class AAM:
'''
Initialise members and train AAM
\param[in] i_dataset Full filepath to training and test dataset
\param[in] i_debug True to display debug info
'''
def __init__(self, i_dataset, i_debug=False):
self.debug = i_debug
self.dataset = i_dataset
if not os.path.exists(self.dataset):
raise RuntimeError('Database dir does not exist in ' +
self.dataset)
self.Train()
self.viola_face_detector = FaceDetectViola(False)
self.menpo_face_detector = FaceDetectMenpo()
if self.debug:
self.PrintDebug()
'''
Load training images and annotated landmarks from a training set in the file system
'''
def LoadDataset(self):
trainset = os.path.join(self.dataset, 'trainset', '*')
training_images = [
self.LoadImage(img)
for img in menpoio.import_images(trainset, verbose=True)
]
return training_images
'''
Crops image landmarks (0.1 referenced from AAMs Basics) and convert to greyscale
\param[in] i_img Menpo image to process
\return processed menpo image
'''
def LoadImage(self, i_img, i_landmark_crop=0.5):
img = i_img.crop_to_landmarks_proportion(i_landmark_crop)
img = GreyscaleConversionMenpo(img)
return img
'''
Train an Active Appearance Model and compute the
\param[in] i_diag Search gradient along model landmark
\param[in] i_scale Scale applied to search direction (search) || (initial, search)
\param[in] i_max_greyscale_dims Dimensionality limit for PCA appearance model
\param[in] i_max_shape_dims Dimensionality limit for PCA keypoint components
'''
def Train(self,
i_diag=150,
i_scale=[0.5, 1.0],
i_max_greyscale_dims=200,
i_max_shape_dims=20):
# laterals tuned for performance gain - Sacrifice mouth modes
self.model = HolisticAAM(
self.LoadDataset(),
group='PTS',
verbose=True,
holistic_features=float32_fast_dsift,
diagonal=i_diag,
scales=i_scale,
max_appearance_components=i_max_greyscale_dims,
max_shape_components=i_max_shape_dims)
self.fitter = LucasKanadeAAMFitter(self.model,
n_shape=[5, 15],
n_appearance=[50, 150])
'''
Fit an appearance model to an image with annotated landmarks
\return Converged candidate fit
'''
def FitAnnotatedImage(self, i_img):
gt = i_img.landmarks['PTS'].lms
initial_shape = self.fitter.perturb_from_bb(gt, gt.bounding_box())
return self.fitter.fit_from_shape(i_img, initial_shape, gt_shape=gt)
'''
Fit an appearance model to an image without annotations using Menpo Face Detection
\return Converged landmarks
'''
def FitWildImageMenpo(self, i_img, i_initial_guess=None, i_max_iters=10):
# Convert menpo image to expected openCV format
i_img = GreyscaleConversionMenpo(i_img)
ret = None
if i_initial_guess is not None:
pts = menpo.shape.PointCloud(i_initial_guess, False)
ret = self.fitter.fit_from_shape(i_img, pts,
i_max_iters).final_shape.points
else:
bb = self.menpo_face_detector.Detect(i_img)
if bb is not None:
ret = self.fitter.fit_from_bb(i_img, bb,
i_max_iters).final_shape.points
return ret
'''
Fit an appearance model to an image without annotations using Viola Face Detection
\return Converged landmarks
'''
def FitWildImageViola(self, i_img, i_initial_guess=None, i_max_iters=10):
# Convert menpo image to expected openCV format
i_img = GreyscaleConversionMenpo(i_img)
img = i_img.pixels[0] * 255
img = numpy.array(img, dtype=numpy.uint8)
# Detect face with experiment tuning according to lfpw testset
ret = None
if i_initial_guess is None:
faces = self.viola_face_detector.Detect(img, 3, 1.1, 0.125, 1.0)
# Fit candidate model
if len(faces) > 1:
faces = [GetLargestROI(faces)]
faces = ConvertRectToMenpoBoundingBox(faces)
fit = self.fitter.fit_from_bb(i_img, faces[0], i_max_iters)
ret = fit.final_shape.points
elif i_initial_guess is not None:
pts = menpo.shape.PointCloud(i_initial_guess, False)
ret = self.fitter.fit_from_shape(i_img, pts,
i_max_iters).final_shape.points
return ret
'''
Print debug information for the AAM class
'''
def PrintDebug(self):
print('Dataset', self.dataset)
print self.model
class AAM:
'''
