示例#1
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    def test__Circular_Gabor__fit_Sf(self):
        self.pattern = np.zeros((256, 256))
        self.pattern = cv2.circle(img=self.pattern, center=(100, 100), radius=75, color=255)
        cosfire = Cosfire(strategy_name='Circular Gabor',
                          center_x=100,
                          center_y=100,
                          rho_list=(0, 75, 100),
                          t1=0.99,
                          t2=0.75,
                          t3=0.9,
                          filter_parameters=GaborParameters(ksize=(10, 10), σ=5,
                                                            θ=np.linspace(start=0, stop=π, num=5,
                                                                          endpoint=True),
                                                            λ=[12],
                                                            # np.linspace(start=1, stop=100, num=100, endpoint=True),
                                                            γ=0.25, ψ=0, ktype=cv2.CV_32F),
                          sigma0=0.67,
                          alpha=0.04,
                          reflection_invariant=0,
                          scale_invariant=[0],
                          rotation_invariant=[0]
                          )

        cosfire._prototype_bank = _Circular_Gabor__compute_bank_of_responses(cosfire, self.pattern)
        cosfire.threshold_prototype_bank_of_responses(threshold=0.85)
        operator = _Circular_Gabor__fit_Sf(cosfire)
        self.assertEqual(operator,[CosfireCircularGaborTuple(λ=12, θ=0.0,                ρ=75, φ=0.06981317007977318),
                                   CosfireCircularGaborTuple(λ=12, θ=3.141592653589793,  ρ=75, φ=0.06981317007977318),
                                   CosfireCircularGaborTuple(λ=12, θ=2.356194490192345,  ρ=75, φ=0.6457718232379019 ),
                                   CosfireCircularGaborTuple(λ=12, θ=2.356194490192345,  ρ=75, φ=4.066617157146788  ),
                                   CosfireCircularGaborTuple(λ=12, θ=1.5707963267948966, ρ=75, φ=4.625122517784973  ),
                                   CosfireCircularGaborTuple(λ=12, θ=0.7853981633974483, ρ=75, φ=5.480333851262195  ),
                                   CosfireCircularGaborTuple(λ=12, θ=0.0,                ρ=75, φ=6.178465552059927  ),
                                   CosfireCircularGaborTuple(λ=12, θ=3.141592653589793,  ρ=75, φ=6.178465552059927  )])
示例#2
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 def test_cosfire__preset_1(self):
     im1 = cv2.imread("media/patron1.jpg", 0)
     im2 = cv2.imread("media/prueba1.jpg", 0)
     for iter in range(1):
         a = Cosfire(**preset_1)
         a.fit(im1)
         r = a.transform(im2)
     # viewPattern(r, im2, 20)
     # cv2.imshow("Response", r)
     expected = np.load('media/test_cosfire__preset_1_expected.npy')
     assert_almost_equal(r, expected)
示例#3
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    def test_shift_responses(self):
        specific_image = np.array([[3, 3, 3, 3, 3],
                                   [3, 2, 2, 2, 3],
                                   [3, 2, 1, 2, 3],
                                   [3, 2, 2, 2, 3],
                                   [3, 3, 3, 3, 3]], dtype=np.float32)
        tupla_collection = [CosfireCircularGaborTuple(ρ=1, ϕ=0, λ=0.1, θ=0),
                            CosfireCircularGaborTuple(ρ=1, ϕ=π * 0.5, λ=0.1, θ=0),
                            CosfireCircularGaborTuple(ρ=1, ϕ=π, λ=0.1, θ=0),
                            CosfireCircularGaborTuple(ρ=1, ϕ=π * 1.5, λ=0.1, θ=0),
                            ]
        response_collection = {tupla: specific_image for tupla in tupla_collection}
        cosfire_filter = Cosfire(**preset_1)
        result_collection = {key: cosfire_filter.shift_responses({key: response})[key]
                             for key, response in response_collection.items()}
        expected_collection = {
            CosfireCircularGaborTuple(ρ=1, ϕ=0, λ=0.1, θ=0): np.array([[3, 3, 3, 3, 0],
                                                                       [2, 2, 2, 3, 0],
                                                                       [2, 1, 2, 3, 0],
                                                                       [2, 2, 2, 3, 0],
                                                                       [3, 3, 3, 3, 0]], dtype=np.float32),
            CosfireCircularGaborTuple(ρ=1, ϕ=π * 0.5, λ=0.1, θ=0): np.array([[0., 0., 0., 0., 0.],
                                                                             [3., 3., 3., 3., 3.],
                                                                             [3., 2., 2., 2., 3.],
                                                                             [3., 2., 1., 2., 3.],
                                                                             [3., 2., 2., 2., 3.]],
                                                                            dtype=np.float32),
            CosfireCircularGaborTuple(ρ=1, ϕ=π, λ=0.1, θ=0): np.array([[0., 3., 3., 3., 3.],
                                                                       [0., 3., 2., 2., 2.],
                                                                       [0., 3., 2., 1., 2.],
                                                                       [0., 3., 2., 2., 2.],
                                                                       [0., 3., 3., 3., 3.]], dtype=np.float32),
            CosfireCircularGaborTuple(ρ=1, ϕ=π * 1.5, λ=0.1, θ=0): np.array([[3., 2., 2., 2., 3.],
                                                                             [3., 2., 1., 2., 3.],
                                                                             [3., 2., 2., 2., 3.],
                                                                             [3., 3., 3., 3., 3.],
                                                                             [0., 0., 0., 0., 0.]],
                                                                            dtype=np.float32), }

