def test_squared_even_patches():
image = mio.import_builtin_asset('breakingbad.jpg')
patch_shape = (16, 16)
patches = extract_local_patches_fast(image, image.landmarks['PTS'].lms,
patch_shape)
print(patches.shape)
assert (patches.shape == (68, ) + patch_shape + (3, ))
def test_squared_even_patches():
image = mio.import_builtin_asset('breakingbad.jpg')
patch_shape = (16, 16)
patches = extract_local_patches_fast(
image, image.landmarks['PTS'].lms, patch_shape)
print patches.shape
assert(patches.shape == (68,) + patch_shape + (3,))
def features(self, image, shape):
r"""
Method that extracts the features for the regression, which in this
case are patch based.
Parameters
----------
image : :map:`MaskedImage`
The current image.
shape : :map:`PointCloud`
The current shape.
"""
# extract patches
patches = extract_local_patches_fast(image, shape, self.patch_shape)
features = np.zeros((shape.n_points, self._feature_patch_length))
for j, patch in enumerate(patches):
# build patch image
patch_img = Image(patch, copy=False)
# compute features
features[j, ...] = compute_features(
patch_img, self.regression_features).as_vector()
return np.hstack((features.ravel(), 1))
def test_nonsquared_odd_patches():
image = mio.import_builtin_asset('breakingbad.jpg')
patch_shape = (15, 17)
patches = extract_local_patches_fast(
image, image.landmarks['PTS'].lms, patch_shape)
assert(patches.shape == (68,) + patch_shape + (3,))
