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Python 人脸识别实现（三种方式）

admin 阅读： 2024-03-25

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所有涉及的配置文件（xml,dat）存储在这里：
https://jhc001.lanzoub.com/iyaeo0w8jkgb
密码:JDBC

所有 sdk 包下内容均为自定义，跑不了直接自己改输入就行

代码功能描述：
识别图像中的人面部位置，并将面部裁切下来，存储到excel里（excel里最多存储8张，按照从左到右顺序存储）

# !/usr/bin/env python3 # -*- coding: UTF-8 -*- """ @author : v_jiaohaicheng@baidu.com @des :基于opencv实现 """ import os import cv2 import xlsxwriter from io import BytesIO class CVFaceCheck(): def get_img_name(self,imgfile): return os.path.split(imgfile)[0],os.path.split(imgfile)[-1].split(".")[0] def get_img_date(self,img_file): image_file = open(img_file, 'rb') image_data = BytesIO(image_file.read()) image_file.close() return image_data def read_img(self,imgfile): return cv2.imread(imgfile) def tran_gray(self,img): return cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) def get_face_opt(self,gray): # face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_alt.xml') face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml') # Detect faces faces = face_cascade.detectMultiScale(gray, 1.1, 4) # Draw rectangle around the faces and crop the faces for (x, y, w, h) in faces: yield x, y, w, h def cut_face(self,gray,img,imgfile): map = {} for index,value in enumerate(self.get_face_opt(gray)): x, y, w, h = value faces = img[y:y + h,x:x + w] save_face_name = os.path.join(self.get_img_name(imgfile)[0],'face_{}.jpg'.format(index)) cv2.imwrite(save_face_name, cv2.resize(faces,[100,100])) map[x] = { "img_name":save_face_name, "face_opt":[y,y + h, x,x + w], } return map def remove_face_cut(self,res_sort): for _,value in res_sort.items(): img_name = value["img_name"] os.remove(img_name) def write_excel(self,imgfile,face_map,save_path = "./",sheet="Sheet1"): save_excel_file = os.path.join(save_path,self.get_img_name(imgfile)[-1])+".xlsx" workbook = xlsxwriter.Workbook(save_excel_file) worksheet = workbook.add_worksheet(sheet) lis = ["人物ID","0","1","2","3","4","5","6","7"] style = { 'font_name': '微软雅黑', 'font_size': 15, 'color':"red", 'bold': True, 'border': 0, 'align': 'center', 'valign': 'vcenter', 'text_wrap': False, } cell_format = workbook.add_format(style) worksheet.merge_range(0,0,0,9,"人物关系矩阵",cell_format=cell_format) for col,value in enumerate(lis): worksheet.write_string( row=1, col=col+1, string=str(value), ) for row,value in enumerate(lis): worksheet.write_string( row=row+2, col=0, string=str(value), ) worksheet.write_string( row=2, col=1, string="人物图片", ) num = 0 for _,val in face_map.items(): num += 1 img_file = val["img_name"] image_data = self.get_img_date(img_file) if num <= 8: worksheet.insert_image( row = 2, col = num+1, filename = img_file, options={"image_data":image_data}, ) worksheet.insert_image( row=num+2, col=1, filename=img_file, options={"image_data": image_data}, ) worksheet.set_column(1, num + 1, 14) worksheet.set_row(2, 100) worksheet.set_row(num+2, 100) workbook.close() def process(self,imgfile,out_path): # Read the input image img = self.read_img(imgfile) # cv2.imshow("test",img) # cv2.waitKey(0) # Convert into grayscale gray = self.tran_gray(img) res = self.cut_face(gray,img,imgfile) res_sort = dict(sorted(res.items(),key=lambda x:x[0])) print(res_sort) self.write_excel(imgfile,res_sort,save_path=out_path) # self.remove_face_cut(res_sort) if __name__ == '__main__': # file = input("输入图片位置:") # out_path = input("输入excel存储路径:(默认为当前路径下)") # if out_path == "": # out_path = "./" cfc = CVFaceCheck() # file = R"D:\PythonDevelopmentTools\tests\cv_test\face_check_cut\npw1.jpg" # out_file = R"D:\PythonDevelopmentTools\tests\cv_test\face_check_cut\res" from sdk.utils.util_folder import FolderPath out_path = R"D:\Desktop\res" os.makedirs(out_path, exist_ok=True) for file in FolderPath.get_absfiles(R"D:\Desktop\111111"): # os.makedirs(out_path,exist_ok=True) _file = file["filepath"] print(_file) cfc.process(_file,out_path) input("回车继续")

