I know how to draw rotated rectangle using RotatedRect. However I would like to draw a rotated Trapeze using opencv.
I found a solution.
A) Define the four points of the trapeze as a contour:
std::vector<std::vector<cv::Point> > MakeRotatedTrapeze(
const cv::Size& BBSize,
const float& angleRad,
const cv::Point2f& trapezeCenter,
const float& topToBottomRatio)
{
//Make trapeze
cv::Point2f deltaDOWN( BBSize.height/2*sin(angleRad), BBSize.height/2*cos(angleRad));//bottom
cv::Point2f deltaUP = -deltaDOWN;//top
cv::Point2f deltaLEFT(-BBSize.width/2*cos(angleRad), BBSize.width/2*sin(angleRad));//left
cv::Point2f deltaRIGHT = -deltaLEFT;//right
cv::Point2f bl = trapezeCenter + deltaDOWN + deltaLEFT;
cv::Point2f br = trapezeCenter + deltaDOWN + deltaRIGHT;
cv::Point2f tl = trapezeCenter + deltaUP + topToBottomRatio*deltaLEFT;
cv::Point2f tr = trapezeCenter + deltaUP + topToBottomRatio*deltaRIGHT;
cv::Point trapez_pts[4] = {tl, tr, br, bl};
vector<vector<Point> > trapeze(1, vector<Point>(trapez_pts, trapez_pts + 4));
return trapeze;
}
B) Draw the trapeze using drawContours
float trapezTopScale = 0.5;
std::vector<std::vector<cv::Point> > trapeze = MakeRotatedTrapeze( cv::Size(100, 50), 45.0, cv::Point2f(320, 240), trapezTopScale);
cv::Mat image(480, 640, CV_8UC1);
image.setTo(0);
drawContours( image, trapeze, 0, cv::Scalar(255), CV_FILLED, 8, vector<Vec4i>() );
Related
I'm doing object reconstruction from structure from motion. The current situation right now, is I'm getting a multiple views for a car and apply a mask rcnn for that object to remove the background, because I only want that object to reconstruct and have a clean object.
My current issues, right now are that The Object is not fully reconstructured.
The mask that I get from Mask RCNN is not always have a fixed size for the SFM to work
A background noise is still present in the scene reconstructed object
Camera parameters are messed up when I use only the mask that are got from different views, how to fix that ?
Here are some results:
Original image of course there are mutliple views of it
Mask RCNN results that I use for SFM
and here is the result from SFM
// Draw the predicted bounding box, colorize and show the mask on the image
void drawBox(Mat& frame, int classId, float conf, Rect box, Mat& objectMask, std::vector<Mat> &contours_images)
{
//Draw a rectangle displaying the bounding box
//rectangle(frame, Point(box.x, box.y), Point(box.x + box.width, box.y + box.height), Scalar(255, 178, 50), 3);
//Get the label for the class name and its confidence
string label = format("%.2f", conf);
if (!classes.empty())
{
CV_Assert(classId < (int)classes.size());
label = classes[classId] + ":" + label;
}
//Display the label at the top of the bounding box
int baseLine;
Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
box.y = max(box.y, labelSize.height);
//rectangle(frame, Point(box.x, box.y - round(1.5 * labelSize.height)), Point(box.x + round(1.5 * labelSize.width), box.y + baseLine), Scalar(255, 255, 255), FILLED);
//putText(frame, label, Point(box.x, box.y), FONT_HERSHEY_SIMPLEX, 0.75, Scalar(0, 0, 0), 1);
// Resize the mask, threshold, color and apply it on the image
// Scalar color = colors[classId % colors.size()];
// Resize the mask, threshold, color and apply it on the image
resize(objectMask, objectMask, Size(box.width, box.height));
Mat mask = (objectMask > maskThreshold);
//Mat coloredRoi = (0.3 * color + 0.7 * frame(box));
// coloredRoi.convertTo(coloredRoi, CV_8UC3);
Mat coloredRoi(frame(box));
// Draw the contours on the image
vector<Mat> contours;
Mat hierarchy;
mask.convertTo(mask, CV_8U);
findContours(mask, contours, hierarchy, RETR_CCOMP, cv::CHAIN_APPROX_NONE);
//drawContours(coloredRoi, contours, -1, color, 5, LINE_8, hierarchy, 100);
