Python实战项目：为人脸照片添加口罩

前言

好想玩点不一样的，感觉平常的已经不能吸引大家了。想了又想，我今天给大家分享如何给人像添加口罩吧。毕竟最近疫情那么

严重，也只能玩玩这个了，大家千万别乱跑啊。

效果展示

数据集展示

数据集来源：使用了开源数据集FaceMask_CelebA

github地址：https://github.com/sevenHsu/FaceMask_CelebA.git

部分人脸数据集：

口罩样本数据集：

为人脸照片添加口罩代码

这部分有个库face_recognition需要安装，如果之前没有用过的小伙伴可能得费点功夫。

Face Recognition 库主要封装了dlib这一 C++ 图形库，通过 Python 语言将它封装为一个非常简单就可以实现人脸识别的 API

库，屏蔽了人脸识别的算法细节，大大降低了人脸识别功能的开发难度。

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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Author  : 2014Vee
import os
import numpy as np
from PIL import Image, ImageFile

__version__ = '0.3.0'

IMAGE_DIR = os.path.dirname
('E:/play/FaceMask_CelebA-master/facemask_image/')
WHITE_IMAGE_PATH = os.path.join
(IMAGE_DIR, 'front_14.png')
BLUE_IMAGE_PATH = os.path.join(IMAGE_DIR, 
'front_14.png')
SAVE_PATH = os.path.dirname
('E:/play/FaceMask_CelebA-
master/save/synthesis/')
SAVE_PATH2 = os.path.dirname
('E:/play/FaceMask_CelebA-
master/save/masks/')

class FaceMasker:    
KEY_FACIAL_FEATURES = ('nose_bridge',
 'chin')
 
    def __init__(self, face_path, 
mask_path, white_mask_path, save_path,
save_path2, model='hog'):        
self.face_path = face_path        
self.mask_path = mask_path        
self.save_path = save_path        
self.save_path2 = save_path2        
self.white_mask_path =
 white_mask_path        
self.model = model        
self._face_img: ImageFile = None        
self._black_face_img = None        
self._mask_img: ImageFile = None       
 self._white_mask_img = None
    def mask(self):       
import face_recognition

        face_image_np = 
face_recognition.load_image_file
(self.face_path)        
face_locations = 
face_recognition.face_locations
(face_image_np, model=self.model)        
face_landmarks = 
face_recognition.face_landmarks
(face_image_np, face_locations)        
self._face_img = Image.fromarray
(face_image_np)        
self._mask_img = Image.open
(self.mask_path)        
self._white_mask_img = Image.open
(self.white_mask_path)        
self._black_face_img = Image.new
('RGB', self._face_img.size, 0)
        found_face = False        
        for face_landmark in face_landmarks:            
        # check whether facial features meet requirement            
        skip = False            
        for facial_feature in self.KEY_FACIAL_FEATURES:                
        if facial_feature not in face_landmark:                    
 skip = True                    
 break           
if skip:                
continue
            
