static int openVideo(cv::VideoCapture &capture, std::string filename){
std::cout << "abriendo video " << filename << std::endl;
capture.open(filename);
if (!capture.isOpened()) {
std::cout << "abriendo camara " << filename << std::endl;
int id_webcam = std::atoi(filename.c_str());
capture.open(id_webcam);
}
if (!capture.isOpened()) {
// std::cout << "no puedo abrir " << filename << std::endl;
return 0;
}
return 1;
}
bool readParameters(int argc, char** argv)
{
if (argc<4) {
usage();
return false;
}
TheInputVideo=argv[1];
/* // read input video
if (TheInputVideo=="live") TheVideoCapturer.open(0);
else TheVideoCapturer.open(argv[1]);
if (!TheVideoCapturer.isOpened())
{
cerr<<"Could not open video"<<endl;
return false;
}*/
//read from camera
if (TheInputVideo=="live") TheVideoCapturer.open(0);
else TheVideoCapturer.open(TheInputVideo);
if (!TheVideoCapturer.isOpened())
{
cerr<<"Could not open video"<<endl;
return -1;
}
// read intrinsic file
try {
CameraParams.readFromXMLFile(argv[2]);
} catch (std::exception &ex) {
cout<<ex.what()<<endl;
return false;
}
// read board file
try {
TheBoardConfig.readFromFile(argv[3]);
} catch (std::exception &ex) {
cout<<ex.what()<<endl;
return false;
}
if(argc>4) TheMarkerSize=atof(argv[4]);
else TheMarkerSize=1.;
return true;
}
void mono_handler(const lcm_recv_buf_t *rbuf, const char* channel, const lcmt_stereo *msg, void *user) {
// open the frame of the video specified by the message
// check to see if the current video is the correct video file
if (current_video_number != msg->video_number) {
video_capture.release();
std::string newfile = GetMonoFilename(msg->timestamp, msg->video_number);
if (newfile.empty()) {
return;
}
std::cout << "Opening file: " << newfile << std::endl;
if (!video_capture.open(newfile)) {
std::cerr << "Failed to open file: " << newfile << std::endl;
return;
}
current_video_number = msg->video_number;
}
video_capture.set(CV_CAP_PROP_POS_FRAMES, msg->frame_number + frame_offset);
cv::Mat frame;
video_capture >> frame;
SendImageOverLcm(lcm_, image_channel, frame);
}
void initVideoStream(cv::VideoCapture &cap)
{
if (cap.isOpened())
cap.release();
cap.open(0); // open the default camera
}
void InitOpenCVModules() //OPENCV 데이터들의 초기화
{
/*----------------------*/
//
//
//
//
/*-----------------------*/
if (Webcam_mode)
{
camera.open(Webcam_number);
if (!camera.isOpened()) //소스 영상 에러체크
{
//error in opening the video input
cerr << "Unable to open Camera Stream" << endl;
exit(EXIT_FAILURE);
}
camera >> Current_Frame; //카메라 소스에서 Current Frame으로 데이터를 넣어준다.
}
else
{
void open(string name, int type){
capture_type = type;
if(capture_type==0){
videoCapture.open(name);
}else{
imageCapture.open(name);
}
};
bool SubsExtractor::open(string file)
{
videofile = file;
bool o = cap->open(videofile);
StartFrame = 0;
EndFrame = cap->get(CV_CAP_PROP_FRAME_COUNT);
return o;
}
bool init_opencv()
{
if (!capture.open(0)) {
std::cerr << "error: capture.open() failed..." << std::endl;
exit(-1);
}
capture.set(CV_CAP_PROP_FRAME_WIDTH, 640);
capture.set(CV_CAP_PROP_FRAME_HEIGHT, 480);
result_img.create(cv::Size(640, 480), CV_8UC3);
return true;
}
void openCamera(cv::VideoCapture& cap, int argc, const char** argv) {
cv::CommandLineParser parser(argc, argv, keys);
int camNum = parser.get<int>("1");
cap.open(camNum);
if (!cap.isOpened()) {
//help();
std::cout << "***Could not initialize capturing...***\n";
std::cout << "Current parameter's value: \n";
exit (-1);
}
cap.set(CV_CAP_PROP_FRAME_WIDTH, 160);
cap.set(CV_CAP_PROP_FRAME_HEIGHT, 120);
