// Main execute funcion---------------------------------------------------------------------------------
vector<FP> QrDetectorMod::find() {
Mat gray = Mat(image.rows, image.cols, CV_8UC1);
Mat edges(image.size(), CV_MAKETYPE(image.depth(), 1));
cvtColor(image, gray, CV_BGR2GRAY);
Canny(gray, edges, 100, 200, 3);
//imshow("edges", edges);
vector<vector<Point>> contours;
vector<Point> approx;
//findContours(edges, contours, RETR_LIST, CHAIN_APPROX_NONE);
uchar** arr = matToArr(edges); /* trik use pointers for images*/ findContours_(arr, &contours);
/*for (int i = 0; i < contours.size() - 1; i++){
vector<Point> fst = contours[i];
for (int j = i + 1; j < contours.size() - 1; j++){
vector<Point> scd = contours[j];
double endbeg = dist(fst[fst.size() - 1], scd[0]);
double endend = dist(fst[fst.size() - 1], scd[scd.size() - 1]);
double begbeg = dist(fst[0], scd[0]);
double begend = dist(fst[0], scd[scd.size() - 1]);
if (endbeg < 2){
fst.insert(fst.end(), scd.begin(), scd.end());
contours[i] = fst;
contours.erase(contours.begin() + j);
}
if (begbeg < 2){
reverse(fst.begin(), fst.end());
fst.insert(fst.end(), scd.begin(), scd.end());
contours[i] = fst;
contours.erase(contours.begin() + j);
}
else
if (endend < 2){
fst.insert(fst.end(), scd.begin(), scd.end());
contours[i] = fst;
contours.erase(contours.begin() + j);
}
else
if (begend < 2){
scd.insert(scd.end(), fst.begin(), fst.end());
contours[j] = scd;
contours.erase(contours.begin() + i);
}
}
}*/
/*RNG rng(12345);
for each (vector<Point> c in contours){
Scalar color = Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255));
for each(Point p in c) circle(image, p, 1, color, -1);
}*/
for (int i = 0; i < contours.size(); i++)
{
approx = approximate(contours[i]);
//for each (Point p in approx) circle(image, Point(p.x, p.y), 1, Scalar(0, 0, 255), -1); // for degug
if (approx.size() == 4){
//drawContours(image, contours, i, Scalar(255, 0, 0), CV_FILLED); //for debug
if (isQuad(&approx) && abs(area(approx)) > 10){
if (!inOtherContour(&approx)){
quadList.push_back(vector<Point>(approx));
}
}
}
}
if (quadList.size() < 2){
return vector<FP>();
}
vector<FP> fps;
for each(vector<Point> quad in quadList){
Point min = minCoord(quad);
Point max = maxCoord(quad);
int x = min.x - 0.7*(max.x - min.x),
y = min.y - 0.7*(max.y - min.y);
if (x < 0) x = 0; if (y < 0) y = 0;
int w = 2.8 * (max.x - min.x),
h = 2.8 * (max.y - min.y);
if (h > 0.7*image.rows || w > 0.7*image.cols) continue;
if (x + w > gray.cols) w = gray.cols - x - 1;
if (h + y > gray.rows) h = gray.rows - y - 1;
Mat partImg = gray(Rect(x, y, w, h));
threshold(partImg, partImg, 128, 255, THRESH_OTSU);
int dif = quad[4].y - y;
if (dif >= h || dif <= 0) continue;
if (singleHorizontalCheck(partImg, dif)) {
fps.push_back(FP(quad[4].x, quad[4].y, module));
}
else {
if (fullHorizontalCheck(partImg)) {
fps.push_back(FP(quad[4].x, quad[4].y, module)); }
}
//imshow("Parts", partImg);//for debug
//waitKey(1200);//for debug
}
int main( int argc, char** argv ) {
/// Load an image
cv::Mat src, greyIm, histeqIm;
src = cv::imread( argv[1] );
if( !src.data ) {
printf("Input file? No? ouuuupsss thooooorryyyyy\n");
return -1;
}
cv::Size s = src.size();
int rows = s.height;
int cols = s.width;
// Setup a rectangle to define your region of interest
cv::Rect myROI(0, rows/2, cols, rows/2);
// Crop the full image to that image contained by the rectangle myROI
// Note that this doesn't copy the data
cv::Mat croppedImage = src(myROI);
cv::imwrite("output/1_low_half.jpg", croppedImage);
cv::cvtColor(croppedImage, greyIm, cv::COLOR_BGR2GRAY);
cv::Size crop_size = croppedImage.size();
int crop_rows = crop_size.height;
int crop_cols = crop_size.width;
cv::imwrite("output/2_grey_scale.jpg", greyIm);
cv::equalizeHist( greyIm, histeqIm );
cv::imwrite("output/3_hist_eq.jpg", histeqIm);
std::vector<std::vector<cv::Point> > contours;
std::vector<cv::Vec4i> hierarchy;
// Reduce noise with kernel 3x3
cv::Mat blurIm;
blur(histeqIm, blurIm, cv::Size(3,3));
cv::imwrite("output/4_blur.jpg", blurIm);
// Canny detector
cv::Mat edgesIm;
Canny(blurIm, edgesIm, thresh, thresh*ratio, kernel_size);
cv::imwrite("output/5_edge.jpg", edgesIm);
// Find contours
cv::findContours(edgesIm, contours, hierarchy, cv::RETR_TREE, cv::CHAIN_APPROX_SIMPLE, cv::Point(0,0));
// Approximate contours to polygons + get bounding rects and circles
std::vector<std::vector<cv::Point> > contours_poly(contours.size());
std::vector<cv::Rect> boundRect(contours.size());
std::vector<cv::Point2f>center(contours.size());
std::vector<float>radius(contours.size());
for (int i = 0; i < contours.size(); i++) {
cv::approxPolyDP(cv::Mat(contours[i]), contours_poly[i], 3, true);
boundRect[i] = cv::boundingRect(cv::Mat(contours_poly[i]));
cv::minEnclosingCircle((cv::Mat)contours_poly[i], center[i], radius[i]);
}
// Draw contours
int j=0;
cv::Mat drawing = cv::Mat::zeros(edgesIm.size(), CV_8UC3);
cv::Mat piece[5], hsvIm[5];
for (int i = 0; i < contours.size(); i++) {
if (!((boundRect[i].height >= boundRect[i].width/5) && (boundRect[i].height <= boundRect[i].width/2)
&& boundRect[i].height<=crop_rows/4 && boundRect[i].width<=crop_cols/2
&& boundRect[i].height>=crop_rows/10 && boundRect[i].width>=crop_cols/6))
continue;
cv::Rect roi = boundRect[i];
piece[j] = croppedImage(roi);
imwrite("output/contour"+std::to_string(j)+".jpg", piece[j]);
j++;
cv::Scalar color = cv::Scalar(rng.uniform(0, 255), rng.uniform(0,255), rng.uniform(0,255));
cv::drawContours(drawing, contours, i, color, 2, 8, hierarchy, 0, cv::Point());
cv::rectangle(drawing, boundRect[i].tl(), boundRect[i].br(), color, 2, 8, 0);
//circle(drawing, center[i], (int)radius[i], color, 2, 8, 0);
}
imwrite("output/6_contours.jpg", drawing);
int h_bins = 50; int s_bins = 60;
int histSize[] = { h_bins, s_bins };
float h_ranges[] = { 0, 180 };
float s_ranges[] = { 0, 256 };
const float* ranges[] = { h_ranges, s_ranges };
int channels[] = { 0, 1 };
cv::Mat hist[5];
for (int i=0; i<j; i++){
cvtColor(piece[i], hsvIm[i], cv::COLOR_BGR2HSV);
imwrite("output/hsvIm"+std::to_string(i)+".jpg", hsvIm[i]);
calcHist( &hsvIm[i], 1, channels, cv::Mat(), hist[i], 2, histSize, ranges, true, false );
//normalize( hsvIm[i], hsvIm[i], 0, 1, cv::NORM_MINMAX, -1, cv::Mat() );
}
return 0;
}
/*
* General videos processing. @TODO Make use of previous frames?
*/
Mat RoadDetection::processVideo(Mat rawFrame)
{
Mat originalFrame, tmpFrame, grayFrame, blurredFrame, contoursFrame, houghFrame, sectionFrame, drawingFrame;
Point vanishingPoint;
vector<Line> houghLines, houghMainLines;
vector<Point> roadShape;
vector<Rect> vehicles;
int sectionOffset;
// convert to grayscale
cvtColor(rawFrame, grayFrame, CV_BGR2GRAY);
equalizeHist(grayFrame, grayFrame);
// smooth and remove noise
GaussianBlur(grayFrame, blurredFrame, Size(BLUR_KERNEL, BLUR_KERNEL), 0);
// edge detection (canny, inverted)
Canny(blurredFrame, contoursFrame, CANNY_MIN_THRESH, CANNY_MAX_THRESH);
threshold(contoursFrame, contoursFrame, 128, 255, THRESH_BINARY);
// hough transform
houghLines = getHoughLines(contoursFrame, true);
// vanishing point
vanishingPoint = getVanishingPoint(houghLines, rawFrame.size());
sectionOffset = vanishingPoint.y;
// if we can't find a point, use the one from a previous frame
if (vanishingPoint.x == 0 && vanishingPoint.y == 0)
{
vanishingPoint = previousVanishingPoint;
}
// if we can, save it for future use
else
{
previousVanishingPoint = vanishingPoint;
}
// section frame (below vanishing point)
sectionFrame = contoursFrame(CvRect(0, sectionOffset, contoursFrame.cols, contoursFrame.rows - sectionOffset));
// re-apply hough transform to section frame
houghLines = getHoughLines(sectionFrame, true);
// shift lines downwards
houghLines = shiftLines(houghLines, sectionOffset);
// best line matches
houghMainLines = getMainLines(houghLines);
// update vanishing point according to the new section
if (houghMainLines.size() >= 2)
{
previousLines = houghMainLines;
}
else
{
houghMainLines = previousLines;
}
if (houghMainLines.size() >= 2)
{
Point intersection = getLineIntersection(houghMainLines[0], houghMainLines[1]);
if (intersection.x > 0 && intersection.x < rawFrame.cols && intersection.y > 0 && intersection.y < rawFrame.rows)
{
vanishingPoint = intersection;
sectionOffset = intersection.y;
}
// get road shape
roadShape = getRoadShape(rawFrame, houghMainLines[0], houghMainLines[1], vanishingPoint);
}
// limit lines
houghLines = getLimitedLines(houghLines, sectionOffset);
houghMainLines = getLimitedLines(houghMainLines, sectionOffset);
// drawing frame and vehicles
drawingFrame = rawFrame(CvRect(0, sectionOffset, contoursFrame.cols, contoursFrame.rows - sectionOffset));
// vehicles = getVehicles(drawingFrame);
// drawing process
drawLines(rawFrame, houghLines, Scalar(0, 0, 255), 2, 0);
drawLines(rawFrame, houghMainLines, Scalar(20, 125, 255), 2, 0);
drawRoadShape(rawFrame, roadShape, Scalar(20, 125, 255), 0.3);
drawCircle(rawFrame, vanishingPoint, Scalar(20, 125, 255), 15, -1, 0);
// drawRects(rawFrame, vehicles, Scalar(255, 0, 0), 1, sectionOffset);
return rawFrame;
}
RawImage* OpenCvCannyEdgeDetection::ProcessInput(CommandLineArgModel* arg, RawImage* image)
{
OpenCvCannyEdgeDetectionModel* model = (OpenCvCannyEdgeDetectionModel*)arg->ParsedModel;
Rectangle roi = model->Roi;
Mat img = image->CloneToMat(roi);
vector<Mat> channels;
blur(img, img, Size(model->Window * 3, model->Window * 3));
switch (model->ColorSpace)
{
case GRAY:
cvtColor(img, img, CV_BGR2GRAY);
Canny(img, img, 0, model->Threshold, model->Window);
cvtColor(img, img, CV_GRAY2RGB);
break;
case RGB:
split(img, channels);
if (model->ColorChannel1)
{
Canny(channels[0], channels[0], 0, model->Threshold, model->Window);
}
else
{
channels[0] = Scalar(0);
}
if (model->ColorChannel2)
{
Canny(channels[1], channels[1], 0, model->Threshold, model->Window);
}
else
{
channels[1] = Scalar(0);
}
if (model->ColorChannel3)
{
Canny(channels[2], channels[2], 0, model->Threshold, model->Window);
}
else
{
channels[2] = Scalar(0);
}
merge(channels, img);
break;
case HSI:
throw runtime_error("HSI is not support with OpenCvCannyEdgeDetection.");
case YCRCB:
cvtColor(img, img, CV_BGR2YCrCb);
split(img, channels);
if (model->ColorChannel1)
{
Canny(channels[0], channels[0], 0, model->Threshold, model->Window);
}
if (model->ColorChannel2)
{
Canny(channels[1], channels[1], 0, model->Threshold, model->Window);
}
if (model->ColorChannel3)
{
Canny(channels[2], channels[2], 0, model->Threshold, model->Window);
}
merge(channels, img);
cvtColor(img, img, CV_YCrCb2BGR);
break;
}
image->Import(img, roi.X, roi.Y);
return image;
}
Mat skinDetector::cannySegmentation(Mat img0, int minPixelSize)
{
// Segments items in gray image (img0)
// minPixelSize=
// -1, returns largest region only
// pixels, threshold for removing smaller regions, with less than minPixelSize pixels
// 0, returns all detected segments
// LB: Zero pad image to remove edge effects when getting regions....
int padPixels=20;
// Rect border added at start...
