/* Trains the classifier on warps of a bounding-box patch.
frame: frame to take warps from
bb: first-frame bounding-box [x, y, width, height] */
void bbWarpPatch(IntegralImage *frame, double *bb) {
// Transformation matrix
float *m = new float[4];
// Loop through various rotations and skews
for (float r = -0.1f; r < 0.1f; r += 0.005f) {
float sine = sin(r);
float cosine = cos(r);
for (float sx = -0.1f; sx < 0.1f; sx += 0.05f) {
for (float sy = -0.1f; sy < 0.1f; sy += 0.05f) {
// Set transformation
/* Rotation matrix * skew matrix =
| cos r sin r | * | 1 sx | =
| -sin r cos r | | sy 1 |
| cos r + sy * sin r sx * cos r + sin r |
| sy * cos r - sin r cos r - sx * sin r | */
m[0] = cosine + sy * sine;
m[1] = sx * cosine + sine;
m[2] = sy * cosine - sine;
m[3] = cosine - sx * sine;
// Create warp and train classifier
IntegralImage *warp = new IntegralImage();
warp->createWarp(frame, bb, m);
classifier->train(warp, 0, 0, (int)bb[2], (int)bb[3], 1);
delete warp;
}
}
}
delete m;
}
// 用边框盒围住的小块的仿射变换来训练分类器
// frame: 输入的用于仿射变换的图像
// bb: 第一帧中的边框盒[x,y,width,height]
void bbWarpPatch(IntegralImage *frame, double *bb) {
// 变换矩阵
float *m = new float[4];
// 循环穿过各种旋转和斜交参数
for (float r = -0.1f; r < 0.1f; r += 0.005f) {
float sine = sin(r);
float cosine = cos(r);
for (float sx = -0.1f; sx < 0.1f; sx += 0.05f) {
for (float sy = -0.1f; sy < 0.1f; sy += 0.05f) {
// 设置转换矩阵
/* Rotation matrix * skew matrix =
| cos r sin r | * | 1 sx | =
| -sin r cos r | | sy 1 |
| cos r + sy * sin r sx * cos r + sin r |
| sy * cos r - sin r cos r - sx * sin r | */
m[0] = cosine + sy * sine;
m[1] = sx * cosine + sine;
m[2] = sy * cosine - sine;
m[3] = cosine - sx * sine;
// 创建仿射变换，然后训练分类器
IntegralImage *warp = new IntegralImage();
warp->createWarp(frame, bb, m);
classifier->train(warp, 1, 1, (int)bb[2], (int)bb[3], 1);
delete warp;
}
}
}
delete m;
}
// 初始化 --------------------------------------------------------
// 参数1帧宽,参数2帧高,参数3初始第一帧,参数4初始边框盒
void BpTld_Init(int Width,int Height,IplImage * firstImage,double * firstbb)
{
// 获取图像参数
frameWidth = Width;
frameHeight = Height;
frameSize = (CvSize *)malloc(sizeof(CvSize));
*frameSize = cvSize(frameWidth, frameHeight);
IntegralImage *firstFrame = new IntegralImage();
firstFrame->createFromIplImage(firstImage);
IplImage *firstFrameIplImage = firstImage;
double *bb = firstbb;
initBBWidth = (float)bb[2];
initBBHeight = (float)bb[3];
//初始化信任度
confidence = 1.0f;
// 初始化分类器,跟踪器和探测器
srand((unsigned int)time(0));
classifier = new Classifier(TOTAL_FERNS, TOTAL_NODES, MIN_FEATURE_SCALE, MAX_FEATURE_SCALE);
tracker = new Tracker(frameWidth, frameHeight, frameSize, firstFrameIplImage, classifier);
detector = new Detector(frameWidth, frameHeight, bb, classifier);
// 用初始图像小块和它的仿射变形来训练分类器
classifier->train(firstFrame, (int)bb[0], (int)bb[1], (int)initBBWidth, (int)initBBHeight, 1);
bbWarpPatch(firstFrame, bb);
trainNegative(firstFrame, bb);
// 释放内存
delete firstFrame;
// 设置bool值initialised
initialised = true;
return;
}
int Detector::detect(const Mat &image, int max_num, CB_RectT *pt_rects, int &subwin_count)
{
int image_w = image.cols;
int image_h = image.rows;
int feature_types = ptr_model->p_ft_param->getFeatureTypes();
IntegralImage intg;
intg.init(image_w, image_h, feature_types);
intg.compute(image.data);
int num = detect(intg, max_num, pt_rects, subwin_count);
return num;
}
int PositiveExtractor::extractSamples(const TrainParamsT *pt_params)
{
IntegralImage intg;
_chdir(pt_params->positive_pool_dir.c_str());
CFileFind file_finder;
bool is_working = file_finder.FindFile();
int count = 0;
while (is_working)
{
is_working = file_finder.FindNextFile();
string file_name = file_finder.GetFileName();
string file_path = file_finder.GetFilePath();
if (file_name == "." || file_name == ".."|| file_name == "Thumbs.db")
{
continue;
}
printf("%s\n", file_path.c_str());
Mat image = imread(file_path, CV_LOAD_IMAGE_GRAYSCALE);
if (image.data == NULL)
{
continue;
}
int width = image.cols;
int height = image.rows;
if (width < pt_params->template_w || height < pt_params->template_h)
{
continue;
}
intg.init(width, height, pt_params->feature_type);
intg.compute(image.data);
intg.save(pt_params->positive_data_path);
count++;
}
return count;
}
int Detector::preTest(IntegralImage &intg, SubwinInfoT &subwin)
{
intg.computeSubwinMeanVar(subwin);
if (subwin.mean >= MAX_MEAN || subwin.mean <= MIN_MEAN)
{
return 0;
}
if (subwin.var < MIN_VAR || subwin.var > MAX_VAR)
{
return 0;
}
return 1;
}
void * buildResponseLayer_work(void * threadarg)
{
struct thread_data * my_data = (struct thread_data *) threadarg;
int thread_id = my_data->thread_id;
FastHessian::ResponseLayer * rl = my_data->rl;