}
void initParams::readAndSet(cv::VideoCapture& cap, char* input){
cv::FileStorage fs(input, cv::FileStorage::READ);
if(fs.isOpened()){
fs.open(input, cv::FileStorage::READ);
fs["frameWidth"] >> frameWidth;
fs["frameHeight"] >> frameHeight;
fs["fps"] >> fps;
fs["camId"] >> camId;
fs["generateData"] >> genData;
fs["visualizeData"] >> visualize;
fs["writeVideo"] >> writeVideo;
fs["generatePerfData"] >> genPerfData;
fs["videoFile"] >> videoFile;
fs.release();
cap.open(videoFile);
if(!cap.isOpened()){
std::cerr << "Could not access video file: " << videoFile << std::endl;
exit(EXIT_FAILURE);
}
}
void init()
{
//摄像头
camera.open(0);
if(!camera.isOpened())
{
std::cout << "Can not find camera!" << std::endl;
exit(-1);
}
camera.set(cv::CAP_PROP_FRAME_WIDTH,160);
camera.set(cv::CAP_PROP_FRAME_HEIGHT,120);
/* double width = camera.get(cv::CAP_PROP_FRAME_WIDTH);
double height = camera.get(cv::CAP_PROP_FRAME_HEIGHT);
std::cout << "width:" << width << "\t";
std::cout << "height:" << height << "\t" << std::endl;*/
face_cascade_name = "/usr/share/OpenCV/haarcascades/haarcascade_frontalface_alt.xml";
if(!face_cascade.load(face_cascade_name))
{
std::cout << "can not find face_cascade_file!" << std::endl;
exit(-1);
}
running = true;
pthread_mutex_init(&mutexLock, NULL);
pthread_create(&grabThread, NULL, grabFunc, NULL);
//Setup edi robot mraa control lib
spider_init();
signal(SIGINT, clear);
signal(SIGTERM, clear);
}
DWORD WINAPI video_test(void* data) {
MyFrame* f = (MyFrame*)data;
wxClientDC dc(f->left_bottom);
cap.open(0);
cv::Mat img;
cap >> img;
Sleep(2000);
initFrame->Hide();
// 开始显示
for (int i = 0; i < 50; i++) {
f->SetTransparent(i + 206);
Sleep(i / 10);
}
while (cap>>img,!img.empty()) {
IplImage image = img.operator IplImage();
wxImage wximg = wx_from_cv(&image);
wximg.Rescale(320, 240);
dc.DrawBitmap(wxBitmap(wximg), wxDefaultPosition);
}
return 0;
}
int main(int argc, char**argv)
{
capture.open(0);
if (capture.isOpened() == false)
{
std::cerr << "no capture device found" << std::endl;
return 1;
}
capture.set(CV_CAP_PROP_FRAME_WIDTH, vgaSize.width);
capture.set(CV_CAP_PROP_FRAME_HEIGHT, vgaSize.height);
if (capture.get(cv::CAP_PROP_FRAME_WIDTH) != (double)vgaSize.width || capture.get(cv::CAP_PROP_FRAME_HEIGHT) != (double)vgaSize.height)
{
std::cerr << "current device doesn't support " << vgaSize.width << "x" << vgaSize.height << " size" << std::endl;
return 2;
}
cv::Mat image;
capture >> image;
cv::namedWindow(windowName);
cv::imshow(windowName, image);
initCuda();
initArray(image);
char key = -1;
enum device statusDevice = useCpuSimd;
enum precision statusPrecision = precisionFloat;
int index = 1;
cv::Mat stub = cv::imread(imagePath[index][0], cv::IMREAD_UNCHANGED);
cv::Mat gain = cv::Mat(stub.rows, stub.cols/2, CV_16SC1, stub.data);
double elapsedTime;
while (isFinish(key) == false)
{
capture >> image;
switch (key)
{
case 'h':
case 'H':
// switch to half precision
statusPrecision = precisionHalf;
std::cout << std::endl << header << "half " << std::endl;
stub = cv::imread(imagePath[index][0], cv::IMREAD_UNCHANGED);
gain = cv::Mat(stub.rows, stub.cols/2, CV_16SC1, stub.data);
break;
case 'f':
case 'F':
// switch to single precision
statusPrecision = precisionFloat;
std::cout << std::endl << header << "single" << std::endl;
stub = cv::imread(imagePath[index][1], cv::IMREAD_UNCHANGED);
gain = cv::Mat(stub.rows, stub.cols, CV_32FC1, stub.data);
break;
case 'b':
case 'B':
// switch to gray gain
statusPrecision = precisionByte;
std::cout << std::endl << header << "char" << std::endl;
