1. 整个识别的流程图：

2. 车牌定位中分割流程图：

三、车牌识别中字符分割流程图：

1.准备数据集

下载车牌相关字符样本用于训练和测试，本文使用14个汉字样本和34个数字跟字母样本，每个字符样本数为40，样本尺寸为28*28。

数据集下载地址

https://download.csdn.net/download/hai411741962/88248392

下载不了，评论区留言
2. 编码训练代码

import cv2
import numpy as np
from numpy.linalg import norm
import sys
import os
import jsonSZ = 20          #训练图片长宽
MAX_WIDTH = 1000 #原始图片最大宽度
Min_Area = 2000  #车牌区域允许最大面积
PROVINCE_START = 1000
#不能保证包括所有省份
provinces = ["zh_cuan", "川","zh_e", "鄂","zh_gan", "赣","zh_gan1", "甘","zh_gui", "贵","zh_gui1", "桂","zh_hei", "黑","zh_hu", "沪","zh_ji", "冀","zh_jin", "津","zh_jing", "京","zh_jl", "吉","zh_liao", "辽","zh_lu", "鲁","zh_meng", "蒙","zh_min", "闽","zh_ning", "宁","zh_qing", "靑","zh_qiong", "琼","zh_shan", "陕","zh_su", "苏","zh_sx", "晋","zh_wan", "皖","zh_xiang", "湘","zh_xin", "新","zh_yu", "豫","zh_yu1", "渝","zh_yue", "粤","zh_yun", "云","zh_zang", "藏","zh_zhe", "浙"
]class StatModel(object):def load(self, fn):self.model = self.model.load(fn)#从文件载入训练好的模型def save(self, fn):self.model.save(fn)#保存训练好的模型到文件中class SVM(StatModel):def __init__(self, C = 1, gamma = 0.5):self.model = cv2.ml.SVM_create()#生成一个SVM模型self.model.setGamma(gamma) #设置Gamma参数，demo中是0.5self.model.setC(C)# 设置惩罚项， 为：1self.model.setKernel(cv2.ml.SVM_RBF)#设置核函数self.model.setType(cv2.ml.SVM_C_SVC)#设置SVM的模型类型：SVC是分类模型，SVR是回归模型#训练svmdef train(self, samples, responses):self.model.train(samples, cv2.ml.ROW_SAMPLE, responses)#训练#字符识别def predict(self, samples):r = self.model.predict(samples)#预测return r[1].ravel()#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 save_traindata(model,modelchinese):if not os.path.exists("module\\svm.dat"):model.save("module\\svm.dat")if not os.path.exists("module\\svmchinese.dat"):modelchinese.save("module\\svmchinese.dat")def train_svm():#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)if os.path.exists("svm.dat"):model.load("svm.dat")else:chars_train = []chars_label = []for root, dirs, files in os.walk("train\\chars2"):if len(os.path.basename(root)) > 1:continueroot_int = ord(os.path.basename(root))for filename in files: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(1)chars_label.append(root_int)chars_train = list(map(deskew, chars_train))#print(chars_train)chars_train = preprocess_hog(chars_train)#print(chars_train)#chars_train = chars_train.reshape(-1, 20, 20).astype(np.float32)chars_label = np.array(chars_label)model.train(chars_train, chars_label)if os.path.exists("svmchinese.dat"):modelchinese.load("svmchinese.dat")else:chars_train = []chars_label = []for root, dirs, files in os.walk("train\\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: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(1)chars_label.append(index)chars_train = list(map(deskew, chars_train))chars_train = preprocess_hog(chars_train)#chars_train = chars_train.reshape(-1, 20, 20).astype(np.float32)chars_label = np.array(chars_label)print(chars_train.shape)modelchinese.train(chars_train, chars_label)save_traindata(model,modelchinese)train_svm()

