def frequency_filter_images(self):
r"""
"""
filter_images = []
for fft_padded_filter in self.fft_padded_filters:
spatial_filter = np.real(ifft2(fft_padded_filter))
spatial_filter = crop(spatial_filter,
self.patch_shape)[:, ::-1, ::-1]
frequency_filter = np.abs(fftshift(fft2(spatial_filter)))
filter_images.append(Image(frequency_filter))
return filter_images
def frequency_filter_images(self):
r"""
"""
filter_images = []
for fft_padded_filter in self.fft_padded_filters:
spatial_filter = np.real(ifft2(fft_padded_filter))
spatial_filter = crop(spatial_filter,
self.patch_shape)[:, ::-1, ::-1]
frequency_filter = np.abs(fftshift(fft2(spatial_filter)))
filter_images.append(Image(frequency_filter))
return filter_images
def frequency_filter_images(self):
r"""
Returns a `list` of `n_experts` filter images on the frequency domain.
:type: `list` of `menpo.image.Image`
"""
filter_images = []
for fft_padded_filter in self.fft_padded_filters:
spatial_filter = np.real(ifft2(fft_padded_filter))
spatial_filter = crop(spatial_filter,
self.patch_shape)[:, ::-1, ::-1]
frequency_filter = np.abs(fftshift(fft2(spatial_filter)))
filter_images.append(Image(frequency_filter))
return filter_images
def frequency_filter_images(self):
r"""
Returns a `list` of `n_experts` filter images on the frequency domain.
:type: `list` of `menpo.image.Image`
"""
filter_images = []
for fft_padded_filter in self.fft_padded_filters:
spatial_filter = np.real(ifft2(fft_padded_filter))
spatial_filter = crop(spatial_filter,
self.patch_shape)[:, ::-1, ::-1]
frequency_filter = np.abs(fftshift(fft2(spatial_filter)))
filter_images.append(Image(frequency_filter))
return filter_images
def _train(self, images, shapes, prefix='', verbose=False,
increment=False):
# Define print_progress partial
wrap = partial(print_progress,
prefix='{}Training experts'
.format(prefix),
end_with_newline=not prefix,
verbose=verbose)
# If increment is False, we need to initialise/reset the ensemble of
# experts
if not increment:
self.fft_padded_filters = []
self.auto_correlations = []
self.cross_correlations = []
# Set number of images
self.n_images = len(images)
else:
# Update number of images
self.n_images += len(images)
# Obtain total number of experts
n_experts = shapes[0].n_points
# Train ensemble of correlation filter experts
fft_padded_filters = []
auto_correlations = []
cross_correlations = []
for i in wrap(range(n_experts)):
patches = []
for image, shape in zip(images, shapes):
# Select the appropriate landmark
landmark = PointCloud([shape.points[i]])
# Extract patch
patch = self._extract_patch(image, landmark)
# Add patch to the list
patches.append(patch)
if increment:
# Increment correlation filter
correlation_filter, auto_correlation, cross_correlation = (
self._icf.increment(self.auto_correlations[i],
self.cross_correlations[i],
self.n_images,
patches,
self.response))
else:
# Train correlation filter
correlation_filter, auto_correlation, cross_correlation = (
self._icf.train(patches, self.response))
# Pad filter with zeros
padded_filter = pad(correlation_filter, self.padded_size)
# Compute fft of padded filter
fft_padded_filter = fft2(padded_filter)
# Add fft padded filter to list
fft_padded_filters.append(fft_padded_filter)
auto_correlations.append(auto_correlation)
cross_correlations.append(cross_correlation)
# Turn list into ndarray
self.fft_padded_filters = np.asarray(fft_padded_filters)
self.auto_correlations = np.asarray(auto_correlations)
self.cross_correlations = np.asarray(cross_correlations)
def _train(self,
images,
shapes,
prefix='',
verbose=False,
increment=False):
# Define print_progress partial
wrap = partial(print_progress,
prefix='{}Training experts'.format(prefix),
end_with_newline=not prefix,
verbose=verbose)
# If increment is False, we need to initialise/reset the ensemble of
# experts
if not increment:
self.fft_padded_filters = []
self.auto_correlations = []
self.cross_correlations = []
# Set number of images
self.n_images = len(images)
else:
# Update number of images
self.n_images += len(images)
# Obtain total number of experts
n_experts = shapes[0].n_points
# Train ensemble of correlation filter experts
fft_padded_filters = []
auto_correlations = []
cross_correlations = []
for i in wrap(range(n_experts)):
patches = []
for image, shape in zip(images, shapes):
# Select the appropriate landmark
landmark = PointCloud([shape.points[i]])
# Extract patch
patch = self._extract_patch(image, landmark)
# Add patch to the list
patches.append(patch)
if increment:
# Increment correlation filter
correlation_filter, auto_correlation, cross_correlation = (
self._icf.increment(self.auto_correlations[i],
self.cross_correlations[i],
self.n_images, patches, self.response))
else:
# Train correlation filter
correlation_filter, auto_correlation, cross_correlation = (
self._icf.train(patches, self.response))
# Pad filter with zeros
padded_filter = pad(correlation_filter, self.padded_size)
# Compute fft of padded filter
fft_padded_filter = fft2(padded_filter)
# Add fft padded filter to list
fft_padded_filters.append(fft_padded_filter)
auto_correlations.append(auto_correlation)
cross_correlations.append(cross_correlation)
# Turn list into ndarray
self.fft_padded_filters = np.asarray(fft_padded_filters)
self.auto_correlations = np.asarray(auto_correlations)
self.cross_correlations = np.asarray(cross_correlations)
