基于python_opencv的车牌识别系统

一、说明

根据现有的车牌识别系统，本人对代码进行了优化，原有功能：

1、对图片中的车牌号进行识别，并对车牌所属地可视化
2、将识别出的车牌号、车牌所属地等信息导出Excel表格
3、根据QtDesinger设计GUI界面，将程序系统化

添加功能：调用摄像头实时识别捕捉到的车牌信息，并可视化

链接: 最新代码传送门

下图分别是调用摄像头和直接识别图像的画面：

二、具体实现流程

整个项目为模块化处理，按文件分为：

1. Recognition.py（识别模块）
2. UI_main(主函数及UI模块）
3. SVM训练模块
4. 路由配置模块

Recognition模块

此模块问本项目的核心，主要包含的功能有：
1、读取图像
使用cv2.imdecode()函数将图片文件转换成流数据，赋值到内存缓存中，便于后续图像操作。使用cv2.resize()函数对读取的图像进行缩放，以免图像过大导致识别耗时过长。

    def __imreadex(self, filename):        return cv2.imdecode(np.fromfile(filename, dtype=np.uint8), cv2.IMREAD_COLOR)    def __point_limit(self, point):        if point[0] < 0:            point[0] = 0        if point[1] < 0:            point[1] = 0

2、图像预处理

    def __preTreatment(self, car_pic):        if type(car_pic) == type("openc"):            img = self.__imreadex(car_pic)        else:            img = car_pic        pic_hight, pic_width = img.shape[:2]        if pic_width > self.MAX_WIDTH:            resize_rate = self.MAX_WIDTH / pic_width            img = cv2.resize(img, (self.MAX_WIDTH, int(pic_hight * resize_rate)),                             interpolation=cv2.INTER_AREA)  # 图片分辨率调整        # cv2.imshow('Image', img)

3、利用投影法，根据设定的阈值和图片直方图，找出波峰，用于分隔字符，得到逐个字符图片

 def __find_waves(self, threshold, histogram):        up_point = -1  # 上升点        is_peak = False        if histogram[0] > threshold:            up_point = 0            is_peak = True        wave_peaks = []        for i, x in enumerate(histogram):            if is_peak and x < threshold:                if i - up_point > 2:                    is_peak = False                    wave_peaks.append((up_point, i))            elif not is_peak and x >= threshold:                is_peak = True                up_point = i        if is_peak and up_point != -1 and i - up_point >             wave_peaks.append((up_point, i))        return wave_peaks    def __seperate_card(self, img, waves):        part_cards = []        for wave in waves:            part_cards.append(img[:, wave[0]:wave[1]])        return part_cards

4、高斯去噪
使用cv2.GaussianBlur()进行高斯去噪。使cv2.morphologyEx()函数进行开运算，再使用cv2.addWeighted()函数将运算结果与原图像做一次融合，从而去掉孤立的小点，毛刺等噪声。

        if blur > 0:            img = cv2.GaussianBlur(img, (blur, blur), 0)        oldimg = img        img = 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

5、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_width            wh_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)            # 框出所有可能的矩形            # oldimg = cv2.drawContours(img, [box], 0, (0, 0, 255), 2)            # cv2.imshow("Test",oldimg )

6、分割字符并识别车牌文字
使用cv2.threshold()函数进行二值化处理，再使用cv2.Canny()函数找到各区域边缘，使用cv2.morphologyEx()和cv2.morphologyEx()两个函数分别进行一次开运算（先腐蚀运算，再膨胀运算）和一个闭运算（先膨胀运算，再腐蚀运算），去掉较小区域，同时填平小孔，弥合小裂缝。将车牌位置凸显出来