运行代码后会生成两个模型文件，下面验证两个模型文件。

import cv2
import numpy as npimport json
import trainSZ = 20          #训练图片长宽
MAX_WIDTH = 1000 #原始图片最大宽度
Min_Area = 2000  #车牌区域允许最大面积
PROVINCE_START = 1000
#读取图片文件
def imreadex(filename):return cv2.imdecode(np.fromfile(filename, dtype=np.uint8), cv2.IMREAD_COLOR)def point_limit(point):if point[0] < 0:point[0] = 0if point[1] < 0:point[1] = 0#根据设定的阈值和图片直方图，找出波峰，用于分隔字符
def find_waves(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 > 4:wave_peaks.append((up_point, i))return wave_peaks#根据找出的波峰，分隔图片，从而得到逐个字符图片
def seperate_card(img, waves):part_cards = []for wave in waves:part_cards.append(img[:, wave[0]:wave[1]])return part_cardsclass CardPredictor:def __init__(self):#车牌识别的部分参数保存在json中，便于根据图片分辨率做调整f = open('config.json')j = json.load(f)for c in j["config"]:if c["open"]:self.cfg = c.copy()breakelse:raise RuntimeError('没有设置有效配置参数')def load_svm(self):#识别英文字母和数字self.model = train.SVM(C=1, gamma=0.5)#SVM(C=1, gamma=0.5)#识别中文self.modelchinese = train.SVM(C=1, gamma=0.5)#SVM(C=1, gamma=0.5)self.model.load("module\\svm.dat")self.modelchinese.load("module\\svmchinese.dat")def accurate_place(self, card_img_hsv, limit1, limit2, color):row_num, col_num = card_img_hsv.shape[:2]xl = col_numxr = 0yh = 0yl = row_num#col_num_limit = self.cfg["col_num_limit"]row_num_limit = self.cfg["row_num_limit"]col_num_limit = col_num * 0.8 if color != "green" else col_num * 0.5#绿色有渐变for i in range(row_num):count = 0for 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 limit1 < H <= limit2 and 34 < S and 46 < V:count += 1if count > col_num_limit:if yl > i:yl = iif yh < i:yh = ifor j in range(col_num):count = 0for i in range(row_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 limit1 < H <= limit2 and 34 < S and 46 < V:count += 1if count > row_num - row_num_limit:if xl > j:xl = jif xr < j:xr = jreturn xl, xr, yh, yldef predict(self, car_pic, resize_rate=1):if type(car_pic) == type(""):img = imreadex(car_pic)else:img = car_picpic_hight, pic_width = img.shape[:2]if resize_rate != 1:img = cv2.resize(img, (int(pic_width*resize_rate), int(pic_hight*resize_rate)), interpolation=cv2.INTER_AREA)pic_hight, pic_width = img.shape[:2]#cv2.imshow('img',img)#cv2.waitKey(0)print("h,w:", pic_hight, pic_width)blur = self.cfg["blur"]#高斯去噪if blur > 0:img = cv2.GaussianBlur(img, (blur, blur), 0)#图片分辨率调整oldimg = imgimg = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)#去掉图像中不会是车牌的区域kernel = np.ones((20, 20), np.uint8)img_opening = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)img_opening = cv2.addWeighted(img, 1, img_opening, -1, 0);#找到图像边缘ret, img_thresh = cv2.threshold(img_opening, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)img_edge = cv2.Canny(img_thresh, 100, 200)#边缘检测#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)img_edge2 = cv2.morphologyEx(img_edge1, cv2.MORPH_OPEN, kernel)#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contours, hierarchy = cv2.findContours(img_edge2, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)print('len(contours)', len(contours))#找出区域contours = [cnt for cnt in contours if cv2.contourArea(cnt) > Min_Area]print('len(contours)', len(contours))#cv2.contourArea计算面积#一一排除不是车牌的矩形区域car_contours = []for cnt in contours:rect = cv2.minAreaRect(cnt)#minAreaRectarea_width, area_height = rect[1]if area_width < area_height:area_width, area_height = area_height, area_widthwh_ratio = area_width / area_height#长宽比#print(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)#cv2.boxPoints()可获取该矩形的四个顶点坐标。