用pyhon的PYQT5写一个漂亮的人脸识别是否戴口罩的客户端

话不多说先看效果
下面是没有戴口罩的效果（先用偶像帮我遮一下）

下面是戴了口罩的效果

再次描述一下这个效果，其实这不是图片，而是摄像头实时读取的，首先感谢AIZOOTech 的开源项目–FaceMaskDetection ，同时也 主要参考了以下几篇博客，在此一一感谢：
(https://blog.youkuaiyun.com/qq_41204464/article/details/104596777)
(https://blog.youkuaiyun.com/qq_41204464/article/details/106026650)
(https://www.freesion.com/article/8339842034/)
(https://zmister.com/archives/477.html)
搭载的环境是PYCHARM(免费版) + Anaconda，编辑器和环境的安装我就不说了，上边几个大神有提到过，自己去看，conda环境下pip安装的内容截图如下（我自己的机器在这些环境下可以正常运行，给朋友的机器上也试过可以用）。如下图自己pip install 就好了。最后别忘了 conda install tensorflow,我的理解是把pip 安装的库搭载到conda里面（欢迎指正错误）。最后conda list 检查一下和我的截图版本是否一致，若不一致再卸载掉某个不符合的，再重新安装和指定版本就可以比如：先 pip uninstall keras 然后再 pip install keras==2.4.3

需要注意的是我安装的是tensorflow 2.2.0版本，对应的是keras 2.4.3版本，之前我安装了keras 2.2版本是会报错的。另外其他几个主要的三方库我已经用红线标注出来了。

下面是主程序代码，为了方便我把UI设计的代码和主程序代码写到了一起。

import sys 
from PyQt5 import QtCore, QtGui, QtWidgets
import qtawesome
from PyQt5.QtCore import Qt
from PyQt5.QtWidgets import *
from PyQt5.QtCore import *
from PyQt5.QtGui import *
import cv2
import tensorflow_infer as tsf
class Ui_Form(object):
    def setupUi(self, Form):
        Form.setObjectName("Form")
        Form.resize(800, 511)
        self.verticalLayoutWidget = QtWidgets.QWidget(Form)
        self.verticalLayoutWidget.setGeometry(QtCore.QRect(10, 60, 131, 441))
        self.verticalLayoutWidget.setObjectName("verticalLayoutWidget")
        self.verticalLayout = QtWidgets.QVBoxLayout(self.verticalLayoutWidget)
        self.verticalLayout.setContentsMargins(0, 0, 0, 0)
        self.verticalLayout.setObjectName("verticalLayout")

        self.label1 = QtWidgets.QPushButton(self.verticalLayoutWidget)
        self.label1.setObjectName("labeltitle")
        self.verticalLayout.addWidget(self.label1)
        self.pushButton1 = QtWidgets.QPushButton(qtawesome.icon('fa.camera',color='white'),"") 
        self.pushButton1.setObjectName("pushButton")
        self.verticalLayout.addWidget(self.pushButton1)
        self.pushButton2 = QtWidgets.QPushButton(qtawesome.icon('fa.medkit',color='white'),"") 
        self.pushButton2.setObjectName("pushButton")
        self.verticalLayout.addWidget(self.pushButton2)
        self.pushButton3 = QtWidgets.QPushButton(qtawesome.icon('fa.smile-o',color='white'),"") 
        self.pushButton3.setObjectName("pushButton")
        self.verticalLayout.addWidget(self.pushButton3)
        self.label2 = QtWidgets.QPushButton(self.verticalLayoutWidget)
        self.label2.setObjectName("labeltitle")
        self.verticalLayout.addWidget(self.label2)
        self.pushButton4 = QtWidgets.QPushButton(qtawesome.icon('fa.hourglass-start',color='white'),"") 
        self.pushButton4.setObjectName("pushButton")
        self.verticalLayout.addWidget(self.pushButton4)
        self.pushButton5 = QtWidgets.QPushButton(qtawesome.icon('fa.hourglass-end',color='white'),"") 
        self.pushButton5.setObjectName("pushButton")
        self.verticalLayout.addWidget(self.pushButton5)
        self.horizontalLayoutWidget = QtWidgets.QWidget(Form)
        self.horizontalLayoutWidget.setGeometry(QtCore.QRect(10, 30, 131, 25))
        self.horizontalLayoutWidget.setObjectName("horizontalLayoutWidget")
        self.horizontalLayout = QtWidgets.QHBoxLayout(self.horizontalLayoutWidget)
        self.horizontalLayout.setContentsMargins(0, 0, 0, 0)
        self.horizontalLayout.setObjectName("horizontalLayout")

