def test_warp_to_mask_boolean():
b = BooleanImage.blank((10, 10))
b.pixels[:, :5] = False
template_mask = BooleanImage.blank((10, 10))
template_mask.pixels[:5, :] = False
t = Affine.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))
def test_mask_false_indices():
mask = BooleanImage.blank((64, 14, 51), fill=True)
mask.mask[0, 2, 5] = False
mask.mask[5, 13, 4] = False
false_indices = mask.false_indices
false_indices_test = np.array([[0, 2, 5], [5, 13, 4]])
assert_equal(false_indices, false_indices_test)
def test_mask_false_indices():
mask = BooleanImage.blank((64, 14, 51), fill=True)
mask.mask[0, 2, 5] = False
mask.mask[5, 13, 4] = False
false_indices = mask.false_indices
false_indices_test = np.array([[0, 2, 5], [5, 13, 4]])
assert_equal(false_indices, false_indices_test)
def test_boolean_image_from_vector_no_copy_raises():
vector = np.zeros(16, dtype=np.bool)
image = BooleanImage.blank((4, 4))
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
image.from_vector(vector[::-1], copy=False)
assert len(w) == 1
def test_mask_true_indices():
mask = BooleanImage.blank((64, 14, 51), fill=False)
mask.mask[0, 2, 5] = True
mask.mask[5, 13, 4] = True
true_indices = mask.true_indices
true_indices_test = np.array([[0, 2, 5], [5, 13, 4]])
assert_equal(true_indices, true_indices_test)
def test_mask_true_indices():
mask = BooleanImage.blank((64, 14, 51), fill=False)
mask.mask[0, 2, 5] = True
mask.mask[5, 13, 4] = True
true_indices = mask.true_indices
true_indices_test = np.array([[0, 2, 5], [5, 13, 4]])
assert_equal(true_indices, true_indices_test)
def test_boolean_bounds_false():
mask = BooleanImage.blank((8, 8), fill=True)
mask.pixels[1, 2] = False
mask.pixels[5, 4] = False
mask.pixels[3:2, 3] = False
min_b, max_b = mask.bounds_false()
assert (np.all(min_b == np.array([1, 2])))
assert (np.all(max_b == np.array([5, 4])))
def test_mask_n_true_n_false():
mask = BooleanImage.blank((64, 14), fill=False)
assert_equal(mask.n_true(), 0)
assert_equal(mask.n_false(), 64 * 14)
mask.mask[0, 0] = True
mask.mask[9, 13] = True
assert_equal(mask.n_true(), 2)
assert_equal(mask.n_false(), 64 * 14 - 2)
def test_boolean_bounds_false():
mask = BooleanImage.blank((8, 8), fill=True)
mask.pixels[1, 2] = False
mask.pixels[5, 4] = False
mask.pixels[3:2, 3] = False
min_b, max_b = mask.bounds_false()
assert(np.all(min_b == np.array([1, 2])))
assert(np.all(max_b == np.array([5, 4])))
def test_mask_n_true_n_false():
mask = BooleanImage.blank((64, 14), fill=False)
assert_equal(mask.n_true, 0)
assert_equal(mask.n_false, 64 * 14)
mask.mask[0, 0] = True
mask.mask[9, 13] = True
assert_equal(mask.n_true, 2)
assert_equal(mask.n_false, 64 * 14 - 2)
def test_warp_to_mask_masked_image():
mask = BooleanImage.blank((10, 10))
# make a funny mask on the original image
mask.pixels[2:, :] = False
img = MaskedImage.blank((10, 10), n_channels=2, mask=mask)
img.pixels[...] = 2.5
template_mask = BooleanImage.blank((10, 10), fill=False)
template_mask.pixels[:5, :5] = True
t = Affine.identity(2)
warped_img = img.warp_to_mask(template_mask, t)
assert(type(warped_img) == MaskedImage)
result = Image.blank((10, 10), n_channels=2).pixels
result[:5, :5, :] = 2.5
result_mask = BooleanImage.blank((10, 10), fill=False).pixels
result_mask[:2, :5] = True
assert(warped_img.n_true_pixels() == 10)
assert(np.all(result == warped_img.pixels))
assert(np.all(result_mask == warped_img.mask.pixels))
def test_mask_true_bounding_extent():
mask = BooleanImage.blank((64, 14, 51), fill=False)
mask.mask[0, 13, 5] = True
mask.mask[5, 2, 4] = True
tbe = mask.bounds_true()
