CGAL Intersection Circle and Vertical Lines (not segments) - c++

In CGAL I need to compute the exact intersection points between a set of lines and a set o circles. Starting from the circles (which can have irrational radius but rational squared_radius) I should compute the vertical line passing through the x_extremal_points of each circle (not segment but lines) and calculate the intersection point of each circle with each line.
I’m using CircularKernel and Circle_2 for the circles and Line_2 for the lines.
Here’s an example of how I compute the circles and the lines and how I check if they intersect.
int main()
{
Point_2 a = Point_2(250.5, 98.5);
Point_2 b = Point_2(156, 139);
//Radius is half distance ab
Circular_k::FT aRad = CGAL::squared_distance(a, b);
Circle_2 circle_a = Circle_2(a, aRad/4);
Circular_arc_point_2 a_left_point = CGAL::x_extremal_point(circle_a, false);
Circular_arc_point_2 a_right_point = CGAL::x_extremal_point(circle_a, true);
//for example use only left extremal point of circle a
CGAL::Bbox_2 a_left_point_bb = a_left_point.bbox();
Line_2 a_left_line = Line_2(Point_2(a_left_point_bb.xmin(), a_left_point_bb.ymin()),
Point_2(a_left_point_bb.xmin(), a_left_point_bb.ymax()));
if ( do_intersect(a_left_line, circle_a) ) {
std::cout << "intersect";
}
else {
std::cout << " do not intersect ";
}
return 0;
}
This flow rises this exception:
CGAL error: precondition violation!
Expression : y != 0
File : c:\dev\cgal-4.7\include\cgal\gmp\gmpq_type.h
Line : 371
Explanation:
Refer to the bug-reporting instructions at http://www.cgal.org/bug_report.html
I can’t figure out how I can calculate the intersection points.
Also, Is there a better way to compute the lines? I know abot the x_extremal_point function but it returns the Circular_arc_point point and I’m not able to construct a vertical line passing through them directly without using Bounding box.

In your code, you seem to compute the intersection of a circle with the vertical line that passes through the extremal point of the circle (I forget the bounding box). Well, then the (double) intersection is the extremal point itself...
More globally, you say in your text of introduction that you want to compute exact intersections. Then you should certainly not use bounding boxes, which by definition introduce some approximation.
If I understand your text correctly,
* for testing the intersection of your vertical lines with the other circles, you don't need to construct the lines, you only need to compare the abscissae of the extremal points of two circles, which you can do with the CGAL circular kernel.
* for computing the intersection of a vertical line that has non-rational coefficients (since its equation is of the form x= +-sqrt(r)) with another circle, then the CGAL circular kernel will not give you a pre-cooked solution. That kernel will help, but you must still compute a few things by hand.
If you don't want to bother, then you can also just take a standard CGAL kernel with Core::Expr as underlying number type. It can do "anything", but it will be slower.

For efficiency, you should look at the underlying 1D problem: projecting the lines and the circle on the X axis, you have a set of points and a set of intervals [Xc-R, Xc+R].
If the L points are sorted increasingly, you can locate the left bound of an interval in time Lg(L) by dichotomy, and scan the list of points until the right bound. This results in a O(Lg(L).C + I) behavior (C circle intervals), where I is the number of intersections reported.
I guess that with a merge-like process using an active list, if the interval bounds are also sorted you can lower to O(L + C + I).
The extension to 2D is elementary.

