def train_aps(experiments_path, fast, group, training_images_options,
training_options, save_model, verbose):
# update training_images_options
training_images_options['save_path'] = os.path.join(experiments_path,
'Databases')
training_images_options['fast'] = fast
training_images_options['group'] = group
training_images_options['verbose'] = verbose
# parse training options
adj, rv = parse_deformation_graph(training_options['graph_deformation'])
training_options['adjacency_array_deformation'] = adj
training_options['root_vertex_deformation'] = rv
adj, fl = parse_appearance_graph(training_options['graph_appearance'])
training_options['adjacency_array_appearance'] = adj
training_options['gaussian_per_patch'] = fl
training_options['features'] = parse_features(training_options['features'],
fast)
graph_deformation_str = training_options['graph_deformation']
graph_appearance_str = training_options['graph_appearance']
del training_options['graph_deformation']
del training_options['graph_appearance']
# Load training images
training_images = load_database(**training_images_options)
# make model filename
filename = model_filename(training_images_options, training_options, group,
fast, graph_deformation_str, graph_appearance_str)
save_path = os.path.join(experiments_path, 'Models', filename)
# train model
if file_exists(save_path):
if verbose:
print_dynamic('Loading model...')
aps = pickle_load(save_path)
if verbose:
print_dynamic('Model loaded.')
else:
training_options['max_shape_components'] = None
# Train model
if fast:
from antonakoscvpr2015.menpofast.builder import APSBuilder
else:
from antonakoscvpr2015.menpo.builder import APSBuilder
if group is not None:
aps = APSBuilder(**training_options).build(training_images,
group=group.__name__,
verbose=verbose)
else:
aps = APSBuilder(**training_options).build(training_images,
verbose=verbose)
# save model
if save_model:
pickle_dump(aps, save_path)
return aps, filename, training_images
def load_database(path_to_images,
save_path,
db_name,
crop_percentage,
fast,
group,
verbose=False):
# create filename
if group is not None:
filename = (db_name + '_' + group.__name__ + '_crop' +
str(int(crop_percentage * 100)))
else:
filename = db_name + 'PTS' + '_crop' + str(int(crop_percentage * 100))
if fast:
filename += '_menpofast.pickle'
else:
filename += '_menpo.pickle'
save_path = os.path.join(save_path, filename)
# check if file exists
if file_exists(save_path):
if verbose:
print_dynamic('Loading images...')
images = pickle_load(save_path)
if verbose:
print_dynamic('Images Loaded.')
else:
# load images
images = []
for i in mio.import_images(path_to_images, verbose=verbose):
if fast:
i = convert_from_menpo(i)
i.crop_to_landmarks_proportion_inplace(crop_percentage,
group='PTS')
if group is not None:
labeller(i, 'PTS', group)
if i.n_channels == 3:
i = i.as_greyscale(mode='average')
images.append(i)
# save images
pickle_dump(images, save_path)
# return images
return images
def load_database(path_to_images, save_path, db_name, crop_percentage,
fast, group, verbose=False):
# create filename
if group is not None:
filename = (db_name + '_' + group.__name__ + '_crop' +
str(int(crop_percentage * 100)))
else:
filename = db_name + 'PTS' + '_crop' + str(int(crop_percentage * 100))
if fast:
filename += '_menpofast.pickle'
else:
filename += '_menpo.pickle'
save_path = os.path.join(save_path, filename)
# check if file exists
if file_exists(save_path):
if verbose:
print_dynamic('Loading images...')
images = pickle_load(save_path)
if verbose:
print_dynamic('Images Loaded.')
