IplImage * find_macbeth( const char *img )
{
IplImage * macbeth_img = cvLoadImage( img,
CV_LOAD_IMAGE_ANYCOLOR|CV_LOAD_IMAGE_ANYDEPTH );
IplImage * macbeth_original = cvCreateImage( cvSize(macbeth_img->width, macbeth_img->height), macbeth_img->depth, macbeth_img->nChannels );
cvCopy(macbeth_img, macbeth_original);
IplImage * macbeth_split[3];
IplImage * macbeth_split_thresh[3];
for(int i = 0; i < 3; i++) {
macbeth_split[i] = cvCreateImage( cvSize(macbeth_img->width, macbeth_img->height), macbeth_img->depth, 1 );
macbeth_split_thresh[i] = cvCreateImage( cvSize(macbeth_img->width, macbeth_img->height), macbeth_img->depth, 1 );
}
cvSplit(macbeth_img, macbeth_split[0], macbeth_split[1], macbeth_split[2], NULL);
if( macbeth_img )
{
int adaptive_method = CV_ADAPTIVE_THRESH_MEAN_C;
int threshold_type = CV_THRESH_BINARY_INV;
int block_size = cvRound(
MIN(macbeth_img->width,macbeth_img->height)*0.02)|1;
fprintf(stderr,"Using %d as block size\n", block_size);
double offset = 6;
// do an adaptive threshold on each channel
for(int i = 0; i < 3; i++) {
cvAdaptiveThreshold(macbeth_split[i], macbeth_split_thresh[i], 255, adaptive_method, threshold_type, block_size, offset);
}
IplImage * adaptive = cvCreateImage( cvSize(macbeth_img->width, macbeth_img->height), IPL_DEPTH_8U, 1 );
// OR the binary threshold results together
cvOr(macbeth_split_thresh[0],macbeth_split_thresh[1],adaptive);
cvOr(macbeth_split_thresh[2],adaptive,adaptive);
for(int i = 0; i < 3; i++) {
cvReleaseImage( &(macbeth_split[i]) );
cvReleaseImage( &(macbeth_split_thresh[i]) );
}
int element_size = (block_size/10)+2;
fprintf(stderr,"Using %d as element size\n", element_size);
// do an opening on the threshold image
IplConvKernel * element = cvCreateStructuringElementEx(element_size,element_size,element_size/2,element_size/2,CV_SHAPE_RECT);
cvMorphologyEx(adaptive,adaptive,NULL,element,CV_MOP_OPEN);
cvReleaseStructuringElement(&element);
CvMemStorage* storage = cvCreateMemStorage(0);
CvSeq* initial_quads = cvCreateSeq( 0, sizeof(*initial_quads), sizeof(void*), storage );
CvSeq* initial_boxes = cvCreateSeq( 0, sizeof(*initial_boxes), sizeof(CvBox2D), storage );
// find contours in the threshold image
CvSeq * contours = NULL;
cvFindContours(adaptive,storage,&contours);
int min_size = (macbeth_img->width*macbeth_img->height)/
(MACBETH_SQUARES*100);
if(contours) {
int count = 0;
for( CvSeq* c = contours; c != NULL; c = c->h_next) {
CvRect rect = ((CvContour*)c)->rect;
// only interested in contours with these restrictions
if(CV_IS_SEQ_HOLE(c) && rect.width*rect.height >= min_size) {
// only interested in quad-like contours
CvSeq * quad_contour = find_quad(c, storage, min_size);
if(quad_contour) {
cvSeqPush( initial_quads, &quad_contour );
count++;
rect = ((CvContour*)quad_contour)->rect;
CvScalar average = contour_average((CvContour*)quad_contour, macbeth_img);
CvBox2D box = cvMinAreaRect2(quad_contour,storage);
cvSeqPush( initial_boxes, &box );
// fprintf(stderr,"Center: %f %f\n", box.center.x, box.center.y);
double min_distance = MAX_RGB_DISTANCE;
CvPoint closest_color_idx = cvPoint(-1,-1);
for(int y = 0; y < MACBETH_HEIGHT; y++) {
for(int x = 0; x < MACBETH_WIDTH; x++) {
double distance = euclidean_distance_lab(average,colorchecker_srgb[y][x]);
if(distance < min_distance) {
closest_color_idx.x = x;
closest_color_idx.y = y;
min_distance = distance;
}
}
}
CvScalar closest_color = colorchecker_srgb[closest_color_idx.y][closest_color_idx.x];
// fprintf(stderr,"Closest color: %f %f %f (%d %d)\n",
// closest_color.val[2],
// closest_color.val[1],
// closest_color.val[0],
// closest_color_idx.x,
// closest_color_idx.y
// );
// cvDrawContours(
// macbeth_img,
// quad_contour,
// cvScalar(255,0,0),
// cvScalar(0,0,255),
// 0,
// element_size
// );
// cvCircle(
// macbeth_img,
// cvPointFrom32f(box.center),
// element_size*6,
// cvScalarAll(255),
// -1
// );
// cvCircle(
// macbeth_img,
// cvPointFrom32f(box.center),
// element_size*6,
// closest_color,
// -1
// );
// cvCircle(
// macbeth_img,
// cvPointFrom32f(box.center),
// element_size*4,
// average,
// -1
// );
// CvRect rect = contained_rectangle(box);
// cvRectangle(
// macbeth_img,
// cvPoint(rect.x,rect.y),
// cvPoint(rect.x+rect.width, rect.y+rect.height),
// cvScalarAll(0),
// element_size
// );
}
}
}
ColorChecker found_colorchecker;
fprintf(stderr,"%d initial quads found", initial_quads->total);
if(count > MACBETH_SQUARES) {
fprintf(stderr," (probably a Passport)\n");
CvMat* points = cvCreateMat( initial_quads->total , 1, CV_32FC2 );
CvMat* clusters = cvCreateMat( initial_quads->total , 1, CV_32SC1 );
CvSeq* partitioned_quads[2];
CvSeq* partitioned_boxes[2];
for(int i = 0; i < 2; i++) {
partitioned_quads[i] = cvCreateSeq( 0, sizeof(**partitioned_quads), sizeof(void*), storage );
partitioned_boxes[i] = cvCreateSeq( 0, sizeof(**partitioned_boxes), sizeof(CvBox2D), storage );
}
// set up the points sequence for cvKMeans2, using the box centers
for(int i = 0; i < initial_quads->total; i++) {
CvBox2D box = (*(CvBox2D*)cvGetSeqElem(initial_boxes, i));
cvSet1D(points, i, cvScalar(box.center.x,box.center.y));
}
// partition into two clusters: passport and colorchecker
cvKMeans2( points, 2, clusters,
cvTermCriteria( CV_TERMCRIT_EPS+CV_TERMCRIT_ITER,
10, 1.0 ) );
for(int i = 0; i < initial_quads->total; i++) {
CvPoint2D32f pt = ((CvPoint2D32f*)points->data.fl)[i];
int cluster_idx = clusters->data.i[i];
cvSeqPush( partitioned_quads[cluster_idx],
cvGetSeqElem(initial_quads, i) );
cvSeqPush( partitioned_boxes[cluster_idx],
cvGetSeqElem(initial_boxes, i) );
// cvCircle(
// macbeth_img,
// cvPointFrom32f(pt),
// element_size*2,
// cvScalar(255*cluster_idx,0,255-(255*cluster_idx)),
// -1
// );
}
ColorChecker partitioned_checkers[2];
// check each of the two partitioned sets for the best colorchecker
for(int i = 0; i < 2; i++) {
partitioned_checkers[i] =
find_colorchecker(partitioned_quads[i], partitioned_boxes[i],
storage, macbeth_img, macbeth_original);
}
// use the colorchecker with the lowest error
found_colorchecker = partitioned_checkers[0].error < partitioned_checkers[1].error ?
partitioned_checkers[0] : partitioned_checkers[1];
cvReleaseMat( &points );
cvReleaseMat( &clusters );
}
else { // just one colorchecker to test
fprintf(stderr,"\n");
found_colorchecker = find_colorchecker(initial_quads, initial_boxes,
storage, macbeth_img, macbeth_original);
}
// render the found colorchecker
draw_colorchecker(found_colorchecker.values,found_colorchecker.points,macbeth_img,found_colorchecker.size);
// print out the colorchecker info
for(int y = 0; y < MACBETH_HEIGHT; y++) {
for(int x = 0; x < MACBETH_WIDTH; x++) {
CvScalar this_value = cvGet2D(found_colorchecker.values,y,x);
CvScalar this_point = cvGet2D(found_colorchecker.points,y,x);
printf("%.0f,%.0f,%.0f,%.0f,%.0f\n",
this_point.val[0],this_point.val[1],
this_value.val[2],this_value.val[1],this_value.val[0]);
}
}
printf("%0.f\n%f\n",found_colorchecker.size,found_colorchecker.error);
}
cvReleaseMemStorage( &storage );
if( macbeth_original ) cvReleaseImage( &macbeth_original );
if( adaptive ) cvReleaseImage( &adaptive );
return macbeth_img;
}
if( macbeth_img ) cvReleaseImage( &macbeth_img );
return NULL;
}
bool photometric_calibration(CalibModel &model, CvCapture *capture,
int nbImages, bool cache)
{
if (cache) model.map.load();
const char *win = "BazAR";
IplImage*gray=0;
cvNamedWindow(win, CV_WINDOW_AUTOSIZE);
cvNamedWindow("LightMap", CV_WINDOW_AUTOSIZE);
IplImage* frame = 0;
IplImage* display=cvCloneImage(cvQueryFrame(capture));
int nbHomography =0;
LightCollector lc(model.map.reflc);
IplImage *lightmap = cvCreateImage(cvGetSize(model.map.map.getIm()), IPL_DEPTH_8U,
lc.avgChannels);
while (1)
{
// acquire image
frame = cvQueryFrame( capture );
/*
if (frame) cvReleaseImage(&frame);
frame = cvLoadImage("model.bmp",1);
*/
if( !frame )
break;
// convert it to gray levels, if required
if (frame->nChannels >1) {
if( !gray )
gray = cvCreateImage( cvGetSize(frame), IPL_DEPTH_8U, 1 );
cvCvtColor(frame, gray, CV_RGB2GRAY);
} else {
gray = frame;
}
// run the detector
if (model.detector.detect(gray)) {
// 2d homography found
nbHomography++;
// Computes 3D pose and surface normal
model.augm.Clear();
add_detected_homography(model.detector, model.augm);
model.augm.Accomodate(4, 1e-4);
CvMat *mat = model.augm.GetObjectToWorld();
float normal[3];
for (int j=0;j<3;j++) normal[j] = cvGet2D(mat, j, 2).val[0];
cvReleaseMat(&mat);
// average pixels over triangles
lc.averageImage(frame,model.detector.H);
// add observations
if (!model.map.isReady())
model.map.addNormal(normal, lc, 0);
if (!model.map.isReady() && nbHomography >= nbImages) {
if (model.map.computeLightParams()) {
model.map.save();
const float *gain = model.map.getGain(0);
const float *bias = model.map.getBias(0);
cout << "Gain: " << gain[0] << ", " << gain[1] << ", " << gain[2] << endl;
cout << "Bias: " << bias[0] << ", " << bias[1] << ", " << bias[2] << endl;
}
}
}
if (model.map.isReady()) {
double min, max;
IplImage *map = model.map.map.getIm();
cvSetImageCOI(map, 2);
cvMinMaxLoc(map, &min, &max);
cvSetImageCOI(map, 0);
assert(map->nChannels == lightmap->nChannels);
cvConvertScale(map, lightmap, 128, 0);
cvShowImage("LightMap", lightmap);
augment_scene(model, frame, display);
} else {
cvCopy(frame,display);
if (model.detector.object_is_detected)
lc.drawGrid(display, model.detector.H);
}
cvShowImage(win, display);
int k=cvWaitKey(10);
if (k=='q' || k== 27)
break;
}
cvReleaseImage(&lightmap);
cvReleaseImage(&display);
if (frame->nChannels > 1)
cvReleaseImage(&gray);
return 0;
}
void DB_Analysis::OnBnClickedBtnSame()
{
Count_File = 0;
C_state.SetWindowTextA(_T("중복처리 진행중..."));
UpdateData(false);
CFileFind finder;
BOOL bWorking = finder.FindFile(_T("C:\\output\\*.bmp"));
while (bWorking)
{
bWorking = finder.FindNextFile();
if (finder.IsDots()) continue;
Count_File++;
}
finder.Close();
// TODO: 여기에 컨트롤 알림 처리기 코드를 추가합니다.
CString first[103]; // 처음 이미지 값의 배열
CString second[103]; // 비교 이미지 값의 배열
IplImage *src_image;// 이미지 불러 올값.
int temp_sum[100] = { 0, }; //일시적 평균값 저장 장소.
