void main()
{
// capture image
IplImage* image1 = cvLoadImage(IMAGE_FILE_NAME_1);
IplImage* image2 = cvLoadImage(IMAGE_FILE_NAME_2);
IplImage* grayImage1 = cvCreateImage(cvGetSize(image1), IPL_DEPTH_8U, 1);
IplImage* grayImage2 = cvCreateImage(cvGetSize(image2), IPL_DEPTH_8U, 1);
IplImage* resultImage = cvCreateImage(cvSize(image1->width*2, image1->height), IPL_DEPTH_8U, 3);
cvCvtColor(image1, grayImage1, CV_BGR2GRAY);
cvCvtColor(image2, grayImage2, CV_BGR2GRAY);
windage::Algorithms::FeatureDetector* detector = new windage::Algorithms::SIFTGPUdetector();
windage::Algorithms::SearchTree* tree = new windage::Algorithms::FLANNtree();
windage::Algorithms::HomographyEstimator* estimator = new windage::Algorithms::RANSACestimator();
windage::Algorithms::OutlierChecker* checker = new windage::Algorithms::OutlierChecker();
std::vector<windage::FeaturePoint>* feature = NULL;
std::vector<windage::FeaturePoint> feature1;
std::vector<windage::FeaturePoint> feature2;
std::vector<windage::FeaturePoint> matching1;
std::vector<windage::FeaturePoint> matching2;
tree->SetRatio(0.3);
estimator->AttatchReferencePoint(&matching1);
estimator->AttatchScenePoint(&matching2);
estimator->SetReprojectionError(REPROJECTION_ERRPR);
checker->AttatchEstimator(estimator);
checker->SetReprojectionError(REPROJECTION_ERRPR);
cvNamedWindow("result");
bool processing = true;
while(processing)
{
feature1.clear();
feature2.clear();
matching1.clear();
matching2.clear();
detector->DoExtractKeypointsDescriptor(grayImage1);
feature = detector->GetKeypoints();
for(unsigned int i=0; i<feature->size(); i++)
{
feature1.push_back((*feature)[i]);
}
detector->DoExtractKeypointsDescriptor(grayImage2);
feature = detector->GetKeypoints();
for(unsigned int i=0; i<feature->size(); i++)
{
feature2.push_back((*feature)[i]);
}
Matching(tree, &feature1, &feature2, &matching1, &matching2);
estimator->Calculate();
checker->Calculate();
cvSetImageROI(resultImage, cvRect(0, 0, image1->width, image1->height));
cvCopyImage(image1, resultImage);
cvSetImageROI(resultImage, cvRect(image1->width, 0, image1->width, image1->height));
cvCopyImage(image2, resultImage);
cvResetImageROI(resultImage);
int count = (int)matching1.size();
for(int i=0; i<count; i++)
{
double R = (count - i)/(double)count * 255.0;
double G = (i+1)/(double)count * 255.0;
if(matching1[i].IsOutlier() == false)
cvLine(resultImage, cvPoint((int)matching1[i].GetPoint().x, (int)matching1[i].GetPoint().y), cvPoint(image1->width + (int)matching2[i].GetPoint().x, (int)matching2[i].GetPoint().y), CV_RGB(0, 255, 0));
else
cvLine(resultImage, cvPoint((int)matching1[i].GetPoint().x, (int)matching1[i].GetPoint().y), cvPoint(image1->width + (int)matching2[i].GetPoint().x, (int)matching2[i].GetPoint().y), CV_RGB(255, 0, 0));
}
cvShowImage("result", resultImage);
char ch = cvWaitKey(1);
switch(ch)
{
case 's':
case 'S':
cvSaveImage("FeaturePairMatching.png", resultImage);
break;
case 'q':
case 'Q':
case 27:
processing = false;
break;
}
}
cvDestroyAllWindows();
}
void recognize(IplImage* original_images, IplImage* processed_original_images, IplImage** templates)
{
double matching_result = 0;
double matching_max = 0;
double matching_first_max = 0;
int template_flag = -1;
IplImage* drawed_original_images = cvCloneImage(original_images);
//extract the sub images first and do the matching image by image.
sub_image* sub_images = extract_sub_image(processed_original_images,MIN_SUB_IMAGE_WIDTH,MIN_SUB_IMAGE_HEIGHT);
int ori_flag = 0;
while(sub_images != NULL)
{
//different original sub image to be matched.
printf("ori_flag = %d\n",ori_flag++);
for(int i = 0; i < TEMPLATES_NUM; i++)
{
//match the given image with the provided templates.
//extract the template as well to cut the black edges which might leads negative impacts to the matching.
sub_image* temp_template = extract_sub_image(templates[i],MIN_SUB_IMAGE_WIDTH,MIN_SUB_IMAGE_HEIGHT);
//match the original sub image with template.
matching_result = template_original_match(sub_images->image, temp_template->image);
printf("with template %d, result = %f\n",i,matching_result);
//find the maximum
if(i == 0)
{
matching_first_max = matching_result;
matching_max = matching_result;
}
else
{
if(matching_result > matching_max)
{
matching_max = matching_result ;
template_flag = i;
}
}
}
if(matching_first_max == matching_max)
{
template_flag = 0;
}
//if the object not found or the match result is not reasonable, do nothing.
if(template_flag == -1 || matching_max < MATCHING_FALIED_THRESHOLD)
{
printf("image not matched\n");
}
else
{
//draw the matched template on the original image.
IplImage* drawed_templates_resized = cvCreateImage(cvSize(30,30),processed_original_images->depth,processed_original_images-> nChannels);
sub_image* min_temp_template = extract_sub_image(templates[template_flag],MIN_SUB_IMAGE_WIDTH,MIN_SUB_IMAGE_HEIGHT);
cvResize(min_temp_template->image,drawed_templates_resized);
printf("draw left = %d, top = %d\n",sub_images->image_left,sub_images->image_top);
//draw the template.
cvSetImageROI(drawed_original_images, cvRect(sub_images->image_left, sub_images->image_top, drawed_templates_resized->width, drawed_templates_resized->height));
cvCopy(drawed_templates_resized, drawed_original_images);
cvResetImageROI(drawed_original_images);
}
sub_images = sub_images->next_image;
}
cvShowImage("result",drawed_original_images);
}
std::string basicOCR::readText(std::string filesrc)
{
IplImage* imgSrc = cvLoadImage(filesrc.data(),CV_LOAD_IMAGE_COLOR);
IplImage* img_gray = cvCreateImage(cvGetSize(imgSrc), IPL_DEPTH_8U, 1);
IplImage* img_check = cvCreateImage(cvGetSize(imgSrc), IPL_DEPTH_8U, 1);
//cvSmooth(img_gray,img_gray,CV_GAUSSIAN,5,5);
//cvCopyImage(imgSrc,img_gray);
cvCvtColor(imgSrc, img_gray, CV_BGR2GRAY);
cvCvtColor(imgSrc, img_check, CV_BGR2GRAY);
cvThreshold(img_check, img_check,160, 255,CV_THRESH_BINARY);
cvThreshold(img_gray, img_gray,160, 255,CV_THRESH_BINARY_INV);// CV_THRESH_BINARY_INV使得背景为黑色，字符为白色，这样找到的最外层才是字符的最外层
//cvShowImage("ThresholdImg",img_gray);
CvSeq* contours = NULL;
CvMemStorage* storage = cvCreateMemStorage(0);
int count = cvFindContours(img_gray, storage, &contours,sizeof(CvContour),CV_RETR_EXTERNAL);
int idx = 0;
char szName[56] = {0};
int tempCount=0;
LISTRECT allrect;
LISTRECT line;
double countH = 0;
double countW = 0;
//取出所有字符边界，根据X轴排序
int buu = 0;
std::string output = "";
for (CvSeq* c = contours; c != NULL; c = c->h_next)
{
bool isInster = false;
CvRect rc =cvBoundingRect(c,0);
countH += rc.height;
countW += rc.width;
for (ILR i = allrect.begin();i!= allrect.end();++i)
{
if(rc.x < i->x)
{
allrect.insert(i,rc);
isInster= true;
break;
}
}
if (isInster == false)
{
allrect.push_back(rc);
}
}
double avgh = countH/allrect.size();
double avgw = countW/allrect.size();
for (line.clear();allrect.size() != 0;line.clear())
{
//find the highest char
ILR i = allrect.begin();
int miny = i->y;
for (++i;i != allrect.end(); ++i)
{
if (miny > i->y)
{
miny = i->y;
}
}
//find first char of line
for (i = allrect.begin();i->y > (miny+avgh)*1.2 ;++i);
//cvDrawRect(imgSrc, cvPoint(i->x, i->y), cvPoint(i->x + i->width, i->y + i->height), CV_RGB(255, 0, 0));
double countY = i->y + avgh;
int lastXb = i->x;
int lastXe = i->x+i->width;
int lastY = i->y + i->height;
int countX = 0;
int countSpace = 0;
//put first char to line list
line.push_back(*i);
i = allrect.erase(i);
for (;i != allrect.end();)
{
//find next char
if(i->y < lastY || i->y < (countY))
{
//cvDrawRect(imgSrc, cvPoint(i->x, i->y), cvPoint(i->x + i->width, i->y + i->height), CV_RGB(255, 0, 0));
countX += i->x - lastXb;
countSpace += i->x - lastXe;
//countY += i->y + i->height;
lastY = i->y + i->height;
lastXb = i->x;
lastXe = i->x+i->width;
line.push_back(*i);
i = allrect.erase(i);
}
else
{
++i;
}
}
for (ILR li = line.begin();li != line.end();)
{
ILR lasti = li;
li++;
if (li == line.end())
{
break;
}
//cvDrawRect(imgSrc, cvPoint(li->x, li->y), cvPoint(li->x + li->width, li->y + li->height), CV_RGB(255, 0, 0));
if (((li->height < avgh/2) || (lasti->height < avgh/2))
&& ((li->x - lasti->x) < (countX/(line.size()-1)/2)
|| (li->x+li->width > lasti->x && li->x+li->width < (lasti->x+lasti->width))
|| (li->x > lasti->x && li->x < (lasti->x+lasti->width))))
{
int x = std::min(li->x,lasti->x);
int y = std::min(li->y, lasti->y);
int height = (std::max(li->y+li->height, lasti->y+lasti->height) - y);
int width = (std::max(li->x+li->width, lasti->x+lasti->width) - x);
CvRect add = {x,y,width,height};
*lasti = add;
li = line.erase(li);
li--;
//line.insert(li,add);
}
}
for (ILR ci = line.begin();ci != line.end();ci++)
{
int rate = ((double)ci->width/(double)ci->height) /((double)avgw/(double)avgh*2);
rate++;
if (rate > 1)
{
int x = ci->x;
int y = ci->y;
int h = ci->height;
int w = ci->width;
ci = line.erase(ci);
for(int a = rate;a > 0 ;a--)
{
CvRect add = {x+w/rate*(rate-a),y,w/rate,h};
if (ci == line.end())
{
line.push_back(add);
ci = line.end();
}
else
{
line.insert(ci,add);
}
}
ci--;
}
}
int c = 0;
i = line.begin();
IplImage* imgNo = cvCreateImage(cvSize(i->width, i->height), IPL_DEPTH_8U, 1);
cvSetImageROI(img_check, *i);
cvCopyImage(img_check, imgNo);
cvResetImageROI(img_check);
char temp;
temp = classify(imgNo,0);
printf("%c",temp);
c = c*10 + classify(imgNo,0);
output += temp;
//cvDrawRect(imgSrc, cvPoint(i->x, i->y), cvPoint(i->x + i->width, i->y + i->height), CV_RGB(255, 0, 0));
int lastX = i->x+i->width;
lastY = i->y;
for (i++;i != line.end(); ++i)
{
buu++;
if (i->x - lastX > (countSpace / (line.size() - 1)))
{
/*
//cvDrawRect(imgSrc, cvPoint(lastX, lastY), cvPoint(lastX + avgw, lastY + avgh), CV_RGB(255, 0, 0));
CvRect space = {lastX, lastY, avgw, avgh};
imgNo = cvCreateImage(cvSize(avgw, avgh), IPL_DEPTH_8U, 1);
cvSetImageROI(img_check, space);
cvCopyImage(img_check, imgNo);
cvResetImageROI(img_check);
temp = classify(imgNo,0);
c = c*10 + classify(imgNo,0);
output += temp;
imgNo = cvCreateImage(cvSize(i->width, i->height), IPL_DEPTH_8U, 1);
cvSetImageROI(img_check, *i);
cvCopyImage(img_check, imgNo);
cvResetImageROI(img_check);
temp = classify(imgNo,0);
*/
printf(" ",temp);
}
lastX = i->x+i->width;
lastY = i->y;
imgNo = cvCreateImage(cvSize(i->width, i->height), IPL_DEPTH_8U, 1);
cvSetImageROI(img_check, *i);
cvCopyImage(img_check, imgNo);
cvResetImageROI(img_check);
temp = classify(imgNo,0);
printf("%c",temp);
c = c*10 + classify(imgNo,0);
output += temp;
//char szName[56] = {0};
//sprintf(szName, "%d", idx++);
//cvNamedWindow(szName);
//cvShowImage(szName, imgNo);
//cvDrawRect(imgSrc, cvPoint(i->x, i->y), cvPoint(i->x + i->width, i->y + i->height), CV_RGB(255, 0, 0));
}
output += "\n";
printf("\n");
//printf("%d\n",c);
}
printf("轮廓个数：%d",++buu);
/*
cvNamedWindow("src");
cvShowImage("src", imgSrc);
cvWaitKey(0);
cvReleaseMemStorage(&storage);
cvReleaseImage(&imgSrc);
cvReleaseImage(&img_gray);
cvDestroyAllWindows();
*/
return output;
}
//추후 수정
void FkPaperKeyboard_TypeA::cornerVerification(IplImage* srcImage){
CvSize size = cvGetSize(srcImage);
IplImage* eigImage = cvCreateImage(size, IPL_DEPTH_8U,1);
IplImage* tempImage = cvCreateImage(size, IPL_DEPTH_8U, 1);
IplImage* grayImage = cvCreateImage(size, IPL_DEPTH_8U, 1);
IplImage* veriImage = cvCreateImage(size, IPL_DEPTH_8U, 1);
IplImage* dstImage = cvCreateImage(size, IPL_DEPTH_8U, 1);
IplImage* mask = cvCreateImage(size, IPL_DEPTH_8U, 1);
IplImage* mask2 = cvCreateImage(size, IPL_DEPTH_8U, 1);
CvRect rect = cvRect(10, 10, 640 - 20, 480 - 20);
CvPoint2D32f srcQuad[4], dstQuad[4];
CvMat* warp_matrix = cvCreateMat(3,3, CV_32FC1);
CvMat* warp_matrix_invert = cvCreateMat(3,3, CV_32FC1);
CvMat* result = cvCreateMat(3, 1, CV_32FC1);
CvMat* dst = cvCreateMat(3, 1,CV_32FC1);
int keyButtonCornerCount = 316;
cvCvtColor(srcImage, grayImage, CV_BGR2GRAY);
cvSetImageROI(grayImage, rect);
cvSetImageROI(mask, rect);
cvSetImageROI(dstImage, rect);
cvSetImageROI(mask2, rect);
// 150~255사이의 값만 추출해서 마스크에 저장
cvInRangeS(grayImage, cvScalar(100, 100, 100), cvScalar(255, 255, 255), mask);
cvCopy(mask, mask2);
//cvShowImage("mask", mask);
//cvShowImage("mask2", mask2);
// 20,20? 150 미만의 값을 제외하기 위해 0인 값(mask)과 추출한 값(mask2)을 XOR 연산 한다.