IntegralImage * img = my_data->img;
int step = my_data->step; // step size for this filter
int b = my_data->b; // border for this filter
int l = my_data->l; // lobe for this filter (filter size / 3)
int w = my_data->w; // filter size
float inverse_area = my_data->inverse_area; // normalisation factor
float Dxx, Dyy, Dxy;
unsigned int start = (rl->height / NUM_THREADS) * thread_id;
unsigned int end = (rl->height / NUM_THREADS) + start;
if (end > rl->height) end = rl->height;
for (int r, c, ar = start, index = start * rl->width; ar < end; ++ar)
{
for (int ac = 0; ac < rl->width; ++ac, index++)
{
// get the image coordinates
r = ar * step;
c = ac * step;
// Compute response components
Dxx = img->BoxIntegral(r - l + 1, c - b, 2 * l - 1, w)
- img->BoxIntegral(r - l + 1, c - l / 2, 2 * l - 1, l) * 3;
Dyy = img->BoxIntegral(r - b, c - l + 1, w, 2 * l - 1)
- img->BoxIntegral(r - l / 2, c - l + 1, l, 2 * l - 1) * 3;
Dxy = + img->BoxIntegral(r - l, c + 1, l, l)
+ img->BoxIntegral(r + 1, c - l, l, l)
- img->BoxIntegral(r - l, c - l, l, l)
- img->BoxIntegral(r + 1, c + 1, l, l);
// Normalise the filter responses with respect to their size
Dxx *= inverse_area;
Dyy *= inverse_area;
Dxy *= inverse_area;
// Get the determinant of hessian response & laplacian sign
rl->responses[index] = (Dxx * Dyy - (float)0.81 * Dxy * Dxy);
rl->laplacian[index] = (unsigned char)(Dxx + Dyy >= 0 ? 1 : 0);
}
}
}
void teste_integral_2(IntegralImage ii){
for(register int i=0;i<4;++i){
for(register int j=0;j<4;++j){
printf("%lu ",ii.data()[i][j]);
}
printf("\n");
}
Point ardis;
ardis.y = 24;
ardis.x = 24;
MaskTwoHorizontalFactory m2hf(ardis,1,1,0,2,1);
MaskTwoVerticalFactory m2vf(ardis,1,1,0,1,2);
MaskThreeHorizontalFactory m3hf(ardis,1,1,0,3,1);
MaskThreeVerticalFactory m3vf(ardis,1,1,0,1,3);
MaskDiagonalFactory mdf(ardis,1,1,0,2,2);
FMF factories[5];
std::vector<FeatureMask> _facesFeatures;
factories[0] = m2hf;
factories[1] = m2vf;
factories[2] = m3hf;
factories[3] = m3vf;
factories[4] = mdf;
int counter=0;
for(int i=0;i<5;i++){
while( factories[i].hasNext()==1 ){
_facesFeatures.push_back( factories[i].next(counter++) );
}
}
printf("%d\n", _facesFeatures.size());
FeatureMask fm = m2hf.next();
printf("M2HF: %lu\n",ii.filter( fm ) );
fm = m2vf.next();
printf("M2VF: %lu\n",ii.filter( fm ) );
fm = m3hf.next();
printf("M3HF: %lu\n",ii.filter( fm ) );
fm = m3vf.next();
printf("M3VF: %lu\n",ii.filter( fm ) );
fm = mdf.next();
printf("MDF: %lu\n",ii.filter( fm ) );
}
/* Entry point for mex.
Call form: [left, hand, side, outs] = Detector(right, hand, side, args)
Either use:
To initialise:
TLD(frame width, frame height, first frame, selected bounding-box)
To process a frame:
new trajectory bounding-box = TLD(current frame, trajectory bounding-box)
nlhs: number of left-hand side outputs
plhs: the left-hand side outputs
nrhs: number of right-hand side arguments
prhs: the right-hand side arguments */
void mexFunction(int nlhs, mxArray *plhs[], int nrhs, const mxArray *prhs[]) {
// Initialisation --------------------------------------------------------
if (nlhs == 0 && nrhs == 4) {
// Get input
frameWidth = (int)*mxGetPr(prhs[0]);
frameHeight = (int)*mxGetPr(prhs[1]);
frameSize = (CvSize *)malloc(sizeof(CvSize));
*frameSize = cvSize(frameWidth, frameHeight);
IntegralImage *firstFrame = new IntegralImage();
firstFrame->createFromMatlab(prhs[2]);
IplImage *firstFrameIplImage = imageFromMatlab(prhs[2]);
double *bb = mxGetPr(prhs[3]);
initBBWidth = (float)bb[2];
initBBHeight = (float)bb[3];
confidence = 1.0f;
// Initialise classifier, tracker and detector
srand((unsigned int)time(0));
classifier = new Classifier(TOTAL_FERNS, TOTAL_NODES, MIN_FEATURE_SCALE, MAX_FEATURE_SCALE);
tracker = new Tracker(frameWidth, frameHeight, frameSize, firstFrameIplImage, classifier);
detector = new Detector(frameWidth, frameHeight, bb, classifier);
// Train the classifier on the bounding-box patch and warps of it
classifier->train(firstFrame, (int)bb[0], (int)bb[1], (int)initBBWidth, (int)initBBHeight, 1);
bbWarpPatch(firstFrame, bb);
trainNegative(firstFrame, bb);
// Free memory and set initialised
delete firstFrame;
initialised = true;
return;
}
// Validate --------------------------------------------------------------
// The remainder of this function handles the frame processing call
// Ensure we get the correct call form Matlab and are initialised
if (!initialised || nlhs != 1 || nrhs != 2) {
// Error
return;
}
// Get Input -------------------------------------------------------------
// Current frame
IplImage *nextFrame = imageFromMatlab(prhs[0]);
IntegralImage *nextFrameIntImg = new IntegralImage();