Rect tempRect;
tempRect.x=padPixels;
tempRect.y=padPixels;
tempRect.width=img0.cols;
tempRect.height=img0.rows;
Mat img1 = Mat::zeros(img0.rows+(padPixels*2), img0.cols+(padPixels*2), CV_8UC1);
img0.copyTo(img1(tempRect));
// apply your filter
Canny(img1, img1, 100, 200, 3); //100, 200, 3);
// find the contours
vector< vector<Point> > contours;
findContours(img1, contours, CV_RETR_EXTERNAL, CV_CHAIN_APPROX_NONE);
// Mask for segmented regiond
Mat mask = Mat::zeros(img1.rows, img1.cols, CV_8UC1);
vector<double> areas(contours.size());
if (minPixelSize==-1)
{ // Case of taking largest region
for(int i = 0; i < contours.size(); i++)
areas[i] = contourArea(Mat(contours[i]));
double max;
Point maxPosition;
minMaxLoc(Mat(areas),0,&max,0,&maxPosition);
drawContours(mask, contours, maxPosition.y, Scalar(1), CV_FILLED);
}
else
{ // Case for using minimum pixel size
for (int i = 0; i < contours.size(); i++)
{
if (contourArea(Mat(contours[i]))>minPixelSize)
drawContours(mask, contours, i, Scalar(1), CV_FILLED);
}
}
// normalize so imwrite(...)/imshow(...) shows the mask correctly!
normalize(mask.clone(), mask, 0.0, 255.0, CV_MINMAX, CV_8UC1);
Mat returnMask;
returnMask=mask(tempRect);
// show the images
if (verboseOutput) imshow("Canny Skin: Img in", img0);
if (verboseOutput) imshow("Canny Skin: Mask", returnMask);
if (verboseOutput) imshow("Canny Skin: Output", img1);
return returnMask;
}
Mat CColor::setColorPosition()
{
Scalar colorContours;
drawingContours = Mat::zeros(imgCanny.size(), CV_8UC3);
///setColorPosition2Piece(CColorsType::RED);
imgFHSV = Frame2HSV(imgSharp, CColorsType::RED);
Canny(imgFHSV, imgCanny, 180, 120);
findContours(imgCanny, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));
for (size_t i = 0; i< contours.size(); i++) {
approxPolyDP(Mat(contours[i]), approx, arcLength(Mat(contours[i]), true)*0.02, true);// 5, true); //
if(fabs(contourArea(approx)) > 1000 && approx.size() == 4)
{
if(!fig.isSquare(approx)) {
poscenter.ChooseSaveColorCenter( CColorsType::RED, contours[i]);
fig.setContourRectangle(imgSharp, contours[i]);
colorContours = CV_RGB(255, 0, 0);
drawContours(drawingContours, contours, (int)i, colorContours, 2, /*CV_AA*/8, hierarchy, 0, Point());
drawContours(imgSharp, contours, (int)i, colorContours, 2, /*CV_AA*/8, hierarchy, 0, Point());
}
}
}
///setColorPosition2Piece(CColorsType::GREEN);
imgFHSV = Frame2HSV(imgSharp, CColorsType::GREEN);
Canny(imgFHSV, imgCanny, 180, 120);
findContours(imgCanny, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));
for (size_t i = 0; i< contours.size(); i++) {
approxPolyDP(Mat(contours[i]), approx, arcLength(Mat(contours[i]), true)*0.02, true);// 5, true); //
if(fabs(contourArea(approx)) > 1000 && approx.size() == 4)
{
if(!fig.isSquare(approx)) {
poscenter.ChooseSaveColorCenter( CColorsType::GREEN, contours[i]);
fig.setContourRectangle(imgSharp, contours[i]);
colorContours = CV_RGB(0, 255, 0);
drawContours(drawingContours, contours, (int)i, colorContours, 2, /*CV_AA*/8, hierarchy, 0, Point());
drawContours(imgSharp, contours, (int)i, colorContours, 2, /*CV_AA*/8, hierarchy, 0, Point());
}
}
}
///setColorPosition2Piece(CColorsType::BLUE);
imgFHSV = Frame2HSV(imgSharp, CColorsType::BLUE);
Canny(imgFHSV, imgCanny, 180, 120);
findContours(imgCanny, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));
for (size_t i = 0; i< contours.size(); i++) {
approxPolyDP(Mat(contours[i]), approx, arcLength(Mat(contours[i]), true)*0.02, true);// 5, true); //
if(fabs(contourArea(approx)) > 1000 && approx.size() == 4)
{
if(!fig.isSquare(approx)) {
poscenter.ChooseSaveColorCenter( CColorsType::BLUE, contours[i]);
fig.setContourRectangle(imgSharp, contours[i]);
colorContours = CV_RGB(0, 0, 255);
drawContours(drawingContours, contours, (int)i, colorContours, 2, /*CV_AA*/8, hierarchy, 0, Point());
drawContours(imgSharp, contours, (int)i, colorContours, 2, /*CV_AA*/8, hierarchy, 0, Point());
}
}
}
///setColorPosition2Piece(CColorsType::YELLOW);
imgFHSV = Frame2HSV(imgSharp, CColorsType::YELLOW);
Canny(imgFHSV, imgCanny, 180, 120);
findContours(imgCanny, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));
for (size_t i = 0; i< contours.size(); i++) {
approxPolyDP(Mat(contours[i]), approx, arcLength(Mat(contours[i]), true)*0.02, true);// 5, true); //
if(fabs(contourArea(approx)) > 1000 && approx.size() == 4)
{
if(!fig.isSquare(approx)) {
poscenter.ChooseSaveColorCenter( CColorsType::YELLOW, contours[i]);
fig.setContourRectangle(imgSharp, contours[i]);
colorContours = CV_RGB(255, 255, 0);
drawContours(drawingContours, contours, (int)i, colorContours, 2, /*CV_AA*/8, hierarchy, 0, Point());
drawContours(imgSharp, contours, (int)i, colorContours, 2, /*CV_AA*/8, hierarchy, 0, Point());
}
}
}
///setColorPosition2Piece(CColorsType::BLACK);
imgFHSV = Frame2HSV(imgSharp, CColorsType::BLACK);
Canny(imgFHSV, imgCanny, 180, 120);
findContours(imgCanny, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));
for (size_t i = 0; i< contours.size(); i++) {
approxPolyDP(Mat(contours[i]), approx, arcLength(Mat(contours[i]), true)*0.02, true);// 5, true); //
if(fabs(contourArea(approx)) > 1000 && approx.size() == 4)
{
if(!fig.isSquare(approx)) {
poscenter.ChooseSaveColorCenter( CColorsType::BLACK, contours[i]);
fig.setContourRectangle(imgSharp, contours[i]);
colorContours = CV_RGB(100, 100, 100);
drawContours(drawingContours, contours, (int)i, colorContours, 2, /*CV_AA*/8, hierarchy, 0, Point());
drawContours(imgSharp, contours, (int)i, colorContours, 2, /*CV_AA*/8, hierarchy, 0, Point());
}
}
}
// imshow("drawingContours", drawingContours);
return drawingContours;
}
QVector<CColorsType> CColor::getCorAsked_CorPlaced(CColorsType colorAsked)
{
Scalar colorContours;
imgFHSV = Frame2HSV(imgSharp, CColorsType::YELLOW);
Canny(imgFHSV, imgCanny, 180, 120);
findContours(imgCanny, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));
for (size_t i = 0; i< contours.size(); i++)
{
approxPolyDP(Mat(contours[i]), approx, arcLength(Mat(contours[i]), true)*0.02, true);
if(fabs(contourArea(approx)) > 1000 && approx.size() == 4)
{
if( ! fig.isSquare(approx)) {/// YELLOW ///As peças jogadas são retangulos
if(colorAsked == CColorsType::YELLOW) ///Right Answer
{
fig.setContourRectangle(imgSharp, contours[i], CV_RGB(0, 255, 0)); //feedback on screen
colorContours = CV_RGB(255, 255, 0);
drawContours(imgSharp, contours, (int)i, colorContours, 2, /*CV_AA*/8, hierarchy, 0, Point());
setMessage2Robot(true, colorAsked, CColorsType::YELLOW);
return QVector<CColorsType>() << colorAsked << CColorsType::YELLOW;
}
else
{
fig.setContourRectangle(imgSharp, contours[i], Scalar(0, 0, 255)); //feedback on screen
setMessage2Robot(false, colorAsked, CColorsType::YELLOW);
return QVector<CColorsType>() << colorAsked << CColorsType::YELLOW;
}
}
}
}
imgFHSV = Frame2HSV(imgSharp, CColorsType::RED);
Canny(imgFHSV, imgCanny, 180, 120);
findContours(imgCanny, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));
for (size_t i = 0; i< contours.size(); i++)
{
approxPolyDP(Mat(contours[i]), approx, arcLength(Mat(contours[i]), true)*0.02, true);// 5, true); //
if(fabs(contourArea(approx)) > 1000 && approx.size() == 4) //este if não deve ser necessario
{
if( ! fig.isSquare(approx)){/// RED ///As peças jogadas são retangulos
if(colorAsked == CColorsType::RED)
{
fig.setContourRectangle(imgSharp, contours[i], CV_RGB(0, 255, 0)); //feedback on screen
colorContours = CV_RGB(255, 0, 0);
drawContours(imgSharp, contours, (int)i, colorContours, 2, /*CV_AA*/8, hierarchy, 0, Point());
setMessage2Robot(true, colorAsked, CColorsType::RED);
return QVector<CColorsType>() << colorAsked << CColorsType::RED;
}
else
{
fig.setContourRectangle(imgSharp, contours[i], Scalar(0, 0, 255)); //feedback on screen
setMessage2Robot(false, colorAsked, CColorsType::RED);
return QVector<CColorsType>() << colorAsked << CColorsType::RED;
}
}
}
}
imgFHSV = Frame2HSV(imgSharp, CColorsType::GREEN);
Canny(imgFHSV, imgCanny, 180, 120);
findContours(imgCanny, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));
for (size_t i = 0; i< contours.size(); i++)
{
approxPolyDP(Mat(contours[i]), approx, arcLength(Mat(contours[i]), true)*0.02, true);// 5, true); //
if(fabs(contourArea(approx)) > 1000 && approx.size() == 4) //este if não deve ser necessario
{
if( ! fig.isSquare(approx)){/// GREEN ///As peças jogadas são retangulos
if(colorAsked == CColorsType::GREEN)
{
fig.setContourRectangle(imgSharp, contours[i], CV_RGB(0, 255, 0)); //feedback on screen
colorContours = CV_RGB(0, 255, 0);
drawContours(imgSharp, contours, (int)i, colorContours, 2, /*CV_AA*/8, hierarchy, 0, Point());
setMessage2Robot(true, colorAsked, CColorsType::GREEN);
return QVector<CColorsType>() << colorAsked << CColorsType::GREEN;
}
else
{
fig.setContourRectangle(imgSharp, contours[i], Scalar(0, 0, 255)); //feedback on screen
setMessage2Robot(false, colorAsked, CColorsType::GREEN);
return QVector<CColorsType>() << colorAsked << CColorsType::GREEN;
}
}
}
}
imgFHSV = Frame2HSV(imgSharp, CColorsType::BLUE);
Canny(imgFHSV, imgCanny, 180, 120);
findContours(imgCanny, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));
for (size_t i = 0; i< contours.size(); i++) {
approxPolyDP(Mat(contours[i]), approx, arcLength(Mat(contours[i]), true)*0.02, true);// 5, true); //
if(fabs(contourArea(approx)) > 1000 && approx.size() == 4) //este if não deve ser necessario
{
if( ! fig.isSquare(approx)){/// BLUE ///As peças jogadas são retangulos
if(colorAsked == CColorsType::BLUE)
{
fig.setContourRectangle(imgSharp, contours[i], CV_RGB(0, 255, 0)); //feedback on screen
colorContours = CV_RGB(0, 0, 255);
drawContours(imgSharp, contours, (int)i, colorContours, 2, /*CV_AA*/8, hierarchy, 0, Point());
setMessage2Robot(true, colorAsked, CColorsType::BLUE);
return QVector<CColorsType>() << colorAsked << CColorsType::BLUE;
}
else
{
fig.setContourRectangle(imgSharp, contours[i], Scalar(0, 0, 255)); //feedback on screen
setMessage2Robot(false, colorAsked, CColorsType::BLUE);
return QVector<CColorsType>() << colorAsked << CColorsType::BLUE;
}
}
}
}
imgFHSV = Frame2HSV(imgSharp, CColorsType::BLACK);
Canny(imgFHSV, imgCanny, 180, 120);
findContours(imgCanny, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));
for (size_t i = 0; i< contours.size(); i++) {
approxPolyDP(Mat(contours[i]), approx, arcLength(Mat(contours[i]), true)*0.02, true);// 5, true); //
if(fabs(contourArea(approx)) > 1000 && approx.size() == 4) //este if não deve ser necessario
{
if( ! fig.isSquare(approx)){/// BLACK ///As peças jogadas são retangulos
if(colorAsked == CColorsType::BLACK)
{
fig.setContourRectangle(imgSharp, contours[i], CV_RGB(0, 255, 0)); //feedback on screen
colorContours = CV_RGB(0, 0, 0);
drawContours(imgSharp, contours, (int)i, colorContours, 2, /*CV_AA*/8, hierarchy, 0, Point());
setMessage2Robot(true, colorAsked, CColorsType::BLACK);
return QVector<CColorsType>() << colorAsked << CColorsType::BLACK;
}
else
{
fig.setContourRectangle(imgSharp, contours[i], Scalar(0, 0, 255)); //feedback on screen
setMessage2Robot(false, colorAsked, CColorsType::BLACK);
return QVector<CColorsType>() << colorAsked << CColorsType::BLACK;
}
}
}
}
return QVector<CColorsType>() << CColorsType::NONE << CColorsType::NONE; ///REVER
}
void DrImage::ConvMatrix(){
double Average = 0.;
double Count = 0.;
Matrice ImIn(NWidth,NHeight);
for(int h=0;h<NHeight;h++){
for(int w=0;w<NWidth;w++){
double Sum = 1.- .3333*(data[0][h*NWidth+w]+data[1][h*NWidth+w]+data[2][h*NWidth+w]);
//double Sum = 1. - (data[0][h*NWidth+w]+data[1][h*NWidth+w]+data[2][h*NWidth+w]);
ImIn.Set(w,h,Sum);
Average += Sum;
Count += 1.;
}
}
Average /= Count;
//---------Canny---------------------
Matematica *Mat = new Matematica;
Matrice Canny(5,5);
Canny.FillCanny();
Canny.Print();
// Mat->ApplyFilter(&ImIn,&Canny);
// Mat->ApplyFilter(&ImIn,&Canny);
//-----------Edge-------------------
SPLINE Weight;
Weight.a0 = 0.;
Weight.a1 = 1.; Weight.a2 = 0.;
Weight.a3 = 0.; Weight.a4 = 0.;
Matrice *Mask = new Matrice(Weight,3);
Mask->Print();
//Mat->ApplyFilter(&ImIn,Mask);
// Mask->Transpose();
// Mat->ApplyFilter(ImIn,Mask);
//-------------Smooth----------------
const int NMatSize = 5;
Matrice GaussEdge(NMatSize,NMatSize);
GaussEdge.FillGaussian(.5,1.);
//double LatPos[5] = {.125,-.25,0.,.25,-.125};
// double LatPos[3] = {-1.,0.,1.};
// for(int w=0;w<NMatSize;w++){
// for(int h=0;h<NMatSize;h++){
// GaussEdge.Set(w,h,GaussEdge.Val(w,h)*LatPos[w]);
// }
// }
GaussEdge.Print();
//Mat->ApplyFilter(ImIn,&GaussEdge);
Matrice GaussSmooth(5,5);
GaussSmooth.FillGaussian(.5,3.);
// Mat->ApplyFilter(ImIn,&GaussSmooth);
//------------PixDev------------------
for(int h=0;h<NHeight;h++){
for(int w=0;w<NWidth;w++){
ImIn.Set(w,h,Average-ImIn.Val(w,h));
}
}
int PixelDev = 5;
