Python util.random_noise函数代码示例

def test_localvar():
    seed = 42
    data = np.zeros((128, 128)) + 0.5
    local_vars = np.zeros((128, 128)) + 0.001
    local_vars[:64, 64:] = 0.1
    local_vars[64:, :64] = 0.25
    local_vars[64:, 64:] = 0.45

    data_gaussian = random_noise(data, mode='localvar', seed=seed,
                                 local_vars=local_vars, clip=False)
    assert 0. < data_gaussian[:64, :64].var() < 0.002
    assert 0.095 < data_gaussian[:64, 64:].var() < 0.105
    assert 0.245 < data_gaussian[64:, :64].var() < 0.255
    assert 0.445 < data_gaussian[64:, 64:].var() < 0.455

    # Ensure local variance bounds checking works properly
    bad_local_vars = np.zeros_like(data)
    with testing.raises(ValueError):
        random_noise(data, mode='localvar', seed=seed,
                     local_vars=bad_local_vars)
    bad_local_vars += 0.1
    bad_local_vars[0, 0] = -1
    with testing.raises(ValueError):
        random_noise(data, mode='localvar', seed=seed,
                     local_vars=bad_local_vars)

def noise(image):
    r = np.random.rand(1)[0]
    # TODO randomize parameters of the noises; check how to init seed
    if r < 0.33:
        return random_noise(x, 's&p', seed=np.random.randint(1000000))
    if r < 0.66:
        return random_noise(x, 'gaussian', seed=np.random.randint(1000000))
    return random_noise(x, 'speckle', seed=np.random.randint(1000000))

def test_gaussian():
    seed = 42
    data = np.zeros((128, 128)) + 0.5
    data_gaussian = random_noise(data, seed=seed, var=0.01)
    assert 0.008 < data_gaussian.var() < 0.012

    data_gaussian = random_noise(data, seed=seed, mean=0.3, var=0.015)
    assert 0.28 < data_gaussian.mean() - 0.5 < 0.32
    assert 0.012 < data_gaussian.var() < 0.018

def test_poisson():
    seed = 42
    data = camera()  # 512x512 grayscale uint8
    cam_noisy = random_noise(data, mode='poisson', seed=seed)
    cam_noisy2 = random_noise(data, mode='poisson', seed=seed, clip=False)

    np.random.seed(seed=seed)
    expected = np.random.poisson(img_as_float(data) * 256) / 256.
    assert_allclose(cam_noisy, np.clip(expected, 0., 1.))
    assert_allclose(cam_noisy2, expected)

    def load(self):
        self._log.info('Initiating load of Chars74K data set')

        image_paths, image_labels = self.get_all_image_paths()
        image_matrices = []
        all_labels = []

        for index in range(len(image_paths)):
            raw_image = io.imread(image_paths[index], as_grey=True)  # As grey to get 2D without RGB
            raw_image = raw_image / 255.0  # Normalize image by dividing image by 255.0
            image_matrices.append(raw_image.reshape((self.config['img_size'][0] ** 2)))
            all_labels.append(image_labels[index])
            if self.config['extend_data_set']:
                # Add noisy images
                for noise in self.config['noise_types']:
                    noisy_img = random_noise(raw_image, mode=noise)
                    image_matrices.append(noisy_img.reshape((400, )))
                    all_labels.append(image_labels[index])

                # Add shifted images
                shifted_images = [np.roll(raw_image, 1, axis=i) for i in range(-1, 2)]
                for image in shifted_images:
                    image_matrices.append(image.reshape((400, )))
                    all_labels.append(image_labels[index])