gain = cv::imread(imagePath[index][2], cv::IMREAD_GRAYSCALE);
break;
case '0':
case '1':
index = key - '0';
switch (statusPrecision)
{
case precisionHalf:
// precision half
stub = cv::imread(imagePath[index][0], cv::IMREAD_UNCHANGED);
gain = cv::Mat(stub.rows, stub.cols/2, CV_16SC1, stub.data);
break;
case precisionFloat:
// precision single
stub = cv::imread(imagePath[index][1], cv::IMREAD_UNCHANGED);
gain = cv::Mat(stub.rows, stub.cols, CV_32FC1, stub.data);
break;
case precisionByte:
// precision single
gain = cv::imread(imagePath[index][2], cv::IMREAD_GRAYSCALE);
break;
default:
break;
}
break;
case 'c':
case 'C':
std::cout << std::endl << "Using CPU SIMD " << std::endl;
statusDevice = useCpuSimd;
break;
case 'g':
case 'G':
std::cout << std::endl << "Using GPU " << std::endl;
statusDevice = useGpu;
break;
default:
break;
}
if (statusDevice == useCpuSimd)
{
elapsedTime = multiplyImage(image, gain);
}
else
{
#ifdef HAVE_CUDA
// CUDA
elapsedTime = multiplyImageCuda(image, gain);
#endif // HAVE_CUDA
}
computeStatistics(elapsedTime, key);
if (key == 's' || key == 'S')
{
cv::imwrite(dumpFilename, image);
}
cv::imshow(windowName, image);
key = cv::waitKey(1);
}
std::cout << std::endl;
cv::destroyAllWindows();
releaseArray();
return 0;
}
void OpenCVTemplateApp::makeGUI() {
interface->clear();
interface->addButton("load image", [this] {
auto path = ci::app::getOpenFilePath();
image = cv::imread(path.string());
std::cout <<"cols "<<image.cols << std::endl;
std::cout <<"rows "<<image.rows << std::endl;
std::cout <<"channels "<<image.channels() << std::endl;
imageTexture = gl::Texture::create(fromOcv(image));
});
interface->addButton("load video", [this] {
auto path = ci::app::getOpenFilePath();
video.open(path.string());
frameWidth = video.get(cv::CAP_PROP_FRAME_WIDTH);
frameHeight = video.get(cv::CAP_PROP_FRAME_HEIGHT);
totalFrames = video.get(cv::CAP_PROP_FRAME_COUNT);
video.read(frame);
if(isGrayScale) {
cv::cvtColor(frame, frame, cv::COLOR_BGR2GRAY);
}
frameTexture = gl::Texture::create(fromOcv(frame));
makeGUI();
});
interface->addSeparator();
if(frameTexture) {
interface->addParam("gray scale", &isGrayScale).updateFn([this] {
video.retrieve(frame);
if(isGrayScale) {
cv::cvtColor(frame, frame, cv::COLOR_BGR2GRAY);
}
frameTexture = gl::Texture::create(fromOcv(frame));
makeGUI();
});
interface->addParam("nb of feature",&nbOfFeaturePoints).min(1).max(1000);
if(isGrayScale) {
interface->addButton("get feature points", [this] {
cv::goodFeaturesToTrack(frame, featurePoints, nbOfFeaturePoints, 0.01, 10, cv::Mat(), 3, 0, 0.04);
});
}
interface->addSeparator();
interface->addParam("frame",&frameIndex).min(0).max(totalFrames-1).step(1).updateFn([this] {
video.set(cv::CAP_PROP_POS_FRAMES,frameIndex);
video.read(frame);
if(isGrayScale) {
cv::cvtColor(frame, frame, cv::COLOR_BGR2GRAY);
}
frameTexture = gl::Texture::create(fromOcv(frame));
});
interface->addSeparator();
interface->addParam("speed", &frameSpeed).min(1).max(1000).step(1);
interface->addButton("play",[this] {
currentState = PLAY;
makeGUI();
});
if(currentState == PLAY) {
interface->addButton("pause",[this] {
currentState = PAUSE;
makeGUI();
});
}
}
}
void *cameraThread(void *arg){
std::vector<cmdData> cmd;
cv::Mat frame;
cv::namedWindow("Color", CV_WINDOW_AUTOSIZE); //create a window with the name "MyWindow"
cv::namedWindow("Thresholded", CV_WINDOW_AUTOSIZE); //create a window with the name "HSV"
//cv::namedWindow( "Contours", CV_WINDOW_AUTOSIZE );
int initShutter = 242;
//int initShutter = 0;