 # mask face            
 found_face = True           
  self._mask_face(face_landmark)
        
 if found_face:            
  # save           
   self._save()       
    else:            
print('Found no face.')
    def _mask_face(self, face_landmark: dict):        
nose_bridge = face_landmark['nose_bridge']        
nose_point = nose_bridge[len(nose_bridge) * 1 // 4]        
nose_v = np.array(nose_point)
chin = face_landmark['chin']        
chin_len = len(chin)        
chin_bottom_point = chin[chin_len // 2]        
chin_bottom_v = np.array(chin_bottom_point)        
chin_left_point = chin[chin_len // 8]        
chin_right_point = chin[chin_len * 7 // 8]
 # split mask and resize        
 width = self._mask_img.width        
 height = self._mask_img.height        
 width_ratio = 1.2       
  new_height = int(np.linalg.norm(nose_v - chin_bottom_v))
# left        
mask_left_img = self._mask_img.crop((0, 0, width // 2, height))        
mask_left_width = self.get_distance_from_point_to_line(chin_left_point, nose_point, chin_bottom_point)        
mask_left_width = int(mask_left_width * width_ratio)        
mask_left_img = mask_left_img.resize((mask_left_width, new_height))
# right        
mask_right_img = self._mask_img.crop((width // 2, 0, width, height))        
mask_right_width = self.get_distance_from_point_to_line(chin_right_point, nose_point, chin_bottom_point)        mask_right_width = int(mask_right_width * width_ratio)        
mask_right_img = mask_right_img.resize((mask_right_width, new_height))
 # merge mask        
 size = (mask_left_img.width + mask_right_img.width, new_height)        
 mask_img = Image.new('RGBA', size)        
 mask_img.paste(mask_left_img, (0, 0), mask_left_img)        
 mask_img.paste(mask_right_img, (mask_left_img.width, 0), mask_right_img)
# rotate mask        
angle = np.arctan2(chin_bottom_point[1] - nose_point[1], chin_bottom_point[0] - nose_point[0])        
rotated_mask_img = mask_img.rotate(angle, expand=True)
# calculate mask location        
center_x = (nose_point[0] + chin_bottom_point[0]) // 2        
center_y = (nose_point[1] + chin_bottom_point[1]) // 2
offset = mask_img.width // 2 - mask_left_img.width        
radian = angle * np.pi / 180        
box_x = center_x + int(offset * np.cos(radian)) - rotated_mask_img.width // 2        
box_y = center_y + int(offset * np.sin(radian)) - rotated_mask_img.height // 2
# add mask        
self._face_img.paste(mask_img, (box_x, box_y), mask_img)
# split mask and resize        
width = self._white_mask_img.width       
 height = self._white_mask_img.height        
  width_ratio = 1.2        
  new_height = int(np.linalg.norm(nose_v - chin_bottom_v))
 # left        
 mask_left_img = self._white_mask_img.crop((0, 0, width // 2, height))        
 mask_left_width = self.get_distance_from_point_to_line
 (chin_left_point, nose_point, chin_bottom_point)        
 mask_left_width = int(mask_left_width * width_ratio)        
 mask_left_img = mask_left_img.resize((mask_left_width, new_height))
# right        
mask_right_img = self._white_mask_img.crop((width // 2, 0, width, height))       
 mask_right_width = self.get_distance_from_point_to_line(chin_right_point, nose_point, chin_bottom_point)        mask_right_width = int(mask_right_width * width_ratio)       
  mask_right_img = mask_right_img.resize((mask_right_width, new_height))
 # merge mask        
 size = (mask_left_img.width + mask_right_img.width, new_height)        
 mask_img = Image.new('RGBA', size)        
 mask_img.paste(mask_left_img, (0, 0), mask_left_img)        
 mask_img.paste(mask_right_img, (mask_left_img.width, 0), mask_right_img)
# rotate mask        
angle = np.arctan2(chin_bottom_point[1] - nose_point[1], chin_bottom_point[0] - nose_point[0])        
rotated_mask_img = mask_img.rotate(angle, expand=True)
 # calculate mask location        
 center_x = (nose_point[0] + chin_bottom_point[0]) // 2        
 center_y = (nose_point[1] + chin_bottom_point[1]) // 2
offset = mask_img.width // 2 - mask_left_img.width        
radian = angle * np.pi / 180        
box_x = center_x + int(offset * np.cos(radian)) - rotated_mask_img.width // 2        
box_y = center_y + int(offset * np.sin(radian)) - rotated_mask_img.height // 2
 # add mask        
 self._black_face_img.paste(mask_img, (box_x, box_y), mask_img)
 def _save(self):        
path_splits = os.path.splitext(self.face_path)        
# new_face_path = self.save_path + '/' + os.path.basename(self.face_path) + '-with-mask' + path_splits[1]       
 # new_face_path2 = self.save_path2 + '/' + os.path.basename(self.face_path) + '-binary' + path_splits[1]        
 new_face_path = self.save_path + '/' + os.path.basename(self.face_path) + '-with-mask' + path_splits[1]        
 new_face_path2 = self.save_path2 + '/'  + os.path.basename(self.face_path) + '-binary' + path_splits[1]        self._face_img.save(new_face_path)        
 self._black_face_img.save(new_face_path2)
    #         print(f'Save to {new_face_path}')
    @staticmethod    def get_distance_from_point_to_line(point, line_point1, line_point2):       
     distance = np.abs((line_point2[1] - line_point1[1]) * point[0] +                          
     (line_point1[0] - line_point2[0]) * point[1] +                         
      (line_point2[0] - line_point1[0]) * line_point1[1] +                          
      (line_point1[1] - line_point2[1]) * line_point1[0]) / \                   
      np.sqrt((line_point2[1] - line_point1[1]) * (line_point2[1] - line_point1[1]) +                           
      (line_point1[0] - line_point2[0]) * 
      (line_point1[0] - line_point2[0]))        
      return int(distance)
    # FaceMasker("/home/aistudio/data/人脸.png", WHITE_IMAGE_PATH, True, 'hog').mask()