nextFrameIntImg->createFromMatlab(prhs[0]);
// Trajectory bounding-box [x, y, width, height]
double *bb = mxGetPr(prhs[1]);
// Track and Detect ------------------------------------------------------
// Only track if we were confident enough in the previous iteration
// The tracker handles the memory freeing of nextFrame from here on
double *tbb;
vector<double *> *dbbs;
if (confidence > MIN_TRACKING_CONF) {
tbb = tracker->track(nextFrame, nextFrameIntImg, bb);
dbbs = detector->detect(nextFrameIntImg, tbb);
} else {
dbbs = detector->detect(nextFrameIntImg, NULL);
tracker->setPrevFrame(nextFrame);
tbb = new double[5];
tbb[0] = 0;
tbb[1] = 0;
tbb[2] = 0;
tbb[3] = 0;
tbb[4] = MIN_TRACKING_CONF;
}
// Learn -----------------------------------------------------------------
// Get greatest detected patch confidence
double dbbMaxConf = 0.0f;
int dbbMaxConfIndex = -1;
for (int i = 0; i < dbbs->size(); i++) {
double dbbConf = dbbs->at(i)[4];
if (dbbConf > dbbMaxConf) {
dbbMaxConf = dbbConf;
dbbMaxConfIndex = i;
}
}
// Reset the tracker bounding-box if a detected patch had highest
// confidence and is more confident than MIN_REINIT_CONF
if (dbbMaxConf > tbb[4] && dbbMaxConf > MIN_REINIT_CONF) {
delete tbb;
tbb = new double[5];
double *dbb = dbbs->at(dbbMaxConfIndex);
tbb[0] = dbb[0];
tbb[1] = dbb[1];
tbb[2] = dbb[2];
tbb[3] = dbb[3];
tbb[4] = dbb[4];
}
// Apply constraints if the tracked patch had the greatest confidence and
// we were confident enough last frame
else if (tbb[4] > dbbMaxConf && confidence > MIN_LEARNING_CONF) {
for (int i = 0; i < dbbs->size(); i++) {
// Train the classifier on positive (overlapping with tracked
// patch) and negative (classed as positive but non-overlapping)
// patches
double *dbb = dbbs->at(i);
if (dbb[5] == 1) {
classifier->train(nextFrameIntImg, (int)dbb[0], (int)dbb[1], (int)dbb[2], (int)dbb[3], 1);
}
else if (dbb[5] == 0) {
classifier->train(nextFrameIntImg, (int)dbb[0], (int)dbb[1], (int)dbb[2], (int)dbb[3], 0);
}
}
}
// Set confidence for next iteration
confidence = tbb[4];
// Set output ------------------------------------------------------------
// We output a list of bounding-boxes; the first bounding-box defines the
// tracked patch, the rest are detected positive match patches.
// Rows correspond to individual bounding boxes
// Columns correspond to [x, y, width, height, confidence, overlapping]
int bbCount = (int)dbbs->size() + 1;
plhs[0] = mxCreateDoubleMatrix(bbCount, 6, mxREAL);
double *outputBBs = mxGetPr(plhs[0]);
// Set the tracked bounding-box
outputBBs[0 * bbCount] = tbb[0];
outputBBs[1 * bbCount] = tbb[1];
outputBBs[2 * bbCount] = tbb[2];
outputBBs[3 * bbCount] = tbb[3];
outputBBs[4 * bbCount] = tbb[4];
outputBBs[5 * bbCount] = 0;
// Set detected bounding-boxes
for (int i = 1; i < bbCount; i++) {
double *bb = dbbs->at(i - 1);
outputBBs[0 * bbCount + i] = bb[0];
outputBBs[1 * bbCount + i] = bb[1];
outputBBs[2 * bbCount + i] = bb[2];
outputBBs[3 * bbCount + i] = bb[3];
outputBBs[4 * bbCount + i] = bb[4];
outputBBs[5 * bbCount + i] = bb[5];
delete bb;
}
// Free memory
free(tbb);
dbbs->clear();
delete nextFrameIntImg;
}
void videocallback(IplImage *image)
{
static IplImage *img_grad = NULL;
static IplImage *img_gray = NULL;
static IplImage *img_ver = NULL;
static IplImage *img_hor = NULL;
static IplImage *img_canny = NULL;
static IntegralImage integ;
static IntegralGradient grad;
assert(image);
if (img_gray == NULL) {
// Following image is toggled visible using key '0'
img_grad = CvTestbed::Instance().CreateImageWithProto("Gradient", image);
CvTestbed::Instance().ToggleImageVisible(0);
img_gray = CvTestbed::Instance().CreateImageWithProto("Grayscale", image, 0, 1);
img_ver = CvTestbed::Instance().CreateImage("Vertical", cvSize(1,image->height), IPL_DEPTH_8U, 1);
img_hor = CvTestbed::Instance().CreateImage("Horizontal", cvSize(image->width,1), IPL_DEPTH_8U, 1);
img_canny = CvTestbed::Instance().CreateImageWithProto("Canny", image, 0, 1);
img_canny->origin = img_ver->origin = img_hor->origin = image->origin;
}
if (image->nChannels > 1) {
cvCvtColor(image, img_gray, CV_RGB2GRAY);
} else {
cvCopy(image, img_gray);
}
// Show PerformanceTimer
//PerformanceTimer timer;
//timer.Start();
// Update the integral images
integ.Update(img_gray);
grad.Update(img_gray);
// Whole image projections
integ.GetSubimage(cvRect(0,0,image->width,image->height), img_ver);
integ.GetSubimage(cvRect(0,0,image->width,image->height), img_hor);
for (int y=1; y<image->height; y++) {
cvLine(image,
cvPoint(int(cvGet2D(img_ver, y-1, 0).val[0]), y-1),
cvPoint(int(cvGet2D(img_ver, y, 0).val[0]), y),
CV_RGB(255,0,0));