int shutterVal = initShutter;
int cannyMin = 30;
int blockSize = 89;
int fps = 60;
// Shutter slider
cv::createTrackbar("Shutter","Color",&shutterVal,4095,shutterCB,NULL);
// Canny treshold
cv::createTrackbar("Threshold","Color",&cannyMin,255,NULL,NULL);
cv::createTrackbar("BlockSize","Color",&blockSize,255,NULL,NULL);
cv::Mat colorFrame;
cv::Mat tresholdedFrame;
cv::Mat hsvFrame;
cv::Mat grey,tmp;
cv::Mat contourOutput;
cap.open(CV_CAP_DC1394); // Open first firewire camera. in 2.3 use CV_CAP, in 2.5 use CV::CAP
cap.set(CV_CAP_PROP_WHITE_BALANCE_BLUE_U,794); // 736
cap.set(CV_CAP_PROP_WHITE_BALANCE_RED_V,437);
cap.set(CV_CAP_PROP_EXPOSURE,initShutter); // "Shutter" in coriander
cap.set(CV_CAP_PROP_FPS,fps);
cap.set(CV_CAP_PROP_GAMMA,0);
cap.set(CV_CAP_PROP_GAIN,30);
while(runState){
cap >> frame;
std::vector<std::vector<cv::Point> > contours;
std::vector<cv::Vec4i> hierarchy;
// Get color image, decode bayer BGGR.
cv::cvtColor(frame,colorFrame,CV_BayerBG2RGB,0);
cv::cvtColor(colorFrame, grey, CV_RGB2GRAY );
// Remove gripper from img
cv::Rect roi = cv::Rect(0,0,640,360);
cv::Mat submatrix = cv::Mat(grey,roi);
//submatrix.setTo(cv::Scalar(255));
cv::threshold(submatrix,tresholdedFrame,cannyMin,255,cv::THRESH_BINARY_INV);
if(blockSize % 2 == 0){
//Adaptive threshold block size SKAL være ulige..
blockSize = blockSize -1;
}
// cv::adaptiveThreshold(submatrix,tresholdedFrame,255,cv::ADAPTIVE_THRESH_GAUSSIAN_C,cv::THRESH_BINARY_INV,blockSize,0);
cv::Moments mu;
mu = cv::moments(tresholdedFrame,true);
// Find center
cv::Point2f mc = cv::Point2f( mu.m10/mu.m00 , mu.m01/mu.m00 );
// Count non zero pixels. Used for determining if we are screwed (getting large "white" areas.)
cameraError.areaOfObject = cv::countNonZero(tresholdedFrame);
// Draw it - convert to RGB to we can draw on it with colors
cv::cvtColor(tresholdedFrame, tresholdedFrame, CV_GRAY2RGB);
//cv::Mat drawing = cv::Mat::zeros( tresholdedFrame.size(), CV_8UC3 );
cv::Scalar color = cv::Scalar( 0,255,0 );
cv::circle( tresholdedFrame, mc, 5, color, -1, 8, 0 );
// Calculate distance from center of image
cv::Size s = tresholdedFrame.size();
cv::Point centerOfFrame = cv::Point(s.width/2,s.height/2);
float distX = centerOfFrame.x-mc.x;
float distY = centerOfFrame.y-mc.y;
cameraError.x = distX;
cameraError.y = distY;
/*
OLD STUFF BELOW
*/
// cv::Canny( tresholdedFrame, tmp, cannyMin, cannyMin*2, 3 );
//
// cv::findContours(tmp,contours,hierarchy,CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE, cv::Point(0, 0));
//
// int blablalba = 0 ;
//
// /// Get the moments
// std::vector<cv::Moments> mu(contours.size() );
// for( int i = 0; i < contours.size(); i++ )
// { mu[i] = moments( contours[i], false ); }
//
// /// Get the mass centers:
// std::vector<cv::Point2f> mc( contours.size() );
// for( int i = 0; i < contours.size(); i++ )
// { mc[i] = cv::Point2f( mu[i].m10/mu[i].m00 , mu[i].m01/mu[i].m00 ); }
//
//
// std::vector<cv::Rect> boundRect( contours.size() );
// std::vector<std::vector<cv::Point> > contours_poly( contours.size() );
// // Draw some countours, and maybe some bounding box
//
//
// cv::Mat drawing = cv::Mat::zeros( tresholdedFrame.size(), CV_8UC3 );
//
//
// cv::Point centerOfBlock;
//
// for( int i = 0; i< contours.size(); i++ )
// {
//
// // Filter out small areas, and filter out contours that are holes
// // See http://stackoverflow.com/questions/8461612/using-hierarchy-in-findcontours-in-opencv
// if(cv::contourArea(contours[i]) < 200 ){
//
//
// }else{
//
//
// cv::Scalar color = cv::Scalar( 0,255,0 );
