Python如何实现人脸识别系统

小编给大家分享一下Python如何实现人脸识别系统，相信大部分人都还不怎么了解，因此分享这篇文章给大家参考一下，希望大家阅读完这篇文章后大有收获，下面让我们一起去了解一下吧！

基本原理
人脸识别和目标检测这些还不太一样，比如大家传统的训练一个目标检测模型，你只有对这个目标训练了之后，你的模型才能找到这样的目标，比如你的目标检测模型如果是检测植物的，那显然就不能检测动物。但是人脸识别就不一样，以你的手机为例，你发现你只录入了一次你的人脸信息，不需要训练，他就能准确的识别你，这里识别的原理是通过人脸识别的模型提取你脸部的特征向量，然后将实时检测到的你的人脸同数据库中保存的人脸进行比对，如果相似度超过一定的阈值之后，就认为比对成功。不过我这里说的只是简化版本的人脸识别，现在手机和门禁这些要复杂和安全的多，也不是简单平面上的人脸识别。

总结下来可以分为下面的步骤：

1.上传人脸到数据库

2.人脸检测

3.数据库比对并返回结果

这里我做了一个简答的示意图，可以帮助大家简单理解一下。

代码实现
废话不多说，这里就是我们的代码实现，代码我已经上传到码云，大家直接下载就行，地址就在博客开头。

不会安装python环境的兄弟请看这里：如何在pycharm中配置anaconda的虚拟环境

创建虚拟环境
创建虚拟环境前请大家先下载博客开头的码云源码到本地。

本次我们需要使用到python3.7的虚拟环境，命令如下：

conda create -n face python==3.7.3
conda activate face

安装必要的库

pip install -r requirements.txt

愉快地开始你的人脸识别吧！

执行下面的主文件即可

python UI.py

或者在pycharm中按照下面的方式直接运行即可

首先将你需要识别的人脸上传到数据库中

通过第二个视频检测功能识别实时的人脸

详细的代码如下：

# -*- coding: utf-8 -*-
"""
-------------------------------------------------
Project Name: yolov5-jungong
File Name: window.py.py
Author: chenming
Create Date: 2021/11/8
Description：图形化界面，可以检测摄像头、视频和图片文件
-------------------------------------------------
"""
# 应该在界面启动的时候就将模型加载出来，设置tmp的目录来放中间的处理结果
import shutil
import PyQt5.QtCore
from PyQt5.QtGui import *
from PyQt5.QtCore import *
from PyQt5.QtWidgets import *
import threading
import argparse
import os
import sys
from pathlib import Path
import cv2
import torch
import torch.backends.cudnn as cudnn
import os.path as osp
FILE = Path(__file__).resolve()
ROOT = FILE.parents[0]  # YOLOv5 root directory
if str(ROOT) not in sys.path:sys.path.append(str(ROOT))  # add ROOT to PATH
ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relativefrom models.common import DetectMultiBackend
from utils.datasets import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreams
from utils.general import (LOGGER, check_file, check_img_size, check_imshow, check_requirements, colorstr,increment_path, non_max_suppression, print_args, scale_coords, strip_optimizer, xyxy2xywh)
from utils.plots import Annotator, colors, save_one_box
from utils.torch_utils import select_device, time_sync# 添加一个关于界面
# 窗口主类
class MainWindow(QTabWidget):# 基本配置不动，然后只动第三个界面def __init__(self):# 初始化界面super().__init__()self.setWindowTitle('Target detection system')self.resize(1200, 800)self.setWindowIcon(QIcon("images/UI/lufei.png"))# 图片读取进程self.output_size = 480self.img2predict = ""self.device = 'cpu'# # 初始化视频读取线程self.vid_source = '0'  # 初始设置为摄像头self.stopEvent = threading.Event()self.webcam = Trueself.stopEvent.clear()self.model = self.model_load(weights="runs/train/exp_yolov5s/weights/best.pt",device="cpu")  # todo 指明模型加载的位置的设备self.initUI()self.reset_vid()'''***模型初始化***'''@torch.no_grad()def model_load(self, weights="",  # model.pt path(s)device='',  # cuda device, i.e. 0 or 0,1,2,3 or cpuhalf=False,  # use FP16 half-precision inferencednn=False,  # use OpenCV DNN for ONNX inference):device = select_device(device)half &= device.type != 'cpu'  # half precision only supported on CUDAdevice = select_device(device)model = DetectMultiBackend(weights, device=device, dnn=dnn)stride, names, pt, jit, onnx = model.stride, model.names, model.pt, model.jit, model.onnx# Halfhalf &= pt and device.type != 'cpu'  # half precision only supported by PyTorch on CUDAif