一起用Python做个车牌自动识别系统，好玩又实用！

前言

前段时间，用PyQt5写了两篇文章，关于Python自制一款炫酷音乐播放器、自定义桌面动画挂件。有粉丝问我，为什么要用PyQt5？之前没接触过PyQt5，能不能多分享一些这方面的开发案例？

今天就继续给大家分享一个实战案例，带大家一起用Python的PyQt5开发一个车牌自动识别系统！

首先一起来看看最终实现的车牌识别系统效果图：
下面，我们就开始介绍如何实现这款自动车牌识别系统。

一、核心功能设计

总体来说，我们首先要进行UI界面构建设计，根据车牌识别系统功能进行画面排版布局；其次我们的这款车牌识别系统的主要功能车辆图片读取识别显示、图片中车牌ROI区域获取、车牌识别结果输出显示。

对于结果输出显示，我们主要包含了读取图片名称、读取录入时间、识别车牌号码、识别车牌颜色、识别车牌所属地。最后我们还可以将车牌识别系统的数据信息导出本地存储。

拆解需求，大致可以整理出核心功能如下：

UI设计排版布局

左侧区域进行识别信息显示，包含图片名称、读取录入时间、识别车牌号码、识别车牌颜色、识别车牌所属地信息
右侧可以分成3个区域，顶部区域包含窗体最小化，最大化，关闭功能；中间区域显示读取车辆图片&#xff120000b;底部区域包含车牌显示区域、图片读取、车牌信息存储功能

