I want to copy the rows 0, 2 and 4 of my matrix A into B, in this order.
Let A = [a0, a1, a2, a3, a4]^T , with a_i being row-vectors,
then B should be: [a0, a2, a4]^T.
The code below does what I want but I wonder whether there is a prettier solution (maybe using Eigen)?
#include <iostream>
#include <vector>
#include <opencv/cv.h>
int main(int argc, char **argv) {
const int num_points = 5;
const int vec_length = 3;
cv::Mat A(num_points, vec_length, CV_32FC1);
cv::RNG rng(0); // Fill A with random values
rng.fill(A, cv::RNG::UNIFORM, 0, 1);
// HACK Ugly way to fill that matrix .
cv::Mat B = cv::Mat(3,vec_length, CV_32FC1);
cv::Mat tmp0 = B(cv::Rect(0,0,vec_length,1));
cv::Mat tmp1 = B(cv::Rect(0,1,vec_length,1));
cv::Mat tmp2 = B(cv::Rect(0,2,vec_length,1));
A.row(0).copyTo(tmp0);
A.row(2).copyTo(tmp1);
A.row(4).copyTo(tmp2);
std::cout << "A: " << A << std::endl;
std::cout << "B: " << B << std::endl;
return 0;
}
The way in OpenCV 2.4.1 is:
A.row(0).copyTo(B.row(0));
A.row(2).copyTo(B.row(1));
A.row(4).copyTo(B.row(2));
I found push_back.
Create B with size 0 x vec_length and then use push_back to add the selected rows from A:
#include <iostream>
#include <vector>
#include <opencv/cv.h>
int main(int argc, char **argv) {
const int num_points = 5;
const int vec_length = 3;
cv::Mat A(num_points, vec_length, CV_32FC1);
cv::RNG rng(0); // Fill A with random values
rng.fill(A, cv::RNG::UNIFORM, 0, 1);
cv::Mat B = cv::Mat(0,vec_length, CV_32FC1);
B.push_back(A.row(0));
B.push_back(A.row(2));
B.push_back(A.row(4));
std::cout << "A: " << A << std::endl;
std::cout << "B: " << B << std::endl;
return 0;
}
Since they are non-contiguous I don't think there's any shortcut. In this particular case you could make the code cleaner with a loop:
for (int i=0; i<3; i++) {
cv::Mat tmp = B(cv::Rect(0,i,vec_length,1));
A.row(i * 2).copyTo(tmp);
}
Mat row = old.row(0);
old.row(0).copyTo(row);
new.push_back(row);
Related
I am currently working on a Computer Vision / Machine Learning project for university. Sadly, they only allow us to upload one single file and restrict the computation time too much. Hence I need to compute the matrices on my machine and store them in the same file as the code (22500 rows, 1 col and 100 rows + 22500 col and 100 rows + 1 col). I already found a way to export the data (link), but I'm not sure how to initialize the matrix.
What I've tried
#include <opencv/cv.h>
#include <opencv/highgui.h>
int main(int argc, char const *argv[])
{
float data[10] = {1,2,3,4,5,7,8,9,10,11};
cv::Mat A;
// Something is wrong with this line
A = cv::Mat(1, 10, cv::CV_32FC1, data);
return 0;
}
When I compile it, I get:
main.cc: In function ‘int main(int, const char**)’:
main.cc:10:16: error: expected primary-expression before ‘(’ token
A = cv::Mat(1, 10, cv::CV_32FC1, data);
^
In file included from /usr/include/opencv2/core/core_c.h:47:0,
from /usr/include/opencv/cv.h:63,
from main.cc:1:
main.cc:10:28: error: expected unqualified-id before ‘(’ token
A = cv::Mat(1, 10, cv::CV_32FC1, data);
^
Second try
#include <opencv/cv.h>
#include <opencv/highgui.h>
int main(int argc, char const *argv[])
{
float dataHeaderMat1[10] = {1,2,3,4,5,7,8,9,10,11};
cv::Mat matrix1;
// Something is wrong with this line
cv::cvInitMatHeader( &matrix1, 10, 1, CV_64FC1, dataHeaderMat1);
return 0;
}
gives
main.cc:10:5: error: ‘cvInitMatHeader’ is not a member of ‘cv’
cv::cvInitMatHeader( &matrix1, 10, 1, CV_64FC1, dataHeaderMat1);
^
The following works to declare and initialize a matrix:
#include <opencv/cv.h>
#include <opencv/highgui.h>
int main(int argc, char const *argv[])
{
float data[10] = {1,2,3,4,5,7,8,9,10,11};
cv::Mat A;
// Something is wrong with this line
A = cv::Mat(1, 10, CV_32FC1, data);
return 0;
}
However, I'm not too sure if this is the best way for big arrays.