        self.verticalLayoutWidget.setStyleSheet('''     
        QPushButton{border:none;color:white;}     
        QPushButton#labeltitle{         
        border:none;         
        border-bottom:1px solid white;         
        font-size:18px;         
        font-weight:700;         
        font-family: "Helvetica Neue", Helvetica, Arial, sans-serif;     }     
        QPushButton#pushButton:hover{border-left:4px solid red;font-weight:700;}
        QWidget#verticalLayoutWidget{     
        background:gray;     
        border-top:1px solid white;     
        border-bottom:1px solid white;     
        border-left:1px solid white; 
        border-top-left-radius:10px;
        border-top-right-radius:10px; 
        border-bottom-right-radius:10px;
        border-bottom-left-radius:10px; }
        ''')

        self.pushButton8 = QtWidgets.QPushButton(qtawesome.icon('fa.minus-square',color='white'),"")  # 最小化按钮
        self.pushButton8.setObjectName("pushButton8")
        self.horizontalLayout.addWidget(self.pushButton8)

        self.pushButton9 = QtWidgets.QPushButton(qtawesome.icon('fa.window-close',color='white'),"") # 关闭按钮 
        self.pushButton9.setObjectName("pushButton9")
        self.horizontalLayout.addWidget(self.pushButton9)

        self.pushButton8.setStyleSheet('''QPushButton{background:#F76677;border-radius:5px;}QPushButton:hover{background:red;}''') 
        self.pushButton9.setStyleSheet('''QPushButton{background:#6DDF6D;border-radius:5px;}QPushButton:hover{background:green;}''')  
        self.pushButton8.clicked.connect(Form.showMinimized)
        self.pushButton9.clicked.connect(Form.closeEvent)


        self.verticalLayoutWidget_2 = QtWidgets.QWidget(Form)
        self.verticalLayoutWidget_2.setGeometry(QtCore.QRect(150, 30, 631, 471))
        self.verticalLayoutWidget_2.setObjectName("verticalLayoutWidget_2")
        self.verticalLayout_2 = QtWidgets.QVBoxLayout(self.verticalLayoutWidget_2)
        self.verticalLayout_2.setContentsMargins(0, 0, 0, 0)
        self.verticalLayout_2.setObjectName("verticalLayout_2")
        self.label = QtWidgets.QLabel(self.verticalLayoutWidget_2)
        self.label.setObjectName("label")
        self.label.setFixedSize(641,481)    #给显示视频的Label设置大小为641x481
        self.verticalLayout_2.addWidget(self.label)

        self.verticalLayoutWidget_2.setStyleSheet('''     
        QWidget#verticalLayoutWidget_2{     
        background:gray;     
        border-top:1px solid white;     
        border-bottom:1px solid white;     
        border-left:1px solid white; 
        border-top-left-radius:10px;
        border-top-right-radius:10px; 
        border-bottom-right-radius:10px;
        border-bottom-left-radius:10px; }
        ''')

        self.retranslateUi(Form)
        QtCore.QMetaObject.connectSlotsByName(Form)

        self.label1.setText( "口罩检测系统")
        self.pushButton1.setText("打开摄像头")
        self.pushButton2.setText("开启口罩检测")
        self.pushButton3.setText("关闭口罩检测")
        self.label2.setText("其他功能")
        self.pushButton4.setText("敬请期待")
        self.pushButton5.setText("敬请期待")
        