cvFloodFill(mask, cvPoint(10, 10), cvScalar(0, 0, 0));
cvXor(mask2, mask, dstImage);
//cvShowImage("mask3", mask);
//cvShowImage("mask4", mask2);
//cvShowImage("dstImage", dstImage);
// 최종 연산된 이미지에서 코너 추출(각 키패드의 코너)
cvGoodFeaturesToTrack(dstImage, eigImage, tempImage, keyButtonCorner, &keyButtonCornerCount, 0.01, 7, NULL, 7, 0);
cvFindCornerSubPix (dstImage, keyButtonCorner, keyButtonCornerCount,cvSize (3, 3), cvSize (-1, -1), cvTermCriteria (CV_TERMCRIT_ITER | CV_TERMCRIT_EPS, 20, 0.03));
cvResetImageROI(dstImage);
for(int i =0 ; i < 316 ; i++){
keyButtonCorner[i].x += rect.x;
keyButtonCorner[i].y += rect.y;
}
initKeyButtonCorner();
srcQuad[CLOCKWISE_1].x = keyButtonCorner[315].x+10;
srcQuad[CLOCKWISE_1].y = keyButtonCorner[315].y-10;
srcQuad[CLOCKWISE_5].x = keyButtonCorner[31].x + 10;
srcQuad[CLOCKWISE_5].y = keyButtonCorner[31].y + 10;
srcQuad[CLOCKWISE_7].x = keyButtonCorner[0].x - 10;
srcQuad[CLOCKWISE_7].y = keyButtonCorner[0].y + 10;
srcQuad[CLOCKWISE_11].x = keyButtonCorner[290].x - 10;
srcQuad[CLOCKWISE_11].y = keyButtonCorner[290].y - 10;
dstQuad[CLOCKWISE_1].x = 640;
dstQuad[CLOCKWISE_1].y = 0;
dstQuad[CLOCKWISE_5].x = 640;
dstQuad[CLOCKWISE_5].y = 480;
dstQuad[CLOCKWISE_7].x = 0;
dstQuad[CLOCKWISE_7].y = 480;
dstQuad[CLOCKWISE_11].x = 0;
dstQuad[CLOCKWISE_11].y = 0;
cvGetPerspectiveTransform(srcQuad, dstQuad, warp_matrix);
cvWarpPerspective(dstImage, veriImage, warp_matrix);
detectKeyButtonCorner(veriImage);
cvInvert(warp_matrix, warp_matrix_invert);
for(int i = 0 ; i < 316 ; i++){
cvmSet(dst, 0, 0, keyButtonCorner[i].x);
cvmSet(dst, 1, 0, keyButtonCorner[i].y);
cvmSet(dst, 2, 0, 1);
cvMatMul(warp_matrix_invert, dst, result);
float t = cvmGet(result, 2,0);
keyButtonCorner[i].x = cvmGet(result, 0,0)/t ;
keyButtonCorner[i].y = cvmGet(result, 1,0)/t ;
}
cvResetImageROI(srcImage);
cvResetImageROI(mask);
cvReleaseImage(&eigImage);
cvReleaseImage(&tempImage);
cvReleaseImage(&grayImage);
cvReleaseImage(&veriImage);
cvReleaseImage(&dstImage);
cvReleaseImage(&mask);
cvReleaseImage(&mask2);
cvReleaseMat(&warp_matrix);
cvReleaseMat(&warp_matrix_invert);
cvReleaseMat(&result);
cvReleaseMat(&dst);
}
// the function draws all the squares in the image and crop and save images
void drawSquaresAndCrop(char *pFileName, IplImage* imgFilter, IplImage* img, CvSeq* squares )
{
CvSeqReader reader;
IplImage* cpy = cvCloneImage( imgFilter );
IplImage* cpyc = cvCloneImage( imgFilter );
int i;
char sFileNameCroped[255];
// initialize reader of the sequence
cvStartReadSeq( squares, &reader, 0 );
// read 4 sequence elements at a time (all vertices of a square)
for(int iCnt=0, i = 0; i < squares->total; i += 4,iCnt++ )
{
CvPoint pt[4], *rect = pt;
int count = 4;
// read 4 vertices
CV_READ_SEQ_ELEM( pt[0], reader );
CV_READ_SEQ_ELEM( pt[1], reader );
CV_READ_SEQ_ELEM( pt[2], reader );
CV_READ_SEQ_ELEM( pt[3], reader );
// draw the square as a closed polyline
cvPolyLine( cpy, &rect, &count, 1, 1, CV_RGB(0,255,0), 3, CV_AA, 0 );
// Get Area to crop
CvRect rc = GetRect(pt);
// Filter the area full image
if (abs(rc.width-img->width)>POINTS_NEAR ||
abs(rc.height-img->height)>POINTS_NEAR){
// Draw area
CvPoint pt1, pt2;
pt1.x = rc.x;
pt1.y = rc.y;
pt2.x = pt1.x+rc.width;
pt2.y = pt1.y+rc.height;
cvRectangle(cpy, pt1, pt2, CV_RGB(0,0,255),2);
// sets the Region of Interest
// Note that the rectangle area has to be __INSIDE__ the image
cvSetImageROI(cpyc, rc);
// create destination image
// Note that cvGetSize will return the width and the height of ROI
IplImage *img1 = cvCreateImage(cvGetSize(cpyc),
cpyc->depth,
cpyc->nChannels);
// copy subimage
cvCopy(cpyc, img1, NULL);
// save file
char stype[32];
char sFile[255];
strcpy(sFile, pFileName);
strcpy(stype, &(pFileName[strlen(pFileName)-3]));
sFile[strlen(pFileName)-4]=NULL;
sprintf(sFileNameCroped, "%s_%d.%s", sFile,iCnt,stype);
cvSaveImage(sFileNameCroped, img1);
// always reset the Region of Interest
cvResetImageROI(img1);
}
}
// show the resultant image
cvShowImage( wndname, cpy );
cvReleaseImage( &cpy );
cvReleaseImage( &cpyc );
}
void WindscreenLocator::locateBottomTop()
{
cvSetImageROI(imgGradH, cvRect(winLeft, 0, winRight - winLeft, imgGradH->height));
histoStat(imgGradH, NULL, horizon2Y, thresholdGrad, 0);
cvResetImageROI(imgGradH);
histoSmooth(horizon2Y, imgGrad->height, 5);
histoSmooth(horizon2Y, imgGrad->height, 10);
histoSmooth(horizon2Y, imgGrad->height, 10);
int yCrest[20];
int yCrestNr = 20;
histoCrest(horizon2Y, imgGrad->height, yCrest, yCrestNr);
int crest = 0;
bool usePrediction = false;
winTop = 0;
winBottom = imgRGB->height;
// if(plate->isValid() && reviser != NULL){
if(plate->isValid()
&& (plate->getMiddlePoint().x > winLeft
&& plate->getMiddlePoint().x < winRight)){
int plateWidth = plate->getPlateWidth();
if(reviser && reviser->getPlateWidth() > 0)
plateWidth = reviser->getPlateWidth();
CvPoint mid = plate->getMiddlePoint();
if(winRight - winLeft > 4.5 * plateWidth)
winBottom = mid.y - 80 / 35.0 * plateWidth;
else
winBottom = mid.y - 60.0/35.0 * plateWidth;
// crest = findLikelytCrest(horizon2Y, imgGrad->height, winBottom, winBottom - plateWidth * 0.3, winBottom + plateWidth * 0.3);
pdebug("winBottom=%d, crest=%d\n", winBottom, crest);
if(crest > 0 && crest < imgRGB->height)
winBottom = crest;
if(reviser)
reviser->statLowerBorder(plate, winLeft, winRight, winBottom);
}else if(reviser != NULL){
int plateWidth = reviser->getPlateWidth();
usePrediction = true;
pdebug("License not Found, Use Prediction.\n");
winBottom = reviser->predictLowerBorder(plate, winLeft, winRight);
// crest = findLikelytCrest(horizon2Y, imgGrad->height, winBottom, winBottom - plateWidth * 0.3, winBottom + plateWidth * 0.3);
crest = reviser->predictLowerBorder(plate, winLeft, winRight); //
if(crest == 0){
crest = imgRGB->height - 1;
winTop = imgRGB->height * 0.25;
winBottom = imgRGB->height * 0.9;
winBottom = std::max(0, winBottom);
winBottom = std::min(imgRGB->height - 1, winBottom);
winTop = std::min(winTop, winBottom - 1);
winTop = std::max(0, winTop);
return;
}
if(crest <= winBottom)
crest += 0.5 * plateWidth; // 预测的放宽范围
if(crest > 0 && crest < imgRGB->height)
winBottom = crest;
}
if(!usePrediction){
if(plate->isValid())
winTop = winBottom - (winRight - winLeft) * 0.65;
else
winTop = 0;
}else{
// winTop = winBottom - (winRight - winLeft) * 0.60; // 预测的放宽范围
winTop = winBottom - (winRight - winLeft) * 0.75; // 预测的放宽范围
}
winBottom = std::max(0, winBottom);
winBottom = std::min(imgRGB->height - 1, winBottom);
winTop = std::min(winTop, winBottom - 1);
winTop = std::max(0, winTop);
}
int main()
{
/*********************************** 主程序用到的参数 ***********************************/
IplImage * srcImg = NULL; // 存放从摄像头读取的每一帧彩色源图像
IplImage * img = NULL; // 存放从摄像头读取的每一帧灰度源图像
CvCapture * capture; // 指向CvCapture结构的指针
CvMemStorage* storage = cvCreateMemStorage(0); // 存放矩形框序列的内存空间
CvSeq* objects = NULL; // 存放检测到人脸的平均矩形框
double scale_factor = 1.2; // 搜索窗口的比例系数
int min_neighbors = 3; // 构成检测目标的相邻矩形的最小个数
int flags = 0; // 操作方式
CvSize min_size = cvSize(40, 40); // 检测窗口的最小尺寸
int i, globalK;
int hist[256]; // 存放直方图的数组
int pixelSum;
int threshold; // 存储二值化最优阈值
clock_t start, stop; // 计时参数
IplImage* faceImg = NULL; // 存储检测出的人脸图像
int temp = 0; // 临时用到的变量
int temp1 = 0; // 临时用到的变量
int count = 0; // 计数用的变量
int flag = 0; // 标记变量
int * tempPtr = NULL; // 临时指针
CvRect* largestFaceRect; // 存储检测到的最大的人脸矩形框
int * horiProject = NULL; // 水平方向的投影结果(数组指针)
int * vertProject = NULL; // 垂直方向的投影结果(数组指针)
int * subhoriProject = NULL; // 水平方向的投影结果(数组指针)
int * subvertProject = NULL; // 垂直方向的投影结果(数组指针)
int WIDTH; // 图像的宽度
int HEIGHT; // 图像的高度
int rEyeCol = 0; // 右眼所在的列数
int lEyeCol = 0; // 左眼所在的列数
int lEyeRow = 0; // 左眼所在的行数
int rEyeRow = 0; // 右眼所在的行数
int eyeBrowThreshold; // 区分眉毛与眼睛之间的阈值
uchar* rowPtr = NULL; // 指向图片每行的指针
uchar* rowPtrTemp = NULL; // 指向图片每行的指针, 中间变量
IplImage* eyeImg = NULL; // 存储眼睛的图像
CvRect eyeRect; // 存储裁剪后的人眼的矩形区域
CvRect eyeRectTemp; // 临时矩形区域
IplImage* lEyeImg = NULL; // 存储左眼的图像
IplImage* rEyeImg = NULL; // 存储右眼的图像
IplImage* lEyeImgNoEyebrow = NULL; // 存储去除眉毛之后的左眼图像
IplImage* rEyeImgNoEyebrow = NULL; // 存储去除眉毛之后的右眼图像
IplImage* lEyeballImg = NULL; // 存储最终分割的左眼框的图像
IplImage* rEyeballImg = NULL; // 存储最终分割的右眼框的图像
IplImage* lMinEyeballImg = NULL; // 存储最终分割的最小的左眼框的图像
IplImage* rMinEyeballImg = NULL; // 存储最终分割的最小的右眼框的图像
int lMinEyeballBlackPixel; // 存储最终分割的最小的左眼框的白色像素个数
int rMinEyeballBlackPixel; // 存储最终分割的最小的右眼框的白色像素个数
double lMinEyeballBlackPixelRate; // 存储最终分割的最小的左眼框的黑色像素占的比例
double rMinEyeballBlackPixelRate; // 存储最终分割的最小的右眼框的黑色像素占的比例
double lMinEyeballRectShape; // 存储最小左眼眶的矩形长宽比值
double rMinEyeballRectShape; // 存储最小右眼眶的矩形长宽比值
double lMinEyeballBeta; // 存储最小左眼眶的中间1/2区域的黑像素比值
double rMinEyeballBeta; // 存储最小右边眼眶的中间1/2区域的黑像素比值
int lEyeState; // 左眼睁（0）、闭（1）状态
int rEyeState; // 右眼睁（0）、闭（1）状态
int eyeState; // 眼睛综合睁（0）、闭（1）状态
int eyeCloseNum = 0; // 统计一次检测过程中闭眼的总数
int eyeCloseDuration = 0; // 统计一次检测过程中连续检测到闭眼状态的次数
int maxEyeCloseDuration = 0; // 一次检测过程中连续检测到闭眼状态的次数的最大值
int failFaceNum = 0; // 统计一次检测过程中未检测到人脸的总数
int failFaceDuration = 0; // 统计一次检测过程中连续未检测到人脸的次数
int maxFailFaceDuration = 0; // 一次检测过程中连续未检测到人脸的次数的最大值
int fatigueState = 1; // 驾驶员的驾驶状态：疲劳驾驶（1），正常驾驶（0）
/********************* 创建显示窗口 **************************/
cvNamedWindow("img", CV_WINDOW_AUTOSIZE); // 显示灰度源图像
cvNamedWindow("分割后的人脸", 1); // 显示分割出大致眼眶区域的人脸
cvNamedWindow("大致的左眼区域", 1); // 显示大致的左眼区域
cvNamedWindow("大致的右眼区域", 1); // 显示大致的右眼区域
cvNamedWindow("l_binary"); // 显示大致右眼区域的二值化图像
cvNamedWindow("r_binary"); // 显示大致左眼区域的二值化图像
cvNamedWindow("lEyeImgNoEyebrow", 1); // 显示去除眉毛区域的左眼图像
cvNamedWindow("rEyeImgNoEyebrow", 1); // 显示去除眉毛区域的右眼图像
cvNamedWindow("lEyeCenter", 1); // 显示标出虹膜中心的左眼图像
cvNamedWindow("rEyeCenter", 1); // 显示标出虹膜中心的右眼图像
cvNamedWindow("lEyeballImg", 1); // 根据lEyeImgNoEyebrow大小的1/2区域重新划分的左眼图像
cvNamedWindow("rEyeballImg", 1); // 根据rEyeImgNoEyebrow大小的1/2区域重新划分的右眼图像
cvNamedWindow("lkai", 1); // 左眼进行开运算之后的图像
cvNamedWindow("rkai", 1); // 右眼进行开运算之后的图像
cvNamedWindow("lMinEyeballImg", 1); // 缩小至边界区域的左眼虹膜图像
cvNamedWindow("rMinEyeballImg", 1); // 缩小至边界区域的右眼眼虹膜图像
capture = cvCreateCameraCapture(0);
if( capture == NULL )
return -1;
for( globalK = 1; globalK <= DETECTTIME; globalK ++ ){
start = clock();
srcImg = cvQueryFrame(capture);
img = cvCreateImage(cvGetSize(srcImg), IPL_DEPTH_8U, 1);
cvCvtColor(srcImg, img, CV_BGR2GRAY);
if( !img )
continue;
cvShowImage("img", img);
cvWaitKey(20);
/************************************* 检测人脸 ****************************************/
cvClearMemStorage(storage); // 将存储块的 top 置到存储块的头部，既清空存储块中的存储内容
detectFace(
img, // 灰度图像
objects, // 输出参数：检测到人脸的矩形框
storage, // 存储矩形框的内存区域
scale_factor, // 搜索窗口的比例系数
min_neighbors, // 构成检测目标的相邻矩形的最小个数
flags, // 操作方式
cvSize(20, 20) // 检测窗口的最小尺寸
);
// 提取人脸区域
if ( !objectsTemp->total ){
printf("Failed to detect face!\n"); // 调试代码
failFaceNum ++; // 统计未检测到人脸的次数
failFaceDuration ++; // 统计连续未检测到人脸的次数
// 检测过程中判断全是闭眼和检测不到人脸的情况，没有睁开眼的情况，导致maxEyeCloseDuration = 0;
(eyeCloseDuration > maxEyeCloseDuration) ? maxEyeCloseDuration = eyeCloseDuration : maxEyeCloseDuration;
eyeCloseDuration = 0;
if( globalK == DETECTTIME ){
// 当一次检测过程中，所有的过程都检测不到人脸，则要在此更新 maxFailFaceDuration
(failFaceDuration > maxFailFaceDuration) ? maxFailFaceDuration = failFaceDuration : maxFailFaceDuration;
printf("\nFATIGUETHRESHOLD: %d\n", FATIGUETHRESHOLD);
printf("eyeCloseNum: %d\tmaxEyeCloseDuration: %d\n", eyeCloseNum, maxEyeCloseDuration);
printf("failFaceNum: %d\tmaxFailFaceDuration: %d\n", failFaceNum, maxFailFaceDuration);
// 进行疲劳状态的判别
fatigueState = recoFatigueState(FATIGUETHRESHOLD, eyeCloseNum, maxEyeCloseDuration, failFaceNum, maxFailFaceDuration);
if( fatigueState == 1 )
printf("驾驶员处于疲劳驾驶状态\n\n");
else if( fatigueState == 0 )
printf("驾驶员处于正常驾驶状态\n\n");
// 进入下一次检测过程前，将变量清零
globalK = 0;
lEyeState = 1;
rEyeState = 1;
eyeState = 1;
eyeCloseNum = 0;
eyeCloseDuration = 0;
maxEyeCloseDuration = 0;
failFaceNum = 0;
failFaceDuration = 0;
maxFailFaceDuration = 0;
fatigueState = 1;
cvWaitKey(0);
}
continue;
}
else{
// 统计连续未检测到人脸的次数中的最大数值
(failFaceDuration > maxFailFaceDuration) ? maxFailFaceDuration = failFaceDuration : maxFailFaceDuration;
failFaceDuration = 0;
// 找到检测到的最大的人脸矩形区域
temp = 0;
for(i = 0; i < (objectsTemp ? objectsTemp->total : 0); i ++) {
CvRect* rect = (CvRect*) cvGetSeqElem(objectsTemp, i);
if ( (rect->height * rect->width) > temp ){
largestFaceRect = rect;
temp = rect->height * rect->width;
}
}
// 根据人脸的先验知识分割出大致的人眼区域
temp = largestFaceRect->width / 8;
largestFaceRect->x = largestFaceRect->x + temp;
largestFaceRect->width = largestFaceRect->width - 3*temp/2;
largestFaceRect->height = largestFaceRect->height / 2;
largestFaceRect->y = largestFaceRect->y + largestFaceRect->height / 2;
largestFaceRect->height = largestFaceRect->height / 2;
cvSetImageROI(img, *largestFaceRect); // 设置ROI为检测到的最大的人脸区域
faceImg = cvCreateImage(cvSize(largestFaceRect->width, largestFaceRect->height), IPL_DEPTH_8U, 1);
cvCopy(img, faceImg, NULL);
cvResetImageROI(img); // 释放ROI
cvShowImage("分割后的人脸", faceImg);
eyeRectTemp = *largestFaceRect;
// 根据人脸的先验知识分割出大致的左眼区域
largestFaceRect->width /= 2;
cvSetImageROI(img, *largestFaceRect); // 设置ROI为检测到的最大的人脸区域
lEyeImg = cvCreateImage(cvSize(largestFaceRect->width, largestFaceRect->height), IPL_DEPTH_8U, 1);
cvCopy(img, lEyeImg, NULL);
cvResetImageROI(img); // 释放ROI
cvShowImage("大致的左眼区域", lEyeImg);
// 根据人脸的先验知识分割出大致的右眼区域
eyeRectTemp.x += eyeRectTemp.width / 2;
eyeRectTemp.width /= 2;
cvSetImageROI(img, eyeRectTemp); // 设置ROI为检测到的最大的人脸区域
rEyeImg = cvCreateImage(cvSize(eyeRectTemp.width, eyeRectTemp.height), IPL_DEPTH_8U, 1);
cvCopy(img, rEyeImg, NULL);
cvResetImageROI(img); // 释放ROI
cvShowImage("大致的右眼区域", rEyeImg);
/********************************** 二值化处理 ***********************************/
// 图像增强：直方图均衡化在detectFace中实现了一次；可尝试非线性点运算
/*** 二值化左眼大致区域的图像 ***/
//lineTrans(lEyeImg, lEyeImg, 1.5, 0); // 线性点运算
cvSmooth(lEyeImg, lEyeImg, CV_MEDIAN); // 中值滤波 默认窗口大小为3*3
nonlineTrans(lEyeImg, lEyeImg, 0.8); // 非线性点运算
memset(hist, 0, sizeof(hist)); // 初始化直方图的数组为0
histogram(lEyeImg, hist); // 计算图片直方图
// 计算最佳阈值
pixelSum = lEyeImg->width * lEyeImg->height;
threshold = ostuThreshold(hist, pixelSum, 45);
cvThreshold(lEyeImg, lEyeImg, threshold, 255, CV_THRESH_BINARY);// 对图像二值化
// 显示二值化后的图像
cvShowImage("l_binary",lEyeImg);
/*** 二值化右眼大致区域的图像 ***/
//lineTrans(rEyeImg, rEyeImg, 1.5, 0); // 线性点运算
cvSmooth(rEyeImg, rEyeImg, CV_MEDIAN); // 中值滤波 默认窗口大小为3*3
nonlineTrans(rEyeImg, rEyeImg, 0.8); // 非线性点运算
memset(hist, 0, sizeof(hist)); // 初始化直方图的数组为0
histogram(rEyeImg, hist); // 计算图片直方图
// 计算最佳阈值
pixelSum = rEyeImg->width * rEyeImg->height;
threshold = ostuThreshold(hist, pixelSum, 45);
cvThreshold(rEyeImg, rEyeImg, threshold, 255, CV_THRESH_BINARY);// 对图像二值化
// 显示二值化后的图像
cvShowImage("r_binary",rEyeImg);
/***************************************** 检测人眼 ********************************************/
/** 如果有明显的眉毛区域，则分割去除眉毛 **/
// 分割左眼眉毛
HEIGHT = lEyeImg->height;
WIDTH = lEyeImg->width;
// 分配内存
horiProject = (int*)malloc(HEIGHT * sizeof(int));
vertProject = (int*)malloc(WIDTH * sizeof(int));
if( horiProject == NULL || vertProject == NULL ){
printf("Failed to allocate memory\n");
cvWaitKey(0);
return -1;
}
// 内存置零
for(i = 0; i < HEIGHT; i ++)
*(horiProject + i) = 0;
for(i = 0; i < WIDTH; i ++)
*(vertProject + i) = 0;
histProject(lEyeImg, horiProject, vertProject); // 计算直方图投影
lEyeRow = removeEyebrow(horiProject, WIDTH, HEIGHT, 10); // 计算分割眉毛与眼框的位置
// 分割右眼眉毛
HEIGHT = rEyeImg->height;
WIDTH = rEyeImg->width;
// 分配内存
horiProject = (int*)malloc(HEIGHT * sizeof(int));
vertProject = (int*)malloc(WIDTH * sizeof(int));
if( horiProject == NULL || vertProject == NULL ){
printf("Failed to allocate memory\n");
cvWaitKey(0);
return -1;
}
// 内存置零
for(i = 0; i < HEIGHT; i ++)
*(horiProject + i) = 0;
for(i = 0; i < WIDTH; i ++)
*(vertProject + i) = 0;
histProject(rEyeImg, horiProject, vertProject); // 计算直方图投影
rEyeRow = removeEyebrow(horiProject, WIDTH, HEIGHT, 10); // 计算分割眉毛与眼框的位置
// 显示去除眉毛后的人眼大致区域
eyeRect = cvRect(0, lEyeRow, lEyeImg->width, (lEyeImg->height - lEyeRow)); // 去眉毛的眼眶区域在lEyeImg中的矩形框区域
cvSetImageROI(lEyeImg, eyeRect); // 设置ROI为去除眉毛的眼眶，在下面释放ROI
lEyeImgNoEyebrow = cvCreateImage(cvSize(eyeRect.width, eyeRect.height), IPL_DEPTH_8U, 1);
cvCopy(lEyeImg, lEyeImgNoEyebrow, NULL);
cvShowImage("lEyeImgNoEyebrow", lEyeImgNoEyebrow);
eyeRectTemp = cvRect(0, rEyeRow, rEyeImg->width, (rEyeImg->height - rEyeRow)); // 去眉毛的眼眶区域在rEyeImg中的矩形框区域
cvSetImageROI(rEyeImg, eyeRectTemp); // 设置ROI为去除眉毛的眼眶，在下面释放ROI
rEyeImgNoEyebrow = cvCreateImage(cvSize(eyeRectTemp.width, eyeRectTemp.height), IPL_DEPTH_8U, 1);
cvCopy(rEyeImg, rEyeImgNoEyebrow, NULL);
cvShowImage("rEyeImgNoEyebrow", rEyeImgNoEyebrow);
///////////////// 定位眼睛中心点在去除眉毛图像中的行列位置 ///////////////////
HEIGHT = lEyeImgNoEyebrow->height;
WIDTH = lEyeImgNoEyebrow->width;
// 分配内存
subhoriProject = (int*)malloc(HEIGHT * sizeof(int));
subvertProject = (int*)malloc(WIDTH * sizeof(int));
if( subhoriProject == NULL || subvertProject == NULL ){
printf("Failed to allocate memory\n");
cvWaitKey(0);
return -1;
}
// 内存置零
for(i = 0; i < HEIGHT; i ++)
*(subhoriProject + i) = 0;
for(i = 0; i < WIDTH; i ++)
*(subvertProject + i) = 0;
histProject(lEyeImgNoEyebrow, subhoriProject, subvertProject); // 重新对分割出的左眼图像进行积分投影
lEyeRow = getEyePos(subhoriProject, HEIGHT, HEIGHT/5); // 定位左眼所在的行
lEyeCol = getEyePos(subvertProject, WIDTH, WIDTH/5); // 定位左眼所在的列
HEIGHT = rEyeImgNoEyebrow->height;
WIDTH = rEyeImgNoEyebrow->width;
// 分配内存
subhoriProject = (int*)malloc(HEIGHT * sizeof(int));
subvertProject = (int*)malloc(WIDTH * sizeof(int));
if( subhoriProject == NULL || subvertProject == NULL ){
printf("Failed to allocate memory\n");
cvWaitKey(0);
return -1;
}
// 内存置零
for(i = 0; i < HEIGHT; i ++)
*(subhoriProject + i) = 0;
for(i = 0; i < WIDTH; i ++)
*(subvertProject + i) = 0;
histProject(rEyeImgNoEyebrow, subhoriProject, subvertProject); // 重新对分割出的右眼图像进行积分投影
rEyeRow = getEyePos(subhoriProject, HEIGHT, HEIGHT/5); // 定位右眼所在的行
rEyeCol = getEyePos(subvertProject, WIDTH, WIDTH/5); // 定位右眼所在的列
/*
printf("************ image of eyes without eyebrow ***********\n");
printf("Left eye: width: %d\theight: %d\n", lEyeImgNoEyebrow->width, lEyeImgNoEyebrow->height);
printf("Right eye: width: %d\theight: %d\n", rEyeImgNoEyebrow->width, rEyeImgNoEyebrow->height);
printf("Right eye: WIDTH: %d\tHEIGHT: %d\n", WIDTH, HEIGHT);
printf("Centers positon of Eyes. lEyeRow: %d lEyeCol: %d\trEyeRow: %d rEyeCol: %d\n\n", lEyeRow, lEyeCol, rEyeRow, rEyeCol);
*/
// 标记眼睛的位置
cvCircle(lEyeImgNoEyebrow, cvPoint(lEyeCol, lEyeRow), 3, CV_RGB(0,0,255), 1, 8, 0);
cvCircle(rEyeImgNoEyebrow, cvPoint(rEyeCol, rEyeRow), 3, CV_RGB(0,0,255), 1, 8, 0);
cvShowImage("lEyeCenter", lEyeImgNoEyebrow);
cvShowImage("rEyeCenter", rEyeImgNoEyebrow);
/********************************** 判断人眼睁闭状态 ***********************************/
////////////////// 分割出以找到的中心为中心的大致眼眶 /////////////////
// 左眼眶
HEIGHT = lEyeImgNoEyebrow->height;
WIDTH = lEyeImgNoEyebrow->width;
// 计算大致眼眶的区域: eyeRect
eyeRect = cvRect(0, 0, WIDTH, HEIGHT);
calEyeSocketRegion(&eyeRect, WIDTH, HEIGHT, lEyeCol, lEyeRow);
/*
printf("************lEyeImgNoEyebrow************\n");
printf("width: %d\theight: %d\n", WIDTH, HEIGHT);
printf("**********lEyeballRect**********\n");
printf("eyeRect.x = %d\teyeRect.width = %d\n", eyeRect.x, eyeRectTemp.width);
printf("eyeRect.y = %d\teyeRect.height = %d\n\n", eyeRectTemp.y, eyeRectTemp.height);
*/
cvSetImageROI(lEyeImgNoEyebrow, eyeRect); // 设置ROI为检测到眼眶区域
lEyeballImg = cvCreateImage(cvGetSize(lEyeImgNoEyebrow), IPL_DEPTH_8U, 1);
cvCopy(lEyeImgNoEyebrow, lEyeballImg, NULL);
cvResetImageROI(lEyeImgNoEyebrow);
cvShowImage("lEyeballImg", lEyeballImg);
// 右眼眶
HEIGHT = rEyeImgNoEyebrow->height;
WIDTH = rEyeImgNoEyebrow->width;
// 计算大致眼眶的区域: eyeRectTemp
eyeRect = cvRect(0, 0, WIDTH, HEIGHT);
calEyeSocketRegion(&eyeRect, WIDTH, HEIGHT, rEyeCol, rEyeRow);
/*
printf("************rEyeImgNoEyebrow************\n");
printf("width: %d\theight: %d\n", WIDTH, HEIGHT);
printf("**********rEyeballRect**********\n");
printf("eyeRect.x = %d\teyeRect.width = %d\n", eyeRect.x, eyeRect.width);
printf("eyeRect.y = %d\teyeRect.height = %d\n\n", eyeRect.y, eyeRect.height);
*/
cvSetImageROI(rEyeImgNoEyebrow, eyeRect); // 设置ROI为检测到眼眶区域
rEyeballImg = cvCreateImage(cvGetSize(rEyeImgNoEyebrow), IPL_DEPTH_8U, 1);
cvCopy(rEyeImgNoEyebrow, rEyeballImg, NULL);
cvResetImageROI(rEyeImgNoEyebrow);
cvShowImage("rEyeballImg", rEyeballImg);
/////////////////////////// 闭运算 ///////////////////////////
cvErode(lEyeballImg, lEyeballImg, NULL, 2); //腐蚀图像
cvDilate(lEyeballImg, lEyeballImg, NULL, 2); //膨胀图像
cvShowImage("lkai", lEyeballImg);
cvErode(rEyeballImg, rEyeballImg, NULL, 1); //腐蚀图像
cvDilate(rEyeballImg, rEyeballImg, NULL, 1); //膨胀图像
cvShowImage("rkai", rEyeballImg);
/////////////////// 计算最小眼睛的矩形区域 ////////////////////
///////////////////////////左眼
HEIGHT = lEyeballImg->height;
WIDTH = lEyeballImg->width;
// 分配内存
subhoriProject = (int*)malloc(HEIGHT * sizeof(int));
subvertProject = (int*)malloc(WIDTH * sizeof(int));
if( subhoriProject == NULL || subvertProject == NULL ){
printf("Failed to allocate memory\n");
cvWaitKey(0);
return -1;
}
// 内存置零
for(i = 0; i < HEIGHT; i ++)
*(subhoriProject + i) = 0;
for(i = 0; i < WIDTH; i ++)
*(subvertProject + i) = 0;
histProject(lEyeballImg, subhoriProject, subvertProject);
// 计算左眼最小的矩形区域
eyeRectTemp = cvRect(0, 0 , 1, 1); // 初始化
getEyeMinRect(&eyeRectTemp, subhoriProject, subvertProject, WIDTH, HEIGHT, 5, 3);
/*
printf("eyeRectTemp.y: %d\n", eyeRectTemp.y);
printf("eyeRectTemp.height: %d\n", eyeRectTemp.height);
printf("eyeRectTemp.x: %d\n", eyeRectTemp.x);
printf("eyeRectTemp.width: %d\n", eyeRectTemp.width);
*/
// 计算最小左眼矩形的长宽比, 判断眼睛状态时用的到
lMinEyeballRectShape = (double)eyeRectTemp.width / (double)eyeRectTemp.height;
//printf("\nlMinEyeballRectShape: %f\n", lMinEyeballRectShape);
cvSetImageROI(lEyeballImg, eyeRectTemp); // 设置ROI为检测到最小面积的眼眶
lMinEyeballImg = cvCreateImage(cvGetSize(lEyeballImg), IPL_DEPTH_8U, 1);
cvCopy(lEyeballImg, lMinEyeballImg, NULL);
cvResetImageROI(lEyeballImg);
cvShowImage("lMinEyeballImg", lMinEyeballImg);
//////////////////////// 统计左眼黑像素个数 /////////////////////
HEIGHT = lMinEyeballImg->height;
WIDTH = lMinEyeballImg->width;
// 分配内存
subhoriProject = (int*)malloc(HEIGHT * sizeof(int));
subvertProject = (int*)malloc(WIDTH * sizeof(int));
if( subhoriProject == NULL || subvertProject == NULL ){
printf("Failed to allocate memory\n");
cvWaitKey(0);
return -1;
}
// 内存置零
for(i = 0; i < HEIGHT; i ++)
*(subhoriProject + i) = 0;
for(i = 0; i < WIDTH; i ++)
*(subvertProject + i) = 0;
histProject(lMinEyeballImg, subhoriProject, subvertProject);
// 统计lEyeballImg中黑色像素的个数
temp = 0; // 白像素个数
for( i = 0; i < WIDTH; i ++ )
temp += *(subvertProject + i);
temp /= 255;
lMinEyeballBlackPixel = WIDTH * HEIGHT - temp;
lMinEyeballBlackPixelRate = (double)lMinEyeballBlackPixel / (double)(WIDTH * HEIGHT);
//printf("WIDTH * HEIGHT: %d\tlMinEyeballBlackSum;%d\n\n", WIDTH * HEIGHT, lMinEyeballBlackPixel);
//printf("lMinEyeballBlackPixelRate;%f\n\n", lMinEyeballBlackPixelRate);
// 统计lMinEyeballImg中的1/2区域内黑像素的比例
lMinEyeballBeta = 0;
lMinEyeballBeta = calMiddleAreaBlackPixRate(subvertProject, &eyeRectTemp, WIDTH, HEIGHT, lEyeCol, lMinEyeballBlackPixel);
//printf("lMinEyeballBeta; %f\n\n", lMinEyeballBeta);
////////////////////////////////////右眼
HEIGHT = rEyeballImg->height;
WIDTH = rEyeballImg->width;
// 分配内存
subhoriProject = (int*)malloc(HEIGHT * sizeof(int));
subvertProject = (int*)malloc(WIDTH * sizeof(int));
if( subhoriProject == NULL || subvertProject == NULL ){
printf("Failed to allocate memory\n");
cvWaitKey(0);
return -1;
}
// 内存置零
for(i = 0; i < HEIGHT; i ++)
*(subhoriProject + i) = 0;
for(i = 0; i < WIDTH; i ++)
*(subvertProject + i) = 0;
histProject(rEyeballImg, subhoriProject, subvertProject);
// 计算右眼最小的矩形区域
eyeRectTemp = cvRect(0, 0 , 1, 1);
getEyeMinRect(&eyeRectTemp, subhoriProject, subvertProject, WIDTH, HEIGHT, 5, 3);
// 计算最小右眼矩形的长宽比，判断眼睛状态时用的到
rMinEyeballRectShape = (double)eyeRectTemp.width / (double)eyeRectTemp.height;
//printf("\nrMinEyeballRectShape: %f\n", rMinEyeballRectShape);
cvSetImageROI(rEyeballImg, eyeRectTemp); // 设置ROI为检测到最小面积的眼眶
rMinEyeballImg = cvCreateImage(cvGetSize(rEyeballImg), IPL_DEPTH_8U, 1);
cvCopy(rEyeballImg, rMinEyeballImg, NULL);
cvResetImageROI(rEyeballImg);
cvShowImage("rMinEyeballImg", rMinEyeballImg);
//////////////////////// 统计右眼黑像素个数 /////////////////////
HEIGHT = rMinEyeballImg->height;
WIDTH = rMinEyeballImg->width;
// 分配内存
subhoriProject = (int*)malloc(HEIGHT * sizeof(int));
subvertProject = (int*)malloc(WIDTH * sizeof(int));
if( subhoriProject == NULL || subvertProject == NULL ){
printf("Failed to allocate memory\n");
cvWaitKey(0);
return -1;
}
// 内存置零
for(i = 0; i < HEIGHT; i ++)
*(subhoriProject + i) = 0;
for(i = 0; i < WIDTH; i ++)
*(subvertProject + i) = 0;
histProject(rMinEyeballImg, subhoriProject, subvertProject);// 计算直方图积分投影
// 统计lEyeballImg中黑色像素的个数
temp = 0;
for( i = 0; i < WIDTH; i ++ )
temp += *(subvertProject + i);
temp /= 255;
rMinEyeballBlackPixel = WIDTH * HEIGHT - temp;
rMinEyeballBlackPixelRate = (double)rMinEyeballBlackPixel / (double)(WIDTH * HEIGHT);
//printf("WIDTH * HEIGHT: %d\trMinEyeballBlackSum;%d\n\n", WIDTH * HEIGHT, rMinEyeballBlackPixel);
//printf("rMinEyeballBlackPixelRate; %f\n\n", rMinEyeballBlackPixelRate);
// 统计lMinEyeballImg中的1/2区域内黑像素的比例
rMinEyeballBeta = 0;
rMinEyeballBeta = calMiddleAreaBlackPixRate(subvertProject, &eyeRectTemp, WIDTH, HEIGHT, rEyeCol, rMinEyeballBlackPixel);
//printf("temp:%d\trMinEyeballBeta; %f\n\n", temp, rMinEyeballBeta);
// 判断眼睛睁闭情况
lEyeState = 1; // 左眼状态，默认闭眼
rEyeState = 1; // 右眼状态，默认闭眼
eyeState = 1; // 眼睛综合状态，默认闭眼
if( lMinEyeballBlackPixel > 50)
lEyeState = getEyeState(lMinEyeballRectShape, lMinEyeballBlackPixelRate, lMinEyeballBeta);
else
lEyeState = 1;
if( rMinEyeballBlackPixel > 50)
rEyeState = getEyeState(rMinEyeballRectShape, rMinEyeballBlackPixelRate, rMinEyeballBeta);
else
rEyeState = 1;
(lEyeState + rEyeState) == 2 ? eyeState = 1 : eyeState=0;
// 统计眼睛闭合的次数
if( eyeState == 1 ){
eyeCloseNum ++; // 统计 eyeCloseNum 眼睛闭合次数
eyeCloseDuration ++;
if( globalK == DETECTTIME){
// 检测过程中判断全是闭眼情况，没有睁眼和检测不到人脸的情况
(eyeCloseDuration > maxEyeCloseDuration) ? maxEyeCloseDuration = eyeCloseDuration : maxEyeCloseDuration;
eyeCloseDuration = 0;
}
}
else{
(eyeCloseDuration > maxEyeCloseDuration) ? maxEyeCloseDuration = eyeCloseDuration : maxEyeCloseDuration;
eyeCloseDuration = 0;
}
} // 承接判断是否检测到人脸的if语句
/*
printf("\n************** 眼睛状态 ***************\n");
printf("lEyeState: %d\trEyeState: %d\n", lEyeState, rEyeState);
printf("eyeState: %d\n\n\n\n", eyeState);
*/
// 计时：执行一次循环的时间
stop = clock();
//printf("run time: %f\n", (double)(stop - start) / CLOCKS_PER_SEC);
printf("eyeState: %d\n", eyeState);
// 调整循环变量，进入下一次检测过程
if( globalK == DETECTTIME ){
printf("\nFATIGUETHRESHOLD*****: %d\n", FATIGUETHRESHOLD);
printf("eyeCloseNum: %d\tmaxEyeCloseDuration: %d\n", eyeCloseNum, maxEyeCloseDuration);
printf("failFaceNum: %d\tmaxFailFaceDuration: %d\n", failFaceNum, maxFailFaceDuration);
// 进行疲劳状态的判别
fatigueState = recoFatigueState(FATIGUETHRESHOLD, eyeCloseNum, maxEyeCloseDuration, failFaceNum, maxFailFaceDuration);
if( fatigueState == 1 )
printf("驾驶员处于疲劳驾驶状态\n\n");
else if( fatigueState == 0 )
printf("驾驶员处于正常驾驶状态\n\n");
// 进入下一次检测过程前，将变量清零
globalK = 0;
lEyeState = 1;
rEyeState = 1;
eyeState = 1;
eyeCloseNum = 0;
eyeCloseDuration = 0;
maxEyeCloseDuration = 0;
failFaceNum = 0;
failFaceDuration = 0;
maxFailFaceDuration = 0;
fatigueState = 1;
char c = cvWaitKey(0);
if( c == 27 )
break;
else
continue;
}
} // 承接检测过程的 for 循环
// 释放内存
cvDestroyWindow("分割后的人脸");
cvDestroyWindow("大致的左眼区域");
cvDestroyWindow("大致的右眼区域");
cvDestroyWindow("l_binary");
cvDestroyWindow("r_binary");
cvDestroyWindow("lEyeImgNoEyebrow");
cvDestroyWindow("rEyeImgNoEyebrow");
cvDestroyWindow("lEyeCenter");
cvDestroyWindow("rEyeCenter");
cvDestroyWindow("lEyeballImg");
cvDestroyWindow("rEyeballImg");
cvDestroyWindow("lkai");
cvDestroyWindow("rkai");
cvDestroyWindow("lMinEyeballImg");
cvDestroyWindow("rMinEyeballImg");
cvReleaseMemStorage(&storage);
cvReleaseImage(&eyeImg);
free(horiProject);
free(vertProject);
free(subhoriProject);
free(subvertProject);
return 0;
}
//=========================================
CvRect camKalTrack(IplImage* frame, camshift_kalman_tracker& camKalTrk) {
//=========================================
if (!frame)
printf("Input frame empty!\n");
cvCopy(frame, camKalTrk.image, 0);
cvCvtColor(camKalTrk.image, camKalTrk.hsv, CV_BGR2HSV); // BGR to HSV
if (camKalTrk.trackObject) {
int _vmin = vmin, _vmax = vmax;
cvInRangeS(camKalTrk.hsv, cvScalar(0, smin, MIN(_vmin,_vmax), 0), cvScalar(180, 256, MAX(_vmin,_vmax), 0), camKalTrk.mask); // MASK
cvSplit(camKalTrk.hsv, camKalTrk.hue, 0, 0, 0); // HUE
if (camKalTrk.trackObject < 0) {
float max_val = 0.f;
boundaryCheck(camKalTrk.originBox, frame->width, frame->height);
cvSetImageROI(camKalTrk.hue, camKalTrk.originBox); // for ROI
cvSetImageROI(camKalTrk.mask, camKalTrk.originBox); // for camKalTrk.mask
cvCalcHist(&camKalTrk.hue, camKalTrk.hist, 0, camKalTrk.mask); //
cvGetMinMaxHistValue(camKalTrk.hist, 0, &max_val, 0, 0);
cvConvertScale(camKalTrk.hist->bins, camKalTrk.hist->bins, max_val ? 255. / max_val : 0., 0); // bin [0,255]
cvResetImageROI(camKalTrk.hue); // remove ROI
cvResetImageROI(camKalTrk.mask);
camKalTrk.trackWindow = camKalTrk.originBox;
camKalTrk.trackObject = 1;
camKalTrk.lastpoint = camKalTrk.predictpoint = cvPoint(camKalTrk.trackWindow.x + camKalTrk.trackWindow.width / 2,
camKalTrk.trackWindow.y + camKalTrk.trackWindow.height / 2);
getCurrState(camKalTrk.kalman, camKalTrk.lastpoint, camKalTrk.predictpoint);//input curent state
}
//(x,y,vx,vy),
camKalTrk.prediction = cvKalmanPredict(camKalTrk.kalman, 0);//predicton=kalman->state_post
camKalTrk.predictpoint = cvPoint(cvRound(camKalTrk.prediction->data.fl[0]), cvRound(camKalTrk.prediction->data.fl[1]));
camKalTrk.trackWindow = cvRect(camKalTrk.predictpoint.x - camKalTrk.trackWindow.width / 2, camKalTrk.predictpoint.y
- camKalTrk.trackWindow.height / 2, camKalTrk.trackWindow.width, camKalTrk.trackWindow.height);
camKalTrk.trackWindow = checkRectBoundary(cvRect(0, 0, frame->width, frame->height), camKalTrk.trackWindow);
camKalTrk.searchWindow = cvRect(camKalTrk.trackWindow.x - region, camKalTrk.trackWindow.y - region, camKalTrk.trackWindow.width + 2
* region, camKalTrk.trackWindow.height + 2 * region);
camKalTrk.searchWindow = checkRectBoundary(cvRect(0, 0, frame->width, frame->height), camKalTrk.searchWindow);
cvSetImageROI(camKalTrk.hue, camKalTrk.searchWindow);
cvSetImageROI(camKalTrk.mask, camKalTrk.searchWindow);