You can try to save image to header file, like this:
#include <iostream>
#include <vector>
#include <string>
#include <fstream>
#include <opencv2/opencv.hpp>
using namespace cv;
using namespace std;
// uncomment for test
//#include "image.h"
int main(int argc, char **argv)
{
// This part creates header file from image.
Mat img=imread("D:\\ImagesForTest\\lena.jpg");
int w=img.cols;
int h=img.rows;
int channels=img.channels();
ofstream os("image.h");
os << "int rows=" << h << ";" << endl;
os << "int cols=" << w << ";" << endl;
os << "unsigned char d[]={" << endl;
for(int i=0;i<h;++i)
{
for(int j=0;j<w;++j)
{
if(i!=(w-1) || j!=(h-1))
{
Vec3b b=img.at<Vec3b>(i,j);
os << format("0x%02x,",b[0]);
os << format("0x%02x,",b[1]);
os << format("0x%02x,",b[2]);
}
}
}
Vec3b b=img.at<Vec3b>(w-1,h-1);
os << format("0x%02x,",b[0]);
os << format("0x%02x,",b[1]);
os << format("0x%02x",b[2]);
os << endl << "};" << endl;
os << "Mat I=Mat(rows,cols,CV_8UC3,d);" << endl;
os.close();
// To test uncomment commented part of code and comment uncommented.
// uncomment for test
/*
namedWindow("I");
imshow("I",I);
waitKey();
return 0;
*/
}
But be careful, not all IDEs likes such large files.
I am trying to classify my images whether characters are printed on surface or not.
For doing it.
First I take surf features of images with real images and manually defect real images to try create bag of words to an xml file and then try to predict.
however unless I use absolutely different image or totally cropped image my SVM classifier predicts as it is correct.
those are the images I used for train
https://www.dropbox.com/sh/xked9ywnibzv3tt/AADC0lP4WYAo3ddEDgvHpFhha/negative?dl=0
Here is my code.
#include <stdio.h>
#include <fcntl.h>
#include <sys/types.h>
#include <sys/stat.h>
#include "opencv2/core/core.hpp"
#include<dirent.h>
#include "opencv2/features2d/features2d.hpp"
#include "opencv2/highgui/highgui.hpp"
#include "opencv2/calib3d/calib3d.hpp"
#include "opencv2/nonfree/nonfree.hpp"
#include "opencv2/imgproc/imgproc.hpp"
#include <opencv2/ml/ml.hpp>
using namespace cv;
using namespace std;
Ptr<FeatureDetector> detector = FeatureDetector::create("SURF");
Ptr<DescriptorExtractor> descriptors = DescriptorExtractor::create("SURF");
string to_string(const int val) {
int i = val;
std::string s;
std::stringstream out;
out << i;
s = out.str();
return s;
}
Mat compute_features(Mat image) {
vector<KeyPoint> keypoints;
Mat features;
detector->detect(image, keypoints);
KeyPointsFilter::retainBest(keypoints, 1500);
descriptors->compute(image, keypoints, features);
return features;
}
BOWKMeansTrainer addFeaturesToBOWKMeansTrainer(String dir, BOWKMeansTrainer& bowTrainer) {
DIR *dp;
struct dirent *dirp;
struct stat filestat;
dp = opendir(dir.c_str());
Mat features;
Mat img;
string filepath;
#pragma loop(hint_parallel(4))
for (; (dirp = readdir(dp));) {
filepath = dir + dirp->d_name;
cout << "Reading... " << filepath << endl;
if (stat( filepath.c_str(), &filestat )) continue;
if (S_ISDIR( filestat.st_mode )) continue;
img = imread(filepath, 0);
features = compute_features(img);
bowTrainer.add(features);
}
return bowTrainer;
}
void computeFeaturesWithBow(string dir, Mat& trainingData, Mat& labels, BOWImgDescriptorExtractor& bowDE, int label) {
DIR *dp;
struct dirent *dirp;
struct stat filestat;
dp = opendir(dir.c_str());
vector<KeyPoint> keypoints;
Mat features;
Mat img;
string filepath;
#pragma loop(hint_parallel(4))
for (;(dirp = readdir(dp));) {
filepath = dir + dirp->d_name;
cout << "Reading: " << filepath << endl;
if (stat( filepath.c_str(), &filestat )) continue;
if (S_ISDIR( filestat.st_mode )) continue;
img = imread(filepath, 0);
detector->detect(img, keypoints);
bowDE.compute(img, keypoints, features);
trainingData.push_back(features);
labels.push_back((float) label);
}
cout << string( 100, '\n' );
}
int main() {
initModule_nonfree();
