Python Affine.init_identity方法代码示例

# 需要导入模块: from menpo.transform import Affine [as 别名]
# 或者: from menpo.transform.Affine import init_identity [as 别名]
    def fit_from_bb(self, image, bounding_box, gt_shape=None, **kwargs):
        algo_result = self.algorithm.run(image, bounding_box, gt_shape=gt_shape)

        # TODO: This should be a basic result instead.
        return MultiFitterResult(image, self, [algo_result],
                                 Affine.init_identity(2),
                                 gt_shape=gt_shape)

# 需要导入模块: from menpo.transform import Affine [as 别名]
# 或者: from menpo.transform.Affine import init_identity [as 别名]
def test_warp_multi():
    rgb_image = mio.import_builtin_asset('takeo.ppm')
    target_transform = Affine.init_identity(2).from_vector(initial_params)
    warped_im = rgb_image.warp_to_mask(template_mask, target_transform)

    assert(warped_im.shape == rgb_template.shape)
    assert_allclose(warped_im.pixels, rgb_template.pixels)

# 需要导入模块: from menpo.transform import Affine [as 别名]
# 或者: from menpo.transform.Affine import init_identity [as 别名]
def extract_targets(frame, target_centre, target_shape, n_perturbations=10,
                    noise_std=0.04):
    # initialize targets
    w, h = target_shape
    targets = np.empty((n_perturbations + 1, frame.n_channels, w, h))

    # extract original target
    targets[0] = frame.extract_patches(
        target_centre, patch_size=target_shape,
        as_single_array=True)

    for j in range(n_perturbations):
        # perturb identity affine transform
        params = noise_std * np.random.randn(6)
        transform = Affine.init_identity(2).from_vector(params)
        # warp frame using previous affine transform
        perturbed_frame = frame.warp_to_mask(frame.as_masked().mask,
                                             transform)
        # apply inverse of affine transform to target centre
        perturbed_centre = transform.pseudoinverse().apply(target_centre)
        # extract perturbed target + context region from frame
        perturbed_target = perturbed_frame.extract_patches(
            perturbed_centre, patch_size=target_shape,
            as_single_array=True)
        # store target
        targets[j+1] = perturbed_target

    return targets

# 需要导入模块: from menpo.transform import Affine [as 别名]
# 或者: from menpo.transform.Affine import init_identity [as 别名]
def test_warp_to_mask_masked_image_all_true():
    img = MaskedImage.init_blank((10, 10), fill=2.5)

    template_mask = BooleanImage.init_blank((10, 10), fill=False)
    template_mask.pixels[:, :5, :5] = True
    t = Affine.init_identity(2)
    warped_img = img.warp_to_mask(template_mask, t)
    assert(type(warped_img) == MaskedImage)

# 需要导入模块: from menpo.transform import Affine [as 别名]
# 或者: from menpo.transform.Affine import init_identity [as 别名]
def test_warp_gray_batch():
    rgb_image = mio.import_builtin_asset('takeo.ppm')
    gray_image = rgb_image.as_greyscale()
    target_transform = Affine.init_identity(2).from_vector(initial_params)
    warped_im = gray_image.warp_to_mask(template_mask, target_transform,
                                        batch_size=100)

    assert(warped_im.shape == gray_template.shape)
    assert_allclose(warped_im.pixels, gray_template.pixels)

# 需要导入模块: from menpo.transform import Affine [as 别名]
# 或者: from menpo.transform.Affine import init_identity [as 别名]
def test_warp_to_mask_image():
    img = Image.init_blank((10, 10), n_channels=2)
    img.pixels[:, :, :5] = 0.5
    template_mask = BooleanImage.init_blank((10, 10))
    template_mask.pixels[:, 5:, :] = False
    t = Affine.init_identity(2)
    warped_img = img.warp_to_mask(template_mask, t)
    assert(type(warped_img) == MaskedImage)
    result = Image.init_blank((10, 10), n_channels=2).pixels
    result[:, :5, :5] = 0.5
    assert(np.all(result == warped_img.pixels))

# 需要导入模块: from menpo.transform import Affine [as 别名]
# 或者: from menpo.transform.Affine import init_identity [as 别名]
def test_warp_to_mask_boolean():
    b = BooleanImage.init_blank((10, 10))
    b.pixels[:, :5] = False
    template_mask = BooleanImage.init_blank((10, 10))
    template_mask.pixels[:5, :] = False
    t = Affine.init_identity(2)
    warped_mask = b.warp_to_mask(template_mask, t)
    assert(type(warped_mask) == BooleanImage)
    result = template_mask.pixels.copy()
    result[:, :5] = False
    assert(np.all(result == warped_mask.pixels))

# 需要导入模块: from menpo.transform import Affine [as 别名]
# 或者: from menpo.transform.Affine import init_identity [as 别名]
def test_warp_to_mask_masked_image():
    mask = BooleanImage.init_blank((15, 15))
    # make a truncated mask on the original image
    mask.pixels[0, -1, -1] = False
    img = MaskedImage.init_blank((15, 15), n_channels=2, mask=mask,
                                 fill=2.5)
    template_mask = BooleanImage.init_blank((10, 10), fill=False)
    template_mask.pixels[:, :5, :5] = True
    t = Affine.init_identity(2)
    warped_img = img.warp_to_mask(template_mask, t)
    assert(type(warped_img) == MaskedImage)

    result = Image.init_blank((10, 10), n_channels=2).pixels
    result[:, :5, :5] = 2.5
    result_mask = BooleanImage.init_blank((10, 10), fill=False).pixels
    result_mask[:, :5, :5] = True
    assert(warped_img.n_true_pixels() == 25)
    assert_allclose(result, warped_img.pixels)
    assert_allclose(result_mask, warped_img.mask.pixels)

# 需要导入模块: from menpo.transform import Affine [as 别名]
# 或者: from menpo.transform.Affine import init_identity [as 别名]
def test_affine_identity_2d():
    assert_allclose(Affine.init_identity(2).h_matrix, np.eye(3))

# 需要导入模块: from menpo.transform import Affine [as 别名]
# 或者: from menpo.transform.Affine import init_identity [as 别名]
def test_affine_compose_inplace_affine():
    a = Affine.init_identity(2)
    b = Affine.init_identity(2)
    a.compose_before_inplace(b)
    assert(np.all(a.h_matrix == b.h_matrix))