print(box)box = np.int0(box) #转成整数print(box)oldimg = cv2.drawContours(oldimg, [box], 0, (0, 0, 255), 2)print(len(car_contours))print("精确定位")card_imgs = []#矩形区域可能是倾斜的矩形，需要矫正，以便使用颜色定位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))point_limit(new_right_point)point_limit(heigth_point)point_limit(left_point)card_img = dst[int(left_point[1]):int(heigth_point[1]), int(left_point[0]):int(new_right_point[0])]if(len(card_img)>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))point_limit(right_point)point_limit(heigth_point)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)#cv2.imshow("card", card_img)#cv2.waitKey(0)#开始使用颜色定位，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):print(len(card_imgs))green = yello = blue = black = white = 0card_img_hsv = cv2.cvtColor(card_img, cv2.COLOR_BGR2HSV)print("card_img_hsv.shape")print(card_img_hsv.shape)#有转换失败的可能，原因来自于上面矫正矩形出错if card_img_hsv is None:continuerow_num, col_num= card_img_hsv.shape[:2]card_img_count = row_num * col_numfor 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yello += 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"#根据HSV判断车牌颜色limit1 = limit2 = 0if yello*2 >= card_img_count:color = "yello"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:#TODOcolor = "bw"colors.append(color)print("blue, green, yello, black, white, card_img_count:")print(blue,"   " ,green,"   ", yello,"   ", black,"   ", white,"   ", card_img_count)print("车牌颜色：",color)# cv2.imshow("color", card_img)# cv2.waitKey(0)if limit1 == 0:continue#以上为确定车牌颜色#以下为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]#以上为车牌定位#以下为识别车牌中的字符predict_result = []roi = Nonecard_color = Nonefor i, color in enumerate(colors):if color in ("blue", "yello", "green"):card_img = card_imgs[i]gray_img = cv2.cvtColor(card_img, cv2.COLOR_BGR2GRAY)#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 == "yello":gray_img = cv2.bitwise_not(gray_img)ret, gray_img = cv2.threshold(gray_img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)#查找水平直方图波峰x_histogram  = np.sum(gray_img, axis=1)x_min = np.min(x_histogram)x_average = np.sum(x_histogram)/x_histogram.shape[0]x_threshold = (x_min + x_average)/2wave_peaks = find_waves(x_threshold, x_histogram)if len(wave_peaks) == 0:print("peak less 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]]#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)#二值化# cv2.waitKey(0)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 = find_waves(y_threshold, y_histogram)#车牌字符数应大于6if len(wave_peaks) <= 6:print("peak less 1:", len(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:", len(wave_peaks))continuepart_cards = seperate_card(gray_img, wave_peaks)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:print("a point")continuepart_card_old = part_cardw = part_card.shape[1] // 3part_card = cv2.copyMakeBorder(part_card, 0, 0, w, w, cv2.BORDER_CONSTANT, value = [0,0,0])part_card = cv2.resize(part_card, (SZ, SZ), interpolation=cv2.INTER_AREA)cv2.destroyAllWindows()part_card = train.preprocess_hog([part_card])#preprocess_hog([part_card])if i == 0:resp = self.modelchinese.predict(part_card)#第一个字符调用中文svm模型charactor = train.provinces[int(resp[0]) - PROVINCE_START]else:resp = self.model.predict(part_card)#其他字符调用字母数字svm模型charactor = chr(resp[0])#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 part_card_old.shape[0]/part_card_old.shape[1] >= 8:#1太细，认为是边缘print(part_card_old.shape)continuepredict_result.append(charactor)roi = card_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if __name__ == '__main__':c = CardPredictor()c.load_svm()#加载训练好的模型img  = cv2.imread("test\\car20.jpg")img = cv2.resize(img, (1000, 1000), interpolation=cv2.INTER_AREA)r, roi, color = c.predict(img)print(r)

运行结果：

车牌颜色： blue
['津', 'N', 'A', 'V', '8', '8', '8']

从结果看比上一节的准确多了。