// //draw center of mass
// cv::circle( drawing, mc[i], 5, color, -1, 8, 0 );
//
//
// cv::Point xx = cv::Point(boundRect[i].tl().x+(boundRect[i].width/2),boundRect[i].tl().y+(boundRect[i].height/2));
// //cv::circle( drawing, xx, 2, color, -1, 8, 0 );
//
// centerOfBlock = mc[i];
//
//
// }
// }
//
//
//
// /*
// * Calculate distance.
//
// */
//
//
//
// cv::Size s = tresholdedFrame.size();
//
// cv::Point centerOfFrame = cv::Point(s.width/2,s.height/2);
//
// float distX = centerOfFrame.x-centerOfBlock.x;
//
// float distY = centerOfFrame.y-centerOfBlock.y;
//
// if(centerOfBlock.x == 0 || centerOfBlock.y == 0 || (fabs(distX) < 3 && fabs(distY) < 3)){
//
//
//
//
// }else{
//
//
// // cout << "(Dist X, Dist Y) " << distX << ", " << distY << endl;
//
// cameraError.x = distX;
// cameraError.y = distY;
//
// // std::cout << outss << std::endl;
// /*cmdData tmp;
//
// tmp.x = x_out;
// tmp.y = y_out;
// tmp.z = 0;
// tmp.r = 0;
// tmp.p =0;
// tmp.ya =0;
// tmp.a = a_out;
// tmp.t_min = t_min_out;
// tmp.distX = distX;
// tmp.distY = distY;
//
// cmd.push_back(tmp);
// */
//
//
// }
/* END OLD STUFF */
if(!frame .data) break;
if(debugMonitor){
cv::imshow("Thresholded",tresholdedFrame); // Uncomment this line to see the actual picture. It will give an unsteady FPS
cv::imshow("Color",grey); // Uncomment this line to see the actual picture. It will give an unsteady FPS
//cv::imshow( "Center", drawing );
}
if(cv::waitKey(1) >= 27){ break; } // We wait 1ms - so that the frame can be drawn. break on ESC
}
cv::destroyWindow("Color"); //destroy the window with the name, "MyWindow"
cv::destroyWindow("Thresholded");
}
int main() {
// 打开摄像头
video_capture.open(0);
if (!video_capture.isOpened()){
std::cerr << "Could not open video" << endl;
return -1;
}
// 获取第一张图像，用于这设置参数
video_capture >> input_image;
// 读取摄像机参数
camera_params.readFromXMLFile("camera.yml");
camera_params.resize(input_image.size());
// 注册窗口
cv::namedWindow("thres",1);
cv::namedWindow("in",1);
marker_dector.getThresholdParams(threshold_param1, threshold_param2);
i_threshold_param1 = threshold_param1;
i_threshold_param2 = threshold_param2;
cv::createTrackbar("ThresParam1", "in",&i_threshold_param1, 13, cvTackBarEvents);
cv::createTrackbar("ThresParam2", "in",&i_threshold_param2, 13, cvTackBarEvents);
char key=0;
int index=0;
//capture until press ESC or until the end of the video
while( key!=27 && video_capture.grab())
{
//copy image
video_capture.retrieve(input_image);
//number of images captured
index++;
//for checking the speed
double tick = (double)cv::getTickCount();
//Detection of markers in the image passed
marker_dector.detect(input_image, markers, camera_params, marker_size);
//chekc the speed by calculating the mean speed of all iterations
average_time.first += ((double)cv::getTickCount() - tick) / cv::getTickFrequency();
average_time.second++;
std::cout << "Time detection=" << 1000 * average_time.first / average_time.second << " milliseconds" << endl;
//print marker info and draw the markers in image
input_image.copyTo(input_image_copy);
for(unsigned int i = 0; i < markers.size(); i++){
std::cout << markers[i] << std::endl;
markers[i].draw(input_image, Scalar(0, 0, 255), 2);
}
//draw a 3d cube in each marker if there is 3d info
if ( camera_params.isValid())
for(unsigned int i = 0; i < markers.size(); i++){
aruco::CvDrawingUtils::draw3dCube(input_image, markers[i], camera_params);
aruco::CvDrawingUtils::draw3dAxis(input_image, markers[i], camera_params);
}
//DONE! Easy, right?
