做设计素材网站有哪些,网站开发意见书,百度营销平台,手机网站免费制作1. 整个识别的流程图#xff1a; 2. 车牌定位中分割流程图#xff1a; 三、车牌识别中字符分割流程图#xff1a; 1.准备数据集
下载车牌相关字符样本用于训练和测试#xff0c;本文使用14个汉字样本和34个数字跟字母样本#xff0c;每个字符样本数为40#xff0c;样本尺…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), flagscv2.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(C1, gamma0.5)#识别中文modelchinese SVM(C1, gamma0.5)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, dtypenp.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(C1, gamma0.5)#SVM(C1, gamma0.5)#识别中文self.modelchinese train.SVM(C1, gamma0.5)#SVM(C1, gamma0.5)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_rate1):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)), interpolationcv2.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 yellolimit1 11limit2 34#有的图片有色偏偏绿elif green*2 card_img_count:color greenlimit1 35limit2 99elif blue*2 card_img_count:color bluelimit1 100limit2 124#有的图片有色偏偏紫elif black white card_img_count*0.7:#TODOcolor bwcolors.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, axis1)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, keylambda 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, axis0)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, keylambda 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[i1:]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), interpolationcv2.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), interpolationcv2.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。
