def test_point_map_zero_div(self): test_p = [1, 1] for dtype, e in [['f', numpy.finfo('f').min], ['d', sys.float_info.min]]: print("Dtype:", dtype) print("E:", e) # where [2,2] = 0 h = Homography.from_matrix([[1, 0, 1], [0, 1, 1], [0, 0, 0]], dtype) nose.tools.assert_raises(RuntimeError, h.map, test_p) # Where [2,2] = e which is approximately 0 e = sys.float_info.min h = Homography.from_matrix([[1, 0, 1], [0, 1, 1], [0, 0, e]], dtype) print("E Matrix:", h.as_matrix()) nose.tools.assert_raises(RuntimeError, h.map, test_p) # Where [2,2] = 0.5, which should be valid h = Homography.from_matrix([[1, 0, 1], [0, 1, 1], [0, 0, .5]], dtype) r = h.map(test_p) nose.tools.assert_almost_equal(r[0], 4) nose.tools.assert_almost_equal(r[1], 4)
def test_numeric_invertibility(self): exp_result = Homography.from_matrix([[-.5, -2.5, 1.5], [-1.5, 1.5, -.5], [1.5, .5, -.5]]) h = Homography.from_matrix([[1, 1, 2], [3, 4, 5], [6, 7, 9]]) h_inv = h.inverse() numpy.testing.assert_array_equal(h_inv, exp_result.as_matrix()) h = Homography.from_matrix([[1, 1, 2], [3, 4, 5], [6, 7, 9]], 'f') h_inv = h.inverse() numpy.testing.assert_array_equal(h_inv, exp_result.as_matrix())
def test_point_map(self): h_f = Homography('f') h_d = Homography('d') p_af = EigenArray.from_array([[2.2, 3.3]], 'f') p_f = p_af.get_matrix()[0] p_ad = EigenArray.from_array([[5.5, 6.6]], 'd') p_d = p_ad.get_matrix()[0] # float-float numpy.testing.assert_almost_equal(h_f.map(p_f), p_f) # float-double numpy.testing.assert_almost_equal(h_f.map(p_d), p_d) # double-float numpy.testing.assert_almost_equal(h_d.map(p_f), p_f) # double-double numpy.testing.assert_almost_equal(h_d.map(p_d), p_d) # Code to generate truth h = numpy.random.rand(3, 3) h = h / numpy.linalg.norm(h) p0 = numpy.random.rand(3) p0[2] = 1 p1 = numpy.dot(h, p0) p1 = p1[:2] / p1[2] h_d = Homography.from_matrix(h, 'd') # map from Numpy array. numpy.testing.assert_almost_equal(h_d.map(p0[:2]).ravel(), p1) # map from EigenArray p0 = EigenArray.from_array([p0[:2]]) numpy.testing.assert_almost_equal( h_d.map(p0.get_matrix()[0]).ravel(), p1) # Another explicit case. p0 = numpy.array([1923.47, 645.676, 1]) h = numpy.array( [[ 5.491496261770000276e-01, -1.125428185150000038e-01, 1.358427031619999923e+02 ], [ -1.429513389049999993e-02, 6.035527375529999849e-01, 5.923971959490000216e+01 ], [-2.042570000000000164e-06, -2.871670000000000197e-07, 1]]) p1 = numpy.dot(h, p0) p1 = p1[:2] / p1[2] H = Homography.from_matrix(h) P = EigenArray.from_array([p0[:2]]) numpy.testing.assert_almost_equal(H.map(P.get_matrix()[0]).ravel(), p1)
def test_equal(self): # Identity should be equal to itself h1 = Homography() h2 = Homography() nose.tools.assert_equal(h1, h2) # manually constructed homographies should be equal m = [[.1, .2, .3], [.4, .5, .6], [.7, .8, .9]] h1 = Homography.from_matrix(m) h2 = Homography.from_matrix(m) nose.tools.assert_equal(h1, h2) # and should also be different than identity nose.tools.assert_not_equal(h1, Homography()) nose.tools.assert_not_equal(h2, Homography())
def test_as_matrix(self): numpy.testing.assert_almost_equal( Homography('d').as_matrix(), [[1, 0, 0], [0, 1, 0], [0, 0, 1]]) numpy.testing.assert_almost_equal( Homography('f').as_matrix(), [[1, 0, 0], [0, 1, 0], [0, 0, 1]]) m = [[1, 2, 3], [4, 5, 6], [7, 8, 9]] numpy.testing.assert_almost_equal( Homography.from_matrix(m, 'd').as_matrix(), m) numpy.testing.assert_almost_equal( Homography.from_matrix(m, 'f').as_matrix(), m) m = [[.1, .2, .3], [.4, .5, .6], [.7, .8, .9]] numpy.testing.assert_almost_equal( Homography.from_matrix(m, 'd').as_matrix(), m) numpy.testing.assert_almost_equal( Homography.from_matrix(m, 'f').as_matrix(), m)
def test_multiply(self): # Test multiplying homographies together h_ident = Homography() h_valued = Homography.from_matrix([[1, 0, 1], [0, 1, 1], [0, 0, .5]]) r1 = h_ident * h_ident nose.tools.assert_equal(h_ident, r1) r2 = h_ident * h_valued nose.tools.assert_equal(r2, h_valued) r3 = h_valued * h_ident nose.tools.assert_equal(r3, h_valued)
def test_matrix_init(self): # Test that construction does not fail when passing valid matrices as # initializer m1 = [[0, 1, 3], [0.3, 0.1, 10], [-1, 8.1, 4.7]] m2_d = EigenArray.from_array(m1, 'd') m2_f = EigenArray.from_array(m1, 'f') Homography.from_matrix(m1, 'd') Homography.from_matrix(m1, 'f') Homography.from_matrix(m2_d.get_matrix(), 'd') Homography.from_matrix(m2_d.get_matrix(), 'f') Homography.from_matrix(m2_f.get_matrix(), 'd') Homography.from_matrix(m2_f.get_matrix(), 'f')
def test_normalize(self): h = Homography.from_matrix([[-.5, -2.5, 1.5], [-1.5, 1.5, -.5], [1.5, .5, -.5]]) e = Homography.from_matrix([[1, 5, -3], [3, -3, 1], [-3, -1, 1]]) numpy.testing.assert_array_equal(h.normalize(), e.as_matrix())