else:
# load images
images = []
for i in mio.import_images(path_to_images, verbose=verbose):
if fast:
i = convert_from_menpo(i)
i.crop_to_landmarks_proportion_inplace(crop_percentage, group='PTS')
if group is not None:
labeller(i, 'PTS', group)
if i.n_channels == 3:
i = i.as_greyscale(mode='average')
images.append(i)
# save images
pickle_dump(images, save_path)
# return images
return images
def fit_aps(aps, modelfilename, experiments_path, fast, group,
fitting_images_options, fitting_options, verbose):
# make results filename
filename2 = results_filename(fitting_images_options, fitting_options, group,
fast)
# final save path
filename = modelfilename[:modelfilename.rfind("_")+1] + '_' + filename2
save_path = os.path.join(experiments_path, 'Results', filename)
# fit model
if file_exists(save_path):
if verbose:
print_dynamic('Loading fitting results...')
fitting_results = pickle_load(save_path)
if verbose:
print_dynamic('Fitting results loaded.')
else:
fitter_cls, algorithm_cls = parse_algorithm(
fast, fitting_options['algorithm'])
fitter = fitter_cls(aps, algorithm=algorithm_cls, n_shape=[3, 6],
use_deformation=fitting_options['use_deformation'])
# get fitting images
fitting_images_options['save_path'] = os.path.join(experiments_path,
'Databases')
fitting_images_options['fast'] = fast
fitting_images_options['group'] = group
fitting_images_options['verbose'] = verbose
fitting_images = load_database(**fitting_images_options)
# fit
np.random.seed(seed=1)
fitting_results = []
n_images = len(fitting_images)
if verbose:
perc1 = 0.
perc2 = 0.
perc3 = 0.
for j, i in enumerate(fitting_images):
# fit
if group is not None:
gt_s = i.landmarks[group.__name__].lms
else:
gt_s = i.landmarks['PTS'].lms
s = fitter.perturb_shape(gt_s,
noise_std=fitting_options['noise_std'])
fr = fitter.fit(i, s, gt_shape=gt_s,
max_iters=fitting_options['max_iters'])
fitting_results.append(fr)
# verbose
if verbose:
final_error = fr.final_error(error_type='me_norm')
initial_error = fr.initial_error(error_type='me_norm')
if final_error <= 0.03:
perc1 += 1.
if final_error <= 0.04:
perc2 += 1.
if final_error <= 0.05:
perc3 += 1.
print_dynamic('- {0} - [<=0.03: {1:.1f}%, <=0.04: {2:.1f}%, '
'<=0.05: {3:.1f}%] - Image {4}/{5} (error: '
'{6:.3f} --> {7:.3f})'.format(
progress_bar_str(float(j + 1.) / n_images,
show_bar=False),
perc1 * 100. / n_images, perc2 * 100. / n_images,
perc3 * 100. / n_images, j + 1, n_images,
initial_error, final_error))
if verbose:
print_dynamic('- Fitting completed: [<=0.03: {0:.1f}%, <=0.04: '
'{1:.1f}%, <=0.05: {2:.1f}%]\n'.format(
perc1 * 100. / n_images, perc2 * 100. / n_images,
perc3 * 100. / n_images))
errors = []
errors.append([fr.final_error() for fr in fitting_results])
errors.append([fr.initial_error() for fr in fitting_results])
pickle_dump(errors, save_path)
return fitting_results, filename
def train_aps(experiments_path, fast, group, training_images_options,
training_options, save_model, verbose):
# update training_images_options
training_images_options['save_path'] = os.path.join(
experiments_path, 'Databases')
training_images_options['fast'] = fast
training_images_options['group'] = group
training_images_options['verbose'] = verbose
# parse training options
adj, rv = parse_deformation_graph(training_options['graph_deformation'])
training_options['adjacency_array_deformation'] = adj
training_options['root_vertex_deformation'] = rv
adj, fl = parse_appearance_graph(training_options['graph_appearance'])
training_options['adjacency_array_appearance'] = adj
training_options['gaussian_per_patch'] = fl
adj, _ = parse_appearance_graph(training_options['graph_shape'])
training_options['adjacency_array_shape'] = adj
training_options['features'] = parse_features(training_options['features'],
fast)
graph_deformation_str = training_options['graph_deformation']
graph_appearance_str = training_options['graph_appearance']
graph_shape_str = training_options['graph_shape']
del training_options['graph_deformation']
del training_options['graph_appearance']
del training_options['graph_shape']
# Load training images
training_images = load_database(**training_images_options)
# make model filename
filename = model_filename(training_images_options, training_options, group,
fast, graph_deformation_str,
graph_appearance_str, graph_shape_str)
save_path = os.path.join(experiments_path, 'Models', filename)
# train model
if file_exists(save_path):
if verbose:
print_dynamic('Loading model...')
