CorrelationFilterExpertEnsemble¶
-
class
menpofit.clm.
CorrelationFilterExpertEnsemble
(images, shapes, icf_cls=<class 'menpofit.clm.expert.base.IncrementalCorrelationFilterThinWrapper'>, patch_shape=(17, 17), context_shape=(34, 34), response_covariance=3, patch_normalisation=functools.partial(<function normalize_norm>, mode='per_channel', error_on_divide_by_zero=False), cosine_mask=True, sample_offsets=None, prefix='', verbose=False)[source]¶ Bases:
ConvolutionBasedExpertEnsemble
Class for defining an ensemble of correlation filter experts.
- Parameters
images (list of menpo.image.Image) – The list of training images.
shapes (list of menpo.shape.PointCloud) – The list of training shapes that correspond to the images.
icf_cls (class, optional) – The incremental correlation filter class. For example
IncrementalCorrelationFilterThinWrapper
.patch_shape ((int, int), optional) – The shape of the patches that will be extracted around the landmarks. Those patches are used to train the experts.
context_shape ((int, int), optional) – The context shape for the convolution.
response_covariance (int, optional) – The covariance of the generated Gaussian response.
patch_normalisation (callable, optional) – A normalisation function that will be applied on the extracted patches.
cosine_mask (bool, optional) – If
True
, then a cosine mask (Hanning function) will be applied on the extracted patches.sample_offsets (
(n_offsets, n_dims)
ndarray orNone
, optional) – The offsets to sample from within a patch. So(0, 0)
is the centre of the patch (no offset) and(1, 0)
would be sampling the patch from 1 pixel up the first axis away from the centre. IfNone
, then no offsets are applied.prefix (str, optional) – The prefix of the printed progress information.
verbose (bool, optional) – If
True
, then information will be printed regarding the training progress.
-
increment
(images, shapes, prefix='', verbose=False)¶ Increments the learned ensemble of convolution-based experts given a new set of training data.
- Parameters
images (list of menpo.image.Image) – The list of training images.
shapes (list of menpo.shape.PointCloud) – The list of training shapes that correspond to the images.
prefix (str, optional) – The prefix of the printed training progress.
verbose (bool, optional) – If
True
, then information about the training progress will be printed.
-
predict_probability
(image, shape)¶ Method for predicting the probability map of the response 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
probability_map (
(n_experts, 1, height, width)
ndarray) – The probability map of the response of each expert.
-
predict_response
(image, shape)¶ 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.
-
property
frequency_filter_images
¶ Returns a list of n_experts filter images on the frequency domain.
- Type
list of menpo.image.Image
-
property
n_experts
¶ Returns the number of experts.
- Type
int
-
property
n_sample_offsets
¶ Returns the number of offsets that are sampled within a patch.
- Type
int
-
property
padded_size
¶ Returns the convolution pad size, i.e.
floor(1.5 * patch_shape - 1)
.- Type
(int, int)
-
property
search_shape
¶ Returns the search shape (patch_shape).
- Type
(int, int)
-
property
spatial_filter_images
¶ Returns a list of n_experts filter images on the spatial domain.
- Type
list of menpo.image.Image