Initialise members and train AAM
\param[in] i_dataset Full filepath to training and test dataset
\param[in] i_debug True to display debug info
'''
def __init__(self, i_dataset, i_debug = False):
self.debug = i_debug
self.dataset = i_dataset
if not os.path.exists(self.dataset):
raise RuntimeError('Database dir does not exist in ' + self.dataset)
self.Train()
self.viola_face_detector = FaceDetectViola(False)
self.menpo_face_detector = FaceDetectMenpo()
if self.debug:
self.PrintDebug()
'''
Load training images and annotated landmarks from a training set in the file system
'''
def LoadDataset(self):
trainset = os.path.join(self.dataset,'trainset','*')
training_images = [self.LoadImage(img) for img in menpoio.import_images(trainset, verbose = True) ]
return training_images
'''
Crops image landmarks (0.1 referenced from AAMs Basics) and convert to greyscale
\param[in] i_img Menpo image to process
\return processed menpo image
'''
def LoadImage(self, i_img, i_landmark_crop = 0.5):
img = i_img.crop_to_landmarks_proportion( i_landmark_crop )
img = GreyscaleConversionMenpo(img)
return img
'''
Train an Active Appearance Model and compute the
\param[in] i_diag Search gradient along model landmark
\param[in] i_scale Scale applied to search direction (search) || (initial, search)
\param[in] i_max_greyscale_dims Dimensionality limit for PCA appearance model
\param[in] i_max_shape_dims Dimensionality limit for PCA keypoint components
'''
def Train(self, i_diag = 150, i_scale = [0.5, 1.0], i_max_greyscale_dims = 200, i_max_shape_dims = 20):
# laterals tuned for performance gain - Sacrifice mouth modes
self.model = HolisticAAM(
self.LoadDataset(),
group='PTS',
verbose=True,
holistic_features=float32_fast_dsift,
diagonal=i_diag,
scales=i_scale,
max_appearance_components = i_max_greyscale_dims,
max_shape_components = i_max_shape_dims)
self.fitter = LucasKanadeAAMFitter(
self.model,
n_shape = [5, 15],
n_appearance= [50, 150]);
'''
Fit an appearance model to an image with annotated landmarks
\return Converged candidate fit
'''
def FitAnnotatedImage(self, i_img):
gt = i_img.landmarks['PTS'].lms
initial_shape = self.fitter.perturb_from_bb( gt, gt.bounding_box() )
return self.fitter.fit_from_shape(i_img, initial_shape, gt_shape=gt)
'''
Fit an appearance model to an image without annotations using Menpo Face Detection
\return Converged landmarks
'''
def FitWildImageMenpo(self, i_img, i_initial_guess = None, i_max_iters = 10):
# Convert menpo image to expected openCV format
i_img = GreyscaleConversionMenpo(i_img)
ret = None
if i_initial_guess is not None:
pts = menpo.shape.PointCloud(i_initial_guess, False)
ret = self.fitter.fit_from_shape(i_img, pts, i_max_iters).final_shape.points
else:
bb = self.menpo_face_detector.Detect(i_img)
if bb is not None:
ret = self.fitter.fit_from_bb(i_img, bb, i_max_iters).final_shape.points
return ret
'''
Fit an appearance model to an image without annotations using Viola Face Detection
\return Converged landmarks
'''
def FitWildImageViola(self, i_img, i_initial_guess = None, i_max_iters = 10):
# Convert menpo image to expected openCV format
i_img = GreyscaleConversionMenpo(i_img)
img = i_img.pixels[0] * 255
img = numpy.array(img, dtype=numpy.uint8)
# Detect face with experiment tuning according to lfpw testset
ret = None
if i_initial_guess is None:
faces = self.viola_face_detector.Detect(img, 3, 1.1, 0.125, 1.0)
# Fit candidate model
if len(faces) > 1:
faces = [GetLargestROI(faces)]
faces = ConvertRectToMenpoBoundingBox(faces)
fit = self.fitter.fit_from_bb(i_img, faces[0], i_max_iters)
ret = fit.final_shape.points
elif i_initial_guess is not None:
pts = menpo.shape.PointCloud(i_initial_guess, False)
ret = self.fitter.fit_from_shape(i_img, pts, i_max_iters).final_shape.points
return ret
'''
Print debug information for the AAM class
'''
def PrintDebug(self):
print('Dataset', self.dataset)
print self.model
