python实时车牌识别（一文看懂车牌识别系统全部内容）

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　车辆号牌是车辆唯一身份证，它的特殊性与重要性决定车牌识别系统成为城市智能交通管理系统中不可或缺的重要组成部分。

未来，随着我国城市化进程发展的提速，交通压力将更加严峻，因此智能化交通管理将是今后交通发展的大方向。今天小编就来带大家探索车牌识别系统！

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Python3、 Pycharm 、PIL、cv2 、tkinter 一些车牌图片的素材大家可以自由选择这里就不展示，

等下会给大家随机找几组效果哈！

第三方库的安装：pip install 模块名 如安装出现问题可以直接找我私信即可哈

小程序界面：

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效果展示：

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三、代码展示

主要有部分源码。这里仅展示部分。需要的找我拿完整的哈！

主程序界面：

import tkinter as tk from tkinter.filedialog import * from tkinter import ttk; import predict import cv2 from PIL import Image,ImageTk import threading import time class Window(ttk.frame): pic_path = "" viewHigh = 600 viewWide = 600 updataTime = 0 thread = None threadRun = False camera = None colorTransform = {"green":("绿","#55ff55"),"yello":("黄","#ffff00"),"blue":{"蓝","#6666ff"}} def __init__(self,win): ttk.Frame.__init__(self,win) frame_left = ttk.Frame(self) frame_right1 = ttk.Frame(self) frame_right2= ttk.Frame(self) win.title("车牌识别") win.state("normal") self.pack(fill=tk.BOTH,expand=tk.YES,padx="5",pady="5") frame_left.pack(side=LEFT,expand=1,fill=BOTH) frame_right1.pack(side=TOP, expand=1, fill=tk.Y) frame_right2.pack(side=RIGHT, expand=0) ttk.Label(frame_left, text='原图：').pack(anchor="nw") ttk.Label(frame_right1, text='车牌位置：').grid(column=0, row=0, sticky=tk.W) from_pic_ctl = ttk.Button(frame_right2, text="打开图片", width=20, command=self.from_pic) self.image_ctl = ttk.Label(frame_left) self.image_ctl.pack(anchor="nw") self.roi_ctl = ttk.Label(frame_right1) self.roi_ctl.grid(column=0, row=1, sticky=tk.W) ttk.Label(frame_right1, text='识别结果：').grid(column=0, row=2, sticky=tk.W) self.r_ctl = ttk.Label(frame_right1, text="") self.r_ctl.grid(column=0, row=3, sticky=tk.W) self.color_ctl = ttk.Label(frame_right1, text="", width="20") self.color_ctl.grid(column=0, row=4, sticky=tk.W) from_pic_ctl.pack(anchor="se", pady="5") self.predictor = predict.CardPredictor() self.predictor.train_svm() def from_pic(self): self.threadRun = False self.pic_path = askopenfilename(title="选择识别图片",filetypes=[("jpg图片","*.jpg")]) if self.pic_path: img_bgr = predict.imreadex(self.pic_path) self.imgtk = self.get_imgtk(img_bgr) self.image_ctl.configure(image=self.imgtk) r,roi,color = self.predictor.predict(img_bgr) self.show_roi(r,roi,color) def show_roi(self,r,roi,color): if r : roi = cv2.cvtColor(roi,cv2.COLOR_BGR2RGB) roi = Image.fromarray(roi) self.imgtk_roi = ImageTk.PhotoImage(image=roi) self.roi_ctl.configure(image=self.imgtk_roi, state='enable') self.r_ctl.configure(text=str(r)) self.update_time = time.time() try: c = self.colorTransform[color] self.color_ctl.configure(text=c[0], background=c[1], state='enable') except: self.color_ctl.configure(state='disabled') elif self.update_time 8 < time.time(): self.roi_ctl.configure(state='disabled') self.r_ctl.configure(text="") self.color_ctl.configure(state='disabled') def get_imgtk(self,img_bgr): img = cv2.cvtColor(img_bgr,cv2.COLOR_BGR2RGB) im = Image.fromarray(img) imgtk = ImageTk.PhotoImage(image=im) wide = imgtk.width() high = imgtk.height() if wide>self.viewWide or high > self.viewHigh: wide_factor = self.viewWide / wide high_factor = self.viewHigh / high factor = min(wide_factor,high_factor) wide = int(wide*factor) if wide <=0 : wide = 1 high = int(high*factor) if high <= 0:high = 1 im = im.resize((wide,high),Image.ANTIALIAS) imgtk = ImageTk.PhotoImage(image=im) return imgtk def close_window(): print("destroy") if window.threadRun : window.threadRun = False window.thread.join(2.0) win.destroy() if __name__ == '__main__': win = tk.Tk() window = Window(win) win.protocol('WM_DELETE_WINDOW', close_window) win.mainloop()

