i < count1; i++) {
output->pdata[i] = c1->pdata[i];
}
if (c2 != 0) {
long count2 = c2->height * c2->width * c2->channel;
for (long i = 0; i < count2; i++) {
output->pdata[count1 + i] = c2->pdata[i];
}
if (c3 != 0) {
long count3 = c3->height * c3->width * c3->channel;
for (long i = 0; i < count3; i++) {
output->pdata[count1 + count2 + i] = c3->pdata[i];
}
if (c4 != 0) {
long count4 = c4->height * c4->width * c4->channel;
for (long i = 0; i < count4; i++) {
output->pdata[count1 + count2 + count3 + i] = c4->pdata[i];
}
}
}
}
} else { cout << "conv_mergeInit" << endl; }
}
void mulandaddInit(const pBox *inpbox, const pBox *temppbox, pBox *outpBox, float scale) {
outpBox->channel = temppbox->channel;
outpBox->width = temppbox->width;
outpBox->height = temppbox->height;
outpBox->pdata = (mydataFmt *) malloc(outpBox->width * outpBox->height * outpBox->channel * sizeof(mydataFmt));
if (outpBox->pdata == NULL)cout << "the mulandaddInit is failed!!" << endl;
memset(outpBox->pdata, 0, outpBox->width * outpBox->height * outpBox->channel * sizeof(mydataFmt));
}
void mulandadd(const pBox *inpbox, const pBox *temppbox, pBox *outpBox, float scale) {
mydataFmt *ip = inpbox->pdata;
mydataFmt *tp = temppbox->pdata;
mydataFmt *op = outpBox->pdata;
long dis = inpbox->width * inpbox->height * inpbox->channel;
for (long i = 0; i < dis; i++) {
op[i] = ip[i] + tp[i] * scale;
}
}
void BatchNormInit(struct BN *var, struct BN *mean, struct BN *beta, int width) {
var->width = width;
var->pdata = (mydataFmt *) malloc(width * sizeof(mydataFmt));
if (var->pdata == NULL)cout << "prelu apply for memory failed!!!!";
memset(var->pdata, 0, width * sizeof(mydataFmt));
mean->width = width;
mean->pdata = (mydataFmt *) malloc(width * sizeof(mydataFmt));
if (mean->pdata == NULL)cout << "prelu apply for memory failed!!!!";
memset(mean->pdata, 0, width * sizeof(mydataFmt));
beta->width = width;
beta->pdata = (mydataFmt *) malloc(width * sizeof(mydataFmt));
if (beta->pdata == NULL)cout << "prelu apply for memory failed!!!!";
memset(beta->pdata, 0, width * sizeof(mydataFmt));
}
void BatchNorm(struct pBox *pbox, struct BN *var, struct BN *mean, struct BN *beta) {
if (pbox->pdata == NULL) {
cout << "Relu feature is NULL!!" << endl;
return;
}
if ((var->pdata == NULL) || (mean->pdata == NULL) || (beta->pdata == NULL)) {
cout << "the BatchNorm bias is NULL!!" << endl;
return;
}
mydataFmt *pp = pbox->pdata;
mydataFmt *vp = var->pdata;
mydataFmt *mp = mean->pdata;
mydataFmt *bp = beta->pdata;
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 = temp * (*pp) + ((bp[channel]) - temp * (mp[channel]));
pp++;
}
}
}

22
src/network.h
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@@ -11,7 +11,6 @@
#include <string>
#include <math.h>
#include "pBox.h"
//#include <cblas.h>
using namespace cv;
@@ -25,17 +24,15 @@ void avePooling(const pBox *pbox, pBox *Matrix, int kernelSize, int stride);
void featurePad(const pBox *pbox, pBox *outpBox, const int pad, const int padw = 0, const int padh = 0);
void prelu(struct pBox *pbox, mydataFmt *pbias, mydataFmt *prelu_gmma);
void relu(struct pBox *pbox, mydataFmt *pbias);
void fullconnect(const Weight *weight, const pBox *pbox, pBox *outpBox);
void readData(string filename, long dataNumber[], mydataFmt *pTeam[], int length = 0);
long initConvAndFc(struct Weight *weight, int schannel, int lchannel, int kersize, int stride, int pad,
long ConvAndFcInit(struct Weight *weight, int schannel, int lchannel, int kersize, int stride, int pad,
int w = 0, int h = 0, int padw = 0, int padh = 0);
void initpRelu(struct pRelu *prelu, int width);
void image2MatrixInit(Mat &image, struct pBox *pbox);
void featurePadInit(const pBox *pbox, pBox *outpBox, const int pad, const int padw = 0, const int padh = 0);
@@ -48,8 +45,21 @@ void convolutionInit(const Weight *weight, pBox *pbox, pBox *outpBox);
void fullconnectInit(const Weight *weight, pBox *outpBox);
void vectorXmatrix(mydataFmt *matrix, mydataFmt *v, int size, int v_w, int v_h, mydataFmt *p);
void vectorXmatrix(mydataFmt *matrix, mydataFmt *v, int v_w, int v_h, mydataFmt *p);
void convolution(const Weight *weight, const pBox *pbox, pBox *outpBox);
void MeanAndDev(const Mat &image, mydataFmt &p, mydataFmt &q);
void conv_merge(pBox *output, pBox *c1 = 0, pBox *c2 = 0, pBox *c3 = 0, pBox *c4 = 0);
void conv_mergeInit(pBox *output, pBox *c1 = 0, pBox *c2 = 0, pBox *c3 = 0, pBox *c4 = 0);
void mulandaddInit(const pBox *inpbox, const pBox *temppbox, pBox *outpBox, float scale);
void mulandadd(const pBox *inpbox, const pBox *temppbox, pBox *outpBox, float scale = 1);
void BatchNormInit(struct BN *var, struct BN *mean, struct BN *beta, int width);
void BatchNorm(struct pBox *pbox, struct BN *var, struct BN *mean, struct BN *beta);
#endif

