def test_frontal_face_detector():
takeo_copy = takeo.copy()
dlib_detector = load_dlib_frontal_face_detector()
pcs = dlib_detector(takeo_copy)
assert len(pcs) == 1
assert takeo_copy.n_channels == 3
assert takeo_copy.landmarks['dlib_0'][None].n_points == 4
def test_frontal_face_detector_rgb():
takeo_copy = takeo.copy()
assert takeo_copy.n_channels == 3
dlib_detector = load_dlib_frontal_face_detector()
pcs = dlib_detector(takeo_copy, greyscale=False)
assert len(pcs) == 1
assert takeo_copy.n_channels == 3
assert takeo_copy.landmarks['dlib_0'].n_points == 4
if args.input_dir:
options['input_dir'] = args.input_dir
if args.output_dir:
options['output_dir'] = args.output_dir
if args.model:
options['model'] = args.model
if args.file:
options['file'] = args.file
if args.output:
options['output'] = args.output
return options
if __name__ == '__main__':
options = parse_options()
fit_image.detect = load_dlib_frontal_face_detector()
fit_image.fitter = DlibWrapper(options['model'])
if 'file' in options:
video_file = options['file']
video_file_basename = os.path.basename(video_file)
print('Generating Landmarks from {}'.format(video_file))
output = options['output'] if 'output' in options else os.path.splitext(video_file_basename)[0] + '.csv'
process_video(video_file, output)
exit()
print('Generating Landmarks from {}'.format(options['input_dir']))
videofiles = find_all_videos(options['input_dir'], relpath=False)
videofiles.sort()
print('Found {} video(s)...'.format(len(videofiles)))
input_dir = os.path.abspath(options['input_dir'])
def __init__(self, model):
self.detector = load_dlib_frontal_face_detector()
self.fitter = DlibWrapper(model)
import os
import shutil
from menpofit.aam import load_balanced_frontal_face_fitter
from menpo.shape import TexturedTriMesh
from menpo3d.morphablemodel import ColouredMorphableModel
from menpodetect.dlib import load_dlib_frontal_face_detector
from menpofit.dlib import DlibWrapper
from menpo3d.morphablemodel.fitter import LucasKanadeMMFitter
from menpo.transform import image_coords_to_tcoords
from PIL import Image
from pathlib import Path
detect = load_dlib_frontal_face_detector()
aam_fitter = load_balanced_frontal_face_fitter()
shape_model, landmarks = mio.import_pickle(
"Child Customisation/pkls/children_under7.pkl")
texture_model = mio.import_pickle('Child Customisation/pkls/fast_dsift.pkl')
tcoords, bcoords_img, tri_index_img = mio.import_pickle(
'Child Customisation/pkls/unwrapped_template_barycentrics.pkl')
def extract_texture(mesh_in_image, background):
TI = tri_index_img.as_vector()
BC = bcoords_img.as_vector(keep_channels=True).T
sample_points_3d = mesh_in_image.project_barycentric_coordinates(BC, TI)