Ptr<DescriptorMatcher> matcher = DescriptorMatcher::create("FlannBased");
TermCriteria tc(CV_TERMCRIT_ITER + CV_TERMCRIT_EPS, 10, 0.001);
int dictionarySize = 1000;
int retries = 1;
int flags = KMEANS_PP_CENTERS;
BOWKMeansTrainer bowTrainer(dictionarySize, tc, retries, flags);
BOWImgDescriptorExtractor bowDE(descriptors, matcher);
string dir = "/positive/", filepath;
DIR *dp;
struct dirent *dirp;
struct stat filestat;
cout << "Add Features to KMeans" << endl;
addFeaturesToBOWKMeansTrainer("/positive/", bowTrainer);
addFeaturesToBOWKMeansTrainer("/negative/", bowTrainer);
cout << endl << "Clustering..." << endl;
Mat dictionary = bowTrainer.cluster();
bowDE.setVocabulary(dictionary);
Mat labels(0, 1, CV_32FC1);
Mat trainingData(0, dictionarySize, CV_32FC1);
cout << endl << "Extract bow features" << endl;
computeFeaturesWithBow("/positive/", trainingData, labels, bowDE, 1);
computeFeaturesWithBow("/negative/", trainingData, labels, bowDE, 0);
CvSVMParams params;
params.kernel_type=CvSVM::LINEAR;
params.svm_type=CvSVM::C_SVC;
params.gamma=5;
params.C=100;
params.term_crit=cvTermCriteria(CV_TERMCRIT_NUMBER,100,0.000001);
CvSVM svm;
cout << endl << "Begin training" << endl;
bool res =svm.train(trainingData,labels,cv::Mat(),cv::Mat(),params);
svm.save("classifier.xml");
//CvSVM svm;
svm.load("classifier.xml");
vector<KeyPoint> cameraKeyPoints;
Mat rotated = imread("test.jpg",0);
Mat featuresFromimage;
detector->detect(rotated, cameraKeyPoints);
bowDE.compute(rotated, cameraKeyPoints, featuresFromimage);
cout <<"anar:"<< svm.predict(featuresFromimage) << endl;
imshow("edges", rotated);
cvWaitKey(0);
return 0;
}
Question 1: since those images are too similiar how can I do prediction like
if similiarity > %80
"correct"
else
"defected"
Question 2 Since this character defection is too rare in a factory it is going to very very tough to get a lot of defected images to train. Manually create defect on this images is a correct solution ? if not what I can actually do ?
Question 3
What kind of preprocessing methods I can actually do on this kind of images to increase accuracy of SVM ?
thank you
I am a newbie to OpenCV, so pls bear with me.. I am trying to dump the histogram Mat object for the given image.. It fails with the below error - Any help appreciated...
The first cout in the below program i.e of the loaded image prints successfully - While the second cout of the hist of the image fails with the below error
OpenCV Error: Assertion failed (m.dims <= 2) in FormattedImpl, file /mycode/ws/opencv/opencv-3.0.0-beta/modules/core/src/out.cpp, line 86
libc++abi.dylib: terminating with uncaught exception of type cv::Exception: /mycode/ws/opencv/opencv-3.0.0-beta/modules/core/src/out.cpp:86: error: (-215) m.dims <= 2 in function FormattedImpl
Here is the complete code
#include <stdio.h>
#include <string>
#include <opencv2/opencv.hpp>
using namespace std;
using namespace cv;
int main(int argc, char** argv) {
if (argc != 2) {
printf("usage: opencv.out <Image_Path>\n");
return -1;
}
string imagePath = (argv[1]);
cout << "loading image..." << imagePath << endl;
Mat image = imread(imagePath, 1);
Mat hist;
int imgCount = 1;
int dims = 3;
const int histSizes[] = {4, 4, 4};
const int channels[] = {0, 1, 2};
float rRange[] = {0, 256};
float gRange[] = {0, 256};
float bRange[] = {0, 256};
const float *ranges[] = {rRange, gRange, bRange};
Mat mask = Mat();
calcHist(&image, imgCount, channels, mask, hist, dims, histSizes, ranges);
cout << image << "Loaded image..." << endl;
cout << "Hist of image..." << hist;
return 0;
}
Based on the OpenCV 2.4.9 source code:
static inline std::ostream& operator << (std::ostream& out, const Mat& mtx)
{
Formatter::get()->write(out, mtx);
return out;
}
Is the function you are calling when using << operator. Formatter::get() returns appropriate
formatter class based on the programming language you are using.
