face-mask人脸自动加口罩工具

本文应用场景在于疫情期间复工后，检查员工口罩佩戴情况，基于这个安排给我尽可能生成各种角度的戴口罩的人脸图片，还有就是针对百度搜索中人脸关键字段的图片爬取。

本文先简单介绍一下第一个部分的工作，就是测试face-mask的效果，简单介绍一下face-mask是基于dlib和face_recognition两大人脸检测的库实现的人脸关键点检测的方法，由于我远程办公的情况所以就只能在自己的windows笔记本上进行环境的配置，而在配置环境的过程中就不可避免的遇到了dlib库怎么都配置不上，因为face_recognition是依赖于dllib的所以我就开启了探索研究如何在windows+py3.7+dlib的环境配置道路。终于在查阅多篇博客之后最后基于扶封的博客（感谢博主：），需要的同学点击链接即可开启配置dlib之路），得到了配置的方法。

dlib配置好了我们就开始按照要求来配置face-mask的环境了，建议使用pip指令下载，如下：

pip install face-mask

理论上在配置完dlib之后应该是顺风顺水，就可以愉快的跑测试代码了。

代码的主题部分在face_mask文件夹的__main__.py里，其中默认的口罩模板存放路径如下

在代码目录下的face文件夹放入图片就OK了，最后识别人脸和给人脸加口罩的代码如下：

#!/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.join(os.path.dirname(os.path.abspath(__file__)), 'images')
DEFAULT_IMAGE_PATH = os.path.join(IMAGE_DIR, 'default-mask.png')
BLACK_IMAGE_PATH = os.path.join(IMAGE_DIR, 'black-mask.png')
BLUE_IMAGE_PATH = os.path.join(IMAGE_DIR, 'blue-mask.png')
RED_IMAGE_PATH = os.path.join(IMAGE_DIR, 'red-mask.png')class FaceMasker:KEY_FACIAL_FEATURES = ('nose_bridge', 'chin')def __init__(self, face_path, mask_path, show=False, model='hog'):self.face_path = face_pathself.mask_path = mask_pathself.show = showself.model = modelself._face_img: ImageFile = Noneself._mask_img: ImageFile = Nonedef mask(self):import face_recognitionface_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)found_face = Falsefor face_landmark in face_landmarks:# check whether facial features meet requirementskip = Falsefor facial_feature in self.KEY_FACIAL_FEATURES:if facial_feature not in face_landmark:skip = Truebreakif skip:continue# mask facefound_face = Trueself._mask_face(face_landmark)if found_face:if self.show:self._face_img.show()# saveself._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 resizewidth = self._mask_img.widthheight = self._mask_img.heightwidth_ratio = 1.2new_height = int(np.linalg.norm(nose_v - chin_bottom_v))# leftmask_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))# rightmask_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 masksize = (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 maskangle = 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 locationcenter_x = (nose_point[0] + chin_bottom_point[0]) // 2center_y = (nose_point[1] + chin_bottom_point[1]) // 2offset = mask_img.width // 2 - mask_left_img.widthradian = angle * np.pi / 180box_x = center_x + int(offset * np.cos(radian)) - rotated_mask_img.width // 2box_y = center_y + int(offset * np.sin(radian)) - rotated_mask_img.height // 2# add maskself._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 = path_splits[0] + '-with-mask' + path_splits[1]self._face_img.save(new_face_path)print(f'Save to {new_face_path}')@staticmethoddef 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)if __name__ == '__main__':FaceMasker("./face/1.jpg", DEFAULT_IMAGE_PATH, True, 'hog').mask()

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