车牌识别

车牌自动识别结果输出

车牌信息显示存储

对录入识别的车辆车牌识别信息存储

二、实现步骤

1. UI设计排版布局

根据车牌识别需要的功能，首先进行UI布局设计，我们这次还是使用的pyqt5。核心设计代码如下：

# author：CSDN-Dragon少年def setupUi(self, MainWindow):MainWindow.setObjectName("MainWindow")MainWindow.resize(1213, 670)MainWindow.setFixedSize(1213, 670)  # 设置窗体固定大小MainWindow.setToolButtonStyle(QtCore.Qt.ToolButtonIconOnly)self.centralwidget = QtWidgets.QWidget(MainWindow)self.centralwidget.setObjectName("centralwidget")self.scrollArea = QtWidgets.QScrollArea(self.centralwidget)self.scrollArea.setGeometry(QtCore.QRect(690, 40, 511, 460))self.scrollArea.setWidgetResizable(True)self.scrollArea.setObjectName("scrollArea")self.scrollAreaWidgetContents = QtWidgets.QWidget()self.scrollAreaWidgetContents.setGeometry(QtCore.QRect(0, 0, 500, 489))self.scrollAreaWidgetContents.setObjectName("scrollAreaWidgetContents")self.label_0 = QtWidgets.QLabel(self.scrollAreaWidgetContents)self.label_0.setGeometry(QtCore.QRect(10, 10, 111, 20))font = QtGui.QFont()font.setPointSize(11)self.label_0.setFont(font)self.label_0.setObjectName("label_0")self.label = QtWidgets.QLabel(self.scrollAreaWidgetContents)self.label.setGeometry(QtCore.QRect(10, 40, 481, 420))self.label.setObjectName("label")self.label.setAlignment(Qt.AlignCenter)self.scrollArea.setWidget(self.scrollAreaWidgetContents)self.scrollArea_2 = QtWidgets.QScrollArea(self.centralwidget)self.scrollArea_2.setGeometry(QtCore.QRect(10, 10, 671, 631))self.scrollArea_2.setWidgetResizable(True)self.scrollArea_2.setObjectName("scrollArea_2")self.scrollAreaWidgetContents_1 = QtWidgets.QWidget()self.scrollAreaWidgetContents_1.setGeometry(QtCore.QRect(0, 0, 669, 629))self.scrollAreaWidgetContents_1.setObjectName("scrollAreaWidgetContents_1")self.label_1 = QtWidgets.QLabel(self.scrollAreaWidgetContents_1)self.label_1.setGeometry(QtCore.QRect(10, 10, 111, 20))font = QtGui.QFont()font.setPointSize(11)self.label_1.setFont(font)self.label_1.setObjectName("label_1")self.tableWidget = QtWidgets.QTableWidget(self.scrollAreaWidgetContents_1)self.tableWidget.setGeometry(QtCore.QRect(10, 40, 651, 581))  # 581))self.tableWidget.setObjectName("tableWidget")self.tableWidget.setColumnCount(5)self.tableWidget.setColumnWidth(0, 140)  # 设置1列的宽度self.tableWidget.setColumnWidth(1, 130)  # 设置2列的宽度self.tableWidget.setColumnWidth(2, 110)  # 设置3列的宽度self.tableWidget.setColumnWidth(3, 90)  # 设置4列的宽度self.tableWidget.setColumnWidth(4, 181)  # 设置5列的宽度self.tableWidget.setHorizontalHeaderLabels(["图片名称", "录入时间", "车牌号码", "车牌类型", "车牌信息"])self.tableWidget.setRowCount(self.RowLength)self.tableWidget.verticalHeader().setVisible(False)  # 隐藏垂直表头)self.tableWidget.setEditTriggers(QAbstractItemView.NoEditTriggers)self.tableWidget.raise_()self.scrollArea_2.setWidget(self.scrollAreaWidgetContents_1)self.scrollArea_3 = QtWidgets.QScrollArea(self.centralwidget)self.scrollArea_3.setGeometry(QtCore.QRect(690, 510, 341, 131))self.scrollArea_3.setWidgetResizable(True)self.scrollArea_3.setObjectName("scrollArea_3")self.scrollAreaWidgetContents_3 = QtWidgets.QWidget()self.scrollAreaWidgetContents_3.setGeometry(QtCore.QRect(0, 0, 339, 129))self.scrollAreaWidgetContents_3.setObjectName("scrollAreaWidgetContents_3")self.label_2 = QtWidgets.QLabel(self.scrollAreaWidgetContents_3)self.label_2.setGeometry(QtCore.QRect(10, 10, 111, 20))font = QtGui.QFont()font.setPointSize(11)self.label_2.setFont(font)self.label_2.setObjectName("label_2")self.label_3 = QtWidgets.QLabel(self.scrollAreaWidgetContents_3)self.label_3.setGeometry(QtCore.QRect(10, 40, 321, 81))self.label_3.setObjectName("label_3")self.scrollArea_3.setWidget(self.scrollAreaWidgetContents_3)self.scrollArea_4 = QtWidgets.QScrollArea(self.centralwidget)self.scrollArea_4.setGeometry(QtCore.QRect(1040, 510, 161, 131))self.scrollArea_4.setWidgetResizable(True)self.scrollArea_4.setObjectName("scrollArea_4")self.scrollAreaWidgetContents_4 = QtWidgets.QWidget()self.scrollAreaWidgetContents_4.setGeometry(QtCore.QRect(0, 