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Convert camera intrinsic parameters from OpenCV to MATLAB

Since R2021b



intrinsics = cameraIntrinsicsFromOpenCV(intrinsicMatrix,distortionCoefficients,imageSize) converts the OpenCV intrinsics, specified by the input arguments, into a MATLAB® object intrinsics, which can be cameraIntrinsics or cameraIntrinsicsKB.

The OpenCV spatial coordinate system specifies the upper-left pixel center at (0,0), whereas the MATLAB spatial coordinate system specifies the pixel center at (1,1). The cameraIntrinsicsFromOpenCV function compensates for this difference by adding 1 to both of the x and y-values for the converted principal point.


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Define OpenCV camera intrinsic parameters in the workspace.

intrinsicMatrix = [729.4644    0       570.6455;
                     0       728.8196  346.0108;
                     0         0         1  ];
distortionCoefficients = [-0.4262  0.5460  0.0038  -0.0051  -0.6176];

Define the image size returned by the camera.

imageSize = [712 1072];

Convert the intrinsic parameters from OpenCV to MATLAB format.

intrinsics = cameraIntrinsicsFromOpenCV(intrinsicMatrix, ...

Load the image to undistort.

filename = fullfile(toolboxdir("vision"),"visiondata","calibration", ...
I = imread(filename);

Undistort the image and diplay the results.

J = undistortImage(I,intrinsics);
title("Original Image (left) vs. Corrected Image (right)");

Define OpenCV fisheye parameters in the workspace.

intrinsicMatrix = [875.88  0.00   1005.62; ...
                   0.00    874.76 741.52; ...
                   0.00,   0.00,  1.00];
distortionCoeffs = [0.08, -0.16, 0.35, -0.26];

Define the image size returned by the camera.

imageSize =[1500 2000];

Import the intrinsic camera parameters for a fisheye lens from OpenCV.

intrinsicsKB = cameraIntrinsicsFromOpenCV(intrinsicMatrix,distortionCoeffs,imageSize)
intrinsicsKB = 
  cameraIntrinsicsKB with properties:

               FocalLength: [875.8800 874.7600]
            PrincipalPoint: [1.0066e+03 742.5200]
                 ImageSize: [1500 2000]
    DistortionCoefficients: [0.0800 -0.1600 0.3500 -0.2600]
                         K: [3x3 double]

Load an image to undistort.

filename = fullfile(toolboxdir("vision"),"visiondata", ...
I = imread(filename);

Undistort the image and display the results.

J = undistortImage(I,intrinsicsKB);
title("Original Image (left) vs. Corrected Image (right)");

The ROS camera calibration package estimates camera intrinsic parameters using the OpenCV camera calibration tools [1]. After calibrating a camera in ROS, you can import its intrinsic parameters to a YAML file using the camera calibration parser in ROS. To use the calibrated camera with Computer Vision Toolbox™ functions, such as undistortImage, you must read the camera parameters from the YAML file and then convert them into a cameraIntrinsics object using cameraIntrinsicsFromOpenCV.

Note: The cameraIntrinsicsFromOpenCV function supports importing camera intrinsic parameters for only those pinhole camera models that use the ROS plumb-bob distortion model.

Read Camera Intrinsic Parameters from a ROS YAML File

Read the camera parameters stored in cameraParams.yaml using the helper function helperReadYAML.

intrinsicsParams = helperReadYAML('cameraParams.yaml');

Create cameraIntrinsics Object Using cameraIntrinsicsFromOpenCV

Use the cameraIntrinsicsFromOpenCV function to create a cameraIntrinsics object from the camera matrix and the distortion coefficients.

imageSize = [intrinsicsParams.image_height intrinsicsParams.image_width];
intrinsicMatrix = intrinsicsParams.camera_matrix;
distortionCoefficients = intrinsicsParams.distortion_coefficients;

intrinsicsObj = cameraIntrinsicsFromOpenCV(intrinsicMatrix,distortionCoefficients,imageSize);

Undistort Image

Use the imported camera intrinsics with undistortImage to undistort an image captured using the calibrated camera.

% Load the captured image.
imageName = fullfile(toolboxdir('vision'),'visiondata','calibration','stereo','left','left01.png');
I = imread(imageName);

% Undistort the image.
J = undistortImage(I,intrinsicsObj,'OutputView','full');

% Display the result.

Figure contains an axes object. The axes object contains an object of type image.

