All MenpoFit’s models are built in a multi-scale manner, i.e. in multiple resolutions. In all our core classes, this is controlled using the following three parameters:
- reference_shape (PointCloud)
- First, the size of the training images is normalized by rescaling them so that the scale of their ground truth shapes matches the scale of this reference shape. In case no reference shape is provided, then the mean of the ground shapes is used. This step is essential in order to ensure consistency between the extracted features of the images.
- diagonal (int)
- This parameter is used to rescale the reference shape so that the diagonal of its bounding box matches the provided value. This rescaling takes place before normalizing the training images’ size. Thus, diagonal controls the size of the model at the highest scale.
- scales (tuple of float)
- A tuple with the scale value at each level, provided in ascending order, i.e. from lowest to highest scale. These values are proportional to the final resolution achieved through the reference shape normalization.
Additionally, all models have a holistic_features argument which expects the callable that will be used for extracting features from the training images.
Given the above assumptions, an example of a typical call for building a
deformable model using
from menpofit.aam import HolisticAAM from menpo.feature import fast_dsift aam = HolisticAAM(training_images, group='PTS', reference_shape=None, diagonal=200, scales=(0.25, 0.5, 1.0), holistic_features=fast_dsift, verbose=True)
Information about any kind of model can be retrieved by:
The next section (Fitting) explains the basics of fitting such a deformable model.