cvSetImageROI(camKalTrk.backproject, camKalTrk.searchWindow);
cvCalcBackProject( &camKalTrk.hue, camKalTrk.backproject, camKalTrk.hist ); // back project
cvAnd(camKalTrk.backproject, camKalTrk.mask, camKalTrk.backproject, 0);
camKalTrk.trackWindow = cvRect(region, region, camKalTrk.trackWindow.width, camKalTrk.trackWindow.height);
if (camKalTrk.trackWindow.height > 5 && camKalTrk.trackWindow.width > 5) {
// calling CAMSHIFT
cvCamShift(camKalTrk.backproject, camKalTrk.trackWindow, cvTermCriteria(CV_TERMCRIT_EPS | CV_TERMCRIT_ITER, 10, 1),
&camKalTrk.trackComp, &camKalTrk.trackBox);
/*cvMeanShift( camKalTrk.backproject, camKalTrk.trackWindow,
cvTermCriteria( CV_TERMCRIT_EPS | CV_TERMCRIT_ITER, 10, 1 ),
&camKalTrk.trackComp);*/
}
else {
camKalTrk.trackComp.rect.x = 0;
camKalTrk.trackComp.rect.y = 0;
camKalTrk.trackComp.rect.width = 0;
camKalTrk.trackComp.rect.height = 0;
}
cvResetImageROI(camKalTrk.hue);
cvResetImageROI(camKalTrk.mask);
cvResetImageROI(camKalTrk.backproject);
camKalTrk.trackWindow = camKalTrk.trackComp.rect;
camKalTrk.trackWindow = cvRect(camKalTrk.trackWindow.x + camKalTrk.searchWindow.x, camKalTrk.trackWindow.y
+ camKalTrk.searchWindow.y, camKalTrk.trackWindow.width, camKalTrk.trackWindow.height);
camKalTrk.measurepoint = cvPoint(camKalTrk.trackWindow.x + camKalTrk.trackWindow.width / 2, camKalTrk.trackWindow.y
+ camKalTrk.trackWindow.height / 2);
camKalTrk.realposition->data.fl[0] = camKalTrk.measurepoint.x;
camKalTrk.realposition->data.fl[1] = camKalTrk.measurepoint.y;
camKalTrk.realposition->data.fl[2] = camKalTrk.measurepoint.x - camKalTrk.lastpoint.x;
camKalTrk.realposition->data.fl[3] = camKalTrk.measurepoint.y - camKalTrk.lastpoint.y;
camKalTrk.lastpoint = camKalTrk.measurepoint;//keep the current real position
//measurement x,y
cvMatMulAdd( camKalTrk.kalman->measurement_matrix/*2x4*/, camKalTrk.realposition/*4x1*/,/*measurementstate*/0, camKalTrk.measurement );
cvKalmanCorrect(camKalTrk.kalman, camKalTrk.measurement);
cvRectangle(frame, cvPoint(camKalTrk.trackWindow.x, camKalTrk.trackWindow.y), cvPoint(camKalTrk.trackWindow.x
+ camKalTrk.trackWindow.width, camKalTrk.trackWindow.y + camKalTrk.trackWindow.height), CV_RGB(255,128,0), 4, 8, 0);
}
// set new selection if it exists
if (camKalTrk.selectObject && camKalTrk.selection.width > 0 && camKalTrk.selection.height > 0) {
cvSetImageROI(camKalTrk.image, camKalTrk.selection);
cvXorS(camKalTrk.image, cvScalarAll(255), camKalTrk.image, 0);
cvResetImageROI(camKalTrk.image);
}
return camKalTrk.trackWindow;
}
IplImage* detect_and_draw(IplImage* img, double scale = 1.3)
{
IplImage* img1;
char * str;
static CvScalar colors[] = {
{{0,0,255}}, {{0,128,255}},{{0,255,255}},{{0,255,0}},
{{255,128,0}},{{255,255,0}},{{255,0,0}}, {{255,0,255}}
}; //Just some pretty colors to draw with
// IMAGE PREPARATION:
//
IplImage* gray = cvCreateImage( cvSize(img->width,img->height), 8, 1 );
IplImage* small_img = cvCreateImage(
cvSize( cvRound(img->width/scale), cvRound(img->height/scale)), 8, 1);
cvCvtColor( img, gray, CV_BGR2GRAY );
cvResize( gray, small_img, CV_INTER_LINEAR );
cvEqualizeHist( small_img, small_img );
// DETECT OBJECTS IF ANY
//
cvClearMemStorage( storage );
fprintf(stderr,"size: %d %d\n",cvGetSize(small_img).width,cvGetSize(small_img).height);
CvSeq* objects = cvHaarDetectObjects(
small_img,
cascade,
storage,
1.1,
2,
0 ,
cvSize(35, 35)
);
// LOOP THROUGH FOUND OBJECTS AND DRAW BOXES AROUND THEM
//
// for(int i = 0; i<(objects ? objects->total : 0); i++ )
fprintf(stderr,"size: %d %d\n",cvGetSize(small_img).width,cvGetSize(small_img).height);
if( 0<(objects ? objects->total : 0))
{
CvRect* r = (CvRect*)cvGetSeqElem( objects, 0 );
cvSetImageROI(img,*r);
img1=cvCreateImage(cvSize(r->width,r->height),img->depth,img->nChannels);
cvCopy(img,img1,NULL);
cvRectangle(
img,
cvPoint(r->x,r->y),
cvPoint(r->x+r->width,r->y+r->height),
colors[0]
);
cvResetImageROI(img);
HAND=1;
}
else
{
HAND=0;
img1=cvCreateImage(cvSize(100,100),img->depth,img->nChannels);
}
cvReleaseImage( &gray);
cvReleaseImage( &small_img );
return img1;
}
void GetFrame(void *data,int *pW,int *pH,int *pBpp,int *lvlStat,
int *lvlstatR,int *lvlstatG,int *lvlstatB)
{
int ret = 0;
*pW = qhyusb->QCam.cameraW;
*pH = qhyusb->QCam.cameraH;
*pBpp = qhyusb->QCam.transferBit;
switch(qhyusb->QCam.CAMERA)
{
case DEVICETYPE_QHY5LII:
case DEVICETYPE_QHY5II:
{
while((ret != (qhyusb->QCam.cameraW * qhyusb->QCam.cameraH + 5)) && (qhyusb->liveabort == 0))
{
ret = qhyusb->qhyccd_readUSB2B(qhyusb->QCam.ccd_handle,(unsigned char *)data,qhyusb->QCam.cameraW * qhyusb->QCam.cameraH + 5,1,&qhyusb->QCam.pos);
#ifdef QHYCCD_DEBUG
printf("%d\n",ret);
#endif
}
if(qhyusb->QCam.transferBit == 16 && qhyusb->QCam.CAMERA == DEVICETYPE_QHY5LII)
{
q5lii->SWIFT_MSBLSBQHY5LII((unsigned char *)data);
}
IplImage *cvImg, *cropImg;
cvImg = cvCreateImage(cvSize(qhyusb->QCam.ImgX, qhyusb->QCam.ImgY), qhyusb->QCam.transferBit, 1);
cropImg = cvCreateImage(cvSize(qhyusb->QCam.ShowImgX, qhyusb->QCam.ShowImgY), qhyusb->QCam.transferBit, 1);
cvImg->imageData = (char *)data;
cvSetImageROI(cvImg, cvRect(qhyusb->QCam.ShowImgX_Start, qhyusb->QCam.ShowImgY_Start, qhyusb->QCam.ShowImgX,qhyusb->QCam.ShowImgY));
cvCopy(cvImg, cropImg, NULL);
cvResetImageROI(cvImg);
memcpy(data,cropImg->imageData,cropImg->imageSize);
cvReleaseImage(&cvImg);
cvReleaseImage(&cropImg);
break;
}
case DEVICETYPE_QHY16000:
{
qhyusb->qhyccd_readUSB2B(qhyusb->QCam.ccd_handle,(unsigned char *)data,qhyusb->QCam.cameraW * qhyusb->QCam.cameraH,1,&qhyusb->QCam.pos);
break;
}
case DEVICETYPE_QHY9:
case DEVICETYPE_IC8300:
case DEVICETYPE_QHY11:
case DEVICETYPE_QHY21:
case DEVICETYPE_QHY22:
case DEVICETYPE_QHY23:
case DEVICETYPE_QHY6:
{
qhyusb->qhyccd_readUSB2B(qhyusb->QCam.ccd_handle,(unsigned char *)data,qhyusb->QCam.P_Size,qhyusb->QCam.Total_P,&qhyusb->QCam.pos);
if(qhyusb->QCam.CAMERA == DEVICETYPE_IC8300 || qhyusb->QCam.CAMERA == DEVICETYPE_QHY22 || qhyusb->QCam.CAMERA == DEVICETYPE_QHY21 ||
qhyusb->QCam.CAMERA == DEVICETYPE_QHY23)
{
if(qhyusb->ccdreg.VBIN == 1)
{
ic8300->ConvertIC8300DataBIN11((unsigned char *)data,qhyusb->QCam.cameraW,qhyusb->QCam.cameraH,qhyusb->ccdreg.TopSkipPix);
}
else if(qhyusb->ccdreg.VBIN == 2)
{
ic8300->ConvertIC8300DataBIN22((unsigned char *)data,qhyusb->QCam.cameraW,qhyusb->QCam.cameraH,qhyusb->ccdreg.TopSkipPix);
}
else if(qhyusb->ccdreg.VBIN == 4)
{
ic8300->ConvertIC8300DataBIN44((unsigned char *)data,qhyusb->QCam.cameraW,qhyusb->QCam.cameraH,qhyusb->ccdreg.TopSkipPix);
}
}
else if(qhyusb->QCam.CAMERA == DEVICETYPE_QHY6)
{
if(qhyusb->ccdreg.VBIN == 1)
{
qhy6->ConvertQHY6PRODataBIN11((unsigned char *)data);
}
else if(qhyusb->ccdreg.VBIN == 2)
{
qhy6->ConvertQHY6PRODataBIN22((unsigned char *)data);
}
}
break;
}
}
if(qhyusb->QCam.bin == 22)
{
Bin2x2((unsigned char *)data,qhyusb->QCam.cameraW,qhyusb->QCam.cameraH);
*pW /= 2;
*pH /= 2;
}
}
ReturnType HandsMotionTracking::onExecute()
{
// 영상을 Inport로부터 취득
opros_any *pData = ImageIn.pop();
RawImage result;
//아웃 데이터
std::vector<PositionDataType> data;
if(pData != NULL){
// 포트로 부터 이미지 취득
RawImage Image = ImageIn.getContent(*pData);
RawImageData *RawImage = Image.getImage();
// 현재영상의 크기를 취득
m_in_width = RawImage->getWidth();
m_in_height = RawImage->getHeight();
// 메모리 한번 해제해주고
if(m_image_buff != NULL)
cvReleaseImage(&m_image_buff);
if(m_image_dest != NULL)
cvReleaseImage(&m_image_dest);
if(m_image_dest2 != NULL)
cvReleaseImage(&m_image_dest2);
if(m_image_th != NULL)
cvReleaseImage(&m_image_th);
if(m_image_th2 != NULL)
cvReleaseImage(&m_image_th2);
// 이미지용 메모리 할당
m_image_buff = cvCreateImage(cvSize(m_in_width, m_in_height), IPL_DEPTH_8U, 3);//원본 이미지
m_image_dest = cvCreateImage(cvSize(m_in_width, m_in_height), IPL_DEPTH_8U, 3);
m_image_dest2 = cvCreateImage(cvSize(m_in_width, m_in_height), IPL_DEPTH_8U, 3);
m_image_th = cvCreateImage(cvSize(m_in_width, m_in_height), IPL_DEPTH_8U, 1);//영역 추출 이미지
m_image_th2 = cvCreateImage(cvSize(m_in_width, m_in_height), IPL_DEPTH_8U, 1);//영역 추출 이미지
if(!video_flag)
{
std::string cpath = getProperty("opros.component.dir");
std::string file = getProperty("VideoFile");
if (file == "") file = "sample.avi";
std::string path = cpath + file;
m_video = NULL;
m_video = cvCreateFileCapture(path.c_str()); //비디오
video_flag = true;// 비디오가 계속 새로 재생됨을 방지
}
// 영상에 대한 정보를 확보!memcpy
memcpy(m_image_buff->imageData, RawImage->getData(), RawImage->getSize());
// 출력용
cvCopy(m_image_buff, m_image_dest, 0);
// 색상 분리용 이미지
IplImage* m_image_YCrCb = cvCreateImage(cvGetSize(m_image_buff), IPL_DEPTH_8U, 3);
IplImage* m_Y = cvCreateImage(cvGetSize(m_image_buff), IPL_DEPTH_8U, 1);
IplImage* m_Cr = cvCreateImage(cvGetSize(m_image_buff), IPL_DEPTH_8U, 1);
IplImage* m_Cb = cvCreateImage(cvGetSize(m_image_buff), IPL_DEPTH_8U, 1);
cvCvtColor(m_image_buff, m_image_YCrCb, CV_RGB2YCrCb); //RGB - > YCrCV 변환
cvSplit(m_image_YCrCb, m_Y, m_Cr, m_Cb, NULL); //채널 분리
//추출이 필요한 영역 픽셀 데이터 저장 변수
unsigned char m_Cr_val = 0;
unsigned char m_Cb_val = 0;
// 살색추출
for(int i=0;i<m_image_buff->height;i++)
{
for(int j=0;j<m_image_buff->width;j++)
{
//Cr 영역과 Cb 영역 추출
m_Cr_val = (unsigned char)m_Cr->imageData[i*m_Cr->widthStep+j];
m_Cb_val = (unsigned char)m_Cb->imageData[i*m_Cb->widthStep+j];
//살색에 해당하는 영역인지 검사
if( (77 <= m_Cr_val) && (m_Cr_val <= 127) && (133 <= m_Cb_val) && (m_Cb_val <= 173) )
{
// 살색부분은 하얀색
m_image_buff->imageData[i*m_image_buff->widthStep+j*3+0] = (unsigned char)255;
m_image_buff->imageData[i*m_image_buff->widthStep+j*3+1] = (unsigned char)255;
m_image_buff->imageData[i*m_image_buff->widthStep+j*3+2] = (unsigned char)255;
}
else
{
// 나머지는 검정색
m_image_buff->imageData[i*m_image_buff->widthStep+j*3+0]= 0;
m_image_buff->imageData[i*m_image_buff->widthStep+j*3+1]= 0;
m_image_buff->imageData[i*m_image_buff->widthStep+j*3+2]= 0;
}
}
}
//살색 추출한 영상을 이진화
cvCvtColor(m_image_buff, m_image_th, CV_RGB2GRAY);
//잡영 제거를 위한 연산
cvDilate (m_image_th, m_image_th, NULL, 2);//팽창
cvErode (m_image_th, m_image_th, NULL, 2);//침식
//변수 및 이미지 메모리 초기화
int temp_num = 0;
int StartX , StartY, EndX , EndY;
int nNumber = 0;
m_nThreshold = 100;
if( m_rec_out != NULL )
{
delete m_rec_out;
m_rec_out = NULL;
m_nBlobs_out = _DEF_MAX_BLOBS;
}
else
{
m_rec_out = NULL;
m_nBlobs_out = _DEF_MAX_BLOBS;
}
if( m_image_th2 != NULL )
cvReleaseImage( &m_image_th2 );
//레이블링 할 영상 따로 생성
m_image_th2 = cvCloneImage( m_image_th );
//레이블링 할 이미지의 크기 저장
int nWidth = m_image_th2->width;
int nHeight = m_image_th2->height;
//해당 영상 크기만큼 버프 설정
unsigned char* tmpBuf = new unsigned char [nWidth * nHeight];
for(int j=0; j<nHeight ;j++)
for(int i=0; i<nWidth ;i++)
//전 픽셀 순회
tmpBuf[j*nWidth+i] = (unsigned char)m_image_th2->imageData[j*m_image_th2->widthStep+i];
////// 레이블링을 위한 포인트 초기화
m_vPoint_out = new Visited [nWidth * nHeight];
for(int nY = 0; nY < nHeight; nY++)
{
for(int nX = 0; nX < nWidth; nX++)
{
m_vPoint_out[nY * nWidth + nX].bVisitedFlag = FALSE;
m_vPoint_out[nY * nWidth + nX].ptReturnPoint.x = nX;
m_vPoint_out[nY * nWidth + nX].ptReturnPoint.y = nY;
}
}
////// 레이블링 수행
for(int nY = 0; nY < nHeight; nY++)
{
for(int nX = 0; nX < nWidth; nX++)
{
if(tmpBuf[nY * nWidth + nX] == 255) // Is this a new component?, 255 == Object
{
temp_num++;
tmpBuf[nY * nWidth + nX] = temp_num;
StartX = nX, StartY = nY, EndX = nX, EndY= nY;
__NRFIndNeighbor(tmpBuf, nWidth, nHeight, nX, nY, &StartX, &StartY, &EndX, &EndY, m_vPoint_out);
if(__Area(tmpBuf, StartX, StartY, EndX, EndY, nWidth, temp_num) < m_nThreshold)
{
for(int k = StartY; k <= EndY; k++)
{
for(int l = StartX; l <= EndX; l++)
{
if(tmpBuf[k * nWidth + l] == temp_num)
tmpBuf[k * nWidth + l] = 0;
}
}
--temp_num;
if(temp_num > 250)
temp_num = 0;
}
}
}
}
// 포인트 메모리 해제
delete m_vPoint_out;
//결과 보존
nNumber = temp_num;
//레이블링 수만큼 렉트 생성
if( nNumber != _DEF_MAX_BLOBS )
m_rec_out = new CvRect [nNumber];
//렉트 만들기
if( nNumber != 0 )
DetectLabelingRegion(nNumber, tmpBuf, nWidth, nHeight,m_rec_out);
for(int j=0; j<nHeight; j++)
for(int i=0; i<nWidth ; i++)
m_image_th2->imageData[j*m_image_th2->widthStep+i] = tmpBuf[j*nWidth+i];
delete tmpBuf;
//레이블링 수 보존
m_nBlobs_out = nNumber;
//레이블링 영역 거르기
int nMaxWidth = m_in_height * 9 / 10; // 영상 가로 전체 크기의 90% 이상인 레이블은 제거
int nMaxHeight = m_in_width * 9 / 10; // 영상 세로 전체 크기의 90% 이상인 레이블은 제거
//최소영역과 최대영역 지정- 화면 크기에 영향 받음..
_BlobSmallSizeConstraint( 5, 150, m_rec_out, &m_nBlobs_out);
_BlobBigSizeConstraint(nMaxWidth, nMaxHeight,m_rec_out, &m_nBlobs_out);
//앞으로 쓸 메모리 등록
storage1 = cvCreateMemStorage(0);
storage2 = cvCreateMemStorage(0);
//변수 초기화
CvPoint point;
CvSeq* seq[10];
CvSeq* hull;
CvPoint end_pt;
CvPoint center;
//내보낼 데이터 초기화
outData[0].x = 0, outData[0].y = 0;
outData[1].x = 0, outData[1].y = 0;
outData[2].x = 0, outData[2].y = 0;
int num = 0;
int temp_x = 0;
int temp_y = 0;
int rect = 0;
//만일을 대비하여 준비한 시퀸스 배열의 크기를 초과하지 않도록 조절
//일단 한곳에서만 영상이 나오도록 조절..
if(m_nBlobs_out > 1)
{
m_nBlobs_out = 1;
}
//레이블링 영역 내의 처리 시작
for( int i=0; i < m_nBlobs_out; i++ )
{
//사각형 그리기에 필요한 두점 저장
CvPoint pt1 = cvPoint( m_rec_out[i].x, m_rec_out[i].y );
CvPoint pt2 = cvPoint( pt1.x + m_rec_out[i].width,pt1.y + m_rec_out[i].height );
// 컬러값 설정
CvScalar color = cvScalar( 0, 0, 255 );
//레이블 사각형 그리기 - 확인용
//cvDrawRect( m_image_dest, pt1, pt2, color);
//레이블을 관심영역으로 지정할 이미지 생성
temp_mask = cvCreateImage(cvSize(m_rec_out[i].width, m_rec_out[i].height),8,1);
temp_mask2 = cvCreateImage(cvSize(m_rec_out[i].width, m_rec_out[i].height),8,1);
//관심영역 지정
cvSetImageROI(m_image_th, m_rec_out[i]);
//관심영역 추출
cvCopy(m_image_th, temp_mask, 0);
//관심영역 해제
cvResetImageROI(m_image_th);
//관심영역 내의 오브젝트 처리를 위한 시퀸스 생성
seq[i] = cvCreateSeq(CV_SEQ_KIND_GENERIC | CV_32SC2,sizeof(CvContour),sizeof(CvPoint), storage1);
//관심영역에서 추출한이미지의 흰색 픽셀값으로 시퀸스 생성
for(int j =0; j < temp_mask ->height ; j++)
{
for(int k = 0; k < temp_mask ->width; k++)
{
if((unsigned char)temp_mask->imageData[j*temp_mask->widthStep+k] == 255)
{
point.x = k; //흰색 픽셀 x좌표 저장
point.y = j; //흰색 픽셀 y좌표 저장
cvSeqPush(seq[i], &point); //시퀸스 구조체에 해당 좌표 삽입
temp_x += point.x; //좌표 누적
temp_y += point.y; //좌표 누적
num++; //픽셀 수 카운트
}
}
}
//좌표 초기화
point.x = 0;
point.y = 0;
end_pt.x = 0;
end_pt.y = 0;
center.x = 0;
center.y = 0;
CvPoint dist_pt; //중심점과의 최대거리를 찾을 컨백스헐 저장
double fMaxDist = 0; //중심점과의 최대거리 저장
double fDist = 0; //거리계산에 사용
//중심점 찾기 - 픽셀의 평균값 찾기
if(num != 0)
{
center.x = (int)temp_x/num; //평균 좌표값 구하기
center.y = (int)temp_y/num; //평균 좌표값 구하기
}
//관심영역 설정
cvSetImageROI(m_image_dest, m_rec_out[i]);
/////////컨백스헐 그리기////////
if(seq[i]->total !=0)
{
//컨백스헐 구하기
hull = cvConvexHull2(seq[i], 0, CV_COUNTER_CLOCKWISE, 0);
point = **CV_GET_SEQ_ELEM(CvPoint*, hull,hull->total-1);
//구한 컨백스헐 라인으로 그리기
for(int x = 0; x < hull->total; x++)
{
CvPoint hull_pt = **CV_GET_SEQ_ELEM(CvPoint*, hull,x);
//컨백스헐 라인 그리기
//cvLine(m_image_dest, point, hull_pt, CV_RGB(255, 255, 0 ),2, 8);
point = hull_pt;
//최대 거리 구하기
dist_pt = **CV_GET_SEQ_ELEM(CvPoint*, hull,x);
fDist = sqrt((double)((center.x - dist_pt.x) * (center.x - dist_pt.x)
+ (center.y - dist_pt.y) * (center.y - dist_pt.y)));
if(fDist > fMaxDist)
{
max_pt = dist_pt;
fMaxDist = fDist;
}
}
}
//중심점그리기
cvCircle(m_image_dest,center,5, CV_RGB(0,0,255), 5);
//내보낼 중심점 데이터 저장
outData[0].x = center.x;
outData[0].y = center.y;
////////마스크 만들기///////
//중심점을 기준으로 그릴 마스크 이미지 생성
circle_mask = cvCreateImage(cvGetSize(temp_mask), 8, 1);
//바탕은 검은색으로
cvSetZero(circle_mask);
//흰색 원 - 손 영상과의 연산을 위해 바이너리 이미지에 그리기
int radi = (int)m_rec_out[i].height/2.9; // 원 크기 수동조절..