pt:model.model.half() if half else model.model.float()print("模型加载完成!")return model'''***界面初始化***'''def initUI(self):# 图片检测子界面font_title = QFont('楷体', 16)font_main = QFont('楷体', 14)# 图片识别界面, 两个按钮，上传图片和显示结果img_detection_widget = QWidget()img_detection_layout = QVBoxLayout()img_detection_title = QLabel("图片识别功能")img_detection_title.setFont(font_title)mid_img_widget = QWidget()mid_img_layout = QHBoxLayout()self.left_img = QLabel()self.right_img = QLabel()self.left_img.setPixmap(QPixmap("images/UI/up.jpeg"))self.right_img.setPixmap(QPixmap("images/UI/right.jpeg"))self.left_img.setAlignment(Qt.AlignCenter)self.right_img.setAlignment(Qt.AlignCenter)mid_img_layout.addWidget(self.left_img)mid_img_layout.addStretch(0)mid_img_layout.addWidget(self.right_img)mid_img_widget.setLayout(mid_img_layout)up_img_button = QPushButton("上传图片")det_img_button = QPushButton("开始检测")up_img_button.clicked.connect(self.upload_img)det_img_button.clicked.connect(self.detect_img)up_img_button.setFont(font_main)det_img_button.setFont(font_main)up_img_button.setStyleSheet("QPushButton{color:white}""QPushButton:hover{background-color: rgb(2,110,180);}""QPushButton{background-color:rgb(48,124,208)}""QPushButton{border:2px}""QPushButton{border-radius:5px}""QPushButton{padding:5px 5px}""QPushButton{margin:5px 5px}")det_img_button.setStyleSheet("QPushButton{color:white}""QPushButton:hover{background-color: rgb(2,110,180);}""QPushButton{background-color:rgb(48,124,208)}""QPushButton{border:2px}""QPushButton{border-radius:5px}""QPushButton{padding:5px 5px}""QPushButton{margin:5px 5px}")img_detection_layout.addWidget(img_detection_title, alignment=Qt.AlignCenter)img_detection_layout.addWidget(mid_img_widget, alignment=Qt.AlignCenter)img_detection_layout.addWidget(up_img_button)img_detection_layout.addWidget(det_img_button)img_detection_widget.setLayout(img_detection_layout)# todo 视频识别界面# 视频识别界面的逻辑比较简单，基本就从上到下的逻辑vid_detection_widget = QWidget()vid_detection_layout = QVBoxLayout()vid_title = QLabel("视频检测功能")vid_title.setFont(font_title)self.vid_img = QLabel()self.vid_img.setPixmap(QPixmap("images/UI/up.jpeg"))vid_title.setAlignment(Qt.AlignCenter)self.vid_img.setAlignment(Qt.AlignCenter)self.webcam_detection_btn = QPushButton("摄像头实时监测")self.mp4_detection_btn = QPushButton("视频文件检测")self.vid_stop_btn = QPushButton("停止检测")self.webcam_detection_btn.setFont(font_main)self.mp4_detection_btn.setFont(font_main)self.vid_stop_btn.setFont(font_main)self.webcam_detection_btn.setStyleSheet("QPushButton{color:white}""QPushButton:hover{background-color: rgb(2,110,180);}""QPushButton{background-color:rgb(48,124,208)}""QPushButton{border:2px}""QPushButton{border-radius:5px}""QPushButton{padding:5px 5px}""QPushButton{margin:5px 5px}")self.mp4_detection_btn.setStyleSheet("QPushButton{color:white}""QPushButton:hover{background-color: rgb(2,110,180);}""QPushButton{background-color:rgb(48,124,208)}""QPushButton{border:2px}""QPushButton{border-radius:5px}""QPushButton{padding:5px 5px}""QPushButton{margin:5px 5px}")self.vid_stop_btn.setStyleSheet("QPushButton{color:white}""QPushButton:hover{background-color: rgb(2,110,180);}""QPushButton{background-color:rgb(48,124,208)}""QPushButton{border:2px}""QPushButton{border-radius:5px}""QPushButton{padding:5px 