Perform calibration on fisheye image - cancelling fisheye effect - c++

Question

I'm currently using opencv library with c++, and my goal is to cancel a fisheye effect on an image ("make it plane")
I'm using the function "undistortImage" to cancel the effect but I need before to perform camera calibration in order to find the parameters K, Knew, and D, but I didn't understand exactly the documentation ( link: http://docs.opencv.org/master/db/d58/group__calib3d__fisheye.html#gga37375a2741e88052ce346884dfc9c6a0a0899eaa2f96d6eed9927c4b4f4464e05).
From my understanding, I should give two lists of points and the function "calibrate" is supposed to return the arrays I need. So my question is the following: given a fisheye image, how am I supposed to pick the two lists of points to get the result ? This is for the moment my code, very basic, just takes the picture, display it, performs the undistortion and displays the new image. The elements in the matrix are random, so currently the result is not as expected. Thanks for the answers.
#include "opencv2\core\core.hpp"
#include "opencv2\highgui\highgui.hpp"
#include "opencv2\calib3d\calib3d.hpp"
#include <stdio.h>
#include <iostream>
using namespace std;
using namespace cv;
int main(){
cout << " Usage: display_image ImageToLoadAndDisplay" << endl;
Mat image;
image = imread("C:/Users/Administrator/Downloads/eiffel.jpg", CV_LOAD_IMAGE_COLOR); // Read the file
if (!image.data) // Check for invalid input
{
cout << "Could not open or find the image" << endl;
return -1;
}
cout << "Input image depth: " << image.depth() << endl;
namedWindow("Display window", WINDOW_AUTOSIZE);// Create a window for display.
imshow("Display window", image); // Show our image inside it.
Mat Ka = Mat::eye(3, 3, CV_64F); // Creating distortion matrix
Mat Da = Mat::ones(1, 4, CV_64F);
Mat dstImage(image.rows, image.cols, CV_32F);
cout << "K matrix depth: " << Ka.depth() << endl;
cout << "D matrix depth: " << Da.depth() << endl;
Mat Knew = Mat::eye(3, 3, CV_64F);
std::vector<cv::Vec3d> rvec;
std::vector<cv::Vec3d> tvec;
int flag = 0;
std::vector<Point3d> objectPoints1 = { Point3d(0,0,0), Point3d(1,1,0), Point3d(2,2,0), Point3d(3,3,0), Point3d(4,4,0), Point3d(5,5,0),
Point3d(6,6,0), Point3d(7,7,0), Point3d(3,0,0), Point3d(4,1,0), Point3d(5,2,0), Point3d(6,3,0), Point3d(7,4,0), Point3d(8,5,0), Point3d(5,4,0), Point3d(0,7,0), Point3d(9,7,0), Point3d(9,0,0), Point3d(4,3,0), Point3d(7,2,0)};
std::vector<Point2d> imagePoints1 = { Point(107,84), Point(110,90), Point(116,96), Point(126,107), Point(142,123), Point(168,147),
Point(202,173), Point(232,192), Point(135,69), Point(148,73), Point(165,81), Point(189,93), Point(219,112), Point(248,133), Point(166,119), Point(96,183), Point(270,174), Point(226,56), Point(144,102), Point(206,75) };
std::vector<std::vector<cv::Point2d> > imagePoints(1);
imagePoints[0] = imagePoints1;
std::vector<std::vector<cv::Point3d> > objectPoints(1);
objectPoints[0] = objectPoints1;
fisheye::calibrate(objectPoints, imagePoints, image.size(), Ka, Da, rvec, tvec, flag); // Calibration
cout << Ka<< endl;
cout << Da << endl;
fisheye::undistortImage(image, dstImage, Ka, Da, Knew); // Performing distortion
namedWindow("Display window 2", WINDOW_AUTOSIZE);// Create a window for display.
imshow("Display window 2", dstImage); // Show our image inside it.
waitKey(0); // Wait for a keystroke in the window
return 0;
}

Answer 1

For calibration with cv::fisheye::calibrate you must provide
objectPoints vector of vectors of calibration pattern points in the calibration pattern coordinate space.
This means to provide KNOWN real-world coordinates of the points (must be corresponding points to the ones in imagePoints), but you can choose the coordinate system positon arbitrarily (but carthesian), so you must know your object - e.g. a planar test pattern.
imagePoints vector of vectors of the projections of calibration pattern points
These must be the same points as in objectPoints, but given in image coordinates, so where the projection of the object points hit your image (read/extract the coordinates from your image).
For example, if your camera did capture this image (taken from here ):
you must know the dimension of your testpattern (up to a scale), for example you could choose the top-left corner of the top-left square to be position (0,0,0), the top-right corner of the top-left square to be (1,0,0), and the bottom-left corner of the top-left square to be (1,1,0), so your whole testpattern would be placed on the xy-plane.
Then you could extract these correspondences:
pixel real-world
(144,103) (4,3,0)
(206,75) (7,2,0)
(109,151) (2,5,0)
(253,159) (8,6,0)
for these points (marked red):
The pixel position could be your imagePoints list while the real-world positions could be your objectPoints list.
Does this answer your question?