Lucas Kanade Models - Basics

The aim of this notebook is to showcase how one can build and fit Lucas Kanade to images using menpofit.

Note that this notebook assumes that the user has previously gone through the AAMs Basics notebook and he/she is already familiar with the basics of Menpo's Deformable Model Fitting framework explained in there.

1. Loading data

In [1]:
%matplotlib inline
from pathlib import Path
import as mio

takeo = mio.import_builtin_asset.takeo_ppm()
takeo = takeo.as_greyscale(mode='luminosity')

# Use a bounding box rather than the facial shape
takeo.landmarks['bounding_box'] = takeo.landmarks['PTS'].lms.bounding_box()

In [2]:

Lucas-Kanade methods align a given template onto a provided image. Therefore, we must create a template that we will seek within a given input image. For example, a sensible template for the Takeo image might be the facial region.

In [3]:
template = takeo.crop_to_landmarks(group='bounding_box')


2. Build a Lucas Kanade fitter

Building an LK fitter using Menpo can be done using a single line of code.

In [4]:
from import LucasKanadeFitter

fitter = LucasKanadeFitter(template, group='bounding_box')

3. Fit using the LK fitter

In Menpo, LK fitters can be fitted to images by creating Fitter objects around them.

Fitting a LucasKanadeFitter to an image is as simple as calling its fit_from_bb method. We will attempt to fit the cropped template we created earlier onto a perturbed version of the original image.

In [5]:
from menpofit.fitter import noisy_shape_from_bounding_box

gt_bb = takeo.landmarks['bounding_box'].lms
# generate perturbed bounding box
init_bb = noisy_shape_from_bounding_box(fitter.reference_shape, gt_bb)
# fit image
fr = fitter.fit_from_bb(takeo, init_bb, gt_shape=gt_bb) 

# print fitting error

Fitting result of 4 landmark points.
Initial error: 0.0520
Final error: 0.0000

In [6]: