1. 关键问题描述

在前面的文章中已经分享了pyqt制作图像处理工具的文章，也知道pyqt通过使用label控件显示图像的方式。在此，对于视频的显示，其本质上一帧一帧的图像，因此也可以使用同样的方式对其显示。但是，有两个关键的问题需要解决，也即：
a. 如何读取视频/或通过摄像头抓取视频
b. 如果连续显示视频图像且不会造成界面假死

2. 通过OpenCV读取视频/打开摄像头抓取视频

主要函数：

python">cv.VideoCapture.isOpened()，检查视频捕获是否初始化成功
cv.VideoCapture.read()，捕获视频文件、视频流或捕获的视频设备
cv.VideoCapture.release()，关闭视频文件或设备，释放对象
cv.VideoWriter.release()，关闭视频写入，释放对象

抓取视频

python">import cv2
import timecap = cv2.VideoCapture(0, cv2.CAP_DSHOW) # 使用cv2.CAP_DSHOW后启动快，但帧率慢了
cap.set(6, cv2.VideoWriter.fourcc('M', 'J', 'P', 'G'))
# cap.set(6, cv2.VideoWriter.fourcc('Y', 'U', 'V', '2'))# 使用cv2.CAP_DSHOW后大小为640
cap.set(3, 2560)  # 640 1280 1920  2560# 宽
# cap.set(4, 720)   # 720  1080  # 高
# cap.set(5, 60)  # 帧数
# cap.set(10, 150)  # 亮度
# cap.set(14, 150)  # 增益
# cap.set(11, 90)  # 对比度
# cap.set(15, 150)  # 曝光print(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
print(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
print(cap.get(cv2.CAP_PROP_FPS))num = 0
start = time.time()
fps = '0'
while(True):ret, frame = cap.read()num = num + 1frame = cv2.flip(frame, 1)# frame = beauty_face(frame)if (time.time() - start) > 1:fps = num / (time.time() - start)fps = str(round(fps, 2))num = 0start = time.time()cv2.putText(frame, "FPS:" + fps, (20, 20), 1, 1.5, (255, 255, 255), 2)cv2.imshow("DST", frame)stop = time.time()elapsed = stop - start# print('time=', elapsed)if 1 < elapsed < 1.5:print('time=', elapsed)print('frame num:', num)if cv2.waitKey(1) & 0xFF == ord('q'):break
cap.release()
cv2.destroyAllWindows()

读取视频

python">import cv2 as cvif __name__ == '__main__':# 创建视频读取/捕获对象vedioRead = "../images/test.mp4"  # 读取视频文件的路径capRead = cv.VideoCapture(vedioRead)  # 实例化 VideoCapture 类# 读取视频文件frameNum = 0  # 视频帧数初值while capRead.isOpened():  # 检查视频捕获是否成功ret, frame = capRead.read()  # 读取下一帧视频图像if ret is True:cv.imshow(vedioRead, frame)  # 播放视频图像if cv.waitKey(1) & 0xFF == ord('q'):  # 按 'q' 退出breakelse:print("Can't receive frame at frameNum {}".format(frameNum))breakcapRead.release()  # 关闭读取视频文件capWrite.release()  # 关闭视频写入对象cv.destroyAllWindows()  # 关闭显示窗口

3. 通过PyQt 中的 QTimer定时器实现视频播放

使用 OpenCV 对视频文件进行解码获得图像帧以后，可以使用 QTime 定时器来控制 QLabel 控件中的图像更新，实现视频播放。
PyQt5 中的 QTimer类提供了重复的和单次的定时器，为计时器提供了高级编程接口。
要使用定时器，需要先创建一个QTimer实例，将定时器的timeout信号连接到相应的槽函数，并调用start()，定时器就会以设定的间隔发出timeout信号。

QTimer类中的常用方法：

start(milliseconds)：启动或重新启动定时器，时间间隔为毫秒。如果定时器已经运行，它将被停止并重新启动。如果singleShot信号为真，定时器将仅被激活一次。
stop()：停止定时器

QTimer类中的常用信号：
singleShot：在给定的时间间隔后调用一个槽函数时发射此信号。
timeout：当定时器超时时发射此信号。

注意：可以设置槽函数的执行次数，默认为定时器开启后周期性调用槽函数。如果设置了setSingleShot(True)，则槽函数仅执行一次。

关键代码：

python"># 初始化定时器，并绑定触发信号
self.timerCam = QtCore.QTimer()  # 定时器，毫秒
self.timerCam.timeout.connect(self.refreshFrame)  # 计时器结束时调用槽函数刷新当前帧# 开始定时
if self.cap.isOpened():      # 检查视频捕获是否成功self.timerCam.start(20)  # 设置计时间隔并启动，定时结束将触发刷新当前帧
# 暂停播放
self.timerCam.blockSignals(True)  # 信号阻塞，暂停定时器
# 继续播放
self.timerCam.blockSignals(False)  # 取消信号阻塞，恢复定时器

4. PyQt 视频播放器实现代码

python">import os
import sys
import cv2 as cv
import numpy as np
from PyQt5 import QtCore
from PyQt5.QtCore import QObject, pyqtSignal, QPoint, QRect, qDebug, Qt
from PyQt5.QtWidgets import *
from PyQt5.QtGui import *
from ui_VideoProV1 import Ui_MainWindow  # 导入 uiDemo8.py 中的 Ui_MainWindow 界面类class MyMainWindow(QMainWindow, Ui_MainWindow):  # 继承 QMainWindow 类和 Ui_MainWindow 界面类def __init__(self, parent=None):super(MyMainWindow, self).__init__(parent)  # 初始化父类self.setupUi(self)  # 继承 Ui_MainWindow 界面类self.timerCam = QtCore.QTimer()  # 定时器，毫秒self.cap = Noneself.frameNum = 1  # 视频帧数初值self.edge_flag = Falseself.smooth_flag = False# 添加可点击执行的菜单self.menu_file = QMenu("文件", self)temp = self.menuBar()temp.addMenu(self.menu_file)t = self.menu_fileself.action_open = QAction("打开", self)self.action_open.triggered.connect(self.openVideo)t.addAction(self.action_open)self.action_save = QAction("保存", self)t.addAction(self.action_save)t.addAction('其他')self.mTest = QAction("帮助", self)self.mTest.triggered.connect(self.trigger_actHelp)temp = self.menuBar()temp.addAction(self.mTest)self.action_close = QAction("退出", self)self.action_close.triggered.connect(self.close)temp = self.menuBar()temp.addAction(self.action_close)## # 菜单栏self.action_save.triggered.connect(self.saveSlot)  # 连接并执行 openSlot 子程序# 通过 connect 建立信号/槽连接，点击按钮事件发射 triggered 信号，执行相应的子程序 click_pushButtonself.pushButton.clicked.connect(self.openVideo)self.pushButton_2.clicked.connect(self.playVideo)self.pushButton_3.clicked.connect(self.pauseVideo)self.pushButton_4.clicked.connect(self.click_pushButton_5)  # # 按钮触发：边缘检测self.pushButton_5.clicked.connect(self.click_pushButton_6)  # 点击 # 按钮触发：双边self.timerCam.timeout.connect(self.refreshFrame)  # 计时器结束时调用槽函数刷新当前帧# 初始化self.frame = np.ndarray(())self.videoPath = 'test'self.textEdit_log.append("欢迎回来！")returndef openVideo(self):  # 读取视频文件，点击 pushButton_1 触发try:self.videoPath, _ = QFileDialog.getOpenFileName(self, "Open Video", "../images/", "*.mp4 *.avi *.flv")print("Open Video: ", self.videoPath)except:print("Open video failed.")returndef playVideo(self):  # 播放视频文件，点击 pushButton_2 触发if self.timerCam.isActive() == False:if self.videoPath.endswith(('avi', 'mp4')):self.cap = cv.VideoCapture(self.videoPath)self.textEdit_log.append('open video successfully')else:self.cap = cv.VideoCapture(0)self.textEdit_log.append('open camera successfully')if self.cap.isOpened():      # 检查视频捕获是否成功self.timerCam.start(20)  # 设置计时间隔并启动，定时结束将触发刷新当前帧else:  #self.timerCam.stop()  # 停止定时器self.cap.release()    # 关闭读取视频文件self.label_1.clear()  # 清除显示内容returndef pauseVideo(self):self.timerCam.blockSignals(False)  # 取消信号阻塞，恢复定时器if self.timerCam.isActive() and self.frameNum % 2 == 1:self.timerCam.blockSignals(True)  # 信号阻塞，暂停定时器self.pushButton_3.setText("继续")  # 