基于opencv的車牌識(shí)別系統(tǒng)（UI界面采用tkinter設(shè)計(jì)）
本系統(tǒng)采用python語(yǔ)言搭配opencv進(jìn)行開發(fā)，在傳統(tǒng)的車牌識(shí)別項(xiàng)目上進(jìn)行改進(jìn)，開發(fā)獨(dú)特的GUI界面，方便使用者的使用。
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先上運(yùn)行截圖（下圖分別為圖片識(shí)別和攝像頭識(shí)別結(jié)果）
項(xiàng)目結(jié)構(gòu)
項(xiàng)目結(jié)構(gòu)很簡(jiǎn)單主要由以下三種文件構(gòu)成：
- predict.py
- surface.py
- svmchinese.dat(用于存放訓(xùn)練好的模型）
其余文件還包括用于訓(xùn)練和測(cè)試的圖片數(shù)據(jù)集，這里就不一一列舉了
項(xiàng)目實(shí)現(xiàn)的流程
利用tkinter設(shè)計(jì)UI界面包括主窗口、按鈕（button)、攝像頭界面，識(shí)別結(jié)果的可視化等控件。
class Surface(ttk.Frame):
pic_path = ""
viewhigh = 400 #攝像頭
viewwide = 400
update_time = 0
thread = None
thread_run = False
camera = None
color_transform = {"green":("綠牌","#55FF55"), "yello":("黃牌","#FFFF00"), "blue":("藍(lán)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("車牌識(shí)別系統(tǒng)")
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='原圖：').pack(anchor="nw")
ttk.Label(frame_right1, text='截取車牌：').grid(column=0, row=0, sticky=tk.W)
from_pic_ctl = ttk.Button(frame_right2, text="圖片識(shí)別", width=10, command=self.from_pic)
from_vedio_ctl = ttk.Button(frame_right2, text="攝像頭識(shí)別", 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='獲取結(jié)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()
打開識(shí)別圖片的按鈕點(diǎn)擊事件的設(shè)計(jì)
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
打開攝像頭識(shí)別按鈕點(diǎn)擊事件的設(shè)計(jì)
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', '攝像頭打開失?。?)
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
打開攝像頭或者圖片識(shí)別，按識(shí)別的方式采用不同方法對(duì)捕捉到的畫面結(jié)果進(jìn)行識(shí)別：
def from_pic(self):
self.thread_run = False
self.pic_path = askopenfilename(title="選擇識(shí)別圖片", filetypes=[("jpg圖片", "*.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")
最后設(shè)計(jì)關(guān)閉窗口的事件：
def close_window():
print("destroy")
if surface.thread_run :
surface.thread_run = False
surface.thread.join(2.0)
win.destroy()
預(yù)測(cè)模型的設(shè)計(jì)
預(yù)測(cè)模型設(shè)計(jì)思路為：
1、訓(xùn)練設(shè)計(jì)所需圖片數(shù)據(jù)的尺寸，并讀取圖片數(shù)據(jù)；
2、根據(jù)設(shè)定的閾值和圖片直方圖，找出波峰，用于分隔字符；
3、根據(jù)找出的波峰，分隔圖片，從而得到逐個(gè)字符圖片；
4、將來自opencv的sample，用于svm訓(xùn)練；
5、訓(xùn)練svm、字符識(shí)別；
6、高斯去噪、去掉圖像中不會(huì)是車牌的區(qū)域、找到圖像邊緣、使用開運(yùn)算和閉運(yùn)算讓圖像邊緣成為一個(gè)整體；
7、查找圖像邊緣整體形成的矩形區(qū)域，可能有很多，車牌就在其中一個(gè)矩形區(qū)域中、矩形區(qū)域可能是傾斜的矩形，需要矯正，以便使用顏色定位、識(shí)別到的字符、定位的車牌圖像、車牌顏色；文章來源：http://www.zghlxwxcb.cn/news/detail-423012.html
class CardPredictor:
def __init__(self):
#車牌識(shí)別的部分參數(shù)保存在js中，便于根據(jù)圖片分辨率做調(diào)整
f = open('config.js')
j = json.load(f) #讀取文件
for c in j["config"]:
if c["open"]:
self.cfg = c.copy()
break
else:
raise RuntimeError('沒有設(shè)置有效配置參數(shù)')
def __del__(self):
self.save_traindata()
def train_svm(self):
self.model = SVM(C=1, gamma=0.5) #識(shí)別英文字母和數(shù)字,c表示容忍度，c越高，說明越不能容忍出現(xiàn)誤差
self.modelchinese = SVM(C=1, gamma=0.5) #識(shí)別中文
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是拼音對(duì)應(yīng)的漢字
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#綠色有漸變
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)#圖片分辨率調(diào)整
oldimg = img
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
#equ = cv2.equalizeHist(img)
#img = np.hstack((img, equ))
#去掉圖像中不會(huì)是車牌的區(qū)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);
#找到圖像邊緣
ret, img_thresh = cv2.threshold(img_opening, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
img_edge = cv2.Canny(img_thresh, 100, 200)
#使用開運(yùn)算和閉運(yùn)算讓圖像邊緣成為一個(gè)整體
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)
#查找圖像邊緣整體形成的矩形區(qū)域，可能有很多，車牌就在其中一個(gè)矩形區(qū)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))
#一一排除不是車牌的矩形區(qū)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)
#要求矩形區(qū)域長(zhǎng)寬比在2到5.5之間，2到5.5是車牌的長(zhǎng)寬比，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("精確定位")
card_imgs = []
#矩形區(qū)域可能是傾斜的矩形，需要矯正，以便使用顏色定位
for rect in car_contours:
if rect[2] > -1 and rect[2] < 1:#創(chuàng)造角度，使得左、高、右、低拿到正確的值
angle = 1
else:
angle = rect[2]
rect = (rect[0], (rect[1][0]+5, rect[1][1]+5), angle)#擴(kuò)大范圍，避免車牌邊緣被排除
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變
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]:#負(fù)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變
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)
創(chuàng)作不易，走過路過不要錯(cuò)過，點(diǎn)個(gè)贊再走吧，需要源碼的朋友可以點(diǎn)贊關(guān)注我，再私信我獲取源碼！?。?！文章來源地址http://www.zghlxwxcb.cn/news/detail-423012.html
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