    def __identification(self, card_imgs, colors,model,modelchinese):        # 识别车牌中的字符        result = {}        predict_result = []        roi = None        card_color = None        for i, color in enumerate(colors):            if color in ("blue", "yellow", "green"):                card_img = card_imgs[i]                # old_img = card_img                # 做一次锐化处理                kernel = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]], np.float32)  # 锐化                card_img = cv2.filter2D(card_img, -1, kernel=kernel)                # cv2.imshow("custom_blur", card_img)                # RGB转GARY                gray_img = cv2.cvtColor(card_img, cv2.COLOR_BGR2GRAY)                # cv2.imshow('gray_img', gray_img)                # 黄、绿车牌字符比背景暗、与蓝车牌刚好相反，所以黄、绿车牌需要反向                if color == "green" or color == "yellow":                    gray_img = cv2.bitwise_not(gray_img)                # 二值化                ret, gray_img = cv2.threshold(gray_img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)                # cv2.imshow('gray_img', gray_img)                # 查找水平直方图波峰                x_histogram = np.sum(gray_img, axis=1)                # 最小值                x_min = np.min(x_histogram)                # 均值                x_average = np.sum(x_histogram) / x_histogram.shape[0]                x_threshold = (x_min + x_average) / 2                wave_peaks = self.__find_waves(x_threshold, x_histogram)                if len(wave_peaks) == 0:                    continue                # 认为水平方向，最大的波峰为车牌区域                wave = max(wave_peaks, key=lambda x: x[1] - x[0])                gray_img = gray_img[wave[0]:wave[1]]                # cv2.imshow('gray_img', gray_img)                # 查找垂直直方图波峰                row_num, col_num = gray_img.shape[:2]                # 去掉车牌上下边缘1个像素，避免白边影响阈值判断                gray_img = gray_img[1:row_num - 1]                # cv2.imshow('gray_img', gray_img)                y_histogram = np.sum(gray_img, axis=0)                y_min = np.min(y_histogram)                y_average = np.sum(y_histogram) / y_histogram.shape[0]                y_threshold = (y_min + y_average) / 5  # U和0要求阈值偏小，否则U和0会被分成两半                wave_peaks = self.__find_waves(y_threshold, y_histogram)                # print(wave_peaks)                # for wave in wave_peaks:                #cv2.line(card_img, pt1=(wave[0], 5), pt2=(wave[1], 5), color=(0, 0, 255), thickness=2)                # 车牌字符数应大于6                if len(wave_peaks) <= 6:                    #   print(wave_peaks)                    continue                wave = max(wave_peaks, key=lambda x: x[1] - x[0])                max_wave_dis = wave[1] - wave[0]                # 判断是否是左侧车牌边缘                if wave_peaks[0][1] - wave_peaks[0][0] < max_wave_dis / 3 and wave_peaks[0][0] == 0:                    wave_peaks.pop(0)                # 组合分离汉字                cur_dis = 0                for i, wave in enumerate(wave_peaks):                    if wave[1] - wave[0] + cur_dis > max_wave_dis * 0.6:                        break                    else:                        cur_dis += wave[1] - wave[0]                if i > 0:                    wave = (wave_peaks[0][0], wave_peaks[i][1])                    wave_peaks = wave_peaks[i + 1:]                    wave_peaks.insert(0, wave)                # 去除车牌上的分隔点                point = wave_peaks[2]                if point[1] - point[0] < max_wave_dis / 3:                    point_img = gray_img[:, point[0]:point[1]]                    if np.mean(point_img) < 255 / 5:                        wave_peaks.pop(2)                if len(wave_peaks) <= 6:                    # print("peak less 2:", wave_peaks)                    continue                # print(wave_peaks)                # 分割牌照字符                part_cards = self.__seperate_card(gray_img, wave_peaks)                # 分割输出                #for i, part_card in enumerate(part_cards):                #    cv2.imshow(str(i), part_card)                # 识别                for i, part_card in enumerate(part_cards):                    # 可能是固定车牌的铆钉                    if np.mean(part_card) < 255 / 5:                        continue                    part_card_old = part_card                    w = abs(part_card.shape[1] - self.SZ) // 2                    # 边缘填充                    part_card = cv2.copyMakeBorder(part_card, 0, 0, w, w, cv2.BORDER_CONSTANT, value=[0, 0, 0])                    # cv2.imshow('part_card', part_card)                    # 图片缩放（缩小）                    part_card = cv2.resize(part_card, (self.SZ, self.SZ), interpolation=cv2.INTER_AREA)                    # cv2.imshow('part_card', part_card)                    part_card = SVM_Train.preprocess_hog([part_card])                    if i == 0:  # 识别汉字                        resp = self.modelchinese.predict(part_card)  # 匹配样本                        charactor = self.provinces[int(resp[0]) - self.PROVINCE_START]                        # print(charactor)                    else:  # 识别字母                        resp = self.model.predict(part_card)  # 匹配样本                        charactor = chr(resp[0])                        # print(charactor)                    # 判断最后一个数是否是车牌边缘，假设车牌边缘被认为是1                    if charactor == "1" and i == len(part_cards) - 1:                        if color == 'blue' and len(part_cards) > 7:                            if part_card_old.shape[0] / part_card_old.shape[1] >= 7:  # 1太细，认为是边缘                                continue                        elif color == 'blue' and len(part_cards) > 7:                            if part_card_old.shape[0] / part_card_old.shape[1] >= 7:  # 1太细，认为是边缘                                continue                        elif color == 'green' and len(part_cards) > 8:                            if part_card_old.shape[0] / part_card_old.shape[1] >= 7:  # 1太细，认为是边缘                                continue                    predict_result.append(charactor)                roi = card_img  # old_img                card_color = color                break        return predict_result, roi, card_color  # 识别到的字符、定位的车牌图像、车牌颜色