}
for (int x=1; x<image->width; x++) {
cvLine(image,
cvPoint(x-1, int(cvGet2D(img_hor, 0, x-1).val[0])),
cvPoint(x, int(cvGet2D(img_hor, 0, x).val[0])),
CV_RGB(0,255,0));
}
// Gradients
// Mark gradients for 4x4 sub-blocks
/*
cvZero(img_grad);
CvRect r = {0,0,4,4};
for (int y=0; y<image->height/4; y++) {
r.y = y*4;
for (int x=0; x<image->width/4; x++) {
r.x = x*4;
double dirx, diry;
grad.GetAveGradient(r, &dirx, &diry);
cvLine(img_grad, cvPoint(r.x+2,r.y+2), cvPoint(r.x+2+int(dirx),r.y+2+int(diry)), CV_RGB(255,0,0));
}
}
*/
// Gradients on canny
cvZero(img_grad);
static int t1=64, t2=192;
cvCreateTrackbar("t1", "Gradient", &t1, 255, NULL);
cvCreateTrackbar("t2", "Gradient", &t2, 255, NULL);
cvCanny(img_gray, img_canny, t1, t2);
CvRect r = {0,0,4,4};
for (r.y=0; r.y<img_canny->height-4; r.y++) {
for (r.x=0; r.x<img_canny->width-4; r.x++) {
if (img_canny->imageData[r.y*img_canny->widthStep+r.x]) {
double dirx, diry;
grad.GetAveGradient(r, &dirx, &diry);
cvLine(img_grad, cvPoint(r.x+2,r.y+2), cvPoint(r.x+2+int(dirx),r.y+2+int(diry)), CV_RGB(0,0,255));
cvLine(img_grad, cvPoint(r.x+2,r.y+2), cvPoint(r.x+2+int(-diry),r.y+2+int(+dirx)), CV_RGB(255,0,0));
cvLine(img_grad, cvPoint(r.x+2,r.y+2), cvPoint(r.x+2+int(+diry),r.y+2+int(-dirx)), CV_RGB(255,0,0));
}
}
}
// Show PerformanceTimer
//cout<<"Processing: "<<1.0 / timer.Stop()<<" fps"<<endl;
}
float HaarFeature::computeFeature(IntegralImage &intg, const SubwinInfoT &subwin, const HaarFeatureInfoT &haar)
{
int cur_scan_pos_x = subwin.win_pos.x;
int cur_scan_pos_y = subwin.win_pos.y;
double cur_scan_scale = (double)subwin.win_size.x / haar.tpl_size.x;
HaarFeatureInfoT haar_feature = haar;
haar_feature.pos1.x = haar_feature.pos1.x * cur_scan_scale;
haar_feature.pos1.y = haar_feature.pos1.y * cur_scan_scale;
haar_feature.pos2.x = haar_feature.pos2.x * cur_scan_scale;
haar_feature.pos2.y = haar_feature.pos2.y * cur_scan_scale;
haar_feature.size.x *= cur_scan_scale;
haar_feature.size.y *= cur_scan_scale;
double real_scale_square = (double)(haar_feature.size.x * haar_feature.size.y) / (haar.size.x * haar.size.y);
double result;
switch (haar_feature.type)
{
case HFT_X_AB:
{
int x_left = cur_scan_pos_x + haar_feature.pos1.x;
int x_middle = x_left + haar_feature.size.x - 1;
int x_right = x_middle + haar_feature.size.x;
int y_top = cur_scan_pos_y + haar_feature.pos1.y;
int y_bottom = y_top + haar_feature.size.y - 1;
CB_RectT rect1 = {x_left, x_right, y_top, y_bottom};
CB_RectT rect2 = {x_middle+1, x_right, y_top, y_bottom};
double result1 = intg.getRectValue_0(rect1);
double result2 = intg.getRectValue_0(rect2);
result = result1 - 2 * result2;
break;
}
case HFT_Y_AB:
{
int x_left = cur_scan_pos_x + haar_feature.pos1.x;
int x_right = x_left + haar_feature.size.x - 1;
int y_top = cur_scan_pos_y + haar_feature.pos1.y;
int y_middle = y_top + haar_feature.size.y - 1;
int y_bottom = y_middle + haar_feature.size.y;
CB_RectT rect1 = {x_left, x_right, y_top, y_bottom};
CB_RectT rect2 = {x_left, x_right, y_middle+1, y_bottom};
double result1 = intg.getRectValue_0(rect1);
double result2 = intg.getRectValue_0(rect2);
result = result1 - 2 * result2;
break;
}
case HFT_X_ABA:
{
int x_left = cur_scan_pos_x + haar_feature.pos1.x;
int x_middleL = x_left + haar_feature.size.x - 1;
int x_middleR = x_middleL + haar_feature.size.x;
int x_right = x_middleR + haar_feature.size.x;
int y_top = cur_scan_pos_y + haar_feature.pos1.y;
int y_bottom = y_top + haar_feature.size.y - 1;
CB_RectT rect1 = {x_left, x_right, y_top, y_bottom};
CB_RectT rect2 = {x_middleL + 1, x_middleR, y_top, y_bottom};
double result1 = intg.getRectValue_0(rect1);
double result2 = intg.getRectValue_0(rect2);
result = result1 - 3 * result2;
break;
}
case HFT_Y_ABA:
{
int x_left = cur_scan_pos_x + haar_feature.pos1.x;
int x_right = x_left + haar_feature.size.x - 1;
int y_top = cur_scan_pos_y + haar_feature.pos1.y;
int y_middleT = y_top + haar_feature.size.y - 1;
int y_middleB = y_middleT + haar_feature.size.y;
int y_bottom = y_middleB + haar_feature.size.y;
CB_RectT rect1 = {x_left, x_right, y_top, y_bottom};
CB_RectT rect2 = {x_left, x_right, y_middleT + 1, y_middleB};
double result1 = intg.getRectValue_0(rect1);
double result2 = intg.getRectValue_0(rect2);
result = result1 - 3 * result2;
break;
}
case HFT_X_ABBA:
{
int x_left = cur_scan_pos_x + haar_feature.pos1.x;
int x_middleL = x_left + haar_feature.size.x - 1;
int x_middleR = x_middleL + haar_feature.size.x * 2;
int x_right = x_middleR + haar_feature.size.x;
int y_top = cur_scan_pos_y + haar_feature.pos1.y;
int y_bottom = y_top + haar_feature.size.y - 1;
CB_RectT rect1 = {x_left, x_right, y_top, y_bottom};