cout << endl << endl << endl;
//show input with augmented information and the thresholded image
cv::imshow("in", input_image);
cv::imshow("thres", marker_dector.getThresholdedImage());
key=cv::waitKey(0);//wait for key to be pressed
}
return 0;
}
int main(int argc, char** argv)
{
//ROS
ROS_INFO("sks_vision without ui");
ros::init(argc, argv, "sks_vision_nui");
ros::NodeHandle nh;
ros::Rate loop_rate(1000);
// image_transport::ImageTransport it(nh);
ros::Publisher pub_m = nh.advertise<geometry_msgs::Vector3>("/sks_vision",1);
ros::Subscriber position_sub = nh.subscribe("/slam_out_pose",10,current_pos);
nh.getParam("/SKS/vision/auto_gray",para_auto_gray);
nh.getParam("/SKS/vision/gray_sum",para_threshold_num);
while(1){
cap.open(200);
if(cap.isOpened()) break;
else std::cout<<"camera isnt opened"<<std::endl;
}
while (nh.ok()){
cap >> cvframe;
if(!cvframe.empty()) {
frame = MatToQImage(cvframe);
// frame = func_Gray(frame);
frame = func_DeepGray(frame);
//==============================================================
int blacknum=0;
// for(int h=0;h<frame.height();h+=2){
// for(int w=0;w<frame.width();w+=2){
// if(qRed(frame.pixel(w,h))<=64&&qRed(frame.pixel(w,h))>=27) blacknum++;//60,20
// }
// }
// std::cout<<blacknum<<std::endl;
//===============================================================
//Threshold_bar --> Threshold
int gray;
if(para_auto_gray){
gray = para_threshold_num;
}else{
// if(blacknum>=BLACK_BLOCK){
// gray=70;
// }else{
gray = Average_Threshold(frame);
// gray = Qtsu(frame);
if(cu_pos.pose.position.x<-1.07+0.2&&cu_pos.pose.position.x>-3.14-0.2&&
cu_pos.pose.position.y<3.87+0.2&&cu_pos.pose.position.y>1.13-0.2/*&& !goal_one_send*/){
gray=24;
}else{
gray=58;
}
// if(gray > THRESHOLD_MAX) gray = THRESHOLD_MAX;
// else if(gray < THRESHOLD_MIN) gray = THRESHOLD_MIN;
}
frame = func_Threshold(frame,gray);
org_frame = frame;
for(int dot=2;dot < frame.height()-2;dot+=(frame.height()/16)){
for(int i=0;i<frame.width();i++){
frame.setPixel(i,dot,QColor(0,255,0).rgb());
}
}
//reset
memset(Position,0,sizeof(Position)/sizeof(Position[0][0]));
//Segmentation
for(int h=2;h < frame.height()-2;h+=(frame.height()/16)){
bw_sw = compareBlackWhite(org_frame,h);
if(bw_sw==0){
Segmentation1(org_frame,&frame,h,255);
}else if(bw_sw==1){
Segmentation1(org_frame,&frame,h,0);
}
}
if(push_p>2){
for(int i=0;i<push_p;i++){
total_x += Position[0][i];
total_y += Position[1][i];
}
double vector_x=total_x/push_p;
double vector_y=total_y/push_p;
double vector_h=frame.height()-1-vector_y;
double vector_w=(ROBOT_CENTER-1-vector_x)/vector_h;
for(int i=0;i<vector_h;i++){
frame.setPixel(ROBOT_CENTER-1-vector_w*i,frame.height()-1-i,QColor(0,0,255).rgb());
}
final_w = vector_x - (ROBOT_CENTER);
final_h = frame.height() - 1 - vector_y;
int sensor_x,sensor_y=100;
sensor_x = sensorPoint(org_frame,&frame,sensor_y);
final_w += sensor_x;
sensor_y=379;
final_h += sensor_y;
// if(sensor_x!=0) final_h += sensor_y;
sensor_y=380;