        assert_almost_equal(result_collection[CosfireCircularGaborTuple(ρ=1, ϕ=π, λ=0.1, θ=0)],
                            expected_collection[CosfireCircularGaborTuple(ρ=1, ϕ=π, λ=0.1, θ=0)])

        assert_almost_equal(result_collection[CosfireCircularGaborTuple(ρ=1, ϕ=0, λ=0.1, θ=0)],
                            expected_collection[CosfireCircularGaborTuple(ρ=1, ϕ=0, λ=0.1, θ=0)])

        assert_almost_equal(result_collection[CosfireCircularGaborTuple(ρ=1, ϕ=π * 0.5, λ=0.1, θ=0)],
                            expected_collection[CosfireCircularGaborTuple(ρ=1, ϕ=π * 0.5, λ=0.1, θ=0)])

        assert_almost_equal(result_collection[CosfireCircularGaborTuple(ρ=1, ϕ=π * 1.5, λ=0.1, θ=0)],
                            expected_collection[CosfireCircularGaborTuple(ρ=1, ϕ=π * 1.5, λ=0.1, θ=0)])
示例#4
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    def test__threshold_prototype_bank_of_responses(self):
        self.pattern = np.zeros((256, 256))
        self.pattern = cv2.circle(img=self.pattern, center=(100, 100), radius=75, color=255)
        cosfire = Cosfire(strategy_name='Circular Gabor',
                          center_x=100,
                          center_y=100,
                          rho_list=range(0, 50, 10),
                          t1=0.99,
                          t2=0.75,
                          t3=0.9,
                          filter_parameters=GaborParameters(ksize=(10, 10), σ=5,
                                                            θ=np.linspace(start=0, stop=π, num=5,
                                                                          endpoint=True),
                                                            λ=[12],
                                                            # np.linspace(start=1, stop=100, num=100, endpoint=True),
                                                            γ=0.25, ψ=0, ktype=cv2.CV_32F),
                          sigma0=0.67,
                          alpha=0.04,
                          reflection_invariant=0,
                          scale_invariant=[1],
                          rotation_invariant=[0]
                          )

        cosfire._prototype_bank = _Circular_Gabor__compute_bank_of_responses(cosfire, self.pattern)
        cosfire.threshold_prototype_bank_of_responses(threshold=0.75)