// coloredRoi.copyTo(frame(box), mask);
Mat outframe;
coloredRoi.copyTo(outframe, mask);
cv::resize(outframe, outframe, cv::Size(400, 400));
contours_images.push_back(outframe);
// imshow("outframe", outframe);
// waitKey(0);
}
// For each frame, extract the bounding box and mask for each detected object
void postprocess(Mat& frame, const vector<Mat>& outs, vector<Mat> & maskes)
{
Mat outDetections = outs[0];
Mat outMasks = outs[1];
// Output size of masks is NxCxHxW where
// N - number of detected boxes
// C - number of classes (excluding background)
// HxW - segmentation shape
const int numDetections = outDetections.size[2];
const int numClasses = outMasks.size[1];
outDetections = outDetections.reshape(1, outDetections.total() / 7);
for (int i = 0; i < numDetections; ++i)
{
float score = outDetections.at<float>(i, 2);
if (score > confThreshold)
{
// Extract the bounding box
int classId = static_cast<int>(outDetections.at<float>(i, 1));
int left = static_cast<int>(frame.cols * outDetections.at<float>(i, 3));
int top = static_cast<int>(frame.rows * outDetections.at<float>(i, 4));
int right = static_cast<int>(frame.cols * outDetections.at<float>(i, 5));
int bottom = static_cast<int>(frame.rows * outDetections.at<float>(i, 6));
left = max(0, min(left, frame.cols - 1));
top = max(0, min(top, frame.rows - 1));
right = max(0, min(right, frame.cols - 1));
bottom = max(0, min(bottom, frame.rows - 1));
Rect box = Rect(left, top, right - left + 1, bottom - top + 1);
// Extract the mask for the object
Mat objectMask(outMasks.size[2], outMasks.size[3], CV_32F, outMasks.ptr<float>(i, classId));
// Draw bounding box, colorize and show the mask on the image
drawBox(frame, classId, score, box, objectMask, maskes);
}
}
}
Im struggling with the shape detection using OpenCV for C++. The edged figures such as triangle and rectangular are detected trouble-free. But when it comes to circle it estimates number of vertices up to 6-8. Could somebody help me?
void getContours(Mat video){
Mat grayscale, canny_output;
cvtColor(video, grayscale,COLOR_RGB2GRAY);//converting image to grayscale
GaussianBlur(grayscale, grayscale, Size(9, 9), 2, 2 );
threshold(grayscale, grayscale,60,255,THRESH_BINARY);
vector <vector<Point>> contours, output_contour;
vector <Vec4i> hierarchy;
findContours( grayscale, contours, hierarchy, RETR_TREE,CHAIN_APPROX_SIMPLE );
Mat drawing = Mat::zeros( grayscale.size(), CV_8UC3 );
vector<Point> c;
for (size_t i = 0; i<contours.size(); i++){
c = contours[i];
Rect crect = boundingRect(c);
// compute the center of the contour, then detect the name of the
// shape using only the contour
Moments M = moments(c);
int cX, cY;
cX = static_cast<int>(M.m10/M.m00);
cY = static_cast<int>(M.m01/M.m00);
string shape = detect(Mat(c));
drawContours( drawing, contours, (int)i, Scalar(0, 255, 0), 2);
Point pt(cX,cY);
putText(drawing,shape,pt, FONT_HERSHEY_SIMPLEX, 0.5, Scalar(255, 255, 255), 2);
imshow("contour", drawing);
}
}
string detect(const Mat &curve){
string shape = "unidentified";
double peri = arcLength(curve, true);
Mat approx;
approxPolyDP(curve, approx, 0.04 * peri, true); // 0.01~0.05
const int num_of_vertices = approx.rows;
if(num_of_vertices == 0){
shape = "circle";
}
if(num_of_vertices==2){
shape = "line";
}
cout<<"\n"<<num_of_vertices;
return to_string(num_of_vertices);
}
I'm trying to build a simple Augmented Reality application using OpenCV 4.1.1 and Aruco. The goal is to overlay an image on top of a marker but have the image go beyond the edges of the marker.
I have calibrated my camera and gotten the camera matrix and distortion coefficients. By using OpenCV's warpPerspective I can draw an image on top of a marker, but I can only tie it to the corners of the marker so it stays within the border of the marker.