0, 159, 129))self.scrollAreaWidgetContents_4.setObjectName("scrollAreaWidgetContents_4")self.pushButton_2 = QtWidgets.QPushButton(self.scrollAreaWidgetContents_4)self.pushButton_2.setGeometry(QtCore.QRect(20, 50, 121, 31))self.pushButton_2.setObjectName("pushButton_2")self.pushButton = QtWidgets.QPushButton(self.scrollAreaWidgetContents_4)self.pushButton.setGeometry(QtCore.QRect(20, 90, 121, 31))self.pushButton.setObjectName("pushButton")self.label_4 = QtWidgets.QLabel(self.scrollAreaWidgetContents_4)self.label_4.setGeometry(QtCore.QRect(10, 10, 111, 20))font = QtGui.QFont()font.setPointSize(11)self.label_4.setFont(font)self.label_4.setObjectName("label_4")self.scrollArea_4.setWidget(self.scrollAreaWidgetContents_4)MainWindow.setCentralWidget(self.centralwidget)self.statusbar = QtWidgets.QStatusBar(MainWindow)self.statusbar.setObjectName("statusbar")MainWindow.setStatusBar(self.statusbar)self.retranslateUi(MainWindow)QtCore.QMetaObject.connectSlotsByName(MainWindow)self.retranslateUi(MainWindow)QtCore.QMetaObject.connectSlotsByName(MainWindow)self.pushButton.clicked.connect(self.__openimage)  # 设置点击事件self.pushButton.setStyleSheet('''QPushButton{background:#222225;border-radius:5px;}QPushButton:hover{background:#2B2B2B;}''')self.pushButton_2.clicked.connect(self.__writeFiles)  # 设置点击事件self.pushButton_2.setStyleSheet('''QPushButton{background:#222225;border-radius:5px;}QPushButton:hover{background:#2B2B2B;}''')self.retranslateUi(MainWindow)self.close_widget = QtWidgets.QWidget(self.centralwidget)self.close_widget.setGeometry(QtCore.QRect(1130, 0, 90, 50))self.close_widget.setObjectName("close_widget")self.close_layout = QGridLayout()  # 创建左侧部件的网格布局层self.close_widget.setLayout(self.close_layout)  # 设置左侧部件布局为网格self.left_close = QPushButton("")  # 关闭按钮self.left_close.clicked.connect(self.close)self.left_visit = QPushButton("")  # 空白按钮self.left_visit.clicked.connect(MainWindow.big)self.left_mini = QPushButton("")  # 最小化按钮self.left_mini.clicked.connect(MainWindow.mini)self.close_layout.addWidget(self.left_mini, 0, 0, 1, 1)self.close_layout.addWidget(self.left_close, 0, 2, 1, 1)self.close_layout.addWidget(self.left_visit, 0, 1, 1, 1)self.left_close.setFixedSize(15, 15)  # 设置关闭按钮的大小self.left_visit.setFixedSize(15, 15)  # 设置按钮大小self.left_mini.setFixedSize(15, 15)  # 设置最小化按钮大小self.left_close.setStyleSheet('''QPushButton{background:#F76677;border-radius:5px;}QPushButton:hover{background:red;}''')self.left_visit.setStyleSheet('''QPushButton{background:#F7D674;border-radius:5px;}QPushButton:hover{background:yellow;}''')self.left_mini.setStyleSheet('''QPushButton{background:#6DDF6D;border-radius:5px;}QPushButton:hover{background:green;}''')QtCore.QMetaObject.connectSlotsByName(MainWindow)self.ProjectPath = os.getcwd()  # 获取当前工程文件位置self.scrollAreaWidgetContents.setStyleSheet(sc)self.scrollAreaWidgetContents_3.setStyleSheet(sc)self.scrollAreaWidgetContents_4.setStyleSheet(sc)b =             '''color:white;background:#2B2B2B;'''self.label_0.setStyleSheet(b)self.label_1.setStyleSheet(b)self.label_2.setStyleSheet(b)self.label_3.setStyleSheet(b)MainWindow.setWindowOpacity(0.95)  # 设置窗口透明度MainWindow.setAttribute(Qt.WA_TranslucentBackground)MainWindow.setWindowFlag(Qt.FramelessWindowHint)  # 隐藏边框# author：CSDN-Dragon少年def retranslateUi(self, MainWindow):_translate = QtCore.QCoreApplication.translateMainWindow.setWindowTitle(_translate("MainWindow", "车牌识别系统"))self.label_0.setText(_translate("MainWindow", "原始图片："))self.label.setText(_translate("MainWindow", ""))self.label_1.setText(_translate("MainWindow", "识别结果："))self.label_2.setText(_translate("MainWindow", "车牌区域："))self.label_3.setText(_translate("MainWindow", ""))self.pushButton.setText(_translate("MainWindow", "打开文件"))self.pushButton_2.setText(_translate("MainWindow", "导出数据"))self.label_4.setText(_translate("MainWindow", "事件："))self.scrollAreaWidgetContents_1.show()

