Python 人脸识别实现（三种方式）

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 osimport cv2import xlsxwriterfrom io import BytesIOclass 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 osimport cv2import dlibimport numpy as npimport xlsxwriterfrom io import BytesIOclass 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()

来源地址：https://blog.csdn.net/CXY00000/article/details/130684381