Psidium's profile - activity

I finished up making my own implementation:

cv::cvtColor(mat, mat, CV_BGR2GRAY);
cv::GaussianBlur(mat, mat, cv::Size(3,3), 0);
cv::Mat kernel = cv::getStructuringElement(cv::MORPH_RECT, cv::Point(9,9));
cv::Mat dilated;
cv::dilate(mat, dilated, kernel);

cv::Mat edges;
cv::Canny(dilated, edges, 84, 3);

lines.clear();
cv::HoughLinesP(edges, lines, 1, CV_PI/180, 25);
std::vector<cv::Vec4i>::iterator it = lines.begin();
for(; it!=lines.end(); ++it) {
    cv::Vec4i l = *it;
    cv::line(edges, cv::Point(l[0], l[1]), cv::Point(l[2], l[3]), cv::Scalar(255,0,0), 2, 8);
}
std::vector< std::vector<cv::Point> > contours;
cv::findContours(edges, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_TC89_KCOS);
std::vector< std::vector<cv::Point> > contoursCleaned;
for (int i=0; i < contours.size(); i++) {
    if (cv::arcLength(contours[i], false) > 100)
        contoursCleaned.push_back(contours[i]);
}
std::vector<std::vector<cv::Point> > contoursArea;

for (int i=0; i < contoursCleaned.size(); i++) {
    if (cv::contourArea(contoursCleaned[i]) > 10000){
        contoursArea.push_back(contoursCleaned[i]);
    }
}
std::vector<std::vector<cv::Point> > contoursDraw (contoursCleaned.size());
for (int i=0; i < contoursArea.size(); i++){
    cv::approxPolyDP(Mat(contoursArea[i]), contoursDraw[i], 40, true);
}
Mat drawing = Mat::zeros( mat.size(), CV_8UC3 );
cv::drawContours(drawing, contoursDraw, -1, cv::Scalar(0,255,0),1);

@GilLevi it happens that it should work for all types of background.

@GilLevi It fails, even with Otsu's method: http://imgur.com/0MCzMMi.png

Hi, I'm really new to image processing and my english is not the best one, sorry.

I've been studying OpenCV to use it to detect text inside a sheet of paper, it is for an iOS app, which will detect the text, guide a (blind) user to take a full sized picture and then OCR it and read it loud with VoiceOver, everything works fine if I take the picture using my eyes, but now I need to use computational eyes.

But I need to detect the text so I can guide my user to make it right for then taking the picture, I am open to any ideas.

If anybody wants to read my entire code, it is at github.

This is a photoshoped picture of what I want the code to detect (please ignore layouts in screenshots):

I tried to implement the following ways:

This method finds the sheet, but if the sheet is against a white background, it don't work, and it can't recognize close sheets too.

- (cv::Rect) contornObjectOnView:(cv::Mat&)img {

cv::Mat m = img.clone();
cv::cvtColor(m, m, CV_RGB2GRAY);
cv::blur(m, m, cv::Size(5,5));
cv::threshold(m, m, dataClass.threshold, 255,dataClass.binarizeSelector);
//dataClass.threshold is set as debug by a slider, normally 230 here, 
//and binarizeSelector is usually 0
cv::erode(m, m, cv::Mat(),cv::Point(-1,-1),n_erode_dilate);
cv::dilate(m, m, cv::Mat(),cv::Point(-1,-1),n_erode_dilate);

std::vector< std::vector<cv::Point> > contours;
std::vector<cv::Point> points;
cv::findContours(m, contours, CV_RETR_LIST, CV_CHAIN_APPROX_NONE);
m.release();
for (size_t i=0; i<contours.size(); i++) {
    for (size_t j = 0; j < contours[i].size(); j++) {
        cv::Point p = contours[i][j];
        points.push_back(p);
    }
}
return cv::boundingRect(cv::Mat(points).reshape(2));
}

Resulting image:

In this other method I try to use Probabilistic Hough Transform to find lines of text, but I don't know how to analyse the data in a way that I know there's a text in front of the camera:

 cv::cvtColor(image, image, CV_RGB2GRAY);
cv::Canny(image, image, 50, 250, 3);
lines.clear();
cv::HoughLinesP(image, lines, 1, CV_PI/180, dataClass.threshold, 50, 10);
std::vector<cv::Vec4i>::iterator it = lines.begin();
for(; it!=lines.end(); ++it) {
    cv::Vec4i l = *it;
    //NSLog(@"inicio x: %d, y: %d, fim x: %d, y: %d",l[0],l[1],l[2],l[3]);

    cv::line(image, cv::Point(l[0], l[1]), cv::Point(l[2], l[3]), cv::Scalar(255,0,0), 2, CV_AA); 
}
cv::erode(image, image, cv::Mat(),cv::Point(-1,-1),0.5);
cv::dilate(image, image, cv::Mat(),cv::Point(-1,-1),0.5);//remove smaller part of image

Resulting image:

Thank you for reading my question, I would really appreciate any help on this, I am stuck with this, started to learn about OpenCV in august and that's all that I could come up with.