Related

How to create a B-Spline with QwtSpline

In QwtSpline there are two different types of splines, but I don't see a difference between both types.
I found a image, that explains my problem:
QwtSpline always creates a spline, like that on the left side of the picture.
But I want to have a Spline, like that one on the right side.
My Code is the following:
QwtSpline spline;
QPolygonF polygon;
QVector<QPointF> result;
polygon.append(startPoint);
polygon.append(rotatedPoint);
polygon.append(endPoint);
spline.setPoints(polygon);
for(double i = startPoint.rx(); i < endPoint.rx(); i++)
{
result << QPointF(i, spline.value(i));
}
result << QPointF(endPoint.rx(), spline.value(endPoint.rx()));
What I want to do, is to draw a spline like that one on the right side of the picture to a QwtPlot. Maybe there is a easier way to solve my problem, than creating a QwtSpline iterate throug it to creat a QwtCurve with every point on the QwtSpline.
If it is easier, to draw a bezier curve to QwtPlot, that's no problem, a bezier curve would be easier for me. I only took a spline, because I didn't find a bezier curve in Qwt.
Try Qwt from one of the branches >= 6.2. It has a complete new implementation of several spline interpolation algorithms.
But if it is about drawing a bezier curve only you can also use QwtShapeItem, what is displaying a QPainterPath. Of course you can also use QPainterPath to create a QPolygon from your bezier curve and then use QwtPlotCurve.
Ok guys because you didn't like my answer bevore, I will try to explain how to calculate a bezier curve:
I think the easiest way is to use the Bernstein-Bézier representation of curves.
To do that, you have to find out the Bernstein polynomials. That's not difficult. There is a formular to do that its
n is the number of points of your curve and
i is the actually point.
That means that you have so many Bernstein polynom, how many points you have.
If you know every Bernstein polynom you are able to use the following formular to calculate the curve.
n is the total number of points and
i is the index of the current point.
P is a point and t always runs from 0 to 1. 0 is the positon at the left side and 1 represents the position on the right side. r is the new point of the curve.
Now you have two calculate the formular above for x and y.
This is the formular for x
This is the formular for y
As you can see the only variable parameter on the right side is t. That means, that you have to calculate this formular many times with t between 0 and 1. The easiest way to do that, is to write a for loop like that:
QList<QPointF> results = QList<QPointF>();
QList<QPointF> points = QList<QPointF>();
for(double i = 0; i <= 1; i+=0.01)
{
double x = //formular for rx
double y = //formular for ry
results << QPointF(x, y);
}
I hope it wasn't to complicated. If you didn't understand this short explanation, you can look at the Handbook of Mathematics. In the Sixth Edition its on site 1000 to 1001.
ISBN: 978-3-662-46220-1

Find intersection point of segments and list of intersected segments for each intersection point

I am attempting to use CGAL to find out "all intersection points" AND "intersected segments for each intersection point" from a list of segments in 2D. I want to use Bentley–Ottmann algorithm for some reasons. CGAL library has an c++ implementation of this algorithm called Sweepline 2 but with this i am able to find intersection points only. Is there any other implementation exist in CGAL? or how can I solve this problem?
Edit:
The really quick fix would just be to generate all intersection points using Sweep line 2, loop through all generated intersection points, and for each intersection point record both the point's coordinates and all line segments that contain that point into a structure of your choice.
A quick solution (albeit not the most efficient) is to:
//Probably start off with a struct to store your information
struct intersection{
//This stores the x and y position of the intersection.
int[2] xyCoords;
//This stores the segment ids or indexes of the intersection line segments.
//For example, if the 1st and 5th line segment intersect, you would store 1 and 5 in here.
int[2] segmentIDs;
}
Then just create a vector of the intersection struct so you can store all of your different intersections.
vector<intersection> intersectionVector;
Then just loop through all of the line segments you have in something similar to the following:
for (int i = 0; i < numSegments; i++){
for (int j = 0; j < numSegments; j++){
//Don't look at the same points.
if (i == j)
continue;
//See below for pseudocode for this part.
}
}
Now to fill in that block, instead of reinventing anything just refer to How do you detect where two line segments intersect?.
As shown in the above link, calculate the values of r × s and (q − p) × r where × is the vector cross product. From there, just use if/else blocks to account for the four cases (ctrl f for "Now there are four cases:") if you care about detail. If you just want what you outlined in your question, just check for the validity of case 3. If it is valid, store the indexes of your line segments as well as the x and y coordinates in the vector/whatever structure you're using.
The only extra thing you need to do is use the calculate u and apply it to your second line segment of the two you're checking, in order to store the x y coordinates of the intersection.

