【界面】pyqt5和Swin Transformer对人脸进行识别

基于python，使用pyqt5模块和swin transformer检测算法对人脸进行识别

工具

语言：
python
主要库：
pyqt5
检测模型：
swin transformer

核心代码

import time, cv2
from PyQt5 import QtCore, QtGui, QtWidgets
from PyQt5.QtCore import *
from PyQt5.QtGui import *
from PyQt5.QtWidgets import QFileDialog, QMainWindow
from PyQt5.QtCore import QTimer, QDateTime
from PyQt5.Qt import QThread, pyqtSignal, QMutex
import qimage2ndarray
from queue import Queue
from Project import Ui_Form
from infer import *

video_steam = Queue()

class Thread1(QThread):        # 线程1
    # 使用自定义信号，一定要记得信号是类变量，必须在类中定义，不能在实例方法中定义，
    thread1_signal2 = pyqtSignal(object)   #定义信号,定义参数为object类型

    def __init__(self):
        super(Thread1, self).__init__()
        self.t = 0
        self.Image_thread1 = None
        self.mutex = QMutex()            # 创建线程锁
        self._isPause = False

    def run(self):
        while True:
            self.mutex.lock()            # 加锁
            time.sleep(0.001)            # 休眠
            _, frame = cap.read()
            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
            self.Image_thread1 = frame
            self.thread1_signal2.emit(self.Image_thread1)    # 传信号
            self.mutex.unlock()          # 解锁


class Thread2(QThread):         # 线程2
    thread1_signal3 = pyqtSignal(object)

    def __init__(self):
        super(Thread2, self).__init__()
        self.t = 0
        self.Image_thread1 = None
        self.mutex = QMutex()
        self._isPause = False
        self.video_flag = 0

    def run(self):
        while True:
            self.mutex.lock()
            time.sleep(0.001)
            # time.sleep(1 / self.a) 在这里改帧率
            _, frame = cap.read()
            config_file = 'mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_adamw_1x_coco.py'
            checkpoint_file = 'epoch_100.pth'
            classes = ['The color of silica gel is abnormal', 'The color of silica gel is normal', 'The door is open',
                       'The door is close', 'breakage', 'dirt', 'rust', 'foreign object', 'oil leakage', 'animal',
                       'hat', 'person']
            model = init_detector(config_file, checkpoint_file, device='cuda:0')
            frame, result_label = InferResult(frame, config_file, checkpoint_file, classes, model)

            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
            if self.video_flag == 0:
                video_steam.put(frame)
            self.Image_thread1 = frame
            self.thread1_signal3.emit(self.Image_thread1)
            self.mutex.unlock()


class PyQtMainEntry(QMainWindow, Ui_Form):
    def __init__(self):
        super().__init__()
        self.setupUi(self)
        self.mark = 0
        self.btnReadImage.clicked.connect(self.btnReadImage_Clicked)
        self.btnShowCamera.clicked.connect(self.btnOpenCamera_Clicked)
        self.btnStartLabel.clicked.connect(self.startRecognize)
        self.btnSaveResult.clicked.connect(self.resultsave)
        self.time()


    def btnReadImage_Clicked(self):
        self.mark = 0
        filename,  _ = QFileDialog.getOpenFileName(self, '打开')
        if filename:
            self.captured = cv2.imread(str(filename))
            self.captured = cv2.cvtColor(self.captured, cv2.COLOR_BGR2RGB)
            rows, cols, channels = self.captured.shape
            bytesPerLine = channels * cols
            QImg = QImage(self.captured.data, cols, rows, bytesPerLine, QImage.Format_RGB888)
            self.Videolabel.setPixmap(QPixmap.fromImage(QImg).scaled(
            self.Videolabel.size(), Qt.KeepAspectRatio, Qt.SmoothTransformation))
            self.Videolabel.setScaledContents(True)

    def btnOpenCamera_Clicked(self):
        self.mark = 1
        self.process_thread = Thread2()
        self.process_thread.thread1_signal3.connect(self.thread2_work2)
        self.preview_thread = Thread1()
        self.preview_thread.thread1_signal2.connect(self.thread1_work2)
        self.preview_thread.start()

    def thread1_work2(self, img):
        self.Image = img
        qimg = qimage2ndarray.array2qimage(img)
        self.Videolabel.setPixmap(QPixmap(qimg))
        self.Videolabel.show()
        self.Videolabel.setScaledContents(True)

    def thread2_work2(self, img):
        self.Image = img
        qimg = qimage2ndarray.array2qimage(img)
        self.DetectImagelabel.setPixmap(QPixmap(qimg))
        self.DetectImagelabel.show()
        self.DetectImagelabel.setScaledContents(True)
        self.Videolabel_2.setPixmap(QPixmap(qimg))
        self.Videolabel_2.show()
        self.Videolabel_2.setScaledContents(True)

    def startRecognize(self):
        if self.mark == 0:
            img = self.captured
            config_file = 'mask_rcnn_swin_tiny_patch4_window7_mstrain_480-800_adamw_1x_coco.py'
            checkpoint_file = 'epoch_100.pth'
            classes = ['The color of silica gel is abnormal', 'The color of silica gel is normal', 'The door is open',
                       'The door is close', 'breakage', 'dirt', 'rust', 'foreign object', 'oil leakage', 'animal',
                       'hat', 'person']
            model = init_detector(config_file, checkpoint_file, device='cuda:0')
            draw_1, result_label = InferResult(img, config_file, checkpoint_file, classes, model)

            self.result = draw_1
            draw_2 = qimage2ndarray.array2qimage(draw_1)
            self.DetectImagelabel.setPixmap(QPixmap(draw_2))
            self.DetectImagelabel.setScaledContents(True)
            self.DetectImagelabel.show()
        else:
            self.process_thread.start()


    # 显示时间
    def showCurrentTime(self, timeLabel):
        time = QDateTime.currentDateTime()
        self.timeDisplay = time.toString('yyyy-MM-dd hh:mm:ss dddd')
        timeLabel.setText(self.timeDisplay)

    def time(self):
        self.timer = QTimer()
        self.timer.timeout.connect(lambda: self.showCurrentTime(self.label_2))
        self.timer.start()

    def resultsave(self):
        path_filename = QFileDialog.getExistingDirectory(self, '结果保存')
        if path_filename:
            self.saveImage = cv2.cvtColor(self.result, cv2.COLOR_RGB2BGR)
            cv2.imwrite(path_filename + '/' + self.timeDisplay[:10]
                       + '_' + str(10) + '.png', self.saveImage)
        # 显示路径
        self.PathLineEdit.setText(path_filename)


if __name__ == "__main__":
    import sys
    app = QtWidgets.QApplication(sys.argv)
    cap = cv2.VideoCapture(0)
    window = PyQtMainEntry()
    window.show()
    sys.exit(app.exec_())

运行结果

源代码

https://download.csdn.net/download/qq_44747572/85757863