std::vector<int> ids;
std::vector<std::vector<Point2f>> corners;
// detect markers
aruco::detectMarkers(image, dictionary, corners, ids);
if (ids.size() > 0) {
// file with image to draw
auto file = "square.png";
// image to draw on the marker
Mat im_src = imread(file);
if (im_src.data == NULL) {
std::cout << file << ": File not found\n" << std::endl;
continue;
}
// flip(im_src, im_src, 1);
// points of corners of the image
std::vector<Point2f> pts_src;
pts_src.push_back(Point2f(0, 0));
pts_src.push_back(Point2f(im_src.cols-1, 0));
pts_src.push_back(Point2f(im_src.cols-1, im_src.rows-1));
pts_src.push_back(Point2f(0, im_src.rows-1));
// use aruco marker
for (int i = 0; i < ids.size(); i++) {
if (ids[i] == 69) {
aruco::drawDetectedMarkers(imageCopy, corners, ids);
std::vector<Point> pts_dst;
pts_dst.push_back(corners[i][0]);
pts_dst.push_back(corners[i][1]);
pts_dst.push_back(corners[i][2]);
pts_dst.push_back(corners[i][3]);
Mat h = findHomography(pts_src, pts_dst);
Mat im_out;
warpPerspective(im_src, im_out, h, imageCopy.size());
fillConvexPoly(imageCopy, pts_dst, 0, 16);
imageCopy = imageCopy + im_out;
}
}
Here is an image of what I have and what I want. I think I need to use 3d points to draw the image but i'm not sure how to do that. Any help would be appreciated.
[
[
As you said in the comment, if the marker length is available, say l0, you can define the length of the desired square as l = l0 * 1.05 or something.
for (int i = 0; i < ids.size(); i++) {
if (ids[i] == 69) {
aruco::drawDetectedMarkers(imageCopy, corners, ids);
// Estimate the pose of the marker
std::vector<cv::Vec3d> rvecs, tvecs;
cv::aruco::estimatePoseSingleMarkers(
corners, l0, camera_matrix, dist_coeffs,
rvecs, tvecs
);
drawSquare(
image_copy, camera_matrix, dist_coeffs, rvecs[i], tvecs[i],
l0
);
}
}
void drawSquare(
cv::InputOutputArray image, cv::InputArray cameraMatrix,
cv::InputArray distCoeffs, cv::InputArray rvec, cv::InputArray tvec,
float l0
)
{
float l = l0 * 1.05; // new square is 5% larger than the aruco marker
float half_l = l / 2.0;
// Define the square on the camera frame (this is 3D since the camera
// frame is 3D).
std::vector<cv::Point3f> squarePoints;
squarePoints.push_back(cv::Point3f(half_l, half_l, 0));
squarePoints.push_back(cv::Point3f(half_l, -half_l, 0));
squarePoints.push_back(cv::Point3f(-half_l, -half_l, 0));
squarePoints.push_back(cv::Point3f(-half_l, half_l, 0));
// Project the square to the image.
std::vector<cv::Point2f> imagePoints;
projectPoints(
squarePoints, rvec, tvec, cameraMatrix, distCoeffs, imagePoints
);
// Draw the square on the image.
cv::line(image, imagePoints[0], imagePoints[1], cv::Scalar(255, 0, 0), 3);
cv::line(image, imagePoints[1], imagePoints[2], cv::Scalar(255, 0, 0), 3);
cv::line(image, imagePoints[2], imagePoints[3], cv::Scalar(255, 0, 0), 3);
cv::line(image, imagePoints[3], imagePoints[0], cv::Scalar(255, 0, 0), 3);
}
I did not test this, but I used a similar code for a different project. If you run into any issues, please let me know. I will update the above code.
I have followed this article on how to calculate and deskew an image for better Tesseract OCR results: http://felix.abecassis.me/2011/10/opencv-rotation-deskewing/
The correct angle is calculated, but the text is never actually rotated.
These are the methods I am using:
+(UIImage *) prepareImage: (UIImage *)image{
return deskew(image, computeSkew(image));
}
// Organization -> Deskewing
double computeSkew(UIImage *image)
{
Mat src;
UIImageToMat(image, src);
cv::Size size = src.size();
bitwise_not(src, src);
vector<Vec4i> lines;
HoughLinesP(src, lines, 1, CV_PI/180, 100, size.width / 2.f, 20);
Mat disp_lines(size, CV_8UC1, Scalar(0, 0, 0));
double angle = 0.;
unsigned nb_lines = lines.size();
for (unsigned i = 0; i < nb_lines; ++i)
{
line(disp_lines, cv::Point(lines[i][0], lines[i][1]),
cv::Point(lines[i][2], lines[i][3]), Scalar(255, 0 ,0));
angle += atan2((double)lines[i][3] - lines[i][1],
(double)lines[i][2] - lines[i][0]);
}
angle /= nb_lines; // mean angle, in radians.
cout << angle << endl;
return angle;
}
UIImage* deskew(UIImage *image, double angle)
{
Mat img;
UIImageToMat(image, img);
bitwise_not(img, img);
vector<cv::Point> points;
Mat_<uchar>::iterator it = img.begin<uchar>();
Mat_<uchar>::iterator end = img.end<uchar>();
for (; it != end; ++it)
if (*it)
points.push_back(it.pos());
RotatedRect box = minAreaRect(Mat(points));
Mat rot_mat = getRotationMatrix2D(box.center, angle, 1);
Mat rotated;
warpAffine(img, rotated, rot_mat, img.size(), INTER_CUBIC);
return MatToUIImage(rotated);
}
UIImageToMat and MatToUIImage are reliable methods that convert back and forth. I have also tried to return the angle in both radians and degrees. Both times the image returned from the prepareImage function is still tilted at the same angle as the original image.