from pathlib import Path
images = Path("E:/play/FaceMask_CelebA-master/bbox_align_celeba").glob("*")cnt = 0for image in images:    
if cnt < 1:        
cnt += 1       
 continue    
 FaceMasker(image, BLUE_IMAGE_PATH, WHITE_IMAGE_PATH, SAVE_PATH, SAVE_PATH2, 'hog').
 mask()    
 cnt += 1    
 print(f"正在处理第{cnt}张图片，还有{99 - cnt}张图片")

掩膜生成代码

这部分其实就是对使用的口罩样本的二值化，因为后续要相关模型会用到

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import os
from PIL import Image
# 源目录
# MyPath = 'E:/play/FaceMask_CelebA
-master/facemask_image/'
MyPath = 'E:/play/FaceMask_CelebA-
master/save/masks/'
# 输出目录
OutPath = 'E:/play/FaceMask_CelebA-
master/save/Binarization/'

def processImage(filesoure, destsoure, 
name, imgtype):   
 '''   
  filesoure是存放待转换图片的目录    
  destsoure是存在输出转换后图片的目录    
  name是文件名    
  imgtype是文件类型   
   '''    
   imgtype = 'bmp' if imgtype == '.bmp' 
   else 'png'   
    # 打开图片    
    im = Image.open(filesoure + name)   
  # =============================================================================    #   
       #缩放比例   
#     rate =max(im.size[0]/640.0 if 
im.size[0] > 60 else 0, im.size[1]/1136.0 
if im.size[1] > 1136 else 0)    
#     if rate:    
#         im.thumbnail((im.size[0]/rate, im.size[1]/rate))    
# =============================================================================
img = im.convert("RGBA")    
pixdata = img.load()    
# 二值化    
for y in range(img.size[1]):        
for x in range(img.size[0]):            
if pixdata[x, y][0] < 90:                
pixdata[x, y] = (0, 0, 0, 255)
    for y in range(img.size[1]):        
    for x in range(img.size[0]):            
    if pixdata[x, y][1] < 136:                
    pixdata[x, y] = (0, 0, 0, 255)
    for y in range(img.size[1]):        
    for x in range(img.size[0]):            
    if pixdata[x, y][2] > 0:                
    pixdata[x, y] = (255, 255, 255, 255)    
    img.save(destsoure + name, imgtype)

def run():    
# 切换到源目录，遍历源目录下所有图片    
os.chdir(MyPath)    
for i in os.listdir(os.getcwd()):        
# 检查后缀        
postfix = os.path.splitext(i)[1]       
 name = os.path.splitext(i)[0]        
 name2 = name.split('.')        
 if name2[1] == 'jpg-binary' or name2[1] == 'png-binary':            
 processImage(MyPath, OutPath, i, postfix)

if __name__ == '__main__':   
 run()

最后

今天又到周末了，祝大家周末愉快，玩够了记得回来学习鸭！下一章见啦~~~