Python|Python实战项目（为人脸照片添加口罩）爬虫|python|爬虫|Python实战项目

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
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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：

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口罩样本数据集：
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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
【Python|Python实战项目（为人脸照片添加口罩）】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。

Python学习交流Q群：906715085### #!/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_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) 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}') @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) # 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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Python学习交流Q群：906715085#### 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 = https://www.it610.com/article/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~~~