CB_RectT rect2 = {x_middleL + 1, x_middleR, y_top, y_bottom};
double result1 = intg.getRectValue_0(rect1);
double result2 = intg.getRectValue_0(rect2);
result = result1 - 2 * result2;
break;
}
case HFT_Y_ABBA:
{
int x_left = cur_scan_pos_x + haar_feature.pos1.x;
int x_right = x_left + haar_feature.size.x - 1;
int y_top = cur_scan_pos_y + haar_feature.pos1.y;
int y_middleT = y_top + haar_feature.size.y - 1;
int y_middleB = y_middleT + haar_feature.size.y * 2;
int y_bottom = y_middleB + haar_feature.size.y;
CB_RectT rect1 = {x_left, x_right, y_top, y_bottom};
CB_RectT rect2 = {x_left, x_right, y_middleT + 1, y_middleB};
double result1 = intg.getRectValue_0(rect1);
double result2 = intg.getRectValue_0(rect2);
result = result1 - 2 * result2;
break;
}
case HFT_XY_ABA:
{
int x_left = cur_scan_pos_x + haar_feature.pos1.x;
int x_middleL = x_left + haar_feature.size.x - 1;
int x_middleR = x_middleL + haar_feature.size.x;
int x_right = x_middleR + haar_feature.size.x;
int y_top = cur_scan_pos_y + haar_feature.pos1.y;
int y_middleT = y_top + haar_feature.size.y - 1;
int y_middleB = y_middleT + haar_feature.size.y;
int y_bottom = y_middleB + haar_feature.size.y;
CB_RectT rect1 = {x_left, x_right, y_top, y_bottom};
CB_RectT rect2 = {x_middleL + 1, x_middleR, y_middleT + 1, y_middleB};
double result1 = intg.getRectValue_0(rect1);
double result2 = intg.getRectValue_0(rect2);
result = result1 - 9 * result2;
break;
}
case HFT_XY_ABBA:
{
int x_left = cur_scan_pos_x + haar_feature.pos1.x;
int x_middleL = x_left + haar_feature.size.x - 1;
int x_middleR = x_middleL + haar_feature.size.x * 2;
int x_right = x_middleR + haar_feature.size.x;
int y_top = cur_scan_pos_y + haar_feature.pos1.y;
int y_middleT = y_top + haar_feature.size.y - 1;
int y_middleB = y_middleT + haar_feature.size.y * 2;
int y_bottom = y_middleB + haar_feature.size.y;
CB_RectT rect1 = {x_left, x_right, y_top, y_bottom};
CB_RectT rect2 = {x_middleL + 1, x_middleR, y_middleT + 1, y_middleB};
double result1 = intg.getRectValue_0(rect1);
double result2 = intg.getRectValue_0(rect2);
result = result1 - 4 * result2;
break;
}
case HFT_L_AB:
{
int x = cur_scan_pos_x + haar_feature.pos1.x;
int y = cur_scan_pos_y + haar_feature.pos1.y;
CB_SlantT slant1 = {x, y, haar_feature.size.x, haar_feature.size.y};
CB_RectangleT rect1;
slantToRect(slant1, rect1, subwin.image_size);
x = rect1.left.x;
y = rect1.left.y;
CB_SlantT slant2 = {x, y, haar_feature.size.x, haar_feature.size.y};
CB_RectangleT rect2;
slantToRect(slant2, rect2, subwin.image_size);
double result1 = intg.getRectValue_45(rect1);
double result2 = intg.getRectValue_45(rect2);
result = result1 - result2;
break;
}
case HFT_R_AB:
{
int x = cur_scan_pos_x + haar_feature.pos1.x;
int y = cur_scan_pos_y + haar_feature.pos1.y;
CB_SlantT slant1 = {x, y, haar_feature.size.x, haar_feature.size.y};
CB_RectangleT rect1;
slantToRect(slant1, rect1, subwin.image_size);
x = rect1.right.x;
y = rect1.right.y;
CB_SlantT slant2 = {x, y, haar_feature.size.x, haar_feature.size.y};
CB_RectangleT rect2;
slantToRect(slant2, rect2, subwin.image_size);
double result1 = intg.getRectValue_45(rect1);
double result2 = intg.getRectValue_45(rect2);
result = result1 - result2;
break;
}
case HFT_L_ABA:
{
int x = cur_scan_pos_x + haar_feature.pos1.x;
int y = cur_scan_pos_y + haar_feature.pos1.y;
CB_SlantT slant1 = {x, y, haar_feature.size.x, haar_feature.size.y};
CB_RectangleT rect1;
slantToRect(slant1, rect1, subwin.image_size);
x = rect1.left.x;
y = rect1.left.y;
CB_SlantT slant2 = {x, y, haar_feature.size.x, haar_feature.size.y};
CB_RectangleT rect2;
slantToRect(slant2, rect2, subwin.image_size);
x = rect2.left.x;
y = rect2.left.y;
CB_SlantT slant3 = {x, y, haar_feature.size.x, haar_feature.size.y};
CB_RectangleT rect3;
slantToRect(slant3, rect3, subwin.image_size);
double result1 = intg.getRectValue_45(rect1);
double result2 = intg.getRectValue_45(rect2);
double result3 = intg.getRectValue_45(rect3);
result = result1 + result3 - 2 * result2;
break;
}
case HFT_R_ABA:
{
int x = cur_scan_pos_x + haar_feature.pos1.x;
int y = cur_scan_pos_y + haar_feature.pos1.y;
CB_SlantT slant1 = {x, y, haar_feature.size.x, haar_feature.size.y};
CB_RectangleT rect1;
slantToRect(slant1, rect1, subwin.image_size);
x = rect1.right.x;
y = rect1.right.y;
CB_SlantT slant2 = {x, y, haar_feature.size.x, haar_feature.size.y};
CB_RectangleT rect2;
slantToRect(slant2, rect2, subwin.image_size);
x = rect2.right.x;
y = rect2.right.y;
CB_SlantT slant3 = {x, y, haar_feature.size.x, haar_feature.size.y};
CB_RectangleT rect3;
slantToRect(slant3, rect3, subwin.image_size);
double result1 = intg.getRectValue_45(rect1);