8
src/pBox.cpp
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@@ -23,3 +23,11 @@ void freeWeight(struct Weight *weight) {
weight->pdata = NULL;
delete weight;
}
void freeBN(struct BN *bn) {
if (bn->pdata == NULL)cout << "weight is NULL!" << endl;
else
free(bn->pdata);
bn->pdata = NULL;
delete bn;
}

42
src/pBox.h
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@@ -6,11 +6,14 @@
#include <opencv2/core/cvstd.hpp>
#include <vector>
/**
* 声明结构体
*/
using namespace std;
//#define mydataFmt double
#define Num 128
typedef double mydataFmt;
typedef float mydataFmt;
struct pBox : public cv::String {
mydataFmt *pdata;
@@ -19,12 +22,15 @@ struct pBox : public cv::String {
int channel;
};
struct pRelu {
mydataFmt *pdata;
int width;
};
struct BN {
mydataFmt *pdata;
int width;
};
struct Weight {
mydataFmt *pdata;
@@ -40,23 +46,21 @@ struct Weight {
int padh;
};
class pBox1 {
public:
vector<vector<vector<mydataFmt>>> pdata;
struct Bbox {
float score;
int x1;
int y1;
int x2;
int y2;
float area;
bool exist;
mydataFmt ppoint[10];
mydataFmt regreCoord[4];
};
class pRelu1 {
public:
vector<mydataFmt> pdata;
};
class Weight1 {
public:
vector<vector<vector<vector<mydataFmt>>>> pdata;
vector<mydataFmt> pbias;
int stride;
int padw;
int padh;
struct orderScore {
mydataFmt score;
int oriOrder;
};
void freepBox(struct pBox *pbox);
@@ -65,4 +69,6 @@ void freeWeight(struct Weight *weight);
void freepRelu(struct pRelu *prelu);
void freeBN(struct BN *bn);
#endif

56
src/pikaqiu.cpp
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@@ -3,52 +3,22 @@
#include <time.h>
int main() {
int b = 0;
if (b == 0) {
Mat image = imread("../1.jpg");
Mat image = imread("../1.jpg");
// Mat image = imread("../2.png");
Mat Image;
resize(image, Image, Size(299, 299), 0, 0, cv::INTER_LINEAR);
facenet ggg;
mydataFmt *o = new mydataFmt[Num];
ggg.run(Image, o, 0);
Mat Image;
resize(image, Image, Size(160, 160), 0, 0, cv::INTER_LINEAR);
facenet ggg;
vector<mydataFmt> o;
ggg.run(Image, o, 0);
// imshow("result", Image);
imwrite("../result.jpg", 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);
if (!cap.isOpened())
cout << "fail to open!" << endl;
cap >> image;
if (!image.data) {
cout << "读取视频失败" << endl;
return -1;
}
clock_t start;
int stop = 1200;
//while (stop--) {
while (true) {
start = clock();
cap >> image;
resize(image, image, Size(299, 299), 0, 0, cv::INTER_LINEAR);
facenet ggg;
mydataFmt *o = new mydataFmt[Num];
ggg.run(image, o, 0);
imshow("result", image);
if (waitKey(1) >= 0) break;
start = clock() - start;
cout << "time is " << (double) start / CLOCKS_PER_SEC * 1000 << "ms" << endl;
}
waitKey(0);
image.release();
for (int i = 0; i < Num; ++i) {
cout << o[i] << endl;
}
waitKey(0);
image.release();
return 0;
}