write() function basicly calls:
static void writeMat(std::ostream& out, const Mat& m, char rowsep, char elembrace, bool singleLine)
{
CV_Assert(m.dims <= 2);
int type = m.type();
char crowbrace = getCloseBrace(rowsep);
char orowbrace = crowbrace ? rowsep : '\0';
if( orowbrace || isspace(rowsep) )
rowsep = '\0';
for( int i = 0; i < m.rows; i++ )
{
if(orowbrace)
out << orowbrace;
if( m.data )
writeElems(out, m.ptr(i), m.cols, type, elembrace);
if(orowbrace)
out << crowbrace << (i+1 < m.rows ? ", " : "");
if(i+1 < m.rows)
{
if(rowsep)
out << rowsep << (singleLine ? " " : "");
if(!singleLine)
out << "\n ";
}
}
}
As you can see if your Mat dimensionality is greater than 2 assertion will be thrown like in your code (CV_Assert(m.dims<=2)).
calcHist() with the parameters you gave produces 3-dimentional Mat and thus it cannot be displayed using << operator
By calling calcHist() function that way you are getting 3-dimentional histogram and I don't see a simple solution to visualize that in OpenCV (which doesn't mean it can't be done). If it's something you must do I would suggest to look into OpenGL for 3D data visualization. If not you could simply call this function for each channel seperatly - you will get 3 one-dimenational histograms which you can print using << operator.
I need help to get the coordinates of the lines that HoughLines produced and extract it to an output file (Notepad, Excel, or any other output files).
I managed to obtain the lines and based on my research on this site I found a post that tells how to obtain the coordinates, however due to my limited understanding I could not get the code to run along my original Hough code and get the intersection points coordinate onto an output file.
Here is my original Hough code:
#pragma once
#include <C:\OpenCV2.2\include\opencv\cv.h>
#include <C:\OpenCV2.2\include\opencv\highgui.h>
#include <C:\OpenCV2.2\include\opencv2\core\core.hpp>
#include <C:\OpenCV2.2\include\opencv2\imgproc\imgproc.hpp>
#include <C:\OpenCV2.2\include\opencv2\highgui\highgui.hpp>
#include <stdio.h>
#include <math.h>
#include <opencv2/opencv.hpp>
#include <iostream>
using namespace std;
using namespace cv;
int main(int argc, char* argv[])
{
cv::Mat dst_img, gray_img, contour_img, contrast_img;
cv::Mat src_img = cv::imread("C:\\Frame-1.bmp"); //Source image path
dst_img = src_img.clone();
dst_img.convertTo(contrast_img, -1, 1.5, 0);
cv::cvtColor(contrast_img, gray_img, CV_BGR2GRAY);
cv::Canny(gray_img, contour_img, 75, 225, 3);
vector<Vec2f> lines_;
HoughLines(contour_img, lines_, 1, CV_PI/180, 200);
for( size_t i = 0; i < lines_.size(); i++ )
{
float rho = lines_[i][0];
float theta = lines_[i][1];
double a = cos(theta), b = sin(theta);
double x0 = a*rho, y0 = b*rho;
Point pt1(cvRound(x0 + 1000*(-b)),
cvRound(y0 + 1000*(a)));
Point pt2(cvRound(x0 - 1000*(-b)),
cvRound(y0 - 1000*(a)));
cv::clipLine(gray_img.size(), pt1, pt2);
if(!dst_img.empty())
line( dst_img, pt1, pt2, Scalar(0, 0, 255), 1, CV_AA);
cv::imwrite("result.bmp", dst_img);
}
namedWindow("My Image");
imshow("My Image", dst_img);
waitKey(0);
return 0;
}
And here is the link to the code that I wanted to put into my original code:
I am struck at finding the point of intersection of most lines in an image
Right now my original code draws Houghlines and exports the image (as result.bmp) and at the same time displays the image on a new window.