I have a binary image:
I want to remove the bottom two crescent shapes(size and area may change with different images) from the image or at-least differentiate it from the rest.
I tried Hough circle transform to detect the curves as it resembles a portion of a circle, but that code was not working:
int main(int argc, char** argv)
{
Mat src, gray;
src = imread("446.bmp", 1);
namedWindow("src", 1);
imshow("src", src);
waitKey(0);
cvtColor(src, gray, CV_BGR2GRAY);
// Reduce the noise so we avoid false circle detection
GaussianBlur(gray, gray, Size(9, 9), 2, 2);
vector<Vec3f> circles;
// Apply the Hough Transform to find the circles
HoughCircles(gray, circles, CV_HOUGH_GRADIENT, 1, 30, 100, 100, 0, 0);
// Draw the circles detected
for (size_t i = 0; i < circles.size(); i++)
{
Point center(cvRound(circles[i][0]), cvRound(circles[i][1]));
int radius = cvRound(circles[i][2]);
circle(src, center, 3, Scalar(0, 255, 0), -1, 8, 0);// circle center
circle(src, center, radius, Scalar(0, 0, 255), 3, 8, 0);// circle outline
cout << "center : " << center << "\nradius : " << radius << endl;
}
// Show your results
namedWindow("Hough Circle Transform Demo", CV_WINDOW_AUTOSIZE);
imshow("Hough Circle Transform Demo", src);
waitKey(0);
return 0;
}
But No circle is being drawn or the crescent moon shapes are not being detected at all. Any idea where I went wrong?
EDIT 1- I have added some other images too:
Edit-2 new image to try:-
i made some modification on the code posted for other question
you could try it
#include "opencv2/imgproc.hpp"
#include "opencv2/highgui.hpp"
using namespace cv;
using namespace std;
//! Compute the distance between two points
/*! Compute the Euclidean distance between two points
*
* #param a Point a
* #param b Point b
*/
static double distanceBtwPoints(const cv::Point2f &a, const cv::Point2f &b)
{
double xDiff = a.x - b.x;
double yDiff = a.y - b.y;
return std::sqrt((xDiff * xDiff) + (yDiff * yDiff));
}
int main( int argc, char** argv )
{
Mat src,gray;
src = imread(argv[1]);
if(src.empty())
return -1;
cvtColor( src, gray, COLOR_BGR2GRAY );
gray = gray < 200;
vector<vector<Point> > contours;
findContours(gray.clone(), contours, CV_RETR_LIST, CV_CHAIN_APPROX_SIMPLE);
RotatedRect _minAreaRect;
for (size_t i = 0; i < contours.size(); ++i)
{
double contour_area = contourArea(contours[i]);
_minAreaRect = minAreaRect( Mat(contours[i]) );
Point2f pts[4];
_minAreaRect.points(pts);
double dist0 = distanceBtwPoints(pts[0], pts[1]);
double dist1 = distanceBtwPoints(pts[1], pts[2]);
double angle = 0;
//if(dist0 > dist1 *1.2)
angle =atan2(pts[0].y - pts[1].y,pts[0].x - pts[1].x) * 180.0 / CV_PI;
//if(dist1 > dist0 *1.2)
angle =atan2(pts[1].y - pts[2].y,pts[1].x - pts[2].x) * 180.0 / CV_PI;
if( fabs(angle) > 91 ) // you can try different values
{
if( contour_area < dist0 * dist1 /2 ) // you can try different values
{
//drawContours(src,contours,i,Scalar(0,0,0),-1); // try to uncomment this line
for( int j = 0; j < 4; j++ )
line(src, pts[j], pts[(j+1)%4], Scalar(0, 0, 255), 1, LINE_AA);
}
}
}
imshow("result", src);
waitKey(0);
return 0;
}
I don't think there is an easy solution here unfortunately.
What you might want to try is to detect and label each image component. From here you need to detect which set of pixels looks like a crescent and which does not : as a crescent can be described by a polynomial equations you only need to describe each component (i.e a set of points) as a mathematical equation (using regression methods such as RANSAC) and see if that might be a crescent equation.