double result2 = intg.getRectValue_45(rect2);
double result3 = intg.getRectValue_45(rect3);
result = result1 + result3 - 2 * result2;
break;
}
case HFT_L_ABBA:
{
int x = cur_scan_pos_x + haar_feature.pos1.x;
int y = cur_scan_pos_y + haar_feature.pos1.y;
CB_SlantT slant1 = {x, y, haar_feature.size.x, haar_feature.size.y};
CB_RectangleT rect1;
slantToRect(slant1, rect1, subwin.image_size);
x = rect1.left.x;
y = rect1.left.y;
CB_SlantT slant2 = {x, y, haar_feature.size.x * 2, haar_feature.size.y};
CB_RectangleT rect2;
slantToRect(slant2, rect2, subwin.image_size);
x = rect2.left.x;
y = rect2.left.y;
CB_SlantT slant3 = {x, y, haar_feature.size.x, haar_feature.size.y};
CB_RectangleT rect3;
slantToRect(slant3, rect3, subwin.image_size);
double result1 = intg.getRectValue_45(rect1);
double result2 = intg.getRectValue_45(rect2);
double result3 = intg.getRectValue_45(rect3);
result = result1 + result3 - result2;
break;
}
case HFT_R_ABBA:
{
int x = cur_scan_pos_x + haar_feature.pos1.x;
int y = cur_scan_pos_y + haar_feature.pos1.y;
CB_SlantT slant1 = {x, y, haar_feature.size.x, haar_feature.size.y};
CB_RectangleT rect1;
slantToRect(slant1, rect1, subwin.image_size);
x = rect1.right.x;
y = rect1.right.y;
CB_SlantT slant2 = {x, y, haar_feature.size.x, haar_feature.size.y * 2};
CB_RectangleT rect2;
slantToRect(slant2, rect2, subwin.image_size);
x = rect2.right.x;
y = rect2.right.y;
CB_SlantT slant3 = {x, y, haar_feature.size.x, haar_feature.size.y};
CB_RectangleT rect3;
slantToRect(slant3, rect3, subwin.image_size);
double result1 = intg.getRectValue_45(rect1);
double result2 = intg.getRectValue_45(rect2);
double result3 = intg.getRectValue_45(rect3);
result = result1 + result3 - result2;
break;
}
case HFT_A_B:
{
int x_left1 = cur_scan_pos_x + haar_feature.pos1.x;
int x_right1 = x_left1 + haar_feature.size.x - 1;
int y_top1 = cur_scan_pos_y + haar_feature.pos1.y;
int y_bottom1 = y_top1 + haar_feature.size.y - 1;
int x_left2 = cur_scan_pos_x + haar_feature.pos2.x;
int x_right2 = x_left2 + haar_feature.size.x - 1;
int y_top2 = cur_scan_pos_y + haar_feature.pos2.y;
int y_bottom2 = y_top2 + haar_feature.size.y - 1;
CB_RectT rect1 = {x_left1, x_right1, y_top1, y_bottom1};
CB_RectT rect2 = {x_left2, x_right2, y_top2, y_bottom2};
double result1 = intg.getRectValue_0(rect1);
double result2 = intg.getRectValue_0(rect2);
result = result1 - result2;
break;
}
case HFT_SQ_X_AB:
{
int x_left = cur_scan_pos_x + haar_feature.pos1.x;
int x_middle = x_left + haar_feature.size.x - 1;
int x_right = x_middle + haar_feature.size.x;
int y_top = cur_scan_pos_y + haar_feature.pos1.y;
int y_bottom = y_top + haar_feature.size.y - 1;
CB_RectT rect1 = {x_left, x_right, y_top, y_bottom};
CB_RectT rect2 = {x_middle+1, x_right, y_top, y_bottom};
double result1 = intg.getRectSqValue_0(rect1);
double result2 = intg.getRectSqValue_0(rect2);
result = result1 - 2 * result2;
break;
}
case HFT_SQ_Y_AB:
{
int x_left = cur_scan_pos_x + haar_feature.pos1.x;
int x_right = x_left + haar_feature.size.x - 1;
int y_top = cur_scan_pos_y + haar_feature.pos1.y;
int y_middle = y_top + haar_feature.size.y - 1;
int y_bottom = y_middle + haar_feature.size.y;
CB_RectT rect1 = {x_left, x_right, y_top, y_bottom};
CB_RectT rect2 = {x_left, x_right, y_middle+1, y_bottom};
double result1 = intg.getRectSqValue_0(rect1);
double result2 = intg.getRectSqValue_0(rect2);
result = result1 - 2 * result2;
break;
}
case HFT_SQ_X_ABA:
{
int x_left = cur_scan_pos_x + haar_feature.pos1.x;
int x_middleL = x_left + haar_feature.size.x - 1;
int x_middleR = x_middleL + haar_feature.size.x;
int x_right = x_middleR + haar_feature.size.x;
int y_top = cur_scan_pos_y + haar_feature.pos1.y;
int y_bottom = y_top + haar_feature.size.y - 1;
CB_RectT rect1 = {x_left, x_right, y_top, y_bottom};
CB_RectT rect2 = {x_middleL + 1, x_middleR, y_top, y_bottom};
double result1 = intg.getRectSqValue_0(rect1);
double result2 = intg.getRectSqValue_0(rect2);
result = result1 - 3 * result2;
break;
}
case HFT_SQ_Y_ABA:
{
int x_left = cur_scan_pos_x + haar_feature.pos1.x;
int x_right = x_left + haar_feature.size.x - 1;
int y_top = cur_scan_pos_y + haar_feature.pos1.y;
int y_middleT = y_top + haar_feature.size.y - 1;
int y_middleB = y_middleT + haar_feature.size.y;
int y_bottom = y_middleB + haar_feature.size.y;
CB_RectT rect1 = {x_left, x_right, y_top, y_bottom};
CB_RectT rect2 = {x_left, x_right, y_middleT + 1, y_middleB};
double result1 = intg.getRectSqValue_0(rect1);
double result2 = intg.getRectSqValue_0(rect2);
result = result1 - 3 * result2;
break;
}
case HFT_SQ_X_ABBA:
{
int x_left = cur_scan_pos_x + haar_feature.pos1.x;
