人脸图像的切割python和matlab 实现

人脸的图像的切割以检测为前提，以下是切割过程总结

1、python实现：  

#!/usr/bin/python
# -*- coding: utf-8 -*-  
#!/usr/bin/python
import dlib         # 人脸识别库
import numpy as np  #数据处理库
import cv2          # 图像处理库

detector = dlib.get_frontal_face_detector()#与人脸检测相同，使用dlib自带的frontal_face_detector作为人脸检测器

predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')#使用官方提供的模型构建特征提取器

#读取图像
path = "/home/sunwen/image/"
img = cv2.imread(path+"friend0.jpg")
#print("img/shape:", img.shape)
dets = detector(img)
print("人脸数", len(dets))

height_max = 0
width_sum = 0

# 计算要生成的图像img_blank大小
for k, d in enumerate(dets):

    # 计算矩形大小
  
    pos_start = tuple([d.left(), d.top()])
    pos_end = tuple([d.right(), d.bottom()])

    # 璁＄畻鐭╁舰妗嗗ぇ灏?
    height = d.bottom()-d.top()
    width = d.right()-d.left()

 
    width_sum += width

    if height > height_max:
        height_max = height
    else:
        height_max = height_max

print("img_blank的大小：")
print("高度", height_max, "宽度", width_sum)

img_blank = np.zeros((height_max, width_sum, 3), np.uint8)

blank_start = 0

for k, d in enumerate(dets):
    height = d.bottom()-d.top()
    width = d.right()-d.left()

    for i in range(height):
        for j in range(width):
                img_blank[i][blank_start+j] = img[d.top()+i][d.left()+j]
 
    blank_start += width
cv2.namedWindow("img_faces", 2)
cv2.imshow("img_faces", img_blank)
#cv2.imwrite("img_new.jpg", img_blank)
cv2.waitKey(0）

结果

 剪切的人脸保存如下：

2、Matlab实现：

clc;
clear;
faceDetector = vision.CascadeObjectDetector();
file_path='E:/database/Normal/';
path_list=dir(strcat(file_path,'*.tif'));
img_num=length(path_list);
if img_num>0
    for j=1:img_num
        image_name=path_list(j).name;
        I=imread(strcat(file_path,image_name));
        bbox=step(faceDetector,I);
        faceOut = insertObjectAnnotation(I,'rectangle',bbox,'face');
        faceout1=imcrop(I,bbox);
        faceout2=imresize(faceout1,[80,80]);
        imwrite(faceout2,strcat(num2str(j+1004),'.tif'));
    end
end

运行结果：