//흰색 원과 흰색 네모로 구성된 마스크 영상 생성을 위한 그리기
cvCircle(circle_mask, center, radi, CV_RGB(255,255,255),CV_FILLED);
cvDrawRect(circle_mask, cvPoint(center.x - radi, center.y),cvPoint(center.x + radi, pt2.y),
CV_RGB(255,255,255),CV_FILLED);
//마스크 추출
cvSub(temp_mask, circle_mask, temp_mask, 0);
///////관심영역 레이블링 - 손가락 끝 추출//////
//변수 및 이미지 메모리 초기화
int temp_num_in = 0;
int StartX_in , StartY_in, EndX_in , EndY_in;
int nNumber_in = 0;
m_nThreshold_in = 10;
if( m_rec_in != NULL )
{
delete m_rec_in;
m_rec_in = NULL;
m_nBlobs_in = _DEF_MAX_BLOBS;
}
else
{
m_rec_in = NULL;
m_nBlobs_in = _DEF_MAX_BLOBS;
}
if( temp_mask2 != NULL )
cvReleaseImage( &temp_mask2 );
temp_mask2 = cvCloneImage( temp_mask );
//들어온 이미지의 크기 저장
int nWidth = temp_mask2->width;
int nHeight = temp_mask2->height;
//영상 크기만큼 버프 설정
unsigned char* tmpBuf_in = new unsigned char [nWidth * nHeight];
for(int j=0; j<nHeight ;j++)
for(int i=0; i<nWidth ;i++)
//전 픽셀 순회
tmpBuf_in[j*nWidth+i] = (unsigned char)temp_mask2->imageData[j*temp_mask2->widthStep+i];
/////// 레이블링을 위한 포인트 초기화 ////////
m_vPoint_in = new Visited [nWidth * nHeight];
for(int nY = 0; nY < nHeight; nY++)
{
for(int nX = 0; nX < nWidth; nX++)
{
m_vPoint_in[nY * nWidth + nX].bVisitedFlag = FALSE;
m_vPoint_in[nY * nWidth + nX].ptReturnPoint.x = nX;
m_vPoint_in[nY * nWidth + nX].ptReturnPoint.y = nY;
}
}
////레이블링 수행
for(int nY = 0; nY < nHeight; nY++)
{
for(int nX = 0; nX < nWidth; nX++)
{
if(tmpBuf_in[nY * nWidth + nX] == 255) // Is this a new component?, 255 == Object
{
temp_num_in++;
tmpBuf_in[nY * nWidth + nX] = temp_num_in;
StartX_in = nX, StartY_in = nY, EndX_in = nX, EndY_in= nY;
__NRFIndNeighbor(tmpBuf_in, nWidth, nHeight, nX, nY,
&StartX_in, &StartY_in, &EndX_in, &EndY_in,m_vPoint_in);
if(__Area(tmpBuf_in, StartX_in, StartY_in, EndX_in, EndY_in, nWidth, temp_num_in) < m_nThreshold_in)
{
for(int k = StartY_in; k <= EndY_in; k++)
{
for(int l = StartX_in; l <= EndX_in; l++)
{
if(tmpBuf_in[k * nWidth + l] == temp_num_in)
tmpBuf_in[k * nWidth + l] = 0;
}
}
--temp_num_in;
if(temp_num_in > 250)
temp_num_in = 0;
}
}
}
}
// 포인트 메모리 해제
delete m_vPoint_in;
//레이블링 수 보존
nNumber_in = temp_num_in;
if( nNumber_in != _DEF_MAX_BLOBS )
m_rec_in = new CvRect [nNumber_in];
if( nNumber_in != 0 )
DetectLabelingRegion(nNumber_in, tmpBuf_in, nWidth, nHeight,m_rec_in);
for(int j=0; j<nHeight; j++)
for(int i=0; i<nWidth ; i++)
temp_mask2->imageData[j*temp_mask2->widthStep+i] = tmpBuf_in[j*nWidth+i];
delete tmpBuf_in;
m_nBlobs_in = nNumber_in;
//최소영역과 최대영역 설정
_BlobSmallSizeConstraint( 5, 5, m_rec_in, &m_nBlobs_in);
_BlobBigSizeConstraint( temp_mask2->width, temp_mask2->height,m_rec_in, &m_nBlobs_in);
//선언 및 초기화
CvPoint center_in;
CvPoint point_in;
point_in.x = 0;
point_in.y = 0;
center_in.x = 0;
center_in.x = 0;
CvSeq* seq_in[20];
//준비한 시퀸스 배열크기를 초과하지 않도록 조절
if(m_nBlobs_in > 20)
{
m_nBlobs_in =20;
}
for( int ni =0; ni < m_nBlobs_in; ni++ )
{
//사각형 그리기에 필요한 두 점 저장
CvPoint pt1 = cvPoint( m_rec_in[ni].x, m_rec_in[ni].y );
CvPoint pt2 = cvPoint( pt1.x + m_rec_in[ni].width,pt1.y + m_rec_in[ni].height );
//색상값 설정
CvScalar color = cvScalar( 255,0 , 255 );
//레이블 사각형 그리기
//cvDrawRect( m_image_dest, pt1, pt2, color);
//처리할 손끝 마스크 생성할 메모리 할당
in_mask = cvCreateImage(cvSize(m_rec_in[ni].width, m_rec_in[ni].height),8,1);
//관심영역 설정
cvSetImageROI(temp_mask, m_rec_in[ni]);
//필요한 영역 복사
cvCopy(temp_mask, in_mask, 0);
//관심영역 해제
cvResetImageROI(temp_mask);
//관심영역 내의 오브젝트 처리를 위한 시퀸스 생성
seq_in[ni] = cvCreateSeq(CV_SEQ_KIND_GENERIC | CV_32SC2,sizeof(CvContour),sizeof(CvPoint), storage2);
//초기화
int temp_x_in = 0;
int temp_y_in = 0;
int num_in = 0;
//관심영역에서 추출한이미지의 흰색 픽셀값으로 시퀸스 생성
for(int j =0; j < in_mask ->height ; j++)
{
for(int k = 0; k < in_mask ->width; k++)
{
if((unsigned char)in_mask->imageData[j*in_mask->widthStep+k] == 255)
{
point_in.x = k; //흰색 픽셀 x좌표 저장
point_in.y = j; //흰색 픽셀 y좌표 저장
cvSeqPush(seq_in[ni], &point_in); //시퀸스 구조체에 해당 좌표 삽입
temp_x_in += point_in.x; //좌표 누적
temp_y_in += point_in.y; //좌표 누적
num_in++; //픽셀 수 카운트
}
}
}
//초기화
max_pt_in.x = 0;
max_pt_in.y = 0;
double fMaxDist_in = 0;
double fDist_in = 0;
//중심점 찾기 - 픽셀의 평균값 찾기
if(num_in != 0)
{
center_in.x = (int)temp_x_in/num_in + pt1.x; //평균 좌표값 구하기
center_in.y = (int)temp_y_in/num_in + pt1.y; //평균 좌표값 구하기
}
//우선 끝점이 2개일때만..
if(m_nBlobs_in == 2)
{
//초기화
finger_pt[ni].x = NULL;
finger_pt[ni].y = NULL;
finger_pt[ni].x = NULL;
finger_pt[ni].y = NULL;
if(seq_in[ni]->total !=0)
{
//컨백스헐 구하기 - 윤곽선의 좌표 정보 겟
CvSeq* hull_in = cvConvexHull2(seq_in[ni], 0, CV_COUNTER_CLOCKWISE, 0);
//point_in = **CV_GET_SEQ_ELEM(CvPoint*, hull_in,hull_in->total-1);
//구한 컨백스헐 라인으로 그리기
for(int nx = 0; nx < hull_in->total; nx++)
{
CvPoint hull_pt_in = **CV_GET_SEQ_ELEM(CvPoint*, hull_in,nx);
hull_pt_in.x = hull_pt_in.x + pt1.x;
hull_pt_in.y = hull_pt_in.y + pt1.y;
//중심점과 해당영역의 컨백스 헐 지점간의 거리 계산
fDist_in = sqrt((double)((center.x - hull_pt_in.x) * (center.x - hull_pt_in.x)
+ (center.y - hull_pt_in.y) * (center.y - hull_pt_in.y)));
//거리가 먼 점 찾기
if(fDist_in > fMaxDist_in)
{
max_pt_in = hull_pt_in;
fMaxDist_in = fDist_in;
}
}
}
//최대점 보존
finger_pt[ni].x = max_pt_in.x ;
finger_pt[ni].y = max_pt_in.y ;
//관심영역 해제할 경우의 값으로 보정
finger_pt[ni].x = finger_pt[ni].x + m_rec_out[i].x;
finger_pt[ni].y = finger_pt[ni].y + m_rec_out[i].y;
}
void FindArenaObjects(IplImage* Image, CvFont Font, _ArenaObject *pRamp, _ArenaObject* pPlatform, _ArenaObject* pRightPit, _ArenaObject* pLeftPit, _Robot* pRobot)
{
IplImage* ImageCopy = cvCloneImage(Image);
IplImage* ImageCopy2 = cvCloneImage(Image);
SelectionNumber = 0;
Select_Object = 0;
int PrevSelectionNumber = -1;
cvNamedWindow("Arena");
cvShowImage("Arena", ImageCopy);
cvSetMouseCallback("Arena", OnMouse);
while(SelectionNumber < 6 && cvWaitKey(10) != 27)
{
if(SelectionNumber - PrevSelectionNumber > 0)
{
PrevSelectionNumber = SelectionNumber;
cvCopyImage(Image, ImageCopy);
switch(SelectionNumber)
{
case 0:
cvPutText(ImageCopy, "Select Temp Ramp", cvPoint(0, 20), &Font, cvScalarAll(255));
break;
case 1:
if(pRamp)
{
pRamp->BoundingRect = Selection;
pRamp->Center = cvPoint(pRamp->BoundingRect.x + pRamp->BoundingRect.width/2, pRamp->BoundingRect.y + pRamp->BoundingRect.height/2);
}
cvPutText(ImageCopy, "Select Temp Platform", cvPoint(0, 20), &Font, cvScalarAll(255));
break;
case 2:
if(pPlatform)
{
pPlatform->BoundingRect = Selection;
pPlatform->Center = cvPoint(pPlatform->BoundingRect.x + pPlatform->BoundingRect.width/2, pPlatform->BoundingRect.y + pPlatform->BoundingRect.height/2);
}
cvPutText(ImageCopy, "Select Right Pit", cvPoint(0, 20), &Font, cvScalarAll(255));
break;
case 3:
if(pRightPit)
{
pRightPit->BoundingRect = Selection;
pRightPit->Center = cvPoint(pRightPit->BoundingRect.x + pRightPit->BoundingRect.width/2, pRightPit->BoundingRect.y + pRightPit->BoundingRect.height/2);
}
cvPutText(ImageCopy, "Select Left Pit", cvPoint(0, 20), &Font, cvScalarAll(255));
break;
case 4:
if(pLeftPit)
{
pLeftPit->BoundingRect = Selection;
pLeftPit->Center = cvPoint(pLeftPit->BoundingRect.x + pLeftPit->BoundingRect.width/2, pLeftPit->BoundingRect.y + pLeftPit->BoundingRect.height/2);
}
cvPutText(ImageCopy, "Select Robot", cvPoint(0, 20), &Font, cvScalarAll(255));
break;
case 5:
if(pRobot)
{
pRobot->BoundingRect = Selection;
}
cvPutText(ImageCopy, "Select Robot Patch", cvPoint(0, 20), &Font, cvScalarAll(255));
break;
case 6:
if(pRobot)
{
pRobot->Patch = Selection;
pRobot->PatchCenter = cvPoint(pRobot->Patch.x + pRobot->Patch.width/2, pRobot->Patch.y + pRobot->Patch.height/2);
pRobot->Updated = 1;
}
cvPutText(ImageCopy, "Press Escape to Continue...", cvPoint(0, 20), &Font, cvScalarAll(255));
break;
default:
break;
}
cvShowImage("Arena", ImageCopy);
}
if(Select_Object && Selection.width > 0 && Selection.height > 0 )
{
cvCopyImage(ImageCopy, ImageCopy2);
cvSetImageROI(ImageCopy2, Selection);
cvXorS(ImageCopy2, cvScalarAll(255), ImageCopy2);
cvResetImageROI(ImageCopy2);
cvShowImage("Arena", ImageCopy2);
}
}
cvReleaseImage(&ImageCopy);
cvReleaseImage(&ImageCopy2);
cvDestroyWindow("Arena");
}
double catcierge_haar_matcher_match(void *octx,
IplImage *img, match_result_t *result, int save_steps)
{
catcierge_haar_matcher_t *ctx = (catcierge_haar_matcher_t *)octx;
catcierge_haar_matcher_args_t *args = ctx->args;
double ret = HAAR_SUCCESS_NO_HEAD;
IplImage *img_eq = NULL;
IplImage *img_gray = NULL;
IplImage *tmp = NULL;
IplImage *thr_img = NULL;
CvSize max_size;
CvSize min_size;
int cat_head_found = 0;
assert(ctx);
assert(ctx->args);
assert(result);
min_size.width = args->min_width;
min_size.height = args->min_height;
max_size.width = 0;
max_size.height = 0;
result->step_img_count = 0;
result->description[0] = '\0';
// Make gray scale if needed.
if (img->nChannels != 1)
{
tmp = cvCreateImage(cvGetSize(img), 8, 1);
cvCvtColor(img, tmp, CV_BGR2GRAY);
img_gray = tmp;
}
else
{
img_gray = img;
}
if (result->direction)
{
result->direction = MATCH_DIR_UNKNOWN;
}
// Equalize histogram.
if (args->eq_histogram)
{
img_eq = cvCreateImage(cvGetSize(img), 8, 1);
cvEqualizeHist(img_gray, img_eq);
}
else
{
img_eq = img_gray;
}
catcierge_haar_matcher_save_step_image(ctx,
img_eq, result, "gray", "Grayscale original", save_steps);
result->rect_count = MAX_MATCH_RECTS;
if (cv2CascadeClassifier_detectMultiScale(ctx->cascade,
img_eq, result->match_rects, &result->rect_count,
1.1, 3, CV_HAAR_SCALE_IMAGE, &min_size, &max_size))
{
ret = -1.0;
goto fail;
}
if (ctx->super.debug) printf("Rect count: %d\n", (int)result->rect_count);
cat_head_found = (result->rect_count > 0);
// Even if we don't find a face we count it as a success.
// Only when a prey is found we consider it a fail.
// Unless args->no_match_is_fail is set.
if (args->no_match_is_fail)
{
// Any return value above 0.0 is considered
// a success. Just so we can distinguish the types of successes.
ret = cat_head_found ?
HAAR_SUCCESS_NO_HEAD_IS_FAIL : HAAR_FAIL;
}
if (cat_head_found)
{
int inverted;
int flags;
CvRect roi;
find_prey_f find_prey = NULL;
// Only use the lower part of the region of interest
// and extend it some towards the "outside" for better result.
// (We only use the first haar cascade match).
roi = result->match_rects[0];
// If we're saving steps, include the original haar cascade
// match rectangle image.
if (save_steps)
{
cvSetImageROI(img_eq, roi);
catcierge_haar_matcher_save_step_image(ctx,
img_eq, result, "haar_roi", "Haar match", save_steps);
}
catcierge_haar_matcher_calculate_roi(ctx, &roi);
cvSetImageROI(img_eq, roi);
catcierge_haar_matcher_save_step_image(ctx,
img_eq, result, "roi", "Cropped region of interest", save_steps);
if (args->prey_method == PREY_METHOD_ADAPTIVE)
{
inverted = 1;
flags = CV_THRESH_BINARY_INV | CV_THRESH_OTSU;
find_prey = catcierge_haar_matcher_find_prey_adaptive;
}
else
{
inverted = 0;
flags = CV_THRESH_BINARY | CV_THRESH_OTSU;
find_prey = catcierge_haar_matcher_find_prey;
}
// Both "find prey" and "guess direction" needs
// a thresholded image, so perform it before calling those.
thr_img = cvCreateImage(cvGetSize(img_eq), 8, 1);
cvThreshold(img_eq, thr_img, 0, 255, flags);
if (ctx->super.debug) cvShowImage("Haar image binary", thr_img);
catcierge_haar_matcher_save_step_image(ctx,
thr_img, result, "thresh", "Global thresholded binary image", save_steps);
result->direction = catcierge_haar_guess_direction(ctx, thr_img, inverted);
if (ctx->super.debug) printf("Direction: %s\n", catcierge_get_direction_str(result->direction));
// Don't bother looking for prey when the cat is going outside.
if ((result->direction) == MATCH_DIR_OUT)
{
if (ctx->super.debug) printf("Skipping prey detection!\n");
snprintf(result->description, sizeof(result->description) - 1,
"Skipped prey detection when going out");
goto done;
}
// Note that thr_img will be modified.
if (find_prey(ctx, img_eq, thr_img, result, save_steps))
{
if (ctx->super.debug) printf("Found prey!\n");
ret = HAAR_FAIL;
snprintf(result->description, sizeof(result->description) - 1,
"Prey detected");
}
else
{
ret = HAAR_SUCCESS;
snprintf(result->description, sizeof(result->description) - 1,
"No prey detected");
}
}
else
{
snprintf(result->description, sizeof(result->description) - 1,
"%sNo cat head detected",
(ret == HAAR_SUCCESS_NO_HEAD_IS_FAIL) ? "Fail ": "");
}
done:
fail:
cvResetImageROI(img);
if (args->eq_histogram)
{
cvReleaseImage(&img_eq);
}
if (tmp)
{
cvReleaseImage(&tmp);
}
if (thr_img)
{
cvReleaseImage(&thr_img);
}
result->result = ret;
result->success = (result->result > 0.0);
return ret;
}
int catcierge_haar_matcher_find_prey_adaptive(catcierge_haar_matcher_t *ctx,
IplImage *img, IplImage *inv_thr_img,
match_result_t *result, int save_steps)
{
IplImage *inv_adpthr_img = NULL;
IplImage *inv_combined = NULL;
IplImage *open_combined = NULL;
IplImage *dilate_combined = NULL;
CvSeq *contours = NULL;
size_t contour_count = 0;
CvSize img_size;
assert(ctx);
assert(img);
assert(ctx->args);
img_size = cvGetSize(img);
// We expect to be given an inverted global thresholded image (inv_thr_img)
// that contains the rough cat profile.
// Do an inverted adaptive threshold of the original image as well.
// This brings out small details such as a mouse tail that fades
// into the background during a global threshold.
inv_adpthr_img = cvCreateImage(img_size, 8, 1);
cvAdaptiveThreshold(img, inv_adpthr_img, 255,
CV_ADAPTIVE_THRESH_GAUSSIAN_C, CV_THRESH_BINARY_INV, 11, 5);
catcierge_haar_matcher_save_step_image(ctx,
inv_adpthr_img, result, "adp_thresh", "Inverted adaptive threshold", save_steps);
// Now we can combine the two thresholded images into one.
inv_combined = cvCreateImage(img_size, 8, 1);
cvAdd(inv_thr_img, inv_adpthr_img, inv_combined, NULL);
catcierge_haar_matcher_save_step_image(ctx,
inv_combined, result, "inv_combined", "Combined global and adaptive threshold", save_steps);
// Get rid of noise from the adaptive threshold.
open_combined = cvCreateImage(img_size, 8, 1);
cvMorphologyEx(inv_combined, open_combined, NULL, ctx->kernel2x2, CV_MOP_OPEN, 2);
catcierge_haar_matcher_save_step_image(ctx,
open_combined, result, "opened", "Opened image", save_steps);
dilate_combined = cvCreateImage(img_size, 8, 1);
cvDilate(open_combined, dilate_combined, ctx->kernel3x3, 3);
catcierge_haar_matcher_save_step_image(ctx,
dilate_combined, result, "dilated", "Dilated image", save_steps);
// Invert back the result so the background is white again.
cvNot(dilate_combined, dilate_combined);
catcierge_haar_matcher_save_step_image(ctx,
dilate_combined, result, "combined", "Combined binary image", save_steps);
cvFindContours(dilate_combined, ctx->storage, &contours,
sizeof(CvContour), CV_RETR_LIST, CV_CHAIN_APPROX_NONE, cvPoint(0, 0));
// If we get more than 1 contour we count it as a prey.
contour_count = catcierge_haar_matcher_count_contours(ctx, contours);
if (save_steps)
{
IplImage *img_contour = cvCloneImage(img);
IplImage *img_final_color = NULL;
CvScalar color;
cvDrawContours(img_contour, contours, cvScalarAll(255), cvScalarAll(0), 1, 1, 8, cvPoint(0, 0));
catcierge_haar_matcher_save_step_image(ctx,
img_contour, result, "contours", "Background contours", save_steps);
// Draw a final color combined image with the Haar detection + contour.