int x_middleL = x_left + haar_feature.size.x - 1;
int x_middleR = x_middleL + haar_feature.size.x * 2;
int x_right = x_middleR + haar_feature.size.x;
int y_top = cur_scan_pos_y + haar_feature.pos1.y;
int y_bottom = y_top + haar_feature.size.y - 1;
CB_RectT rect1 = {x_left, x_right, y_top, y_bottom};
CB_RectT rect2 = {x_middleL + 1, x_middleR, y_top, y_bottom};
double result1 = intg.getRectSqValue_0(rect1);
double result2 = intg.getRectSqValue_0(rect2);
result = result1 - 2 * result2;
break;
}
case HFT_SQ_Y_ABBA:
{
int x_left = cur_scan_pos_x + haar_feature.pos1.x;
int x_right = x_left + haar_feature.size.x - 1;
int y_top = cur_scan_pos_y + haar_feature.pos1.y;
int y_middleT = y_top + haar_feature.size.y - 1;
int y_middleB = y_middleT + haar_feature.size.y * 2;
int y_bottom = y_middleB + haar_feature.size.y;
CB_RectT rect1 = {x_left, x_right, y_top, y_bottom};
CB_RectT rect2 = {x_left, x_right, y_middleT + 1, y_middleB};
double result1 = intg.getRectSqValue_0(rect1);
double result2 = intg.getRectSqValue_0(rect2);
result = result1 - 2 * result2;
break;
}
case HFT_SQ_XY_ABA:
{
int x_left = cur_scan_pos_x + haar_feature.pos1.x;
int x_middleL = x_left + haar_feature.size.x - 1;
int x_middleR = x_middleL + haar_feature.size.x;
int x_right = x_middleR + haar_feature.size.x;
int y_top = cur_scan_pos_y + haar_feature.pos1.y;
int y_middleT = y_top + haar_feature.size.y - 1;
int y_middleB = y_middleT + haar_feature.size.y;
int y_bottom = y_middleB + haar_feature.size.y;
CB_RectT rect1 = {x_left, x_right, y_top, y_bottom};
CB_RectT rect2 = {x_middleL + 1, x_middleR, y_middleT + 1, y_middleB};
double result1 = intg.getRectSqValue_0(rect1);
double result2 = intg.getRectSqValue_0(rect2);
result = result1 - 9 * result2;
break;
}
case HFT_SQ_XY_ABBA:
{
int x_left = cur_scan_pos_x + haar_feature.pos1.x;
int x_middleL = x_left + haar_feature.size.x - 1;
int x_middleR = x_middleL + haar_feature.size.x * 2;
int x_right = x_middleR + haar_feature.size.x;
int y_top = cur_scan_pos_y + haar_feature.pos1.y;
int y_middleT = y_top + haar_feature.size.y - 1;
int y_middleB = y_middleT + haar_feature.size.y * 2;
int y_bottom = y_middleB + haar_feature.size.y;
CB_RectT rect1 = {x_left, x_right, y_top, y_bottom};
CB_RectT rect2 = {x_middleL + 1, x_middleR, y_middleT + 1, y_middleB};
double result1 = intg.getRectSqValue_0(rect1);
double result2 = intg.getRectSqValue_0(rect2);
result = result1 - 4 * result2;
break;
}
case HFT_SQ_A_B:
{
int x_left1 = cur_scan_pos_x + haar_feature.pos1.x;
int x_right1 = x_left1 + haar_feature.size.x - 1;
int y_top1 = cur_scan_pos_y + haar_feature.pos1.y;
int y_bottom1 = y_top1 + haar_feature.size.y - 1;
int x_left2 = cur_scan_pos_x + haar_feature.pos2.x;
int x_right2 = x_left2 + haar_feature.size.x - 1;
int y_top2 = cur_scan_pos_y + haar_feature.pos2.y;
int y_bottom2 = y_top2 + haar_feature.size.y - 1;
CB_RectT rect1 = {x_left1, x_right1, y_top1, y_bottom1};
CB_RectT rect2 = {x_left2, x_right2, y_top2, y_bottom2};
double result1 = intg.getRectSqValue_0(rect1);
double result2 = intg.getRectSqValue_0(rect2);
result = result1 - result2;
break;
}
default:
break;
}
result *= haar_feature.inv_area;
result = result / subwin.var;
result /= real_scale_square;
if (haar_feature.type <HFT_STEP1 && haar.is_abs == 1)
{
result = fabs(result);
}
return result;
}
void GlutOrientation::Apply(ImageOf<PixelRgb>& src,
ImageOf<PixelRgb>& dest,
ImageOf<PixelFloat>& xdata,
ImageOf<PixelFloat>& ydata,
ImageOf<PixelFloat>& mdata)
{
int view = 1; //(dest.width()>0);
ImageOf<PixelFloat>& orient = fine_orientation_data.Orient();
static ImageOf<PixelMono> mask;
static IntegralImage ii;
if (view)
{
dest.resize(src);
}
for (int i=0;i<SHIFTS;i++)
{
shifts[i].resize(src);
}
mean.resize(src);
static int shifts_x[SHIFTS], shifts_y[SHIFTS];
int ct = 0;
for (int dx=-1; dx<=2; dx++)
{
for (int dy=-1; dy<=2; dy++)
{
assert(ct<SHIFTS);
ii.Offset(src,shifts[ct],dx,dy,1);
shifts_x[ct] = dx;
shifts_y[ct] = dy;
ct++;
}
}
static ImageOf<PixelFloat> mean, var, lx, ly, agree;
ImageOf<PixelRgb>& mono = src;
SatisfySize(mono,mean);
SatisfySize(mono,var);
SatisfySize(mono,lx);
SatisfySize(mono,ly);
SatisfySize(mono,agree);
SatisfySize(mono,xdata);
SatisfySize(mono,ydata);
SatisfySize(mono,mdata);
int response_ct = 0;
IMGFOR(mono,x,y)
{
float total = 0;
float total2 = 0;
PixelRgb& pix0 = src(x,y);
for (int k=0; k<SHIFTS; k++)
{
PixelRgb& pix1 = shifts[k](x,y);
float v = pdist(pix0,pix1);
total += v;
#ifdef USE_LUMINANCE_FILTER
total2 += v*v;
#endif
}
mean(x,y) = total/SHIFTS;
#ifdef USE_LUMINANCE_FILTER
var(x,y) = total2/SHIFTS - (total/SHIFTS)*(total/SHIFTS);
#endif
}
/* MEX入口点.