true_extends_mins = np.array([0, 2, 4])
true_extends_maxs = np.array([5, 13, 5])
assert_equal(tbe[0], true_extends_mins)
assert_equal(tbe[1], true_extends_maxs)
def test_mask_true_bounding_extent():
mask = BooleanImage.blank((64, 14, 51), fill=False)
mask.mask[0, 13, 5] = True
mask.mask[5, 2, 4] = True
tbe = mask.bounds_true()
true_extends_mins = np.array([0, 2, 4])
true_extends_maxs = np.array([5, 13, 5])
assert_equal(tbe[0], true_extends_mins)
assert_equal(tbe[1], true_extends_maxs)
def test_warp_to_mask_image():
img = Image.blank((10, 10), n_channels=2)
img.pixels[:, :5, :] = 0.5
template_mask = BooleanImage.blank((10, 10))
template_mask.pixels[5:, :] = False
t = Affine.identity(2)
warped_img = img.warp_to_mask(template_mask, t)
assert(type(warped_img) == MaskedImage)
result = Image.blank((10, 10), n_channels=2).pixels
result[:5, :5, :] = 0.5
assert(np.all(result == warped_img.pixels))
def test_mask_blank_rounding_floor():
mask = BooleanImage.blank((56.1, 12.1), round='floor')
assert_allclose(mask.shape, (56, 12))
def test_boolean_image_invert_inplace():
image = BooleanImage.blank((4, 4))
image.invert_inplace()
assert (np.all(image.pixels == False))
def test_boolean_prevent_order_kwarg():
mask = BooleanImage.blank((8, 8), fill=True)
mask.warp_to_mask(mask, None, order=4)
def test_boolean_image_false_indices():
image = BooleanImage.blank((2, 3))
assert(image.__str__() == '3W x 2H 2D mask, 100.0% of which is True')
def test_boolean_image_from_vector_no_copy():
vector = np.zeros(16, dtype=np.bool)
image = BooleanImage.blank((4, 4))
image2 = image.from_vector(vector, copy=False)
assert(is_same_array(image2.pixels.ravel(), vector))
def test_boolean_image_from_vector_no_copy_raises():
vector = np.zeros(16, dtype=np.bool)
image = BooleanImage.blank((4, 4))
image.from_vector(vector[::-1], copy=False)
def test_boolean_image_proportion_sums():
image = BooleanImage.blank((10, 10))
image.pixels[:7] = False
assert(image.proportion_true() + image.proportion_false() == 1)
def test_boolean_image_from_vector_no_copy():
vector = np.zeros(16, dtype=np.bool)
image = BooleanImage.blank((4, 4))
image2 = image.from_vector(vector, copy=False)
assert (is_same_array(image2.pixels.ravel(), vector))
def test_boolean_image_invert_inplace_double_noop():
image = BooleanImage.blank((4, 4))
image.invert_inplace()
image.invert_inplace()
assert (np.all(image.pixels))
def test_boolean_image_str():
image = BooleanImage.blank((2, 3))
assert (image.__str__() == '3W x 2H 2D mask, 100.0% of which is True')
def test_boolean_image_from_vector():
vector = np.zeros(16, dtype=np.bool)
image = BooleanImage.blank((4, 4))
image2 = image.from_vector(vector)
assert (np.all(image2.as_vector() == vector))
def test_boolean_image_false_indices():
image = BooleanImage.blank((2, 3))
image.pixels[0, 1] = False
image.pixels[1, 2] = False
assert (np.all(image.false_indices() == np.array([[0, 1], [1, 2]])))
def test_boolean_image_proportion_sums():
image = BooleanImage.blank((10, 10))
image.pixels[:7] = False
assert (image.proportion_true() + image.proportion_false() == 1)
def test_boolean_image_wrong_round():
BooleanImage.blank((12, 12), round='ads')
def test_mask_blank_rounding_round():
mask = BooleanImage.blank((56.1, 12.6), round='round')
assert_allclose(mask.shape, (56, 13))
def test_boolean_image_invert():
image = BooleanImage.blank((4, 4))
image2 = image.invert()
assert (np.all(image.pixels))
assert (np.all(~image2.pixels))
def test_warp_to_shape_boolean_preserves_path():
i1 = BooleanImage.blank((10, 10))
i1.path = Path('.')