CString new_image_path("C:\\output\\");
char cut_file_name[13];
sprintf(cut_file_name, "%05d.bmp", 1);
DB_array = new CString*[Count_File];
for (int i = 0; i < Count_File; i++)
{
DB_array[i] = new CString[103];
}
first[0] = new_image_path + cut_file_name;
first[1].Format("%d", 1);
src_image = cvLoadImage(first[0]);
///영상을 10 x 10으로 분할 후 한칸당 값들의 평균값 저장.
int real_width, real_height;
CvScalar temp_scalar;
for (int y_block = 0; y_block < 10; y_block++)
{
for (int x_block = 0; x_block < 10; x_block++)
{
for (int col = 0; col < 64; col++)
{
for (int row = 0; row < 48; row++)
{
real_width = x_block * 64 + col;
real_height = y_block * 48 + row;
temp_scalar = cvGet2D(src_image, real_height, real_width);
temp_sum[x_block + 10 * y_block] += (temp_scalar.val[0] + temp_scalar.val[1] + temp_scalar.val[2]) / 3.0f;
}
}
}
}
for (int i = 0; i < 100; i++)
{
temp_sum[i] = temp_sum[i] / 3185;
first[i + 2].Format("%d", temp_sum[i]);
}
for (int i = 0; i < 102; i++)
{
DB_array[0][i].Format(first[i]);
}
cvReleaseImage(&src_image);
for (int j = 2; j <= Count_File; j++)
{
sprintf(cut_file_name, "%05d.bmp", j);
second[0] = new_image_path + cut_file_name;
second[1].Format("%d", j);
src_image = cvLoadImage(second[0]);
for (int y_block = 0; y_block < 10; y_block++)
{
for (int x_block = 0; x_block < 10; x_block++)
{
for (int col = 0; col < 64; col++)
{
for (int row = 0; row < 48; row++)
{
real_width = x_block * 64 + col;
real_height = y_block * 48 + row;
temp_scalar = cvGet2D(src_image, real_height, real_width);
temp_sum[x_block + 10 * y_block] += (temp_scalar.val[0] + temp_scalar.val[1] + temp_scalar.val[2]) / 3.0f;
}
}
}
}
for (int i = 0; i < 100; i++)
{
temp_sum[i] = temp_sum[i] / 3185;
second[i + 2].Format("%d", temp_sum[i]);
}
cvReleaseImage(&src_image);
int count_differnet = 0;
for (int count = 0; count < 100; count++)
{
if (atoi(first[2 + count]) != temp_sum[count])
count_differnet++;
if (count_differnet>5) break;
}
if (count_differnet > 5)
{
number_array++;
for (int i = 0; i < 102; i++)
{
DB_array[number_array][i].Format(second[i]);
first[i].Format(second[i]);
}
}
}
C_state.SetWindowTextA("중복처리 완료");
CString count, time;
count.Format("%d 개", Count_File - number_array);
time.Format("%f 초", (float)(Count_File*0.06667));
Same_count.SetWindowTextA(count);
C_time.SetWindowTextA(time);
UpdateData(false);
}
void display()
{
glClearColor(0.0, 0.0, 0.0, 0.0);
glClear(GL_COLOR_BUFFER_BIT|GL_DEPTH_BUFFER_BIT);
/* glPushMatrix();
glTranslatef(xavg,yavg,0);
glutSolidCube(200);
glPopMatrix();
/*
glBegin(GL_QUADS);
glVertex3f(xr,xb,0);
glVertex3f(xb,yb,0);
glVertex3f(xl,yl,0);
glVertex3f(xt,yt,0);
glEnd();
*/
///////////////////////////////////////////////////////////nishanthprakash20///////////////////////////////////////////////////
captured=cvQueryFrame(video1);
disp=cvCreateImage(cvGetSize(captured),IPL_DEPTH_8U,3);
eroded=cvCreateImage(cvGetSize(captured),IPL_DEPTH_8U,3);
dilated=cvCreateImage(cvGetSize(captured),IPL_DEPTH_8U,3);
// data=cvGet2D(captured,240,320);
// printf("%f,%f,%f\n",data.val[0],data.val[1],data.val[2]);
thresh1=150;
thresh2=100;
thresh3=100;
for(i=0;i<disp->height;i++)
for(j=0;j<disp->width;j++)
{
data=cvGet2D(captured,i,j);
if(data.val[1]>thresh1&&data.val[2]<thresh2&&data.val[0]<thresh3)
{
cvSet2D(disp,i,j,data);
}
}
cvErode(disp,eroded,NULL,1);
cvDilate(eroded,dilated,NULL,4);
for(i=0;i<disp->height;i++)
for(j=0;j<disp->width;j++)
{
data=cvGet2D(dilated,i,j);
if(data.val[1]>thresh1&&data.val[2]<thresh2&&data.val[0]<thresh3)
{ goto donetop;
}
}
donetop:
xt=j;
yt=i;
for(i=479;i>0;i--)
for(j=0;j<disp->width;j++)
{
data=cvGet2D(dilated,i,j);
if(data.val[1]>thresh1&&data.val[2]<thresh2&&data.val[0]<thresh3)
{ goto doneleft;
}
}
doneleft:
xb=j;
yb=i;
inclination=((float)atan((yt-yb)/(xt-xb))-(float)atan(10.0/21))*180/3.14;
if(inclination<0) inclination+=60;
printf("%f\n",inclination);
cvNamedWindow("Cap");
cvShowImage("Cap",dilated);
cvWaitKey(3);
//*/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
glColor3f(1.0, 1.0, 1.0);
glPushMatrix();
glTranslatef(0,0,-5);
glRotatef(inclination,0,0,1);
glScalef(100,100,100);
glColor3f(0.0f,0.0f,0.0f);
drawmodel_box();
glColor3f(1.0f,1.0f,1.0f);
drawmodel_box2();
glColor3f(1.0f,1.0f,1.0f);
drawmodel_box3();
glColor3f(1.0f,1.0f,1.0f);
drawmodel_box4();
glColor3f(0.2f,0.2f,1.0f);
drawmodel_box5(); //remove this
//glScalef(0.01,0.01,0.01);
//glTranslatef(0,0,5);
glPopMatrix();
glutSwapBuffers();
}
int _tmain(int argc, _TCHAR* argv[])
{
int i, j, n, m;
CvScalar tempValue;
double S= 0.0;
double Mask[3][3] = {{255., 255., 255.}, {255., 255., 255.}, {255., 255., 255.}};
//침식 연산을 위한 마스크
IplImage *inputImage = cvLoadImage("lena.bmp", CV_LOAD_IMAGE_GRAYSCALE);
IplImage *binaryImage = cvCreateImage(cvGetSize(inputImage), 8, 1);
IplImage *tempImage = cvCreateImage(cvSize(inputImage->width+2, inputImage->height+2), 8, 1);
IplImage *outputImage = cvCreateImage(cvGetSize(inputImage), 8, 1);
for(i=0; i<inputImage->height; i++){ //원본영상의 이진화
for(j=0; j<inputImage->width; j++){
tempValue = cvGet2D(inputImage, i, j);
if(tempValue.val[0] > THRESHOLD) {
cvSet2D(binaryImage, i, j, cvScalar(255));
} else {
cvSet2D(binaryImage, i, j, cvScalar(0));
}
}
}
for(i=0; i<binaryImage->height; i++) {
for(j=0; j<binaryImage->width; j++){
cvSet2D(tempImage, i+1, j+1, cvGet2D(binaryImage, i, j));
}
}
for(i=0; i<binaryImage->height; i++){
for(j=0; j<binaryImage->width; j++){
for(n=0; n<3; n++){
for(m=0; m<3; m++){
tempValue = cvGet2D(tempImage, i+n, j+m);
if(Mask[n][m] == tempValue.val[0]){
//마스크와 같은 값이 있는지 조사
S += 1.0;
}
}
}
if(S==9.0){
cvSet2D(outputImage, i, j, cvScalar(255));
//값이 모두 일치하면 출력 값은 255
} else {
cvSet2D(outputImage, i, j, cvScalar(0));
//모두 일치하지 않으면 출력 값은 0
}
S =0.0; // reset
}
}
cvShowImage("Input Image", inputImage);
cvShowImage("Binary Image", binaryImage);
cvShowImage("Output Image", outputImage);
cvWaitKey();
cvReleaseImage(&inputImage);
cvReleaseImage(&binaryImage);
cvReleaseImage(&tempImage);
cvReleaseImage(&outputImage);
return 0;
}
CvScalar MaskShift::GetPixelValue(int x, int y)
{
return cvGet2D(_imageData, y + _shift.y, x + _shift.x);
}
IplImage *preImg(std::string caminho) {
int quadratico = 200;
CvSize tamanho = cvSize(quadratico, quadratico);
IplImage *in = cvLoadImage(caminho.c_str(), CV_LOAD_IMAGE_GRAYSCALE);
IplImage *src = cvCreateImage(tamanho, in->depth, in->nChannels);
IplImage *dst = cvCreateImage(tamanho, in->depth, in->nChannels);
IplImage *fn = cvCreateImage(cvSize(mh, mw), in->depth, in->nChannels);
cvResize(in, src);
cvThreshold(src, src, 220, 255, CV_THRESH_BINARY);
cvShowImage("tresh", src);
cvCanny(src, src, 100, 120, 3);
//cvShowImage("canny", src);
cvMorphologyEx(src, src, 0, cvCreateStructuringElementEx(4, 4, 0, 0, CV_SHAPE_RECT), cv::MORPH_DILATE, 1);
//cvShowImage("Dilatacao", src);
std::vector<CvPoint> pontos;
for (int y = 0; y < src->height; y++) {
for (int x = 0; x < src->width; x++) {
if (cvGet2D(src, x, y).val[0] == 255) {
//inversão dos eixos
pontos.push_back(cvPoint(y, x));
}
}
}
std::sort(pontos.begin(), pontos.end(), sortPontos);
CvPoint interpol = getInterpolado(pontos[0], pontos[pontos.size() - 1]);
// CvScalar color = cvScalar(255, 255, 255);
// int radius = 6;
// int thickness = 2;
//
// cvCircle(src, pontos[0], radius, color, thickness);
//
// cvCircle(src, pontos[pontos.size() - 1], radius, color, thickness);
//cvCircle(src, interpol, radius, color, thickness);
// std::cout << cvGetReal2D(src, pontos.begin()->x, pontos.begin()->y)
// << std::endl;
// cvShowImage("teste", src);
//-----------------------------
cvLogPolar(src, dst, cvPoint2D32f(interpol.x, interpol.y), 40,
CV_INTER_LINEAR + CV_WARP_FILL_OUTLIERS);
//cvNamedWindow("log-polar", 1);
//cvShowImage("log-polar", dst);
//cvShowImage("LogPolar",dst);
cvResize(dst, fn);
//cvShowImage("teste saida", fn);
return fn;
}
//参数说明：nCuster为聚类的类数
int color_cluster(char *filename,int nCuster )
{
IplImage* img=cvLoadImage(filename);
int i,j;
CvMat *samples=cvCreateMat((img->width)*(img->height),1,CV_32FC3);//创建样本矩阵，CV_32FC3代表32位浮点3通道（彩色图像）
CvMat *clusters=cvCreateMat((img->width)*(img->height),1,CV_32SC1);//创建类别标记矩阵，CV_32SF1代表32位整型1通道
int k=0;
for (i=0;i<img->width;i++)
{
for (j=0;j<img->height;j++)
{
CvScalar s;
//获取图像各个像素点的三通道值（BGR）
s.val[0]=(float)cvGet2D(img,j,i).val[0];//B
s.val[1]=(float)cvGet2D(img,j,i).val[1];//G
s.val[2]=(float)cvGet2D(img,j,i).val[2];//R
cvSet2D(samples,k++,0,s);//将像素点三通道的值按顺序排入样本矩阵
}
}
//聚类类别数，后期可以通过学习确定分类数。
cvKMeans2(samples,nCuster,clusters,cvTermCriteria(CV_TERMCRIT_ITER,100,1.0));//开始聚类，迭代100次，终止误差1.0
//创建用于显示的图像，二值图像
IplImage *binimg=cvCreateImage(cvSize(img->width,img->height),IPL_DEPTH_8U,1);
//创建用于单独显示每个聚类结果的图像
IplImage *cluster_img0=cvCreateImage(cvSize(img->width,img->height),IPL_DEPTH_8U,1);
IplImage *cluster_img1=cvCreateImage(cvSize(img->width,img->height),IPL_DEPTH_8U,1);
IplImage *cluster_img2=cvCreateImage(cvSize(img->width,img->height),IPL_DEPTH_8U,1);
k=0;
int val=0;
float step=255/(nCuster-1);
CvScalar bg={255,0,0,0};//背景设置为白色
for (i=0;i<img->width;i++)
{
for (j=0;j<img->height;j++)
{
cvSet2D(cluster_img0,j,i,bg);
cvSet2D(cluster_img1,j,i,bg);
cvSet2D(cluster_img2,j,i,bg);
}
}
for (i=0;i<img->width;i++)
{
for (j=0;j<img->height;j++)
{
val=(int)clusters->data.i[k++];
CvScalar s;
s.val[0]=255-val*step;//这个是将不同类别取不同的像素值，
cvSet2D(binimg,j,i,s);
//将每个聚类进行分离
switch(val)
{
case 0:
cvSet2D(cluster_img0,j,i,s);break;//白色类
case 1:
cvSet2D(cluster_img1,j,i,s);break;//灰色类
case 2:
cvSet2D(cluster_img2,j,i,s);break;//黑色类
default:
break;
}
}
}
cvSaveImage("PicVideo//cluster_img0.png",cluster_img0);
cvSaveImage("PicVideo//cluster_img1.png",cluster_img1);
cvSaveImage("PicVideo//cluster_img2.png",cluster_img2);
cvNamedWindow( "原始图像", 1 );
cvShowImage( "原始图像", img );
cvNamedWindow( "聚类图像", 1 );
cvShowImage( "聚类图像", binimg );
cvSaveImage("PicVideo//clusterimg.png",binimg);
cvWaitKey(0); //等待按键
cvDestroyWindow( "原始图像" );
cvDestroyWindow( "聚类图像" );
cvReleaseImage( &img );
cvReleaseImage( &binimg );
cvReleaseImage(&cluster_img0);
cvReleaseImage(&cluster_img1);
cvReleaseImage(&cluster_img0);
return 0;
}
ImageRAII hysteresis( IplImage * image, IplImage * orientation, std::pair< int, int > thresh )
{
const char * WINDOW_NAME = "Hysteresis Threshold";
CvSize image_size = cvGetSize( image );
ImageRAII hysteresis_image( cvCreateImage( image_size, image->depth, image->nChannels ) );
// key: pixel position
// value: visited = true, unvisited = false
std::map< CvPoint, bool, classcomp > pixels;
std::map< CvPoint, bool, classcomp >::iterator it;
std::vector< std::vector< CvPoint > > edges;
// initialize map
for( int i = 0; i < image_size.width; i++ )
{
for( int j = 0; j < image_size.height; j++ )
{
pixels[cvPoint( i, j )] = false;
}
}
// visit all pixels