sensor_x = sensorPoint(org_frame,&frame,sensor_y);
final_w += sensor_x;
sensor_y=99;
final_h += sensor_y;
// if(sensor_x!=0) final_h += sensor_y;
ang=atan(final_w/final_h);
frame.setPixel(vector_x-1,vector_y-1,QColor(0,0,255).rgb());
frame.setPixel(vector_x-1,vector_y,QColor(0,0,255).rgb());
frame.setPixel(vector_x-1,vector_y+1,QColor(0,0,255).rgb());
frame.setPixel(vector_x,vector_y-1,QColor(0,0,255).rgb());
frame.setPixel(vector_x,vector_y,QColor(0,0,255).rgb());
frame.setPixel(vector_x,vector_y+1,QColor(0,0,255).rgb());
frame.setPixel(vector_x+1,vector_y-1,QColor(0,0,255).rgb());
frame.setPixel(vector_x+1,vector_y,QColor(0,0,255).rgb());
frame.setPixel(vector_x+1,vector_y+1,QColor(0,0,255).rgb());
vec3.z = ang;
}else{
vec3.z = 999;
}
//reset
total_x =0,total_y =0,push_p =0;//,h_max=0;
final_w =0,final_h =0;
total_black=0;total_white=0;
cvGray = QImageToCvMat(frame);
// cv::imshow(WINDOW,cvGray);
// cv::waitKey(1);
//ROS
pub_m.publish(vec3);
}
loop_rate.sleep();
ros::spinOnce();
}
}
int main(int argc, char* argv[])
{
signal(SIGINT, quitFunction);
// Simple parsing of the parameters related to the image acquisition
int xRes = 640;
int yRes = 480;
int cameraIndex = 0;
if (argc > 2) {
xRes = std::atoi(argv[1]);
yRes = std::atoi(argv[2]);
}
if (argc > 3) {
cameraIndex = std::atoi(argv[3]);
}
// The source of input images
capture.open(cameraIndex);
if (!capture.isOpened())
{
std::cerr << "Unable to initialise video capture." << std::endl;
return 1;
}
#ifdef OPENCV3
capture.set(cv::CAP_PROP_FRAME_WIDTH, xRes);
capture.set(cv::CAP_PROP_FRAME_HEIGHT, yRes);
#else
capture.set(CV_CAP_PROP_FRAME_WIDTH, xRes);
capture.set(CV_CAP_PROP_FRAME_HEIGHT, yRes);
#endif
cv::Mat inputImage;
// The tag detection happens in the Chilitags class.
chilitags::Chilitags chilitags;
// The detection is not perfect, so if a tag is not detected during one frame,
// the tag will shortly disappears, which results in flickering.
// To address this, Chilitags "cheats" by keeping tags for n frames
// at the same position. When tags disappear for more than 5 frames,
// Chilitags actually removes it.
// Here, we cancel this to show the raw detection results.
chilitags.setFilter(0, 0.0f);
cv::namedWindow("DisplayChilitags");
// Main loop, exiting when 'q is pressed'
for (; 'q' != (char) cv::waitKey(1) && sRunning; ) {
// Capture a new image.
capture.read(inputImage);
// Start measuring the time needed for the detection
int64 startTime = cv::getTickCount();
// Detect tags on the current image (and time the detection);
// The resulting map associates tag ids (between 0 and 1023)
// to four 2D points corresponding to the corners positions
// in the picture.
chilitags::TagCornerMap tags = chilitags.find(inputImage);
// Measure the processing time needed for the detection
int64 endTime = cv::getTickCount();
float processingTime = 1000.0f*((float) endTime - startTime)/cv::getTickFrequency();
// Now we start using the result of the detection.