i2 = i1.rescale(0.8)
assert hasattr(i2, 'path')
assert i2.path == i1.path
def test_boolean_image_proportion_false():
image = BooleanImage.blank((10, 10))
image.pixels[:7] = False
assert(image.proportion_false == 0.7)
def test_boolean_image_wrong_round():
BooleanImage.blank((12, 12), round='ads')
def test_boolean_prevent_order_kwarg():
mask = BooleanImage.blank((8, 8), fill=True)
mask.warp_to_mask(mask, None, order=4)
def test_boolean_image_false_indices():
image = BooleanImage.blank((2, 3))
image.pixels[0, 1] = False
image.pixels[1, 2] = False
assert(np.all(image.false_indices() == np.array([[0, 1],
[1, 2]])))
def test_boolean_image_invert_inplace():
image = BooleanImage.blank((4, 4))
image.invert_inplace()
assert(np.all(image.pixels == False))
def test_boolean_image_from_vector():
vector = np.zeros(16, dtype=np.bool)
image = BooleanImage.blank((4, 4))
image2 = image.from_vector(vector)
assert(np.all(image2.as_vector() == vector))
def test_boolean_image_invert():
image = BooleanImage.blank((4, 4))
image2 = image.invert()
assert(np.all(image.pixels == True))
assert(np.all(image2.pixels == False))
from menpo.image import BooleanImage
import menpo.io as pio
from menpo.transform import Affine
from menpo.fit.lucaskanade.image import (ImageInverseCompositional,
ImageForwardAdditive)
from menpo.fit.lucaskanade.residual import *
target_shape = (90, 90)
# Setup the static assets (the takeo image)
takeo = pio.import_builtin_asset('takeo.ppm')
image = takeo.as_greyscale()
template_mask = BooleanImage.blank(target_shape)
# Setup global conditions
initial_params = np.array([0, 0, 0, 0, 70.5, 30.5])
target_params = np.array([0, 0.2, 0.1, 0, 70, 30])
def compute_rms_point_error(test_pts, template_affine, M):
iteration_pts = M.apply(template_affine.T)
diff_pts = test_pts - iteration_pts
return np.sqrt(np.mean(diff_pts ** 2))
def setup_conditions(interpolator):
target_transform = Affine.identity(2).from_vector(target_params)
image_warped = image.warp_to(template_mask, target_transform,
def test_boolean_image_proportion_false():
image = BooleanImage.blank((10, 10))
image.pixels[:7] = False
assert (image.proportion_false == 0.7)
def test_boolean_image_invert_inplace_double_noop():
image = BooleanImage.blank((4, 4))
image.invert_inplace()
image.invert_inplace()
assert(np.all(image.pixels == True))
def test_warp_to_mask_preserves_path():
bb = menpo.io.import_builtin_asset.breakingbad_jpg()
no_op = UniformScale(1.0, n_dims=2)
bb2 = bb.warp_to_mask(BooleanImage.blank((10, 10)), no_op)
assert hasattr(bb2, 'path')
assert bb2.path == bb.path
import numpy as np
import menpo
from numpy.testing import assert_allclose
from menpo.image import BooleanImage, Image, MaskedImage
from menpo.transform import Affine
import menpo.io as mio
# do the import to generate the expected outputs
rgb_image = mio.import_builtin_asset('takeo.ppm')