double ValStep[3] = {0.,0.,0.};
int ValNStep[3] = {0,0,0};
for(int h=0;h<NHeight;h++){
for(int w=0;w<NWidth;w++){
ValNStep[0] = w - PixelDev;
if(ValNStep[0] < 0 ) continue;
if(ValNStep[0] >= NWidth) continue;
ValNStep[1] = w;
ValNStep[2] = w + PixelDev;
if(ValNStep[2] < 0 ) continue;
if(ValNStep[2] >= NWidth) continue;
for(int d=0;d<3;d++){
ValStep[d] = ImIn.Val(ValNStep[d],h);
if(d == 1){
ValStep[d] = ValStep[d] > 0. ? ValStep[d] : 0.;
continue;
}
ValStep[d] = ValStep[d] < 0. ? -ValStep[d] : 0.;
}
double Resp = ValStep[0]*ValStep[1]*ValStep[2];
//double Resp = ValStep[1];
//ImIn.Set(w,h,Resp);
}
}
char cImage[160];
sprintf(cImage,"Canny.png");
pngwriter ImageOut(NWidth,NHeight,1.0,cImage);
FILE *Ciccia = fopen("Pos3d.dat","w");
double NormH = 1./(double)NHeight;
double NormW = 1./(double)NWidth;
for(int h=0;h<NHeight;h++){
for(int w=0;w<NWidth;w++){
fprintf(Ciccia,"%lf %lf %lf\n",w*NormH,h*NormH,ImIn.Val(w,h));
ImageOut.plot(w,h,ImIn.Val(w,h),ImIn.Val(w,h),ImIn.Val(w,h));
}
}
fclose(Ciccia);
ImageOut.close();
}
void ProcessingThread::run()
{
while(1)
{
/////////////////////////////////
// Stop thread if stopped=TRUE //
/////////////////////////////////
stoppedMutex.lock();
if (stopped)
{
stopped=false;
stoppedMutex.unlock();
break;
}
stoppedMutex.unlock();
/////////////////////////////////
/////////////////////////////////
// Save processing time
processingTime=t.elapsed();
// Start timer (used to calculate processing rate)
t.start();
// Get frame from queue, store in currentFrame, set ROI
currentFrame=Mat(imageBuffer->getFrame(),currentROI);
updateMembersMutex.lock();
///////////////////
// PERFORM TASKS //
///////////////////
// Note: ROI changes will take effect on next frame
if(resetROIFlag)
resetROI();
else if(setROIFlag)
setROI();
////////////////////////////////////
// PERFORM IMAGE PROCESSING BELOW //
////////////////////////////////////
// Grayscale conversion
if(grayscaleOn)
cvtColor(currentFrame,currentFrameGrayscale,CV_BGR2GRAY);
// Smooth (in-place operations)
if(smoothOn)
{
if(grayscaleOn)
{
switch(smoothType)
{
// BLUR
case 0:
blur(currentFrameGrayscale,currentFrameGrayscale,Size(smoothParam1,smoothParam2));
break;
// GAUSSIAN
case 1:
GaussianBlur(currentFrameGrayscale,currentFrameGrayscale,Size(smoothParam1,smoothParam2),smoothParam3,smoothParam4);
break;
// MEDIAN
case 2:
medianBlur(currentFrameGrayscale,currentFrameGrayscale,smoothParam1);
break;
}
}
else
{
switch(smoothType)
{
// BLUR
case 0:
blur(currentFrame,currentFrame,Size(smoothParam1,smoothParam2));
break;
// GAUSSIAN
case 1:
GaussianBlur(currentFrame,currentFrame,Size(smoothParam1,smoothParam2),smoothParam3,smoothParam4);
break;
// MEDIAN
case 2:
medianBlur(currentFrame,currentFrame,smoothParam1);
break;
}
}
}
// Dilate
if(dilateOn)
{
if(grayscaleOn)
dilate(currentFrameGrayscale,currentFrameGrayscale,Mat(),Point(-1,-1),dilateNumberOfIterations);
else
dilate(currentFrame,currentFrame,Mat(),Point(-1,-1),dilateNumberOfIterations);
}
// Erode
if(erodeOn)
{
if(grayscaleOn)
erode(currentFrameGrayscale,currentFrameGrayscale,Mat(),Point(-1,-1),erodeNumberOfIterations);
else
erode(currentFrame,currentFrame,Mat(),Point(-1,-1),erodeNumberOfIterations);
}
// Flip
if(flipOn)
{
if(grayscaleOn)
flip(currentFrameGrayscale,currentFrameGrayscale,flipCode);
else
flip(currentFrame,currentFrame,flipCode);
}
// Canny edge detection
if(cannyOn)
{
// Frame must be converted to grayscale first if grayscale conversion is OFF
if(!grayscaleOn)
cvtColor(currentFrame,currentFrameGrayscale,CV_BGR2GRAY);
Canny(currentFrameGrayscale,currentFrameGrayscale,
cannyThreshold1,cannyThreshold2,
cannyApertureSize,cannyL2gradient);
}
////////////////////////////////////
// PERFORM IMAGE PROCESSING ABOVE //
////////////////////////////////////
// Convert Mat to QImage: Show grayscale frame [if either Grayscale or Canny processing modes are ON]
if(grayscaleOn||cannyOn)
frame=MatToQImage(currentFrameGrayscale);
// Convert Mat to QImage: Show BGR frame
else
frame=MatToQImage(currentFrame);
updateMembersMutex.unlock();
// Update statistics
updateFPS(processingTime);
currentSizeOfBuffer=imageBuffer->getSizeOfImageBuffer();
// Inform GUI thread of new frame (QImage)
emit newFrame(frame);
}
qDebug() << "Stopping processing thread...";
}
void SurfaceComputer::init(Mat& image0)
{
Mat image_bright = image0;
// Mat image_bright = Mat::zeros(image0.size(), CV_8UC3);
// for (int i = 0; i < WIDTH_LARGE; ++i)
// for (int j = 0; j < HEIGHT_LARGE; ++j)
// {
// int b = image0.ptr<uchar>(j, i)[0] + 100;
// int g = image0.ptr<uchar>(j, i)[1] + 100;
// int r = image0.ptr<uchar>(j, i)[2] + 100;
// if (b > 255)
// b = 255;
// if (g > 255)
// g = 255;
// if (r > 255)
// r = 255;
// image_bright.ptr<uchar>(j, i)[0] = b;
// image_bright.ptr<uchar>(j, i)[1] = g;
// image_bright.ptr<uchar>(j, i)[2] = r;
// }
Mat image_canny;
Canny(image_bright, image_canny, 20, 60, 3);
y_reflection = HEIGHT_LARGE;
int intensity_array[HEIGHT_LARGE];
for (int j = 0; j < HEIGHT_LARGE; ++j)
{
int intensity = 0;
for (int i = 0; i < WIDTH_LARGE; ++i)
if (image_canny.ptr<uchar>(j, i)[0] > 0)
++intensity;
intensity_array[j] = intensity;
}
Mat image_histogram = Mat::zeros(HEIGHT_LARGE, WIDTH_LARGE, CV_8UC1);
vector<Point3f> concave_pt_index_vec;
vector<Point3f> convex_pt_index_vec;
Point pt_old = Point(-1, -1);
Point pt_old_old = Point(-1, -1);
int index = 0;
for (int j = 0; j < HEIGHT_LARGE; ++j)
{
int i = intensity_array[j];
low_pass_filter.compute(i, 0.1, "i");
Point pt = Point(i, j);
if (pt_old.x != -1)
{
line(image_histogram, pt, pt_old, Scalar(254), 1);
if (pt_old_old.x != -1)
{
if (pt_old.x < pt_old_old.x && pt_old.x < pt.x)
concave_pt_index_vec.push_back(Point3f(pt_old.x, pt_old.y, index - 1));
if (pt_old.x > pt_old_old.x && pt_old.x > pt.x)
convex_pt_index_vec.push_back(Point3f(pt_old.x, pt_old.y, index - 1));
}
pt_old_old = pt_old;
}
pt_old = pt;
++index;
}
int x_diff_sum_max = -1;
for (Point3f& concave_pt_index : concave_pt_index_vec)
{
int before_x_max = -1;
int after_x_max = -1;
for (Point3f& convex_pt_index : convex_pt_index_vec)
{
if (convex_pt_index.z < concave_pt_index.z)
{
if (convex_pt_index.x > before_x_max)
before_x_max = convex_pt_index.x;
}
else
{
if (convex_pt_index.x > after_x_max)
after_x_max = convex_pt_index.x;
}
}
if (before_x_max != -1 && after_x_max != -1)
{
int x_diff_before = before_x_max - concave_pt_index.x;
int x_diff_after = after_x_max - concave_pt_index.x;
int x_diff_sum = (x_diff_before * x_diff_after) + (concave_pt_index.z * 100);
if (x_diff_sum > x_diff_sum_max)
{
x_diff_sum_max = x_diff_sum;
y_reflection = concave_pt_index.z;
}
}
}
y_reflection /= 4;
// vector<Vec4i> lines(10000);
// HoughLinesP(image_canny, lines, 1, CV_PI / 180, 20, 50, 10);
// for (size_t i = 0; i < lines.size(); ++i)
// {
// Vec4i l = lines[i];
// Point pt0 = Point(l[0], l[1]);
// Point pt1 = Point(l[2], l[3]);
// if (abs(pt0.y - pt1.y) <= 5 && pt0.y < y_reflection)
// line(image_canny, pt0, pt1, Scalar(127), 3);
// }
}
int main(int argc, char ** argv)
{
string gauss = "Gaussino";
string canny = "Canny";
string hough = "Hough";
string binarizar = "Binarizar";
string Otsu = "Otsu";
string image_name = "";
int number;
Point min, max, start;
ofstream myfile;
myfile.open("data.txt");
myfile << "ESCREVE QUALQUER COISA\n";
clock_t t1, t2, t3, t4;
double threshold1, threshold2, thres, minLength, maxGap;
bool f1, f2, f3, f4, f5, f6, f7, f8, f9;
string Result;
ostringstream convert;
//int i;
float temp;
//for (i = 1; i <= 6; i++){
//number = i;
//convert << number;
//Result = convert.str();
//image_name = "a" + Result + ".JPG";
image_name = "a2.JPG";
//number++;
//cout << number << endl;
cout << image_name;
myfile << image_name;
myfile << "\n";
t1 = clock();
f1 = false;
f2 = true;
f3 = false;
f4 = false;
f5 = false;
f6 = true;
f7 = true;
if (f7 == true){
threshold1 = 10;
threshold2 = 19;
}
f8 = false;
f9 = true;
if (f9 == true){
thres = 10;// 40
minLength = 20; //50
maxGap = 30; //80
/*
CvCapture* capture = cvCaptureFromCAM( CV_CAP_ANY );
if ( !capture ) {
fprintf( stderr, "ERROR: capture is NULL \n" );
getchar();
return -1;
}
string original = "original.jpg";
string foto ="img";
IplImage* frame = cvQueryFrame( capture );
Mat img(frame);
Mat I, I1, imge;
cvtColor(img,imge,CV_RGB2GRAY);
imge.convertTo(I, CV_8U);
equalizeHist(I,I1);
Mat aux = I1;
savePictures(I1, original, foto);
*/
//realiza a leitura e carrega a imagem para a matriz I1
// a imagem tem apenas 1 canal de cor e por isso foi usado o parametro CV_LOAD_IMAGE_GRAYSCALE
Mat lara = imread("lara.JPG", CV_LOAD_IMAGE_GRAYSCALE);
Mat I = imread(image_name, CV_LOAD_IMAGE_GRAYSCALE);
if (I.empty())
return -1;
resize(I, I, lara.size(), 1.0, 1.0, INTER_LINEAR);
Mat I1;
//Mat aux = imread(argv[1], CV_LOAD_IMAGE_GRAYSCALE);
equalizeHist(I, I1);
Mat aux, original;
aux = I1;
//ShowImage(I, I1);
// verifica se carregou e alocou a imagem com sucesso
if (I1.empty())
return -1;
// tipo Size contem largura e altura da imagem, recebe o retorno do metodo .size()
//imSize = I1.size();
// Cria uma matriz do tamanho imSize, de 8 bits e 1 canal
Mat I2 = Mat::zeros(I1.size(), CV_8UC1);
if (f2 == true) {
t2 = clock();
for (int i = 1; i < MAX_KERNEL_LENGTH; i = i + 2)
GaussianBlur(I1, I1, Size(i, i), 0, 0, BORDER_DEFAULT);
//ShowImage(aux, I1);
cout << "Guassiano tempo : ";
temp = tempo(t2);
savePictures(I1, image_name, gauss);
myfile << "Gauss: ";
myfile << temp;
myfile << "\n";
}
if (f1 == true){
t2 = clock();
binarizacao(I1, 125);
//ShowImage(aux, I1);
cout << "binarizacao : ";
temp = tempo(t2);
savePictures(I1, image_name, binarizar);
myfile << "Binarizacao: ";
myfile << temp;
myfile << "\n";
}
if (f3 == true){
t2 = clock();
inversao(I1);
cout << "inversao : ";
tempo(t2);
}
if (f4 == true){
adaptiveThreshold(I1, I1, 255, ADAPTIVE_THRESH_GAUSSIAN_C, CV_THRESH_BINARY, 7, 0);
}
if (f5 == true)
Laplacian(I1, I1, 125, 1, 1, 0, BORDER_DEFAULT);
if (f7 == true){
t2 = clock();
Canny(I1, I2, threshold1, threshold2, 3, false);
cout << "canny : ";
temp = tempo(t2);
savePictures(I2, image_name, canny);
myfile << "Canny: " + (int)(temp * 1000);
myfile << "\n";
}
if (f9 == true){
t2 = clock();
Hough(I2, aux, thres, minLength, maxGap);
cout << "hough : ";
temp = tempo(t2);
savePictures(aux, image_name, hough);
myfile << "Hough: ";
myfile << temp;
myfile << "\n";
}
if (f6 == true){
t2 = clock();
threshold_type = THRESH_BINARY;
threshold(aux, I1, 9, max_BINARY_value, threshold_type);
cout << "Threshold : ";
//savePictures(aux, image_name, Otsu);
temp = tempo(t2);
myfile << "Threshold/OTSU: ";
myfile << temp;
myfile << "\n";
}
string name = Otsu + image_name;
imwrite(name, aux);
ShowImage(I1, aux);
t2 = clock();
max = maxPoint(aux);
min = minPoint(aux);
/*start.y = (max.y + min.y) / 2;
start.x = (max.x + min.x) /2;*/
start.x = max.x;
start.y = max.y;
Point end;
end.x = start.x;
end.y = aux.size().height;
MyLine(I, start, end, image_name, 0.3);
temp = tempo(t2);
ShowImage(I, aux);
myfile << "Rota: ";
myfile << temp;
myfile << "\n";
temp = tempo(t1);
cout << "Final time : ";
myfile << "Final Time: ";
myfile << temp;
myfile << "\n";
//float angle = Angle(aux, min, 5);
//cout << angle;
}
//}
myfile.close();
//ShowImage(aux, I1);
//imwrite(argv[2], I2); // salva imagem I2 no arquivo definido pelo usuario em argv[2]
//}
return 0;
}
void RectangleDetector::findRectangles()
{
std::cout << "Finding rectangles..." << std::endl;
// Clear Rectangle Vectors
allRectangles.clear();
finalRectangles.clear();
// Variable Declarations
cv::Mat pyr;
cv::Mat timg;
cv::Mat gray0(image.size(), CV_8U);
cv::Mat gray;
cv::pyrDown(image, pyr, cv::Size(image.cols / 2, image.rows / 2));
cv::pyrUp(pyr, timg, image.size());
// Variable Declarations
std::vector<std::vector<cv::Point> > contours;
int ch[] = {0, 0};
cv::mixChannels(&image, 1, &gray0, 1, ch, 1); // Extract Channel
// Canny helps to catch squares with gradient shading
// apply Canny. Take the upper threshold from slider
// and set the lower to 0 (which forces edges merging)
Canny(gray0, gray, 0, constList->detectionCannyThreshold, 5);
// dilate canny output to remove potential
// holes between edge segments
dilate(gray, gray, cv::Mat(), cv::Point(-1,-1));
findContours(gray, contours, CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE); // Find Contours
// Variable Declarations
std::vector<cv::Point> approx;
// Test Each Contour
for (size_t i = 0; i < contours.size(); ++i) {
// approximate contour with accuracy proportional
// to the contour perimeter
cv::approxPolyDP(cv::Mat(contours.at(i)), approx, cv::arcLength(cv::Mat(contours.at(i)), true) * 0.02, true);
// rectangular contours should have 4 vertices after approximation
// relatively large area (to filter out noisy contours)
// and be convex.