for( it = pixels.begin(); it != pixels.end(); it++ )
{
std::vector< CvPoint > edge;
// find next unvisited edge pixel
bool run = true;
while( run )
{
if( it->second == false && check_boundaries( image, it->first ) && cvGet2D( image, it->first.y, it->first.x ).val[0] > thresh.second )
run = false;
if( it == pixels.end() )
run = false;
it++;
}
// mark pixel as visited
CvPoint current_pixel = it->first;
it->second = true;
edge.push_back( current_pixel );
// follow links forward
std::pair< CvPoint, CvPoint > positions = get_edge_positions( orientation, current_pixel.x, current_pixel.y );
// go forward
CvPoint forward = positions.first;
while( check_boundaries( image, forward ) && cvGet2D( image, forward.y, forward.x ).val[0] > thresh.first )
{
// mark pixel as visited
edge.push_back( forward );
pixels.find( forward )->second = true;
std::pair< CvPoint, CvPoint > forward_positions = get_edge_positions( orientation, forward.x, forward.y );
forward = forward_positions.first;
}
// go backward
CvPoint backward = positions.second;
while( check_boundaries( image, backward ) && cvGet2D( image, backward.y, backward.x ).val[0] > thresh.first )
{
// mark pixel as visited
edge.push_back( backward );
pixels.find( backward )->second = true;
std::pair< CvPoint, CvPoint > backward_positions = get_edge_positions( orientation, backward.x, backward.y );
backward = backward_positions.second;
}
// store this edge
edges.push_back( edge );
}
int size = 0;
// set the edges in the image
std::vector< std::vector< CvPoint > >::iterator edges_iterator;
for( edges_iterator = edges.begin(); edges_iterator < edges.end(); edges_iterator++ )
{
std::vector< CvPoint >::iterator edge_iterator;
std::vector< CvPoint > edge = *edges_iterator;
for( edge_iterator = edge.begin(); edge_iterator < edge.end(); edge_iterator++ )
{
size++;
CvPoint pixel = *edge_iterator;
CvScalar e;
e.val[0] = GRAY;
cvSet2D( hysteresis_image.image, pixel.y, pixel.x, e );
}
}
cvNamedWindow( WINDOW_NAME );
cvShowImage( WINDOW_NAME, hysteresis_image.image );
cvMoveWindow( WINDOW_NAME, image_size.width * 3, 0 );
return hysteresis_image;
}
int writeDataToImage(IplImage *img, PixelMap *pixelmap, FILE *file, ConfigInfo config, unsigned int useable_pixels)
{
CvScalar pixel;
int index, number;
int i,j, written_bytes=0;
int message_length;
unsigned int img_capacity;
char byte;
int img_channels = 0;
if(img->nChannels == 1)
{
img_channels = 1;
}
if(img->nChannels >= 3){
img_channels = 3;
}
img_capacity = (unsigned int)( ((useable_pixels * img_channels) - MESSAGE_LENGTH_BITS) / BYTE );
message_length = getFileLength(file);
if(img_capacity < message_length)
{
printf("Image capacity is only: %u Byte -> message size is: %d\n", img_capacity, message_length);
return 0;
}
printf("Image capacity is: %u Byte\n", img_capacity);
printf("Message size is: %d Byte\n", message_length);
DEBUG( ("writing message length bits to image\n") );
for(i=MESSAGE_LENGTH_BITS-1; i>=0; )
{
//get next free pixel
do
{
number = getNextRandom();
index = number % useable_pixels;
}
while( pixelmap[index].used == TRUE );
//get values at this place
pixel = cvGet2D(img, pixelmap[index].x_coord, pixelmap[index].y_coord);
//if we have more than one channel
for(j=0; j<img_channels; j++)
{
pixel.val[j] = LSB_BIT( (int)pixel.val[j], ((message_length >> i) & 1) );
DEBUG( ("%u", ((message_length >> i) & 1)) );
i--;
if(i < 0)
{
break;
}
}
cvSet2D(img, pixelmap[index].x_coord, pixelmap[index].y_coord, pixel);
pixelmap[index].used = TRUE;
}
DEBUG( ("\n") );
getNextRandom();//fix -> asynchron
/////////////////////////////////////////////////////////////////////////////
DEBUG( ("writing bytes to image...") );
j=3;
while( (EOF != (byte = getByteFromFile(file))) && (written_bytes < img_capacity) && (written_bytes < message_length) )
{
for(i=BYTE-1; i>=0; )
{
if(j>=img_channels)
{
j=0;
do
{
number = getNextRandom();
index = number % useable_pixels;
}
while( pixelmap[index].used == TRUE );
pixel = cvGet2D(img, pixelmap[index].x_coord, pixelmap[index].y_coord);
}
else
{
j++;
}
while(j<img_channels)
{
pixel.val[j] = LSB_BIT( (int)pixel.val[j], ((byte >> i) & 1) );
i--;
if(i < 0)
{
break;
}
j++;
}
cvSet2D(img, pixelmap[index].x_coord, pixelmap[index].y_coord, pixel);
pixelmap[index].used = TRUE;
}
written_bytes++;
}
DEBUG( ("done\n") );
printf("%d Bytes written to image\n", written_bytes);
return written_bytes;
}
void cveGet2D(CvArr* arr, int idx0, int idx1, CvScalar* value)
{
*value = cvGet2D(arr, idx0, idx1);
}
int readDataFromImage(IplImage *img, PixelMap *pixelmap, FILE *file, ConfigInfo config, unsigned int useable_pixels)
{
CvScalar pixel;
int index, number;
int i,j;
unsigned int message_length=0;
unsigned int img_capacity;
unsigned int extracted_bytes=0;
char byte=0;
int img_channels = 0;
if(img->nChannels == 1)
{
img_channels = 1;
}
if(img->nChannels >= 3){
img_channels = 3;
}
img_capacity = (unsigned int)( ((useable_pixels * img_channels) - MESSAGE_LENGTH_BITS) / BYTE );
printf("Image capacity is: %u Byte\n", img_capacity);
initFile(file);
DEBUG( ("extracting message size bits \n") );
for(i=0; i<=MESSAGE_LENGTH_BITS; )
{
//get next free pixel
do
{
number = getNextRandom();
index = number % useable_pixels;
}
while( pixelmap[index].used == TRUE );
//get values at this place
pixel = cvGet2D(img, pixelmap[index].x_coord, pixelmap[index].y_coord);
//if we have more than one channel
for(j=0; j<img_channels; j++)
{
message_length |= (int)pixel.val[j] & 1;
i++;
if(i < MESSAGE_LENGTH_BITS)
{
message_length <<= 1;
}
else
{
break;
}
DEBUG( ("%u", ((int)pixel.val[j] & 1) ) );
}
pixelmap[index].used = TRUE;
}
DEBUG( ("\n") );
if(message_length > img_capacity)
{
printf("The embedded message size (%d) does not fix to image's capacity (%d)\n", message_length, img_capacity);
printf("Please check if you have chosen the correct parameters and password\n");
return 0;
}
printf("Integrated message size is: %d Byte\n", message_length);
////////////////////////////////////////////////////////////////////////////////////////
DEBUG( ("extracting bytes out of image...") );
j=3;
while( extracted_bytes < message_length )
{
byte=0;
for(i=0; i<BYTE; )
{
if(j>=img_channels)
{
j=0;
do
{
number = getNextRandom();
index = number % useable_pixels;
}
while( pixelmap[index].used == TRUE );
pixel = cvGet2D(img, pixelmap[index].x_coord, pixelmap[index].y_coord);
}
else
{
j++;
}
while(j<img_channels)
{
byte |= (int)pixel.val[j] & 1;
i++;
if(i < BYTE)
{
byte <<= 1;
}
else
{
break;
}
j++;
}
pixelmap[index].used = TRUE;
}
writeByteToFile(file, byte);
extracted_bytes++;
}
DEBUG( ("done\n") );
printf("%d Bytes extracted from image\n", extracted_bytes);
return extracted_bytes;
}
int main (int argc, char ** argv)
{
const char *fileName = "../Images/lena.jpg";
double** V;
double** VDCT;
int i;
int j;
CvScalar s;
CvScalar s_img;
if (argc > 1)
{
fileName = argv[1];
}
IplImage* src = cvLoadImage ( fileName, 3);
IplImage* dct = cvCreateImage ( cvGetSize(src) , src->depth, src->nChannels);
IplImage* img = cvCreateImage( cvGetSize(src), src->depth, 1);
/**
*Y = (0.257 * R) + (0.504 * G) + (0.098 * B) + 16
*Cr = V = (0.439 * R) - (0.368 * G) - (0.071 * B) + 128
*Cb = U = -(0.148 * R) - (0.291 * G) + (0.439 * B) + 128
**/
cvCvtColor (src, dct, CV_BGR2HSV);
/**
*On va travailler sur le canal V
**/
//Allocation de la matrice correspondant au canal V
V = (double**) malloc(dct->width* sizeof(double*));
for (i = 0; i < dct->width; i++)
{
V[i] = (double*) malloc(dct->height*sizeof(double));
}
//Initialisation de la matrice V à 0
for (i = 0; i <src->width; i++)
{
for (j = 0; j < src->height; j++)
{
V[i][j] = 0;
}
}
//On récupère les valeurs du canal V dans la matrice associée à celui-ci
for (i = 0; i < src->width; i++)
{
for (j = 0; j < src->height; j++)
{
s = cvGet2D(dct,i,j);
V[i][j] = s.val[2];
}
}
//Allocation de la matrice VDCT (deuxième méthode)
VDCT = alocamd( src->width, src->height);
//Initialisation à 0 de VDCT
for (i = 0; i <src->width; i++)
{
for (j = 0; j < src->height; j++)
{
VDCT[i][j] = 0;
}
}
//Transformation DCT de la matrice V associé au canal "V" vers la matrice VDCT
dct2dim( V, VDCT, src->width, src->height);
//On met le VDCT comprenant le canal V (et seulement celui-ci) après transformation DCT dans le canal 0 de img
for (i = 0; i < src->width; i++)
{
for (j = 0; j < src->height; j++)
{
s_img = cvGet2D(img,i,j);
s_img.val[0] = VDCT[i][j];
cvSet2D(img, i, j, s_img);
}
}
//Affichage
cvNamedWindow("YUV", CV_WINDOW_AUTOSIZE);
cvShowImage("YUV", img);
cvWaitKey(0);
cvDestroyAllWindows();
}
int main_video_mask_alpha_join(int argc, char* argv[]) {
int _black_min = 20;
int _black_max = 50;
cv::namedWindow("Color");
cv::createTrackbar("Black", "Color", &_black_min, _black_max);
//CvCapture* capture_mask = cvCreateFileCapture("Split/01BW.mp4");
//CvCapture* capture_color = cvCreateFileCapture("Split/01color.mp4");
//CvCapture* capture_mask = cvCreateFileCapture("Split/02BW.mp4");
//CvCapture* capture_color = cvCreateFileCapture("Split/02color.mp4");
//CvCapture* capture_mask = cvCreateFileCapture("Split/03BW.mp4");
//CvCapture* capture_color = cvCreateFileCapture("Split/03color.mp4");
CvCapture* capture_mask = cvCreateFileCapture("Split/04BW.mp4");
CvCapture* capture_color = cvCreateFileCapture("Split/04color.mp4");
// Create IplImage to point to each frame
IplImage* frame_color;
IplImage* frame_mask;
IplImage* frame_processed;
// Loop until frame ended or ESC is pressed
frame_color = cvQueryFrame(capture_color);
frame_mask = cvQueryFrame(capture_mask);
frame_processed = cvCloneImage(frame_color);
printf("Color:%dx%d\n", frame_color->width, frame_color->height);
printf("Mask:%dx%d\n", frame_mask->width, frame_mask->height);
double fps = cvGetCaptureProperty(capture_color, CV_CAP_PROP_FPS);
CvSize size = cvSize(cvGetCaptureProperty(capture_color, CV_CAP_PROP_FRAME_WIDTH), cvGetCaptureProperty(capture_color, CV_CAP_PROP_FRAME_HEIGHT));
CvVideoWriter* writer = cvCreateVideoWriter("Split/result04.avi", CV_FOURCC('D', 'I', 'V', 'X'), fps, size);
CvScalar _green;
_green.val[0] = 0;
_green.val[1] = 255;
_green.val[2] = 0;
char c;
int ac = 0;
while (1) {
c = cvWaitKey(33);
//processKey(c);
if (c == 27) break;
//if (c == 'n') {
// frame_color = cvQueryFrame(capture_color);
// frame_mask = cvQueryFrame(capture_mask);
// frame_processed = cvCloneImage(frame_color);
//}
//else {
frame_color = cvQueryFrame(capture_color);
frame_mask = cvQueryFrame(capture_mask);
if (frame_color == NULL) {
printf("NULL");
}
// exit loop if fram is null / movie end
if (!frame_color || !frame_mask) break;
frame_processed = cvCloneImage(frame_color);
//}
for( int i = 0; i < frame_mask->height; i++ ) {
for( int j = 0; j < frame_mask->width-1; j++ ) {
CvScalar _aux;
_aux=cvGet2D(frame_mask,i,j);