gray_image = rgb_image.as_greyscale()
gray_template = gray_image.crop(np.array([70, 30]),
np.array([169, 129]))
rgb_template = rgb_image.crop(np.array([70, 30]),
np.array([169, 129]))
template_mask = BooleanImage.blank(gray_template.shape)
initial_params = np.array([0, 0, 0, 0, 70, 30])
row_indices, col_indices = np.meshgrid(np.arange(50, 100), np.arange(50, 100),
indexing='ij')
row_indices, col_indices = row_indices.flatten(), col_indices.flatten()
multi_expected = rgb_image.crop([50, 50],
[100, 100]).pixels.flatten()
def test_warp_gray():
rgb_image = mio.import_builtin_asset('takeo.ppm')
gray_image = rgb_image.as_greyscale()
target_transform = Affine.identity(2).from_vector(initial_params)
warped_im = gray_image.warp_to_mask(template_mask, target_transform)
assert(warped_im.shape == gray_template.shape)
def test_mask_blank_rounding_ceil():
mask = BooleanImage.blank((56.1, 12.1), round='ceil')
assert_allclose(mask.shape, (57, 13))
def test_mask_blank():
mask = BooleanImage.blank((56, 12, 3))
assert (np.all(mask.pixels))
def test_mask_blank_rounding_round():
mask = BooleanImage.blank((56.1, 12.6), round='round')
assert_allclose(mask.shape, (56, 13))
def test_mask_blank_rounding_ceil():
mask = BooleanImage.blank((56.1, 12.1), round='ceil')
assert_allclose(mask.shape, (57, 13))
def test_mask_blank_false_fill():
mask = BooleanImage.blank((56, 12, 3), fill=False)
assert (np.all(~mask.pixels))
def test_mask_blank_false_fill():
mask = BooleanImage.blank((56, 12, 3), fill=False)
assert (np.all(~mask.pixels))
def test_rescale_boolean():
mask = BooleanImage.blank((100, 100))
mask.resize((10, 10))
def test_mask_blank():
mask = BooleanImage.blank((56, 12, 3))
assert (np.all(mask.pixels))
from numpy.testing import assert_approx_equal
from menpo.image import BooleanImage
import menpo.io as pio
from menpo.transform import Affine
from menpo.fit.lucaskanade.image import (ImageInverseCompositional,
ImageForwardAdditive)
from menpo.fit.lucaskanade.residual import *
target_shape = (90, 90)
# Setup the static assets (the takeo image)
takeo = pio.import_builtin_asset('takeo.ppm')
image = takeo.as_greyscale()
template_mask = BooleanImage.blank(target_shape)
# Setup global conditions
initial_params = np.array([0, 0, 0, 0, 70.5, 30.5])
target_params = np.array([0, 0.2, 0.1, 0, 70, 30])
def compute_rms_point_error(test_pts, template_affine, M):
iteration_pts = M.apply(template_affine.T)
diff_pts = test_pts - iteration_pts
return np.sqrt(np.mean(diff_pts**2))
def setup_conditions(interpolator):
target_transform = Affine.identity(2).from_vector(target_params)
image_warped = image.warp_to(template_mask,
def test_mask_blank_rounding_floor():
mask = BooleanImage.blank((56.1, 12.1), round='floor')
assert_allclose(mask.shape, (56, 12))
def test_boolean_image_as_masked_raises_not_implemented_error():
b_img = BooleanImage.blank((4, 5))
b_img.as_masked()