        self.label.setText("摄像头在开启的过程中会比较慢，请稍等，别乱点，以免卡死。")

    def retranslateUi(self, Form):
        _translate = QtCore.QCoreApplication.translate
        Form.setWindowTitle(_translate("Form", "Form"))
    
    def setNoTittle(self,Form):
        Form.setWindowFlags(Qt.FramelessWindowHint)
        Form.setWindowOpacity(1) # 设置窗口透明度 
        Form.setAttribute(Qt.WA_TranslucentBackground) # 设置窗口背景透明
    


class MainUi(QtWidgets.QMainWindow):  
    
    def mouseMoveEvent(self, e: QMouseEvent):  # 重写移动事件    
        if self._tracking:
            self._endPos = e.pos() - self._startPos
            self.move(self.pos() + self._endPos)

    def mousePressEvent(self, e: QMouseEvent):  
        if e.button() == Qt.LeftButton:
            self._startPos = QPoint(e.x(), e.y())
            self._tracking = True      

    def mouseReleaseEvent(self, e: QMouseEvent):
        if e.button() == Qt.LeftButton:
            self._tracking = False
            self._startPos = None
            self._endPos = None

    def closeEvent(self):
        reply = QMessageBox.question(self, '提示信息', '确认退出吗？', 
        QMessageBox.Yes | QMessageBox.No, QMessageBox.No)
        if reply == QMessageBox.Yes:
            QCoreApplication.instance().quit()
        else:
            pass

class MyMainForm(QtWidgets.QWidget,Ui_Form):
    def __init__(self,parent=None):  
        super().__init__(parent) #父类的构造函数
        self.timer_camera = QtCore.QTimer() #定义定时器，用于控制显示视频的帧率
        self.cap = cv2.VideoCapture()       #视频流
        self.CAM_NUM = 0                    #为0时表示视频流来自笔记本内置摄像头
        self.openkz = False
        
    def setupUi(self,Form):
        Ui_Form.setupUi(self,Form)
        
    def slot_init(self):
        self.pushButton1.clicked.connect(self.open_camera_clicked)    #若该按键被点击，则调用button_open_camera_clicked()
        self.timer_camera.timeout.connect(self.show_camera) #若定时器结束，则调用show_camera()

        self.pushButton2.clicked.connect(self.openkouzhao)
        self.pushButton3.clicked.connect(self.closekouzhao)
    def openkouzhao(self):
        self.openkz = True
    def closekouzhao(self):
        self.openkz = False

    def open_camera_clicked(self):
        self.label.setText("")
        if self.timer_camera.isActive() == False:   #若定时器未启动
            
            flag = self.cap.open(self.CAM_NUM) #参数是0，表示打开笔记本的内置摄像头，参数是视频文件路径则打开视频
            if flag == False:       #flag表示open()成不成功
                msg = QtWidgets.QMessageBox.warning(self,'warning',"请检查相机于电脑是否连接正确",buttons=QtWidgets.QMessageBox.Ok)
            else:
                self.timer_camera.start(30)  #定时器开始计时30ms，结果是每过30ms从摄像头中取一帧显示
                self.pushButton1.setText('关闭摄像头')
        else:
            self.timer_camera.stop()  #关闭定时器
            self.cap.release()        #释放视频流
            self.label.clear()  #清空视频显示区域
            self.pushButton1.setText('打开摄像头')
            self.label.setText("摄像头在开启的过程中会比较慢，请稍等，别乱点，以免卡死。")
    def show_camera(self):
        flag,self.image = self.cap.read()  #从视频流中读取
        #
        
