def generate_bfm_base_obj():
bfm = MorphabelModel('../Data/BFM/Out/BFM.mat')
sp = bfm.get_shape_para('zero')
ep = bfm.get_exp_para('zero')
tp = bfm.get_tex_para('random')
vertices = bfm.generate_vertices(sp, ep)
colors = bfm.generate_colors(tp)
colors = np.minimum(np.maximum(colors, 0), 255)
write_obj_with_colors('head_fit.obj', vertices, bfm.triangles, colors)
from time import time
import matplotlib.pyplot as plt
sys.path.append('..')
import face3d
from face3d import mesh
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)
import matplotlib.pyplot as plt
sys.path.append('..')
import face3d
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
from time import time
import matplotlib.pyplot as plt
sys.path.append('..')
import face3d
from face3d import mesh
from face3d.morphable_model import MorphabelModel
# --------------------- Forward: parameters(shape, expression, pose) --> 3D obj --> 2D image ---------------
# --- 1. load model 【载入3D参数，即BFM参数，用于生成mesh数据，从而进一步地生成2D人脸图像】
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') # shape参数
ep = bfm.get_exp_para('random') # expression参数
vertices = bfm.generate_vertices(sp, ep)
tp = bfm.get_tex_para('random') # texture参数，也即colors——颜色
colors = bfm.generate_colors(tp)
colors = np.minimum(np.maximum(colors, 0), 1)
# --- 3. transform vertices to proper position # 转换坐标系
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) # 渲染，将3d转变为颜色正常的2d
# set prop of rendering
sys.path.append('..')
from face3d import mesh
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')
# print(bfm.n_tex_para)
# --- 2. generate face mesh: vertices(represent shape) & colors(represent texture)
# sp = bfm.get_shape_para('random')
sp = bfm.get_shape_para('zero')
# print(sp_sigma)
# print(sp_sigma.shape)
ep = bfm.get_exp_para('zero')
print(ep.shape)
ep[:10] = np.array([[-2.4658144 ],
[ 1.5232908 ],
[-0.58274204],
[ 1.1200945 ],
[ 0.35412225],
[-1.2616647 ],
[-1.0544292 ],
[-1.6552277 ],
[-0.41645545],
[-1.2801003 ]])
exp_sigma = bfm.get_exp_sigma()