        generating_results = False
        if generating_results:
            # The first time I used this to generate expected results
            np.save('media/test__threshold_prototype_bank_of_responses__pattern.npy', self.pattern)
            with PdfPages('test__threshold_prototype_bank_of_responses.pdf') as pdf:
                for key, value in cosfire._prototype_bank.items():
                    file_name = 'media/test__threshold_prototype_bank_of_responses_' + str(key) + '.npy'
                    np.save(file_name, value)
                    fig = plt.figure()
                    plt.imshow(value, cmap='cool')
                    plt.title(key)
                    pdf.savefig(fig)

        for key in cosfire._prototype_bank:
            file_name = 'media/test__threshold_prototype_bank_of_responses_' + str(key) + '.npy'
            expected = np.load(file_name)
            result = cosfire._prototype_bank[key]
            assert_almost_equal(result, expected)
示例#5
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    def setUp(self):
        self.pattern = np.zeros((256, 256))
        cv2.rectangle(img=self.pattern, pt1=(50, 100), pt2=(100, 106), color=255, thickness=-1)
        cv2.rectangle(img=self.pattern, pt1=(100, 100), pt2=(97, 50), color=255, thickness=-1)

        self.cosfire = Cosfire(strategy_name='Circular Gabor',
                               center_x=100,
                               center_y=100,
                               rho_list=range(0, 100, 10),
                               t1=0.99,
                               t2=0.75,
                               t3=0.9,
                               filter_parameters=GaborParameters(ksize=(10, 10), σ=5,
                                                                 θ=np.linspace(start=0, stop=π, num=30,
                                                                               endpoint=False),
                                                                 λ=np.linspace(start=7, stop=8, num=10,
                                                                               endpoint=False),
                                                                 γ=0.5, ψ=π, ktype=cv2.CV_32F),
                               sigma0=0.67,
                               alpha=0.04,
                               reflection_invariant=0,
                               scale_invariant=[1],
                               rotation_invariant=[0]
                               )
示例#6
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# Now let's fit a COSFIRE filter using the demo pattern as prototype:
##############################################################################

some_cosfire = Cosfire(strategy_name='Circular Gabor',
                       center_x=100,
                       center_y=100,
                       rho_list=range(0, 100, 10),
                       t1=0.99,
                       t2=0.75,
                       t3=0.9,
                       filter_parameters=GaborParameters(
                           ksize=(10, 10),
                           σ=5,
                           θ=np.linspace(start=0,
                                         stop=π,
                                         num=30,
                                         endpoint=False),
                           λ=np.linspace(start=7,
                                         stop=8,
                                         num=10,
                                         endpoint=False),
                           γ=0.5,
                           ψ=π,
                           ktype=cv2.CV_32F),
                       sigma0=0.67,
                       alpha=0.04,
                       reflection_invariant=True,
                       scale_invariant=[0.5, 1, 2],
                       rotation_invariant=[0, 0.5 * π, 1.5 * π])

some_cosfire.fit(pattern)
for parameters in some_cosfire._Sf:
示例#7
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class TestCosfireCircularGabor(unittest.TestCase):
    def setUp(self):
        self.pattern = np.zeros((256, 256))
        cv2.rectangle(img=self.pattern, pt1=(50, 100), pt2=(100, 106), color=255, thickness=-1)
        cv2.rectangle(img=self.pattern, pt1=(100, 100), pt2=(97, 50), color=255, thickness=-1)

        self.cosfire = Cosfire(strategy_name='Circular Gabor',
                               center_x=100,
                               center_y=100,
                               rho_list=range(0, 100, 10),
                               t1=0.99,
                               t2=0.75,
                               t3=0.9,
                               filter_parameters=GaborParameters(ksize=(10, 10), σ=5,
                                                                 θ=np.linspace(start=0, stop=π, num=30,
                                                                               endpoint=False),
                                                                 λ=np.linspace(start=7, stop=8, num=10,
                                                                               endpoint=False),
                                                                 γ=0.5, ψ=π, ktype=cv2.CV_32F),
                               sigma0=0.67,
                               alpha=0.04,
                               reflection_invariant=0,
                               scale_invariant=[1],
                               rotation_invariant=[0]
                               )