I just need to figure how and where to put the new code plus an additional code to obtain the raw data of the coordinates onto an output file like Notepad, most desirably in the same folder as result.bmp (the name of the output file could be anything, just needed it to be there).
Sorry if this question sounds like a beginner`s question (I really am) and any help is much appreciated. Many thanks in advance.
Additional information: I am using OpenCV 2.2 and Microsoft Visual Studio Academic 2010
EDIT: This is all three codes (Hough, Coordinate extraction, and Exporting data to notepad) but as a complete beginner I don`t know to make them all work in a single code.
#pragma once
#include <C:\OpenCV2.2\include\opencv\cv.h>
#include <C:\OpenCV2.2\include\opencv\highgui.h>
#include <C:\OpenCV2.2\include\opencv2\core\core.hpp>
#include <C:\OpenCV2.2\include\opencv2\imgproc\imgproc.hpp>
#include <C:\OpenCV2.2\include\opencv2\highgui\highgui.hpp>
#include <stdio.h>
#include <math.h>
#include <opencv2/opencv.hpp>
#include <iostream>
#define PointMinusPoint(P,Q,R) {(P).x = (Q).x - (R).x; (P).y = (Q).y - (R).y;}
#define PointCross(P,Q) (((P).x*(Q).y)-((P).y*(Q).x))
#define SIGN(X) (((X)>=0)? 1:-1 )
#define ABS(a) ((a) >= 0 ? (a) : (-(a)))
#define ROUND(a) ((SIGN(a)) * ( ( int )( ABS(a) + 0.5 ) ) )
typedef struct{
int x,y;
} MYintPOINT;
typedef struct {
MYintPOINT pStart;
MYintPOINT pEnd;
} MyLine;
using namespace std;
using namespace cv;
int main(int argc, char* argv[])
{
cv::Mat dst_img, gray_img, contour_img, contrast_img;
cv::Mat src_img = cv::imread("C:\\Frame-1.bmp"); //Source image path
dst_img = src_img.clone();
dst_img.convertTo(contrast_img, -1, 1.5, 0);
cv::cvtColor(contrast_img, gray_img, CV_BGR2GRAY);
cv::Canny(gray_img, contour_img, 75, 225, 3);
vector<Vec2f> lines_;
HoughLines(contour_img, lines_, 1, CV_PI/180, 200);
for( size_t i = 0; i < lines_.size(); i++ )
{
float rho = lines_[i][0];
float theta = lines_[i][1];
double a = cos(theta), b = sin(theta);
double x0 = a*rho, y0 = b*rho;
Point pt1(cvRound(x0 + 1000*(-b)),
cvRound(y0 + 1000*(a)));
Point pt2(cvRound(x0 - 1000*(-b)),
cvRound(y0 - 1000*(a)));
cv::clipLine(gray_img.size(), pt1, pt2);
if(!dst_img.empty())
line( dst_img, pt1, pt2, Scalar(0, 0, 255), 1, CV_AA);
cv::imwrite("result.bmp", dst_img);
}
int findLinesIntersectionPoint(const MyLine*l1, const MyLine*l2, MYintPOINT *res){
MYintPOINT p = l1->pStart;
MYintPOINT dp;
MYintPOINT q = l2->pStart;
MYintPOINT dq;
MYintPOINT qmp; // q-p
int dpdq_cross; // 2 cross products
int qpdq_cross; // dp with dq, q-p with dq
float a;
PointMinusPoint(dp,l1->pEnd,l1->pStart);
PointMinusPoint(dq,l2->pEnd,l2->pStart);
PointMinusPoint(qmp,q,p);
dpdq_cross = PointCross(dp,dq);
if (!dpdq_cross){
// Perpendicular Lines
return 0;
}
qpdq_cross = PointCross(qmp,dq);
a = (qpdq_cross*1.0f/dpdq_cross);
res->x = ROUND(p.x+a*dp.x);
res->y = ROUND(p.y+a*dp.y);
return 1;
}
string FileName= FileName_S.c_str();
string::size_type Extension = FileName_S.find_last_of('.'); // Find extension point
Mat mInputImg;
mInputImg= imread(FileName_S,1);
Size szInput= mInputImg.size();
const string DestinationFileName = FileName_S.substr(0, Extension) + "_ImageData.csv"; // Form the new name with container
ofstream myfile (DestinationFileName.c_str());
if (!myfile.is_open())
{
MessageBox(L"Unable to Open File");
}
string Text= format("Row, Col , Pixel Data,\n");
myfile << Text;
for (int Row = 0; Row < szInput.height; Row++)
{
for (int Col = 0; Col < szInput.width; Col++)
{
string Text= format("%d , %d , %d",Row,Col,mInputImg.at<uchar>(Row,Col));
myfile << Text;
myfile << "\n";
}
}
myfile.close();
namedWindow("My Image");
imshow("My Image", dst_img);
waitKey(0);
return 0;
}
It is very easy to export your data to Notepad or excel file. Here is the code to Export a mat to a csv File. Format your String with your desired data to export your desired data.