Given camera matrices, how to find point correspondances using OpenCV?

I'm following this tutorial, which uses Features2D + Homography. If I have known camera matrix for each image, how can I optimize the result? I tried some images, but it didn't work well.
//Edit
After reading some materials, I think I should rectify two image first. But the rectification is not perfect, so a vertical line on image 1 correspond a vertical band on image 2 generally. Are there any good algorithms?
I'm not sure if I understand your problem. You want to find corresponding points between the images or you want to improve the correctness of your matches by use of the camera intrinsics?
In principle, in order to use camera geometry for finding matches, you would need the fundamental or essential matrix, depending on wether you know the camera intrinsics (i.e. calibrated camera). That means, you would need an estimate for the relative rotation and translation of the camera. Then, by computing the epipolar lines corresponding to the features found in one image, you would need to search along those lines in the second image to find the best match. However, I think it would be better to simply rely on automatic feature matching. Given the fundamental/essential matrix, you could try your luck with correctMatches, which will move the correspondences such that the reprojection error is minimised.
Tips for better matches
To increase the stability and saliency of automatic matches, it usually pays to
Adjust the parameters of the feature detector
Try different detection algorithms
Perform a ratio test to filter out those keypoints which have a very similar second-best match and are therefore unstable. This is done like this:
Mat descriptors_1, descriptors_2; // obtained from feature detector
BFMatcher matcher;
vector<DMatch> matches;
matcher = BFMatcher(NORM_L2, false); // norm depends on feature detector
vector<vector<DMatch>> match_candidates;
const float ratio = 0.8; // or something
matcher.knnMatch(descriptors_1, descriptors_2, match_candidates, 2);
for (int i = 0; i < match_candidates.size(); i++)
{
if (match_candidates[i][0].distance < ratio * match_candidates[i][1].distance)
matches.push_back(match_candidates[i][0]);
}
A more involved way of filtering would be to compute the reprojection error for each keypoint in the first frame. This means to compute the corresponding epipolar line in the second image and then checking how far its supposed matching point is away from that line. Throwing away those points whose distance exceeds some threshold would remove the matches which are incompatible with the epiploar geometry (which I assume would be known). Computing the error can be done like this (I honestly do not remember where I took this code from and I may have modified it a bit, also the SO editor is buggy when code is inside lists, sorry for the bad formatting):
double computeReprojectionError(vector& imgpts1, vector& imgpts2, Mat& inlier_mask, const Mat& F)
{
double err = 0;
vector lines[2];
int npt = sum(inlier_mask)[0];
// strip outliers so validation is constrained to the correspondences
// which were used to estimate F
vector imgpts1_copy(npt),
imgpts2_copy(npt);
int c = 0;
for (int k = 0; k < inlier_mask.size().height; k++)
{
if (inlier_mask.at(0,k) == 1)
{
imgpts1_copy[c] = imgpts1[k];
imgpts2_copy[c] = imgpts2[k];
c++;
}
}
Mat imgpt[2] = { Mat(imgpts1_copy), Mat(imgpts2_copy) };
computeCorrespondEpilines(imgpt[0], 1, F, lines[0]);
computeCorrespondEpilines(imgpt1, 2, F, lines1);
for(int j = 0; j < npt; j++ )
{
// error is computed as the distance between a point u_l = (x,y) and the epipolar line of its corresponding point u_r in the second image plus the reverse, so errij = d(u_l, F^T * u_r) + d(u_r, F*u_l)
Point2f u_l = imgpts1_copy[j], // for the purpose of this function, we imagine imgpts1 to be the "left" image and imgpts2 the "right" one. Doesn't make a difference
u_r = imgpts2_copy[j];
float a2 = lines1[j][0], // epipolar line
b2 = lines1[j]1,
c2 = lines1[j][2];
float norm_factor2 = sqrt(pow(a2, 2) + pow(b2, 2));
float a1 = lines[0][j][0],
b1 = lines[0][j]1,
c1 = lines[0][j][2];
float norm_factor1 = sqrt(pow(a1, 2) + pow(b1, 2));