// First, we set up some constants related to the information overlaid
// on the captured image
const static cv::Scalar COLOR(255, 0, 255);
// OpenCv can draw with sub-pixel precision with fixed point coordinates
static const int SHIFT = 16;
static const float PRECISION = 1<<SHIFT;
// We dont want to draw directly on the input image, so we clone it
cv::Mat outputImage = inputImage.clone();
for (const std::pair<int, chilitags::Quad> & tag : tags) {
int id = tag.first;
// We wrap the corner matrix into a datastructure that allows an
// easy access to the coordinates
const cv::Mat_<cv::Point2f> corners(tag.second);
// We start by drawing the borders of the tag
for (size_t i = 0; i < 4; ++i) {
cv::line(
outputImage,
PRECISION*corners(i),
PRECISION*corners((i+1)%4),
#ifdef OPENCV3
COLOR, 1, cv::LINE_AA, SHIFT);
#else
COLOR, 1, CV_AA, SHIFT);
#endif
}
// Other points can be computed from the four corners of the Quad.
// Chilitags are oriented. It means that the points 0,1,2,3 of
// the Quad coordinates are consistently the top-left, top-right,
// bottom-right and bottom-left corners.
// (i.e. clockwise, starting from top-left)
// Using this, we can compute (an approximation of) the center of
// tag.
cv::Point2f center = 0.5f*(corners(0) + corners(2));
cv::putText(outputImage, cv::format("%d", id), center,
cv::FONT_HERSHEY_SIMPLEX, 0.5f, COLOR);
}
// Some stats on the current frame (resolution and processing time)
cv::putText(outputImage,
cv::format("%dx%d %4.0f ms (press q to quit)",
outputImage.cols, outputImage.rows,
processingTime),
cv::Point(32,32),
cv::FONT_HERSHEY_SIMPLEX, 0.5f, COLOR);
// Finally...
cv::imshow("DisplayChilitags", outputImage);
}
cv::destroyWindow("DisplayChilitags");
capture.release();
return 0;
}
/**
* @function main
*/
int main( int argc, char* argv[] ) {
// Filter
gFilterLimits.resize(6);
//gFilterLimits << -0.35, 0.35, -0.70, 0.70, 1.5, 2.4; // Kinect
gFilterLimits << -1.0, 1.0, -1.5, 1.5, 0.35, 2.0; // Asus on top of Crichton
ObjectsDatabase mOd;
mOd.init_classifier();
mOd.load_dataset();
gCapture.open( cv::CAP_OPENNI2 );
if( !gCapture.isOpened() ) {
printf("\t [ERROR] Could not open the capture object \n");
return -1;
}
gCapture.set( cv::CAP_PROP_OPENNI2_MIRROR, 0.0 );
gCapture.set( cv::CAP_PROP_OPENNI_REGISTRATION, -1.0 );
gF = (float)gCapture.get( cv::CAP_OPENNI_DEPTH_GENERATOR_FOCAL_LENGTH );
cv::namedWindow( gWindowName, cv::WINDOW_AUTOSIZE );
::google::InitGoogleLogging( argv[0] );
for(;;) {
if( !gCapture.grab() ) {
printf("\t * ERROR Could not grab a frame \n");
return -1;
}
gCapture.retrieve( gRgbImg, cv::CAP_OPENNI_BGR_IMAGE );
if( gIsSegmentedFlag ) { drawSegmented(); }
cv::imshow( gWindowName, gRgbImg );
gCapture.retrieve( gPclMap, cv::CAP_OPENNI_POINT_CLOUD_MAP );
cv::imshow( gWindowName, gRgbImg );
char k = cv::waitKey(30);
if( k == 'q' ) {
printf("\t Finishing program \n");
break;
}
/** Recognize */
else if( k == 'i' ) {
// Process image
process();
gLabels.resize(gClusters.size() );
gIndex.resize(gClusters.size() );
// Store images
for( int i = 0; i < gClusters.size(); ++i ) {
int xl = gBoundingBoxes[i](0);
int yl = gBoundingBoxes[i](1);
int xw = gBoundingBoxes[i](2)-gBoundingBoxes[i](0);
int yw = gBoundingBoxes[i](3)-gBoundingBoxes[i](1);
cv::Mat img( gRgbImg, cv::Rect( xl, yl,
xw, yw ) );
// Predict
mOd.classify( img, gIndex[i], gLabels[i] );
cv::putText( gRgbImg,
gLabels[i], cv::Point(gBoundingBoxes[i](0), gBoundingBoxes[i](1) ),
cv::FONT_HERSHEY_SIMPLEX, 1,
gColors[i],
2 );
mOd.sayIt(gIndex[i]);
}
} // else
} // for
} // main
void Preprocessing_Wrap::VideoCaptureOpen(void)
{
cap.open(0);
}