/*Exporting a Mat to Excel(.csv) file*/
string FileName= FileName_S.c_str();
string::size_type Extension = FileName_S.find_last_of('.'); // Find extension point
Mat mInputImg;
mInputImg= imread(FileName_S,1);
Size szInput= mInputImg.size();
const string DestinationFileName = FileName_S.substr(0, Extension) + "_ImageData.csv"; // Form the new name with container
ofstream myfile (DestinationFileName.c_str());
if (!myfile.is_open())
{
MessageBox(L"Unable to Open File");
}
string Text= format("Row, Col , Pixel Data,\n");
myfile << Text;
for (int Row = 0; Row < szInput.height; Row++)
{
for (int Col = 0; Col < szInput.width; Col++)
{
string Text= format("%d , %d , %d",Row,Col,mInputImg.at<uchar>(Row,Col));
myfile << Text;
myfile << "\n";
}
}
myfile.close();
I live in Turkey, and in Turkey, TV commercials are making some countdowns which tells us how many minutes left to the tv program or film starts.
What I'am trying to do is catch the 00:59 seconds or just the 2nd "0" on the counter. The alghoritm must understand that the "0" is "0" not any other number.
After that I've tried template matching with template images but it detects wrong numbers too.
So I couldn't figured out which is the best way to do it...
I am trying to detect from these frames:
as you see it detects "1" instead of "0".
Below is my code with template matching;
#include<opencv2/highgui/highgui.hpp>
#include <iostream>
#include <stdio.h>
#include <opencv2/core/core.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <sstream>
using namespace cv;
using namespace std;
Mat frame;
Mat img;
Mat templ;
Mat templ_resized;
Mat templ_resized_bw;
Mat result;
cv::Mat sel;
cv::Mat img_final;
//**************
int main(int argc, char** argv)
{
VideoCapture cap("/home/semih/Desktop/OPENCV_ON_LINUX/dizifiles/yenisoncalismalar/kanaldkucukaga.avi");
if ( !cap.isOpened() )
{
cout << "Cannot open the video file" << endl;
return -1;
}
double fps = cap.get(CV_CAP_PROP_FPS); //get the frames per seconds of the video
cout << "Frame per seconds : " << fps << endl;
namedWindow("1",CV_WINDOW_AUTOSIZE);
namedWindow("2",CV_WINDOW_AUTOSIZE);
namedWindow("3",CV_WINDOW_AUTOSIZE);
namedWindow("4",CV_WINDOW_AUTOSIZE);
namedWindow("5",CV_WINDOW_AUTOSIZE);
int counter=0;
int check_counter=0;
std::string s;
cv::Rect myROI(699, 474, 10,16); //location of the countdown Timer
cv::Mat cropped;
templ = imread("/home/semih/Desktop/OPENCV_ON_LINUX/dizifiles/yenisoncalismalar/sifir00.png",CV_LOAD_IMAGE_COLOR);
cv::resize(templ,templ_resized,Size(8,11),CV_INTER_LINEAR); //8 11
Mat cropped_bw;
double minVal;
double maxVal;
Point minLoc;
Point maxLoc;
Point matchLoc;
cv::Mat pic;
while(1)
{
bool bSuccess = cap.read(frame);
if (!bSuccess)
{
cout << "Cannot read the frame from video file" << endl;
break;
}
counter=counter+1;
cv::Mat croppedRef(frame, myROI);
cvtColor(croppedRef,cropped_bw,CV_RGB2GRAY);
cropped_bw = cropped_bw > 200;
cvtColor(templ_resized,templ_resized_bw,CV_RGB2GRAY);
templ_resized_bw=templ_resized_bw>200;
imshow("1",cropped_bw);
imshow("2",frame);
imshow("3",templ);
imshow("4",templ_resized_bw);
int result_cols = cropped_bw.cols - templ_resized_bw.cols + 1;
int result_rows = cropped_bw.rows - templ_resized_bw.rows + 1;
result.create( result_cols, result_rows, CV_32FC1 );
matchTemplate( cropped_bw,templ_resized_bw, result, CV_TM_SQDIFF_NORMED);
normalize( result, result, 0, 1, NORM_MINMAX, -1, Mat() );
minMaxLoc( result, &minVal, &maxVal, &minLoc, &maxLoc, Mat() );
matchLoc=minLoc;
int dogrula;
if(matchLoc.x>0){
check_counter=check_counter+1;
}
if(check_counter>20){ // if it stays 20 frames, assume "detected
cout<<"0 number detected"<<endl;
}
rectangle(cropped_bw, matchLoc, Point( matchLoc.x + templ_resized_bw.cols , matchLoc.y + templ_resized_bw.rows ), Scalar::all(100), 1, 8, 0 );
imshow("5",cropped_bw);
if(waitKey(30) == 27)
{
cout << "esc key is pressed by user" << endl;
break;
}
}
return 0;
}
As I am trying to search specific and same number I found this solution.
