Python实战——为人脸照片添加口罩

文章目录

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。

#!/usr/bin/env python# -*- coding: utf-8 -*-# @Author  : 2014Veeimport 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/')classFaceMasker:
    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 =Nonedefmask(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 =Falsefor face_landmark in face_landmarks:# check whether facial features meet requirement
            skip =Falsefor facial_feature in self.KEY_FACIAL_FEATURES:if facial_feature notin face_landmark:
                    skip =Truebreakif 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}')@staticmethoddefget_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]))returnint(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 +=1continue
    FaceMasker(image, BLUE_IMAGE_PATH, WHITE_IMAGE_PATH, SAVE_PATH, SAVE_PATH2,'hog').mask()
    cnt +=1print(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

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/'defprocessImage(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 inrange(img.size[1]):for x inrange(img.size[0]):if pixdata[x, y][0]<90:
                pixdata[x, y]=(0,0,0,255)for y inrange(img.size[1]):for x inrange(img.size[0]):if pixdata[x, y][1]<136:
                pixdata[x, y]=(0,0,0,255)for y inrange(img.size[1]):for x inrange(img.size[0]):if pixdata[x, y][2]>0:
                pixdata[x, y]=(255,255,255,255)
    img.save(destsoure + name, imgtype)defrun():# 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()