if(_aux.val[0] <= _black_min && _aux.val[1] <= _black_min && _aux.val[2] <= _black_min){
cvSet2D(frame_processed, i, j, _green);
}
}
}
//for (int i = 0; i < frame_color->height; i++) {
// for (int j = frame_mask->width-4; j < frame_mask->width; j++) {
// cvSet2D(frame_processed, i, j, _green);
// }
//}
//for (int i = 0; i < 2; i++) {
// for (int j = 0; j < frame_mask->width; j++) {
// cvSet2D(frame_processed, i, j, _green);
// }
//}
cvShowImage("Color", frame_color);
cvShowImage("Mask", frame_mask);
cvShowImage("Processed", frame_processed);
cvWriteFrame(writer, frame_processed);
cvReleaseImage(&frame_processed);
//cvReleaseImage(&frame_mask);
}
cvReleaseVideoWriter(&writer);
// destroy pointer to video
cvReleaseCapture(&capture_color);
cvReleaseCapture(&capture_mask);
// delete window
//cvDestroyWindow("Example2");
return 0;
}
// mask is of the same size as input
MaskShift* CreateMaskShift(IplImage* input, IplImage* mask, CvPoint shift, CvSize outputSize)
{
IplImage* outputMask = cvCreateImage(outputSize, IPL_DEPTH_8U, 3);
IplImage* outputTest = cvCreateImage(outputSize, IPL_DEPTH_8U, 3);
IplImage* outputData = cvCreateImage(outputSize, IPL_DEPTH_8U, 3);
for(int i = 0; i < outputMask->width; i++)
for(int j = 0; j < outputMask->height; j++)
{
CvPoint inputPixel;
inputPixel.x = shift.x + i;
inputPixel.y = shift.y + j;
if(!IsOutside(inputPixel, cvSize(input->width, input->height)))
{
cvSet2D(outputMask, j, i, cvGet2D(mask, inputPixel.y, inputPixel.x));
}
else
{
cvSet2D(outputMask, j, i, cvScalar(255));
}
}
for(int i = 0; i < outputSize.width; i++)
for(int j = 0; j < outputSize.height; j++)
{
cvSet2D(outputData,j,i, cvScalar(231,233,233));
}
for(int i = 0; i < outputSize.width; i++)
for(int j = 0; j < outputSize.height; j++)
{
if(IsMaskedPixel(i, j, outputMask))
{
// check neighbor
CvPoint inputPixel;
inputPixel.x = shift.x + i;
inputPixel.y = shift.y + j;
CvScalar value;
CvSize inputSize = cvSize(input->width, input->height);
if(!IsOutside(cvPoint(inputPixel.x, inputPixel.y), inputSize) &&
!IsOutside(cvPoint(i,j), outputSize))
{
value = cvGet2D(input, inputPixel.y, inputPixel.x);
cvSet2D(outputData, j, i, value);
}
if(!IsOutside(cvPoint(inputPixel.x+1, inputPixel.y), inputSize) &&
!IsOutside(cvPoint(i+1,j), outputSize))
{
value = cvGet2D(input, inputPixel.y, inputPixel.x + 1);
cvSet2D(outputData, j, i+1, value);
}
if(!IsOutside(cvPoint(inputPixel.x - 1, inputPixel.y), inputSize) &&
!IsOutside(cvPoint(i-1,j), outputSize))
{
value = cvGet2D(input, inputPixel.y, inputPixel.x - 1);
cvSet2D(outputData, j, i-1, value);
}
if(!IsOutside(cvPoint(inputPixel.x, inputPixel.y+1), inputSize) &&
!IsOutside(cvPoint(i,j+1), outputSize))
{
value = cvGet2D(input, inputPixel.y + 1, inputPixel.x);
cvSet2D(outputData, j+1, i, value);
}
if(!IsOutside(cvPoint(inputPixel.x, inputPixel.y-1), inputSize) &&
!IsOutside(cvPoint(i,j-1), outputSize))
{
value = cvGet2D(input, inputPixel.y - 1, inputPixel.x);
cvSet2D(outputData, j-1, i, value);
}
}
}
int j2 =21;
int i2 = 5;
CvScalar value2 = cvGet2D(outputData, j2+1, i2);
for(int i = 0; i < outputSize.width; i++)
for(int j = 0; j < outputSize.height; j++)
{
if(IsMaskedPixel(i, j, outputMask))
{
// check neighbor
CvScalar value;
value = cvGet2D(outputData, j, i);
if(value.val[0] == 231 && value.val[1] == 233 && value.val[2] == 233)
printf("Test");
value = cvGet2D(outputData, j+1, i);
if(value.val[0] == 231 && value.val[1] == 233 && value.val[2] == 233)
printf("Test");
value = cvGet2D(outputData, j-1, i);
if(value.val[0] == 231 && value.val[1] == 233 && value.val[2] == 233)
printf("Test");
value = cvGet2D(outputData, j, i+1);
if(value.val[0] == 231 && value.val[1] == 233 && value.val[2] == 233)
printf("Test");
value = cvGet2D(outputData, j, i-1);
if(value.val[0] == 231 && value.val[1] == 233 && value.val[2] == 233)
printf("Test");
}
}
//cvNamedWindow("Test");
//while(1)
//{
// cvShowImage("Test", outputData);
// cvWaitKey(100);
//}
MaskShift* maskShift = new MaskShift(outputMask, input);
maskShift->SetMaskData(outputData);
return maskShift;
}
int image_analysis(const int argc, const char * argv[])
{
#define NUM 5
char fname[4096];
unsigned u, v, q, vec_per_class, vec_num, cl_ind, yes, res, vec_class[5], max_iter[NUM], * d, * test_d;
double C[NUM], tau[NUM], epsilon[NUM], ** x, ** test_x;
double (* K[NUM])(const double *, const double *, unsigned);
int (* selector[NUM])(const fmll_svm *, const double **, const char *, const unsigned, int *, int *, const double, const double, const double, const double *, const double *, const double **);
IplImage * src_teach, * src_test, * dst;
CvSize size_teach, size_test;
CvScalar pixel, pixel_white, pixel_red, pixel_green, pixel_blue, pixel_violet, pixel_black, pixel_yellow;
fmll_svm_net * svm_net;
/* ############################################################################ */
if(argc != 3)
{
/* У C90 "проблемы" с максимальной длиной строки кода */
printf("\nДемонстрация нейронной сети, состоящей из нескольких машин опорных векторов.\n\n");
printf("Запуск программы:\n\n");
printf("ex_svm_net DIR IMAGE_1\n\n");
printf("Где:\n\n");
printf("\tDIR - путь и имя каталога, в котором должны содержаться следующие файлы:\n\n");
printf("\t\tteach.png - файл с изображением, на основании которого будет составлена обучающая выборка;\n");
printf("\t\ttest.png - файл с изображением, классификация пикселей которого будет выполнена;\n\n");
printf("\tIMAGE_1 - путь и имя файла, в который будет сохранено результирующее изображение ");
printf("(белый, красный, зеленый, синий, фиолетовый - правильно классифицированные пиксели; черный - неправильно классифицированные пиксели; ");
printf("желтый - неклассифицированные пиксели).\n\n");
return -1;
}
/* ############################################################################ */
memset(vec_class, 0, sizeof(unsigned) * 5);
sprintf(fname, "%s/teach.png", argv[1]);
src_teach = cvLoadImage(fname, CV_LOAD_IMAGE_COLOR);
size_teach = cvGetSize(src_teach);
sprintf(fname, "%s/test.png", argv[1]);
src_test = cvLoadImage(fname, CV_LOAD_IMAGE_COLOR);
size_test = cvGetSize(src_test);
dst = cvCreateImage(size_test, IPL_DEPTH_8U, 3);
pixel_white.val[0] = 255;
pixel_white.val[1] = 255;
pixel_white.val[2] = 255;
pixel_white.val[3] = 0;
pixel_red.val[0] = 0;
pixel_red.val[1] = 0;
pixel_red.val[2] = 255;
pixel_red.val[3] = 0;
pixel_green.val[0] = 0;
pixel_green.val[1] = 255;
pixel_green.val[2] = 0;
pixel_green.val[3] = 0;
pixel_black.val[0] = 0;
pixel_black.val[1] = 0;
pixel_black.val[2] = 0;
pixel_black.val[3] = 0;
pixel_blue.val[0] = 255;
pixel_blue.val[1] = 0;
pixel_blue.val[2] = 0;
pixel_blue.val[3] = 0;
pixel_violet.val[0] = 255;
pixel_violet.val[1] = 0;
pixel_violet.val[2] = 255;
pixel_violet.val[3] = 0;
pixel_yellow.val[0] = 0;
pixel_yellow.val[1] = 255;
pixel_yellow.val[2] = 255;
pixel_yellow.val[3] = 0;
vec_per_class = size_teach.height * size_teach.width / 2000;
vec_num = vec_per_class * 5;
x = (double **) fmll_alloc(sizeof(double), 2, vec_num, 3);
d = fmll_alloc(sizeof(unsigned), 1, vec_num);
test_x = (double **) fmll_alloc(sizeof(double), 2, size_test.height * size_test.width, 3);
test_d = fmll_alloc(sizeof(unsigned), 1, size_test.height * size_test.width);
for(v = 0, q = 0; v < size_teach.height; v++)
for(u = 0; u < size_teach.width; u++)
{
pixel = cvGet2D(src_teach, v, u);
if(pixel.val[0] == 0 && pixel.val[1] == 237 && pixel.val[2] == 95)
cl_ind = 0;
else if(pixel.val[0] == 10 && pixel.val[1] == 169 && pixel.val[2] == 203)
cl_ind = 1;
else if(pixel.val[0] == 0 && pixel.val[1] == 255 && pixel.val[2] == 255)
cl_ind = 2;
else if(pixel.val[0] == 255 && pixel.val[1] == 0 && pixel.val[2] == 12)
cl_ind = 3;
else
cl_ind = 4;
if(vec_class[cl_ind] < vec_per_class)
{
x[q][0] = pixel.val[0];
x[q][1] = pixel.val[1];
x[q][2] = pixel.val[2];
d[q] = cl_ind;
vec_class[cl_ind]++;
q++;
}
}
for(v = 0, q = 0; v < size_test.height; v++)
for(u = 0; u < size_test.width; u++, q++)
{
pixel = cvGet2D(src_test, v, u);
if(pixel.val[0] == 0 && pixel.val[1] == 237 && pixel.val[2] == 95)
test_d[q] = 0;
else if(pixel.val[0] == 10 && pixel.val[1] == 169 && pixel.val[2] == 203)
test_d[q] = 1;
else if(pixel.val[0] == 0 && pixel.val[1] == 255 && pixel.val[2] == 255)
test_d[q] = 2;
else if(pixel.val[0] == 255 && pixel.val[1] == 0 && pixel.val[2] == 12)
test_d[q] = 3;
else
test_d[q] = 4;
test_x[q][0] = pixel.val[0];
test_x[q][1] = pixel.val[1];
test_x[q][2] = pixel.val[2];
}
cvReleaseImage(& src_teach);
cvReleaseImage(& src_test);
/* ############################################################################ */
for(u = 0; u < NUM; u++)
K[u] = & fmll_kernel_radial;
svm_net = fmll_svm_net_init(NUM, 3, K);
/* ############################################################################ */
for(u = 0; u < NUM; u++)
{
C[u] = 1;
tau[u] = 1E-12;
selector[u] = & fmll_svm_teach_smo_selector_fan_chen_lin;
max_iter[u] = 10000;
epsilon[u] = 1E-3;
}
fmll_svm_net_teach_smo(svm_net, (const double **) x, d, vec_num, C, tau, selector, max_iter, epsilon);
/* ############################################################################ */
yes = fmll_svm_net_test(svm_net, (const double **) test_x, test_d, size_test.width * size_test.height, NULL, NULL);
printf("\nВерно классифицированных пикселей: %u из %u (%.7f %%)\n",
yes, (size_test.width * size_test.height), (100.0 * yes) / (size_test.width * size_test.height));
/* ############################################################################ */
fmll_svm_net_save(svm_net, "svm_net");
fmll_svm_net_destroy(svm_net);
svm_net = fmll_svm_net_load("svm_net", K);
for(v = 0, q = 0, yes = 0; v < size_test.height; v++)
for(u = 0; u < size_test.width; u++, q++)
{
res = fmll_svm_net_run(svm_net, test_x[q], NULL);
if(res >= 0)
{
if(res == test_d[q])
{
yes++;
switch(res)
{
case 0:
{
cvSet2D(dst, v, u, pixel_white);
break;
}
case 1:
{
cvSet2D(dst, v, u, pixel_red);
break;
}
case 2:
{
cvSet2D(dst, v, u, pixel_green);
break;
}
case 3:
{
cvSet2D(dst, v, u, pixel_blue);
break;
}
case 4:
{
cvSet2D(dst, v, u, pixel_violet);
break;
}
}
}
else
cvSet2D(dst, v, u, pixel_black);
}
else if(res == -1)
cvSet2D(dst, v, u, pixel_yellow);
else
cvSet2D(dst, v, u, pixel_black);
}
printf("Верно классифицированных пикселей: %u из %u (%.7f %%)\n",
yes, (size_test.width * size_test.height), (100.0 * yes) / (size_test.width * size_test.height));
/* ############################################################################ */
fmll_free(x);
fmll_free(d);
fmll_free(test_x);
fmll_free(test_d);
fmll_svm_net_destroy(svm_net);
cvSaveImage(argv[2], dst, NULL);
cvReleaseImage(& dst);
return 0;
}
void CreateMask(IplImage* input, IplImage* mask, CvPoint shift, CvSize outputSize, IplImage* outputMask, IplImage* outputData)
{
// check size
if(outputMask->width != outputSize.width || outputMask->height != outputSize.height)