// Note: absolute value of an area is used because
// area may be positive or negative - in accordance with the
// contour orientation
if (approx.size() >= 4 &&
//approx.size() <= 6 &&
fabs(cv::contourArea(cv::Mat(approx))) > 200// &&
//cv::isContourConvex(cv::Mat(approx))
) {
double maxCosine = 0;
for(int j = 2; j < 5; ++j) {
// find the maximum cosine of the angle between joint edges
double cosine = fabs(angle(approx.at(j%4), approx.at(j-2), approx.at(j-1)));
maxCosine = MAX(maxCosine, cosine);
}
// if cosines of all angles are small
// (all angles are ~90 degrees) then write quandrange
// vertices to resultant sequence
if(maxCosine < constList->detectionMaxCosine)
allRectangles.push_back(approx);
}
}
std::cout << allRectangles.size();
if (allRectangles.size() == 0)
foundRectangle = false;
}
HoughLineTransform::HoughLineTransform(Mat srcInput)
{
src = srcInput;
Canny(src, dst, 50, 200, 3);
cvtColor(dst, cdst, CV_GRAY2BGR);
}
int main()
{
cv::Mat imCalibColor;
cv::Mat imCalibGray;
cv::vector<cv::vector<cv::Point> > contours;
cv::vector<cv::Vec4i> hierarchy;
cv::vector<cv::Point2f> pointQR;
cv::Mat imCalibNext;
cv::Mat imQR;
cv::vector<cv::Mat> tabQR;
/*cv::vector<cv::Point2f> corners1;
cv::vector<cv::Point2f> corners2;
cv::vector<cv::Point2f> corners3;
cv::vector<cv::Point2f> corners4;
cv::vector<cv::Point2f> corners5;*/
double qualityLevel = 0.01;
double minDistance = 10;
int blockSize = 3;
bool useHarrisDetector = false;
double k = 0.04;
int maxCorners = 600;
int A = 0, B= 0, C= 0;
char key;
int mark;
bool patternFound = false;
cv::VideoCapture vcap("../rsc/capture2.avi");
for (int i = 1; i < 5; i++)
{
std::ostringstream oss;
oss << "../rsc/QrCodes/QR" << i << ".jpg";
imQR = cv::imread(oss.str());
cv::cvtColor(imQR, imQR, CV_BGR2GRAY);
std::cout<< "Bouh!!!!!!" << std::endl;
tabQR.push_back(imQR);
}
do
{
while(imCalibColor.empty())
{
vcap >> imCalibColor;
}
vcap >> imCalibColor;
cv::Mat edges(imCalibColor.size(),CV_MAKETYPE(imCalibColor.depth(), 1));
cv::cvtColor(imCalibColor, imCalibGray, CV_BGR2GRAY);
Canny(imCalibGray, edges, 100 , 200, 3);
cv::findContours( edges, contours, hierarchy, cv::RETR_TREE, cv::CHAIN_APPROX_SIMPLE);
cv::imshow("pointInteret", imCalibColor);
mark = 0;
cv::vector<cv::Moments> mu(contours.size());
cv::vector<cv::Point2f> mc(contours.size());
for( int i = 0; i < contours.size(); i++ )
{
mu[i] = moments( contours[i], false );
mc[i] = cv::Point2f( mu[i].m10/mu[i].m00 , mu[i].m01/mu[i].m00 );
}
for( int i = 0; i < contours.size(); i++ )
{
int k=i;
int c=0;
while(hierarchy[k][2] != -1)
{
k = hierarchy[k][2] ;
c = c+1;
}
if(hierarchy[k][2] != -1)
c = c+1;
if (c >= 5)
{
if (mark == 0) A = i;
else if (mark == 1) B = i; // i.e., A is already found, assign current contour to B
else if (mark == 2) C = i; // i.e., A and B are already found, assign current contour to C
mark = mark + 1 ;
}
}
if (A !=0 && B !=0 && C!=0)
{
cv::Mat imagecropped = imCalibColor;
cv::Rect ROI(280/*pointQR[0].x*/, 260/*pointQR[0].y*/, 253, 218);
cv::Mat croppedRef(imagecropped, ROI);
cv::cvtColor(croppedRef, imagecropped, CV_BGR2GRAY);
cv::threshold(imagecropped, imagecropped, 180, 255, CV_THRESH_BINARY);
pointQR.push_back(mc[A]);
cv::circle(imCalibColor, cv::Point(pointQR[0].x, pointQR[0].y), 3, cv::Scalar(0, 0, 255), 1, 8, 0);
pointQR.push_back(mc[B]);
cv::circle(imCalibColor, cv::Point(pointQR[1].x, pointQR[1].y), 3, cv::Scalar(0, 0, 255), 1, 8, 0);
pointQR.push_back(mc[C]);
cv::circle(imCalibColor, cv::Point(pointQR[2].x, pointQR[2].y), 3, cv::Scalar(0, 0, 255), 1, 8, 0);
cv::Point2f D(0.0f,0.0f);
cv::Point2f E(0.0f,0.0f);
cv::Point2f F(0.0f,0.0f);
D.x = (mc[A].x + mc[B].x)/2;
E.x = (mc[B].x + mc[C].x)/2;
F.x = (mc[C].x + mc[A].x)/2;
D.y = (mc[A].y + mc[B].y)/2;
E.y = (mc[B].y + mc[C].y)/2;
F.y = (mc[C].y + mc[A].y)/2;
pointQR.push_back(D);
cv::circle(imCalibColor, cv::Point(pointQR[3].x, pointQR[3].y), 3, cv::Scalar(0, 0, 255), 1, 8, 0);
pointQR.push_back(E);
cv::circle(imCalibColor, cv::Point(pointQR[4].x, pointQR[4].y), 3, cv::Scalar(0, 0, 255), 1, 8, 0);
pointQR.push_back(F);
cv::circle(imCalibColor, cv::Point(pointQR[5].x, pointQR[5].y), 3, cv::Scalar(0, 0, 255), 1, 8, 0);
patternFound = true;
std::cout << "patternfound" << std::endl;
cv::SiftFeatureDetector detector;
cv::vector<cv::KeyPoint> keypoints1, keypoints2;
detector.detect(tabQR[3], keypoints1);
detector.detect(imagecropped, keypoints2);
cv::Ptr<cv::DescriptorExtractor> descriptor = cv::DescriptorExtractor::create("SIFT");
cv::Mat descriptors1, descriptors2;
descriptor->compute(tabQR[3], keypoints1, descriptors1 );
descriptor->compute(imagecropped, keypoints2, descriptors2 );
cv::FlannBasedMatcher matcher;
std::vector< cv::DMatch > matches;
matcher.match( descriptors1, descriptors2, matches );
double max_dist = 0; double min_dist = 100;
for( int i = 0; i < descriptors1.rows; i++ )
{
double dist = matches[i].distance;
if( dist < min_dist ) min_dist = dist;
if( dist > max_dist ) max_dist = dist;
}
std::vector< cv::DMatch > good_matches;
for( int i = 0; i < descriptors1.rows; i++ )
if( matches[i].distance <= 2*min_dist )
good_matches.push_back( matches[i]);
cv::Mat imgout;
drawMatches(tabQR[3], keypoints1, imagecropped, keypoints2, good_matches, imgout);
std::vector<cv::Point2f> pt_img1;
std::vector<cv::Point2f> pt_img2;
for( int i = 0; i < (int)good_matches.size(); i++ )
{
pt_img1.push_back(keypoints1[ good_matches[i].queryIdx ].pt );
pt_img2.push_back(keypoints2[ good_matches[i].trainIdx ].pt );
}
cv::Mat H = findHomography( pt_img1, pt_img2, CV_RANSAC );
cv::Mat result;
warpPerspective(tabQR[3],result,H,cv::Size(tabQR[3].cols+imagecropped.cols,tabQR[3].rows));
cv::Mat half(result,cv::Rect(0,0,imagecropped.cols,imagecropped.rows));
imagecropped.copyTo(half);
imshow( "Result", result );
break;
}
key = (char)cv::waitKey(67);
}while(patternFound != true && key != 27);
if(patternFound)
imCalibNext = imCalibColor;
return patternFound;
}
void KeyPointsDeliverer::extractMouthCornerKeyPoints(Mat &mouthImg, int thresholdDifferenceToAvg, int totalLineCheck, int kp1Break, int kp5Break)
{
QList<PossibleKeyPoint> possibleKeyPoints;
PossibleKeyPoint possibleKeyPoint;
for (int i = 0; i < rMidFinal.cols; ++i) {
for (int j = rMidFinal.rows-totalLineCheck; j > totalLineCheck/2; --j) {
int currentDiffToAvg = 0;
for (int k = 1; k < totalLineCheck/2 + 1; ++k) {
currentDiffToAvg += rMidFinal.at<uchar>(j-k,i) + rMidFinal.at<uchar>(j+k,i);
}
currentDiffToAvg = currentDiffToAvg / totalLineCheck;
if(currentDiffToAvg > 0){
currentDiffToAvg = 100 - (rMidFinal.at<uchar>(j,i) * 100 / currentDiffToAvg);
}
if(currentDiffToAvg > thresholdDifferenceToAvg){
possibleKeyPoint.differenceToAvg = currentDiffToAvg;
possibleKeyPoint.keyPoint.x = i;
possibleKeyPoint.keyPoint.y = j;
possibleKeyPoints.append(possibleKeyPoint);
}
}
}
Mat contourImg(rMidFinal.rows, rMidFinal.cols, CV_8UC1, Scalar(0,0,0));
Point p;
for (int i = 0; i < possibleKeyPoints.size(); ++i) {
p = possibleKeyPoints.at(i).keyPoint;
if(i % 3 == 0)
circle(rMidFinal, p, 1, Scalar(255,255,255));
contourImg.at<uchar>(p.y, p.x) = 255;
}
Mat _img;
double otsu_thresh_val = cv::threshold(
contourImg, _img, 0, 255, CV_THRESH_BINARY | CV_THRESH_OTSU
);
Canny(contourImg, contourImg, otsu_thresh_val*0.5, otsu_thresh_val, 3, true);
vector<vector<Point> > contours;
vector<Vec4i> hierarchy;
findContours( contourImg, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0) );
for( uint i = 0; i< contours.size(); i++ )
{
drawContours( contourImg, contours, i, Scalar(255,255,255), 1, 8, hierarchy, 1, Point() );
}
double luminanceMean = 0.0;
double pseudoHueMean = 0.0;
double pseudoHuePxl = 0.0;
int luminancePxl = 0;
for (int i = 0; i < mouthImg.cols/2; ++i) {
for (int j = 0; j < mouthImg.rows; ++j) {
pseudoHuePxl = imageProcessing.pseudoHuePxl(mouthImg, j, i);
luminancePxl = imageProcessing.luminancePxl(mouthImg, j, i);
luminanceMean += luminancePxl;
pseudoHueMean += pseudoHuePxl;
}
}
luminanceMean /= (mouthImg.cols/2*mouthImg.rows);
pseudoHueMean /= (mouthImg.cols/2*mouthImg.rows);
QList<PossibleKeyPoint> pKPoints;
PossibleKeyPoint pKPoint;
for (int i = mouthImg.cols/2-(mouthImg.cols/2*0.4); i > 0; --i) {
for (int j = mouthImg.rows-(mouthImg.rows/2*0.4); j > 0; --j) {
pseudoHuePxl = imageProcessing.pseudoHuePxl(mouthImg, j, i);
luminancePxl = imageProcessing.luminancePxl(mouthImg, j, i);
if(contourImg.at<uchar>(j,i) == 255
){
pKPoint.keyPoint.x = i;
pKPoint.keyPoint.y = j;
pKPoints.append(pKPoint);
break;
}
}
}
keyPoint1.x = 1000;
for (int i = 0; i < pKPoints.size(); ++i) {
int diffY = 0;
if(i > 0){
diffY = abs(pKPoints.at(i).keyPoint.y - pKPoints.at(i-1).keyPoint.y);
//ROS_INFO("diff: %d", abs(pKPoints.at(i).keyPoint.y - pKPoints.at(i-1).keyPoint.y));
}
if(diffY > kp1Break){
break;
}
if(keyPoint1.x > pKPoints.at(i).keyPoint.x){
keyPoint1.x = pKPoints.at(i).keyPoint.x;
keyPoint1.y = pKPoints.at(i).keyPoint.y;
}
//circle(rMidFinal, pKPoints.at(i).keyPoint, 2, Scalar(255,255,255));
}
luminanceMean = 0.0;
pseudoHueMean = 0.0;
for (int i = mouthImg.cols/2; i < mouthImg.cols; ++i) {
for (int j = 0; j < mouthImg.rows-(mouthImg.rows/2*0.4); ++j) {
pseudoHuePxl = imageProcessing.pseudoHuePxl(mouthImg, j, i);
luminancePxl = imageProcessing.luminancePxl(mouthImg, j, i);
luminanceMean += luminancePxl;
pseudoHueMean += pseudoHuePxl;
}
}
luminanceMean /= (mouthImg.cols/2*mouthImg.rows);
pseudoHueMean /= (mouthImg.cols/2*mouthImg.rows);
pKPoints.clear();
for (int i = mouthImg.cols/2+(mouthImg.cols/2*0.3); i < mouthImg.cols; ++i) {
for (int j = mouthImg.rows; j > 0; --j) {
pseudoHuePxl = imageProcessing.pseudoHuePxl(mouthImg, j, i);
luminancePxl = imageProcessing.luminancePxl(mouthImg, j, i);
if(contourImg.at<uchar>(j,i) == 255
){
pKPoint.keyPoint.x = i;
pKPoint.keyPoint.y = j;
pKPoints.append(pKPoint);
break;
}
}
}
// for (int i = 0; i < pKPoints.size(); ++i) {
// circle(rMidFinal, pKPoints.at(i).keyPoint, 1, Scalar(255,255,255));
// }
// for (int i = 0; i < contourImg.cols; ++i) {
// for (int j = 0; j < contourImg.rows; ++j) {
// if(contourImg.at<uchar>(j,i) == 255)
// circle(rMidFinal, Point(i,j), 1, Scalar(255,255,255));
// }
// }
Point kpTmp;
kpTmp.x = 0;
kpTmp.y = 0;
//ROS_INFO("pkPoint size %d", pKPoints.size());
for (int i = 0; i < pKPoints.size(); ++i) {
int diffY = 0;
if(i > 0){
diffY = abs(pKPoints.at(i).keyPoint.y - pKPoints.at(i-1).keyPoint.y);
//ROS_INFO("diff: %d", abs(pKPoints.at(i).keyPoint.y - pKPoints.at(i-1).keyPoint.y));
}
if(diffY > kp5Break){
break;
}
if(kpTmp.x < pKPoints.at(i).keyPoint.x){
kpTmp.x = pKPoints.at(i).keyPoint.x;
keyPoint5.x = pKPoints.at(i).keyPoint.x;
keyPoint5.y = pKPoints.at(i).keyPoint.y;
}
//circle(rMidFinal, pKPoints.at(i).keyPoint, 2, Scalar(255,255,255));
}
}
void FieldLineDetector::findTransformation(cv::Mat& src, cv::Mat& imgDst,
std::vector<cv::Point2f>& modelBots, cv::Mat& H)
{
this->botPosField = modelBots;
Mat imgBw;
blur(src, imgBw, Size(5, 5));
cvtColor(imgBw, imgBw, CV_BGR2GRAY);
Mat imgEdges;
Canny(imgBw, imgEdges, 50, 100, 3);
// imshow("bw", imgBw);
// imshow("edges", imgEdges);
std::vector<cv::Vec4i> lines;
HoughLinesP(imgEdges, lines, 1, CV_PI / 180, min_threshold + p_trackbar,
minLineLength, maxLineGap);
// Expand the lines little bit (by scaleFactor)
for (int i = 0; i < lines.size(); i++)
{
cv::Vec4i v = lines[i];
cv::Point2f p1 = Point2f(v[0], v[1]);
cv::Point2f p2 = Point2f(v[2], v[3]);
cv::Point2f p1p2 = p2 - p1;
float length = norm(p1p2);
cv::Point2f scaleP2 = p2 + p1p2 * (scaleFactor / 10.0f);
cv::Point2f scaleP1 = p1 - p1p2 * (scaleFactor / 10.0f);
lines[i][0] = scaleP1.x;
lines[i][1] = scaleP1.y;
lines[i][2] = scaleP2.x;
lines[i][3] = scaleP2.y;
}
createThresholdedImg(src);
// do line detection!
