1.场景介绍
3D人脸点云不容易直接训练，需要将其转化为二维图片。大部分论文是这样做的：首先求出每个点所在平面的法向量，发向量与水平面和竖直平面的夹角组成两个通道，深度图是一个通道。然后，将这三个通道归一到[0~255]，形成我们人眼可见的图片。最后，就可以像训练图片一样训练人脸识别了。
2. 原始的点云
将点云MashLab中打开，如下图所示：
如果你没有安装MeshLab，也可用代码打开，更加直观的查看点云，代码如下：
#coding:UTF-8
import os
import sys
reload(sys)
sys.setdefaultencoding("utf-8")
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3Ddef ply_reader(ply_file):with open(ply_file, "r") as f:lines = f.readlines()lines = lines[12:-3]x = []y = []z = []for line in lines:parts = line.split(" ")x.append(float(parts[0]))y.append(float(parts[1]))z.append(float(parts[2]))return x, y, zdef obj_reader(obj_file):alpha, beta, theta, x, y, z = [], [], [], [], [], []with open(obj_file, "r") as f:lines = f.readlines()lines = [i for i in lines if i[0]=="v"]for line in lines:if line[:2] == "vn":parts = line.rstrip("\n").split(" ")alpha.append(float(parts[1]))beta.append(float(parts[2]))theta.append(float(parts[3]))else:parts = line.rstrip("\n").split(" ")x.append(float(parts[1]))y.append(float(parts[2]))z.append(float(parts[3]))return x, y, z, alpha, beta, thetadef points_show(x, y, z):fig = plt.figure()ax = Axes3D(fig)ax.set_xlabel('X label',color='r')ax.set_ylabel('Y label',color='r')ax.set_zlabel('Z label')ax.scatter(x,y,z,c='b',marker='.',s=2,linewidth=0,alpha=1,cmap='spectral')plt.show()if __name__ == "__main__":x, y, z, alpha, beta, theta = obj_reader("60.obj")points_show(x,y,z)打开的点云如下：
3. 点云法向量三个分和深度分量四个通道的图片如图所示
四张图片从左到右分别是法向量与三维空间三个平面夹角的余弦值，最后一张图是深度值。
将第一张图、第三张图、第四张图三个通道叠加起来，形成训练的彩色图，如下图所示：
代码实现如下：
#coding:UTF-8
import sys
reload(sys)
sys.setdefaultencoding("utf-8")
import numpy as np
import cv2
import mathdef obj_reader(obj_file):alpha, beta, theta, x, y, z = [], [], [], [], [], []with open(obj_file, "r") as f:lines = f.readlines()lines = [i for i in lines if i[0]=="v"]for line in lines:if line[:2] == "vn":parts = line.rstrip("\n").split(" ")alpha.append(float(parts[1]))beta.append(float(parts[2]))theta.append(float(parts[3]))else:parts = line.rstrip("\n").split(" ")x.append(float(parts[1]))y.append(float(parts[2]))z.append(float(parts[3]))return x, y, z, alpha, beta, thetadef img_norm(img):img_max = (img[img != 0]).max()img_min = (img[img != 0]).min()img_new = (img-img_min)*255.0/(img_max-img_min)th = (0-img_min)*255.0/(img_max - img_min)img_new[img_new==th] = 0img_new = cv2.medianBlur(img_new.astype(np.float32), 3)return img_newdef point2gray(x, y, z, alpha, beta, theta):u_list, v_list, z_list = [], [], []for i, j, k in zip(x, y, z):u_list.append((i*616.009)/k)v_list.append((j*614.024)/k)z_list.append(k/1000)width = int(max(u_list) - min(u_list))height = int(max(v_list) - min(v_list))gray_img = np.zeros((width+1, height+1, 1))alpha_img = np.zeros((width+1, height+1, 1))beta_img = np.zeros((width+1, height+1, 1))theta_img = np.zeros((width+1, height+1, 1))rst_img = np.zeros((width+1, height+1, 3))u_min = min(u_list)v_min = min(v_list)u_list = [int(i-u_min) for i in u_list]v_list = [int(i-v_min) for i in v_list]for u, v, z, al, be, th in zip(u_list, v_list, z_list, alpha, beta, theta):gray_img[u,v] = z# al,be,th are anglesalpha_img[u,v] = abs(al)beta_img[u,v] = abs(be)theta_img[u,v] = abs(th)img_gray = img_norm(gray_img)cv2.imwrite("gray.jpg", img_gray)alpha_img = img_norm(alpha_img)cv2.imwrite("alpha.jpg", alpha_img)beta_img = img_norm(beta_img)cv2.imwrite("beta.jpg", beta_img) theta_img = img_norm(theta_img)cv2.imwrite("theta.jpg", theta_img)rst_img[:,:,0] = img_grayrst_img[:,:,1] = alpha_imgrst_img[:,:,2] = theta_imgcv2.imwrite("rst.jpg", rst_img)if __name__ == "__main__":x, y, z, alpha, beta, theta = obj_reader("60.obj")point2gray(y,x,z, alpha, beta, theta)注：将人脸点云转化为上述图片，就可以进行3D人脸识别了。
RGBD人脸数据的处理，请参考我的博客：https://blog.csdn.net/Guo_Python/article/details/115080436。
如需技术交流，请留言，或添加作者微信（下面二维码）。
