基于opencv的车牌识别系统（UI界面采用tkinter设计）
本系统采用python语言搭配opencv进行开发，在传统的车牌识别项目上进行改进，开发独特的GUI界面，方便使用者的使用。
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先上运行截图（下图分别为图片识别和摄像头识别结果）
项目结构
项目结构很简单主要由以下三种文件构成：
- predict.py
- surface.py
- svmchinese.dat(用于存放训练好的模型）
其余文件还包括用于训练和测试的图片数据集，这里就不一一列举了
项目实现的流程
利用tkinter设计UI界面包括主窗口、按钮（button)、摄像头界面，识别结果的可视化等控件。
class Surface(ttk.Frame): pic_path = "" viewhigh = 400 #摄像头 viewwide = 400 update_time = 0 thread = None thread_run = False camera = None color_transform = {"green":("绿牌","#55FF55"), "yello":("黄牌","#FFFF00"), "blue":("蓝牌","#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("车牌识别系统") win.geometry('700x500') 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=0.5) ttk.Label(frame_left, text='原图：').pack(anchor="nw") ttk.Label(frame_right1, text='截取车牌：').grid(column=0, row=0, sticky=tk.W) from_pic_ctl = ttk.Button(frame_right2, text="图片识别", width=10, command=self.from_pic) from_vedio_ctl = ttk.Button(frame_right2, text="摄像头识别", width=10, command=self.from_vedio) 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='获取结果：').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_vedio_ctl.pack(anchor="se", pady="5") from_pic_ctl.pack(anchor="se", pady="5") self.predictor = predict.CardPredictor() self.predictor.train_svm()
打开识别图片的按钮点击事件的设计
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
打开摄像头识别按钮点击事件的设计
def from_vedio(self): if self.thread_run: return if self.camera is None: self.camera = cv2.VideoCapture(0) if not self.camera.isOpened(): mBox.showwarning('警告', '摄像头打开失败！') self.camera = None return self.thread = threading.Thread(target=self.vedio_thread, args=(self,)) self.thread.setDaemon(True) self.thread.start() self.thread_run = True
打开摄像头或者图片识别，按识别的方式采用不同方法对捕捉到的画面结果进行识别：
def from_pic(self): self.thread_run = 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) resize_rates = (1, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4) for resize_rate in resize_rates: print("resize_rate:", resize_rate) r, roi, color = self.predictor.predict(img_bgr, resize_rate) if r: break self.show_roi(r, roi, color) @staticmethod def vedio_thread(self): self.thread_run = True predict_time = time.time() while self.thread_run: _, img_bgr = self.camera.read() self.imgtk = self.get_imgtk(img_bgr) self.image_ctl.configure(image=self.imgtk) if time.time() - predict_time > 2: r, roi, color = self.predictor.predict(img_bgr) self.show_roi(r, roi, color) predict_time = time.time() print("run end")
最后设计关闭窗口的事件：
def close_window(): print("destroy") if surface.thread_run : surface.thread_run = False surface.thread.join(2.0) win.destroy()
预测模型的设计
预测模型设计思路为：
1、训练设计所需图片数据的尺寸，并读取图片数据；
2、根据设定的阈值和图片直方图，找出波峰，用于分隔字符；
3、根据找出的波峰，分隔图片，从而得到逐个字符图片；
4、将来自opencv的sample，用于svm训练；
5、训练svm、字符识别；
6、高斯去噪、去掉图像中不会是车牌的区域、找到图像边缘、使用开运算和闭运算让图像边缘成为一个整体；
7、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、矩形区域可能是倾斜的矩形，需要矫正，以便使用颜色定位、识别到的字符、定位的车牌图像、车牌颜色；
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) #读取文件 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): self.model = SVM(C=1, gamma=0.5) #识别英文字母和数字,c表示容忍度，c越高，说明越不能容忍出现误差 self.modelchinese = SVM(C=1, gamma=0.5) #识别中文 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(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) 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, resize_rate=1): 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: pic_rate = MAX_WIDTH / pic_width img = cv2.resize(img, (MAX_WIDTH, int(pic_hight*pic_rate)), interpolation=cv2.INTER_LANCZOS4) if resize_rate != 1: img = cv2.resize(img, (int(pic_width*resize_rate), int(pic_hight*resize_rate)), interpolation=cv2.INTER_LANCZOS4) pic_hight, pic_width = img.shape[:2] 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 = img img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) #equ = cv2.equalizeHist(img) #img = np.hstack((img, equ)) #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 try: contours, hierarchy = cv2.findContours(img_edge2, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) except ValueError: 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.drawContours(oldimg, [box], 0, (0, 0, 255), 2) #cv2.imshow("edge4", oldimg) #cv2.waitKey(0) 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) 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])] 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)
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