cvResetImageROI(img_contour);
img_final_color = cvCreateImage(cvGetSize(img_contour), 8, 3);
cvCvtColor(img_contour, img_final_color, CV_GRAY2BGR);
color = (contour_count > 1) ? CV_RGB(255, 0, 0) : CV_RGB(0, 255, 0);
cvRectangleR(img_final_color, result->match_rects[0], color, 2, 8, 0);
catcierge_haar_matcher_save_step_image(ctx,
img_final_color, result, "final", "Final image", save_steps);
cvReleaseImage(&img_contour);
cvReleaseImage(&img_final_color);
}
cvReleaseImage(&inv_adpthr_img);
cvReleaseImage(&inv_combined);
cvReleaseImage(&open_combined);
cvReleaseImage(&dilate_combined);
return (contour_count > 1);
}
int WindscreenLocator::locateLeftRight()
{
CvRect roiRect = cvRect(0, imgGradH->height / 3, imgGradH->width, imgGradH->height * 0.9 - imgGradH->height/3);
thresholdGrad = max(averageGrad, 1.0 ) * 2.5;
cvSetImageROI(imgGradV, roiRect);
histoStat(imgGradV, vertical2X, NULL, thresholdGrad, 0);
cvResetImageROI(imgGradV);
cvSetImageROI(imgGradH, roiRect);
histoStat(imgGradH, horizon2X, NULL, thresholdGrad, 0);
cvResetImageROI(imgGradH);
int margin = 0.04 * imgGrad->width;
memset(vertical2X, 0, sizeof(double) * margin);
memset(vertical2X + imgGrad->width - margin, 0, sizeof(double) * margin);
histoSmooth(vertical2X, imgGrad->width, 5);
histoSmooth(vertical2X, imgGrad->width, 10);
histoSmooth(vertical2X, imgGrad->width, 20);
histoSmooth(horizon2X, imgGrad->width, 5);
histoSmooth(horizon2X, imgGrad->width, 10);
histoSmooth(horizon2X, imgGrad->width, 40);
// 计算车辆左右边界
int topList[10];
int topNr = 3;
histoCrest(vertical2X, imgGrad->width, topList, topNr);
if(!plate->isValid()){
if(topNr >= 2){
int a = min(topList[0], topList[1]);
int b = max(topList[0], topList[1]);
pdebug("horizon=%f\n", horizon2X[(b+a) / 2]);
pdebug("bike Width = %d, lpWIdth=%f\n", b - a, plate->defaultWidth *2);
double f = isDay(imgGray) ? 0.09 : 0.07;
if(horizon2X[(b+a) / 2] > imgRGB->height * (0.9 - 1.0 / 3) * f){
}else if((double)(b - a) < plate->defaultWidth * 2){
if(b - a < plate->defaultWidth * 1.5){
int ext = -(b - a - plate->defaultWidth * 1.5) / 2;
a = max((a - ext), 0);
b = min((b + ext), (imgGrad->width - 1));
}
if(existCrest(horizon2X, imgGrad->width, a, b, 0.9) != 0){
return -1;
}
}else if((double)(b - a) < plate->defaultWidth * 3.2 && b < imgRGB->width * 0.7){
if(existCrest(horizon2X, imgGrad->width, a, b, 0.8) != 0){
CvRect rect;
double shrunkRatio = imgRGB->width / double(imgOrigin->width);
rect.x = a;
rect.width = b - a;
rect.y = imgRGB->height * 0.25;
rect.height = imgRGB->height * (0.3 - 0.25);
rect = rectScale(rect, 1 / shrunkRatio, cvSize(imgOrigin->width, imgOrigin->height));
vector<FaceInfo> faceVec;
faceDetector->detectTriWheelMotor(imgOrigin, rect, plate->defaultWidth, faceVec);
if(faceVec.size() < 1){
return -1;
}
bool isTriWheel = true;
for(size_t i = 0; i < faceVec.size(); i++){
int mid = faceVec[i].getMiddlePoint().x;
if(mid < rect.x + 0.4 * rect.width || mid > rect.x + 0.6 * rect.width){
isTriWheel = false;
pdebug("face pos=%d %d\n", faceVec[i].getMiddlePoint().x, faceVec[i].getMiddlePoint().y);
break;
}
}
if(isTriWheel)
return -1;
}
}
}
}
int intervalA;
int intervalB;
histoSegment(horizon2X, imgGrad->width, histoSegmentRatio(averageGrad), intervalA, intervalB);
judgeVerticalBorder(imgGrad->width, topList, topNr, intervalA, intervalB, winLeft, winRight);
return 0;
}
int main(int argc,char ** argv){
int key;
int w,h;
IplImage* img,*img1;
CvCapture* capture = NULL;
CvCapture* cap2=NULL;
CvRect pr;
CvRect * r;
w=150;
h=100;
int start=100;
if(NULL==(capture = cvCaptureFromCAM(0))){
printf("\nError on cvCaptureFromCAM");
return -1;
}
/*if(NULL==(cap2 = cvCaptureFromAVI("../TestingSData/infile.ogv"))){
printf("\nError on cvCaptureFromCAM");
return -1;
}*/
cvNamedWindow("Capture", CV_WINDOW_AUTOSIZE);
cvNamedWindow("Captured",CV_WINDOW_AUTOSIZE);
cvMoveWindow("Capture", 550, 250);
cvSetCaptureProperty(capture,CV_CAP_PROP_FRAME_WIDTH,640);
cvSetCaptureProperty(capture,CV_CAP_PROP_FRAME_HEIGHT,480);
for(;;){
if(NULL==(img=cvQueryFrame(capture))){
printf("\nError on cvQueryFrame");
break;
}
pr=cvRect(start,start,start+w,start+h);
r=≺
img1=cvCreateImage(cvSize(r->width,r->height),img->depth,img->nChannels);
cvSetImageROI(img,*r);
cvCopy(img,img1,NULL);
cvResetImageROI(img);
cvRectangle(
img,
cvPoint(r->x,r->y),
cvPoint(r->x+r->width,r->y+r->height),
colors[0]
);
cvShowImage("Capture",img);
cvShowImage("Captured",img1);
key = cvWaitKey(100);
if((key&0x7f)==27)
break;
}
fprintf(stderr,"successful exit\n");
IplImage* skin=cvCreateImage( cvGetSize(img1), img1->depth,3);
cvCvtColor(img1,skin,CV_BGR2HSV);
for(;;){
if(NULL==(img=cvQueryFrame(capture))){
printf("\nError on cvQueryFrame");
break;
}
pr=cvRect(start,start,start+w,start+h);
r=≺
img1=cvCreateImage(cvSize(r->width,r->height),img->depth,img->nChannels);
cvSetImageROI(img,*r);
cvCopy(img,img1,NULL);
cvResetImageROI(img);
cvRectangle(
img,
cvPoint(r->x,r->y),
cvPoint(r->x+r->width,r->y+r->height),
colors[0]
);
cvShowImage("Capture",img);
cvShowImage("Captured",img1);
key = cvWaitKey(100);
if((key&0x7f)==27)
break;
}
fprintf(stderr,"successful exit\n");
IplImage* background=cvCreateImage( cvGetSize(img1), img1->depth,3);
cvCvtColor(img1,background,CV_BGR2HSV);
IplImage* h_plane = cvCreateImage( cvGetSize(img1), 8, 1 );
IplImage* s_plane = cvCreateImage( cvGetSize(img1), 8, 1 );
IplImage* s2_plane = cvCreateImage( cvGetSize(background), 8, 1 );
IplImage* h2_plane = cvCreateImage( cvGetSize(background), 8, 1 );
IplImage* v_plane = cvCreateImage( cvGetSize(img1), 8, 1 );
//IplImage* planes[] = { h_plane, s_plane };
//IplImage* planes2[] = { h2_plane, s2_plane };
cvCvtPixToPlane( skin, h_plane, s_plane, v_plane, 0 );
cvCvtPixToPlane( background, h2_plane, s2_plane, v_plane, 0 );
CvHistogram *hist_skins,*hist_skinh, *hist_backs,*hist_backh;
int h_bins = 181, s_bins = 256;
{
int hist_size[] = { h_bins, s_bins };
float h_ranges[] = { 0, 180 }; // hue is [0,180]
float s_ranges[] = { 0, 255 };
float* ranges[] = { h_ranges, s_ranges };
hist_skinh = cvCreateHist(
1,
&hist_size[0],
CV_HIST_ARRAY,
&ranges[0],
1
);
hist_skins = cvCreateHist(
1,
&hist_size[1],
CV_HIST_ARRAY,
&ranges[1],
1
);
hist_backh = cvCreateHist(
1,
&hist_size[0],
CV_HIST_ARRAY,
&ranges[0],
1
);
hist_backs = cvCreateHist(
1,
&hist_size[1],
CV_HIST_ARRAY,
&ranges[1],
1
);
}
cvCalcHist( &h_plane, hist_skinh, 0, 0 );
cvCalcHist( &s_plane, hist_skins, 0, 0 );
cvCalcHist( &h2_plane, hist_backh, 0, 0 );
cvCalcHist( &s2_plane, hist_backs, 0, 0 );
float min[4],max[4];
int min_indx[4],max_indx[4];
/*//cvNormalizeHist( hist_skinh, 1.0 );
cvGetMinMaxHistValue( hist_skinh, &min[0], &max[0], &min_indx[0], &max_indx[0] );
//cvNormalizeHist( hist_skins, 1.0 );
cvGetMinMaxHistValue( hist_skins, &min[1], &max[1], &min_indx[1], &max_indx[1] );
//cvNormalizeHist( hist_backh, 1.0 );
cvGetMinMaxHistValue( hist_backh, &min[2], &max[2], &min_indx[2], &max_indx[2] );
//cvNormalizeHist( hist_backs, 1.0 );
cvGetMinMaxHistValue( hist_backs, &min[3], &max[3], &min_indx[3], &max_indx[3] );
*/
int hskin[2];
int hback[2];
int sskin[2];
int sback[2];
range_finder(hskin,0, 180,hist_skinh,w*h,0.0001);
range_finder(sskin,0, 255,hist_skins,w*h,0.01);
range_finder(hback,0, 180,hist_backh,w*h,0.01);
range_finder(sback,0, 255,hist_backs,w*h,0.01);
fprintf(stderr,"hskin min=%d max=%d\n",hskin[0],hskin[1]);
fprintf(stderr,"sskin min=%d max=%d\n",sskin[0],sskin[1]);
fprintf(stderr,"hback min=%d max=%d\n",hback[0],hback[1]);
fprintf(stderr,"sback min=%d max=%d\n",sback[0],sback[1]);
int hrange[2],srange[2];
decision(hrange,hskin[0], hskin[1],hback[0],hback[1]);
decision(srange,sskin[0], sskin[1],sback[0],sback[1]);
//decide on thresholds
fprintf(stderr,"hmin=%d hmax=%d\n",hrange[0],hrange[1]);
fprintf(stderr,"smin=%d smax=%d\n",srange[0],srange[1]);
for(;;){
if(NULL==(img=cvQueryFrame(capture))){
printf("\nError on cvQueryFrame");
break;
}
// h-0 179 s-61 227
cvReleaseImage(&img1);
img1=binary_threshold_hsl(img,0,179,srange[0],srange[1]);
cvShowImage("Capture",img);
cvShowImage("Captured",img1);
key = cvWaitKey(50);
if((key&0x7f)==27)
break;
}
cvReleaseImage(&img1);
char * filename = "../TestingSData/11-1.png";
img = cvLoadImage( filename );
img1=binary_threshold_hsl(img,0,179,61,227);
IplImage* img2=cvCreateImage(cvSize(img1->width,img1->height),img1->depth,img1->nChannels);
IplConvKernel * myKernel= cvCreateStructuringElementEx(8,8,0,0,CV_SHAPE_RECT,NULL);
//set the top corner to 0;
cvDilate(img1,img2,myKernel,1);
cvShowImage("Capture",img);
cvShowImage("Captured",img1);
cvNamedWindow("dilated",CV_WINDOW_AUTOSIZE);
cvShowImage("dilated",img2);
key = cvWaitKey(0);
cvReleaseCapture(&capture);
cvDestroyWindow("Capture");
cvReleaseImage( &img );
exit(1);
return 0;
}
void WindscreenLocator::locateBottomTop2()
{
int margin = (winRight - winLeft) * 0.22;
int height = winBottom - winTop;
cvSetImageROI(imgRGB, cvRect(winLeft + margin, winTop, winRight - winLeft - margin * 2, height));
double* vertical2Y = (double*)malloc(sizeof(double) * height);
if(!vertical2Y){
fprintf(stderr, "malloc failed\n");
fflush(stderr);
exit(-1);
}
memset(vertical2Y, 0, sizeof(double) * height);
IplImage* ver;
IplImage* hor;
IplImage* sla;
IplImage* res;
diagonal_origin(imgRGB, hor, ver, sla, res);
double thres = statThreshold(ver);
histoStat(ver, NULL, vertical2Y, thres * 0.65, 0);
cvResetImageROI(imgRGB);
histoSmooth(vertical2Y, height, 5);
histoSmooth(vertical2Y, height, 10);
histoSmooth(vertical2Y, height, 20);
int intervalA = 0;
int intervalB = height - 1;
histoSegment(vertical2Y, height, 0.5, intervalA, intervalB, 0.7);
vertical2Y[intervalA] = 100;
vertical2Y[intervalB] = 80;
cvReleaseImage(&ver);
cvReleaseImage(&hor);
cvReleaseImage(&sla);
cvReleaseImage(&res);
free(vertical2Y);
if((plate->isValid() && (plate->getMiddlePoint().x > winLeft
&& plate->getMiddlePoint().x < winRight))
|| reviser != NULL){
int plateWidth = 35;
if(plate->isValid())
plateWidth= plate->getPlateWidth();
if(reviser && reviser->getPlateWidth() > 0)
plateWidth = reviser->getPlateWidth();
if(winRight - winLeft < 4.5 * plateWidth){
if(intervalA != 0 && intervalB - intervalA > plateWidth * 0.65){
pdebug("[%d %d]\n", intervalA, intervalB);
winBottom = intervalB + winTop;
winTop = intervalA + winTop;
// int newHeight = intervalB - intervalA;
int w = winRight - winLeft;
winBottom += w * 0.05;
winTop -= w * 0.14;
}
}
}
winBottom = std::max(0, winBottom);
winBottom = std::min(imgRGB->height - 1, winBottom);
winTop = std::min(winTop, winBottom - 1);
winTop = std::max(0, winTop);
}
static GstFlowReturn
kms_crowd_detector_transform_frame_ip (GstVideoFilter * filter,
GstVideoFrame * frame)
{
KmsCrowdDetector *crowddetector = KMS_CROWD_DETECTOR (filter);
GstMapInfo info;
kms_crowd_detector_initialize_images (crowddetector, frame);
if ((crowddetector->priv->num_rois == 0)
&& (crowddetector->priv->rois != NULL)) {
kms_crowd_detector_extract_rois (crowddetector);
}
if (crowddetector->priv->pixels_rois_counted == TRUE &&
crowddetector->priv->actual_image != NULL) {
kms_crowd_detector_count_num_pixels_rois (crowddetector);
crowddetector->priv->pixels_rois_counted = FALSE;
}
gst_buffer_map (frame->buffer, &info, GST_MAP_READ);
crowddetector->priv->actual_image->imageData = (char *) info.data;
IplImage *frame_actual_gray =
cvCreateImage (cvSize (crowddetector->priv->actual_image->width,
crowddetector->priv->actual_image->height),
IPL_DEPTH_8U, 1);
cvZero (frame_actual_gray);
IplImage *actual_lbp =
cvCreateImage (cvSize (crowddetector->priv->actual_image->width,
crowddetector->priv->actual_image->height),
IPL_DEPTH_8U, 1);
cvZero (actual_lbp);
IplImage *lbp_temporal_result =
cvCreateImage (cvSize (crowddetector->priv->actual_image->width,
crowddetector->priv->actual_image->height),
IPL_DEPTH_8U, 1);
cvZero (lbp_temporal_result);
IplImage *add_lbps_result =
cvCreateImage (cvSize (crowddetector->priv->actual_image->width,
crowddetector->priv->actual_image->height),
IPL_DEPTH_8U, 1);
cvZero (add_lbps_result);
IplImage *lbps_alpha_result_rgb =
cvCreateImage (cvSize (crowddetector->priv->actual_image->width,
crowddetector->priv->actual_image->height),
IPL_DEPTH_8U, 3);
cvSet (lbps_alpha_result_rgb, CV_RGB (0, 0, 0), 0);
IplImage *actual_image_masked =
cvCreateImage (cvSize (crowddetector->priv->actual_image->width,
crowddetector->priv->actual_image->height), IPL_DEPTH_8U, 1);
cvZero (actual_image_masked);
IplImage *substract_background_to_actual =
cvCreateImage (cvSize (crowddetector->priv->actual_image->width,
crowddetector->priv->actual_image->height),
IPL_DEPTH_8U, 1);
cvZero (substract_background_to_actual);
IplImage *low_speed_map =
cvCreateImage (cvSize (crowddetector->priv->actual_image->width,
crowddetector->priv->actual_image->height),
IPL_DEPTH_8U, 1);
cvZero (low_speed_map);
IplImage *high_speed_map =
cvCreateImage (cvSize (crowddetector->priv->actual_image->width,
crowddetector->priv->actual_image->height),
IPL_DEPTH_8U, 1);
cvZero (high_speed_map);
IplImage *actual_motion =
cvCreateImage (cvSize (crowddetector->priv->actual_image->width,
crowddetector->priv->actual_image->height),
IPL_DEPTH_8U, 3);
cvSet (actual_motion, CV_RGB (0, 0, 0), 0);
IplImage *binary_actual_motion =
cvCreateImage (cvSize (crowddetector->priv->actual_image->width,
crowddetector->priv->actual_image->height),
IPL_DEPTH_8U, 1);
cvZero (binary_actual_motion);
uint8_t *low_speed_pointer;
uint8_t *low_speed_pointer_aux;
uint8_t *high_speed_pointer;
uint8_t *high_speed_pointer_aux;
uint8_t *actual_motion_pointer;
uint8_t *actual_motion_pointer_aux;
uint8_t *binary_actual_motion_pointer;
uint8_t *binary_actual_motion_pointer_aux;
int w, h;
if (crowddetector->priv->num_rois != 0) {
cvFillPoly (actual_image_masked, crowddetector->priv->curves,
crowddetector->priv->n_points, crowddetector->priv->num_rois,
cvScalar (255, 255, 255, 0), CV_AA, 0);
}
cvCvtColor (crowddetector->priv->actual_image, frame_actual_gray,
CV_BGR2GRAY);
kms_crowd_detector_mask_image (frame_actual_gray, actual_image_masked, 0);
if (crowddetector->priv->background == NULL) {
cvCopy (frame_actual_gray, crowddetector->priv->background, 0);
} else {
cvAddWeighted (crowddetector->priv->background, BACKGROUND_ADD_RATIO,
frame_actual_gray, 1 - BACKGROUND_ADD_RATIO, 0,
crowddetector->priv->background);
}
kms_crowd_detector_compute_temporal_lbp (frame_actual_gray, actual_lbp,
actual_lbp, FALSE);
kms_crowd_detector_compute_temporal_lbp (frame_actual_gray,
lbp_temporal_result, crowddetector->priv->frame_previous_gray, TRUE);
cvAddWeighted (crowddetector->priv->previous_lbp, LBPS_ADD_RATIO, actual_lbp,
(1 - LBPS_ADD_RATIO), 0, add_lbps_result);
cvSub (crowddetector->priv->previous_lbp, actual_lbp, add_lbps_result, 0);
cvThreshold (add_lbps_result, add_lbps_result, 70.0, 255.0, CV_THRESH_OTSU);
cvNot (add_lbps_result, add_lbps_result);
cvErode (add_lbps_result, add_lbps_result, 0, 4);
cvDilate (add_lbps_result, add_lbps_result, 0, 11);
cvErode (add_lbps_result, add_lbps_result, 0, 3);
cvCvtColor (add_lbps_result, lbps_alpha_result_rgb, CV_GRAY2BGR);
cvCopy (actual_lbp, crowddetector->priv->previous_lbp, 0);
cvCopy (frame_actual_gray, crowddetector->priv->frame_previous_gray, 0);
if (crowddetector->priv->acumulated_lbp == NULL) {
cvCopy (add_lbps_result, crowddetector->priv->acumulated_lbp, 0);
} else {
cvAddWeighted (crowddetector->priv->acumulated_lbp, TEMPORAL_LBPS_ADD_RATIO,
add_lbps_result, 1 - TEMPORAL_LBPS_ADD_RATIO, 0,
crowddetector->priv->acumulated_lbp);
}
cvThreshold (crowddetector->priv->acumulated_lbp, high_speed_map,
150.0, 255.0, CV_THRESH_BINARY);