detectCorners(lines);
filterCorners();
findNeighbors(lines);
findCornerMapping(mappedEdges);
for (int i = 0; i < mappedEdges.size(); i++)
{
cout << (*mappedEdges[i]) << endl;
}
findFieldMatch(mappedEdges, H);
if (imgDst.cols > 0)
{
// Draw lines
for (int i = 0; i < lines.size(); i++)
{
cv::Vec4i v = lines[i];
cv::line(imgDst, cv::Point(v[0], v[1]), cv::Point(v[2], v[3]),
cv::Scalar(0, 255, 0), 2);
}
// draw corners
for (int i = 0; i < cornerBuffer.size(); i++)
{
cv::circle(imgDst, cornerBuffer.at(i), 1, cv::Scalar(255, 0, 0), 2);
}
// draw filtered corners
for (int i = 0; i < detectedCorners.size(); i++)
{
circle(imgDst, detectedCorners[i]->point, (int) 20,
Scalar(0, 255, 255), 1);
}
// draw detected corner coordinates
for (int i = 0; i < detectedCorners.size(); i++)
{
stringstream ss;
ss << detectedCorners[i]->point;
putText(imgDst, ss.str(),
detectedCorners[i]->point + Point2f(0, 10),
FONT_HERSHEY_PLAIN, 1, Scalar(250, 0, 0));
}
}
}
// Process an image containing a line and return the angle with respect to NAO.
double NaoVision::calculateAngleToBlackLine() {
// Convert image to gray and blur it.
cvtColor(src, src_gray, CV_BGR2GRAY);
blur(src_gray, src_gray, Size(3,3));
if(!local)
imshow("src", src);
Mat canny_output;
vector<vector<Point> > contours;
vector<Vec4i> hierarchy;
// Detect edges using canny.
Canny(src_gray, canny_output, thresh, thresh * 2, 3);
// Find contours.
findContours(canny_output, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));
// Get the moments.
vector<Moments> mu(contours.size());
for(int i = 0; i < contours.size(); i++)
mu[i] = moments(contours[i], false);
// Get the mass centers.
vector<Point2f> mc( contours.size());
for(int i = 0; i < contours.size(); i++)
mc[i] = Point2f( mu[i].m10/mu[i].m00 , mu[i].m01/mu[i].m00);
// Eliminate contours without area.
contoursClean.clear();
int indMax = 0;
int lengthMax = 0;
for(int i = 0; i < contours.size(); i++) {
area = mu[i].m00;
length = arcLength(contours[i], true);
punto = mc[i];
if(area != 0 && length > 200 && punto.x > 0 && punto.y > 0)
contoursClean.push_back(contours.at(i));
}
if(contoursClean.size() != 0) {
// Get moments and mass for new vector.
vector<Moments> muClean(contoursClean.size());
for(int i = 0; i < contoursClean.size(); i++)
muClean[i] = moments(contoursClean[i], false);
// Get the mass centers.
vector<Point2f> mcClean( contoursClean.size());
for(int i = 0; i < contoursClean.size(); i++)
mcClean[i] = Point2f(muClean[i].m10/muClean[i].m00, muClean[i].m01/muClean[i].m00);
for(int i = 0; i < contoursClean.size(); i++) {
punto = mcClean[i];
length = arcLength(contoursClean[i], true);
}
// Find the longest.
for(int i = 0; i < contoursClean.size(); i++) {
length = arcLength(contoursClean[i], true);
lengthMax = arcLength(contoursClean[indMax], true);
if(i > 0) {
if(length > lengthMax)
indMax = i;
} else
indMax = 0;
}
// Draw contours.
Mat drawing = Mat::zeros(canny_output.size(), CV_8UC3);
Scalar color = Scalar( rng.uniform(0, 255), rng.uniform(0,255), rng.uniform(0,255));
drawContours( drawing, contoursClean, indMax, color, 2, 8, hierarchy, 0, Point());
circle(drawing, mcClean[indMax], 4, color, 5, 8, 0 );
// Calculate the angle of the line.
angleToALine = getAngleDegrees(contoursClean[indMax], drawing);
puntoMax = mcClean[indMax];
lengthMax = arcLength(contoursClean[indMax], true);
// Show in a window.
if(!local) {
namedWindow("Contours", CV_WINDOW_AUTOSIZE);
imshow("Contours", drawing);
// Draw grid.
line(drawing, Point(260,0), Point(260, drawing.rows), Scalar(255,255,255));
line(drawing, Point(umbral,0), Point(umbral, drawing.rows), Scalar(255,255,255));
line(drawing, Point((drawing.cols/2),0), Point((drawing.cols/2), drawing.rows), Scalar(255,255,255));
line(drawing, Point(0,120), Point(320,120), Scalar(255,255,255));
imshow("Contours", drawing);
}
}
else { // Go straight.
angleToALine = 90.0;
}
return angleToALine;
}
void FindLargest_ProjectionVoxel(int ImageNum, vector<OctVoxel>& Octree, vector<cv::Mat>& Silhouette, Cpixel*** vertexII, CMatrix* ART){
int thresh = 70;
int max_thresh = 210;
RNG rng(12345);
Mat src_gray;
Mat drawing;
double scale(0.7);
Size ssize;
CVector M(4); //Homogeneous coordinates of the vertices(x,y,z,1) world coordinate
CVector m(4); //That the image coordinates (normalized) expressed in homogeneous coordinates
M[3] = 1.0;
//8 vertices world coordinates of the voxel (x,y,z)
CVector3d vertexW[8];
ofstream fout("larget_boundingbox_contour.txt");
int Boundingbox_line[12][2] = { { 0, 1 }, { 1, 2 }, { 2, 3 }, { 3, 0 },
{ 0, 4 }, { 1, 5 }, { 2, 6 }, { 3, 7 },
{ 4, 5 }, { 5, 6 }, { 6, 7 }, { 7, 4 } };
//---------------------------------------------------------------
for (auto h(0); h < ImageNum; h++){
//src_gray = Silhouette[h];
Silhouette[h].copyTo(src_gray);
cout << "Silhouette_" << h << endl;
for (auto j(0); j < Octree.size(); j++){
Octree[j].SetVertexWorld_Rotated(vertexW);
for (int k = 0; k < 8; ++k){ //8 vertices of the voxel
M[0] = vertexW[k].x;
M[1] = vertexW[k].y;
M[2] = vertexW[k].z;
m = ART[h] * M;
vertexII[h][j][k].setPixel_u_v((int)(m[0] / m[2]), (int)(m[1] / m[2])); // normalize
}
//-------------------------------------- bounding box ------------------------
for (auto k(0); k < 12; k++){
//Draw 12 lines of the voxel in img.
Start_point.x = vertexII[h][j][Boundingbox_line[k][0]].getPixel_u();
Start_point.y = vertexII[h][j][Boundingbox_line[k][0]].getPixel_v();
PointStart.push_back(Start_point);
End_point.x = vertexII[h][j][Boundingbox_line[k][1]].getPixel_u();
End_point.y = vertexII[h][j][Boundingbox_line[k][1]].getPixel_v();
PointEnd.push_back(End_point);
//line(src_gray, Start_point, End_point, Scalar(225, 225,255), 2.0, CV_AA);
}
}
Mat canny_output;
vector<vector<Point> > contours;
vector<Vec4i> hierarchy;
double max_contour_area(0.0);
int largest_contour_index(0);
/// Detect edges using canny
Canny(src_gray, canny_output, thresh, max_thresh, 3);
/// Find contours
//findContours(canny_output, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));
findContours(canny_output, contours, hierarchy, CV_RETR_CCOMP, CV_CHAIN_APPROX_NONE, Point(0, 0));
/// Draw contours
drawing = Mat::zeros(canny_output.size(), CV_8UC3);
for (auto n(0); n < PointEnd.size(); n++){
line(drawing, PointStart[n], PointEnd[n], Scalar(225, 225, 225), 1.0, 1, 0);
}
/// Get the moments
vector<Moments> mu(contours.size());
for (int i = 0; i < contours.size(); i++)
{
mu[i] = moments(contours[i], false);
//cout << "# of contour points: " << contours[i].size() << endl;
for (int j = 0; j < contours[i].size(); j++)
{
//cout << "Point(x,y)=" <<i<<" j "<<j<<" "<< contours[i][j] << endl;
}
}
//// Get the mass centers:
vector<Point2f> mc(contours.size());
for (int i = 0; i < contours.size(); i++)
{
mc[i] = Point2f(mu[i].m10 / mu[i].m00, mu[i].m01 / mu[i].m00);
}
//// ---------- - Find the convex hull object for each contour
vector<vector<Point>>hull(contours.size());
for (int i = 0; i < contours.size(); i++){
convexHull(Mat(contours[i]), hull[i], false);
}
// Calculate the area with the moments 00 and compare with the result of the OpenCV function
//printf("\t Info: Area and Contour Length \n");
//cout << "contours.size() " << contours.size() << endl;
double countour_Area(0.0);
double arc_Length(0.0);
for (int i = 0; i < contours.size(); i++)
{
countour_Area = (double)contourArea(contours[i]);
arc_Length = (double)arcLength(contours[i], true);
//cout << "contourArea [" << i << "] " << ": Moment " << mu[i].m00
// << " OpenCV " << countour_Area << " arcLength " << arc_Length << endl;
//cout << "countour_Area "<< countour_Area << " " << endl;
if (countour_Area > max_contour_area){
max_contour_area = countour_Area;
largest_contour_index = i;
}
//------- draw all contour ---------------
//Scalar color = Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255));
//drawContours(drawing, contours, i, color, 2, 8, hierarchy, 0, Point());
//circle(drawing, mc[i], 4, color, -1, 8, 0);
//drawContours(drawing, hull, i, color, 1, 8, vector<Vec4i>(), 0, Point());
//drawContours(drawing, contours, i, Scalar(255, 255, 255), 0.10, 8, hierarchy, 0, Point());
}
//------- draw largest contour ---------------
Scalar color = Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255));
drawContours(drawing, contours, largest_contour_index, color, 2, 8, hierarchy, 0, Point());
//circle(drawing, mc[largest_contour_index], 4, color, -1, 8, 0);
//drawContours(drawing, contours, largest_contour_index, Scalar(0, 255, 255), 2, 8, hierarchy, 0, Point());
//drawContours(drawing, hull, largest_contour_index, color, 2, 8, vector<Vec4i>(), 0, Point());
//drawContours(drawing, contours, largest_contour_index, Scalar(255, 255, 255), 1, 8, hierarchy, 0, Point());
fout << max_contour_area << endl;
cout << "max_contour_area " << max_contour_area << endl;
//----------------------- Show in a window --------------------------------------
//resize(drawing, drawing, ssize, INTER_NEAREST);
namedWindow("Contours", CV_WINDOW_AUTOSIZE);
imshow("Contours", drawing);
//output white boundary
imwrite("../../data2016/input/newzebra/contour_voxel/contour_voxel" + to_string(h) + ".bmp", drawing);
waitKey(0);
destroyWindow("silhouette");
PointStart.clear();
PointStart.shrink_to_fit();
PointEnd.clear();
PointEnd.shrink_to_fit();
}
//getchar();
}
QVector<CColorsType> CColor::getCorBoard_CorPlaced()
{
imgFHSV = Frame2HSV(imgSharp, CColorsType::YELLOW);
Canny(imgFHSV, imgCanny, 180, 120);
findContours(imgCanny, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));
for (size_t i = 0; i< contours.size(); i++)
{
approxPolyDP(Mat(contours[i]), approx, arcLength(Mat(contours[i]), true)*0.02, true);
if(fabs(contourArea(approx)) > 1000 && approx.size() == 4)
{
if( ! fig.isSquare(approx)) {/// YELLOW ///As peças jogadas são retangulos
CColorsType colorBoard = poscenter.getBoardColor(CColorsType::YELLOW);
if(colorBoard == CColorsType::YELLOW) ///Right Answer
{
setMessage2Robot(true, colorBoard, CColorsType::YELLOW);
return QVector<CColorsType>() << colorBoard << CColorsType::YELLOW;
}
else
{
setMessage2Robot(false, colorBoard, CColorsType::YELLOW);
return QVector<CColorsType>() << colorBoard << CColorsType::YELLOW;
}
}
}
}
imgFHSV = Frame2HSV(imgSharp, CColorsType::RED);
Canny(imgFHSV, imgCanny, 180, 120);
findContours(imgCanny, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));
for (size_t i = 0; i< contours.size(); i++)
{
approxPolyDP(Mat(contours[i]), approx, arcLength(Mat(contours[i]), true)*0.02, true);// 5, true); //
if(fabs(contourArea(approx)) > 1000 && approx.size() == 4) //este if não deve ser necessario
{
if( ! fig.isSquare(approx)){/// RED ///As peças jogadas são retangulos
CColorsType colorBoard = poscenter.getBoardColor(CColorsType::RED);
if(colorBoard == CColorsType::RED)
{
setMessage2Robot(true, colorBoard, CColorsType::RED);
return QVector<CColorsType>() << colorBoard << CColorsType::RED;
}
else
{
setMessage2Robot(false, colorBoard, CColorsType::RED);
return QVector<CColorsType>() << colorBoard << CColorsType::RED;
}
}
}
}
imgFHSV = Frame2HSV(imgSharp, CColorsType::GREEN);
Canny(imgFHSV, imgCanny, 180, 120);
findContours(imgCanny, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));
for (size_t i = 0; i< contours.size(); i++)
{
approxPolyDP(Mat(contours[i]), approx, arcLength(Mat(contours[i]), true)*0.02, true);// 5, true); //
if(fabs(contourArea(approx)) > 1000 && approx.size() == 4) //este if não deve ser necessario
{
if( ! fig.isSquare(approx)){/// GREEN ///As peças jogadas são retangulos
CColorsType colorBoard = poscenter.getBoardColor(CColorsType::GREEN);
if(colorBoard == CColorsType::GREEN)
{
setMessage2Robot(true, colorBoard, CColorsType::GREEN);
return QVector<CColorsType>() << colorBoard << CColorsType::GREEN;
}
else
{
setMessage2Robot(false, colorBoard, CColorsType::GREEN);
return QVector<CColorsType>() << colorBoard << CColorsType::GREEN;
}
}
}
}
imgFHSV = Frame2HSV(imgSharp, CColorsType::BLUE);
Canny(imgFHSV, imgCanny, 180, 120);
findContours(imgCanny, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));
for (size_t i = 0; i< contours.size(); i++) {
approxPolyDP(Mat(contours[i]), approx, arcLength(Mat(contours[i]), true)*0.02, true);// 5, true); //
if(fabs(contourArea(approx)) > 1000 && approx.size() == 4) //este if não deve ser necessario
{
if( ! fig.isSquare(approx)){/// BLUE ///As peças jogadas são retangulos
CColorsType colorBoard = poscenter.getBoardColor(CColorsType::BLUE);
if(colorBoard == CColorsType::BLUE)
{
setMessage2Robot(true, colorBoard, CColorsType::BLUE);
return QVector<CColorsType>() << colorBoard << CColorsType::BLUE;
}
else
{
setMessage2Robot(false, colorBoard, CColorsType::BLUE);
return QVector<CColorsType>() << colorBoard << CColorsType::BLUE;
}
}
}
}
imgFHSV = Frame2HSV(imgSharp, CColorsType::BLACK);
Canny(imgFHSV, imgCanny, 180, 120);
findContours(imgCanny, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));
for (size_t i = 0; i< contours.size(); i++) {
approxPolyDP(Mat(contours[i]), approx, arcLength(Mat(contours[i]), true)*0.02, true);// 5, true); //
if(fabs(contourArea(approx)) > 1000 && approx.size() == 4) //este if não deve ser necessario
{
if( ! fig.isSquare(approx)){/// BLACK ///As peças jogadas são retangulos
CColorsType colorBoard = poscenter.getBoardColor(CColorsType::BLACK);
if(colorBoard == CColorsType::BLACK)
{
setMessage2Robot(true, colorBoard, CColorsType::BLACK);
return QVector<CColorsType>() << colorBoard << CColorsType::BLACK;
}
else
{
setMessage2Robot(false, colorBoard, CColorsType::BLACK);