5px}""QPushButton{margin:5px 5px}")self.webcam_detection_btn.clicked.connect(self.open_cam)self.mp4_detection_btn.clicked.connect(self.open_mp4)self.vid_stop_btn.clicked.connect(self.close_vid)# 添加组件到布局上vid_detection_layout.addWidget(vid_title)vid_detection_layout.addWidget(self.vid_img)vid_detection_layout.addWidget(self.webcam_detection_btn)vid_detection_layout.addWidget(self.mp4_detection_btn)vid_detection_layout.addWidget(self.vid_stop_btn)vid_detection_widget.setLayout(vid_detection_layout)# todo 关于界面about_widget = QWidget()about_layout = QVBoxLayout()about_title = QLabel('欢迎使用目标检测系统\n\n 提供付费指导：有需要的好兄弟加下面的QQ即可')  # todo 修改欢迎词语about_title.setFont(QFont('楷体', 18))about_title.setAlignment(Qt.AlignCenter)about_img = QLabel()about_img.setPixmap(QPixmap('images/UI/qq.png'))about_img.setAlignment(Qt.AlignCenter)# label4.setText("<a href='https://oi.wiki/wiki/学习率的调整'>如何调整学习率</a>")label_super = QLabel()  # todo 更换作者信息label_super.setText("<a href='https://blog.csdn.net/ECHOSON'>或者你可以在这里找到我-->肆十二</a>")label_super.setFont(QFont('楷体', 16))label_super.setOpenExternalLinks(True)# label_super.setOpenExternalLinks(True)label_super.setAlignment(Qt.AlignRight)about_layout.addWidget(about_title)about_layout.addStretch()about_layout.addWidget(about_img)about_layout.addStretch()about_layout.addWidget(label_super)about_widget.setLayout(about_layout)self.left_img.setAlignment(Qt.AlignCenter)self.addTab(img_detection_widget, '图片检测')self.addTab(vid_detection_widget, '视频检测')self.addTab(about_widget, '联系我')self.setTabIcon(0, QIcon('images/UI/lufei.png'))self.setTabIcon(1, QIcon('images/UI/lufei.png'))self.setTabIcon(2, QIcon('images/UI/lufei.png'))'''***上传图片***'''def upload_img(self):# 选择录像文件进行读取fileName, fileType = QFileDialog.getOpenFileName(self, 'Choose file', '', '*.jpg *.png *.tif *.jpeg')if fileName:suffix = fileName.split(".")[-1]save_path = osp.join("images/tmp", "tmp_upload." + suffix)shutil.copy(fileName, save_path)# 应该调整一下图片的大小，然后统一防在一起im0 = cv2.imread(save_path)resize_scale = self.output_size / im0.shape[0]im0 = cv2.resize(im0, (0, 0), fx=resize_scale, fy=resize_scale)cv2.imwrite("images/tmp/upload_show_result.jpg", im0)# self.right_img.setPixmap(QPixmap("images/tmp/single_result.jpg"))self.img2predict = fileNameself.left_img.setPixmap(QPixmap("images/tmp/upload_show_result.jpg"))# todo 上传图片之后右侧的图片重置，self.right_img.setPixmap(QPixmap("images/UI/right.jpeg"))'''***检测图片***'''def detect_img(self):model = self.modeloutput_size = self.output_sizesource = self.img2predict  # file/dir/URL/glob, 0 for webcamimgsz = 640  # inference size (pixels)conf_thres = 0.25  # confidence thresholdiou_thres = 0.45  # NMS IOU thresholdmax_det = 1000  # maximum detections per imagedevice = self.device  # cuda device, i.e. 0 or 0,1,2,3 or cpuview_img = False  # show resultssave_txt = False  # save results to *.txtsave_conf = False  # save confidences in --save-txt labelssave_crop = False  # save cropped prediction boxesnosave = False  # do not save images/videosclasses = None  # filter by class: --class 0, or --class 0 2 3agnostic_nms = False  # class-agnostic NMSaugment = False  # ugmented inferencevisualize = False  # visualize featuresline_thickness = 3  # bounding box thickness (pixels)hide_labels = False  # hide