点击"继续"，恢复播放print("信号阻塞，暂停播放。", self.frameNum)self.textEdit_log.append("信号阻塞，暂停播放。" + str(self.frameNum))else:self.pushButton_3.setText("暂停")  # 点击"暂停"，暂停播放print("取消阻塞，恢复播放。", self.frameNum)self.textEdit_log.append("取消阻塞，恢复播放。" + str(self.frameNum))self.frameNum = self.frameNum + 1def refreshFrame(self):  # 刷新视频图像ret, self.frame = self.cap.read()  # 读取下一帧视频图像self.frame = cv.flip(self.frame, 1)if self.smooth_flag:self.frame = cv.bilateralFilter(self.frame, 15, 30, 40, None)if self.edge_flag:# self.frame = cv.Canny(self.frame, 10, 60)self.frame = self.apply_heat_effect(self.frame)qImg = self.cvToQImage(self.frame)  # OpenCV 转为 PyQt 图像格式self.label_1.setScaledContents(True)self.label_1.setPixmap((QPixmap.fromImage(qImg)))  # 加载 PyQt 图像# self.frameNum = self.frameNum + 1# print("视频帧数：", self.frameNum)returndef cvToQImage(self, image):if image.dtype == np.uint8:channels = 1 if len(image.shape) == 2 else image.shape[2]if channels == 3:qImg = QImage(image, image.shape[1], image.shape[0], image.strides[0], QImage.Format_RGB888)return qImg.rgbSwapped()elif channels == 1:qImg = QImage(image, image.shape[1], image.shape[0], image.strides[0], QImage.Format_Indexed8)return qImgelse:QtCore.qDebug("ERROR: numpy.ndarray could not be converted to QImage. Channels = %d" % image.shape[2])return QImage()def click_pushButton_5(self):if self.edge_flag:self.edge_flag = Falseself.textEdit_log.append('close edge mode successfully')else:self.edge_flag = Trueself.textEdit_log.append('open edge mode successfully')returndef click_pushButton_6(self):if self.smooth_flag:self.smooth_flag = Falseself.textEdit_log.append('close smooth mode successfully')else:self.smooth_flag = Trueself.textEdit_log.append('open smooth mode successfully')returndef saveSlot(self):  # 保存图像文件# 选择存储文件 dialogtry:fileName, tmp = QFileDialog.getSaveFileName(self, "Save Image", "../images/", '*.png; *.jpg; *.tif')if self.frame.size == 1:return# OpenCV 写入图像文件ret = cv.imwrite(fileName, self.frame)if ret:print(fileName, self.frame.shape)except:print('save failed.')returndef cvToQImage(self, image):# 8-bits unsigned, NO. OF CHANNELS=1if image.dtype == np.uint8:channels = 1 if len(image.shape) == 2 else image.shape[2]if channels == 3:  # CV_8UC3# Create QImage with same dimensions as input MatqImg = QImage(image, image.shape[1], image.shape[0], image.strides[0], QImage.Format_RGB888)return qImg.rgbSwapped()elif channels == 1:# Create QImage with same dimensions as input MatqImg = QImage(image, image.shape[1], image.shape[0], image.strides[0], QImage.Format_Indexed8)return qImgelse:qDebug("ERROR: numpy.ndarray could not be converted to QImage. Channels = %d" % image.shape[2])return QImage()def qPixmapToCV(self, qPixmap):  # PyQt图像 转换为 OpenCV图像qImg = qPixmap.toImage()  # QPixmap 转换为 QImageshape = (qImg.height(), qImg.bytesPerLine() * 8 // qImg.depth())shape += (4,)ptr = qImg.bits()ptr.setsize(qImg.byteCount())image = np.array(ptr, dtype=np.uint8).reshape(shape)  # 定义 OpenCV 图像image = image[..., :3]return imagedef trigger_actHelp(self):  # 动作 actHelp 触发QMessageBox.about(self, "About", """打开视频或摄像头 v1.0""")returndef closeEvent(self):self.timerCam.stop()  # 停止定时器self.cap.release()  # 关闭读取视频文件self.label_1.clear()  # 清除显示内容if __name__ == '__main__':app = QApplication(sys.argv)myWin = MyMainWindow()myWin.show()sys.exit(app.exec_())

参考文献

[1] OpenCV-PyQT项目实战（9）项目案例04：视频播放