return;
if(outputData->width != outputSize.width || outputData->height != outputSize.height)
return;
for(int i = 0; i < outputMask->width; i++)
for(int j = 0; j < outputMask->height; j++)
{
CvPoint inputPixel;
inputPixel.x = shift.x + i;
inputPixel.y = shift.y + j;
if(!IsOutside(inputPixel, cvSize(input->width, input->height)))
{
cvSet2D(outputMask, j, i, cvGet2D(mask, inputPixel.y, inputPixel.x));
}
else
{
cvSet2D(outputMask, j, i, cvScalar(255));
}
}
for(int i = 0; i < outputSize.width; i++)
for(int j = 0; j < outputSize.height; j++)
{
cvSet2D(outputData,j,i, cvScalar(231,233,233));
}
for(int i = 0; i < outputSize.width; i++)
for(int j = 0; j < outputSize.height; j++)
{
if(IsMaskedPixel(i, j, outputMask))
{
// check neighbor
CvPoint inputPixel;
inputPixel.x = shift.x + i;
inputPixel.y = shift.y + j;
CvScalar value;
CvSize inputSize = cvSize(input->width, input->height);
if(!IsOutside(cvPoint(inputPixel.x, inputPixel.y), inputSize) &&
!IsOutside(cvPoint(i,j), outputSize))
{
value = cvGet2D(input, inputPixel.y, inputPixel.x);
cvSet2D(outputData, j, i, value);
}
if(!IsOutside(cvPoint(inputPixel.x+1, inputPixel.y), inputSize) &&
!IsOutside(cvPoint(i+1,j), outputSize))
{
value = cvGet2D(input, inputPixel.y, inputPixel.x + 1);
cvSet2D(outputData, j, i+1, value);
}
if(!IsOutside(cvPoint(inputPixel.x - 1, inputPixel.y), inputSize) &&
!IsOutside(cvPoint(i-1,j), outputSize))
{
value = cvGet2D(input, inputPixel.y, inputPixel.x - 1);
cvSet2D(outputData, j, i-1, value);
}
if(!IsOutside(cvPoint(inputPixel.x, inputPixel.y+1), inputSize) &&
!IsOutside(cvPoint(i,j+1), outputSize))
{
value = cvGet2D(input, inputPixel.y + 1, inputPixel.x);
cvSet2D(outputData, j+1, i, value);
}
if(!IsOutside(cvPoint(inputPixel.x, inputPixel.y-1), inputSize) &&
!IsOutside(cvPoint(i,j-1), outputSize))
{
value = cvGet2D(input, inputPixel.y - 1, inputPixel.x);
cvSet2D(outputData, j-1, i, value);
}
}
}
for(int i = 0; i < outputSize.width - 1; i++)
for(int j = 0; j < outputSize.height - 1; j++)
{
if(IsMaskedPixel(i, j, outputMask))
{
// check neighbor
CvScalar value;
value = cvGet2D(outputData, j, i);
if(value.val[0] == 231 && value.val[1] == 233 && value.val[2] == 233)
printf("Test");
value = cvGet2D(outputData, j+1, i);
if(value.val[0] == 231 && value.val[1] == 233 && value.val[2] == 233)
printf("Test");
value = cvGet2D(outputData, j-1, i);
if(value.val[0] == 231 && value.val[1] == 233 && value.val[2] == 233)
printf("Test");
value = cvGet2D(outputData, j, i+1);
if(value.val[0] == 231 && value.val[1] == 233 && value.val[2] == 233)
printf("Test");
value = cvGet2D(outputData, j, i-1);
if(value.val[0] == 231 && value.val[1] == 233 && value.val[2] == 233)
printf("Test");
}
}
}
void detectAndDisplay(IplImage *frame)
{
IplImage *frame_gray;
int ForCount1, ForCount2;
ForCount1 = ForCount2 = 0;
memset(pointXY, 0, sizeof(char)*column*row);
frame_gray = cvCreateImage( cvGetSize(frame),IPL_DEPTH_8U,1);
//frame_gray = cvCreateImage( cvSize(column, row),IPL_DEPTH_8U,1);
dst = cvCreateImage(cvGetSize(frame),IPL_DEPTH_8U,1);
//dst = cvCreateImage(cvSize(column,row),IPL_DEPTH_8U,1);
#ifdef showXY
showSumX = cvCreateImage(cvGetSize(frame),IPL_DEPTH_8U,1);
showSumY = cvCreateImage(cvSize(row,column),IPL_DEPTH_8U,1);
#endif
cvCvtColor(frame, frame_gray, CV_BGR2GRAY);
//cvCanny(frame_gray, dst, 40, 40*3, 3);
cvThreshold(frame_gray, dst, Threshold, 255, CV_THRESH_BINARY);
for(ForCount1 = 0; ForCount1 < column; ForCount1++)
{
for(ForCount2 = 0; ForCount2 < row; ForCount2++)
{
CvScalar s = cvGet2D(dst,ForCount2,ForCount1);
//char s = ((uchar *)(dst->imageData + ForCount1*dst->widthStep))[ForCount2];
#ifdef Detail
printf("%3d %3d %f\n",ForCount1,ForCount2, s.val[0]);
#endif
/*if( s.val[0] <= Threshold)
{
pointXY[ForCount1][ForCount2] = 1;
}*/
if(s.val[0] <= Threshold)
{
pointXY[ForCount1][ForCount2] = 0;
}
else
{
pointXY[ForCount1][ForCount2] = 1;
}
}
}
Integral(row);
Integral(column);
Error1();
Error2();
cvShowImage("Webcam",dst);
cvShowImage("Webcam1",frame_gray);
#ifdef Detail
for(ForCount1 = 0; ForCount1 < column; ForCount1++)
{
printf("x[%3d]:%d\n", ForCount1, SumX[ForCount1]);
}
printf("\n");
for(ForCount1 = 0; ForCount1 < row; ForCount1++)
{
printf("y[%3d]:%d\n", ForCount1, SumY[ForCount1]);
}
printf("\n");
#endif
#ifdef showXY
for(ForCount1 = 0; ForCount1 < column; ForCount1++)
{
for(ForCount2 = 0; ForCount2 < (int)SumX[ForCount1]; ForCount2++)
{
CvScalar s = cvGet2D(showSumX,ForCount2,ForCount1);
s.val[0] = 255;
cvSet2D(showSumX, ForCount2, ForCount1, s);
}
}
cvShowImage("SumX", showSumX);
for(ForCount1 = 0; ForCount1 < row; ForCount1++)
{
for(ForCount2 = 0; ForCount2 < (int)SumY[ForCount1]; ForCount2++)
{
CvScalar s = cvGet2D(showSumY,ForCount2,ForCount1);
s.val[0] = 255;
cvSet2D(showSumY, ForCount2, ForCount1, s);
}
}
#endif
//cvShowImage("SumY", showSumY);
cvReleaseImage( &dst );
cvReleaseImage( &frame_gray );
#ifdef showXY
cvReleaseImage( &showSumX );
cvReleaseImage( &showSumY );
#endif
}
CvScalar MaskShift::GetPixelValue(int x, int y, IplImage* image)
{
return cvGet2D(image, y + _shift.y, x + _shift.x);
}
void Error1()
{
int ForCount1 = 0;
int ForCount2 = 0;
memset(SumX1, 0, sizeof(short)*column);
/* for(ForCount1 = 0; ForCount1 < column; ForCount1++)
{
for(ForCount2 = YUpperBoundPos -1; ForCount2 >= (YUpperBoundPos/2); ForCount2--)
{
SumX1[ForCount1] = SumX1[ForCount1] + pointXY[ForCount1][ForCount1];
}
if(ForCount1 > 3 || SumX1[ForCount1] > 4 && SumX1[ForCount1-1] > 4 && SumX1[ForCount1-2] > 4 && SumX1[ForCount1-3] >4 )
{
for(ForCount2 = YUpperBoundPos -1; ForCount2 >= (YUpperBoundPos/2); ForCount2--)
{
if(pointXY[ForCount1][ForCount2] == 1)
{
CvScalar s = cvGet2D(dst,ForCount2,ForCount1);
s.val[0] = 150;
cvSet2D(dst, ForCount2, ForCount1, s);
}
if(pointXY[ForCount1-1][ForCount2] == 1)
{
CvScalar s = cvGet2D(dst,ForCount2,ForCount1-1);
s.val[0] = 150;
cvSet2D(dst, ForCount2, ForCount1-1, s);
}
if(pointXY[ForCount1-2][ForCount2] == 1)
{
CvScalar s = cvGet2D(dst,ForCount2,ForCount1-2);
s.val[0] = 150;
cvSet2D(dst, ForCount2, ForCount1-2, s);
}
if(pointXY[ForCount1-3][ForCount2] == 1)
{
CvScalar s = cvGet2D(dst,ForCount2,ForCount1-3);
s.val[0] = 150;
cvSet2D(dst, ForCount2, ForCount1-3, s);
}
}
printf("Error1\n");
break;
}
}
*/
for(ForCount1 = YUpperBoundPos-1; ForCount1 >= (YUpperBoundPos/2); ForCount1--)
{
if(SumY[ForCount1] > Error1YThreshold && SumY[ForCount1-1] > Error1YThreshold && SumY[ForCount1-2] > Error1YThreshold )
{
for(ForCount2 = 0; ForCount2 < column; ForCount2++)
{
if(pointXY[ForCount2][ForCount1] == 1)
{
CvScalar s = cvGet2D(dst,ForCount1,ForCount2);
s.val[0] = 150;
cvSet2D(dst, ForCount1, ForCount2, s);
cvSet2D(dst, ForCount1-1, ForCount2, s);
cvSet2D(dst, ForCount1-2, ForCount2, s);
}
}
printf("Error1\n\n");
break;
}
}
}
bool CvNeuralGas::train( CvScalar* _input ) {
CV_FUNCNAME( "CvNeuralGas::train" );
__BEGIN__;
//if( input != NULL )
// delete input;
if( _input != NULL ) {
input = _input;
} else {
// peak random
int x = rng.next() % (distribution->width - 1);
int y = rng.next() % (distribution->height - 1);
input = &(cvGet2D( distribution, y, x ));
}
// Calculate the distance of each node`s reference vector from the
// projected input vector.
double temp = 0.0;
double val = 0.0;
for( unsigned long int i=0; i<total_nodes; i++ ) {
CvGasNode* curr = nodes->at( i );
curr->distance = 0.0;
CvScalar* ref_vector = &(curr->ref_vector);
for( int x=0; x<4; x++ ) {
val = input->val[x] - ref_vector->val[x];
temp += pow( val, 2.0 );
}
curr->distance = sqrt( temp );
temp = 0.0;
val = 0.0;
}
//Sort the nodes based on their distance.
std::sort( nodes->begin(), nodes->end(), Compare);
//Fetch the bmu/smu/wmu.
bmu = nodes->at( 0 );
smu = nodes->at( 1 );
wmu = nodes->at( total_nodes - 1 );
// Adapt the nodes.
double epsilon_t = epsilon0 * pow( ( epsilonT / epsilon0 ), (float)iteration/max_iterations );
double sqr_sigma = lambda0 * pow( ( lambdaT / lambda0 ), (float)iteration/max_iterations );
for( unsigned long int i=0; i<total_nodes; i++ ) {
CvGasNode* curr = nodes->at( i );
curr->rank = -i;
double h = exp( ((double)curr->rank) / sqr_sigma );
CvScalar* ref_vector = &(curr->ref_vector);
CvScalar delta;
for(int x=0;x<4;x++){
delta.val[x] = (input->val[x] - ref_vector->val[x]) * h * epsilon_t;
ref_vector->val[x] += delta.val[x];
}
}
iteration++;
__END__;
return true;
}
void compresie_decompresie_cu_DCT( IplImage* image )
{
int w, h;
int i, j;
int k, l;
double pi = 3.1415926;
CvScalar pixel;
CvScalar valoare;
IplImage *imagine_rezultat;
IplImage *imagine_diferenta;
IplImage *cosinus;
int N, nivel;
double max = 0, min = 10000;
double value;
CvMat *C; //matricea transformarii cosinus
CvMat *U; //matricea imaginii in spatiul original
CvMat *V; //matricea imaginii in spatiul transformatei
CvMat *AUX; //matrice auxiliara pentru rezultatul partial
double m, M;
double prag;
int nr; //numarul de coeficienti anulati in spatiul transformatei
w = image->width;
h = image->height;
if( w == h )
{
imagine_rezultat = cvCreateImage( cvSize( w, h ), IPL_DEPTH_8U, 1 );
N = w;
C = cvCreateMat( N, N, CV_32FC1 );
U = cvCreateMat( N, N, CV_32FC1 );
V = cvCreateMat( N, N, CV_32FC1 );
AUX = cvCreateMat( N, N, CV_32FC1 );
//formarea matricii C a transformarii cosinus discreta
for( i = 0; i < N; i++ )
cvmSet( C, 0, i, 1. / sqrt( (float)N ) );
for( i = 1; i < N; i++ )
for( j = 0; j < N; j++ )
{
value = sqrt( 2./N ) * cos( pi * ( 2*j + 1 ) * i / ( 2*N ) );
cvmSet( C, i, j, value );
if( value > max )
max = value;
}
//popularea matricei U cu valorile pixelilor imaginii
for( i = 0; i < N; i++ )
for( j = 0; j < N; j++ )
{
pixel = cvGet2D( image, i, j );
cvmSet( U, i, j, pixel.val[ 0 ] );
}
//V = C*U*Ct
//mai intii vom calcula AUX = C * U
for( i = 0; i < N; i++ )
for( j = 0; j < N; j++ )
{
value = 0;
for( k = 0; k < N; k++ )
value += cvmGet( C, i, k ) * cvmGet( U, k, j );
cvmSet( AUX, i, j, value );
}
//apoi V = AUX * Ct
max = 0;
for( i = 0; i < N; i++ )
for( j = 0; j < N; j++ )
{
value = 0;
for( k = 0; k < N; k++ )
value += cvmGet( AUX, i, k ) * cvmGet( C, j, k );
cvmSet( V, i, j, value );
if( value > max )
max = value;
else
if( value < min )
min = value;
}
// /*
//anularea coeficientilor de energie mica
prag = 800;
nr = 0;
for( i = 0; i < N; i++ )
for( j = 0; j < N; j++ )
{
value = fabs( cvmGet( V, i, j ) );
if( value < prag )
{
cvmSet( V, i, j, 0 );
nr++;
}
}
printf( "Procentul de valori retinute in spatiul transformatei = %5.2lf%%\n", 100.*(N*N - nr) / (N*N) );
if( nr != 0 )
printf( "Factorul de compresie obtinut = %5.2lf\n", 1. * (N * N) / (N*N - nr) );
// */
// DECOMPRESIA IMAGINII
//U = Ct * V * C
//AUX = Ct * V
for( i = 0; i < N; i++ )
for( j = 0; j < N; j++ )
{
value = 0;
for( k = 0; k < N; k++ )
value += cvmGet( C, k, i ) * cvmGet( V, k, j );
