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] = 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 cv2 import os import numpy as np from numpy.linalg import norm from args import args class 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_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 = 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, 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=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)