double errij =
fabs(u_l.x * a2 + u_l.y * b2 + c2) / norm_factor2 +
fabs(u_r.x * a1 + u_r.y * b1 + c1) / norm_factor1; // distance of (x,y) to line (a,b,c) = ax + by + c / (a^2 + b^2)
err += errij; // at this point, apply threshold and mark bad matches
}
return err / npt;
}
The point is, grab the fundamental matrix, use it to compute epilines for all the points and then compute the distance (the lines are given in a parametric form so you need to do some algebra to get the distance). This is somewhat similar in outcome to what findFundamentalMat with the RANSAC method does. It returns a mask wherein for each match there is either a 1, meaning that it was used to estimate the matrix, or a 0 if it was thrown out. But estimating the fundamental Matrix like this will probably be less accurate than using chessboards.
EDIT: Looks like oarfish beat me to it, but I'll leave this here.
The fundamental matrix (F) defines a mapping from a point in the left image to a line in the right image on which the corresponding point must lie, assuming perfect calibration. This is the epipolar line, i.e. the line though the point in the left image and the two epipoles of the stereo camera pair. For references, see these lecture notes and this chapter of the HZ book.
Given a set of point correspondences in the left and right images: (p_L, p_R), from SURF (or any other feature matcher), and given F, the constraint from epipolar geometry of the stereo pair says that p_R should lie on the epipolar line projected by p_L onto the right image, i.e.
In practice, calibration errors from noise as well as erroneous feature matches lead to a non-zero value.
However, using this idea, you can then perform outlier removal by rejecting those feature matches for which this equation is greater than a certain threshold value, i.e. reject (p_L, p_R) if and only if:
When selecting this threshold, keep in mind that it is the distance in image space of a point from an epipolar line that you are willing to tolerate, which in some sense is your epipolar error tolerance.
Degenerate case: To visually imagine what this means, let us assume that the stereo pair differ only in a pure X-translation. Then the epipolar lines are horizontal. This means that you can connect the feature matched point pairs by a line and reject those pairs whose line slope is not close to zero. The equation above is a generalization of this idea to arbitrary stereo rotation and translation, which is accounted for by the matrix F.
Your specific images: It looks like your feature matches are sparse. I suggest instead to use a dense feature matching approach so that after outlier removal, you are still left with a sufficient number of good-quality matches. I'm not sure which dense feature matcher is already implemented in OpenCV, but I suggest starting here.
Giving your pictures, your are trying to do a stereo matching.
This page will be helpfull. The rectification you want can be done using stereoCalibrate then stereoRectify.
The result (from the doc):
In order to find the Fundamental Matrix, you need correct correspondances, but in order to get good correspondances, you need a good estimate of the fundamental matrix. This might sound like an impossible chicken-and-the-egg-problem, but there is well established methods to do this; RANSAC.
It randomly selects a small set of correspondances, uses those to calculate a fundamental matrix (using the 7 or 8 point algorithm) and then tests how many of the other correspondences that comply with this matrix (using the method described by scribbleink for measuring the distance between point and epipolar line). It keeps testing new combinations of correspondances for a certain number of iterations and selects the one with the most inliers.
This is already implemented in OpenCV as cv::findFundamentalMat (http://docs.opencv.org/2.4/modules/calib3d/doc/camera_calibration_and_3d_reconstruction.html#findfundamentalmat). Select the method CV_FM_RANSAC to use ransac to remove bad correspondances. It will output a list of all the inlier correspondances.
The requirement for this is that all the points does not lie on the same plane.

