diff --git a/python.doc b/python.doc
index f0bc6d8..19cba0a 100644
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diff --git a/python/chepai.jpg b/python/chepai.jpg
deleted file mode 100644
index 2e5dc72..0000000
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diff --git a/python/chepai1.jpg b/python/jinH.jpg
similarity index 100%
rename from python/chepai1.jpg
rename to python/jinH.jpg
diff --git a/python/chepai2.jpg b/python/jingA.jpg
similarity index 100%
rename from python/chepai2.jpg
rename to python/jingA.jpg
diff --git a/python/p/kcsj.py b/python/p/kcsj.py
index 22b5d4a..bc99217 100644
--- a/python/p/kcsj.py
+++ b/python/p/kcsj.py
@@ -3,7 +3,9 @@ import cv2
 from matplotlib import pyplot as plt
 import os
 import numpy as np
-
+import tkinter as tk
+from tkinter import ttk
+from PIL import ImageTk, Image
 
 # plt显示彩色图片
 def plt_show0(img):
@@ -27,11 +29,14 @@ def gray_guss(image):
     return gray_image
 
 # 读取待检测图片
-origin_image = cv2.imread('../chepai1.jpg')
+origin_image = cv2.imread('../xiangA.jpg')
+
 # 复制一张图片，在复制图上进行图像操作，保留原图
 image = origin_image.copy()
+
 # 图像去噪灰度处理
 gray_image = gray_guss(image)
+
 # x方向上的边缘检测（增强边缘信息）
 Sobel_x = cv2.Sobel(gray_image, cv2.CV_16S, 1, 0)
 absX = cv2.convertScaleAbs(Sobel_x)
@@ -39,27 +44,35 @@ image = absX
 
 # 图像阈值化操作——获得二值化图
 ret, image = cv2.threshold(image, 0, 255, cv2.THRESH_OTSU)
+
 # 显示灰度图像
 plt_show(image)
+
 # 形态学（从图像中提取对表达和描绘区域形状有意义的图像分量）——闭操作
 kernelX = cv2.getStructuringElement(cv2.MORPH_RECT, (30, 10))
 image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernelX,iterations = 1)
+
 # 显示灰度图像
 plt_show(image)
 
 # 腐蚀（erode）和膨胀（dilate）
 kernelX = cv2.getStructuringElement(cv2.MORPH_RECT, (50, 1))
 kernelY = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 20))
+
 #x方向进行闭操作（抑制暗细节）
 image = cv2.dilate(image, kernelX)
 image = cv2.erode(image, kernelX)
+
 #y方向的开操作
 image = cv2.erode(image, kernelY)
 image = cv2.dilate(image, kernelY)
+
 # 中值滤波（去噪）
 image = cv2.medianBlur(image, 21)
+
 # 显示灰度图像
 plt_show(image)
+
 # 获得轮廓
 contours, hierarchy = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
 
@@ -77,11 +90,12 @@ for item in contours:
 #车牌字符分割
 # 图像去噪灰度处理
 gray_image = gray_guss(image)
+
 # 图像阈值化操作——获得二值化图
 ret, image = cv2.threshold(gray_image, 0, 255, cv2.THRESH_OTSU)
 plt_show(image)
 
-#膨胀操作，使“苏”字膨胀为一个近似的整体，为分割做准备
+#膨胀操作，使“津”字膨胀为一个近似的整体，为分割做准备
 kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (2, 2))
 image = cv2.dilate(image, kernel)
 plt_show(image)
@@ -90,6 +104,7 @@ plt_show(image)
 contours, hierarchy = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
 words = []
 word_images = []
+
 #对所有轮廓逐一操作
 for item in contours:
     word = []
@@ -98,14 +113,21 @@ for item in contours:
     y = rect[1]
     weight = rect[2]
     height = rect[3]
+
     word.append(x)
+
     word.append(y)
+
     word.append(weight)
+
     word.append(height)
+
     words.append(word)
+
 # 排序，车牌号有顺序。words是一个嵌套列表
 words = sorted(words,key=lambda s:s[0],reverse=False)
 i = 0
+
 #word中存放轮廓的起始点和宽高
 for word in words:
     # 筛选字符的轮廓
@@ -113,8 +135,8 @@ for word in words:
         i = i+1
         splite_image = image[word[1]:word[1] + word[3], word[0]:word[0] + word[2]]
         word_images.append(splite_image)
-        print(i)
-print(words)
+        #print(i)
+#print(words)
 
 for i,j in enumerate(word_images):
     plt.subplot(1,7,i+1)
@@ -124,9 +146,12 @@ plt.show()
 #模版匹配
 # 准备模板(template[0-9]为数字模板；)
 template = ['0','1','2','3','4','5','6','7','8','9',
-            'A','B','C','D','E','F','G','H','J','K','L','M','N','P','Q','R','S','T','U','V','W','X','Y','Z',
-            '藏','川','鄂','甘','赣','贵','桂','黑','沪','吉','冀','津','晋','京','辽','鲁','蒙','闽','宁',
-            '青','琼','陕','苏','皖','湘','新','渝','豫','粤','云','浙']
+            'A','B','C','D','E','F','G','H','J','K',
+            'L','M','N','P','Q','R','S','T','U','V',
+            'W','X','Y','Z','藏','川','鄂','甘','赣',
+            '贵','桂','黑','沪','吉','冀','津','晋','京',
+            '辽','鲁','蒙','闽','宁','青','琼','陕','苏',
+            '皖','湘','新','渝','豫','粤','云','浙']
 
 # 读取一个文件夹下的所有图片，输入参数是文件名，返回模板文件地址列表
 def read_directory(directory_name):
@@ -234,16 +259,18 @@ def template_matching(word_images):
 
 word_images_ = word_images.copy()
 # 调用函数获得结果
+
 result = template_matching(word_images_)
-print(result)
+#print(result)
 # "".join(result)函数将列表转换为拼接好的字符串，方便结果显示
-print( "".join(result))
+#print( "".join(result))
+
 
 from PIL import ImageFont, ImageDraw, Image
 
 height,weight = origin_image.shape[0:2]
-print(height)
-print(weight)
+#print(height)
+#print(weight)
 
 image_1 = origin_image.copy()
 cv2.rectangle(image_1, (int(0.2*weight), int(0.75*height)), (int(weight*0.9), int(height*0.95)), (0, 255, 0), 5)
@@ -257,6 +284,26 @@ draw = ImageDraw.Draw(img_pil)
 #绘制文字信息
 draw.text((int(0.2*weight)+25, int(0.75*height)),  "".join(result), font = font, fill = (255, 255, 0))
 bk_img = np.array(img_pil)
-print(result)
+#print(result)
 print( "".join(result))
 plt_show0(bk_img)
+
+# 使用PIL将numpy数组转换为Image对象
+image_tk = Image.fromarray(cv2.cvtColor(bk_img, cv2.COLOR_BGR2RGB))
+
+# 初始化Tkinter窗口
+root = tk.Tk()
+root.title("车牌识别结果")
+
+# 将Image对象转换为Tkinter支持的PhotoImage对象
+photo_image = ImageTk.PhotoImage(image_tk)
+
+# 创建标签以显示图像
+label = ttk.Label(root, image=photo_image)
+label.pack()  # 将标签添加到窗口中并自动调整大小
+
+# 防止Tkinter在图像数据被垃圾回收时删除图像
+label.image = photo_image
+
+# 运行Tkinter事件循环
+root.mainloop()
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diff --git a/python/xiangA.jpg b/python/xiangA.jpg
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