Utility functions to distort and undistort an image or a set of points. The code is for the most part ported over from OpenCV to native Julia.
Pkg.add("https://github.com/jc211/ImageDistortion.jl")
using ImageDistortionOpenCV implements the following distortion model
This implementation currently only supports the k1, k2, k3, p1, p2 parameters. They are represented by distcoeffs where a sample distcoeffs is in the form
distcoeffs = [k1, k2, p1, p2, k3]
In general, this package has functions to move around the blocks illustrated above.
The function distortpoints takes a matrix of shape (n,2) representing points (e.g. [0 0; 10 15; 32 10]) and firstly undoes the linear distortion provided by newcameramatrix and subsequently applies nonlinear distortion by distcoeffs, followed by a linear distortion by cameramatrix
points = [10 23; 100 100.3; 23.1 20.5]
distortedpoints = distortpoints(points, cameramatrix=[517.3 0 318.6; 0 516.5 244.3; 0 0 1], distcoeffs=[0.2624, -0.9531, -0.0054, 0.0026, 1.1633], cameramatrix=[517.3 0 318.6; 0 516.5 244.3; 0 0 1])
The function undistortpoints takes a matrix of shape (n,2) representing points (e.g. [0 0; 10 15; 32 10]) and undoes the linear distortion given by cameramatrix, undoes the nonlinear distortion by distcoeffs, and optionally reapplies a linear distortion by newcameramatrix
points = [10 23; 100 100.3; 23.1 20.5]
undistortedpoints = undistortpoints(points, cameramatrix=[517.3 0 318.6; 0 516.5 244.3; 0 0 1], distcoeffs=[0.2624, -0.9531, -0.0054, 0.0026, 1.1633])
The function get_optimalnewcameramatrix outputs a new camera matrix that transforms an undistorted image (in normalized coordinates) to one that contains all the information in the original source image if the parameter alpha = 0, or to one that has no missing information (i.e. no black parts in the image) if alpha = 1. A value of alpha in between does some mixture of the two extremes.
K_original = [517.3 0 318.6; 0 516.5 244.3; 0 0 1] # camera calibration of raw image
d_orignal = [0.2624, -0.9531, -0.0054, 0.0026, 1.1633]; # distortion coefficients of raw image
newcameramatrix = get_optimalnewcameramatrix(cameramatrix=K_original, distcoeffs=d_orignal, imgsize=(640,480), alpha=0, newimgsize=(640,480))
The function init_undistortrectifymap precomputes the map shown above.
K_original = [517.3 0 318.6; 0 516.5 244.3; 0 0 1] # camera calibration of raw image
d_orignal = [0.2624, -0.9531, -0.0054, 0.0026, 1.1633]; # distortion coefficients of raw image
newcameramatrix = get_optimalnewcameramatrix(cameramatrix=K_original, distcoeffs=d_orignal, imgsize=(640,480), alpha=0, newimgsize=(640,480))
xmap, ymap = init_undistortrectifymap(cameramatrix=K_original, distcoeffs=d_orignal, newcameramatrix=newcameramatrix, imgsize=(640,480))
The function remap uses bilinear interpolation to generate the domain of the maps which are generally computed by init_undistortrectifymap
K_original = [517.3 0 318.6; 0 516.5 244.3; 0 0 1] # camera calibration of raw image
d_orignal = [0.2624, -0.9531, -0.0054, 0.0026, 1.1633]; # distortion coefficients of raw image
newcameramatrix = get_optimalnewcameramatrix(cameramatrix=K_original, distcoeffs=d_orignal, imgsize=(640,480), alpha=0, newimgsize=(640,480))
xmap, ymap = init_undistortrectifymap(cameramatrix=K_original, distcoeffs=d_orignal, newcameramatrix=newcameramatrix, imgsize=(640,480))
newimg = remap(src, xmap, ymap)