两种使用方法:
初始化:
TLD(帧宽, 帧高, 第一帧, 选择出的边框盒)
处理每一帧:
新的轨迹边框盒 = TLD(当前帧, 轨迹边框盒)
*/
void BpTld_Process(IplImage * NewImage,double * ttbb,double * outPut) {
// 获得输入 -------------------------------------------------------------
// 当前帧
IplImage *nextFrame = NewImage;
IntegralImage *nextFrameIntImg = new IntegralImage();
nextFrameIntImg->createFromIplImage(NewImage);
// 轨迹边框盒[x, y, width, height]
double *bb = ttbb;
// 跟踪 + 探测 ------------------------------------------------------
// 只有在上个迭代中我们足够自信的时候,才跟踪
// 从这开始，跟踪器处理下一帧内存
double *tbb;
vector<double *> *dbbs;
if (confidence > MIN_TRACKING_CONF) {
tbb = tracker->track(nextFrame, nextFrameIntImg, bb);
if (tbb[4] > MIN_TRACKING_CONF)
{
dbbs = detector->detect(nextFrameIntImg, tbb);
}
else
{
dbbs = detector->detect(nextFrameIntImg, NULL);
}
} else {
dbbs = detector->detect(nextFrameIntImg, NULL);
tracker->setPrevFrame(nextFrame);
tbb = new double[5];
tbb[0] = 0;
tbb[1] = 0;
tbb[2] = 0;
tbb[3] = 0;
tbb[4] = MIN_TRACKING_CONF;
}
// 学习 -----------------------------------------------------------------
// 获得最好的探测出的小块的信任度
double dbbMaxConf = 0.0f;
int dbbMaxConfIndex = -1;
for (int i = 0; i < dbbs->size(); i++) {
double dbbConf = dbbs->at(i)[4];
if (dbbConf > dbbMaxConf) {
dbbMaxConf = dbbConf;
dbbMaxConfIndex = i;
}
}
// //
//if (dbbMaxConfIndex >= 0)
//{ testDetector.x = dbbs->at(dbbMaxConfIndex)[0];
//testDetector.y = dbbs->at(dbbMaxConfIndex)[1];
//testDetector.width = dbbs->at(dbbMaxConfIndex)[2];
//testDetector.height = dbbs->at(dbbMaxConfIndex)[3];
//}
////
// 如果探测出的小块中有一个信任度最高，并且大于MIN_REINIT_CONF
// 那么就重置跟踪器的边框盒
if (dbbMaxConf > tbb[4] && dbbMaxConf > MIN_REINIT_CONF) {
delete tbb;
tbb = new double[5];
double *dbb = dbbs->at(dbbMaxConfIndex);
tbb[0] = dbb[0];
tbb[1] = dbb[1];
tbb[2] = dbb[2];
tbb[3] = dbb[3];
tbb[4] = dbb[4];
}
// 如果跟踪出的小块的信任度最高并且最后一帧时足够自信，那么就启动约束。
else if (tbb[4] > dbbMaxConf && confidence > MIN_LEARNING_CONF) {
for (int i = 0; i < dbbs->size(); i++) {
// 用正向和负向小块训练分类器
// 正向-与被跟踪的小块重合
// 负向-被分类为正向但与被跟踪的小块不重合
double *dbb = dbbs->at(i);
if (dbb[5] == 1) {
classifier->train(nextFrameIntImg, (int)dbb[0], (int)dbb[1], (int)dbb[2], (int)dbb[3], 1);
}
else if (dbb[5] == 0) {
classifier->train(nextFrameIntImg, (int)dbb[0], (int)dbb[1], (int)dbb[2], (int)dbb[3], 0);
}
}
}
// 为下个迭代设置信任度
confidence = tbb[4];
//设置输出
outPut[0] = tbb[0];
outPut[1] = tbb[1];
outPut[2] = tbb[2];
outPut[3] = tbb[3];
//////////////////////////////////////////////////////////////////////////
//此处tbb[0],tbb[1],tbb[2],tbb[3]即是最终的边框盒
//如果tbb[2],tbb[3]全都大于0,,就表明估算出物体位置
//////////////////////////////////////////////////////////////////////////
// 设置输出 ------------------------------------------------------------
// 我们输出一系列边框盒;第一个是跟踪出的小块，剩下的是探测出的小块
// Rows correspond to individual bounding boxes
// Columns correspond to [x, y, width, height, confidence, overlapping]
// 释放内存
free(tbb);
dbbs->clear();
delete nextFrameIntImg;
}
int main(int argc, char **argv) {
if(argc!=3){
printf("Usage: imageFindSoma img soma.gr\n");
exit(0);
}
string nameImg(argv[1]);
string nameGraph(argv[2]);
Image<float> * img = new Image<float>(nameImg);
IntegralImage* iimg = new IntegralImage(img);
// Image<float>* res1 = img->create_blank_image_float("/media/neurons/findSoma/n7s1.png");
// Image<float>* res2 = res1->create_blank_image_float("/media/neurons/findSoma/n7s2.png");
// Image<float>* res = res2->create_blank_image_float("/media/neurons/findSoma/n7s.png");
//And now finds the soma (just a simple integral over 50x50 px
int step = 30;
// res1->put_all(255);
// res2->put_all(255);
// res ->put_all(255);
int x1, y1, xS, yS;
float value;
float minValue = 255;
printf("Doing the first pass\n");
//Now the displacement will be included into the computation
for(int disp = 0; disp < step; disp+=5){
printf("disp = %i\n", disp);
//Initial search
for(int y0 = disp; y0 < img->height - step; y0+=step){
for(int x0 = disp; x0 < img->width - step; x0+=step){
value = iimg->integral(x0,y0,x0+step,y0+step)/(step*step);
if(value < minValue){
minValue = value;
xS = x0;
yS = y0;
}
// for(int i = x0; i <= x0+step; i++)
// for(int j = y0; j <= y0+step; j++)
// res1->put(i,j,value);
}
}
}
// printf("Saving the result\n");
// for(int x = xS; x < xS+step; x++)
// for(int y = yS; y < yS+step; y++){
// res->put(x,y,0);
// }
printf("Saving the images\n");
// iimg->save();
// res1->save();
// res2->save();
// res->save();
//And now the graph with the contour will be saved
printf("Saving the graph\n");
Graph<Point2Do, EdgeW<Point2Do> >* gr =
new Graph<Point2Do, EdgeW<Point2Do> >();
int x, y;
y = yS;
vector<float> smicrom(2);
vector<int> sindex(2);
int stepContour = 3;
sindex[0] = xS; sindex[1]=yS;
img->indexesToMicrometers(sindex, smicrom);
for(x = 0; x <= step; x+=stepContour){
gr->cloud->points.push_back
(new Point2Do(smicrom[0]+x, smicrom[1], M_PI/2));
}
for(y = 1; y <= step-1; y+=stepContour){
gr->cloud->points.push_back
(new Point2Do(smicrom[0]+step, smicrom[1]-y, 0));
}
for(x = step; x>=0; x-=stepContour){
gr->cloud->points.push_back
(new Point2Do(smicrom[0]+x, smicrom[1]-step, M_PI/2));
}
for(y = step-1; y>=1; y-=stepContour){
gr->cloud->points.push_back
(new Point2Do(smicrom[0], smicrom[1]-y, 0));
}
for(int i = 0; i < gr->cloud->points.size()-1; i++)
gr->eset.edges.push_back
(new EdgeW<Point2Do>(&gr->cloud->points, i, i+1, 1));
gr->eset.edges.push_back
(new EdgeW<Point2Do>(&gr->cloud->points, 0, gr->cloud->points.size()-1, 1));
gr->v_radius = 0.2;
gr->cloud->v_radius = 0.2;
gr->saveToFile(nameGraph);
}