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：

Recognition.py（识别模块）

UI_main(主函数及UI模块）

SVM训练模块

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] = 0if 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_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# 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 = Falseif histogram[0] > threshold:up_point = 0is_peak = Truewave_peaks = []for i, x in enumerate(histogram):if is_peak and x < threshold:if i - up_point > 2:is_peak = Falsewave_peaks.append((up_point, i))elif not is_peak and x >= threshold:is_peak = Trueup_point = iif is_peak and up_point != -1 and i - up_point > wave_peaks.append((up_point, i))return wave_peaksdef __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 = 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

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_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)# 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 = Nonecard_color = Nonefor 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GARYgray_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) / 2wave_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6if len(wave_peaks) <= 6:#   print(wave_peaks)continuewave = 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 = 0for i, wave in enumerate(wave_peaks):if wave[1] - wave[0] + cur_dis > max_wave_dis * 0.6:breakelse: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:continuepart_card_old = part_cardw = 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1if 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continueelif 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continueelif 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continuepredict_result.append(charactor)roi = card_img  # old_imgcard_color = colorbreakreturn 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 = 0self.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.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.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 + 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(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returnif 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returnfilename = path.split('/')[-1]# 尺寸适配size = cv2.imdecode(np.fromfile(path, dtype=np.uint8), cv2.IMREAD_COLOR).shapeif 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jpgresult = 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:breakelif 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 cv2
import os
import numpy as np
from numpy.linalg import norm
from 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svmdef 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_START
provinces = 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 = 16bin = 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 kerneleps = 1e-7hist /= hist.sum() + epshist = np.sqrt(hist)hist /= norm(hist) + epssamples.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:continueroot_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_"):continuepinyin = 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, jsonify
from json_utils import jsonify
import numpy as np
import cv2
import time
from collections import OrderedDict
from 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=falseapp.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)