labelshide_conf = False  # hide confidenceshalf = False  # use FP16 half-precision inferencednn = False  # use OpenCV DNN for ONNX inferenceprint(source)if source == "":QMessageBox.warning(self, "请上传", "请先上传图片再进行检测")else:source = str(source)device = select_device(self.device)webcam = Falsestride, names, pt, jit, onnx = model.stride, model.names, model.pt, model.jit, model.onnximgsz = check_img_size(imgsz, s=stride)  # check image sizesave_img = not nosave and not source.endswith('.txt')  # save inference images# Dataloaderif webcam:view_img = check_imshow()cudnn.benchmark = True  # set True to speed up constant image size inferencedataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt and not jit)bs = len(dataset)  # batch_sizeelse:dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt and not jit)bs = 1  # batch_sizevid_path, vid_writer = [None] * bs, [None] * bs# Run inferenceif pt and device.type != 'cpu':model(torch.zeros(1, 3, *imgsz).to(device).type_as(next(model.model.parameters())))  # warmupdt, seen = [0.0, 0.0, 0.0], 0for path, im, im0s, vid_cap, s in dataset:t1 = time_sync()im = torch.from_numpy(im).to(device)im = im.half() if half else im.float()  # uint8 to fp16/32im /= 255  # 0 - 255 to 0.0 - 1.0if len(im.shape) == 3:im = im[None]  # expand for batch dimt2 = time_sync()dt[0] += t2 - t1# Inference# visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else Falsepred = model(im, augment=augment, visualize=visualize)t3 = time_sync()dt[1] += t3 - t2# NMSpred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)dt[2] += time_sync() - t3# Second-stage classifier (optional)# pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)# Process predictionsfor i, det in enumerate(pred):  # per imageseen += 1if webcam:  # batch_size >= 1p, im0, frame = path[i], im0s[i].copy(), dataset.counts += f'{i}: 'else:p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)p = Path(p)  # to Paths += '%gx%g ' % im.shape[2:]  # print stringgn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwhimc = im0.copy() if save_crop else im0  # for save_cropannotator = Annotator(im0, line_width=line_thickness, example=str(names))if len(det):# Rescale boxes from img_size to im0 sizedet[:, :4] = scale_coords(im.shape[2:], det[:, :4], im0.shape).round()# Print resultsfor c in det[:, -1].unique():n = (det[:, -1] == c).sum()  # detections per classs += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string# Write resultsfor *xyxy, conf, cls in reversed(det):if save_txt:  # Write to filexywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywhline = (cls, *xywh, conf) if save_conf else (cls, *xywh)  # label format# with open(txt_path + '.txt', 'a') as f:#     f.write(('%g ' * len(line)).rstrip() % line + '\n')if save_img or save_crop or view_img:  # Add bbox to imagec = int(cls)  # integer classlabel = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')annotator.box_label(xyxy, label, color=colors(c, True))# if save_crop:#     save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg',#                  BGR=True)# Print time (inference-only)LOGGER.info(f'{s}Done. ({t3 - t2:.3f}s)')# Stream resultsim0 = annotator.result()# if view_img:#     cv2.imshow(str(p), im0)#     cv2.waitKey(1)  # 1 millisecond# Save results (image with detections)resize_scale = output_size / im0.shape[0]im0 = cv2.resize(im0, (0, 0), fx=resize_scale, fy=resize_scale)cv2.imwrite("images/tmp/single_result.jpg", im0)# 目前的情况来看，应该只是ubuntu下会出问题，但是在windows下是完整的，所以继续self.right_img.setPixmap(QPixmap("images/tmp/single_result.jpg"))# 视频检测，逻辑基本一致，有两个功能，分别是检测摄像头的功能和检测视频文件的功能，先做检测摄像头的功能。'''