# !/usr/bin/env python3 # -*- coding: UTF-8 -*- """ @author : v_jiaohaicheng@baidu.com @des :基于dblib实现 """ import os import cv2 import dlib import numpy as np import xlsxwriter from io import BytesIO class DlibFaceCheck(): def read_img(self,imgfile): return cv2.imread(imgfile) def get_img_name(self,imgfile): return os.path.split(imgfile)[0],os.path.split(imgfile)[-1].split(".")[0] def get_img_date(self,img_file): image_file = open(img_file, 'rb') image_data = BytesIO(image_file.read()) image_file.close() return image_data def get_face_opt(self,img): predictor_path = 'shape_predictor_68_face_landmarks.dat' detector = dlib.get_frontal_face_detector() predictor = dlib.shape_predictor(predictor_path) faces = detector(img, 0) if len(faces): print('==> Found %d face in this image.' % len(faces)) for i in range(len(faces)): landmarks = np.matrix([[p.x, p.y] for p in predictor(img, faces[i]).parts()]) lis = landmarks.tolist() opt1 = (min([i[0] for i in lis]), min([i[1] for i in lis])) opt2 = (max([i[0] for i in lis]), max([i[1] for i in lis])) # print(opt1,opt2) yield opt1,opt2 else: print('Face not found!') def get_map(self,imgfile,save_path): map = {} img = self.read_img(imgfile) for index, value in enumerate(self.get_face_opt(img)): opt1, opt2 = value faces = img[opt1[-1]:opt2[-1], opt1[0]:opt2[0]] save_img_file = os.path.join(save_path,"res_{}.jpg".format(index)) cv2.imwrite(save_img_file, cv2.resize(faces,(100,100))) map[index] = { "img_name": save_img_file, "face_opt": [[opt1[0], opt1[-1]], [opt2[-1], opt2[0]]] } return map def write_excel(self,imgfile,face_map,save_path = "./",sheet="Sheet1"): save_excel_file = os.path.join(save_path,self.get_img_name(imgfile)[-1])+".xlsx" workbook = xlsxwriter.Workbook(save_excel_file) worksheet = workbook.add_worksheet(sheet) lis = ["人物ID","0","1","2","3","4","5","6","7"] style = { 'font_name': '微软雅黑', 'font_size': 15, 'color':"red", 'bold': True, 'border': 0, 'align': 'center', 'valign': 'vcenter', 'text_wrap': False, } cell_format = workbook.add_format(style) worksheet.merge_range(0,0,0,9,"人物关系矩阵",cell_format=cell_format) for col,value in enumerate(lis): worksheet.write_string( row=1, col=col+1, string=str(value), ) for row,value in enumerate(lis): worksheet.write_string( row=row+2, col=0, string=str(value), ) worksheet.write_string( row=2, col=1, string="人物图片", ) num = 0 for _,val in face_map.items(): num += 1 img_file = val["img_name"] image_data = self.get_img_date(img_file) if num <= 8: worksheet.insert_image( row = 2, col = num+1, filename = img_file, options={"image_data":image_data}, ) worksheet.insert_image( row=num+2, col=1, filename=img_file, options={"image_data": image_data}, ) worksheet.set_column(1, num + 1, 14) worksheet.set_row(2, 100) worksheet.set_row(num+2, 100) workbook.close() def remove_face_cut(self,res_sort): for _,value in res_sort.items(): img_name = value["img_name"] os.remove(img_name) def process(self,imgfile,save_path): map = self.get_map(imgfile,save_path) sort_map = dict(sorted(map.items(),key=lambda x:x[0])) self.write_excel(imgfile, sort_map, save_path=save_path) self.remove_face_cut(sort_map) if __name__ == '__main__': file = input("输入图片位置:") out_path = input("输入excel存储路径:(默认为当前路径下)") if out_path == "": out_path = "./" else: os.makedirs(out_path,exist_ok=True) dfc = DlibFaceCheck() dfc.process(file,out_path)

# !/usr/bin/env python3 # -*- coding: UTF-8 -*- """ @author : v_jiaohaicheng@baidu.com @des :基于百度api实现 """ import requests # 自己注册后填在这 APP_ID = "" API_KEY = "" SECRET_KEY= "" class ApiFaceCheck(): def get_access_token(self,ak,sk): url = "https://aip.baidubce.com/oauth/2.0/token?grant_type=client_credentials&client_id={}&client_secret={}".format( ak,sk ) headers = { 'Content-Type': 'application/json', 'Accept': 'application/json' } payload = "" response = requests.request("POST", url, headers=headers, data=payload) return response.json()["access_token"] def get_face_opt(self,up_url,ak=API_KEY,sk=SECRET_KEY): request_url = "https://aip.baidubce.com/rest/2.0/face/v3/detect" params = { "image":"{}".format(up_url), "image_type":"URL", "max_face_num":8, "face_type":"LIVE" } access_token = self.get_access_token(ak,sk) request_url = request_url + "?access_token=" + access_token headers = {'content-type': 'application/json'} response = requests.post(request_url, data=params, headers=headers) return response.json()

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