cvSmooth (high_speed_map, high_speed_map, CV_MEDIAN, 3, 0, 0.0, 0.0);
kms_crowd_detector_substract_background (frame_actual_gray,
crowddetector->priv->background, substract_background_to_actual);
cvThreshold (substract_background_to_actual, substract_background_to_actual,
70.0, 255.0, CV_THRESH_OTSU);
cvCanny (substract_background_to_actual,
substract_background_to_actual, 70.0, 150.0, 3);
if (crowddetector->priv->acumulated_edges == NULL) {
cvCopy (substract_background_to_actual,
crowddetector->priv->acumulated_edges, 0);
} else {
cvAddWeighted (crowddetector->priv->acumulated_edges, EDGES_ADD_RATIO,
substract_background_to_actual, 1 - EDGES_ADD_RATIO, 0,
crowddetector->priv->acumulated_edges);
}
kms_crowd_detector_process_edges_image (crowddetector, low_speed_map, 3);
cvErode (low_speed_map, low_speed_map, 0, 1);
low_speed_pointer = (uint8_t *) low_speed_map->imageData;
high_speed_pointer = (uint8_t *) high_speed_map->imageData;
actual_motion_pointer = (uint8_t *) actual_motion->imageData;
binary_actual_motion_pointer = (uint8_t *) binary_actual_motion->imageData;
for (h = 0; h < low_speed_map->height; h++) {
low_speed_pointer_aux = low_speed_pointer;
high_speed_pointer_aux = high_speed_pointer;
actual_motion_pointer_aux = actual_motion_pointer;
binary_actual_motion_pointer_aux = binary_actual_motion_pointer;
for (w = 0; w < low_speed_map->width; w++) {
if (*high_speed_pointer_aux == 0) {
actual_motion_pointer_aux[0] = 255;
binary_actual_motion_pointer_aux[0] = 255;
}
if (*low_speed_pointer_aux == 255) {
*actual_motion_pointer_aux = 0;
actual_motion_pointer_aux[2] = 255;
binary_actual_motion_pointer_aux[0] = 255;
} else if (*high_speed_pointer_aux == 0) {
actual_motion_pointer_aux[0] = 255;
}
low_speed_pointer_aux++;
high_speed_pointer_aux++;
actual_motion_pointer_aux = actual_motion_pointer_aux + 3;
binary_actual_motion_pointer_aux++;
}
low_speed_pointer += low_speed_map->widthStep;
high_speed_pointer += high_speed_map->widthStep;
actual_motion_pointer += actual_motion->widthStep;
binary_actual_motion_pointer += binary_actual_motion->widthStep;
}
int curve;
for (curve = 0; curve < crowddetector->priv->num_rois; curve++) {
if (crowddetector->priv->rois_data[curve].send_optical_flow_event == TRUE) {
CvRect container =
kms_crowd_detector_get_square_roi_contaniner (crowddetector, curve);
cvSetImageROI (crowddetector->priv->actual_image, container);
cvSetImageROI (crowddetector->priv->previous_image, container);
cvSetImageROI (actual_motion, container);
kms_crowd_detector_compute_optical_flow (crowddetector,
binary_actual_motion, container, curve);
cvResetImageROI (crowddetector->priv->actual_image);
cvResetImageROI (crowddetector->priv->previous_image);
}
}
{
uint8_t *orig_row_pointer =
(uint8_t *) crowddetector->priv->actual_image->imageData;
uint8_t *overlay_row_pointer = (uint8_t *) actual_motion->imageData;
for (h = 0; h < crowddetector->priv->actual_image->height; h++) {
uint8_t *orig_column_pointer = orig_row_pointer;
uint8_t *overlay_column_pointer = overlay_row_pointer;
for (w = 0; w < crowddetector->priv->actual_image->width; w++) {
int c;
for (c = 0; c < crowddetector->priv->actual_image->nChannels; c++) {
if (overlay_column_pointer[c] != 0) {
orig_column_pointer[c] = overlay_column_pointer[c];
}
}
orig_column_pointer += crowddetector->priv->actual_image->nChannels;
overlay_column_pointer += actual_motion->nChannels;
}
orig_row_pointer += crowddetector->priv->actual_image->widthStep;
overlay_row_pointer += actual_motion->widthStep;
}
}
if (crowddetector->priv->num_rois != 0) {
cvPolyLine (crowddetector->priv->actual_image, crowddetector->priv->curves,
crowddetector->priv->n_points, crowddetector->priv->num_rois, 1,
cvScalar (255, 255, 255, 0), 1, 8, 0);
}
cvNot (high_speed_map, high_speed_map);
kms_crowd_detector_roi_analysis (crowddetector, low_speed_map,
high_speed_map);
cvReleaseImage (&frame_actual_gray);
cvReleaseImage (&actual_lbp);
cvReleaseImage (&lbp_temporal_result);
cvReleaseImage (&add_lbps_result);
cvReleaseImage (&lbps_alpha_result_rgb);
cvReleaseImage (&actual_image_masked);
cvReleaseImage (&substract_background_to_actual);
cvReleaseImage (&low_speed_map);
cvReleaseImage (&high_speed_map);
cvReleaseImage (&actual_motion);
cvReleaseImage (&binary_actual_motion);
gst_buffer_unmap (frame->buffer, &info);
return GST_FLOW_OK;
}
feature::vec2DFloat feature::calFeatureVec(IplImage* eachImage, bool debug)
{
auto imgVec = analyse::process(eachImage, debug);
//准备图片，整个细胞bgr， gray，轮廓分析备份， 细胞核bgr， gray，
IplImage* wholeCell = imgVec[0];
IplImage* wholeCellb = cvCreateImage(cvGetSize(wholeCell), 8, 1);
IplImage* wholeCell4Contour= cvCreateImage(cvGetSize(wholeCell), 8, 1);
IplImage* nuclei = imgVec[1];
IplImage* nucleib = cvCreateImage(cvGetSize(nuclei), 8, 1);
cvCvtColor(wholeCell, wholeCellb, CV_BGR2GRAY);
cvCopy(wholeCellb, wholeCell4Contour);
cvCvtColor(nuclei, nucleib, CV_BGR2GRAY);
// analyse::showImg(wholeCell, "wholeCell");
// analyse::showImg(nuclei, "nuclei");
// cvWaitKey(0);
//返回值， n * dimension 维向量
feature::vec2DFloat features;
//对整个细胞gray图进行轮廓分析，找出一个个细胞，然后ROI， 再接着处理
CvMemStorage* storage = cvCreateMemStorage(0);
CvSeq* contours = 0;
cvFindContours(wholeCell4Contour, storage, &contours, sizeof(CvContour), CV_RETR_LIST);
if (contours) {
CvSeq* c = contours;
//int i = 0;
for (; c != NULL; c = c->h_next) {
if (cvContourArea(c) < 2000) continue;
//qDebug("i = %d, area = %f, perimeter = %f", ++i, cvContourArea(c), cvArcLength(c));
//开始设置ROI
CvRect rect = cvBoundingRect(c,0);
cvSetImageROI(wholeCell, rect);
cvSetImageROI(wholeCellb, rect);
cvSetImageROI(nuclei, rect);
cvSetImageROI(nucleib, rect);
//为临时图片申请空间，复制图片
IplImage* temp1 = cvCreateImage(cvGetSize(nuclei), 8, 3);//细胞核bgr
IplImage* temp2 = cvCreateImage(cvGetSize(nucleib), 8, 1);//细胞核gray
IplImage* temp22 = cvCreateImage(cvGetSize(nucleib), 8, 1);//细胞核gray
IplImage* temp3 = cvCreateImage(cvGetSize(wholeCell), 8, 3);//整个细胞bgr
IplImage* temp4 = cvCreateImage(cvGetSize(wholeCellb), 8, 1);//整个细胞gray
IplImage* temp5 = cvCreateImage(cvGetSize(wholeCell), 8, 3);//细胞质bgr
cvCopy(nuclei, temp1);
cvCopy(nucleib, temp2);
cvCopy(nucleib, temp22);
cvCopy(wholeCell, temp3);
cvCopy(wholeCellb, temp4);
//细胞质图片
cvSub(temp3, temp1, temp5);
//取消ROI设置
cvResetImageROI(wholeCell);
cvResetImageROI(wholeCellb);
cvResetImageROI(nuclei);
cvResetImageROI(nucleib);
//开始提取特征， 用tempX图片
auto t1 = feature::meanRgb(temp1);
auto t3 = feature::meanRgb(temp3);
auto t5 = feature::meanRgb(temp5);
auto t2 = feature::getPAR(temp2, 2);//4-d
auto t4 = feature::getPAR(temp4, 4);//3-d
auto t6 = vector<float> ({t2.at(1) - t2.at(1), t2.at(1) / t2.at(1)});
auto t22 = feature::getLBP(temp22);
//细胞核面积为0，说明ROI设置有错，跳过，特征值不要啦
if (t2.at(1) == 0) {
continue;
}
//debug mode, display the pic
if (debug == true) {
analyse::showImg(temp1, "Nuclei-bgr");
analyse::showImg(temp3, "Cell-bgr");
analyse::showImg(temp5, "Cryto-bgr");
cvWaitKey(waitTime * 2);
}
//如果细胞质面积为0，说明没有细胞质， 应该对BGR分量另外处理
vector<float> eachFeature;
auto insertItr = std::back_inserter(eachFeature);
move(t2.begin(), t2.end(), insertItr);
move(t1.begin(), t1.end(), insertItr);
move(t4.begin(), t4.end(), insertItr);
move(t3.begin(), t3.end(), insertItr);
if (t6.at(0) == 0) {
eachFeature.push_back(0);
eachFeature.push_back(0);
eachFeature.push_back(0);
}
else {
move(t5.begin(), t5.end(), insertItr);
// eachFeature << t2.join("'") << t1.join("'") << t4.join("'") << t3.join("'") << t5.join("'") << t6.join("'") << t22.join("'");
}
move(t6.begin(), t6.end(), insertItr);
move(t22.begin(), t22.end(), insertItr);
//把当前细胞的特征值放入图片总特征值中
features.push_back(eachFeature);
// analyse::showImg(temp1, "temp1");
// analyse::showImg(temp3, "temp3");
// if (cvWaitKey(0)== 27) break;
cvReleaseImage(&temp1);
cvReleaseImage(&temp2);
cvReleaseImage(&temp22);
cvReleaseImage(&temp3);
cvReleaseImage(&temp4);
cvReleaseImage(&temp5);
}
}
cvReleaseMemStorage(&storage);
cvReleaseImage(&wholeCell);
cvReleaseImage(&wholeCellb);
cvReleaseImage(&wholeCell4Contour);
cvReleaseImage(&nuclei);
cvReleaseImage(&nucleib);
return features;
}
/*
* Performs the face detection
*/
static GstFlowReturn
gst_face_detect_transform_ip (GstOpencvVideoFilter * base, GstBuffer * buf,
IplImage * img)
{
GstFaceDetect *filter = GST_FACE_DETECT (base);
if (filter->cvFaceDetect) {
GstMessage *msg = NULL;
GstStructure *s;
GValue facelist = { 0 };
GValue facedata = { 0 };
CvSeq *faces;
CvSeq *mouth = NULL, *nose = NULL, *eyes = NULL;
gint i;
gboolean do_display = FALSE;
if (filter->display) {
if (gst_buffer_is_writable (buf)) {
do_display = TRUE;
} else {
GST_LOG_OBJECT (filter, "Buffer is not writable, not drawing faces.");
}
}
cvCvtColor (img, filter->cvGray, CV_RGB2GRAY);
cvClearMemStorage (filter->cvStorage);
faces = gst_face_detect_run_detector (filter, filter->cvFaceDetect,
filter->min_size_width, filter->min_size_height);
msg = gst_face_detect_message_new (filter, buf);
g_value_init (&facelist, GST_TYPE_LIST);
for (i = 0; i < (faces ? faces->total : 0); i++) {
CvRect *r = (CvRect *) cvGetSeqElem (faces, i);
guint mw = filter->min_size_width / 8;
guint mh = filter->min_size_height / 8;
guint rnx = 0, rny = 0, rnw, rnh;
guint rmx = 0, rmy = 0, rmw, rmh;
guint rex = 0, rey = 0, rew, reh;
gboolean have_nose, have_mouth, have_eyes;
/* detect face features */
if (filter->cvNoseDetect) {
rnx = r->x + r->width / 4;
rny = r->y + r->height / 4;
rnw = r->width / 2;
rnh = r->height / 2;
cvSetImageROI (filter->cvGray, cvRect (rnx, rny, rnw, rnh));
nose =
gst_face_detect_run_detector (filter, filter->cvNoseDetect, mw, mh);
have_nose = (nose && nose->total);
cvResetImageROI (filter->cvGray);
} else {
have_nose = FALSE;
}
if (filter->cvMouthDetect) {
rmx = r->x;
rmy = r->y + r->height / 2;
rmw = r->width;
rmh = r->height / 2;
cvSetImageROI (filter->cvGray, cvRect (rmx, rmy, rmw, rmh));
mouth =
gst_face_detect_run_detector (filter, filter->cvMouthDetect, mw,
mh);
have_mouth = (mouth && mouth->total);
cvResetImageROI (filter->cvGray);
} else {
have_mouth = FALSE;
}
if (filter->cvEyesDetect) {
rex = r->x;
rey = r->y;
rew = r->width;
reh = r->height / 2;
cvSetImageROI (filter->cvGray, cvRect (rex, rey, rew, reh));
eyes =
gst_face_detect_run_detector (filter, filter->cvEyesDetect, mw, mh);
have_eyes = (eyes && eyes->total);
cvResetImageROI (filter->cvGray);
} else {
have_eyes = FALSE;
}
GST_LOG_OBJECT (filter,
"%2d/%2d: x,y = %4u,%4u: w.h = %4u,%4u : features(e,n,m) = %d,%d,%d",
i, faces->total, r->x, r->y, r->width, r->height,
have_eyes, have_nose, have_mouth);
s = gst_structure_new ("face",
"x", G_TYPE_UINT, r->x,
"y", G_TYPE_UINT, r->y,
"width", G_TYPE_UINT, r->width,
"height", G_TYPE_UINT, r->height, NULL);
if (have_nose) {
CvRect *sr = (CvRect *) cvGetSeqElem (nose, 0);
GST_LOG_OBJECT (filter, "nose/%d: x,y = %4u,%4u: w.h = %4u,%4u",
nose->total, rnx + sr->x, rny + sr->y, sr->width, sr->height);
gst_structure_set (s,
"nose->x", G_TYPE_UINT, rnx + sr->x,
"nose->y", G_TYPE_UINT, rny + sr->y,
"nose->width", G_TYPE_UINT, sr->width,
"nose->height", G_TYPE_UINT, sr->height, NULL);
}
if (have_mouth) {
CvRect *sr = (CvRect *) cvGetSeqElem (mouth, 0);
GST_LOG_OBJECT (filter, "mouth/%d: x,y = %4u,%4u: w.h = %4u,%4u",
mouth->total, rmx + sr->x, rmy + sr->y, sr->width, sr->height);
gst_structure_set (s,
"mouth->x", G_TYPE_UINT, rmx + sr->x,
"mouth->y", G_TYPE_UINT, rmy + sr->y,
"mouth->width", G_TYPE_UINT, sr->width,
"mouth->height", G_TYPE_UINT, sr->height, NULL);
}
if (have_eyes) {
CvRect *sr = (CvRect *) cvGetSeqElem (eyes, 0);
GST_LOG_OBJECT (filter, "eyes/%d: x,y = %4u,%4u: w.h = %4u,%4u",
eyes->total, rex + sr->x, rey + sr->y, sr->width, sr->height);
gst_structure_set (s,
"eyes->x", G_TYPE_UINT, rex + sr->x,
"eyes->y", G_TYPE_UINT, rey + sr->y,
"eyes->width", G_TYPE_UINT, sr->width,
"eyes->height", G_TYPE_UINT, sr->height, NULL);
}
g_value_init (&facedata, GST_TYPE_STRUCTURE);
g_value_take_boxed (&facedata, s);
gst_value_list_append_value (&facelist, &facedata);
g_value_unset (&facedata);
s = NULL;
if (do_display) {
CvPoint center;
CvSize axes;
gdouble w, h;
gint cb = 255 - ((i & 3) << 7);
gint cg = 255 - ((i & 12) << 5);
gint cr = 255 - ((i & 48) << 3);
w = r->width / 2;
h = r->height / 2;
center.x = cvRound ((r->x + w));
center.y = cvRound ((r->y + h));
axes.width = w;
axes.height = h * 1.25; /* tweak for face form */
cvEllipse (img, center, axes, 0.0, 0.0, 360.0, CV_RGB (cr, cg, cb),
3, 8, 0);
if (have_nose) {
CvRect *sr = (CvRect *) cvGetSeqElem (nose, 0);
w = sr->width / 2;
h = sr->height / 2;
center.x = cvRound ((rnx + sr->x + w));
center.y = cvRound ((rny + sr->y + h));
axes.width = w;
axes.height = h * 1.25; /* tweak for nose form */
cvEllipse (img, center, axes, 0.0, 0.0, 360.0, CV_RGB (cr, cg, cb),
1, 8, 0);
}
if (have_mouth) {
CvRect *sr = (CvRect *) cvGetSeqElem (mouth, 0);
w = sr->width / 2;
h = sr->height / 2;
center.x = cvRound ((rmx + sr->x + w));
center.y = cvRound ((rmy + sr->y + h));
axes.width = w * 1.5; /* tweak for mouth form */
axes.height = h;
cvEllipse (img, center, axes, 0.0, 0.0, 360.0, CV_RGB (cr, cg, cb),
1, 8, 0);
}
if (have_eyes) {
CvRect *sr = (CvRect *) cvGetSeqElem (eyes, 0);
w = sr->width / 2;
h = sr->height / 2;
center.x = cvRound ((rex + sr->x + w));
center.y = cvRound ((rey + sr->y + h));
axes.width = w * 1.5; /* tweak for eyes form */
axes.height = h;
cvEllipse (img, center, axes, 0.0, 0.0, 360.0, CV_RGB (cr, cg, cb),
1, 8, 0);
}
}
}
gst_structure_set_value ((GstStructure *) gst_message_get_structure (msg),
"faces", &facelist);
g_value_unset (&facelist);
gst_element_post_message (GST_ELEMENT (filter), msg);
}
return GST_FLOW_OK;
}
void BleImageProcessThread::run()
{
CvSize dstSize;
dstSize.width = m_width;
dstSize.height = m_height;
IplImage* dstImg = cvCreateImage(dstSize, IPL_DEPTH_8U, 3);
cvZero(dstImg);
QRect dstRect(0, 0, m_width, m_height);
while (!m_stop) {
QElapsedTimer elapsedTimer;
elapsedTimer.start();
m_updateMutex.lock();
for (int i = 0; i < m_sources.size(); ++i) {
SourcePair & pair = m_sources[i];
BleImage bleImage = pair.source->getImage();
if (bleImage.dataSize <= 0) continue;
IplImage *cvImage = cvCreateImageHeader(cvSize(bleImage.width, bleImage.height), 8, 3);
cvImage->imageData = bleImage.data;
cvImage->imageDataOrigin = bleImage.data;
// if the image size not qual to area size
// then resize it.
IplImage *resizedImage = cvImage;
if (cvImage->width != pair.rect.width() ||
cvImage->height != pair.rect.height())
{
resizedImage = cvCreateImage(cvSize(pair.rect.width(), pair.rect.height()), cvImage->depth, cvImage->nChannels);
cvResize(cvImage, resizedImage, CV_INTER_LINEAR);
}
if (bleImage.format != BleImage_Format_BGR24) {
cvConvertImage(resizedImage, resizedImage, CV_CVTIMG_SWAP_RB);
}
// get the intersection of dst rect
if (!dstRect.intersects(pair.rect)) continue;
QRect intersectionRect_1 = dstRect.intersected(pair.rect);
QRect intersectionRect_2 = pair.rect.intersected(dstRect);
// intersectionRect_2 should relative to pair.rect
intersectionRect_2.moveTopLeft(QPoint(intersectionRect_2.x() - pair.rect.x(),
intersectionRect_2.y() - pair.rect.y()));
cvSetImageROI(dstImg, QRect2CvRect(intersectionRect_1));
cvSetImageROI(resizedImage, QRect2CvRect(intersectionRect_2));
cvCopy(resizedImage, dstImg);
cvResetImageROI(dstImg);
cvResetImageROI(resizedImage);
// if resizedImage is cvCreateImage created ?
if (resizedImage != cvImage) {
cvReleaseImage(&resizedImage);
}
cvReleaseImageHeader(&cvImage);
}
m_updateMutex.unlock();
m_modifyOutputMutex.lock();
// if delayed about 1s , then discard some image.