UI_main模块

此模块主要包含UI界面的设计的控件，图片识别的入口函数，摄像头识别入口函数，Excel表格生成函数：

1、UI界面主类

class Ui_MainWindow(object):    def __init__(self):        self.RowLength = 0        self.Data = [['文件名称', '录入时间', '车牌号码', '车牌类型', '识别耗时', '车牌信息']]    def setupUi(self, MainWindow):        MainWindow.setObjectName("MainWindow")        MainWindow.resize(1213, 680)        MainWindow.setFixedSize(1213, 680)  # 设置窗体固定大小        MainWindow.setToolButtonStyle(QtCore.Qt.ToolButtonIconOnly)        self.centralwidget = QtWidgets.QWidget(MainWindow)    #图片区域        self.centralwidget.setObjectName("centralwidget")        self.scrollArea = QtWidgets.QScrollArea(self.centralwidget)        self.scrollArea.setGeometry(QtCore.QRect(690, 10, 511, 491))        self.scrollArea.setWidgetResizable(False)        self.scrollArea.setObjectName("scrollArea")        self.scrollAreaWidgetContents = QtWidgets.QWidget()        self.scrollAreaWidgetContents.setGeometry(QtCore.QRect(10, 10, 509, 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, 441))        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)  #设置布局        self.tableWidget.setGeometry(QtCore.QRect(10, 40, 651, 581))  # 581))        self.tableWidget.setObjectName("tableWidget")        self.tableWidget.setColumnCount(6)        self.tableWidget.setColumnWidth(0, 106)  # 设置1列的宽度        self.tableWidget.setColumnWidth(1, 106)  # 设置2列的宽度        self.tableWidget.setColumnWidth(2, 106)  # 设置3列的宽度        self.tableWidget.setColumnWidth(3, 106)  # 设置4列的宽度        self.tableWidget.setColumnWidth(4, 106)  # 设置5列的宽度        self.tableWidget.setColumnWidth(5, 106)  # 设置6列的宽度        self.tableWidget.setHorizontalHeaderLabels(["图片名称", "录入时间", "识别耗时", "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(10, 50, 80, 30))        self.pushButton_2.setObjectName("pushButton_2")        self.pushButton = QtWidgets.QPushButton(self.scrollAreaWidgetContents_4)        self.pushButton.setGeometry(QtCore.QRect(10, 90, 80, 30))        self.pushButton.setObjectName("pushButton")        self.pushButton_3 = QtWidgets.QPushButton(self.scrollAreaWidgetContents_4)        self.pushButton_3.setGeometry(QtCore.QRect(100, 50, 50, 70))        self.pushButton_3.setObjectName("pushButton_3")        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_2.clicked.connect(self.__writeFiles)  # 设置点击事件        self.pushButton_3.clicked.connect(self.__openVideo)  #设置事件        self.retranslateUi(MainWindow)        QtCore.QMetaObject.connectSlotsByName(MainWindow)        self.ProjectPath = os.getcwd()  # 获取当前工程文件位置    def retranslateUi(self, MainWindow):        _translate = QtCore.QCoreApplication.translate        MainWindow.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.pushButton_3.setText(_translate("MainWindow", "摄像"))        self.label_4.setText(_translate("MainWindow", "控制面板："))        self.scrollAreaWidgetContents_1.show()

2、识别入口函数

    def __vlpr(self, path):        PR = PlateRecognition()        result = PR.VLPR(path)        return result

3、写入及导出Excel表格文件

    def __show(self, result, FileName):        # 显示表格        self.RowLength = self.RowLength + 1        if 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(str(result['UseTime']) + '秒'))        self.tableWidget.setItem(self.RowLength - 1, 3, QTableWidgetItem(result['Number']))        self.tableWidget.setItem(self.RowLength - 1, 4, QTableWidgetItem(result['Type']))        if result['Type'] == '蓝色牌照':            self.tableWidget.item(self.RowLength - 1, 4).setBackground(QBrush(QColor(3, 128, 255)))        elif result['Type'] == '绿色牌照':            self.tableWidget.item(self.RowLength - 1, 4).setBackground(QBrush(QColor(98, 198, 148)))        elif result['Type'] == '黄色牌照':            self.tableWidget.item(self.RowLength - 1, 4).setBackground(QBrush(QColor(242, 202, 9)))        self.tableWidget.setItem(self.RowLength - 1, 5, QTableWidgetItem(result['From']))        # 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))    def __writexls(self, DATA, path):        wb = xlwt.Workbook();        ws = wb.add_sheet('Data');        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', '识别耗时', '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            return        if filetype == 'Excel 工作簿(*.xls)':            self.__writexls(self.Data, path)        elif filetype == 'CSV (逗号分隔)(*.csv)': #逗号分隔开            self.__writecsv(self.Data, path)