UI实现效果如下：

2. 车牌识别

接下来我们需要实现两个核心功能，包括获取车牌ROI区域和车牌自动识别功能。

车牌ROI区域提取：

根据读取的车辆图片，预处理进行车牌ROI区域提取，主要通过Opencv的图像处理相关知识点来完成。主要包括对图像去噪、二值化、边缘轮廓提取、矩形区域矫正、蓝绿黄车牌颜色定位识别。核心代码如下：

# author：CSDN-Dragon少年# 预处理def pretreatment(self, car_pic):if type(car_pic) == type(""):img = self.__imreadex(car_pic)else:img = car_picpic_hight, pic_width = img.shape[:2]if pic_width > self.MAX_WIDTH:resize_rate = self.MAX_WIDTH / pic_widthimg = cv2.resize(img, (self.MAX_WIDTH, int(pic_hight * resize_rate)),interpolation=cv2.INTER_AREA)  # 图片分辨率调整blur = self.cfg["blur"]# 高斯去噪if blur > 0:img = cv2.GaussianBlur(img, (blur, blur), 0)oldimg = imgimg = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)kernel = np.ones((20, 20), np.uint8)img_opening = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)  # 开运算img_opening = cv2.addWeighted(img, 1, img_opening, -1, 0);  # 与上一次开运算结果融合# cv2.imshow('img_opening', img_opening)# 找到图像边缘ret, img_thresh = cv2.threshold(img_opening, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)  # 二值化img_edge = cv2.Canny(img_thresh, 100, 200)# cv2.imshow('img_edge', img_edge)# 使用开运算和闭运算让图像边缘成为一个整体kernel = np.ones((self.cfg["morphologyr"], self.cfg["morphologyc"]), np.uint8)img_edge1 = cv2.morphologyEx(img_edge, cv2.MORPH_CLOSE, kernel)  # 闭运算img_edge2 = cv2.morphologyEx(img_edge1, cv2.MORPH_OPEN, kernel)  # 开运算# cv2.imshow('img_edge2', img_edge2)# cv2.imwrite('./edge2.png', img_edge2)# 查找图像边缘整体形成的矩形区域，可能有很多，车牌就在其中一个矩形区域中image, contours, hierarchy = cv2.findContours(img_edge2, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)contours = [cnt for cnt in contours if cv2.contourArea(cnt) > self.Min_Area]# 逐个排除不是车牌的矩形区域car_contours = []for cnt in contours:# 框选 生成最小外接矩形 返回值（中心(x,y), (宽,高), 旋转角度）rect = cv2.minAreaRect(cnt)# print('宽高:',rect[1])area_width, area_height = rect[1]# 选择宽大于高的区域if area_width < area_height:area_width, area_height = area_height, area_widthwh_ratio = area_width / area_height# print('宽高比：',wh_ratio)# 要求矩形区域长宽比在2到5.5之间，2到5.5是车牌的长宽比，其余的矩形排除if wh_ratio > 2 and wh_ratio < 5.5:car_contours.append(rect)box = cv2.boxPoints(rect)box = np.int0(box)# 矩形区域可能是倾斜的矩形，需要矫正，以便使用颜色定位card_imgs = []for rect in car_contours:if rect[2] > -1 and rect[2] < 1:  # 创造角度，使得左、高、右、低拿到正确的值angle = 1else:angle = rect[2]rect = (rect[0], (rect[1][0] + 5, rect[1][1] + 5), angle)  # 扩大范围，避免车牌边缘被排除box = cv2.boxPoints(rect)heigth_point = right_point = [0, 0]left_point = low_point = [pic_width, pic_hight]for point in box:if left_point[0] > point[0]:left_point = pointif low_point[1] > point[1]:low_point = pointif heigth_point[1] < point[1]:heigth_point = pointif right_point[0] < point[0]:right_point = pointif left_point[1] <= right_point[1]:  # 正角度new_right_point = [right_point[0], heigth_point[1]]pts2 = np.float32([left_point, heigth_point, new_right_point])  # 字符只是高度需要改变pts1 = np.float32([left_point, heigth_point, right_point])M = cv2.getAffineTransform(pts1, pts2)dst = cv2.warpAffine(oldimg, M, (pic_width, pic_hight))self.__point_limit(new_right_point)self.