        if self.openkz:
            img_raw = cv2.cvtColor(self.image, cv2.COLOR_BGR2RGB)
            tsf.inference(img_raw,
                        conf_thresh=0.5,
                        iou_thresh=0.5,
                        target_shape=(260, 260),
                        draw_result=True,
                        show_result=False)
            show = img_raw[:, :, ::-1]
        else:
            show = cv2.resize(self.image,(640,480))     #把读到的帧的大小重新设置为 640x480
        #
        
        
        show = cv2.cvtColor(show,cv2.COLOR_BGR2RGB) #视频色彩转换回RGB，这样才是现实的颜色
        showImage = QtGui.QImage(show.data,show.shape[1],show.shape[0],QtGui.QImage.Format_RGB888) #把读取到的视频数据变成QImage形式
        self.label.setPixmap(QtGui.QPixmap.fromImage(showImage))  #往显示视频的Label里 显示QImage

if __name__ =='__main__':
    app = QtWidgets.QApplication(sys.argv)
    Form = MainUi() 
    ui = MyMainForm()
    ui.setupUi(Form)
    ui.setNoTittle(Form)
    ui.slot_init()
    Form.show()
    sys.exit(app.exec_())

应该有人能够发现我的QT是用designer做的然后转的源码，没错，所以可以有更简单的写法，但是我懒，所以就不停的继承再继承。qt这里主要用了几个关键的方法，一个是重写移动函数、一个是关掉窗口自己设计缩小和关闭按钮、另一个就是样式。然后视频流传送到lable里面，这样就可以实现视频的打开和关闭。
开源的tensorflow_infer.py的代码，做了一丢丢修改，其实就是注释掉几行而已，然后把视频流作为比对的样品，经tsf.inference方法检验，返回结果，最后再赋值给label就完成了。

下面是的tensorflow_infer.py的代码（想要模型和源码的去下方地址下载）

# -*- coding:utf-8 -*-
import cv2
import time
import argparse

import numpy as np
from PIL import Image
from keras.models import model_from_json
from utils.anchor_generator import generate_anchors
from utils.anchor_decode import decode_bbox
from utils.nms import single_class_non_max_suppression
from load_model.tensorflow_loader import load_tf_model, tf_inference

sess, graph = load_tf_model('models/face_mask_detection.pb')

# anchor configuration
feature_map_sizes = [[33, 33], [17, 17], [9, 9], [5, 5], [3, 3]]
anchor_sizes = [[0.04, 0.056], [0.08, 0.11], [0.16, 0.22], [0.32, 0.45], [0.64, 0.72]]
anchor_ratios = [[1, 0.62, 0.42]] * 5

# generate anchors
anchors = generate_anchors(feature_map_sizes, anchor_sizes, anchor_ratios)

# for inference , the batch size is 1, the model output shape is [1, N, 4],
# so we expand dim for anchors to [1, anchor_num, 4]
anchors_exp = np.expand_dims(anchors, axis=0)

id2class = {0: 'YES', 1: 'NO'}


def inference(image,
              conf_thresh=0.5,
              iou_thresh=0.4,
              target_shape=(160, 160),
              draw_result=True,
              show_result=True
              ):
    '''
    Main function of detection inference
    :param image: 3D numpy array of image
    :param conf_thresh: the min threshold of classification probabity.
    :param iou_thresh: the IOU threshold of NMS
    :param target_shape: the model input size.
    :param draw_result: whether to daw bounding box to the image.
    :param show_result: whether to display the image.
    :return:
    '''
    # image = np.copy(image)
    output_info = []
    height, width, _ = image.shape
    image_resized = cv2.resize(image, target_shape)
    image_np = image_resized / 255.0  # 归一化到0~1
    image_exp = np.expand_dims(image_np, axis=0)
    y_bboxes_output, y_cls_output = tf_inference(sess, graph, image_exp)

    # remove the batch dimension, for batch is always 1 for inference.
    y_bboxes = decode_bbox(anchors_exp, y_bboxes_output)[0]
    y_cls = y_cls_output[0]
    # To speed up, do single class NMS, not multiple classes NMS.
    bbox_max_scores = np.max(y_cls, axis=1)
    bbox_max_score_classes = np.argmax(y_cls, axis=1)