模型训练：

import cv2 import numpy as np from numpy.linalg import norm import sys import os import json from matplotlib import pyplot as plt SZ = 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] = 0 if 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 = 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 > 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_cards # 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) # 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) 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) # 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() class CardPredictor: def __init__(self): # 车牌识别的部分参数保存在js中，便于根据图片分辨率做调整 f = open('config.js') j = json.load(f) for c in j["config"]: if c["open"]: self.cfg = c.copy() break else: raise RuntimeError('没有设置有效配置参数') def __del__(self): self.save_traindata() def train_svm(self): # 识别英文字母和数字 self.model = SVM(C=1, gamma=0.5) # 识别中文 self.modelchinese = SVM(C=1, gamma=0.5) if os.path.exists("svm.dat"): self.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: continue root_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)) 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) self.model.train(chars_train, chars_label) if os.path.exists("svmchinese.dat"): self.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_"): 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: 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) self.modelchinese.train(chars_train, chars_label) def save_traindata(self): if not os.path.exists("svm.dat"): self.model.save("svm.dat") if not os.path.exists("svmchinese.dat"): self.modelchinese.save("svmchinese.dat") def accurate_place(self, card_img_hsv, limit1, limit2, color): row_num, col_num = card_img_hsv.shape[:2] xl = col_num xr = 0 yh = 0 yl = 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 = 0 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 limit1 < H <= limit2 and 34 < S and 46 < V: count = 1 if count > col_num_limit: if yl > i: yl = i if yh < i: yh = i for j in range(col_num): count = 0 for 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 = 1 if count > row_num - row_num_limit: if xl > j: xl = j if xr < j: xr = j return xl, xr, yh, yl def predict(self, car_pic): print("config: " str(self.cfg)) if type(car_pic) == type(""): img = imreadex(car_pic) else: img = car_pic pic_hight, pic_width = img.shape[:2] if pic_width > MAX_WIDTH: resize_rate = MAX_WIDTH / pic_width img = cv2.resize(img, (MAX_WIDTH, int(pic_hight * resize_rate)), interpolation=cv2.INTER_AREA) 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 = img img = 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) # 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 image, contours, hierarchy = cv2.findContours(img_edge2, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) contours = [cnt for cnt in contours if cv2.contourArea(cnt) > Min_Area] print('len(contours)', len(contours)) # 一一排除不是车牌的矩形区域 car_contours = [] for cnt in contours: rect = cv2.minAreaRect(cnt) area_width, area_height = rect[1] if area_width < area_height: area_width, area_height = area_height, area_width wh_ratio = area_width / area_height # 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) box = np.int0(box) # oldimg = cv2.drawConours(oldimg, [box], 0, (0, 0, 255), 2) # cv2.imshow("edge4", oldimg) # print(rect) 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 = 1 else: 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) print("box_ :" str(box) ) 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 = point if low_point[1] > point[1]: low_point = point if heigth_point[1] < point[1]: heigth_point = point if right_point[0] < point[0]: right_point = point if 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])] print("card_img :" str(card_img)) card_imgs.append(card_img) # cv2.imshow("card", card_img) # cv2.waitKey(0) 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): green = yello = blue = black = white = 0 card_img_hsv = cv2.cvtColor(card_img, cv2.COLOR_BGR2HSV) # 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: continue row_num, col_num = card_img_hsv.shape[:2] card_img_count = row_num * col_num for 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 = 1 elif 35 < H <= 99 and S > 34: # 图片分辨率调整 green = 1 elif 99 < H <= 124 and S > 34: # 图片分辨率调整 blue = 1 if 0 < H < 180 and 0 < S < 255 and 0 < V < 46: black = 1 elif 0 < H < 180 and 0 < S < 43 and 221 < V < 225: white = 1 color = "no" limit1 = limit2 = 0 if yello * 2 >= card_img_count: color = "yello" limit1 = 11 limit2 = 34 # 有的图片有色偏偏绿 elif green * 2 >= card_img_count: color = "green" limit1 = 35 limit2 = 99 elif blue * 2 >= card_img_count: color = "blue" limit1 = 100 limit2 = 124 # 有的图片有色偏偏紫 elif black white >= card_img_count * 0.7: # TODO color = "bw" print(color) colors.append(color) print(blue, green, yello, black, white, card_img_count) # 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 # 以上为车牌定位 # 以下为识别车牌中的字符 predict_result = [] roi = None card_color = None for 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) / 2 wave_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] 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) # 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("peak less 1:", len(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:", len(wave_peaks)) continue part_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") continue part_card_old = part_card w = abs(part_card.shape[1] - SZ) // 2 part_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) # part_card = deskew(part_card) part_card = preprocess_hog([part_card]) if i == 0: resp = self.modelchinese.predict(part_card) charactor = provinces[int(resp[0]) - PROVINCE_START] else: resp = self.model.predict(part_card) 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1 if charactor == "1" and i == len(part_cards) - 1: 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 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

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车牌识别小程序就写到这里，之后会更新更多内容哦~记得关注我啦

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