import face3d
from face3d import mesh
from face3d.morphable_model import MorphabelModel
# --------------------- Forward: parameters(shape, expression, pose) --> 3D obj ---------------
# --- 1. load model
bfm = MorphabelModel('Data/BFM/Out/BFM.mat')
print('init bfm model success')
# --- 2. generate face mesh: vertices(represent shape) & colors(represent texture)
sp = bfm.get_shape_para('random')
ep = bfm.get_exp_para('random')
vertices = bfm.generate_vertices(sp, ep)
tp = bfm.get_tex_para('random')
colors = bfm.generate_colors(tp)
colors = np.minimum(np.maximum(colors, 0), 1)
# --- 3. create a mesh in blender and load 3dmm vertices and faces
mesh = bpy.data.meshes.new("3dmm_face") # add the new mesh
obj = bpy.data.objects.new(mesh.name, mesh)
col = bpy.data.collections.get("Collection")
col.objects.link(obj)
bpy.context.view_layer.objects.active = obj
edges = []
verts = vertices.tolist()
faces = bfm.triangles.tolist()
mesh.from_pydata(verts, edges, faces)
from face3d import mesh
from face3d import mesh_cython
from face3d.morphable_model import MorphabelModel
# --------------------- Forward: parameters(shape, expression, pose) --> 3D obj --> 2D image ---------------
# --- 1. load model
bfm = MorphabelModel('Data/BFM/Out/BFM.mat')
print('init bfm model success')
# --- 2. generate face mesh: vertices(represent shape) & colors(represent texture)
sp = bfm.get_shape_para('random')
ep = bfm.get_exp_para('random')
vertices = bfm.generate_vertices(sp, ep)
tp = bfm.get_tex_para('random')
colors = bfm.generate_colors(tp)
colors = np.minimum(np.maximum(colors, 0), 1)
# --- 3. transform vertices to proper position
s = 8e-04
angles = [10, 30, 20]
t = [0, 0, 0]
transformed_vertices = bfm.transform(vertices, s, angles, t)
projected_vertices = transformed_vertices.copy() # using stantard camera & orth projection
# --- 4. render(3d obj --> 2d image)
# set prop of rendering
h = w = 256; c = 3
image_vertices = mesh.transform.to_image(projected_vertices, h, w)
image = mesh_cython.render.render_colors(image_vertices, bfm.triangles, colors, h, w)
br_corner_y = rects[0].center().y + rects[0].height()/2 # added
rects = [(tl_corner_x, tl_corner_y), (br_corner_x, br_corner_y)]
landmarks = np.zeros((68, 2))
for i, p in enumerate(shape.parts()):
landmarks[i] = [p.x, p.y]
im = cv2.circle(im, (p.x, p.y), radius=3, color=(0, 0, 255), thickness=5)
bfm = MorphabelModel('Data/BFM/Out/BFM.mat')
x = mesh.transform.from_image(landmarks, h, w)
X_ind = bfm.kpt_ind
global colors
global triangles
fitted_sp, fitted_ep, fitted_s, fitted_angles, fitted_t = bfm.fit(x, X_ind, max_iter=200, isShow=False)
colors = bfm.generate_colors(np.random.rand(bfm.n_tex_para, 1))
colors = np.minimum(np.maximum(colors, 0), 1)
fitted_vertices = bfm.generate_vertices(fitted_sp, fitted_ep)
transformed_vertices = bfm.transform(fitted_vertices, fitted_s, fitted_angles, fitted_t)
image_vertices = mesh.transform.to_image(transformed_vertices, h, w)
vertices = image_vertices
triangles = bfm.triangles
colors = colors/np.max(colors)
# load data
#C = sio.loadmat('Data/example1.mat')
#vertices = C['vertices']; colors = C['colors']; triangles = C['triangles']
#colors = colors/np.max(colors)
# transform
def main(args):
with open(args.img_list) as f:
img_list = [x.strip() for x in f.readlines()]
landmark_list = []
if not os.path.exists(args.save_dir):
os.mkdir(args.save_dir)
if not os.path.exists(args.save_lmk_dir):
os.mkdir(args.save_lmk_dir)
for img_idx, img_fp in enumerate(tqdm(img_list)):
im = cv2.imread(os.path.join(args.img_prefix, img_fp), 1)
gray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)
h, w, c = im.shape
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(
"face3d/models/shape_predictor_68_face_landmarks.dat")
rects = detector(gray, 1)
shape = predictor(gray, rects[0])
tl_corner_x = rects[0].center().x - rects[0].width() / 2
tl_corner_y = rects[0].center().y - rects[0].height() / 2
br_corner_x = rects[0].center().x + rects[0].width() / 2
br_corner_y = rects[0].center().y + rects[0].height() / 2
rects = [(tl_corner_x, tl_corner_y), (br_corner_x, br_corner_y)]
landmarks = np.zeros((68, 2))
for i, p in enumerate(shape.parts()):
landmarks[i] = [p.x, p.y]
im = cv2.circle(im, (p.x, p.y),
radius=3,
color=(0, 0, 255),
thickness=5)
bfm = MorphabelModel('face3d/Data/BFM/Out/BFM.mat')
x = mesh_numpy.transform.from_image(landmarks, h, w)
X_ind = bfm.kpt_ind
fitted_sp, fitted_ep, fitted_s, fitted_angles, fitted_t = bfm.fit(
x, X_ind, max_iter=200, isShow=False)
colors = bfm.generate_colors(np.random.rand(bfm.n_tex_para, 1))
colors = np.minimum(np.maximum(colors, 0), 1)
fitted_vertices = bfm.generate_vertices(fitted_sp, fitted_ep)
transformed_vertices = bfm.transform(fitted_vertices, fitted_s,
fitted_angles, fitted_t)
image_vertices = mesh_numpy.transform.to_image(transformed_vertices, h,
w)
triangles = bfm.triangles
colors = colors / np.max(colors)
attribute = colors
color_image = mesh_numpy.render.render_colors(image_vertices,
triangles,
attribute,
h,
w,
c=3)
io.imsave(os.path.join(args.save_lmk_dir,
str(img_idx) + ".png"),
img_as_ubyte(color_image))