__point_limit(heigth_point)self.__point_limit(left_point)card_img = dst[int(left_point[1]):int(heigth_point[1]), int(left_point[0]):int(new_right_point[0])]card_imgs.append(card_img)elif left_point[1] > right_point[1]:  # 负角度new_left_point = [left_point[0], heigth_point[1]]pts2 = np.float32([new_left_point, heigth_point, right_point])  # 字符只是高度需要改变pts1 = np.float32([left_point, heigth_point, right_point])M = cv2.getAffineTransform(pts1, pts2)dst = cv2.warpAffine(oldimg, M, (pic_width, pic_hight))self.__point_limit(right_point)self.__point_limit(heigth_point)self.__point_limit(new_left_point)card_img = dst[int(right_point[1]):int(heigth_point[1]), int(new_left_point[0]):int(right_point[0])]card_imgs.append(card_img)#使用颜色定位，排除不是车牌的矩形，目前只识别蓝、绿、黄车牌colors = []for card_index, card_img in enumerate(card_imgs):green = yellow = blue = black = white = 0try:# 有转换失败的可能，原因来自于上面矫正矩形出错card_img_hsv = cv2.cvtColor(card_img, cv2.COLOR_BGR2HSV)except:print('BGR转HSV失败')card_imgs = colors = Nonereturn card_imgs, colorsif card_img_hsv is None:continuerow_num, col_num = card_img_hsv.shape[:2]card_img_count = row_num * col_num# 确定车牌颜色for i in range(row_num):for j in range(col_num):H = card_img_hsv.item(i, j, 0)S = card_img_hsv.item(i, j, 1)V = card_img_hsv.item(i, j, 2)if 11 < H <= 34 and S > 34:  # 图片分辨率调整yellow += 1elif 35 < H <= 99 and S > 34:  # 图片分辨率调整green += 1elif 99 < H <= 124 and S > 34:  # 图片分辨率调整blue += 1if 0 < H < 180 and 0 < S < 255 and 0 < V < 46:black += 1elif 0 < H < 180 and 0 < S < 43 and 221 < V < 225:white += 1color = "no"# print('黄：{:<6}绿：{:<6}蓝：{:<6}'.format(yellow,green,blue))limit1 = limit2 = 0if yellow * 2 >= card_img_count:color = "yellow"limit1 = 11limit2 = 34  # 有的图片有色偏偏绿elif green * 2 >= card_img_count:color = "green"limit1 = 35limit2 = 99elif blue * 2 >= card_img_count:color = "blue"limit1 = 100limit2 = 124  # 有的图片有色偏偏紫elif black + white >= card_img_count * 0.7:color = "bw"# print(color)colors.append(color)# print(blue, green, yellow, black, white, card_img_count)if limit1 == 0:continue# 根据车牌颜色再定位，缩小边缘非车牌边界xl, xr, yh, yl = self.accurate_place(card_img_hsv, limit1, limit2, color)if yl == yh and xl == xr:continueneed_accurate = Falseif yl >= yh:yl = 0yh = row_numneed_accurate = Trueif xl >= xr:xl = 0xr = col_numneed_accurate = Truecard_imgs[card_index] = card_img[yl:yh, xl:xr] \\if color != "green" or yl < (yh - yl) // 4 else card_img[yl - (yh - yl) // 4:yh, xl:xr]if need_accurate:  # 可能x或y方向未缩小，需要再试一次card_img = card_imgs[card_index]card_img_hsv = cv2.cvtColor(card_img, cv2.COLOR_BGR2HSV)xl, xr, yh, yl = self.accurate_place(card_img_hsv, limit1, limit2, color)if yl == yh and xl == xr:continueif yl >= yh:yl = 0yh = row_numif xl >= xr:xl = 0xr = col_numcard_imgs[card_index] = card_img[yl:yh, xl:xr] \\if color != "green" or yl < (yh - yl) // 4 else card_img[yl - (yh - yl) // 4:yh, xl:xr]# cv2.imshow("result", card_imgs[0])# cv2.imwrite('1.jpg', card_imgs[0])# print('颜色识别结果：' + colors[0])return card_imgs, colors

