def AlignTwoFaceWithRandomPoints(FirstFace_verts,
SecondFace_verts,
numberof_points=7):
"""
将第二个人脸对齐到第一个人脸, 通过计算旋转矩阵, 平移矩阵等等 注意第二个像第一个人脸对齐 即要对齐的人脸是第二个参数
:param FirstFace: 第一个人脸顶点数据 n x 3
:param SecondFace: 第二个人脸顶点数据 第一个人脸和第二个人最好定数一样 n x 3
:param numberof_points:选用多少点进行刚体变换
:return: 返回第二个人脸对齐后的数据
"""
Number_points = len(FirstFace_verts)
select_index = np.random.randint(low=0,
high=Number_points,
size=numberof_points)
Face1 = np.array(FirstFace_verts)
Face2 = np.array(SecondFace_verts)
Face1_select = Face1[select_index, :].T
Face2_selcet = Face2[select_index, :].T
s, R, t, _ = align_sim3(Face1_select, Face2_selcet)
# print("align error is {}".format(error))
model_aligned = s * R.dot(Face2.T) + t
alignment_error = model_aligned - Face1.T
t_error = np.sqrt(np.sum(np.multiply(alignment_error, alignment_error), 0))
print("model aligned error is {}".format(np.mean(t_error)))
return model_aligned.T.tolist()
def AlignTwoFaceWithFixedPoints(FirstFace_verts, SecondFace_verts, pointsindex, non_linear_align=False, return_sRt=False):
"""
非线性对齐要求对齐点数大于8
将第二个人脸对齐到第一个人脸, 通过计算旋转矩阵, 平移矩阵等等 注意第二个像第一个人脸对齐 即要对齐的人脸是第二个参数
:param FirstFace: 第一个人脸顶点数据 n x 3
:param SecondFace: 第二个人脸顶点数据 第一个人脸和第二个人最好点数一样 n x 3
:param pointsindex: 需要對齊所使用的點序 一維list
:return: 返回第二个人脸对齐后的数据
"""
if non_linear_align:
select_index = np.array(pointsindex)
Face1 = np.array(FirstFace_verts)
Face2 = np.array(SecondFace_verts)
Face1_select = Face1[select_index, :]
Face2_selcet = Face2[select_index, :]
R, t, s, res = caculate_transform(Face1_select, Face2_selcet)
t = t[:, np.newaxis]
model_aligned = s * R.dot(Face2.T) + t
alignment_error = model_aligned - Face1.T
t_error = np.sqrt(np.sum(np.multiply(alignment_error, alignment_error), 0))
res = np.mean(t_error)
print("model aligned error is {}".format(res))
model_aligned = model_aligned.T
else:
select_index = np.array(pointsindex)
Face1 = np.array(FirstFace_verts)
Face2 = np.array(SecondFace_verts)
Face1_select = Face1[select_index, :].T
Face2_selcet = Face2[select_index, :].T
# s, R, t, _ = align_sim3(Face1_select, Face2_selcet) # demo from web
s, R, t, _ = align_sim3(Face1_select, Face2_selcet)
# print("align error is {}".format(error))
model_aligned = s * R.dot(Face2.T) + t
# print("R is {}".format(R))
# print("t is {}".format(t))
alignment_error = model_aligned - Face1.T
t_error = np.sqrt(np.sum(np.multiply(alignment_error, alignment_error), 0))
res = np.mean(t_error)
print("model aligned error is {}".format(res))
model_aligned = model_aligned.T
if return_sRt:
return model_aligned.tolist(), s, R, t, res
else:
return model_aligned.tolist()
Face2_selcet = Face2[select_index, :]
R, t, s, res = caculate_transform(Face1_select, Face2_selcet)
t = t[:, np.newaxis]
model_aligned = s * R.dot(Face2.T) + t
alignment_error = model_aligned - Face1.T
t_error = np.sqrt(np.sum(np.multiply(alignment_error, alignment_error), 0))
res = np.mean(t_error)
print("model aligned error is {}".format(res))
model_aligned = model_aligned.T
else:
select_index = np.array(pointsindex)
Face1 = np.array(FirstFace_verts)
Face2 = np.array(SecondFace_verts)
Face1_select = Face1[select_index, :].T
Face2_selcet = Face2[select_index, :].T
s, R, t, _ = align_sim3(Face1_select, Face2_selcet)
# print("align error is {}".format(error))
model_aligned = s * R.dot(Face2.T) + t
alignment_error = model_aligned - Face1.T
t_error = np.sqrt(np.sum(np.multiply(alignment_error, alignment_error), 0))
print("model aligned error is {}".format(np.mean(t_error)))
model_aligned = model_aligned.T
if return_sRt:
return model_aligned.tolist(), s, R, t
else:
return model_aligned.tolist()
def BatchAlignFacewithRandomPoints(FaceToalignPath, FaceAligned_verts, SavePath):
"""
批量稳定人脸