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copied!<p>The <a href="http://wiki.panotools.org/Fisheye_Projection" rel="noreferrer">description you mention</a> states that the projection by a pin-hole camera (one that does not introduce lens distortion) is modeled by</p> <pre><code>R_u = f*tan(theta) </code></pre> <p>and the projection by common fisheye lens cameras (that is, distorted) is modeled by</p> <pre><code>R_d = 2*f*sin(theta/2) </code></pre> <p>You already know R_d and theta and if you knew the camera's focal length (represented by f) then correcting the image would amount to computing R_u in terms of R_d and theta. In other words, </p> <pre><code>R_u = f*tan(2*asin(R_d/(2*f))) </code></pre> <p>is the formula you're looking for. Estimating the focal length f can be solved by calibrating the camera or other means such as letting the user provide feedback on how well the image is corrected or using knowledge from the original scene.</p> <p>In order to solve the same problem using OpenCV, you would have to obtain the camera's intrinsic parameters and lens distortion coefficients. See, for example, Chapter 11 of <a href="http://oreilly.com/catalog/9780596516130" rel="noreferrer">Learning OpenCV</a> (don't forget to check the <a href="http://oreilly.com/catalog/errataunconfirmed.csp?isbn=9780596516130" rel="noreferrer">correction</a>). Then you can use a program such as this one (written with the Python bindings for OpenCV) in order to reverse lens distortion:</p> <pre><code>#!/usr/bin/python # ./undistort 0_0000.jpg 1367.451167 1367.451167 0 0 -0.246065 0.193617 -0.002004 -0.002056 import sys import cv def main(argv): if len(argv) < 10: print 'Usage: %s input-file fx fy cx cy k1 k2 p1 p2 output-file' % argv[0] sys.exit(-1) src = argv[1] fx, fy, cx, cy, k1, k2, p1, p2, output = argv[2:] intrinsics = cv.CreateMat(3, 3, cv.CV_64FC1) cv.Zero(intrinsics) intrinsics[0, 0] = float(fx) intrinsics[1, 1] = float(fy) intrinsics[2, 2] = 1.0 intrinsics[0, 2] = float(cx) intrinsics[1, 2] = float(cy) dist_coeffs = cv.CreateMat(1, 4, cv.CV_64FC1) cv.Zero(dist_coeffs) dist_coeffs[0, 0] = float(k1) dist_coeffs[0, 1] = float(k2) dist_coeffs[0, 2] = float(p1) dist_coeffs[0, 3] = float(p2) src = cv.LoadImage(src) dst = cv.CreateImage(cv.GetSize(src), src.depth, src.nChannels) mapx = cv.CreateImage(cv.GetSize(src), cv.IPL_DEPTH_32F, 1) mapy = cv.CreateImage(cv.GetSize(src), cv.IPL_DEPTH_32F, 1) cv.InitUndistortMap(intrinsics, dist_coeffs, mapx, mapy) cv.Remap(src, dst, mapx, mapy, cv.CV_INTER_LINEAR + cv.CV_WARP_FILL_OUTLIERS, cv.ScalarAll(0)) # cv.Undistort2(src, dst, intrinsics, dist_coeffs) cv.SaveImage(output, dst) if __name__ == '__main__': main(sys.argv) </code></pre> <p>Also note that OpenCV uses a very different lens distortion model to the one in the web page you linked to.</p>

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