cvmSet( AUX, i, j, value );
}
//apoi U = AUX * C
for( i = 0; i < N; i++ )
for( j = 0; j < N; j++ )
{
value = 0;
for( k = 0; k < N; k++ )
value += cvmGet( AUX, i, k ) * cvmGet( C, k, j );
cvmSet( U, i, j, value );
pixel.val[ 0 ] = value;
cvSet2D( imagine_rezultat, i, j, pixel );
}
cvNamedWindow( "Imaginea dupa decompresie", 1 );
cvShowImage( "Imaginea dupa decompresie", imagine_rezultat );
//calcul pseudo-imagine diferenta (zgomot cauzat de compresia cu pierderi)
imagine_diferenta = cvCreateImage( cvSize( w, h ), IPL_DEPTH_8U, 1 );
for( i = 0; i < N; i++ )
for( j = 0; j < N; j++ )
{
pixel = cvGet2D( image, i, j );
value = (int)( fabs( pixel.val[ 0 ] - cvmGet( U, i, j ) ) );
valoare.val[ 0 ] = value;
cvSet2D( imagine_diferenta, i, j, valoare );
}
cvNamedWindow( "Pseudo-imaginea diferenta", 1 );
cvShowImage( "Pseudo-imaginea diferenta", imagine_diferenta );
cvWaitKey(0);
cvReleaseImage( &imagine_rezultat );
cvReleaseImage( &imagine_diferenta );
cvDestroyWindow( "Imaginea dupa decompresie" );
cvDestroyWindow( "Pseudo-imaginea diferenta" );
cvReleaseMat( &C );
cvReleaseMat( &U );
cvReleaseMat( &V );
cvReleaseMat( &AUX );
}
else
printf( "Imaginea de intrare nu este patrata! (w != h)\n" );
}
void adjust_bodybbox_w_clusters(CvMat* mask, IplImage* cluster, int numclusters, CvRect facebox )
{
double a=1.15; //extra growing of body box region
double A=1.00; // body bbox is lower than face bbox (neck)
double b=1+a;
int bodyx0 = ((facebox.x-facebox.width* a) < 0 ) ? 0 : (facebox.x-facebox.width* a);
int bodyy0 = ((facebox.y+facebox.height * A) > cluster->height) ? cluster->height : (facebox.y+facebox.height*A );
int bodyx1 = ((facebox.x+facebox.width* b) > cluster->width) ? cluster->width : (facebox.x+facebox.width*b);
int bodyy1 = (cluster->height);
int x,y, i=0;
int *accu;
int eqclass_1st; // equivalence class chosen as most populated
int tmp_eqclass; // temp, to hold the equivalence class associated to a pixel
int eqclass_2nd; // equivalence class chosen as 2nd most populated
accu = (int*)malloc( numclusters*sizeof(int));
bzero( accu, numclusters*sizeof(int));
eqclass_1st = 0;
eqclass_2nd = numclusters-1; // just initialised to sth != eqclass_1st
// 1st: get to know the amount of pixels per equivalence class ("cluster")
// but not blindly: only those in the FG mask already
for( x = 0; x < cluster->width; x++ ){
for( y = 0; y < cluster->height; y++ ){
// filter the equ_classes using the mask
if( ( x >= bodyx0) && ( x <= bodyx1) && ( y >= bodyy0) && ( y <= bodyy1)){
tmp_eqclass = (int) ( round(cvGet2D( cluster, y, x).val[1]*numclusters/255.0) -1);
accu[ tmp_eqclass ] ++;
}
}
}
// 2nd: get the most populated and the 2nd most populated cluster
for( i = 0; i< numclusters; i++ ){
eqclass_1st = ( accu[i] > accu[eqclass_1st] ) ? i : eqclass_1st;
eqclass_2nd = ( accu[i] > accu[eqclass_2nd] ) ? ((accu[i] < accu[eqclass_1st]) ? i : eqclass_2nd ): eqclass_2nd;
printf(" %.8d ", accu[i]);
}
printf(" (eqclass_1st %d 2nd %d) \n", eqclass_1st, eqclass_2nd);
// 3rd: Using the pixels inside of a seed of the body bbox, calculated from the face box,
// we calculate the (minx,miny)-(maxx,maxy) bounding rectangle due to the largest cluster
int minx=10000, miny=10000, maxx=0, maxy=0;
for( x = 0; x < cluster->width; x++ ){
for( y = 0; y < cluster->height; y++ ){
if(!( ( x >= bodyx0) && ( x <= bodyx1) && ( y >= bodyy0) && ( y <= bodyy1) )){
cvSet2D(cluster, y, x, cvScalar(0, 0, 0 ) );
continue;
}
tmp_eqclass = (int) ( round(cvGet2D( cluster, y, x).val[1]*numclusters/255.0) -1);
if(tmp_eqclass == eqclass_1st){
cvSet2D(cluster, y, x, cvScalar(255, 0, 0 ) ); // for display purposes
maxx = ( maxx > x ) ? maxx : x;
maxy = ( maxy > y ) ? maxy : y;
minx = ( minx < x ) ? minx : x;
miny = ( miny < y ) ? miny : y;
}
else if (tmp_eqclass == eqclass_2nd) {
cvSet2D(cluster, y, x, cvScalar(100, 0, 0 ) ); // for display purposes
}
else{
cvSet2D(cluster, y, x, cvScalar(10, 0, 0 ) ); // for display purposes
}
}
}
cvRectangle(cluster, cvPoint(minx, miny), cvPoint(maxx, maxy), cvScalar(255, 0, 255), 1);
// Last: compose in the mask a body-box based on the largest cluster bbox
// the rectangle is needed otherwise the largest cluster has loads of holes
cvRectangle(mask, cvPoint(minx, miny), cvPoint(maxx, cluster->height), cvScalar(GC_PR_FGD, 0, 0), CV_FILLED);
free( accu );
}
void CTWithWater::imageAndDepthToWorld(double u, double v, double d,
double* x, double* y, double* z,
bool undistort)
{
double xx, yy, t;
CvMat* r = cvCreateMat(3, 1, CV_32FC1);
if(undistort)
{
CvMat* I = cvCreateMat(1, 1, CV_32FC2);
CvMat* Io = cvCreateMat(1, 1, CV_32FC2);
cvSet2D(I, 0, 0, cvScalar(u,v));
cvUndistortPoints(I, Io, m_CameraMatrix, m_DistCoeffs, NULL, m_CameraMatrixNorm);
CvScalar s = cvGet2D(Io, 0, 0);
xx = s.val[0];//cvGetReal1D(Io, 0);
yy = s.val[1];//cvGetReal1D(Io, 1);
cvReleaseMat(&I);
cvReleaseMat(&Io);
}
else
{
xx = u;
yy = v;
}
xx = (xx - cvGetReal2D(m_CameraMatrixNorm, 0, 2))/cvGetReal2D(m_CameraMatrixNorm, 0, 0);
yy = (yy - cvGetReal2D(m_CameraMatrixNorm, 1, 2))/cvGetReal2D(m_CameraMatrixNorm, 1, 1);
cvSetReal1D(r, 0, xx);
cvSetReal1D(r, 1, yy);
cvSetReal1D(r, 2, 1.0);
/* Rt_(3,:)*r = sum of third column of R times elements of r */
t = xx*cvGetReal2D(m_R, 0, 2) + yy*cvGetReal2D(m_R, 1, 2) + cvGetReal2D(m_R, 2, 2);
if(t == 0)
{
t = 1.0;
}
if(d <= 0)
{
/* d<= 0 => above water surface */
t = (-m_dCameraHeightAboveWater-d)/t;
/* r = t*R'*r + C */
cvGEMM(m_R, r, t, m_CameraWorld, 1.0, r, CV_GEMM_A_T);
}
else
{
/* d > 0 => below water surface */
t = -m_dCameraHeightAboveWater/t;
/* S = t*R'*r */
cvGEMM(m_R, r, t, NULL, 0, m_S, CV_GEMM_A_T);
double Sx = cvGetReal1D(m_S, 0);
double Sy = cvGetReal1D(m_S, 1);
double phi = atan2(Sy, Sx);
double rS = sqrt(Sx*Sx + Sy*Sy);
double rP = calculateRpFromRs(rS, d, m_dCameraHeightAboveWater);
cvSetReal1D(r, 0, rP*cos(phi));
cvSetReal1D(r, 1, rP*sin(phi));
cvSetReal1D(r, 2, -m_dCameraHeightAboveWater-d);
cvAdd(r, m_CameraWorld, r);
}
*x = cvGetReal1D(r, 0);
*y = cvGetReal1D(r, 1);
*z = cvGetReal1D(r, 2);
cvReleaseMat(&r);
}
// callback function to extract the pixel coordinate value from an image
unsigned getimgdata_fn(void *p, unsigned x, unsigned y)
{
CvScalar s = cvGet2D((IplImage*)p, y, x);
return (unsigned)s.val[0];
}
bool GrayImageSegmentByKMeans2 ( const IplImage * pImg , IplImage * pResult , int nClusters, int sortFlag )
{
assert ( pImg != NULL && pImg -> nChannels == 1);
// 创建样本矩阵， CV_32FC1 代表位浮点通道（灰度图像）
CvMat * samples = cvCreateMat (( pImg -> width )* ( pImg -> height ),1, CV_32FC1 );
// 创建类别标记矩阵， CV_32SF1 代表位整型通道
CvMat * clusters = cvCreateMat (( pImg -> width )* ( pImg -> height ),1, CV_32SC1 );
// 创建类别中心矩阵
CvMat * centers = cvCreateMat ( nClusters , 1, CV_32FC1 );
// 将原始图像转换到样本矩阵
{
int k = 0;
CvScalar s ;
for ( int i = 0; i < pImg -> width ; i ++)
{
for ( int j =0; j < pImg -> height ; j ++)
{
s . val [0] = ( float ) cvGet2D ( pImg , j , i ). val [0];
cvSet2D ( samples , k ++, 0, s );
}
}
}
// 开始聚类，迭代次，终止误差 .0
cvKMeans2 ( samples , nClusters , clusters , cvTermCriteria ( CV_TERMCRIT_ITER + CV_TERMCRIT_EPS ,50, 1.0), 1, 0, 0, centers );
// 无需排序直接输出时
if ( sortFlag == 0)
{
int k = 0;
int val = 0;
float step = 255 / (( float ) nClusters - 1);
CvScalar s ;
for ( int i = 0; i < pImg -> width ; i ++)
{
for ( int j = 0; j < pImg -> height ; j ++)
{
val = ( int ) clusters -> data . i [ k ++];
s . val [0] = 255- val * step ; // 这个是将不同类别取不同的像素值，
cvSet2D ( pResult , j , i , s ); // 将每个像素点赋值
}
}
return true ;
}
}
int opticaltri( CvMat * &clean_texture, int verts )
{
char * im1fname = "conhull-dirty-thresh.jpg";
char * im2fname = "conhull-clean-thresh.jpg";
int count = MAX_COUNT;
char * status;
CvPoint2D32f * source_points;
CvPoint2D32f * dest_points;
CvPoint2D32f * delaunay_points = (CvPoint2D32f*)cvAlloc(MAX_COUNT*sizeof(CvPoint2D32f));
// count = opticalflow( im1fname, im2fname, source_points, dest_points, status );
count = findsiftpoints( "conhull-dirty.jpg", "conhull-clean.jpg", source_points, dest_points, status );
IplImage * image1 = cvLoadImage(im1fname, CV_LOAD_IMAGE_COLOR);
CvMemStorage * storage = cvCreateMemStorage(0);
CvSubdiv2D * delaunay = cvCreateSubdivDelaunay2D( cvRect(0,0,image1->width,image1->height), storage);
IplImage * image2 = cvLoadImage(im2fname, CV_LOAD_IMAGE_COLOR);
cvSet( image1, cvScalarAll(255) );
std::map<CvPoint, CvPoint> point_lookup_map;
std::vector<std::pair<CvPoint, CvPoint> > point_lookup;
int num_matches = 0;
int num_out_matches = 0;
int max_dist = 50;
int offset = 200;
// put corners in the point lookup as going to themselves
point_lookup_map[cvPoint(0,0)] = cvPoint(0,0);
point_lookup_map[cvPoint(0,image1->height-1)] = cvPoint(0,image1->height-1);
point_lookup_map[cvPoint(image1->width-1,0)] = cvPoint(image1->width-1,0);
point_lookup_map[cvPoint(image1->width-1,image1->height-1)] = cvPoint(image1->width-1,image1->height-1);
point_lookup.push_back(std::pair<CvPoint,CvPoint>(cvPoint(0,0), cvPoint(0,0)));
point_lookup.push_back(std::pair<CvPoint,CvPoint>(cvPoint(0,image1->height-1), cvPoint(0,image1->height-1)));
point_lookup.push_back(std::pair<CvPoint,CvPoint>(cvPoint(image1->width-1,0), cvPoint(image1->width-1,0)));
point_lookup.push_back(std::pair<CvPoint,CvPoint>(cvPoint(image1->width-1,image1->height-1), cvPoint(image1->width-1,image1->height-1)));
printf("Inserting corners...");
// put corners in the Delaunay subdivision
for(unsigned int i = 0; i < point_lookup.size(); i++) {
cvSubdivDelaunay2DInsert( delaunay, cvPointTo32f(point_lookup[i].first) );
}
printf("done.\n");
CvSubdiv2DEdge proxy_edge;
for(int i = 0; i < count; i++) {
if(status[i]) {
CvPoint source = cvPointFrom32f(source_points[i]);
CvPoint dest = cvPointFrom32f(dest_points[i]);
if((((int)fabs((double)(source.x - dest.x))) > max_dist) ||
(((int)fabs((double)(source.y - dest.y))) > max_dist)) {
num_out_matches++;
}
else if((dest.x >= 0) && (dest.y >= 0) && (dest.x < (image1->width)) && (dest.y < (image1->height))) {
if(point_lookup_map.find(source) == point_lookup_map.end()) {
num_matches++;
point_lookup_map[source] = dest;
point_lookup.push_back(std::pair<CvPoint,CvPoint>(source,dest));
// delaunay_points[i] =
(cvSubdivDelaunay2DInsert( delaunay, cvPointTo32f(source) ))->pt;
cvSetImageROI( image1, cvRect(source.x-8,source.y-8,8*2,8*2) );
cvResetImageROI( image2 );
cvGetRectSubPix( image2, image1, dest_points[i] );
}
/*
cvSet2D( image1, source.y, source.x, cvGet2D( image2, dest.y, dest.x ) );
cvSet2D( image1, source.y, source.x+1, cvGet2D( image2, dest.y, dest.x+1 ) );
cvSet2D( image1, source.y, source.x-1, cvGet2D( image2, dest.y, dest.x-1 ) );
cvSet2D( image1, source.y+1, source.x, cvGet2D( image2, dest.y+1, dest.x ) );
cvSet2D( image1, source.y-1, source.x, cvGet2D( image2, dest.y-1, dest.x ) );