if (m_outputQueue.size() > 20) {
log_trace("queue has many mang image, maybe your encoder is too slow!");
goto end;
}
if (true) {
// to BleImage
BleImage *be = new BleImage;
be->width = dstImg->width;
be->height = dstImg->height;
be->data = new char[dstImg->imageSize];
memcpy(be->data, dstImg->imageData, dstImg->imageSize);
be->dataSize = dstImg->imageSize;
be->format = BleImage_Format_BGR24;
m_timestampBuilder.setVideoCaptureInternal(m_internal);
be->pts = m_timestampBuilder.addVideoFrame();
m_outputQueue.enqueue(be);
}
end:
m_modifyOutputMutex.unlock();
int elapsedMs = elapsedTimer.elapsed();
int needSleepMs = m_internal - elapsedMs;
if (needSleepMs < 0) {
needSleepMs = 0;
}
msleep(needSleepMs);
// reset bg image to black
cvZero(dstImg);
}
log_trace("BleImageProcessThread exit normally.");
}
/**
* Locate the user's eye with template matching
*
* @param IplImage* img the source image
* @param IplImage* tpl the eye template
* @param CvRect* window search within this window,
* will be updated with the recent search window
* @param CvRect* eye output parameter, will contain the current
* location of user's eye
* @return int '1' if found, '0' otherwise
*/
int
locate_eye(IplImage* img, IplImage* tpl, CvRect* window, CvRect* eye)
{
IplImage* tm;
CvRect win;
CvPoint minloc, maxloc, point;
double minval, maxval;
int w, h;
/* get the centroid of eye */
point = cvPoint(
(*eye).x + (*eye).width / 2,
(*eye).y + (*eye).height / 2
);
/* setup search window
replace the predefined WIN_WIDTH and WIN_HEIGHT above
for your convenient */
win = cvRect(
point.x - WIN_WIDTH / 2,
point.y - WIN_HEIGHT / 2,
WIN_WIDTH,
WIN_HEIGHT
);
/* make sure that the search window is still within the frame */
if (win.x < 0)
win.x = 0;
if (win.y < 0)
win.y = 0;
if (win.x + win.width > img->width)
win.x = img->width - win.width;
if (win.y + win.height > img->height)
win.y = img->height - win.height;
/* create new image for template matching result where:
width = W - w + 1, and
height = H - h + 1 */
w = win.width - tpl->width + 1;
h = win.height - tpl->height + 1;
tm = cvCreateImage(cvSize(w, h), IPL_DEPTH_32F, 1);
/* apply the search window */
cvSetImageROI(img, win);
/* template matching */
cvMatchTemplate(img, tpl, tm, CV_TM_SQDIFF_NORMED);
cvMinMaxLoc(tm, &minval, &maxval, &minloc, &maxloc, 0);
/* release things */
cvResetImageROI(img);
cvReleaseImage(&tm);
/* only good matches */
if (minval > TM_THRESHOLD)
return 0;
/* return the search window */
*window = win;
/* return eye location */
*eye = cvRect(
win.x + minloc.x,
win.y + minloc.y,
TPL_WIDTH,
TPL_HEIGHT
);
return 1;
}
void MapMaker::image_callback(const sensor_msgs::ImageConstPtr& msg) {
// printf("callback called\n");
try
{
// if you want to work with color images, change from mono8 to bgr8
if(input_image==NULL){
input_image = cvCloneImage(bridge.imgMsgToCv(msg, "mono8"));
rotationImage=cvCloneImage(input_image);
// printf("cloned image\n");
}
else{
cvCopy(bridge.imgMsgToCv(msg, "mono8"),input_image);
// printf("copied image\n");
}
}
catch (sensor_msgs::CvBridgeException& e)
{
ROS_ERROR("Could not convert from '%s' to 'mono8'.", msg->encoding.c_str());
return;
}
if(input_image!=NULL) {
//get tf transform here and put in map
ros::Time acquisition_time = msg->header.stamp;
geometry_msgs::PoseStamped basePose;
geometry_msgs::PoseStamped mapPose;
basePose.pose.orientation.w=1.0;
ros::Duration timeout(3);
basePose.header.frame_id="/base_link";
mapPose.header.frame_id="/map";
try {
tf_listener_.waitForTransform("/base_link", "/map", acquisition_time, timeout);
tf_listener_.transformPose("/map", acquisition_time,basePose,"/base_link",mapPose);
printf("pose #%d %f %f %f\n",pic_number,mapPose.pose.position.x, mapPose.pose.position.y, tf::getYaw(mapPose.pose.orientation));
/*
char buffer [50];
sprintf (buffer, "/tmp/test%02d.jpg", pic_number);
if(!cvSaveImage(buffer,input_image,0)) printf("Could not save: %s\n",buffer);
else printf("picture taken!!!\n");
pic_number++;
*/
cv::Point_<double> center;
center.x=input_image->width/2;
center.y=input_image->height/2;
double tranlation_arr[2][3];
CvMat translation;
cvInitMatHeader(&translation,2,3,CV_64F,tranlation_arr);
cvSetZero(&translation);
cv2DRotationMatrix(center, (tf::getYaw(mapPose.pose.orientation)*180/3.14159) -90,1.0,&translation);
cvSetZero(rotationImage);
cvWarpAffine(input_image,rotationImage,&translation,CV_INTER_LINEAR+CV_WARP_FILL_OUTLIERS,cvScalarAll(0));
CvRect roi;
roi.width=rotationImage->width;
roi.height=rotationImage->height;
if(init_zero_x==0){
init_zero_x=(int)(mapPose.pose.position.x*(1.0/map_meters_per_pixel));
init_zero_y=(int)(mapPose.pose.position.y*(-1.0/map_meters_per_pixel));
}
roi.x=(int)(mapPose.pose.position.x*(1.0/map_meters_per_pixel))-init_zero_x+map_zero_x-roi.width/2;
roi.y=(int)(mapPose.pose.position.y*(-1.0/map_meters_per_pixel))-init_zero_y+map_zero_y-roi.height/2;
printf("x %d, y %d, rot %f\n",roi.x,roi.y, (tf::getYaw(mapPose.pose.orientation)*180/3.14159) -90);
cvSetImageROI(map,roi);
cvMax(map,rotationImage,map);
cvResetImageROI(map);
cvShowImage("map image",map);
}
catch (tf::TransformException& ex) {
ROS_WARN("[map_maker] TF exception:\n%s", ex.what());
printf("[map_maker] TF exception:\n%s", ex.what());
return;
}
catch(...){
printf("opencv shit itself cause our roi is bad\n");
}
}
}
int fbtrack(IplImage *imgI, IplImage *imgJ, float *bb, float *bbnew,
float *scaleshift)
{
char level = 5;
int numAdd = 50;
// find good points
const int margin = 5;
CvRect rect = cvRect(bb[0],bb[1],bb[2]-bb[0]+1,bb[3]-bb[1]+1);
cvSetImageROI(imgI, rect);
IplImage *eig_image = cvCreateImage(cvGetSize(imgI), 32, 1);
IplImage *temp_image = cvCreateImage(cvGetSize(imgI), 32, 1);
CvPoint2D32f corners [numAdd];
cvGoodFeaturesToTrack(imgI, eig_image, temp_image, corners, &numAdd, 0.01, 0, NULL, 2, 0, 0.04);
cvReleaseImage(&(eig_image));
cvReleaseImage(&(temp_image));
cvResetImageROI(imgI);
//printf("%d - number of features\n", numAdd);
if (numAdd > 50) {
numAdd = 50;
}
int numM = sqrt(100 - numAdd);
int numN = sqrt(100 - numAdd);
const int nPoints = numM * numN + numAdd;
const int sizePointsArray = nPoints * 2;
float fb[nPoints];
float ncc[nPoints];
char status[nPoints];
float pt[sizePointsArray];
float ptTracked[sizePointsArray];
int nlkPoints;
CvPoint2D32f *startPoints;
CvPoint2D32f *targetPoints;
float *fbLkCleaned;
float *nccLkCleaned;
int i, M;
int nRealPoints;
float medFb;
float medNcc;
int nAfterFbUsage;
getFilledBBPoints(bb, numM, numN, margin, pt);
//getFilledBBPoints(bb, numM, numN, 5, &ptTracked);
//show good points
//IplImage *tmp_show = cvCreateImage(cvGetSize(imgI), imgI->depth, imgI->nChannels);
//cvCopy(imgI, tmp_show, NULL);
//for(i = numN+numM; i < numN+numM+numAdd; i++) {
// cvCircle(tmp_show, CvPoint{bb[0]+corners[i-(numN+numM)].x, bb[1]+corners[i-(numN+numM)].y}, 2, CvScalar{0,0,255}, 1, 8, 0);
//}
//cvRectangle(tmp_show, CvPoint{bb[0],bb[1]},CvPoint{bb[2],bb[3]},CvScalar{0,0,255},1,8,0);
//cvShowImage("name",tmp_show);
//copy good points
for(i = numN*numM; i < numN*numM+numAdd; i++)
{
pt[2*i + 0] = (int)(corners[i-(numN*numM)].x+bb[0]);
pt[2*i + 1] = (int)(corners[i-(numN*numM)].y+bb[1]);
}
memcpy(ptTracked, pt, sizeof(float) * sizePointsArray);
initImgs();
trackLK(imgI, imgJ, pt, nPoints, ptTracked, nPoints, level, fb, ncc, status);
initImgs();
// char* status = *statusP;
nlkPoints = 0;
for(i = 0; i < nPoints; i++)
{
nlkPoints += status[i];
}
startPoints = (CvPoint2D32f *) malloc(nlkPoints * sizeof(CvPoint2D32f));
targetPoints = (CvPoint2D32f *) malloc(nlkPoints * sizeof(CvPoint2D32f));
fbLkCleaned = (float *) malloc(nlkPoints * sizeof(float));
nccLkCleaned = (float *) malloc(nlkPoints * sizeof(float));
M = 2;
nRealPoints = 0;
for(i = 0; i < nPoints; i++)
{
//TODO:handle Missing Points
//or status[i]==0
if(ptTracked[M * i] == -1)
{
}
else
{
startPoints[nRealPoints].x = pt[2 * i];
startPoints[nRealPoints].y = pt[2 * i + 1];
targetPoints[nRealPoints].x = ptTracked[2 * i];
targetPoints[nRealPoints].y = ptTracked[2 * i + 1];
fbLkCleaned[nRealPoints] = fb[i];
nccLkCleaned[nRealPoints] = ncc[i];
nRealPoints++;
}
}
//assert nRealPoints==nlkPoints
medFb = getMedian(fbLkCleaned, nlkPoints);
medNcc = getMedian(nccLkCleaned, nlkPoints);
/* printf("medianfb: %f\nmedianncc: %f\n", medFb, medNcc);
printf("Number of points after lk: %d\n", nlkPoints);*/
nAfterFbUsage = 0;
for(i = 0; i < nlkPoints; i++)
{
if((fbLkCleaned[i] <= medFb) & (nccLkCleaned[i] >= medNcc))
{
startPoints[nAfterFbUsage] = startPoints[i];
targetPoints[nAfterFbUsage] = targetPoints[i];
nAfterFbUsage++;
}
}
/*printf("Number of points after fb correction: %d\n", nAfterFbUsage);*/
// showIplImage(IMGS[1]);
// show "OpticalFlow" fb filtered.
// drawLinesCvPoint2D32f(imgI, startPoints, nRealPoints, targetPoints,
// nRealPoints);
// showIplImage(imgI);
predictbb(bb, startPoints, targetPoints, nAfterFbUsage, bbnew, scaleshift);
/*printf("bbnew: %f,%f,%f,%f\n", bbnew[0], bbnew[1], bbnew[2], bbnew[3]);
printf("relative scale: %f \n", scaleshift[0]);*/
//show picture with tracked bb
// drawRectFromBB(imgJ, bbnew);
// showIplImage(imgJ);
free(startPoints);
free(targetPoints);
free(fbLkCleaned);
free(nccLkCleaned);
if(medFb > 10) return 0;
else return 1;
}
//extract the sub image from the whole image.
//The first node of a linkedlist with images and coordinates in the original image is returned.
sub_image* extract_sub_image(IplImage* original_image, int min_width, int min_height)
{
int left = 0;
int top = 0;
int right = 0;
int buttom = 0;
int temp_x = 0;
int temp_y = 0;
int contour_flag = 0;
int flag = 0;
sub_image* temp_result = (sub_image*)malloc(sizeof(sub_image));;
sub_image* result = NULL;
sub_image* prev = NULL;
IplImage* temp_original_image = cvCloneImage(original_image);
IplImage* temp = cvCloneImage(original_image);
//search the connected components(here search the connected area)
CvSeq* contours = connected_components(temp_original_image, temp);
for (CvSeq* c=contours ;c!=NULL; c=c->h_next)
{
for (int i = 0; i < c->total; i ++)
{
CvPoint* p = (CvPoint*)cvGetSeqElem(c, i);
temp_x = p->x;
temp_y = p->y;
//printf("x = %d, y = %d\n",temp_x,temp_y);
//do the approximation
if(i == 0)
{
left = temp_x;
right = temp_x;
top = temp_y;
buttom = temp_y;
}
else
{
if(temp_x <= left) left = temp_x;
if(temp_x >= right) right = temp_x;
if(temp_y <= top) top = temp_y;
if(temp_y >= buttom) buttom = temp_y;
}
}
//Not a correct area.To define the value of min_width and min_height I estimate the smallest scale of sub image.
if(right-left < min_width || buttom - top < min_height)
{
continue;
}
else
{
//printf("find subimage %d\n",flag++);
cvSetImageROI(temp_original_image, cvRect(left,top,right - left,buttom - top));
if(contour_flag++ == 0)
{
result = temp_result;
}
temp_result->image = cvCreateImage(cvSize(right - left, buttom - top),temp_original_image->depth,temp_original_image-> nChannels);
temp_result->image_left = left;
temp_result->image_top = top;
temp_result->image_right = right;
temp_result->image_buttom = buttom;
//copy the region of interesting(the sub image) to the destination image.
cvCopy(temp_original_image, temp_result->image, NULL);
//cvShowImage(string[flag++],temp_result->image);
//organize the linkedlist.
temp_result->next_image = (sub_image*)malloc(sizeof(sub_image));
prev = temp_result;
temp_result = temp_result->next_image;
cvResetImageROI(temp_original_image);
}
}
temp_result = prev;
temp_result->next_image= NULL;
//the head node of the linkedlist is returned.
return result;
}
//--------------------------------------------------------------------------------
void ofxCvImage::resetROI() {
cvResetImageROI( cvImage );
cvResetImageROI( cvImageTemp );
}
void CvFaceElement::FindContours(IplImage* img, IplImage* thresh, int nLayers, int dMinSize)
{
CvSeq* seq;
CvRect roi = m_rROI;
Extend(roi, 1);
cvSetImageROI(img, roi);
cvSetImageROI(thresh, roi);
// layers
int colors[MAX_LAYERS] = {0};
int iMinLevel = 0, iMaxLevel = 255;
float step, power;
ThresholdingParam(img, nLayers / 2, iMinLevel, iMaxLevel, step, power, 4);
int iMinLevelPrev = iMinLevel;
int iMaxLevelPrev = iMinLevel;
if (m_trPrev.iColor != 0)
{
iMinLevelPrev = m_trPrev.iColor - nLayers / 2;
iMaxLevelPrev = m_trPrev.iColor + nLayers / 2;
}
if (iMinLevelPrev < iMinLevel)
{
iMaxLevelPrev += iMinLevel - iMinLevelPrev;
iMinLevelPrev = iMinLevel;
}
if (iMaxLevelPrev > iMaxLevel)
{
iMinLevelPrev -= iMaxLevelPrev - iMaxLevel;
if (iMinLevelPrev < iMinLevel)
iMinLevelPrev = iMinLevel;
iMaxLevelPrev = iMaxLevel;
}
int n = nLayers;
n -= (iMaxLevelPrev - iMinLevelPrev + 1) / 2;
step = float(iMinLevelPrev - iMinLevel + iMaxLevel - iMaxLevelPrev) / float(n);
int j = 0;
float level;
for (level = (float)iMinLevel; level < iMinLevelPrev && j < nLayers; level += step, j++)
colors[j] = int(level + 0.5);
for (level = (float)iMinLevelPrev; level < iMaxLevelPrev && j < nLayers; level += 2.0, j++)
colors[j] = int(level + 0.5);
for (level = (float)iMaxLevelPrev; level < iMaxLevel && j < nLayers; level += step, j++)
colors[j] = int(level + 0.5);
//
for (int i = 0; i < nLayers; i++)
{
cvThreshold(img, thresh, colors[i], 255.0, CV_THRESH_BINARY);
if (cvFindContours(thresh, m_mstgRects, &seq, sizeof(CvContour), CV_RETR_CCOMP, CV_CHAIN_APPROX_SIMPLE))
{
CvTrackingRect cr;
for (CvSeq* external = seq; external; external = external->h_next)
{
cr.r = cvContourBoundingRect(external);
Move(cr.r, roi.x, roi.y);
if (RectInRect(cr.r, m_rROI) && cr.r.width > dMinSize && cr.r.height > dMinSize)
{
cr.ptCenter = Center(cr.r);
cr.iColor = colors[i];
cvSeqPush(m_seqRects, &cr);
}
for (CvSeq* internal = external->v_next; internal; internal = internal->h_next)
{
cr.r = cvContourBoundingRect(internal);
Move(cr.r, roi.x, roi.y);
if (RectInRect(cr.r, m_rROI) && cr.r.width > dMinSize && cr.r.height > dMinSize)
{
cr.ptCenter = Center(cr.r);
cr.iColor = colors[i];
cvSeqPush(m_seqRects, &cr);
}
}
}
cvClearSeq(seq);
}
}
cvResetImageROI(img);
cvResetImageROI(thresh);
}//void CvFaceElement::FindContours(IplImage* img, IplImage* thresh, int nLayers)
//--------------------------------------------------------------------------------
void ofxCvImage::resetImageROI( IplImage* img ) {
cvResetImageROI(img);
}
// parameters:
// img - input video frame
// dst - resultant motion picture
// args - optional parameters
void update_mhi( IplImage* img, IplImage* dst, int diff_threshold )
{
double timestamp = (double)clock()/CLOCKS_PER_SEC; // get current time in seconds
CvSize size = cvSize(img->width,img->height); // get current frame size
int i, idx1 = last, idx2;
IplImage* silh;
CvSeq* seq;
CvRect comp_rect;
double count;
double angle;
CvPoint center;
double magnitude;
CvScalar color;
// allocate images at the beginning or
// reallocate them if the frame size is changed
if( !mhi || mhi->width != size.width || mhi->height != size.height ) {
if( buf == 0 ) {
buf = (IplImage**)malloc(N*sizeof(buf[0]));
memset( buf, 0, N*sizeof(buf[0]));
}
for( i = 0; i < N; i++ ) {
cvReleaseImage( &buf[i] );
buf[i] = cvCreateImage( size, IPL_DEPTH_8U, 1 );
cvZero( buf[i] );
}
cvReleaseImage( &mhi );
cvReleaseImage( &orient );
cvReleaseImage( &segmask );
cvReleaseImage( &mask );
mhi = cvCreateImage( size, IPL_DEPTH_32F, 1 );
cvZero( mhi ); // clear MHI at the beginning
orient = cvCreateImage( size, IPL_DEPTH_32F, 1 );
segmask = cvCreateImage( size, IPL_DEPTH_32F, 1 );
mask = cvCreateImage( size, IPL_DEPTH_8U, 1 );
}
cvCvtColor( img, buf[last], CV_BGR2GRAY ); // convert frame to grayscale
idx2 = (last + 1) % N; // index of (last - (N-1))th frame
last = idx2;
silh = buf[idx2];
cvAbsDiff( buf[idx1], buf[idx2], silh ); // get difference between frames
cvThreshold( silh, silh, diff_threshold, 1, CV_THRESH_BINARY ); // and threshold it
cvUpdateMotionHistory( silh, mhi, timestamp, MHI_DURATION ); // update MHI
// convert MHI to blue 8u image
cvCvtScale( mhi, mask, 255./MHI_DURATION,
(MHI_DURATION - timestamp)*255./MHI_DURATION );
cvZero( dst );
cvMerge( mask, 0, 0, 0, dst );
// calculate motion gradient orientation and valid orientation mask
cvCalcMotionGradient( mhi, mask, orient, MAX_TIME_DELTA, MIN_TIME_DELTA, 3 );
if( !storage )
storage = cvCreateMemStorage(0);
else
cvClearMemStorage(storage);
// segment motion: get sequence of motion components
// segmask is marked motion components map. It is not used further
seq = cvSegmentMotion( mhi, segmask, storage, timestamp, MAX_TIME_DELTA );
// iterate through the motion components,
// One more iteration (i == -1) corresponds to the whole image (global motion)
for( i = -1; i < seq->total; i++ ) {
if( i < 0 ) { // case of the whole image
comp_rect = cvRect( 0, 0, size.width, size.height );
color = CV_RGB(255,255,255);
magnitude = 100;
}
else { // i-th motion component
comp_rect = ((CvConnectedComp*)cvGetSeqElem( seq, i ))->rect;
if( comp_rect.width + comp_rect.height < 100 ) // reject very small components
continue;
color = CV_RGB(255,0,0);
magnitude = 30;
}
// select component ROI
cvSetImageROI( silh, comp_rect );
cvSetImageROI( mhi, comp_rect );
cvSetImageROI( orient, comp_rect );
cvSetImageROI( mask, comp_rect );
// calculate orientation
angle = cvCalcGlobalOrientation( orient, mask, mhi, timestamp, MHI_DURATION);
angle = 360.0 - angle; // adjust for images with top-left origin
count = cvNorm( silh, 0, CV_L1, 0 ); // calculate number of points within silhouette ROI
cvResetImageROI( mhi );
cvResetImageROI( orient );
cvResetImageROI( mask );
cvResetImageROI( silh );
// check for the case of little motion
if( count < comp_rect.width*comp_rect.height * 0.05 )
continue;
// draw a clock with arrow indicating the direction
center = cvPoint( (comp_rect.x + comp_rect.width/2),
(comp_rect.y + comp_rect.height/2) );
cvCircle( dst, center, cvRound(magnitude*1.2), color, 3, CV_AA, 0 );
cvLine( dst, center, cvPoint( cvRound( center.x + magnitude*cos(angle*CV_PI/180)),
cvRound( center.y - magnitude*sin(angle*CV_PI/180))), color, 3, CV_AA, 0 );
}
}
void
MotionCapture::HUD(char* title, int nArgs, ...) {
// img - Used for getting the arguments
IplImage *img;
// DispImage - the image in which input images are to be copied
IplImage *DispImage;
int size;
int i;
int m, n;
int x, y;
// w - Maximum number of images in a row
// h - Maximum number of images in a column
int w, h;
// scale - How much we have to resize the image
float scale;
int max;
// If the number of arguments is lesser than 0 or greater than 12
// return without displaying
if(nArgs <= 0) {
printf("Number of arguments too small....\n");
return;
}
else if(nArgs > 12) {
printf("Number of arguments too large....\n");
return;
}
// Determine the size of the image,
// and the number of rows/cols
// from number of arguments
else if (nArgs == 1) {
w = h = 1;
size = 300;
}
else if (nArgs == 2) {
w = 2; h = 1;
size = 300;
}
else if (nArgs == 3 || nArgs == 4) {
w = 2; h = 2;
size = 350;
}
else if (nArgs == 5 || nArgs == 6) {
w = 3; h = 2;
size = 200;
}
else if (nArgs == 7 || nArgs == 8) {
w = 4; h = 2;
size = 200;
}
else {
w = 4; h = 3;
size = 150;
}
// Create a new 3 channel image
DispImage = cvCreateImage( cvSize( 50 + size*w, 60 + size*h), 8, 3 );
// Used to get the arguments passed
va_list args;
va_start(args, nArgs);
// Loop for nArgs number of arguments
for (i = 0, m = 20, n = 20; i < nArgs; i++, m += (20 + size)) {
// Get the Pointer to the IplImage
img = va_arg(args, IplImage*);
// Check whether it is NULL or not
// If it is NULL, release the image, and return
if(img == 0) {
printf("Invalid arguments");
cvReleaseImage(&DispImage);
return;
}
// Find the width and height of the image
x = img->width;
y = img->height;
// Find whether height or width is greater in order to resize the image
max = (x > y)? x: y;
// Find the scaling factor to resize the image
scale = (float) ( (float) max / size );
// Used to Align the images
if( i % w == 0 && m!= 20) {
m = 20;
n+= 20 + size;
}
// Set the image ROI to display the current image
cvSetImageROI(DispImage, cvRect(m, n, (int)( x/scale ), (int)( y/scale )));
// Resize the input image and copy the it to the Single Big Image
cvResize( img, DispImage );
// Reset the ROI in order to display the next image
cvResetImageROI(DispImage);
}
// Create a new window, and show the Single Big Image
cvNamedWindow( title, 1 );
cvShowImage( title, DispImage);
//cvWaitKey();
//cvDestroyWindow(title);
// End the number of arguments
va_end(args);
// Release the Image Memory
//cvReleaseImage(&DispImage);
}