    def test__cosfire_process(self):
        self.cosfire.fit(self.pattern)
        expected = [CosfireCircularGaborTuple(λ=7.5, θ=0.0, ρ=10, φ=1.3089969389957472),
                    CosfireCircularGaborTuple(λ=7.5, θ=0.0, ρ=10, φ=2.0420352248333655),
                    CosfireCircularGaborTuple(λ=7.5, θ=1.5707963267948966, ρ=10, φ=2.9845130209103035),
                    CosfireCircularGaborTuple(λ=7.5, θ=1.5707963267948966, ρ=10, φ=4.171336912266447),
                    CosfireCircularGaborTuple(λ=7.5, θ=0.0, ρ=20, φ=1.7976891295541595),
                    CosfireCircularGaborTuple(λ=7.5, θ=1.5707963267948966, ρ=20, φ=3.07177948351002),
                    CosfireCircularGaborTuple(λ=7.5, θ=1.5707963267948966, ρ=20, φ=3.5779249665883754),
                    CosfireCircularGaborTuple(λ=7.5, θ=0.0, ρ=30, φ=1.710422666954443),
                    CosfireCircularGaborTuple(λ=7.5, θ=1.5707963267948966, ρ=30, φ=3.420845333908886),
                    CosfireCircularGaborTuple(λ=7.5, θ=0.0, ρ=40, φ=1.6755160819145565),
                    CosfireCircularGaborTuple(λ=7.5, θ=1.5707963267948966, ρ=40, φ=3.351032163829113)]

        result = self.cosfire._Sf
        self.assertEqual(result, expected)

    def test__Circular_Gabor__compute_bank_of_responses__no_kwargs(self):
        self.pattern = np.zeros((256, 256))
        self.pattern = cv2.circle(img=self.pattern, center=(100,100), radius=75, color=255)
        cosfire = Cosfire(strategy_name='Circular Gabor',
                          center_x=100,
                          center_y=100,
                          rho_list=range(0, 50, 10),
                          t1=0.99,
                          t2=0.75,
                          t3=0.9,
                          filter_parameters=GaborParameters(ksize=(10, 10), σ=5,
                                                            θ=np.linspace(start=0, stop=π, num=5,
                                                                          endpoint=True),
                                                            λ=[12],#np.linspace(start=1, stop=100, num=100, endpoint=True),
                                                            γ=0.25, ψ=0, ktype=cv2.CV_32F),
                          sigma0=0.67,
                          alpha=0.04,
                          reflection_invariant=0,
                          scale_invariant=[1],
                          rotation_invariant=[0]
                          )

        results_bank = _Circular_Gabor__compute_bank_of_responses(cosfire, self.pattern)

        generating_results = False
        if generating_results:
            #The first time I used this to generate expected results
            np.save('media/test__Circular_Gabor__compute_bank_of_responses__no_kwargs__pattern.npy', self.pattern)
            with PdfPages('test__Circular_Gabor__compute_bank_of_responses.pdf') as pdf:
                for key, value in results_bank.items():
                    file_name = 'media/test__Circular_Gabor__compute_bank_of_responses__no_kwargs__' + str(key) + '.npy'
                    np.save(file_name, value)
                    fig = plt.figure()
                    plt.imshow(value, cmap='cool')
                    plt.title(key)
                    pdf.savefig(fig)

        for key in results_bank:
            file_name = 'media/test__Circular_Gabor__compute_bank_of_responses__no_kwargs__' + str(key) + '.npy'
            expected = np.load(file_name)
            result = results_bank[key]
            assert_almost_equal(result, expected)