cvSet2D( image1, source.y+1, source.x+1, cvGet2D( image2, dest.y+1, dest.x+1 ) );
cvSet2D( image1, source.y-1, source.x-1, cvGet2D( image2, dest.y-1, dest.x-1 ) );
cvSet2D( image1, source.y+1, source.x-1, cvGet2D( image2, dest.y+1, dest.x-1 ) );
cvSet2D( image1, source.y-1, source.x+1, cvGet2D( image2, dest.y-1, dest.x+1 ) );
*/
// cvCircle( image1, source, 4, CV_RGB(255,0,0), 2, CV_AA );
// cvCircle( image2, dest, 4, CV_RGB(255,0,0), 2, CV_AA );
}
/*
cvSetImageROI( image1, cvRect(source.x-offset,source.y-offset,offset*2,offset*2) );
cvSetImageROI( image2, cvRect(dest.x-offset,dest.y-offset,offset*2,offset*2) );
cvNamedWindow("image1",0);
cvNamedWindow("image2",0);
cvShowImage("image1",image1);
cvShowImage("image2",image2);
printf("%d,%d -> %d,%d\n",source.x,source.y,dest.x,dest.y);
cvWaitKey(0);
cvDestroyAllWindows();
*/
}
}
printf("%d %d\n",num_matches,num_out_matches);
printf("%d lookups\n",point_lookup_map.size());
cvResetImageROI( image1 );
cvSaveImage("sparse.jpg", image1);
cvReleaseImage(&image1);
image1 = cvLoadImage(im1fname, CV_LOAD_IMAGE_COLOR);
cvSet( image1, cvScalarAll(255) );
printf("Warping image...");
CvSeqReader reader;
int total = delaunay->edges->total;
int elem_size = delaunay->edges->elem_size;
std::vector<Triangle> trivec;
std::vector<CvMat *> baryinvvec;
for( int i = 0; i < total*2; i++ ) {
if((i == 0) || (i == total)) {
cvStartReadSeq( (CvSeq*)(delaunay->edges), &reader, 0 );
}
CvQuadEdge2D* edge = (CvQuadEdge2D*)(reader.ptr);
if( CV_IS_SET_ELEM( edge )) {
CvSubdiv2DEdge curedge = (CvSubdiv2DEdge)edge;
CvSubdiv2DEdge t = curedge;
Triangle temptri;
int count = 0;
// construct a triangle from this edge
do {
CvSubdiv2DPoint* pt = cvSubdiv2DEdgeOrg( t );
if(count < 3) {
pt->pt.x = pt->pt.x >= image1->width ? image1->width-1 : pt->pt.x;
pt->pt.y = pt->pt.y >= image1->height ? image1->height-1 : pt->pt.y;
pt->pt.x = pt->pt.x < 0 ? 0 : pt->pt.x;
pt->pt.y = pt->pt.y < 0 ? 0 : pt->pt.y;
temptri.points[count] = cvPointFrom32f( pt->pt );
}
else {
printf("More than 3 edges\n");
}
count++;
if(i < total)
t = cvSubdiv2DGetEdge( t, CV_NEXT_AROUND_LEFT );
else
t = cvSubdiv2DGetEdge( t, CV_NEXT_AROUND_RIGHT );
} while( t != curedge );
// check that triangle is not already in
if( std::find(trivec.begin(), trivec.end(), temptri) == trivec.end() ) {
// push triangle in and draw
trivec.push_back(temptri);
cvLine( image1, temptri.points[0], temptri.points[1], CV_RGB(255,0,0), 1, CV_AA, 0 );
cvLine( image1, temptri.points[1], temptri.points[2], CV_RGB(255,0,0), 1, CV_AA, 0 );
cvLine( image1, temptri.points[2], temptri.points[0], CV_RGB(255,0,0), 1, CV_AA, 0 );
// compute barycentric computation vector for this triangle
CvMat * barycen = cvCreateMat( 3, 3, CV_32FC1 );
CvMat * baryceninv = cvCreateMat( 3, 3, CV_32FC1 );
barycen->data.fl[3*0+0] = temptri.points[0].x;
barycen->data.fl[3*0+1] = temptri.points[1].x;
barycen->data.fl[3*0+2] = temptri.points[2].x;
barycen->data.fl[3*1+0] = temptri.points[0].y;
barycen->data.fl[3*1+1] = temptri.points[1].y;
barycen->data.fl[3*1+2] = temptri.points[2].y;
barycen->data.fl[3*2+0] = 1;
barycen->data.fl[3*2+1] = 1;
barycen->data.fl[3*2+2] = 1;
cvInvert( barycen, baryceninv, CV_LU );
baryinvvec.push_back(baryceninv);
cvReleaseMat( &barycen );
}
}
CV_NEXT_SEQ_ELEM( elem_size, reader );
}
printf("%d triangles...", trivec.size());
cvSaveImage("triangles.jpg", image1);
cvSet( image1, cvScalarAll(255) );
IplImage * clean_nonthresh = cvLoadImage( "conhull-clean.jpg", CV_LOAD_IMAGE_COLOR );
// for each triangle
for(unsigned int i = 0; i < trivec.size(); i++) {
Triangle curtri = trivec[i];
CvMat * curpoints = cvCreateMat( 1, 3, CV_32SC2 );
Triangle target;
std::map<CvPoint,CvPoint>::iterator piter[3];
printf("Triangle %d / %d\n",i,trivec.size());
int is_corner = 0;
for(int j = 0; j < 3; j++) {
/*
curpoints->data.i[2*j+0] = curtri.points[j].x;
curpoints->data.i[2*j+1] = curtri.points[j].y;
*/
CV_MAT_ELEM( *curpoints, CvPoint, 0, j ) = curtri.points[j];
printf("%d,%d\n",curtri.points[j].x,curtri.points[j].y);
if((curtri.points[j] == cvPoint(0,0)) || (curtri.points[j] == cvPoint(0,image1->height - 1)) ||(curtri.points[j] == cvPoint(image1->width - 1,0)) ||(curtri.points[j] == cvPoint(image1->width - 1,image1->height - 1))) {
is_corner++;
}
for(unsigned int k = 0; k < point_lookup.size(); k++) {
std::pair<CvPoint,CvPoint> thispair = point_lookup[k];
if(thispair.first == curtri.points[j]) {
target.points[j] = thispair.second;
break;
}
}
/*
piter[j] = point_lookup_map.find(curtri.points[j]);
if(piter[j] != point_lookup_map.end() ) {
target.points[j] = piter[j]->second;
}
*/
}
// if((piter[0] != point_lookup_map.end()) && (piter[1] != point_lookup_map.end()) && (piter[2] != point_lookup_map.end())) {
if(is_corner < 3) {
CvMat * newcorners = cvCreateMat( 3, 3, CV_32FC1 );
newcorners->data.fl[3*0+0] = target.points[0].x;
newcorners->data.fl[3*0+1] = target.points[1].x;
newcorners->data.fl[3*0+2] = target.points[2].x;
newcorners->data.fl[3*1+0] = target.points[0].y;
newcorners->data.fl[3*1+1] = target.points[1].y;
newcorners->data.fl[3*1+2] = target.points[2].y;
newcorners->data.fl[3*2+0] = 1;
newcorners->data.fl[3*2+1] = 1;
newcorners->data.fl[3*2+2] = 1;
CvContour hdr;
CvSeqBlock blk;
CvRect trianglebound = cvBoundingRect( cvPointSeqFromMat(CV_SEQ_KIND_CURVE+CV_SEQ_FLAG_CLOSED, curpoints, &hdr, &blk), 1 );
printf("Bounding box: %d,%d,%d,%d\n",trianglebound.x,trianglebound.y,trianglebound.width,trianglebound.height);
for(int y = trianglebound.y; (y < (trianglebound.y + trianglebound.height)) && ( y < image1->height); y++) {
for(int x = trianglebound.x; (x < (trianglebound.x + trianglebound.width)) && (x < image1->width); x++) {
// check to see if we're inside this triangle
/*
CvPoint v0 = cvPoint( curtri.points[2].x - curtri.points[0].x, curtri.points[2].y - curtri.points[0].y );
CvPoint v1 = cvPoint( curtri.points[1].x - curtri.points[0].x, curtri.points[1].y - curtri.points[0].y );
CvPoint v2 = cvPoint( x - curtri.points[0].x, y - curtri.points[0].y );
int dot00 = v0.x * v0.x + v0.y * v0. y;
int dot01 = v0.x * v1.x + v0.y * v1. y;
int dot02 = v0.x * v2.x + v0.y * v2. y;
int dot11 = v1.x * v1.x + v1.y * v1. y;
int dot12 = v1.x * v2.x + v1.y * v2. y;
double invDenom = 1.0 / (double)(dot00 * dot11 - dot01 * dot01);
double u = (double)(dot11 * dot02 - dot01 * dot12) * invDenom;
double v = (double)(dot00 * dot12 - dot01 * dot02) * invDenom;
*/
CvMat * curp = cvCreateMat(3, 1, CV_32FC1);
CvMat * result = cvCreateMat(3, 1, CV_32FC1);
curp->data.fl[0] = x;
curp->data.fl[1] = y;
curp->data.fl[2] = 1;
cvMatMul( baryinvvec[i], curp, result );
// double u = result->data.fl[0]/result->data.fl[2];
// double v = result->data.fl[1]/result->data.fl[2];
/*
if((i == 3019) && (y == 1329) && (x > 2505) && (x < 2584)) {
printf("Range %d: %f, %f, %f\t%f, %f, %f\n",x,result->data.fl[0],result->data.fl[1],result->data.fl[2],
sourcepoint->data.fl[0],sourcepoint->data.fl[1],sourcepoint->data.fl[2]);
}
*/
if( (result->data.fl[0] > MIN_VAL) && (result->data.fl[1] > MIN_VAL) && (result->data.fl[2] > MIN_VAL) && (fabs(1.0 - (result->data.fl[0]+result->data.fl[1]+result->data.fl[2])) <= 0.01) ) {
// if((u > 0) || (v > 0) /*&& ((u +v) < 1)*/ )
// printf("Barycentric: %f %f %f\n", result->data.fl[0], result->data.fl[1], result->data.fl[2]);
// this point is inside this triangle
// printf("Point %d,%d inside %d,%d %d,%d %d,%d\n",x,y,trivec[i].points[0].x,trivec[i].points[0].y,
// trivec[i].points[1].x,trivec[i].points[1].y,trivec[i].points[2].x,trivec[i].points[2].y);
CvMat * sourcepoint = cvCreateMat(3, 1, CV_32FC1);
cvMatMul( newcorners, result, sourcepoint );
double sourcex = sourcepoint->data.fl[0]/*/sourcepoint->data.fl[2]*/;
double sourcey = sourcepoint->data.fl[1]/*/sourcepoint->data.fl[2]*/;
if((sourcex >= 0) && (sourcey >= 0) && (sourcex < (image1->width)) && (sourcey < (image1->height))) {
// printf("%d,%d %d,%d\n",x,y,(int)sourcex,(int)sourcey);
cvSet2D( image1, y, x, cvGet2D( clean_nonthresh, (int)sourcey, (int)sourcex ) );
}
// printf("Point %d,%d inside %d,%d %d,%d %d,%d\n",x,y,trivec[i].points[0].x,trivec[i].points[0].y,
// trivec[i].points[1].x,trivec[i].points[1].y,trivec[i].points[2].x,trivec[i].points[2].y);
cvReleaseMat( &sourcepoint );
}
cvReleaseMat( &result );
cvReleaseMat( &curp );
}
}
for(int k = 0; k < verts; k++) {
double x = clean_texture->data.fl[2*k+0];
double y = clean_texture->data.fl[2*k+1];
// check to see if we're inside this triangle
CvMat * curp = cvCreateMat(3, 1, CV_32FC1);
CvMat * result = cvCreateMat(3, 1, CV_32FC1);
curp->data.fl[0] = x;
curp->data.fl[1] = y;
curp->data.fl[2] = 1;
cvMatMul( baryinvvec[i], curp, result );
if( (result->data.fl[0] > MIN_VAL) && (result->data.fl[1] > MIN_VAL) && (result->data.fl[2] > MIN_VAL) && (fabs(1.0 - (result->data.fl[0]+result->data.fl[1]+result->data.fl[2])) <= 0.01) ) {
CvMat * sourcepoint = cvCreateMat(3, 1, CV_32FC1);
cvMatMul( newcorners, result, sourcepoint );
double sourcex = sourcepoint->data.fl[0]/*/sourcepoint->data.fl[2]*/;
double sourcey = sourcepoint->data.fl[1]/*/sourcepoint->data.fl[2]*/;
if((sourcex >= 0) && (sourcey >= 0) && (sourcex < (image1->width)) && (sourcey < (image1->height))) {
clean_texture->data.fl[2*k+0] = sourcex;
clean_texture->data.fl[2*k+1] = sourcey;
// cvSet2D( image1, y, x, cvGet2D( clean_nonthresh, (int)sourcey, (int)sourcex ) );
}
cvReleaseMat( &sourcepoint );
}
cvReleaseMat( &result );
cvReleaseMat( &curp );
}
cvReleaseMat( &newcorners );
}
cvReleaseMat( &curpoints );
}
cvReleaseImage( &clean_nonthresh );
printf("done.\n");
cvSaveImage("fullwarp.jpg", image1);
printf("Drawing subdivisions on warped image...");
draw_subdiv( image1, delaunay, NULL, NULL, 0, NULL );
// draw_subdiv( image1, delaunay, delaunay_points, source_points, count, status );
printf("done.\n");
cvSaveImage("edgeswarp.jpg", image1);
cvReleaseImage(&image2);
image2 = cvLoadImage(im2fname, CV_LOAD_IMAGE_COLOR);
// cvCreateImage( cvGetSize(image2), IPL_DEPTH_8U, 3 );
// cvCalcSubdivVoronoi2D( delaunay );
printf("Drawing subdivisions on unwarped image...");
// draw_subdiv( image2, delaunay, delaunay_points, dest_points, count, status );
// draw_subdiv( image2, delaunay, NULL, NULL, 0, NULL );
printf("done.\n");
cvSaveImage("edges.jpg",image2);
cvReleaseImage(&image1);
cvFree(&source_points);
cvFree(&dest_points);
cvFree(&status);
cvReleaseMemStorage(&storage);
cvFree(&delaunay_points);
cvReleaseImage(&image2);
return 0;
}
bool ColorImageSegmentByKMeans2 ( const IplImage * img , IplImage * pResult, int nClusters, int sortFlag )
{
assert ( img != NULL && pResult != NULL );
assert ( img -> nChannels == 3 && pResult -> nChannels == 1);
int i , j ;
CvMat * samples = cvCreateMat (( img -> width )*( img -> height ),1, CV_32FC3 ); // 创建样本矩阵， CV_32FC3 代表位浮点通道（彩色图像）