Which is comparing two images as if they are same.
#include<opencv2/highgui/highgui.hpp>
#include <iostream>
#include <stdio.h>
#include <opencv2/core/core.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <sstream>
using namespace cv;
using namespace std;
Mat frame;
Mat img;
Mat templ;
Mat templ_resized;
Mat templ_resized_bw;
Mat result;
cv::Mat sel;
cv::Mat img_final;
//**************
int detect()
{
VideoCapture cap("/home/semih/Desktop/OPENCV_ON_LINUX/dizifiles/yenisoncalismalar/Final/kucukaga2.avi");
if ( !cap.isOpened() )
{
cout << "Cannot open the video file" << endl;
return -1;
}
double fps = cap.get(CV_CAP_PROP_FPS); //get the frames per seconds of the video
cout << "Frame per seconds : " << fps << endl;
int counter=0;
int check_counter=0;
std::string s;
cv::Rect myROI(702, 476, 11,16); // location of countdown timer
cv::Mat cropped;
templ = imread("/home/semih/Desktop/OPENCV_ON_LINUX/dizifiles/yenisoncalismalar/Final/thresh747.png",CV_LOAD_IMAGE_COLOR);
cv::resize(templ,templ_resized,Size(45,45),CV_INTER_LINEAR); //8 11
Mat cropped_bw;
double minVal;
double maxVal;
Point minLoc;
Point maxLoc;
Point matchLoc;
cv::Mat pic;
while(1)
{
bool bSuccess = cap.read(frame);
if (!bSuccess)
{
cout << "Cannot read the frame from video file" << endl;
break;
}
counter=counter+1;
cv::Mat croppedRef(frame, myROI);
cvtColor(croppedRef,cropped_bw,CV_RGB2GRAY);
cv::resize(cropped_bw,cropped_bw,Size(45,45),CV_INTER_LINEAR);
cropped_bw = cropped_bw > 200;
cvtColor(templ_resized,templ_resized_bw,CV_RGB2GRAY);
templ_resized_bw=templ_resized_bw>200;
cv::Mat result2;
Mat croppedsimilar;
Mat templ_resized_re;
Mat templ_cvt;
cvtColor(templ_resized, templ_cvt, CV_BGR2GRAY);
cv::resize(templ_cvt,templ_resized_re,Size(45,45),CV_INTER_LINEAR);
cv::resize(cropped_bw,croppedsimilar,Size(45,45),CV_INTER_LINEAR);
templ_resized_re=templ_resized_re>200;
croppedsimilar=croppedsimilar>200;
imshow("111",croppedsimilar);
imshow("222",templ_resized_re);
int threshold = (double)(templ_resized_re.rows * templ_resized_re.cols) * 0.97;
// Search for almost same match
cv::compare(croppedsimilar , templ_resized_re , result2 , cv::CMP_EQ );
int similarPixels = countNonZero(result2);
if ( similarPixels > threshold ) {
cout << "number '0' found !!!!!" << endl;
}
if(waitKey(30) == 27)
{
cout << "esc key is pressed by user" << endl;
break;
}
}
return 0;
}