    def test__threshold_prototype_bank_of_responses(self):
        self.pattern = np.zeros((256, 256))
        self.pattern = cv2.circle(img=self.pattern, center=(100, 100), radius=75, color=255)
        cosfire = Cosfire(strategy_name='Circular Gabor',
                          center_x=100,
                          center_y=100,
                          rho_list=range(0, 50, 10),
                          t1=0.99,
                          t2=0.75,
                          t3=0.9,
                          filter_parameters=GaborParameters(ksize=(10, 10), σ=5,
                                                            θ=np.linspace(start=0, stop=π, num=5,
                                                                          endpoint=True),
                                                            λ=[12],
                                                            # np.linspace(start=1, stop=100, num=100, endpoint=True),
                                                            γ=0.25, ψ=0, ktype=cv2.CV_32F),
                          sigma0=0.67,
                          alpha=0.04,
                          reflection_invariant=0,
                          scale_invariant=[1],
                          rotation_invariant=[0]
                          )

        cosfire._prototype_bank = _Circular_Gabor__compute_bank_of_responses(cosfire, self.pattern)
        cosfire.threshold_prototype_bank_of_responses(threshold=0.75)

        generating_results = False
        if generating_results:
            # The first time I used this to generate expected results
            np.save('media/test__threshold_prototype_bank_of_responses__pattern.npy', self.pattern)
            with PdfPages('test__threshold_prototype_bank_of_responses.pdf') as pdf:
                for key, value in cosfire._prototype_bank.items():
                    file_name = 'media/test__threshold_prototype_bank_of_responses_' + str(key) + '.npy'
                    np.save(file_name, value)
                    fig = plt.figure()
                    plt.imshow(value, cmap='cool')
                    plt.title(key)
                    pdf.savefig(fig)

        for key in cosfire._prototype_bank:
            file_name = 'media/test__threshold_prototype_bank_of_responses_' + str(key) + '.npy'
            expected = np.load(file_name)
            result = cosfire._prototype_bank[key]
            assert_almost_equal(result, expected)

    def test__Circular_Gabor__fit_Sf(self):
        self.pattern = np.zeros((256, 256))
        self.pattern = cv2.circle(img=self.pattern, center=(100, 100), radius=75, color=255)
        cosfire = Cosfire(strategy_name='Circular Gabor',
                          center_x=100,
                          center_y=100,
                          rho_list=(0, 75, 100),
                          t1=0.99,
                          t2=0.75,
                          t3=0.9,
                          filter_parameters=GaborParameters(ksize=(10, 10), σ=5,
                                                            θ=np.linspace(start=0, stop=π, num=5,
                                                                          endpoint=True),
                                                            λ=[12],
                                                            # np.linspace(start=1, stop=100, num=100, endpoint=True),
                                                            γ=0.25, ψ=0, ktype=cv2.CV_32F),
                          sigma0=0.67,
                          alpha=0.04,
                          reflection_invariant=0,
                          scale_invariant=[0],
                          rotation_invariant=[0]
                          )

        cosfire._prototype_bank = _Circular_Gabor__compute_bank_of_responses(cosfire, self.pattern)
        cosfire.threshold_prototype_bank_of_responses(threshold=0.85)
        operator = _Circular_Gabor__fit_Sf(cosfire)
        self.assertEqual(operator,[CosfireCircularGaborTuple(λ=12, θ=0.0,                ρ=75, φ=0.06981317007977318),
                                   CosfireCircularGaborTuple(λ=12, θ=3.141592653589793,  ρ=75, φ=0.06981317007977318),
                                   CosfireCircularGaborTuple(λ=12, θ=2.356194490192345,  ρ=75, φ=0.6457718232379019 ),
                                   CosfireCircularGaborTuple(λ=12, θ=2.356194490192345,  ρ=75, φ=4.066617157146788  ),
                                   CosfireCircularGaborTuple(λ=12, θ=1.5707963267948966, ρ=75, φ=4.625122517784973  ),
                                   CosfireCircularGaborTuple(λ=12, θ=0.7853981633974483, ρ=75, φ=5.480333851262195  ),
                                   CosfireCircularGaborTuple(λ=12, θ=0.0,                ρ=75, φ=6.178465552059927  ),
                                   CosfireCircularGaborTuple(λ=12, θ=3.141592653589793,  ρ=75, φ=6.178465552059927  )])