return QVector<CColorsType>() << colorBoard << CColorsType::BLACK;
}
}
}
}
return QVector<CColorsType>() << CColorsType::NONE << CColorsType::NONE; ///REVER
}
/** @function thresh_callback */
void thresh_callback(Mat src_gray, Mat& drawing, double scale, Size& ssize)
{
RNG rng(12345);
int thresh = 100;
ofstream fout("larget_contour_area.txt");
Mat canny_output;
vector<vector<Point> > contours;
vector<Vec4i> hierarchy;
double max_contour_area(0.0);
int largest_contour_index(0);
/// Detect edges using canny
Canny(src_gray, canny_output, thresh, thresh * 2, 3);
/// Find contours
findContours(canny_output, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));
/// Get the moments
vector<Moments> mu(contours.size());
for (int i = 0; i < contours.size(); i++)
{
mu[i] = moments(contours[i], false);
//cout << "# of contour points: " << contours[i].size() << endl;
for (int j = 0; j < contours[i].size(); j++)
{
//cout << "Point(x,y)=" <<i<<" j "<<j<<" "<< contours[i][j] << endl;
}
}
/// Get the mass centers:
vector<Point2f> mc(contours.size());
for (int i = 0; i < contours.size(); i++)
{
mc[i] = Point2f(mu[i].m10 / mu[i].m00, mu[i].m01 / mu[i].m00);
}
//----------- Find the convex hull object for each contour
vector<vector<Point>>hull(contours.size());
for (int i = 0; i < contours.size(); i++){
convexHull(Mat(contours[i]), hull[i], false);
}
//------------ Draw contours
drawing = Mat::zeros(canny_output.size(), CV_8UC3);
//Size ssize;
ssize = Size((int)(drawing.size().width * scale), (int)(drawing.size().height*scale));
//the dst image size,e.g.100x100
// Calculate the area with the moments 00 and compare with the result of the OpenCV function
//printf("\t Info: Area and Contour Length \n");
//cout << "contours.size() " << contours.size() << endl;
double countour_Area(0.0), arc_Length(0.0);
for (int i = 0; i < contours.size(); i++)
{
countour_Area = (double)contourArea(contours[i]);
arc_Length = (double)arcLength(contours[i], true);
//cout << "contourArea [" << i << "] " << ": Moment " << mu[i].m00
// << " OpenCV " << countour_Area << " arcLength " << arc_Length << endl;
//cout << "countour_Area "<< countour_Area << " " << endl;
if (countour_Area > max_contour_area){
max_contour_area = countour_Area;
largest_contour_index = i;
}
//------- draw all contour ---------------
//Scalar color = Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255));
//drawContours(drawing, contours, i, color, 2, 8, hierarchy, 0, Point());
//circle(drawing, mc[i], 4, color, -1, 8, 0);
}
//------- draw largest contour ---------------
if (contours.size() > 0){
Scalar color = Scalar(rng.uniform(0, 255), rng.uniform(0, 255), rng.uniform(0, 255));
drawContours(drawing, contours, largest_contour_index, color, 1, 8, hierarchy, 0, Point());
circle(drawing, mc[largest_contour_index], 4, color, -1, 8, 0);
drawContours(drawing, hull, largest_contour_index, color, 1, 8, vector<Vec4i>(), 0, Point());
}
fout << max_contour_area << endl;
cout << "max_contour_area " << max_contour_area << endl;
}
///Level 1
void CColor::setALLBoardColorPosition()
{
Scalar colorContours;
imgFHSVBoard = Frame2HSV(imgSharp, CColorsType::NOTWHITE);
///setColorPosition2Board(CColorsType::RED);
imgFHSV = Frame2HSV(imgSharp, CColorsType::RED);
bitwise_and(imgFHSVBoard, imgFHSV, imgFComper); //sumar a figura imgFHSVBoard com imgFHSV para obter só a cor
Canny(imgFComper, imgCanny, 180, 120);
findContours(imgCanny, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));
drawingContours = Mat::zeros(imgCanny.size(), CV_8UC3);
for (size_t i = 0; i< contours.size(); i++) {
approxPolyDP(Mat(contours[i]), approx, arcLength(Mat(contours[i]), true)*0.02, true);// 5, true); //
if(fabs(contourArea(approx)) > 1000 && approx.size() == 4)
{
if(fig.isSquare(approx)) {
poscenter.ChooseSaveBoardColorCenter( CColorsType::RED, contours[i]);
colorContours = CV_RGB(255, 0, 0);
drawContours(drawingContours, contours, (int)i, colorContours, 2, /*CV_AA*/8, hierarchy, 0, Point());
}
}
}
///setColorPosition2Board(CColorsType::GREEN);
imgFHSV = Frame2HSV(imgSharp, CColorsType::GREEN);
bitwise_and(imgFHSVBoard, imgFHSV, imgFComper); //sumar a figura imgFHSVBoard com imgFHSV para obter só a cor
Canny(imgFComper, imgCanny, 180, 120);
findContours(imgCanny, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));
for (size_t i = 0; i< contours.size(); i++) {
approxPolyDP(Mat(contours[i]), approx, arcLength(Mat(contours[i]), true)*0.02, true);// 5, true); //
if(fabs(contourArea(approx)) > 1000 && approx.size() == 4)
{
if(fig.isSquare(approx)) {
poscenter.ChooseSaveBoardColorCenter( CColorsType::GREEN, contours[i]);
colorContours = CV_RGB(0, 255, 0);
drawContours(drawingContours, contours, (int)i, colorContours, 2, /*CV_AA*/8, hierarchy, 0, Point());
}
}
}
///setColorPosition2Board(CColorsType::BLUE);
imgFHSV = Frame2HSV(imgSharp, CColorsType::BLUE);
bitwise_and(imgFHSVBoard, imgFHSV, imgFComper); //sumar a figura imgFHSVBoard com imgFHSV para obter só a cor
Canny(imgFComper, imgCanny, 180, 120);
findContours(imgCanny, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));
for (size_t i = 0; i< contours.size(); i++) {
approxPolyDP(Mat(contours[i]), approx, arcLength(Mat(contours[i]), true)*0.02, true);// 5, true); //
if(fabs(contourArea(approx)) > 1000 && approx.size() == 4)
{
if(fig.isSquare(approx)) {
poscenter.ChooseSaveBoardColorCenter( CColorsType::BLUE, contours[i]);
colorContours = CV_RGB(0, 0, 255);
drawContours(drawingContours, contours, (int)i, colorContours, 2, /*CV_AA*/8, hierarchy, 0, Point());
}
}
}
////setColorPosition2Board(CColorsType::YELLOW);
imgFHSV = Frame2HSV(imgSharp, CColorsType::YELLOW);
bitwise_and(imgFHSVBoard, imgFHSV, imgFComper); //sumar a figura imgFHSVBoard com imgFHSV para obter só a cor
Canny(imgFComper, imgCanny, 180, 120);
findContours(imgCanny, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));
for (size_t i = 0; i< contours.size(); i++) {
approxPolyDP(Mat(contours[i]), approx, arcLength(Mat(contours[i]), true)*0.02, true);// 5, true); //
if(fabs(contourArea(approx)) > 1000 && approx.size() == 4)
{
if(fig.isSquare(approx)) {
poscenter.ChooseSaveBoardColorCenter( CColorsType::YELLOW, contours[i]);
colorContours = CV_RGB(255, 255, 0);
drawContours(drawingContours, contours, (int)i, colorContours, 2, /*CV_AA*/8, hierarchy, 0, Point());
}
}
}
///setColorPosition2Board(CColorsType::BLACK);
imgFHSV = Frame2HSV(imgSharp, CColorsType::BLACK );
bitwise_and(imgFHSVBoard, imgFHSV, imgFComper); //sumar a figura imgFHSVBoard com imgFHSV para obter só a cor
Canny(imgFComper, imgCanny, 180, 120);
findContours(imgCanny, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));
for (size_t i = 0; i< contours.size(); i++) {
approxPolyDP(Mat(contours[i]), approx, arcLength(Mat(contours[i]), true)*0.02, true);// 5, true); //
if(fabs(contourArea(approx)) > 1000 && approx.size() == 4)
{
if(fig.isSquare(approx)) {
poscenter.ChooseSaveBoardColorCenter( CColorsType::BLACK, contours[i]);
colorContours = CV_RGB(255, 255, 0);
drawContours(drawingContours, contours, (int)i, colorContours, 2, /*CV_AA*/8, hierarchy, 0, Point());
}
}
}
//imshow("drawingContours", drawingContours);
}
void AndarPelaParedeAteLinha::execute(Robotino *robotino)
{
float Vx = 200, Vy, w, distParede;
float erroDist = 0;
int paredeAlvo = robotino->paredeAlvo();
static State<Robotino> * voltar;
static float a = std::sin(60*PI/180)/std::sin(80*PI/180);
static float cos20 = std::cos(20*PI/180);
static float K = R*(a-1);
static float erro_int = 0;
float e1 = robotino->irDistance(Robotino::IR_ESQUERDO_1);
float e2 = robotino->irDistance(Robotino::IR_ESQUERDO_2);
float ref_e1 = e2*a+K;
float d1 = robotino->irDistance(Robotino::IR_DIREITO_1);
float d2 = robotino->irDistance(Robotino::IR_DIREITO_2);
float ref_d1 = 1.15*(d2*a+K);
float distancia_da_esquerda, distancia_da_direita;
float erro;
vector<Vec4i> lines;
Vec4i l, l2;
Mat img, cdst;
int num_linha = 0;
int min_Hough = 70, dist_Hough = 50;
int min_canny =150 , max_canny = 3*min_canny;
distParede = robotino->getRefDistParede();
distParede += R;
img = robotino->getImage();
cvtColor( img, cdst, CV_BGR2GRAY );
Canny( cdst, cdst, (double)min_canny, (double)max_canny, 3 );
convertScaleAbs(cdst, cdst);
//cv::imshow("Canny",cdst);
//cv::waitKey(1);
threshold(cdst, cdst, (double)5, (double)255, CV_THRESH_BINARY);
HoughLinesP(cdst, lines, 1, CV_PI/180, min_Hough, min_Hough, dist_Hough );
cvtColor( cdst, cdst, CV_GRAY2BGR );
if (paredeAlvo == Robotino::NORTEN90 || paredeAlvo == Robotino::OESTE0 || paredeAlvo == Robotino::SUL90 || paredeAlvo == Robotino::LESTE180){
erro = (e1-ref_e1);
erro_int += erro*dt;
w = Kp*erro+Ki*erro_int;
distancia_da_esquerda = ((e1+ref_e1+2*R)*cos20)/2;
erroDist = (distancia_da_esquerda) - distParede;
Vy = Kpy*erroDist;
std::cout << "erro dist: " << erroDist << "\n";
std::cout<< "Esquerda 1: " << e1 << std::endl;
std::cout<< "RefEsquerda 1: " << ref_e1 << std::endl;
std::cout<< "Esquerda 2: " << e2 << std::endl;
std::cout << "Distância da esquerda: " << distancia_da_esquerda << "\n";
if (lines.size() > numeroLinhasMin){
if (paredeAlvo == Robotino::OESTE0) {
robotino->setOdometry(robotino->odometryX(),-(distancia_da_esquerda*10+15),0);
}
if (paredeAlvo == Robotino::NORTEN90) {
robotino->setOdometry((robotino->getAlturaMapa())*10 -(distancia_da_esquerda*10+15),robotino->odometryY(),-90);
}
if (paredeAlvo == Robotino::SUL90) {
robotino->setOdometry((distancia_da_esquerda*10+15),robotino->odometryY(),90);
}
if (paredeAlvo == Robotino::LESTE180) {
robotino->setOdometry(robotino->odometryX(),-((robotino->getLarguraMapa())*10 -(distancia_da_esquerda*10+15)),180);
}
}
}else if (paredeAlvo == Robotino::SULN90 || paredeAlvo == Robotino::LESTE0 || paredeAlvo == Robotino::NORTE90 || paredeAlvo == Robotino::OESTE180) {
erro = (d1-ref_d1);
erro_int += erro*dt;
w = -Kp*erro-Ki*erro_int;
distancia_da_direita = ((d1+ref_d1+2*R)*cos20)/2;
erroDist = distParede - ( distancia_da_direita );
Vy = Kpy*erroDist;
std::cout<< "Direita 1: " << d1 << std::endl;
std::cout<< "RefDireita 1: " << ref_d1 << std::endl;
std::cout<< "Direita 2: " << d2 << std::endl;
std::cout << "Distância da direita: " << distancia_da_direita << "\n";
if (lines.size() > numeroLinhasMin){
if (paredeAlvo == Robotino::SULN90) {
robotino->setOdometry((distancia_da_direita*10+15),robotino->odometryY(),-90);
}
if (paredeAlvo == Robotino::LESTE0) {
robotino->setOdometry(robotino->odometryX(),-((robotino->getLarguraMapa()) * 10-(distancia_da_direita*10+15)),0);
}
if (paredeAlvo == Robotino::NORTE90) {
robotino->setOdometry((robotino->getAlturaMapa()*10 - (distancia_da_direita*10+15)),robotino->odometryY(),90);
}
if (paredeAlvo == Robotino::OESTE180) {
robotino->setOdometry(robotino->odometryX(),-((distancia_da_direita*10+15)),180);
}
}
}
if(distParede > 99){
robotino->setVelocity(Vx,0,0);
}else{
robotino->setVelocity(Vx,Vy,w);
}
for( size_t i = 0; i < lines.size(); i++ ){
Vec4i l = lines[i];
//if (l[3] > 100 || l[1] > 100){
num_linha++;
//}
}
if (num_linha > numeroLinhasMin){
robotino->setVelocity(0,0,0);
robotino->change_state(robotino->previous_state());
}
}
void DkPageSegmentation::findRectangles(const cv::Mat& img, std::vector<DkPolyRect>& rects, int channel, int threshold) const {
cv::Mat imgL;
cv::normalize(img, imgL, 255, 0, cv::NORM_MINMAX);
// downscale
if (scale != 1.0f)
cv::resize(imgL, imgL, cv::Size(), scale, scale, CV_INTER_LINEAR);
std::vector<std::vector<cv::Point> > contours;
int threshStep = dsc::round(255.0 / numThresh);
//std::cout << "thresh step: " << threshStep << std::endl;
cv::Mat gray;
std::vector<DkPolyRect> rectsL;
std::vector<int> indexes;
if (threshold == -1) {
// use less thresholds for a/b channels
if (channel > 0)
threshStep *= 2;
for (int idx = 0; idx < 255; idx += threshStep)
indexes.push_back(idx);
}
else
indexes.push_back(threshold);
// try several threshold levels
for (int thr : indexes) {
if (thr == 0) {
int thresh = 80;
Canny(imgL, gray, thresh, thresh*3, 5);
// dilate canny output to remove potential
// holes between edge segments
//dilate(gray, gray, cv::Mat(), cv::Point(-1,-1));
//cv::imwrite("C:/VSProjects/DocScan/img/tests/edge.png", gray);
}
else
gray = imgL >= thr;
cv::erode(gray, gray, cv::Mat(), cv::Point(-1,-1));
// find contours and store them all as a list
findContours(gray, contours, CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE);
if (looseDetection) {
std::vector<std::vector<cv::Point> > hull;
for (int i = 0; i < (int)contours.size(); i++) {
double cArea = contourArea(cv::Mat(contours[i]));
if (fabs(cArea) > mMinArea*scale*scale && (!mMaxArea || fabs(cArea) < mMaxArea*(scale*scale))) {
std::vector<cv::Point> cHull;
cv::convexHull(cv::Mat(contours[i]), cHull, false);
hull.push_back(cHull);
}
}
contours = hull;
}
std::vector<cv::Point> approx;
// DEBUG ------------------------
//cv::Mat pImg = imgL.clone();
//cv::cvtColor(pImg, pImg, CV_GRAY2BGR);
// DEBUG ------------------------
// test each contour
for (size_t i = 0; i < contours.size(); i++) {
// approxicv::Mate contour with accuracy proportional
// to the contour perimeter
approxPolyDP(cv::Mat(contours[i]), approx, arcLength(cv::Mat(contours[i]), true)*0.02, true);
double cArea = contourArea(cv::Mat(approx));
// DEBUG ------------------------
//if (fabs(cArea) < mMaxArea)
//fillConvexPoly(pImg, &approx[0], (int)approx.size(), cv::Scalar(255,0,0));
// DEBUG ------------------------
// square contours should have 4 vertices after approxicv::Mation
// relatively large area (to filter out noisy contours)
// and be convex.