C++ recognize shape from points

I'am trying to find out an algorithm to recognize circle in array of points.
Lets say that I've got points array where circle could or could not be stored (that also means array doesn't have to store only circle's points, there could be some "extra" points before or after circle's data).
I've already tried some algorithms but none of them work properly with those "extra" points. Have you got any ideas how to deal with this problem?
EDIT// I didn't mention that before. I want this algorithm to be used on circle gesture recognition. I've thought I would have data in array (for last few seconds) and by analysing this data in every tracking frame I would be able to say if there was or was not a circle gesture.
First I calculate the geometric mean (not the aritmetic mean) for each X and Y component.
I choose geometric mean because one feature is that small values ​​(with respect to the arithmetic mean ) of the values ​​are much more influential than the large values.
This lead me to the theoretical center of all points: circ_center
Then I calculate the standard deviation of distance of each point to center: stddev. This gives me the "indicator" to quantify the amount of variation. One property of circle is that all circumference point is at the same distance of it's center. With standard dev I try to test if your points are (with max variance threshold: max_dispersion) equally distance.
Last I calculates the average distance of points inside max_dispersion threshold from center, this give me the radius of the circle: avg_dist.
Parameters:
max_dispersion represents the "cicle precision". Smaller means more precise.
min_points_needed is the minimun number of points valid to be considered as circumference.
This is just an attempt, I have not tried. Let me know.
I will try this (in pseudo language)
points_size = 100; //number_of_user_points
all_poins[points_size]; //coordinates of points
//thresholds to be defined by user
max_dispersion = 20; //value of max stddev accepted, expressed in geometric units
min_points_needed = 5; //minimum number of points near the circumference
stddev = 0; //standard deviation of points from center
circ_center; //estimated circumference center, using Geometric mean
num_ok_points = 0; //points with distance under standard eviation
avg_dist = 0; //distance from center of "ok points"
all_x = 1; all_y = 1;
for(i = 0 ; i < points_size ; i++)
{
all_x = all_x * all_poins[i].x;
all_y = all_y * all_poins[i].y;
}
//pow(x, 1/y) = nth root
all_x = pow(all_x, 1 / points_size); //Geometric mean
all_y = pow(all_y, 1 / points_size); //Geometric mean
circ_center = make_point(all_x, all_y);
for(i = 0 ; i < points_size ; i++)
{
dist = distance(all_poins[i], circ_center);
stddev = stddev + (dist * dist);
}
stddev = square_root(stddev / points_size);
for(i = 0 ; i < points_size ; i++)
{
if( distance(all_poins[i], circ_center) < max_dispersion )
{
num_ok_points++;
avg_dist = avg_dist + distance(all_poins[i], circ_center);
}
}
avg_dist = avg_dist / num_ok_points;
if(stddev <= max_dispersion && num_ok_points >= min_points_needed)
{
circle recognized; it's center is circ_center; it's radius is avg_dist;
}
Can we assume the array of points are mostly on or near to the circumference of the circle?
A circle has a center and radius. If you can determine the circle's center coordinates, via the intersection of perpendiculars of two chords, then all the true circle points should be equidistant(r), from the center point.
The false points can be eliminated by not being equidistant (+-)tolerance from the center point.
The weakness of this approach is how well can you determine the center and radius? You may want to try a least squares approach to computing the center coordinates.
To answer the initially stated question, my approach would be to iterate through the points and derive the center of a circle from each consecutive set of three points. Then, take the longest contiguous subset of points that create circles with centers that fall within some absolute range. Then determine if the points wind consistently around the average of the circles. You can always perform some basic heuristics on any discarded data to determine if a circle is actually what the user wanted to make though.
Now, since you say that you want to perform gesture recognition, I would suggest you think of a completely different method. Personally, I would first create a basic sort of language that can be used to describe gestures. It should be very simple; the only words I would consider having are:
Start - Denotes the start of a stroke
Angle - The starting angle of the stroke. This should be one of the eight major cardinal directions (N, NW, W, SW, S, SE, E, NE) or Any for unaligned gestures. You could also add combining mechanisms, or perhaps "Axis Aligned" or other such things.
End - Denotes the end of a stroke
Travel - Denotes a straight path in the stroke
Distance - The percentage of the total length of the path that this particular operation will consume.
Turn - Denotes a turn in the stroke
Direction - The direction to turn in. Choices would be Left, Right, Any, Previous, or Opposite.
Angle - The angle of the turn. I would suggest you use just three directions (90 deg, 180 deg, 270 deg)
Tolerance - The maximum tolerance for deviation from the specified angle. This should have a default of somewhere around 45 degrees in either direction for a high chance of matching the angle in a signature.
Type - Hard or Radial. Radial angles would be a stroke along a radius. Hard angles would be a turn about a point.
Radius - If the turn is radial, this is the radius of the turn (units are in percentage of total path length, with appropriate conversions of course)
Obviously you can make the angles much more fine, but the coarser the ranges are, the more tolerant of input error it can be. Being too tolerant can lead to misinterpretation though.
If you apply some fuzzy logic, it wouldn't be hard to break just about any gesture down into a language like this. You could then create a bunch of gesture "signatures" that describe various gestures that can be performed. For instance:
//Circle
Start Angle=Any
Turn Type=Radial Direction=Any Angle=180deg Radius=50%
Turn Type=Radial Direction=Previous Angle=180deg Radius=50%
End
//Box
Start Angle=AxisAligned
Travel Distance=25%
Turn Type=Hard Direction=Any Angle=90deg Tolerance=10deg
Travel Distance=25%
Turn Type=Hard Direction=Previous Angle=90deg Tolerance=10deg
Travel Distance=25%
Turn Type=Hard Direction=Previous Angle=90deg Tolerance=10deg
Travel Distance=25%
End
If you want, I could work on an algorithm that could take a point cloud and degenerate it into a series of commands like this so you can compare them with pre-generated signatures.

