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Open Source Computer Vision Library
https://opencv.org/
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166 lines
5.3 KiB
166 lines
5.3 KiB
% Matlab binding test cases |
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% Uses Matlab's builtin testing framework |
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classdef OpenCVTest < matlab.unittest.TestCase |
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methods(Test) |
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% ------------------------------------------------------------------------- |
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% EXCEPTIONS |
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% Check that errors and exceptions are thrown correctly |
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% ------------------------------------------------------------------------- |
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% check that std exception is thrown |
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function stdException(testcase) |
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try |
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std_exception(); |
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testcase.verifyFail(); |
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catch |
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% TODO: Catch more specific exception |
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testcase.verifyTrue(true); |
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end |
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end |
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% check that OpenCV exceptions are correctly caught |
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function cvException(testcase) |
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try |
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cv_exception(); |
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testcase.verifyFail(); |
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catch |
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% TODO: Catch more specific exception |
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testcase.verifyTrue(true); |
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end |
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end |
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% check that all exceptions are caught |
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function allException(testcase) |
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try |
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exception(); |
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testcase.verifyFail(); |
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catch |
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% TODO: Catch more specific exception |
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testcase.verifyTrue(true); |
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end |
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end |
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% ------------------------------------------------------------------------- |
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% SIZES AND FILLS |
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% Check that matrices are correctly filled and resized |
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% ------------------------------------------------------------------------- |
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% check that a matrix is correctly filled with random numbers |
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function randomFill(testcase) |
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sz = [7 11]; |
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mat = zeros(sz); |
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mat = cv.randn(mat, 0, 1); |
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testcase.verifyEqual(size(mat), sz, 'Matrix should not change size'); |
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testcase.verifyNotEqual(mat, zeros(sz), 'Matrix should be nonzero'); |
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end |
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function transpose(testcase) |
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m = randn(19, 81); |
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mt1 = transpose(m); |
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mt2 = cv.transpose(m); |
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testcase.verifyEqual(size(mt1), size(mt2), 'Matrix transposed to incorrect dimensionality'); |
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testcase.verifyLessThan(norm(mt1 - mt2), 1e-8, 'Too much precision lost in tranposition'); |
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end |
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% multiple return |
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function multipleReturn(testcase) |
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A = randn(10); |
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A = A'*A; |
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[V1, D1] = eig(A); D1 = diag(D1); |
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[~, D2, V2] = cv.eigen(A); |
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testcase.verifyLessThan(norm(V1 - V2), 1e-6, 'Too much precision lost in eigenvectors'); |
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testcase.verifyLessThan(norm(D1 - D2), 1e-6, 'Too much precision lost in eigenvalues'); |
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end |
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% complex output from SVD |
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function complexOutputSVD(testcase) |
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A = randn(10); |
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[V1, D1] = eig(A); |
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[~, D2, V2] = cv.eigen(A); |
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testcase.verifyTrue(~isreal(V2) && size(V2,3) == 1, 'Output should be complex'); |
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testcase.verifyLessThan(norm(V1 - V2), 1e-6, 'Too much precision lost in eigenvectors'); |
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end |
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% complex output from Fourier Transform |
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function complexOutputFFT(testcase) |
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A = randn(10); |
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F1 = fft2(A); |
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F2 = cv.dft(A, cv.DFT_COMPLEX_OUTPUT); |
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testcase.verifyTrue(~isreal(F2) && size(F2,3) == 1, 'Output should be complex'); |
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testcase.verifyLessThan(norm(F1 - F2), 1e-6, 'Too much precision lost in eigenvectors'); |
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end |
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% ------------------------------------------------------------------------- |
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% TYPE CASTS |
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% Check that types are correctly cast |
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% ------------------------------------------------------------------------- |
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% ------------------------------------------------------------------------- |
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% PRECISION |
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% Check that basic operations are performed with sufficient precision |
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% ------------------------------------------------------------------------- |
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% check that summing elements is within reasonable precision |
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function sumElements(testcase) |
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a = randn(5000); |
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b = sum(a(:)); |
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c = cv.sum(a); |
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testcase.verifyLessThan(norm(b - c), 1e-8, 'Matrix reduction with insufficient precision'); |
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end |
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% check that adding two matrices is within reasonable precision |
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function addPrecision(testcase) |
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a = randn(50); |
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b = randn(50); |
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c = a+b; |
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d = cv.add(a, b); |
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testcase.verifyLessThan(norm(c - d), 1e-8, 'Matrices are added with insufficient precision'); |
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end |
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% check that performing gemm is within reasonable precision |
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function gemmPrecision(testcase) |
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a = randn(10, 50); |
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b = randn(50, 10); |
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c = randn(10, 10); |
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alpha = 2.71828; |
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gamma = 1.61803; |
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d = alpha*a*b + gamma*c; |
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e = cv.gemm(a, b, alpha, c, gamma); |
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testcase.verifyLessThan(norm(d - e), 1e-8, 'Matrices are multiplied with insufficient precision'); |
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end |
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% ------------------------------------------------------------------------- |
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% MISCELLANEOUS |
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% Miscellaneous tests |
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% ------------------------------------------------------------------------- |
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% check that cv::waitKey waits for at least specified time |
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function waitKey(testcase) |
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tic(); |
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cv.waitKey(500); |
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elapsed = toc(); |
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testcase.verifyGreaterThan(elapsed, 0.5, 'Elapsed time should be at least 0.5 seconds'); |
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end |
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% check that highgui window can be created and destroyed |
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function createAndDestroyWindow(testcase) |
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try |
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cv.namedWindow('test window'); |
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catch |
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testcase.verifyFail('could not create window'); |
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end |
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try |
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cv.destroyWindow('test window'); |
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catch |
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testcase.verifyFail('could not destroy window'); |
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end |
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testcase.verifyTrue(true); |
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end |
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end |
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end
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