int main(int argc, const char * argv[])
{
int threshold;
int row,col,maxcolor;
char* pgmFile="/Users/oo/Desktop/Eclipse_Projects/CS464_P1_PatternRecog/TestImages/square_and_circle_1024_1024.pgm";
int* image;
//read image data into one array from pgm file
image = getImage(pgmFile, &row, &col, &maxcolor);
//Step 1: calculate and print the histoGram data
int* histo = getHistoGram((int*)image, row*col);
//Step 2: calculate the threshold value via Ostu algo.
threshold = otsuThreshold(histo,maxcolor);
//Step 3: convert the grey image into binary image
int* binaryImage = convertToBinary((int*)image, row*col, threshold);
//Step 4: connectivity analysis
int* connectImage = connectAnalysis(binaryImage, row,col);
//Step5: pattern analysis, can differentiate circle and square only
patternAnalysis(connectImage, row, col);
//release the dynamic memory allocated
free(histo);
free(binaryImage);
free(connectImage);
free(image);
return 0;
}
Mat Processor::Binaryzation_otsu(Mat &image)
{
int ThresNum1 = peak_valleyThreshold(image);
int ThresNum2 = otsuThreshold(image);
imwrite("C:\\Users\\43597_000\\Desktop\\roi.jpg",image);
int ThresNum = (ThresNum1 + ThresNum2) / 2;
// 二值化
threshold(image, image, ThresNum, 255, THRESH_BINARY);
return image;
}
/*************************************************************************
dis_angle(float* distance,float* angle )
描述：角度与偏移量的读取函数
输入参数：
输出参数：float* distance,float* angle
*************************************************************************/
void dis_angle(float* distance,float* angle ) //通过前排巡线板标记点最大、小值与后排标记点比较，得出角度和偏移量
{
u8 a,b,c,d;
otsuThreshold(buffer_blue_front,&a,&b); // 前排巡线板最大值，最小值
Now.Front_Data_Min=a;
Now.Front_Data_Max=b;
// otsuThreshold(buffer_blue_behind,&c,&d); // 后排巡线板最大值，最小值
// Now.Behind_Data_Min=c;
// Now.Behind_Data_Max=d;
// if(abs(Now.Front_Data_Min-Last.Front_Data_Min)<5) //比较上一次与现在值
// { //计算角度
// *angle=atan((Now.Front_Data_Min+Now.Front_Data_Max-Now.Behind_Data_Min-Now.Behind_Data_Max)*L/2*L_distance);
// //计算偏移量
// *distance=(Now.Front_Data_Min+Now.Front_Data_Max-Now.Behind_Data_Min-Now.Behind_Data_Max)/2-middle_number;
// }
// else
// Last.Front_Data_Min=Now.Front_Data_Min;
// Last.Front_Data_Max=Now.Front_Data_Max;
// Last.Behind_Data_Min=Now.Behind_Data_Min;
// Last.Behind_Data_Max=Now.Behind_Data_Max;
}
//Main tracking Algorithm
void AnalysisModule::larvaFind(uchar * img, int imWidth, int imHeight, int frameInd){
input = cv::Mat(imHeight,imWidth,CV_8UC1,NULL);
input.data = img;
if(output.rows != imHeight | output.cols != imWidth) output.create(imHeight,imWidth,CV_8UC1);
int nextInd = (index+1)%sampleInd.size();
//for Profiling
tic();
sampleInd[nextInd] = frameInd;
sampleTime[nextInd] = frameInd * frameIntervalMS;
//On first image, automatically determine threshold level using the Otsu method
// Minimizes within group variance of thresholded classes. Should land on the best boundary between backlight and larva
if(index == -1) threshold = otsuThreshold(img,imWidth*imHeight);
//Can speed this up by applying to a roi bounding box a bit larger than the previous one
//Simple inverted binary threshold of the image
cv::threshold(input,output,threshold,255,CV_THRESH_BINARY_INV); profile[0] = toctic();
//Detect Contours in the binary image
cv::findContours(output,contours,CV_RETR_EXTERNAL,CV_CHAIN_APPROX_NONE); profile[1] = toctic();
//No contours detected
if (contours.size() == 0) {
return;
}
//find contour with largest perimeter length
double maxLen = 0; int maxInd = -1;
double cLen;
for(int i=0; i<contours.size(); i++){
cLen = cv::arcLength(cv::Mat(contours[i]), false);
if(cLen >= maxLen){ maxLen = cLen; maxInd = i; };
}
//Check to make sure that the perimeter is a larva by simple size analysis
//(larva should have a certain perimeter length at 8.1um/pixel)
cLarva[nextInd] = contours[maxInd];
//calculate bounding box
bBox[nextInd] = cv::boundingRect(cv::Mat(cLarva[nextInd])); profile[2] = toctic();
//Calculate fourier coefficients
fourierDecompose(cLarva[nextInd],nFourier,fourier[nextInd]);
centroid[nextInd] = cv::Point2f(fourier[nextInd][0][AX],fourier[nextInd][0][AY]); profile[3] = toctic();
//Reconstruct the estimated boundary
fourierReconstruct(fourier[nextInd],cFit,fitRes); profile[4] = toctic();
//Calculate Curvature
perimeterCurvature(cFit,curve,fitRes/8); profile[5] = toctic();
//Find head and tail based on curvature minimums (small angle = sharp region)
findHeadTail(cFit,curve,headTail);
head[nextInd] = headTail[0];
tail[nextInd] = headTail[1]; profile[6] = toctic();
//Calculate Skeleton
skeletonCalc(cFit,skeleton,headTail,length[nextInd],neck[nextInd]); profile[7] = toctic();
//Calculate bearing and head angle to bearing
bodyAngles(tailBearingAngle[nextInd], headToBodyAngle[nextInd], head[nextInd], neck[nextInd], tail[nextInd]); profile[8] = toctic();
//Capture stage position
stagePos[nextInd] = cv::Point(gui->stageThread->xpos,gui->stageThread->ypos);
//Keep track of entire history with a sample every 30 frames
if((nextInd % 30) == 0){
fullTrack[(fullTrackInd+1)%fullTrack.size()].x = stagePos[nextInd].x/gui->stageThread->tickPerMM_X+centroid[nextInd].x*gui->camThread->umPerPixel/1000.0;
fullTrack[(fullTrackInd+1)%fullTrack.size()].y = stagePos[nextInd].y/gui->stageThread->tickPerMM_Y+centroid[nextInd].y*gui->camThread->umPerPixel/1000.0;
fullTrackStim[(fullTrackInd+1)%fullTrack.size()] = binStimMax;
binStimMax = 0; //updated from stimThread
fullTrackInd++;
}
//Calculate Velocities of head and tail
calcVelocities(nextInd);
//Spew out profiling info
//for(int i=0; i<9; i++) qDebug("%d: %.4fms",i,profile[i]*1000);
//qDebug("\n");
index++;
};
