def predict_response(self, image, shape):
r"""
Method for predicting the response of the experts on a given image. Note
that the provided shape must have the same number of points as the
number of experts.
Parameters
----------
image : `menpo.image.Image` or `subclass`
The test image.
shape : `menpo.shape.PointCloud`
The shape that corresponds to the image from which the patches
will be extracted.
Returns
-------
response : ``(n_experts, 1, height, width)`` `ndarray`
The response of each expert.
"""
# Extract patches
patches = self._extract_patches(image, shape)
# Predict responses
return fft_convolve2d_sum(patches,
self.fft_padded_filters,
fft_filter=True,
axis=1)
def predict_response(self, image, shape):
r"""
"""
# Extract patches
patches = self._extract_patches(image, shape)
# Predict responses
return fft_convolve2d_sum(patches, self.fft_padded_filters,
fft_filter=True, axis=1)
def predict_response(self, image, shape):
r"""
"""
# Extract patches
patches = self._extract_patches(image, shape)
# Predict responses
return fft_convolve2d_sum(patches,
self.fft_padded_filters,
fft_filter=True,
axis=1)
def predict_response(self, image, shape):
r"""
Method for predicting the response of the experts on a given image. Note
that the provided shape must have the same number of points as the
number of experts.
Parameters
----------
image : `menpo.image.Image` or `subclass`
The test image.
shape : `menpo.shape.PointCloud`
The shape that corresponds to the image from which the patches
will be extracted.
Returns
-------
response : ``(n_experts, 1, height, width)`` `ndarray`
The response of each expert.
"""
# Extract patches
patches = self._extract_patches(image, shape)
# Predict responses
return fft_convolve2d_sum(patches, self.fft_padded_filters,
fft_filter=True, axis=1)
