def train(img_generator):
# clean up the images with the standard menpo pre-processing
images = [menpo_img_process(img) for img in img_generator]
fitter = SDMTrainer(normalization_diagonal=150,
downscale=1.1,
n_perturbations=15).train(images, group='gt',
verbose=True)
# return a callable that wraps the menpo fitter in order to integrate with
# menpobench
return MenpoFitWrapper(fitter)
def train(img_generator):
# clean up the images with the standard menpo pre-processing
images = [menpo_img_process(img) for img in img_generator]
fitter = SDMTrainer(normalization_diagonal=150,
downscale=1.1,
n_perturbations=15).train(images,
group='gt',
verbose=True)
# return a callable that wraps the menpo fitter in order to integrate with
# menpobench
return MenpoFitWrapper(fitter)
def train(img_generator):
# clean up the images with the standard menpo pre-processing
images = [menpo_img_process(img) for img in img_generator]
# build the AAM
# TODO implement settings for strongest AAM we can for menpofit
aam = AAMBuilder(features=fast_dsift,
normalization_diagonal=120).build(images, verbose=True,
group='gt')
fitter = LucasKanadeAAMFitter(aam, n_shape=[3, 6, 12],
n_appearance=0.9)
# return a callable that wraps the menpo fitter in order to integrate with
# menpobench
return MenpoFitWrapper(fitter)
def __call__(self, img_generator):
from menpo.transform import AlignmentSimilarity
results = []
ref_shape = self.fitter.reference_shape
for img in img_generator:
# note that we don't want to crop the image in our preprocessing
# that's because the gt on the image we are passed is what will
# be used for assessment - we will introduce large errors if this
# is modified in size.
img = menpo_img_process(img, crop=False)
bbox = img.landmarks['bbox'].lms
shape_bb = ref_shape.bounding_box()
init_shape = AlignmentSimilarity(shape_bb, bbox).apply(ref_shape)
menpofit_fr = self.fitter.fit(img, init_shape)
results.append(menpofit_to_result(menpofit_fr))
return results
def __call__(self, img_generator):
from menpo.transform import AlignmentSimilarity
results = []
ref_shape = self.fitter.reference_shape
for img in img_generator:
# note that we don't want to crop the image in our preprocessing
# that's because the gt on the image we are passed is what will
# be used for assessment - we will introduce large errors if this
# is modified in size.
img = menpo_img_process(img, crop=False)
bbox = img.landmarks['bbox'].lms
shape_bb = ref_shape.bounding_box()
init_shape = AlignmentSimilarity(shape_bb, bbox).apply(ref_shape)
menpofit_fr = self.fitter.fit(img, init_shape)
results.append(menpofit_to_result(menpofit_fr))
return results