// Note: absolute value of an area is used because
// area may be positive or negative - in accordance with the
// contour orientation
if (approx.size() == 4 &&
fabs(cArea) > mMinArea*scale*scale &&
(!mMaxArea || fabs(cArea) < mMaxArea*scale*scale) &&
isContourConvex(cv::Mat(approx))) {
DkPolyRect cr(approx);
//std::cout << mMinArea*scale*scale << " < " << fabs(cArea) << " < " << mMaxArea*scale*scale << std::endl;
// if cosines of all angles are small
// (all angles are ~90 degree)
if(/*cr.maxSide() < std::max(tImg.rows, tImg.cols)*maxSideFactor && */
(!maxSide || cr.maxSide() < maxSide*scale) &&
cr.getMaxCosine() < 0.3 ) {
cr.setChannel(channel);
cr.setThreshold(thr);
rectsL.push_back(cr);
}
}
}
// DEBUG ------------------------
//cv::cvtColor(pImg, pImg, CV_RGB2BGR);
//cv::imwrite("C:/VSProjects/DocScan/img/tests/poly" + Utils::num2str(thr) + ".png", pImg);
// DEBUG ------------------------
}
for (size_t idx = 0; idx < rectsL.size(); idx++)
rectsL[idx].scale(1.0f/scale);
// filter rectangles which are found because of the image border
for (const DkPolyRect& p : rectsL) {
DkBox b = p.getBBox();
if (b.size().height < img.rows*maxSideFactor &&
b.size().width < img.cols*maxSideFactor) {
rects.push_back(p);
}
}
//cv::normalize(dbgImg, dbgImg, 255, 0, cv::NORM_MINMAX);
}
bool ShapeFilter::findCirc(cv::Mat img){
//getting the contours
cv::Mat canny;
std::vector<std::vector<cv::Point> > contours;
std::vector<cv::Vec4i> hierarchy;
float radius;
cv::Point2f center;
this->radius.clear();
this->center.clear();
int thresh = 100;
// Detect edges using canny
Canny(img, canny, thresh, thresh*2, 3 );
// Find contours
findContours( canny, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, cv::Point(0, 0) );
int circlesFound = 0;
//constants
unsigned int minPoints = 6;
double minArea = 10;
float minRad = 100;
for (std::vector<cv::Point> co: contours){
if (co.size() < minPoints){
println("Circle Not enough Points");
continue;
}
double area = cv::contourArea(co);
if (area < minArea) {
println ("Circle not enough area " + std::to_string(area));
continue;
}
/*
/// Get the moments
std::vector<cv::Moments> mu(co.size() );
for( int i = 0; i < co.size(); i++ ){
mu[i] = moments( co[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 ); }
*/
cv::Moments cvmoments = moments(co, false);
double nu11 = cvmoments.nu11;
double nu20 = cvmoments.nu02;
double nu02 = cvmoments.nu20;
double nu21 = cvmoments.nu21;
double nu12 = cvmoments.nu12;
double nu03 = cvmoments.nu03;
double nu30 = cvmoments.nu30;
double r03 = fabs(nu30 / nu03);
r03 = (r03 > 1) ? r03 : 1.0/r03;
double r12 = fabs(nu12 / nu21);
r12 = (r12 > 1) ? r12 : 1.0/r12;
double r02 = fabs(nu02 / nu20);
r02 = (r02 > 1) ? r02 : 1.0/r02;
double r11 = fabs( MEAN2(nu02,nu20) / nu11);
double R = MEAN2(nu20,nu02) / std::max((MEAN2(nu21,nu12)), (MEAN2(nu30,nu03)));
bool pass = true;
pass = (r03 <= 25.0) && (r12 <= 12.0) && (r02 <= 12.0) && (r11 > 2.5) && (R > 25);
if (!pass){
println("Circle failed math test");
continue;
}
// get min enclosing circle and radius
//CvPoint2D32f centroid32f;
//cv::minEnclosingCircle(co, ¢roid32f, &radius);
cv::minEnclosingCircle(co, center, radius);
if (radius > minRad || radius < 0) {
println("Circle radius too small");
continue;
}
// do checks on area and perimeter
double area_ratio = area / (CV_PI*radius*radius);
//double perimeter_ratio = perimeter / (2*CV_PI*radius);
if (area_ratio < 0.7) {
println("Circle fail Area");
continue;
}
bool repeat = false;
//check if circle is found already
for (unsigned int i = 0; i < this->center.size(); i++){
cv::Point2f c = this->center[i];
if (std::abs(c.x-center.x) < 20 && std::abs(c.y - center.y) < 20){
repeat = true;
break;
}
}
if (!repeat){
//check if i found the number of circles requested
if (this->center.size() < max){
println("Found circle");
this->radius.push_back(radius);
this->center.push_back(center);
circlesFound++;
}else{
println("Already found enough circles");
}
}else{
println("Already found this circle");
}
}
return circlesFound != 0;
}
void MainWindow::capturePhoto() {
if (photosCaptured < QString(ui->numPhotoLineEdit->text()).toInt()) {
try {
CGrabResultPtr ptrGrabResult;
CInstantCamera Camera(CTlFactory::GetInstance().CreateDevice(camera));
if (Camera.GrabOne(QString(ui->delayLineEdit->text()).toInt(), ptrGrabResult)) {
time_t t = time(0);
struct tm now = *localtime(&t);
strftime(timebuf, sizeof(timebuf), "%Y-%m-%d %X", &now);
strftime(filebuf, sizeof(filebuf), "PlanktonTracker_Files/%Y-%m-%d_%X.png", &now);
CImagePersistence::Save(ImageFileFormat_Png, filebuf, ptrGrabResult);
QImage img = QImage(filebuf);
QImage scaledImage = img.scaled(ui->imageWidget->size(), Qt::KeepAspectRatio, Qt::SmoothTransformation);
ui->imageWidget->setPixmap(QPixmap::fromImage(scaledImage));
// Find contours
cv::Mat im = cv::imread(filebuf, cv::IMREAD_COLOR);
cv::Mat gray;
cvtColor(im, gray, CV_BGR2GRAY);
Canny(gray, gray, cv_minContourTresh, cv_minContourTresh * 2, 3);
vector<vector<Point> > contours;
vector<Vec4i> hierarchy;
findContours(gray, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0));
// Draw contours
// RNG rng(12345);
// Mat drawing = Mat::zeros(gray.size(), CV_8UC3);
contoursCount = 0;
for (int i = 0; i < contours.size(); i++) {
// Scalar color = Scalar( rng.uniform(0, 255), rng.uniform(0,255), rng.uniform(0,255) );
// drawContours( drawing, contours, i, color, 2, 8, hierarchy, 0, Point() );
double area = contourArea(contours[i], false);
if (area >= cv_minContourArea) {
++contoursCount;
//cout << i << " area: " << area << "\t|\t count: " << contoursCount <<endl;
}
}
csvFile.open("PlanktonTracker_Files/output.csv", std::ios_base::app | std::ios_base::out);
if (csvFile.is_open()) {
csvFile << timebuf << "," << filebuf << "," << contoursCount << "\n";
csvFile.close();
}
// imshow( "Result window", drawing );
// waitKey(0);
++photosCaptured;
}
} catch (GenICam::GenericException &e) {
QString status = QString("Could not grab an image: %1").arg((QString)e.GetDescription());
ui->statusBar->showMessage(status);
}
} else {
stopCapture();
}
}
void KeyPointsDeliverer::extractCupidsBowKeyPoints(int thresholdDifferenceToAvg, int totalLineCheck)
{
QList<PossibleKeyPoint> possibleKeyPoints;
PossibleKeyPoint possibleKeyPoint;
for (int i = 0; i < rTopFinal.cols; ++i) {
for (int j = rTopFinal.rows/2; j > totalLineCheck/2; --j) {
int currentDiffToAvg = 0;
for (int k = 1; k < totalLineCheck/2 + 1; ++k) {
currentDiffToAvg += rTopFinal.at<uchar>(j-k,i) + rTopFinal.at<uchar>(j+k,i);
}
currentDiffToAvg = currentDiffToAvg / totalLineCheck;
if(currentDiffToAvg > 0){
currentDiffToAvg = 100 - (rTopFinal.at<uchar>(j,i) * 100 / currentDiffToAvg);
}
if(currentDiffToAvg > thresholdDifferenceToAvg){
possibleKeyPoint.differenceToAvg = currentDiffToAvg;
possibleKeyPoint.keyPoint.x = i;
possibleKeyPoint.keyPoint.y = j;
possibleKeyPoints.append(possibleKeyPoint);
}
}
}
Mat contourImg(rTopFinal.rows, rTopFinal.cols, CV_8UC1, Scalar(0,0,0));
Point p;
for (int i = 0; i < possibleKeyPoints.size(); ++i) {
if(i % 3 == 0)
circle(rTopFinal, p, 1, Scalar(255,255,255));
p = possibleKeyPoints.at(i).keyPoint;
contourImg.at<uchar>(p.y, p.x) = 255;
}
Mat _img;
double otsu_thresh_val = cv::threshold(
contourImg, _img, 0, 255, CV_THRESH_BINARY | CV_THRESH_OTSU
);
Canny(contourImg, contourImg, otsu_thresh_val*0.5, otsu_thresh_val);
vector<vector<Point> > contours;
vector<Vec4i> hierarchy;
findContours( contourImg, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0) );
for( uint i = 0; i< contours.size(); i++ ){
drawContours( contourImg, contours, i, Scalar(255,255,255), 1, 8, hierarchy, 1, Point() );
}
keyPoint2.y = 1000;
for (int i = 0; i < rTopFinal.rows; ++i) {
for (int j = rTopFinal.cols/2; j > 0; --j) {
if(contourImg.at<uchar>(i,j) == 255){
if(keyPoint2.y >= i){
keyPoint2.y = i;
keyPoint2.x = j;
}
}
}
}
keyPoint4.y = 1000;
for (int i = 0; i < rTopFinal.rows; ++i) {
for (int j = rTopFinal.cols/2; j < rTopFinal.cols; ++j) {
if(contourImg.at<uchar>(i,j) == 255){
if(keyPoint4.y >= i){
keyPoint4.y = i;
keyPoint4.x = j;
}
}
}
}
keyPoint3.y = 0;
int kp2kp3Width = keyPoint4.x - keyPoint2.x;
kp2kp3Width = kp2kp3Width/2;
for (int i = keyPoint2.x; i < keyPoint4.x; ++i) {
for (int j = 0; j < keyPoint1.y-14; ++j) {
if(contourImg.at<uchar>(j,i) == 255){
if(keyPoint3.y <= j && i <= (keyPoint2.x + kp2kp3Width + (kp2kp3Width*0.2)) ){
keyPoint3.y = j;
keyPoint3.x = i;
}
}
}
}
}
void PlateLines::processImage(Mat inputImage, vector<TextLine> textLines, float sensitivity)
{
if (this->debug)
cout << "PlateLines findLines" << endl;
timespec startTime;
getTimeMonotonic(&startTime);
// Ignore input images that are pure white or pure black
Scalar avgPixelIntensity = mean(inputImage);
if (avgPixelIntensity[0] >= 252)
return;
else if (avgPixelIntensity[0] <= 3)
return;
// Do a bilateral filter to clean the noise but keep edges sharp
Mat smoothed(inputImage.size(), inputImage.type());
adaptiveBilateralFilter(inputImage, smoothed, Size(3,3), 45, 45);
int morph_elem = 2;
int morph_size = 2;
Mat element = getStructuringElement( morph_elem, Size( 2*morph_size + 1, 2*morph_size+1 ), Point( morph_size, morph_size ) );
Mat edges(inputImage.size(), inputImage.type());
Canny(smoothed, edges, 66, 133);
// Create a mask that is dilated based on the detected characters
Mat mask = Mat::zeros(inputImage.size(), CV_8U);
for (unsigned int i = 0; i < textLines.size(); i++)
{
vector<vector<Point> > polygons;
polygons.push_back(textLines[i].textArea);
fillPoly(mask, polygons, Scalar(255,255,255));
}
dilate(mask, mask, getStructuringElement( 1, Size( 1 + 1, 2*1+1 ), Point( 1, 1 ) ));
bitwise_not(mask, mask);
// AND canny edges with the character mask
bitwise_and(edges, mask, edges);
vector<PlateLine> hlines = this->getLines(edges, sensitivity, false);
vector<PlateLine> vlines = this->getLines(edges, sensitivity, true);
for (unsigned int i = 0; i < hlines.size(); i++)
this->horizontalLines.push_back(hlines[i]);
for (unsigned int i = 0; i < vlines.size(); i++)
this->verticalLines.push_back(vlines[i]);
// if debug is enabled, draw the image
if (this->debug)
{
Mat debugImgHoriz(edges.size(), edges.type());
Mat debugImgVert(edges.size(), edges.type());
edges.copyTo(debugImgHoriz);
edges.copyTo(debugImgVert);
cvtColor(debugImgHoriz,debugImgHoriz,CV_GRAY2BGR);
cvtColor(debugImgVert,debugImgVert,CV_GRAY2BGR);
for( size_t i = 0; i < this->horizontalLines.size(); i++ )
{
line( debugImgHoriz, this->horizontalLines[i].line.p1, this->horizontalLines[i].line.p2, Scalar(0,0,255), 1, CV_AA);
}
for( size_t i = 0; i < this->verticalLines.size(); i++ )
{
line( debugImgVert, this->verticalLines[i].line.p1, this->verticalLines[i].line.p2, Scalar(0,0,255), 1, CV_AA);
}
vector<Mat> images;
images.push_back(debugImgHoriz);
images.push_back(debugImgVert);
Mat dashboard = drawImageDashboard(images, debugImgVert.type(), 1);
displayImage(pipelineData->config, "Hough Lines", dashboard);
}
if (pipelineData->config->debugTiming)
{
timespec endTime;
getTimeMonotonic(&endTime);
cout << "Plate Lines Time: " << diffclock(startTime, endTime) << "ms." << endl;
}
}
void KeyPointsDeliverer::extractLowerLipKeyPoint(int thresholdDifferenceToAvg, int totalLineCheck)
{
QList<PossibleKeyPoint> possibleKeyPoints;
PossibleKeyPoint possibleKeyPoint;
QList<QList<int> > val;
int aPixelsumme;
int dPixeldurchschnitt;
int diffPixeldurchschnitt;
int rlow;
for (int i = 0; i < rLowFinal.cols; ++i) {
for (int j = rLowFinal.rows/2; j < rLowFinal.rows-totalLineCheck/2; ++j) {
QList<int> brabs;
int currentDiffToAvg = 0;
for (int k = 1; k < totalLineCheck/2 + 1; ++k) {
currentDiffToAvg += rLowFinal.at<uchar>(j-k,i) + rLowFinal.at<uchar>(j+k,i);
}
aPixelsumme = currentDiffToAvg;
currentDiffToAvg = currentDiffToAvg / totalLineCheck;
dPixeldurchschnitt = currentDiffToAvg;
if(currentDiffToAvg > 0){
currentDiffToAvg = 100 - (rLowFinal.at<uchar>(j,i) * 100 / currentDiffToAvg);
}
diffPixeldurchschnitt = currentDiffToAvg;
rlow = rLowFinal.at<uchar>(j,i);
if(currentDiffToAvg > thresholdDifferenceToAvg){
brabs << aPixelsumme << dPixeldurchschnitt << diffPixeldurchschnitt << rlow;
val << brabs;
possibleKeyPoint.differenceToAvg = currentDiffToAvg;
possibleKeyPoint.keyPoint.x = i;
possibleKeyPoint.keyPoint.y = j;
possibleKeyPoints.append(possibleKeyPoint);
}
}
}
// foreach (QList<int> v, val) {
// ROS_INFO("#################1##################");
// ROS_INFO("aPixelsumme %d", v.at(0));
// ROS_INFO("dPixeldurchschnitt %d", v.at(1));
// ROS_INFO("diffPixeldurchschnitt %d Rlow %d", v.at(2), v.at(3));
// ROS_INFO("#################2##################");
// }
Mat contourImg(rLowFinal.rows, rLowFinal.cols, CV_8UC1, Scalar(0,0,0));
Point p;
for (int i = 0; i < possibleKeyPoints.size(); ++i) {
if(i % 3 == 0)
circle(rLowFinal, p, 1, Scalar(255,255,255));
p = possibleKeyPoints.at(i).keyPoint;
contourImg.at<uchar>(p.y, p.x) = 255;
}
Mat _img;
double otsu_thresh_val = cv::threshold(
contourImg, _img, 0, 255, CV_THRESH_BINARY | CV_THRESH_OTSU
);
Canny(contourImg, contourImg, otsu_thresh_val*0.5, otsu_thresh_val);
vector<vector<Point> > contours;
vector<Vec4i> hierarchy;
findContours( contourImg, contours, hierarchy, CV_RETR_TREE, CV_CHAIN_APPROX_SIMPLE, Point(0, 0) );
for( uint i = 0; i< contours.size(); i++ ){
drawContours( contourImg, contours, i, Scalar(255,255,255), 1, 8, hierarchy, 1, Point() );
}
Point kpTmp;
kpTmp.y = 0;
int kp2kp3Width = keyPoint4.x - keyPoint2.x;
kp2kp3Width = kp2kp3Width/2;
for (int i = keyPoint2.x; i < keyPoint4.x; ++i) {
for (int j = rLowFinal.rows-(rLowFinal.rows*0.2); j > keyPoint1.y; --j) {
if(contourImg.at<uchar>(j, i) == 255){
if(kpTmp.y <= j && i <= (keyPoint2.x + kp2kp3Width)){
kpTmp.y = j;
keyPoint6.y = j;
keyPoint6.x = i;
break;
}
}
}
}
}
/*
* General image processing. Returns the original image with objects drawn on top of it.
*/
Mat RoadDetection::processImage()
{
Mat rawFrame, tmpFrame, grayFrame, blurredFrame, contoursFrame, houghFrame, sectionFrame;
Point vanishingPoint;
vector<Line> houghLines, houghMainLines;
vector<Point> roadShape;
int sectionOffset;
// save a copy of the original frame
original.copyTo(rawFrame);
// smooth and remove noise
GaussianBlur(rawFrame, blurredFrame, Size(BLUR_KERNEL, BLUR_KERNEL), 0, 0);
// edge detection (canny, inverted)
Canny(blurredFrame, contoursFrame, CANNY_MIN_THRESH, CANNY_MAX_THRESH);
threshold(contoursFrame, contoursFrame, 128, 255, THRESH_BINARY);
// hough transform lines
houghLines = getHoughLines(contoursFrame, true);
// vanishing point
vanishingPoint = getVanishingPoint(houghLines, rawFrame.size());
sectionOffset = vanishingPoint.y;
// section frame (below vanishing point)
contoursFrame.copyTo(tmpFrame);
sectionFrame = tmpFrame(CvRect(0, vanishingPoint.y, contoursFrame.cols, contoursFrame.rows - vanishingPoint.y));
// re-apply hough transform to section frame
houghLines = getHoughLines(sectionFrame, true);
// shift lines downwards
houghLines = shiftLines(houghLines, sectionOffset);
houghLines = getFilteredLines(houghLines);
// best line matches
houghMainLines = getMainLines(houghLines);
if (houghMainLines.size() >= 2)
{
Point intersection = getLineIntersection(houghMainLines[0], houghMainLines[1]);
if (intersection.x > 0 && intersection.y >= 0)
{
vanishingPoint = intersection;
sectionOffset = intersection.y;
}
// get road shape
roadShape = getRoadShape(rawFrame, houghMainLines[0], houghMainLines[1], vanishingPoint);
}
// limit lines
houghLines = getLimitedLines(houghLines, sectionOffset);
houghMainLines = getLimitedLines(houghMainLines, sectionOffset);
// drawing process
drawLines(rawFrame, houghLines, Scalar(0, 0, 255), 2, 0);
drawLines(rawFrame, houghMainLines, Scalar(20, 125, 255), 2, 0);
drawRoadShape(rawFrame, roadShape, Scalar(20, 125, 255), 0.4);
drawCircle(rawFrame, vanishingPoint, Scalar(20, 125, 255), 15, -1, 0);
return rawFrame;
}
void ImageProcessor::process(MultispectralImage frame)
{
MultispectralImage frame8Bit;
QList<imgDesc> results;
quint8 i;
Mat filterMask;
Mat maskedFCImage;
double maxVal = 0;
double maxTemp = 0.0;
Mat temp;
double spread;
Mat motionMask;
errorOccurred = false;
lockConfig.lockForRead();
//main processing tasks
//*********************
//subtract dark image, if enabled
if(myConfig.calibration.subtractDark && !frame.getDarkSubtracted())
{
for(i = 1; i < frame.getChannelCount(); i++)
{
//subtract dark image from current image
Mat tmp;
cv::subtract(frame.getImageByChannelNumber(i),
frame.getDarkImage(),
tmp);
//set result as new channel image
frame.setChannelImage(frame.getWavebands().at(i), tmp);
}
frame.setDarkSubtracted(true);
}
//perform skin detection by using quotient filters, if enabled
if(myConfig.detectSkinByQuotient && (myConfig.quotientFilters.size() > 0))
{
//clear result list
skinDetectionResults.clear();
//signal processing of all filters
emit doSkinDetection(frame);
}
//if image depth is more than 8bit, image has to be resampled to be displayed
if(frame.getDepth() > 8)
{
//if automatic contrast is enabled, find the brightest spot in all channels
if(myConfig.contrastAutomatic)
{
//iterate through all bands (except dark) to find maximum value
for(i = 1; i < frame.getChannelCount(); i++)
{
minMaxLoc(frame.getImageByChannelNumber(i), NULL, &maxTemp);
if ( maxTemp > maxVal )
{
maxVal = maxTemp;
}
}
//subtract contrast dark offset from maximum
maxVal -= myConfig.contrastOffset;
//slowly increase or decrease contrast value
if((maxVal / myConfig.contrastValue) < 220)
{
myConfig.contrastValue -= (myConfig.contrastValue - (maxVal / 255)) / 10;
}
else if((maxVal / myConfig.contrastValue) > 250)
{
myConfig.contrastValue += ((maxVal / 255) - myConfig.contrastValue) / 10;
}
}
//calculate spread factor
spread = 1.0 / (double)myConfig.contrastValue;
//configure GUI image object
frame8Bit.setSize(frame.getWidth(), frame.getHeight());
frame8Bit.setDepth(8);
//scale down every band
for (i = 0; i < frame.getChannelCount(); i++)
{
//subtract contrast offset, if enabled
Mat tempOffset;
if(myConfig.contrastOffset > 0)
{
subtract(frame.getImageByChannelNumber(i),
Scalar(myConfig.contrastOffset),
tempOffset);
}
else
{
tempOffset = frame.getImageByChannelNumber(i);
}
//convert to 8 bit using spread factor
tempOffset.convertTo(temp, 8, spread );
frame8Bit.setChannelImage(frame.getWavebands().at(i), temp.clone());
}
}
else
{
frame8Bit = frame;
}
//detect edges
if(myConfig.edgeDetection)
{
QMapIterator<qint16, Mat> it(frame8Bit.getImages());
while(it.hasNext())
{
it.next();
Mat edges = doEdgeDetection(it.value(), myConfig.edgeThreshold);
struct imgDesc edgeResult;
edgeResult.desc = QString("Edges %1nm").arg(it.key());
edgeResult.img = edges;
results.append(edgeResult);
}
}
//Estimate distance (in separate thread)
if (myConfig.estimateDistance)
{
//make edge mask on selected image
Mat edges;
if(autoSelectCannyImage) //automatically select sharpest band image for edge detection
{
Canny(frame8Bit.getImageByChannelNumber(lastSharpestBand), edges, cannyLowThresh, cannyHighThresh);
}
else //use band image selected by the user (in GUI)
{
Canny(frame8Bit.getImageByChannelNumber(cannyImage), edges, cannyLowThresh, cannyHighThresh);
}
//emit signals to distance estimation thread
distEstimationResults.clear();
emit setDistEstimParams((int)myConfig.sharpMetric, edges, myConfig.sharpnessNbrhdSize, medianKernel);
emit doDistanceEstimation(frame8Bit);
//wait for thread to finish
while (!errorOccurred && distEstimationResults.size() < 1) //frame8Bit.getChannelCount()-1)
{
QCoreApplication::processEvents();
}
if(errorOccurred)
{
emit errorProcessing(ImageSourceException("Error in task: estimateDistanceByChromAberr."));
return;
}
//append distance estimation result to results in order to display them
if(!distEstimationResults.empty())
{
//get 8 bit image from 1st list entry (at position 0)
results.append(distEstimationResults.at(0));
}
}
//wait for threads to finish:
//***************************
//wait until all threads are finished, get results and delete them
if(myConfig.detectSkinByQuotient && (myConfig.quotientFilters.size() > 0))
{
maskedFCImage = Mat::zeros(frame8Bit.getDarkImage().rows,
frame8Bit.getDarkImage().cols, CV_8UC3);
//wait until all threads are finished and get results
while(!errorOccurred &&
(myConfig.quotientFilters.size() > skinDetectionResults.size()))
{
QCoreApplication::processEvents(QEventLoop::AllEvents);
}
if(errorOccurred)
{
emit errorProcessing(ImageSourceException("Error in task: detectSkinByQuotients."));
return;
}
//multiply (cut) the filter masks
filterMask = skinDetectionResults.at(0);
for(i = 1; i < skinDetectionResults.size(); i++ )
{
multiply(filterMask, skinDetectionResults.at(i),
filterMask, 1.0);
}
//remove positive pixels with motion artifacts
if(myConfig.suppressMotion && (lastFrame.getChannelCount() == frame.getChannelCount()))
{
motionMask = Mat::ones(maskedFCImage.rows, maskedFCImage.cols, CV_8UC1);
for(i= 0; i < frame.getChannelCount(); i++)
{
Mat diffF, threshF, thresh;
Mat curF, prevF;
//get frame channels and convert to float
frame.getImageByChannelNumber(i).convertTo(curF, CV_32F);
lastFrame.getImageByChannelNumber(i).convertTo(prevF, CV_32F);
//calculate absolute difference between current and previous frame
absdiff(curF, prevF, diffF);
//threshold the absolute difference
threshold(diffF, threshF, myConfig.motionThreshold, 1.0, THRESH_BINARY_INV);
//convert to 8 bit unsigned
threshF.convertTo(thresh, CV_8U);
//update motion mask with new thresholded difference mask
multiply(motionMask, thresh, motionMask);
}
//now multiply motion mask with filter mask to remove positive filter results
//where there was motion detected
multiply(motionMask, filterMask, filterMask);
//add motion mask to results
struct imgDesc motionResult;
motionResult.desc = "Motion";
threshold(motionMask, motionResult.img, 0, 255, THRESH_BINARY_INV) ;
results.append(motionResult);
}
//Morph result:
if(myConfig.morphResult)
{
Mat element(4,4,CV_8U,Scalar(1));
morphologyEx(filterMask, filterMask, MORPH_OPEN, element);
}
//set mask on top of (8bit) false colour image
bitwise_or(maskedFCImage,
frame8Bit.getFalseColorImage(myConfig.falseColorChannels),
maskedFCImage, filterMask);
if(myConfig.showMaskContours)
{
vector<vector<Point> > contours;
CvScalar green = CV_RGB(0,255,0);
//CvScalar blue = CV_RGB(0,0,255);
findContours(filterMask, contours,
CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE);
drawContours(maskedFCImage, contours, -1, green, 2, 8);
}
struct imgDesc skinMask;
struct imgDesc skinResult;
skinMask.desc = "QF Mask";
threshold(filterMask, skinMask.img, 0, 255, THRESH_BINARY) ;
results.append(skinMask);
skinResult.desc = "Masked FC Image";
skinResult.img = maskedFCImage;
results.append(skinResult);
}
lockConfig.unlock();
emit finishedProcessing(frame, frame8Bit, results);
lastFrame = frame;
}