4、图片识别入口

    def __openimage(self):        path, filetype = QFileDialog.getOpenFileName(None, "选择文件", self.ProjectPath,                                                     "JPEG Image (*.jpg);;PNG Image (*.png);;JFIF Image (*.jfif)")  # ;;All Files (*)        if path == "":  # 未选择文件            return        filename = path.split('/')[-1]        # 尺寸适配        size = cv2.imdecode(np.fromfile(path, dtype=np.uint8), cv2.IMREAD_COLOR).shape        if size[0] / size[1] > 1.0907:            w = size[1] * self.label.height() / size[0]            h = self.label.height()            jpg = QtGui.QPixmap(path).scaled(w, h)        elif size[0] / size[1] < 1.0907:            w = self.label.width()            h = size[0] * self.label.width() / size[1]            jpg = QtGui.QPixmap(path).scaled(w, h)        else:            jpg = QtGui.QPixmap(path).scaled(self.label.width(), self.label.height())        self.label.setPixmap(jpg)    #保存jpg        result = self.__vlpr(path)  #识别        if result is not None:            self.Data.append(                [filename, result['InputTime'], result['Number'], result['Type'], str(result['UseTime']) + '秒',                 result['From']])            self.__show(result, filename)        else:            QMessageBox.warning(None, "Error", "无法识别此图像！", QMessageBox.Yes)

5、摄像头识别入口

    def __openVideo(self):        cap = cv2.VideoCapture(0)        while True:            success, img = cap.read()            img1 = cv2.flip(img, 1)            cv2.imshow("VideoData", img1)            k = cv2.waitKey(1)            if cv2.getWindowProperty('VideoData', cv2.WND_PROP_VISIBLE) < 1:                break            elif k == ord("s"):                cv2.imwrite("index2.jpg", img1)   #读取摄像头                cv2.destroyAllWindows()        cap.release()

6、重写MainWindow窗口

class MainWindow(QtWidgets.QMainWindow):    def closeEvent(self, event):        reply = QtWidgets.QMessageBox.question(self, '提示',                                               "是否要退出程序？\n提示：退出后将丢失所有识别数据",                                               QtWidgets.QMessageBox.Yes | QtWidgets.QMessageBox.No,                                               QtWidgets.QMessageBox.No)        if reply == QtWidgets.QMessageBox.Yes:            event.accept()        else:            event.ignore()