### 界面关闭事件 ### '''def closeEvent(self, event):reply = QMessageBox.question(self,'quit',"Are you sure?",QMessageBox.Yes | QMessageBox.No,QMessageBox.No)if reply == QMessageBox.Yes:self.close()event.accept()else:event.ignore()'''### 视频关闭事件 ### '''def open_cam(self):self.webcam_detection_btn.setEnabled(False)self.mp4_detection_btn.setEnabled(False)self.vid_stop_btn.setEnabled(True)self.vid_source = '0'self.webcam = Trueth = threading.Thread(target=self.detect_vid)th.start()'''### 开启视频文件检测事件 ### '''def open_mp4(self):fileName, fileType = QFileDialog.getOpenFileName(self, 'Choose file', '', '*.mp4 *.avi')if fileName:self.webcam_detection_btn.setEnabled(False)self.mp4_detection_btn.setEnabled(False)# self.vid_stop_btn.setEnabled(True)self.vid_source = fileNameself.webcam = Falseth = threading.Thread(target=self.detect_vid)th.start()'''### 视频开启事件 ### '''# 视频和摄像头的主函数是一样的，不过是传入的source不同罢了def detect_vid(self):# passmodel = self.modeloutput_size = self.output_size# source = self.img2predict  # file/dir/URL/glob, 0 for webcamimgsz = 640  # inference size (pixels)conf_thres = 0.25  # confidence thresholdiou_thres = 0.45  # NMS IOU thresholdmax_det = 1000  # maximum detections per image# device = self.device  # cuda device, i.e. 0 or 0,1,2,3 or cpuview_img = False  # show resultssave_txt = False  # save results to *.txtsave_conf = False  # save confidences in --save-txt labelssave_crop = False  # save cropped prediction boxesnosave = False  # do not save images/videosclasses = None  # filter by class: --class 0, or --class 0 2 3agnostic_nms = False  # class-agnostic NMSaugment = False  # ugmented inferencevisualize = False  # visualize featuresline_thickness = 3  # bounding box thickness (pixels)hide_labels = False  # hide labelshide_conf = False  # hide confidenceshalf = False  # use FP16 half-precision inferencednn = False  # use OpenCV DNN for ONNX inferencesource = str(self.vid_source)webcam = self.webcamdevice = select_device(self.device)stride, names, pt, jit, onnx = model.stride, model.names, model.pt, model.jit, model.onnximgsz = check_img_size(imgsz, s=stride)  # check image sizesave_img = not nosave and not source.endswith('.txt')  # save inference images# Dataloaderif webcam:view_img = check_imshow()cudnn.benchmark = True  # set True to speed up constant image size inferencedataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt and not jit)bs = len(dataset)  # batch_sizeelse:dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt and not jit)bs = 1  # batch_sizevid_path, vid_writer = [None] * bs, [None] * bs# Run inferenceif pt and device.type != 'cpu':model(torch.zeros(1, 3, *imgsz).to(device).type_as(next(model.model.parameters())))  # warmupdt, seen = [0.0, 0.0, 0.0], 0for path, im, im0s, vid_cap, s in dataset:t1 = time_sync()im = torch.from_numpy(im).to(device)im = im.half() if half else im.float()  # uint8 to fp16/32im /= 255  # 0 - 255 to 0.0 - 1.0if len(im.shape) == 3:im = im[None]  # expand for batch dimt2 = time_sync()dt[0] += t2 - t1# Inference# visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else Falsepred = model(im, augment=augment, visualize=visualize)t3 = time_sync()dt[1] += t3 - t2# NMSpred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det)dt[2] += time_sync() - t3# Second-stage classifier (optional)# pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)# Process predictionsfor i, det in enumerate(pred):  # per imageseen += 1if webcam:  # batch_size >= 1p, im0, frame = path[i], im0s[i].copy(), dataset.counts += f'{i}: 'else:p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)p = Path(p)  # to Path# save_path = str(save_dir / p.name)  # im.jpg# txt_path = str(save_dir / 'labels' / p.stem) + (#     '' if dataset.mode == 'image' else f'_{frame}')  # im.txts += '%gx%g ' % im.shape[2:]  # print stringgn = torch.tensor(im0.shape)[[1, 0, 1, 0]]  # normalization gain whwhimc = im0.copy() if save_crop else im0  # for save_cropannotator = Annotator(im0, line_width=line_thickness, example=str(names))if len(det):# Rescale boxes from img_size to im0 sizedet[:, :4] = scale_coords(im.shape[2:], det[:, :4], im0.shape).round()# Print resultsfor c in det[:, -1].unique():n = (det[:, -1] == c).sum()  # detections per classs += f"{n} {names[int(c)]}{'s' * (n > 1)}, "  # add to string# Write resultsfor *xyxy, conf, cls in reversed(det):if save_txt:  # Write to filexywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywhline = (cls, *xywh, conf) if save_conf else (cls, *xywh)  # label format# with open(txt_path + '.txt', 'a') as f:#     f.write(('%g ' * len(line)).rstrip() % line + '\n')if save_img or save_crop or view_img:  # Add bbox to imagec = int(cls)  # integer classlabel = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')annotator.box_label(xyxy, label, color=colors(c, True))# if save_crop:#     save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg',#                  BGR=True)# Print time (inference-only)LOGGER.info(f'{s}Done. ({t3 - t2:.3f}s)')# Stream results# Save results (image with detections)im0 = annotator.result()frame = im0resize_scale = output_size / frame.shape[0]frame_resized = cv2.resize(frame, (0, 0), fx=resize_scale, fy=resize_scale)cv2.imwrite("images/tmp/single_result_vid.jpg", frame_resized)self.vid_img.setPixmap(QPixmap("images/tmp/single_result_vid.jpg"))# self.vid_img# if view_img:# cv2.imshow(str(p), im0)# self.vid_img.setPixmap(QPixmap("images/tmp/single_result_vid.jpg"))# cv2.waitKey(1)  # 1 millisecondif cv2.waitKey(25) & self.stopEvent.is_set() == True:self.stopEvent.clear()self.webcam_detection_btn.setEnabled(True)self.mp4_detection_btn.setEnabled(True)self.reset_vid()break# self.reset_vid()'''### 界面重置事件 ### '''def reset_vid(self):self.webcam_detection_btn.setEnabled(True)self.mp4_detection_btn.setEnabled(True)self.vid_img.setPixmap(QPixmap("images/UI/up.jpeg"))self.vid_source = '0'self.webcam = True'''### 视频重置事件 ### '''def close_vid(self):self.stopEvent.set()self.reset_vid()if __name__ == "__main__":app = QApplication(sys.argv)mainWindow = MainWindow()mainWindow.show()sys.exit(app.exec_())

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