detectCheckerboardPoints

Detect checkerboard pattern in image

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Syntax

[imagePoints,boardSize] = detectCheckerboardPoints(I)
[imagePoints,boardSize,imagesUsed] = detectCheckerboardPoints(imageFileNames)
[imagePoints,boardSize,imagesUsed] = detectCheckerboardPoints(images)
[imagePoints,boardSize,pairsUsed] = detectCheckerboardPoints(imageFileNames1,imageFileNames2)
[imagePoints,boardSize,pairsUsed] = detectCheckerboardPoints(images1,images2)
[imagePoints,boardSize,pairsUsed] = detectCheckerboardPoints(___,'MinCornerMetric',0.15)

Description

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[imagePoints,boardSize] = detectCheckerboardPoints(I) detects a black and white checkerboard of size greater than 4-by-4 squares in a 2-D truecolor or grayscale image. The function returns the detected points and dimensions of the checkerboard.

[imagePoints,boardSize,imagesUsed] = detectCheckerboardPoints(imageFileNames) detects a checkerboard pattern in a set of input images, provided as an array of file names.

[imagePoints,boardSize,imagesUsed] = detectCheckerboardPoints(images) detects a checkerboard pattern in a set of input images, provided as an array of grayscale or truecolor images.

[imagePoints,boardSize,pairsUsed] = detectCheckerboardPoints(imageFileNames1,imageFileNames2) detects a checkerboard pattern in stereo pairs of images, provided as cell arrays of file names.

[imagePoints,boardSize,pairsUsed] = detectCheckerboardPoints(images1,images2) detects a checkerboard pattern in stereo pairs of images, provided as arrays of grayscale or truecolor images.

[imagePoints,boardSize,pairsUsed] = detectCheckerboardPoints(___,'MinCornerMetric',0.15) sets the corner metric threshold, specified as a nonnegative scalar. When the image is noisy or highly textured, increase this value to reduce the number of false corners.

Examples

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Open Live Script

Create a cell array of file names of calibration images.

for i = 1:5
  imageFileName = sprintf('image%d.tif', i);
  imageFileNames{i} = fullfile(matlabroot,'toolbox','vision',...
       'visiondata','calibration','webcam',imageFileName);
end

Detect calibration pattern in the images.

[imagePoints,boardSize,imagesUsed] = detectCheckerboardPoints(imageFileNames);

Display the detected points.

imageFileNames = imageFileNames(imagesUsed);
for i = 1:numel(imageFileNames)
  I = imread(imageFileNames{i});
  subplot(2, 2, i);
  imshow(I);
  hold on;
  plot(imagePoints(:,1,i),imagePoints(:,2,i),'ro');
end

Open Live Script

Read in stereo images.

numImages = 4;
images1 = cell(1, numImages);
images2 = cell(1, numImages);
for i = 1:numImages
    images1{i} = fullfile(matlabroot,'toolbox','vision',...
        'visiondata','calibration','stereo','left',sprintf('left%02d.png',i));
    images2{i} = fullfile(matlabroot,'toolbox','vision',...
        'visiondata','calibration','stereo','right',sprintf('right%02d.png',i));
end

Detect the checkerboards in the images.

[imagePoints,boardSize,pairsUsed] = ...
    detectCheckerboardPoints(images1,images2);

Display points from images1.

images1 = images1(pairsUsed);
figure;
for i = 1:numel(images1)
      I = imread(images1{i});
      subplot(2,2,i);
      imshow(I); 
      hold on; 
      plot(imagePoints(:,1,i,1),imagePoints(:,2,i,1),'ro');
end 
annotation('textbox',[0 0.9 1 0.1],'String','Camera 1',...
    'EdgeColor','none','HorizontalAlignment','center')

Display points from images2.

images2 = images2(pairsUsed);
figure;
for i = 1:numel(images2)
      I = imread(images2{i});
      subplot(2, 2, i);
      imshow(I);
      hold on; 
      plot(imagePoints(:,1,i,2),imagePoints(:,2,i,2),'ro');
end 
annotation('textbox',[0 0.9 1 0.1],'String','Camera 2',...
    'EdgeColor','none','HorizontalAlignment','center')

Input Arguments

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Input image, specified in either an M-by-N-by-3 truecolor or M-by-N 2-D grayscale. The input image must be real and nonsparse. The function can detect checkerboards with a minimum size of 4-by-4 squares.

Data Types: single | double | int16 | uint8 | uint16 | logical

Image file names, specified as an N-element cell array of N file names.

File names for camera 1 images, specified as an N-element cell array of N file names. The images contained in this array must be in the same order as images contained in imageFileNames2, forming stereo pairs.

File names for camera 2 images, specified as an N-element cell array of N file names. The images contained in this array must be in the same order as images contained in imageFileNames1, forming stereo pairs.

Images, specified as an H-by-W-by-B-by-F array containing a set of grayscale or truecolor images. The input dimensions are:

H represents the image height.
W represents the image width.
B represents the color channel. A value of 1 indicates a grayscale image, and a value of 3 indicates a truecolor image.
F represents the number of image frames.

Images, specified as an H-by-W-by-B-by-F array containing a set of grayscale or truecolor images. The input dimensions are:

H represents the image height.
W represents the image width.
B represents the color channel. A value of 1 indicates a grayscale image, and a value of 3 indicates a truecolor image.
F represents the number of image frames.

Images, specified as an H-by-W-by-B-by-F array containing a set of grayscale or truecolor images. The input dimensions are:

H represents the image height.
W represents the image width.
B represents the color channel. A value of 1 indicates a grayscale image, and a value of 3 indicates a truecolor image.
F represents the number of image frames.

Output Arguments

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Detected checkerboard corner coordinates, returned as an M-by-2 matrix for one image. For multiple images, points are returned as an M-by-2-by-number of images array, and for stereo pairs of images, the function returns points as an M-by-2-by-number of pairs-by-number of cameras array.

For stereo pairs, imagePoints(:,:,:,1) are the points from the first set of images, and imagePoints(:,:,:,2) are the points from the second set of images. The output contains M number of [x y] coordinates. Each coordinate represents a point where square corners are detected on the checkerboard. The number of points the function returns depends on the value of boardSize, which indicates the number of squares detected. The function detects the points with sub-pixel accuracy.

The function calculates the number of points, M, as follows:

M = prod(boardSize-1).

If the checkerboard cannot be detected:
imagePoints = []
boardSize = [0,0]

When you specify the imageFileNames input, the function can return imagePoints as an M-by-2-by-N array. In this array, N represents the number of images in which a checkerboard is detected.

Checkerboard dimensions, returned as a 2-element [height, width] vector. The dimensions of the checkerboard are expressed in terms of the number of squares.

Pattern detection flag, returned as an N-by-1 logical vector of N logicals. The function outputs the same number of logicals as there are input images. A true value indicates that the pattern was detected in the corresponding image. A false value indicates that the function did not detect a pattern.

Stereo pair pattern detection flag, returned as an N-by-1 logical vector of N logicals. The function outputs the same number of logicals as there are input images. A true value indicates that the pattern is detected in the corresponding stereo image pair. A false value indicates that the function does not detect a pattern.

References

[1] Geiger, A., F. Moosmann, O. Car, and B. Schuster. "Automatic Camera and Range Sensor Calibration using a Single Shot," International Conference on Robotics and Automation (ICRA), St. Paul, USA, May 2012.

Extended Capabilities

See Also

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Topics

Introduced in R2014a

Computer Vision Toolbox Documentation

Support