Find all tiles intersected by line segment

I have to find all tiles that intersected by line segment but Bresenham's line algorithm doesnt fit to my requirements. I need to find all cells. I dont need to know intersection points, only the fact of intersection. Thanks for help.
I thought to find direction vector of line and step by step find cells by division on tile size. But i dont know how to select correct step size. 1 px step is bad i think.
Here is article of Amanatides and Woo "A Fast Voxel Traversal Algorithm for Ray Tracing" for 2D and 3D cases. Practical implementation.
You might use one of the many line equations found at: http://www.cut-the-knot.org/Curriculum/Calculus/StraightLine.shtml or http://mathworld.wolfram.com/Line.html
Supposedly you have your line in your coordinate system going through two points you deduce the y=mx+n equation and just match against your tiles and see if they intersect while moving x in the unit of your coordinate system in any direction you prefer from the smallest x of your tiles till the biggest. If your coordinate system is the screen, 1 pixel should be enough.
This is the closes I can hint right know without knowing more about the exact nature of the problem you are facing.
It is easy to modify the Bresenham's algorithm such that it tracks what you need. Here's the relevant fragment of the algorithm:
plot(x,y);
error = error + deltaerr;
if (error >= 0.5)
{
y = y + ystep;
error = error - 1.0;
}
To keep track of all the cells we need another variable. Note tat I have not rigorously checked this.
plot(x,y);
olderror = error.
error = error + deltaerr;
if (error >= 0.5)
{
y = y + ystep;
error = error - 1.0;
extra = error+olderror;
if (extra > 0)
{
plot (x,y); /* not plot (x-1,y); */
}
else if (extra < 0)
{
plot (x+1,y-1); /* not plot (x+1,y); */
}
else
{
// the line goes right through the cell corner
// either do nothing, or do both plot (x-1,y) and plot (x+1,y)
// depending on your definition of intersection
}
}