Supporting Functions


The helperReadYAML function reads the monocular camera parameters from the input YAML file that was exported from ROS.

function cameraParams = helperReadYAML(filename)
% helperReadYAML reads a ROS YAML file, filename, and returns a structure 
% with these fields: image_width, image_height, camera_name,
% camera_matrix, distortion_model, distortion_coefficients,
% rectification_matrix, and projection_matrix. These fields are stored 
% in the YAML file colon separated from their values in different lines.

    f = fopen(filename,'r');
    stringFields = {'camera_name','distortion_model'};
    while ~feof(f)

        [name,value,isEmptyLine] = helperReadYAMLLine(f);
        if isEmptyLine

        if ~isempty(value)
            % Convert all values to numbers except for known string
            % fields.
            if ~any(contains(name, stringFields))
                value = str2num(value); %#ok
            % An empty value in ROS YAML files indicates a matrix in
            % upcoming lines. Read the matrix from the upcoming lines.
            value = helperReadYAMLMatrix(f);

        % Store post-processed value.
        cameraParams.(name) = value;


The helperReadYAMLMatrix function reads the rows, columns and data fields of a matrix in the ROS YAML file.

function matrix = helperReadYAMLMatrix(f)
%   helperReadYAMLMatrix reads a matrix from the ROS YAML file. A matrix in
%   a ROS YAML file has three fields: rows, columns and data. rows and col
%   describe the matrix size. data is a continguous array of the matrix
%   elements in row-major order. This helper function assumes the presence
%   of all three fields of a matrix to return the correct matrix.

    numRows = 0;
    numCols = 0;
    data = [];

    % Read numRows, numCols and matrix data.
    while ~feof(f)
        [name,value,isEmptyLine] = helperReadYAMLLine(f);

        if isEmptyLine

        switch name
            case 'rows'
                numRows = str2num(value); %#ok
            case 'cols'
                numCols = str2num(value); %#ok
            case 'data'
                data    = str2num(value); %#ok

                % Terminate the while loop as data is the last 
                % field of a matrix in the ROS YAML file.
                % Terminate the while loop if any other field is
                % encountered.

    if numel(data) == numRows*numCols
        % Reshape the matrix using row-major order.
        matrix = reshape(data,[numCols numRows])';


The helperReadYAMLLine function reads a line of a ROS YAML file.

function [name,value,isEmptyLine] = helperReadYAMLLine(f)

    % Read line from file.
    line = fgetl(f); 

    % Trim leading and trailing whitespaces.
    line = strtrim(line);

    if isempty(line) || line(1)=='#'
        % Empty line or comment.
        name = '';
        value = '';
        isEmptyLine = true;
        % Split the line to get name and value.
        c = strsplit(line,':');
        assert(length(c)==2,'Unexpected file format')
        name = c{1};
        value = strtrim(c{2}); % Trim leading whitespace.
        isEmptyLine = false;


[1] Camera Calibration

Input Arguments

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Camera intrinsic matrix from OpenCV, specified as a 3-by-3 matrix of the form:


where fx and fy are the focal lengths in the x and y-directions, and (cx,cy) is the principal point in OpenCV.

Camera distortion coefficients from OpenCV, specified as a 4- or 5-element vector. The distortion coefficients represent either a Kannala-Brandt model [1] or a pinhole model.

  • Pinhole model — 5-element vector of the form [k1 k2 p1 p2 k3]. The values of k1, k2, and k3 describe the radial distortion and p1 and p2 describe the tangential distortion.

  • Kannala-Brandt model — 4-element vector of the form [k1 k2 k3 k4]. These values are the distortion coefficients from the OpenCV fisheye camera model.

Image size, specified as a 2-element vector in the form [mrows,ncols].

Output Arguments

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Camera intrinsic parameters, returned as a cameraIntrinsics or cameraIntrinsicsKB object.

The cameraIntrinsicsKB object contains camera intrinsic calibration parameters for a fisheye lens, using the Kannala-Brandt model. The cameraIntrinsics object contains OpenCV intrinsics for a pinhole camera model. When the OpenCV pinhole model uses more than five distortion coefficients, the intrinsics cannot be converted to the MATLAB format. In this case, you can recalibrate the camera using the Camera Calibrator app.


[1] Juho Kannala and Sami Brandt. A generic camera model and calibration method for conventional, wide-angle, and fish-eye lenses. IEEE transactions on pattern analysis and machine intelligence, 28:1335–40, 09 2006.

Version History

Introduced in R2021b