SVM训练模块

此模块主要用于对模型的准确性进行训练，包含字符中英文、数字的识别、图片尺寸的训练，最后将模型保存在svmchinese.dat中：

import cv2import osimport numpy as npfrom numpy.linalg import normfrom args import argsclass StatModel(object):    def load(self, fn):        self.model = self.model.load(fn)    def save(self, fn):        self.model.save(fn)class SVM(StatModel):    def __init__(self, C=1, gamma=0.5):        self.model = cv2.ml.SVM_create()        self.model.setGamma(gamma)        self.model.setC(C)        self.model.setKernel(cv2.ml.SVM_RBF)        self.model.setType(cv2.ml.SVM_C_SVC)        # 不能保证包括所有省份    # 训练svm    def train(self, samples, responses):        self.model.train(samples, cv2.ml.ROW_SAMPLE, responses)    # 字符识别    def predict(self, samples):        r = self.model.predict(samples)        return r[1].ravel()# 定义参数SZ = args.Size  # 训练图片长宽MAX_WIDTH = args.MAX_WIDTH  # 原始图片最大宽度Min_Area = args.Min_Area  # 车牌区域允许最大面积PROVINCE_START = args.PROVINCE_STARTprovinces = args.provinces# 来自opencv的sample，用于svm训练def deskew(img):    m = cv2.moments(img)    if abs(m['mu02']) < 1e-2:        return img.copy()    skew = m['mu11'] / m['mu02']    M = np.float32([[1, skew, -0.5 * SZ * skew], [0, 1, 0]])    img = cv2.warpAffine(img, M, (SZ, SZ), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_LINEAR)    return img# 来自opencv的sample，用于svm训练def preprocess_hog(digits):    samples = []    for img in digits:        gx = cv2.Sobel(img, cv2.CV_32F, 1, 0)        gy = cv2.Sobel(img, cv2.CV_32F, 0, 1)        mag, ang = cv2.cartToPolar(gx, gy)        bin_n = 16        bin = np.int32(bin_n * ang / (2 * np.pi))        bin_cells = bin[:10, :10], bin[10:, :10], bin[:10, 10:], bin[10:, 10:]        mag_cells = mag[:10, :10], mag[10:, :10], mag[:10, 10:], mag[10:, 10:]        hists = [np.bincount(b.ravel(), m.ravel(), bin_n) for b, m in zip(bin_cells, mag_cells)]        hist = np.hstack(hists)        # transform to Hellinger kernel        eps = 1e-7        hist /= hist.sum() + eps        hist = np.sqrt(hist)        hist /= norm(hist) + eps        samples.append(hist)    return np.float32(samples)def train_svm(path):    # 识别英文字母和数字    Model = SVM(C=1, gamma=0.5)    # 识别中文    Modelchinese = SVM(C=1, gamma=0.5)    # 英文字母和数字部分训练    chars_train = []    chars_label = []    for root, dirs, files in os.walk(os.path.join(path,'chars')):        if len(os.path.basename(root)) > 1:            continue        root_int = ord(os.path.basename(root))        for filename in files:            print('input：{}'.format(filename))            filepath = os.path.join(root, filename)            digit_img = cv2.imread(filepath)            digit_img = cv2.cvtColor(digit_img, cv2.COLOR_BGR2GRAY)            chars_train.append(digit_img)            chars_label.append(root_int)    chars_train = list(map(deskew, chars_train))    chars_train = preprocess_hog(chars_train)    chars_label = np.array(chars_label)    Model.train(chars_train, chars_label)    if not os.path.exists("svm.dat"):        # 保存模型        Model.save("svm.dat")    else:        # 更新模型        os.remove("svm.dat")        Model.save("svm.dat")    # 中文部分训练    chars_train = []    chars_label = []    for root, dirs, files in os.walk(os.path.join(path,'charsChinese')):        if not os.path.basename(root).startswith("zh_"):            continue        pinyin = os.path.basename(root)        index = provinces.index(pinyin) + PROVINCE_START + 1  # 1是拼音对应的汉字        for filename in files:            print('input：{}'.format(filename))            filepath = os.path.join(root, filename)            digit_img = cv2.imread(filepath)            digit_img = cv2.cvtColor(digit_img, cv2.COLOR_BGR2GRAY)            chars_train.append(digit_img)            chars_label.append(index)    chars_train = list(map(deskew, chars_train))    chars_train = preprocess_hog(chars_train)    chars_label = np.array(chars_label)    Modelchinese.train(chars_train, chars_label)    if not os.path.exists("svmchinese.dat"):        # 保存模型        Modelchinese.save("svmchinese.dat")    else:        # 更新模型        os.remove("svmchinese.dat")        Modelchinese.save("svmchinese.dat")if __name__ == '__main__':    train_svm('train')    print('完成')

路由配置模块

from _collections import OrderedDict# 导入Flask类from flask import Flask, request, jsonifyfrom json_utils import jsonifyimport numpy as npimport cv2import timefrom collections import OrderedDictfrom Recognition import PlateRecognition# 实例化app = Flask(__name__)PR = PlateRecognition()# 设置编码-否则返回数据中文时候-乱码app.config['JSON_AS_ASCII'] = False# route()方法用于设定路由；类似spring路由配置@app.route('/', methods=['POST'])  # 在线识别def forecast():    # 获取输入数据    stat = time.time()    file = request.files['image']    img_bytes = file.read()    image = np.asarray(bytearray(img_bytes), dtype="uint8")    image = cv2.imdecode(image, cv2.IMREAD_COLOR)    RES = PR.VLPR(image)    if RES is not None:        result = OrderedDict(            Error=0,            Errmsg='success',            InputTime=RES['InputTime'],            UseTime='{:.2f}'.format(time.time() - stat),  # RES['UseTime'],            Number=RES['Number'],            From=RES['From'],            Type=RES['Type'],            List=RES['List'])    else:        result = OrderedDict(            Error=1,            Errmsg='unsuccess')    return jsonify(result)if __name__ == '__main__':    # app.run(host, port, debug, options)    # 默认值：host=127.0.0.1（localhost）, port=5000, debug=false    app.run()    # 本地路由地址，局域网下的主机均可通过该地址完成POST请求    # app.run(host='192.168.1.100' )    # 部署到服务器    # from waitress import serve    # serve(app, host=' IP  ', port=5000)