至此我们就可以输出车牌ROI区域和车牌颜色了，效果如下：

车牌自动识别：

车牌识别博主自己写了一个基于Opencv和SVM的识别系统，由于代码篇幅较长，本篇不进行展示（感兴趣的可以私信博主获取源码）。本篇介绍调用百度AI提供的车牌识别接口 – 百度AI开放平台链接，识别效果也非常不错。

这里面我们可以创建一个车牌识别的应用，其中的API Key及Secret Key后面我们调用车牌识别检测接口时会用到。

我们可以看到官方提供的帮助文档，介绍了如何调用请求URL数据格式，向API服务地址使用POST发送请求，必须在URL中带上参数access_token，可通过后台的API Key和Secret Key生成。这里面的API Key和Secret Key就是我们上面提到的。

接下来我们看看调用车牌识别接口代码示例。

那我们如何获取识别的车牌号码呢？API文档可以看到里面有个words_result字典 ，其中的color代表车牌颜色 ，number代表车牌号码 。这样我就可以知道识别的车牌颜色和车牌号了。

车牌识别的接口调用流程基本已经清楚了，下面就可以进行代码实现了。

# author：CSDN-Dragon少年def get_token(self):host = 'https://aip.baidubce.com/oauth/2.0/token?grant_type=client_credentials&client_id=' + self.client_id + '&client_secret=' + self.client_secretresponse = requests.get(host)if response:token_info = response.json()token_key = token_info['access_token']return token_key# author：CSDN-Dragon少年def get_license_plate(self, car_pic):result = {}card_imgs, colors = self.pretreatment(car_pic)request_url = "https://aip.baidubce.com/rest/2.0/ocr/v1/license_plate"# 二进制方式打开图片文件f = open(car_pic, 'rb')img = base64.b64encode(f.read())params = {"image": img}access_token = self.get_token()request_url = request_url + "?access_token=" + access_tokenheaders = {'content-type': 'application/x-www-form-urlencoded'}response = requests.post(request_url, data=params, headers=headers)if response:print(response.json())license_result = response.json()['words_result']['number']card_color = response.json()['words_result']['color']if license_result != []:result['InputTime'] = time.strftime("%Y-%m-%d %H:%M:%S")result['Type'] = self.cardtype[card_color]result['Picture'] = card_imgs[0]result['Number'] = ''.join(license_result[:2]) + '·' + ''.join(license_result[2:])try:result['From'] = ''.join(self.Prefecture[license_result[0]][license_result[1]])except:result['From'] = '未知'return resultelse:return None

这样我们就可以拿到车牌颜色和车牌号码了，效果如下：

3. 车牌信息显示存储

3.1 车牌信息显示：

# author：CSDN-Dragon少年def __show(self, result, FileName):# 显示表格self.RowLength = self.RowLength + 1if self.RowLength > 18:self.tableWidget.setColumnWidth(5, 157)self.tableWidget.setRowCount(self.RowLength)self.tableWidget.setItem(self.RowLength - 1, 0, QTableWidgetItem(FileName))self.tableWidget.setItem(self.RowLength - 1, 1, QTableWidgetItem(result['InputTime']))self.tableWidget.setItem(self.RowLength - 1, 2, QTableWidgetItem(result['Number']))self.tableWidget.setItem(self.RowLength - 1, 3, QTableWidgetItem(result['Type']))if result['Type'] == '蓝色牌照':self.tableWidget.item(self.RowLength - 1, 3).setBackground(QBrush(QColor(3, 128, 255)))elif result['Type'] == '绿色牌照':self.tableWidget.item(self.RowLength - 1, 3).setBackground(QBrush(QColor(98, 198, 148)))elif result['Type'] == '黄色牌照':self.tableWidget.item(self.RowLength - 1, 3).setBackground(QBrush(QColor(242, 202, 9)))self.tableWidget.setItem(self.RowLength - 1, 4, QTableWidgetItem(result['From']))self.tableWidget.item(self.RowLength - 1, 4).setBackground(QBrush(QColor(255, 255, 255)))# 显示识别到的车牌位置size = (int(self.label_3.width()), int(self.label_3.height()))shrink = cv2.resize(result['Picture'], size, interpolation=cv2.INTER_AREA)shrink = cv2.cvtColor(shrink, cv2.COLOR_BGR2RGB)self.QtImg = QtGui.QImage(shrink[:], shrink.shape[1], shrink.shape[0], shrink.shape[1] * 3,QtGui.QImage.Format_RGB888)self.label_3.setPixmap(QtGui.QPixmap.fromImage(self.QtImg))

效果如下：

3.2 信息导出存储：

# author：CSDN-Dragon少年def __writexls(self, DATA, path):wb = xlwt.Workbook();ws = wb.add_sheet('Data');# DATA.insert(0, ['文件名称','录入时间', '车牌号码', '车牌类型', '车牌信息'])for i, Data in enumerate(DATA):for j, data in enumerate(Data):ws.write(i, j, data)wb.save(path)QMessageBox.information(None, "成功", "数据已保存！", QMessageBox.Yes)def __writecsv(self, DATA, path):f = open(path, 'w')# DATA.insert(0, ['文件名称','录入时间', '车牌号码', '车牌类型', '车牌信息'])for data in DATA:f.write((',').join(data) + '\\n')f.close()QMessageBox.information(None, "成功", "数据已保存！", QMessageBox.Yes)def __writeFiles(self):path, filetype = QFileDialog.getSaveFileName(None, "另存为", self.ProjectPath,"Excel 工作簿(*.xls);;CSV (逗号分隔)(*.csv)")if path == "":  # 未选择returnif filetype == 'Excel 工作簿(*.xls)':self.__writexls(self.Data, path)elif filetype == 'CSV (逗号分隔)(*.csv)':self.__writecsv(self.Data, path)

效果如下：

导出车牌信息数据如下：

至此，整个车牌自动识别系统就完成了~今天我们就到这里，明天继续努力！

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