def run_2d_lmk_fitting(model_fname, template_fname, flame_lmk_path, texture_mapping, target_img_path, target_lmk_path, out_path):
if 'generic' not in model_fname:
print('You are fitting a gender specific model (i.e. female / male). Please make sure you selected the right gender model. Choose the generic model if gender is unknown.')
if not os.path.exists(template_fname):
print('Template mesh (in FLAME topology) not found - %s' % template_fname)
return
if not os.path.exists(flame_lmk_path):
print('FLAME landmark embedding not found - %s ' % flame_lmk_path)
return
if not os.path.exists(target_img_path):
print('Target image not found - s' % target_img_path)
return
if not os.path.exists(target_lmk_path):
print('Landmarks of target image not found - s' % target_lmk_path)
return
if not os.path.exists(out_path):
os.makedirs(out_path)
lmk_face_idx, lmk_b_coords = load_embedding(flame_lmk_path)
target_img = cv2.imread(target_img_path)
lmk_2d = np.load(target_lmk_path)
weights = {}
# Weight of the landmark distance term
weights['lmk'] = 1.0
# Weight of the shape regularizer
weights['shape'] = 1e-3
# Weight of the expression regularizer
weights['expr'] = 1e-3
# Weight of the neck pose (i.e. neck rotationh around the neck) regularizer
weights['neck_pose'] = 100.0
# Weight of the jaw pose (i.e. jaw rotation for opening the mouth) regularizer
weights['jaw_pose'] = 1e-3
# Weight of the eyeball pose (i.e. eyeball rotations) regularizer
weights['eyeballs_pose'] = 10.0
result_mesh, result_scale = fit_lmk2d(target_img, lmk_2d, template_fname, model_fname, lmk_face_idx, lmk_b_coords, weights)
if sys.version_info >= (3, 0):
texture_data = np.load(texture_mapping, allow_pickle=True, encoding='latin1').item()
else:
texture_data = np.load(texture_mapping, allow_pickle=True).item()
texture_map = compute_texture_map(target_img, result_mesh, result_scale, texture_data)
out_mesh_fname = os.path.join(out_path, os.path.splitext(os.path.basename(target_img_path))[0] + '.obj')
out_img_fname = os.path.join(out_path, os.path.splitext(os.path.basename(target_img_path))[0] + '.png')
cv2.imwrite(out_img_fname, texture_map)
result_mesh.set_vertex_colors('white')
result_mesh.vt = texture_data['vt']
result_mesh.ft = texture_data['ft']
result_mesh.set_texture_image(out_img_fname)
result_mesh.write_obj(out_mesh_fname)
np.save(os.path.join(out_path, os.path.splitext(os.path.basename(target_img_path))[0] + '_scale.npy'), result_scale)
mv = MeshViewers(shape=[1,2], keepalive=True)
mv[0][0].set_static_meshes([Mesh(result_mesh.v, result_mesh.f)])
mv[0][1].set_static_meshes([result_mesh])
def run_3d_lmk_fitting():
# Path of the Tensorflow FLAME model
tf_model_fname = './models/generic_model'
# tf_model_fname = './models/female_model'
# tf_model_fname = './models/male_model'
# Path of a template mesh in FLAME topology
template_fname = './data/template.ply'
# Path of the landamrk embedding file into the FLAME surface
flame_lmk_path = './data/flame_static_embedding.pkl'
# 3D landmark file that should be fitted (landmarks must be corresponding with the defined FLAME landmarks)
# see "img1_lmks_visualized.jpeg" or "see the img2_lmks_visualized.jpeg" for the order of the landmarks
target_lmk_path = './data/landmark_3d.npy'
# Output filename
out_mesh_fname = './results/landmark_3d.ply'
lmk_face_idx, lmk_b_coords = load_embedding(flame_lmk_path)
lmk_3d = np.load(target_lmk_path)
weights = {}
# Weight of the landmark distance term
weights['lmk'] = 1.0
# Weight of the shape regularizer
weights['shape'] = 1.0
# Weight of the expression regularizer
weights['expr'] = 1.0
# Weight of the neck pose (i.e. neck rotationh around the neck) regularizer
weights['neck_pose'] = 100.0
# Weight of the jaw pose (i.e. jaw rotation for opening the mouth) regularizer
weights['jaw_pose'] = 1.0
# Weight of the eyeball pose (i.e. eyeball rotations) regularizer
weights['eyeballs_pose'] = 10.0
# Show landmark fitting (default: red = target landmarks, blue = fitting landmarks)
show_fitting = True
result_mesh = fit_lmk3d(lmk_3d,
template_fname,
tf_model_fname,
lmk_face_idx,
lmk_b_coords,
weights,
show_fitting=show_fitting)
if not os.path.exists(os.path.dirname(out_mesh_fname)):
os.makedirs(os.path.dirname(out_mesh_fname))
result_mesh.write_ply(out_mesh_fname)