aps = pickle_load(save_path)
if verbose:
print_dynamic('Model loaded.')
else:
training_options['max_shape_components'] = None
# Train model
if fast:
from antonakoscvpr2015.menpofast.builder import APSBuilder
else:
from antonakoscvpr2015.menpo.builder import APSBuilder
if group is not None:
aps = APSBuilder(**training_options).build(training_images,
group=group.__name__,
verbose=verbose)
else:
aps = APSBuilder(**training_options).build(training_images,
verbose=verbose)
# save model
if save_model:
pickle_dump(aps, save_path)
return aps, filename, training_images
def fit_aps(aps, modelfilename, experiments_path, fast, group,
fitting_images_options, fitting_options, verbose):
# make results filename
filename2 = results_filename(fitting_images_options, fitting_options,
group, fast)
# final save path
filename = modelfilename[:modelfilename.rfind("_") + 1] + '_' + filename2
save_path = os.path.join(experiments_path, 'Results', filename)
# fit model
if file_exists(save_path):
if verbose:
print_dynamic('Loading fitting results...')
fitting_results = pickle_load(save_path)
if verbose:
print_dynamic('Fitting results loaded.')
else:
fitter_cls, algorithm_cls = parse_algorithm(
fast, fitting_options['algorithm'])
fitter = fitter_cls(aps,
algorithm=algorithm_cls,
n_shape=[3, 6],
use_deformation=fitting_options['use_deformation'])
# get fitting images
fitting_images_options['save_path'] = os.path.join(
experiments_path, 'Databases')
fitting_images_options['fast'] = fast
fitting_images_options['group'] = group
fitting_images_options['verbose'] = verbose
fitting_images = load_database(**fitting_images_options)
# fit
np.random.seed(seed=1)
fitting_results = []
n_images = len(fitting_images)
if verbose:
perc1 = 0.
perc2 = 0.
perc3 = 0.
for j, i in enumerate(fitting_images):
# fit
if group is not None:
gt_s = i.landmarks[group.__name__].lms
else:
gt_s = i.landmarks['PTS'].lms
s = fitter.perturb_shape(gt_s,
noise_std=fitting_options['noise_std'])
fr = fitter.fit(i,
s,
gt_shape=gt_s,
max_iters=fitting_options['max_iters'])
fitting_results.append(fr)
# verbose
if verbose:
final_error = fr.final_error(error_type='me_norm')
initial_error = fr.initial_error(error_type='me_norm')
if final_error <= 0.03:
perc1 += 1.
if final_error <= 0.04:
perc2 += 1.
if final_error <= 0.05:
perc3 += 1.
print_dynamic('- {0} - [<=0.03: {1:.1f}%, <=0.04: {2:.1f}%, '
'<=0.05: {3:.1f}%] - Image {4}/{5} (error: '
'{6:.3f} --> {7:.3f})'.format(
progress_bar_str(float(j + 1.) / n_images,
show_bar=False),
perc1 * 100. / n_images,
perc2 * 100. / n_images,
perc3 * 100. / n_images, j + 1, n_images,
initial_error, final_error))
if verbose:
print_dynamic('- Fitting completed: [<=0.03: {0:.1f}%, <=0.04: '
'{1:.1f}%, <=0.05: {2:.1f}%]\n'.format(
perc1 * 100. / n_images, perc2 * 100. / n_images,
perc3 * 100. / n_images))
errors = []
errors.append([fr.final_error() for fr in fitting_results])
errors.append([fr.initial_error() for fr in fitting_results])
pickle_dump(errors, save_path)
return fitting_results, filename