        # Split data set into (X_train, y_train, X_test and y_test)
        data_set_tuple = self.split_data_set(image_matrices, all_labels)
        log.info('Loaded %i images of %s pixels' % (len(all_labels), self.config['img_size']))
        return data_set_tuple

def filtre2D(img):
    N = 3
    type_bruit = 'AG'

    selem = np.ones([N, N])
    if type_bruit == 'AG':
        bruit = util.random_noise(np.zeros(img.shape), mode='gaussian')
        img_bruitee = util.random_noise(img, mode='gaussian')
    else:
        bruit = util.random_noise(np.zeros(img.shape), mode='s&p')
        img_bruitee = util.random_noise(img, mode='s&p')

    img_bruit_median = filters.median(img_bruitee, selem)
    img_bruit_linear = ndimage.convolve(img_bruitee, selem)

    fig = plt.figure()

    if type_bruit == 'AG':
        bruit_linear = ndimage.convolve(bruit, selem)
        img_linear = ndimage.convolve(img, selem)
        fig.add_subplot(3, 3, 1)
        plt.imshow(img, cmap='gray')
        fig.add_subplot(3, 3, 2)
        plt.imshow(bruit, cmap='gray')
        fig.add_subplot(3, 3, 3)
        plt.imshow(img_bruitee, cmap='gray')
        fig.add_subplot(3, 3, 4)
        plt.imshow(img_linear, cmap='gray')
        fig.add_subplot(3, 3, 5)
        plt.imshow(bruit_linear, cmap='gray')
        fig.add_subplot(3, 3, 6)
        plt.imshow(img_bruit_linear, cmap='gray')
        fig.add_subplot(3, 3, 9)
        plt.imshow(img_bruit_median, cmap='gray')
    else:
        fig.add_subplot(2, 2, 1)
        plt.imshow(img, cmap='gray')
        fig.add_subplot(2, 2, 2)
        plt.imshow(img_bruitee, cmap='gray')
        fig.add_subplot(2, 2, 3)
        plt.imshow(img_bruit_linear, cmap='gray')
        fig.add_subplot(2, 2, 4)
        plt.imshow(img_bruit_median, cmap='gray')
    # fig.tight_layout()
    plt.show()

def test_extended_search_area():
    """ test of the extended area PIV """
    frame_a = np.zeros((64,64))
    frame_a = random_noise(frame_a)
    frame_a = img_as_ubyte(frame_a)
    frame_b = np.roll(np.roll(frame_a,3,axis=1),2,axis=0)
    u,v = piv(frame_a.astype(np.int32),frame_b.astype(np.int32),window_size=16,search_area_size=32,overlap=0)
    # print u,v
    assert(np.max(np.abs(u-3)+np.abs(v+2)) <= 0.5)

def test_clip_poisson():
    seed = 42
    data = camera()                             # 512x512 grayscale uint8
    data_signed = img_as_float(data) * 2. - 1.  # Same image, on range [-1, 1]

    # Signed and unsigned, clipped
    cam_poisson = random_noise(data, mode='poisson', seed=seed, clip=True)
    cam_poisson2 = random_noise(data_signed, mode='poisson', seed=seed,
                                clip=True)
    assert (cam_poisson.max() == 1.) and (cam_poisson.min() == 0.)
    assert (cam_poisson2.max() == 1.) and (cam_poisson2.min() == -1.)

    # Signed and unsigned, unclipped
    cam_poisson = random_noise(data, mode='poisson', seed=seed, clip=False)
    cam_poisson2 = random_noise(data_signed, mode='poisson', seed=seed,
                                clip=False)
    assert (cam_poisson.max() > 1.15) and (cam_poisson.min() == 0.)
    assert (cam_poisson2.max() > 1.3) and (cam_poisson2.min() == -1.)

def test_speckle():
    seed = 42
    data = np.zeros((128, 128)) + 0.1
    np.random.seed(seed=42)
    noise = np.random.normal(0.1, 0.02 ** 0.5, (128, 128))
    expected = np.clip(data + data * noise, 0, 1)

    data_speckle = random_noise(data, mode="speckle", seed=seed, m=0.1, v=0.02)
    assert_allclose(expected, data_speckle)

def test_clip_gaussian():
    seed = 42
    data = camera()                             # 512x512 grayscale uint8
    data_signed = img_as_float(data) * 2. - 1.  # Same image, on range [-1, 1]

