def get_landmark_points(mesh, img_shape=(320, 240), verbose=False):
fitter = load_balanced_frontal_face_fitter()
detector = load_dlib_frontal_face_detector()
camera = perspective_camera_for_template(img_shape)
# Pre-process - align the mesh roughly with the template
aligned_mesh = align_mesh_to_template(mesh, load_template()).apply(mesh)
mesh_in_img = camera.apply(aligned_mesh)
bcs = rasterize_barycentric_coordinate_images(mesh_in_img, img_shape)
img = rasterize_mesh_from_barycentric_coordinate_images(mesh_in_img, *bcs)
shape_img = rasterize_shape_image_from_barycentric_coordinate_images(
mesh, *bcs)
# 2. Find the one bounding box in the rendered image
bboxes = detector(img)
if len(bboxes) != 1:
raise ValueError("Expected to find one face - found {}".format(
len(bboxes)))
else:
if verbose:
print('Detected 1 face')
# 3. Fit from the bounding box
fr = fitter.fit_from_bb(img, bboxes[0])
if verbose:
print('AMM fitting successfully completed')
# 4. Sample the XYZ image to build back the landmarks
img_lms = fr.final_shape.from_mask(LANDMARK_MASK)
# test to see if the landmark fell on the 3D surface or not
occlusion_mask = img.mask.sample(img_lms).ravel()
img.landmarks['__lsfm_on_surface'] = img_lms.from_mask(occlusion_mask)
img.landmarks['__lsfm_off_surface'] = img_lms.from_mask(~occlusion_mask)
return PointCloud(shape_img.sample(img.landmarks['__lsfm_on_surface']).T)
def landmark_template(mesh, img_shape=(320, 240), verbose=False):
fitter = load_balanced_frontal_face_fitter()
detector = load_dlib_frontal_face_detector()
camera = perspective_camera_for_template(img_shape)
# Pre-process - align the mesh roughly with the template
aligned_mesh = prepare_template_reference_space(mesh)
mesh_in_img = camera.apply(aligned_mesh)
bcs = rasterize_barycentric_coordinate_images(mesh_in_img, img_shape)
img = rasterize_mesh_from_barycentric_coordinate_images(mesh_in_img, *bcs)
shape_img = rasterize_shape_image_from_barycentric_coordinate_images(
mesh, *bcs)
# 2. Find the one bounding box in the rendered image
bboxes = detector(img)
if len(bboxes) != 1:
raise ValueError("Expected to find one face - found {}".format(
len(bboxes)))
else:
if verbose:
print('Detected 1 face')
# 3. Fit from the bounding box
fr = fitter.fit_from_bb(img, bboxes[0])
if verbose:
print('AMM fitting successfully completed')
# 4. Sample the XYZ image to build back the landmarks
img_lms = fr.final_shape
# test to see if the landmark fell on the 3D surface or not
mesh.landmarks["ibug68"] = PointCloud(Image.sample(shape_img, img_lms).T)
mask = np.zeros(68, dtype=np.bool)
mask[30] = True
mesh.landmarks["nosetip"] = mesh.landmarks["ibug68"].lms.from_mask(mask)
def landmark_mesh(mesh, img_shape=(320, 240), verbose=False, template_fn=None):
fitter = load_balanced_frontal_face_fitter()
detector = load_dlib_frontal_face_detector()
camera = perspective_camera_for_template(img_shape)
# Pre-process - align the mesh roughly with the template
aligned_mesh = align_mesh_to_template(mesh, load_template(template_fn)).apply(mesh)
mesh_in_img = camera.apply(aligned_mesh)
bcs = rasterize_barycentric_coordinate_images(mesh_in_img, img_shape)
img = rasterize_mesh_from_barycentric_coordinate_images(mesh_in_img, *bcs)
shape_img = rasterize_shape_image_from_barycentric_coordinate_images(
mesh, *bcs)
# 2. Find the one bounding box in the rendered image
bboxes = detector(img)
if len(bboxes) != 1:
raise ValueError(
"Expected to find one face - found {}".format(len(bboxes)))
else:
if verbose:
print('Detected 1 face')
# 3. Fit from the bounding box
fr = fitter.fit_from_bb(img, bboxes[0])
if verbose:
print('AMM fitting successfully completed')
# 4. Sample the XYZ image to build back the landmarks
img_lms = fr.final_shape.from_mask(LANDMARK_MASK)
# test to see if the landmark fell on the 3D surface or not
occlusion_mask = img.mask.sample(img_lms).ravel()
img.landmarks['__lsfm_on_surface'] = img_lms.from_mask(occlusion_mask)
img.landmarks['__lsfm_off_surface'] = img_lms.from_mask(~occlusion_mask)
return_dict = {
'landmarks_2d': img_lms,
'occlusion_mask': occlusion_mask,
'landmarks_3d_masked': PointCloud(shape_img.sample(
img.landmarks['__lsfm_on_surface']).T)
}
if (~occlusion_mask).sum() != 0:
groups = ['dlib_0', '__lsfm_on_surface', '__lsfm_off_surface']
marker_edge_colours = ['blue', 'yellow', 'red']
else:
groups = ['dlib_0', '__lsfm_on_surface']
marker_edge_colours = ['blue', 'yellow']
lm_img = img.rasterize_landmarks(group=groups,
line_colour='blue',
marker_edge_colour=marker_edge_colours)
return_dict['landmarked_image'] = lm_img
return return_dict
from menpodetect import load_dlib_frontal_face_detector
from menpofit.aam import load_balanced_frontal_face_fitter
detect = load_dlib_frontal_face_detector()
fitter = load_balanced_frontal_face_fitter()
def detect_and_fit(image):
bboxes = detect(image)
if len(bboxes) == 0:
raise ValueError('no detections!')
fr = fitter.fit_from_bb(image, bboxes[0])
image.landmarks['ibug68'] = fr.final_shape
def load_fitter():
return load_balanced_frontal_face_fitter()