CvMat * clusters = cvCreateMat (( img -> width )*( img -> height ),1, CV_32SC1 ); // 创建类别标记矩阵， CV_32SF1 代表位整型通道
int k =0;
for ( i =0; i < img -> width ; i ++)
{
for ( j =0; j < img -> height ; j ++)
{
CvScalar s ;
// 获取图像各个像素点的三通道值（ RGB ）
s . val [0]=( float ) cvGet2D ( img , j , i ). val [0];
s . val [1]=( float ) cvGet2D ( img , j , i ). val [1];
s . val [2]=( float ) cvGet2D ( img , j , i ). val [2];
cvSet2D ( samples , k ++,0, s ); // 将像素点三通道的值按顺序排入样本矩阵
}
}
cvKMeans2 ( samples , nClusters , clusters , cvTermCriteria ( CV_TERMCRIT_ITER ,50,1.0)); // 开始聚类，迭代次，终止误差 .0
k =0;
int val =0;
float step =255/( nClusters -1);
for ( i =0; i < img -> width ; i ++)
{
for ( j =0; j < img -> height ; j ++)
{
val =( int ) clusters -> data . i [ k ++];
CvScalar s ;
s . val [0]=255- val * step ; // 这个是将不同类别取不同的像素值，
cvSet2D ( pResult , j , i , s ); // 将每个像素点赋值
}
}
cvReleaseMat (& samples );
cvReleaseMat (& clusters );
return true ;
}
void draw_lateral_offset(ProbabilisticMapParams map_params, IplImage *dst_image)
{
int x_offset = (int)ceil(2.0 / map_params.grid_res);
int x_limit = (int)ceil(48.0 / map_params.grid_res);
int rect_size_x = (int)ceil(3.0 / map_params.grid_res);
int rect_size_y = (int)ceil(1.0 / map_params.grid_res);
int n_size_x = (int)(MIN(dst_image->width, x_limit + x_offset) - x_offset) / rect_size_x;
int n_size_y = (int)dst_image->height / rect_size_y;
static double **values;
if (values == NULL)
{
values = (double**)malloc(n_size_y * sizeof(double*));
for (int i = 0; i < n_size_y; i++)
values[i] = (double*)malloc(n_size_x * sizeof(double));
}
for (int j = 0; j < n_size_y; j++)
{
//cvLine(dst_image, cvPoint(0, j * rect_size_y), cvPoint(stereo_width - 1, j * rect_size_y), CV_RGB(0,0,255));
for (int i = 0; i < n_size_x; i++)
{
CvRect rect = cvRect(x_offset + i * rect_size_x, j * rect_size_y, rect_size_x, rect_size_y);
//cvLine(dst_image, cvPoint(x_offset + i * rect_size_x, 0), cvPoint(x_offset + i * rect_size_x, stereo_height - 1), CV_RGB(0,0,255));
unsigned long sum = 0;
for (int y = rect.y; y < rect.y + rect.height; y++)
{
for (int x = rect.x; x < rect.x + rect.width; x++)
{
sum += (int)cvGet2D(dst_image, y, x).val[0];
}
}
values[j][i] = (double) sum;
//printf("%f\t", values[j][i]);
}
//printf("\n");
}
for (int j = 0; j < n_size_x; j++)
{
double sum = 0.0;
int n = 0;
for (int i = 0; i < n_size_y; i++)
{
if (values[i][j] > 0.0)
{
sum += values[i][j];
n++;
}
}
double mean = (double)sum / n;
double acum = 0.0;
for (int i = 0; i < n_size_y; i++)
{
if (values[i][j] > 0.0)
{
acum = acum + (values[i][j] - mean) * (values[i][j] - mean);
}
}
double variance = (double)acum / n;
double std_dev = sqrt(variance);
double alpha = 0.8;
int start_index = INT_MIN;
int n_iters = 0, start_cand = -1;
int n_end_y = 24;
int n_max_iters = 12;
while (start_index < 0 || start_index > n_end_y)
{
alpha += 0.1;
double threshould = mean - alpha * std_dev;
for (int i = n_end_y; i >= 0; i--)
{
if (values[i][j] >= threshould)
{
if (start_index < i && n_iters == 0)
start_cand = i;
if (n_iters >= 3)
{
start_index = start_cand;
break;
}
n_iters++;
}
else
{
n_iters = 0;
start_index = INT_MIN;
}
}
n_max_iters--;
if (n_max_iters <= 0)
break;
}
if (start_index >= 0 && start_index < n_end_y)
{
CvRect rect_ri = cvRect(x_offset + j * rect_size_x, start_index * rect_size_y, rect_size_x, rect_size_y);
cvRectangle(dst_image, cvPoint(rect_ri.x, rect_ri.y), cvPoint(rect_ri.x + rect_ri.width, rect_ri.y - rect_ri.height), CV_RGB(0,255,0), 1, 8, 0);
CvRect rect_ro = cvRect(x_offset + j * rect_size_x, (start_index + 1) * rect_size_y, rect_size_x, rect_size_y);
cvRectangle(dst_image, cvPoint(rect_ro.x, rect_ro.y), cvPoint(rect_ro.x + rect_ro.width, rect_ro.y - rect_ro.height), CV_RGB(255,0,0), 1, 8, 0);
}
}
}
float blurriness( IplImage* image)
{
IplImage* clm_dst = cvCreateImage(cvSize(image->width,image->height),image->depth,image->nChannels);
IplImage* row_dst = cvCreateImage(cvSize(image->width,image->height),image->depth,image->nChannels);
IplImage* act_var_v = cvCreateImage(cvSize(image->width,image->height),image->depth,image->nChannels);
IplImage* act_var_h = cvCreateImage(cvSize(image->width,image->height),image->depth,image->nChannels);
IplImage* blur_var_v = cvCreateImage(cvSize(image->width,image->height),image->depth,image->nChannels);
IplImage* blur_var_h = cvCreateImage(cvSize(image->width,image->height),image->depth,image->nChannels);
IplImage* var_v = cvCreateImage(cvSize(image->width,image->height),image->depth,image->nChannels);
IplImage* var_h = cvCreateImage(cvSize(image->width,image->height),image->depth,image->nChannels);
IplImage* diff_v = cvCreateImage(cvSize(image->width,image->height),image->depth,image->nChannels);
IplImage* diff_h = cvCreateImage(cvSize(image->width,image->height),image->depth,image->nChannels);
IplImage* temp = cvCreateImage( cvSize(image->width,image->height),image->depth,image->nChannels);
cvCopy( image,temp);
#if PRINT_DEBUG
if(glb_img==NULL) {
cout<<"global image is Null\n";
}
cout<<"global image type:"<<glb_img->width<<","<<glb_img->height<<endl;
cout<<"temp img type:"<<temp->width<<","<<temp->height<<endl;
#endif
// clm_dst = cvCreateImage( cvSize(image->width,image->height),image->depth,image->nChannels);
// row_dst = cvCreateImage( cvSize(image->width,image->height),image->depth,image->nChannels);
cvSmooth( temp,row_dst,CV_BLUR,1,9);
cvSmooth( temp,clm_dst,CV_BLUR,9,1);
#if SHOW_IMAGE
cvShowImage("act image",image);
cvWaitKey(0);
cvShowImage("row dst",row_dst);
cvWaitKey(0);
cvShowImage("clm dst",clm_dst);
cvWaitKey(0);
cvCvtColor(row_dst,row_dst,CV_RGB2YCrCb);
cvCvtColor(clm_dst,clm_dst,CV_RGB2YCrCb);
cvShowImage("row dst",row_dst);
cvWaitKey(0);
cvShowImage("clm dst",clm_dst);
cvWaitKey(0);
#endif
// act_var_v = cvCreateImage(cvSize(image->width,image->height),IPL_DEPTH_32F,image->nChannels);
// act_var_h = cvCreateImage(cvSize(image->width,image->height),IPL_DEPTH_32F,image->nChannels);
// blur_var_v = cvCreateImage(cvSize(image->width,image->height),IPL_DEPTH_32F,image->nChannels);
// blur_var_h = cvCreateImage(cvSize(image->width,image->height),IPL_DEPTH_32F,image->nChannels);
int i,j;
CvScalar scal;
CvScalar scal1;
CvScalar scal2;
for( i=0;i<image->height;i++) {
for( j=0;j<(image->width-1);j++) {
scal1 = cvGet2D(image,i,j);
scal2 = cvGet2D(image,i,j+1);
scal.val[0] = abs(scal1.val[0] - scal2.val[0]);
cvSet2D(act_var_h,i,j,scal);
// cout<<scal.val[0]<<" ";
}
scal.val[0] = 0;
cvSet2D(act_var_h,i,j,scal);
// cout<<"\n";
}
// cout<<"*********************************************\n";
// cout<<"2nd part"<<endl;
for( i=0;i<image->width;i++) {
for( j=0;j<(image->height-1);j++) {
scal1 = cvGet2D( image,j,i);
scal2 = cvGet2D( image,j+1,i);
scal.val[0] = abs(scal1.val[0] - scal2.val[0]);
cvSet2D(act_var_v,j,i,scal);
// cout<<scal.val[0]<<" ";
}
scal.val[0] = 0;
cvSet2D(act_var_v,j,i,scal);
// cout<<"\n";
}
for( i=0;i<image->height;i++) {
for( j=0;j<(image->width-1);j++) {
scal1 = cvGet2D(row_dst,i,j);
scal2 = cvGet2D(row_dst,i,j+1);
scal.val[0] = abs(scal1.val[0] - scal2.val[0]);
cvSet2D(blur_var_h,i,j,scal);
// cout<<scal.val[0]<<" ";
}
scal.val[0] = 0;
cvSet2D(blur_var_h,i,j,scal);
// cout<<"\n";
}
// cout<<"*********************************************\n";
// cout<<"2nd part"<<endl;
for( i=0;i<image->width;i++) {
for( j=0;j<(image->height-1);j++) {
scal1 = cvGet2D( clm_dst,j,i);
scal2 = cvGet2D( clm_dst,j+1,i);
scal.val[0] = abs(scal1.val[0] - scal2.val[0]);
cvSet2D(blur_var_v,j,i,scal);
// cout<<scal.val[0]<<" ";
}
scal.val[0] = 0;
cvSet2D(blur_var_v,j,i,scal);
// cout<<"\n";
}
#if PRINT_DEBUG
cout<<"image:"<<image->width<<" "<<image->height<<endl;
cout<<"image:"<<image->width<<" "<<image->height<<endl;
cout<<"act_v:"<<act_var_v->width<<" "<<act_var_h->height<<endl;
cout<<"act_h:"<<act_var_h->width<<" "<<act_var_h->height<<endl;
cout<<"blur_v:"<<blur_var_v->width<<" "<<blur_var_h->height<<endl;
cout<<"blur_h:"<<blur_var_h->width<<" "<<blur_var_h->height<<endl;
#endif
// diff_v = cvCreateImage(cvSize(image->width,image->height),IPL_DEPTH_32F,image->nChannels);
// diff_h = cvCreateImage( cvSize(image->width,image->height),IPL_DEPTH_32F,image->nChannels);
for(i=0;i<image->height;i++) {
for(j=0;j<image->width;j++) {
scal1 = cvGet2D(act_var_v,i,j);
scal2 = cvGet2D(blur_var_v,i,j);
scal.val[0] = abs(scal1.val[0] - scal2.val[0]);
// cout<<scal.val[0]<<" ";
cvSet2D( diff_v,i,j,scal);
}
// cout<<"("<<i<<","<<j<<")\n";
}
// cout<<"diff: ("<<diff_v->height<<","<<diff_v->width<<")\n";
for( i=0;i<act_var_h->height;i++) {
for( j=0;j<act_var_h->width;j++) {
scal1 = cvGet2D(act_var_h,i,j);
scal2 = cvGet2D(blur_var_h,i,j);
scal.val[0] = abs(scal1.val[0] - scal2.val[0]);
// cout<<scal.val[0]<<" ";
cvSet2D( diff_h,i,j,scal);
}
// cout<<endl;
}
int max = 0;
// var_v = cvCreateImage(cvSize(image->width,image->height),IPL_DEPTH_32F,image->nChannels);
// var_h = cvCreateImage(cvSize(image->width,image->height),IPL_DEPTH_32F,image->nChannels);
#if PRINT_DEBUG
for(i=0;i<image->height;i++) {
for( j=0;j<image->width;j++) {
scal = cvGet2D(diff_v,i,j);
if(max<scal.val[0]) {
max = scal.val[0];
}
}
}
cout<<"max buff_v:"<<max<<endl;
for(i=0;i<image->height;i++) {
for(j=0;j<image->width;j++) {
scal.val[0] = max;
cvSet2D(var_v,i,j,scal);
}
}
max = 0;
for(i=0;i<image->height;i++) {
for( j=0;j<image->width;j++) {
scal = cvGet2D(diff_h,i,j);
if(max<scal.val[0]) {
max = scal.val[0];
}
}
}
cout<<"max buff_h:"<<max<<endl;
#endif
for(i=0;i<image->height;i++) {
for(j=0;j<image->width;j++) {
scal.val[0] = max;
cvSet2D(var_h,i,j,scal);
}
}
int act_sum_v =0;
int act_sum_h =0;
int diff_sum_v =0;
int diff_sum_h =0;
for(i=0;i<image->height;i++) {
for(j=0;j<image->width;j++) {
scal = cvGet2D(act_var_v,i,j);
act_sum_v += scal.val[0];
}
}
for(i=0;i<image->height;i++) {
for(j=0;j<image->width;j++) {
scal = cvGet2D( act_var_h,i,j);
act_sum_h += scal.val[0];
}
}
for(i=0;i<image->height;i++) {
for(j=0;j<image->width;j++) {
scal = cvGet2D( diff_v,i,j);
diff_sum_v += scal.val[0];
}
}
for(i=0;i<image->height;i++) {
for(j=0;j<image->width;j++) {
scal = cvGet2D( diff_h,i,j);
diff_sum_h += scal.val[0];
}
}
#if PRINT_DEBUG
cout<<"actual image sum of vertical variation : "<<act_sum_v<<endl;
cout<<"horizontal : "<<act_sum_h<<endl;
cout<<"sum of defference of variation vertically:"<<diff_sum_v<<endl;
cout<<"horizontally:"<<diff_sum_h<<endl;
#endif
float blur_v=0 ;
float blur_h=0;
float blur =0;
blur_v = (float)abs(act_sum_v - diff_sum_v)/act_sum_v;
blur_h = (float)abs(act_sum_h - diff_sum_h)/act_sum_h;
#if PRINT_DEBUG
cout<<"blur metrics: vertically:"<<blur_v<<endl;
cout<<" horizontally:"<<blur_h<<endl;
#endif
if( blur_v> blur_h) {
blur = blur_v;
}
else
blur = blur_h;
#if PRINT_DEBUG
cout<<"blur:"<<blur<<endl;
#endif
return(blur);
}