    # Signed and unsigned, clipped
    cam_gauss = random_noise(data, mode='gaussian', seed=seed, clip=True)
    cam_gauss2 = random_noise(data_signed, mode='gaussian', seed=seed,
                              clip=True)
    assert (cam_gauss.max() == 1.) and (cam_gauss.min() == 0.)
    assert (cam_gauss2.max() == 1.) and (cam_gauss2.min() == -1.)

    # Signed and unsigned, unclipped
    cam_gauss = random_noise(data, mode='gaussian', seed=seed, clip=False)
    cam_gauss2 = random_noise(data_signed, mode='gaussian', seed=seed,
                              clip=False)
    assert (cam_gauss.max() > 1.22) and (cam_gauss.min() < -0.36)
    assert (cam_gauss2.max() > 1.219) and (cam_gauss2.min() < -1.337)

def test_clip_speckle():
    seed = 42
    data = camera()                             # 512x512 grayscale uint8
    data_signed = img_as_float(data) * 2. - 1.  # Same image, on range [-1, 1]

    # Signed and unsigned, clipped
    cam_speckle = random_noise(data, mode='speckle', seed=seed, clip=True)
    cam_speckle2 = random_noise(data_signed, mode='speckle', seed=seed,
                                clip=True)
    assert (cam_speckle.max() == 1.) and (cam_speckle.min() == 0.)
    assert (cam_speckle2.max() == 1.) and (cam_speckle2.min() == -1.)

    # Signed and unsigned, unclipped
    cam_speckle = random_noise(data, mode='speckle', seed=seed, clip=False)
    cam_speckle2 = random_noise(data_signed, mode='speckle', seed=seed,
                                clip=False)
    assert (cam_speckle.max() > 1.219) and (cam_speckle.min() == 0.)
    assert (cam_speckle2.max() > 1.219) and (cam_speckle2.min() < -1.306)

def test_piv_smaller_window():
    """ test of the simplest PIV run """
    frame_a = np.zeros((32,32))
    frame_a = random_noise(frame_a)
    frame_a = img_as_ubyte(frame_a)
    frame_b = np.roll(np.roll(frame_a,-3,axis=1),-2,axis=0)
    u,v = piv(frame_a.astype(np.int32),frame_b.astype(np.int32),window_size=16,search_area_size=32)
    # print u,v
    assert(np.max(np.abs(u+3)) < 0.2)
    assert(np.max(np.abs(v-2)) < 0.2)

def AddNoise(img):
    # mean = 0
    # sigma = 0.045
    # gauss = np.random.normal(mean, sigma, img.shape)
    # gauss = gauss.reshape(img.shape[0], img.shape[1])
    # noisy = img + gauss
    noisy = util.random_noise(img, mode='gaussian', var=0.002)
    # noisy = util.random_noise(img, mode='gaussian', var=0.0015)

    # noisy = util.random_noise(img, mode='s&p')
    return noisy

def SaltPepper(request):

    from skimage.util import random_noise

    image, response = process(request, False)

    image = random_noise(image, mode = 's&p')

    io.imsave(response['filename'], image)

    return JsonResponse(response)

def test_piv():
    """ test of the simplest PIV run """
    frame_a = np.zeros((32,32))
    frame_a = random_noise(frame_a)
    with warnings.catch_warnings():
        warnings.simplefilter("ignore")
        frame_a = img_as_ubyte(frame_a)
    frame_b = np.roll(np.roll(frame_a,3,axis=1),2,axis=0)
    u,v = piv(frame_a.astype(np.int32),frame_b.astype(np.int32),window_size=32)
    # print u,v
    assert(np.max(np.abs(u-3)) < 0.2)
    assert(np.max(np.abs(v+2)) < 0.2)