    # keep_idx is the alive bounding box after nms.
    keep_idxs = single_class_non_max_suppression(y_bboxes,
                                                 bbox_max_scores,
                                                 conf_thresh=conf_thresh,
                                                 iou_thresh=iou_thresh,
                                                 )

    for idx in keep_idxs:
        conf = float(bbox_max_scores[idx])
        class_id = bbox_max_score_classes[idx]
        bbox = y_bboxes[idx]
        # clip the coordinate, avoid the value exceed the image boundary.
        xmin = max(0, int(bbox[0] * width))
        ymin = max(0, int(bbox[1] * height))
        xmax = min(int(bbox[2] * width), width)
        ymax = min(int(bbox[3] * height), height)

        if draw_result:
            if class_id == 0:
                color = (0, 255, 0)
            else:
                color = (255, 0, 0)
            cv2.rectangle(image, (xmin, ymin), (xmax, ymax), color, 2)
            cv2.putText(image, "%s: %.2f" % (id2class[class_id], conf), (xmin + 2, ymin - 2),
                        cv2.FONT_HERSHEY_SIMPLEX, 0.8, color)
        output_info.append([class_id, conf, xmin, ymin, xmax, ymax])

    if show_result:
        Image.fromarray(image).show()
    return output_info


def run_on_video(video_path, output_video_name, conf_thresh):
    cap = cv2.VideoCapture(video_path)
    height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
    width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
    fps = cap.get(cv2.CAP_PROP_FPS)
    fourcc = cv2.VideoWriter_fourcc(*'XVID')
    # writer = cv2.VideoWriter(output_video_name, fourcc, int(fps), (int(width), int(height)))
    total_frames = cap.get(cv2.CAP_PROP_FRAME_COUNT)
    if not cap.isOpened():
        raise ValueError("Video open failed.")
        return
    status = True
    idx = 0
    while status:
        start_stamp = time.time()
        status, img_raw = cap.read()
        img_raw = cv2.cvtColor(img_raw, cv2.COLOR_BGR2RGB)
        read_frame_stamp = time.time()
        if (status):
            inference(img_raw,
                      conf_thresh,
                      iou_thresh=0.5,
                      target_shape=(260, 260),
                      draw_result=True,
                      show_result=False)
            cv2.imshow('image', img_raw[:, :, ::-1])
            cv2.waitKey(1)
            inference_stamp = time.time()
            # writer.write(img_raw)
            write_frame_stamp = time.time()
            idx += 1
            # print("%d of %d" % (idx, total_frames))
            # print("read_frame:%f, infer time:%f, write time:%f" % (read_frame_stamp - start_stamp,
            #                                                        inference_stamp - read_frame_stamp,
            #                                                        write_frame_stamp - inference_stamp))
    # writer.release()


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Face Mask Detection")
    parser.add_argument('--img-mode', type=int, default=0, help='set 1 to run on image, 0 to run on video.')
    parser.add_argument('--img-path', type=str, help='path to your image.')
    parser.add_argument('--video-path', type=str, default='0', help='path to your video, `0` means to use camera.')
    # parser.add_argument('--hdf5', type=str, help='keras hdf5 file')
    args = parser.parse_args()
    if args.img_mode:
        imgPath = args.img_path
        img = cv2.imread(imgPath)
        img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        inference(img, show_result=True, target_shape=(260, 260))
    else:
        video_path = args.video_path
        if args.video_path == '0':
            video_path = 0
        run_on_video(video_path, '', conf_thresh=0.5)

需要tensorflow2.2 版本工程的朋友可以到网盘中拿：
链接：
https://pan.baidu.com/s/10IQ1uscONZOYkgdtjcx2pQ
提取码：g8g9
————————————————
毕竟是从别人那里拿的，所以就把【一颗小树x】的链接拿过来让大家下载，欢迎交流。