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main(final).py

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+import numpy as np
+import cv2
+import matplotlib.pyplot as plt
+from matplotlib import font_manager
+import ddddocr
+import threadsafe_tkinter as tk
+from tkinter import filedialog
+font_path = "C:/Users/Lenovo/Downloads/OPPOSans3.0/OPPOSans-Regular.ttf"
+
+# 动态添加字体到 matplotlib 的字体列表
+font = font_manager.FontProperties(fname=font_path)
+
+
+
+
+class Get_license():
+
+    #图像拉伸函数
+    def stretch(self, img):
+
+        maxi = float(img.max())
+        mini = float(img.min())
+
+        for i in range(img.shape[0]):
+            for j in range(img.shape[1]):
+                img[i, j] = (255 / (maxi - mini) * img[i, j] - (255 * mini) / (maxi - mini))
+
+        return img
+
+    #二值化处理函数
+    def dobinaryzation(self, img):
+        # 使用OpenCV的自动阈值方法
+        ret, thresh = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
+        return thresh
+
+    #寻找矩形的轮廓
+    def find_rectangle(self, contour):
+
+        y, x = [],[]
+
+        for p in contour:
+            y.append(p[0][0])
+            x.append(p[0][1])
+
+        return [min(y), min(x), max(y), max(x)]
+
+    #定位车牌号
+    def locate_license(self, img, afterimg):
+
+        contours, hierarchy = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+        #找出最大的三个区域
+        block = []
+        for c in contours:
+            #找出轮廓的左上点和右下点
+            #由此计算它的面积和长度比
+            r = self.find_rectangle(c)
+            a = (r[2] - r[0]) * (r[3] - r[1])  #面积
+            s = (r[2] - r[0]) * (r[3] - r[1])  #长度比
+
+            block.append([r, a, s])
+
+        #选出面积最大的3个区域
+        block = sorted(block, key=lambda b: b[1])[-3:]
+
+        #使用颜色识别判断找出最像车牌的区域
+        maxweight, maxindex = 0, -1
+        for i in range(len(block)):
+            b = afterimg[block[i][0][1]:block[i][0][3], block[i][0][0]:block[i][0][2]]
+            hsv = cv2.cvtColor(b, cv2.COLOR_BGR2HSV)
+            lower = np.array([100, 50, 50])
+            upper = np.array([140, 255, 255])
+            mask = cv2.inRange(hsv, lower, upper)
+
+            # 计算mask中白色像素（即HSV范围内的像素）的数量
+            weight = np.sum(mask == 255)  # 或者简单地使用 np.count_nonzero(mask)
+
+            # 选出最大权值的区域
+            if weight > maxweight:
+                maxindex = i
+                maxweight = weight
+
+        return block[maxindex][0]
+
+    #预处理函数
+    def find_license(self, img):
+
+        m = 400 * img.shape[0] / img.shape[1]
+
+        #压缩图像
+        img = cv2.resize(img, (400, int(m)), interpolation=cv2.INTER_CUBIC)
+
+        #BGR转换为灰度图像
+        gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+        #灰度拉伸
+        stretchedimg = self.stretch(gray_img)
+
+        '''进行开运算，用来去除噪声'''
+        r = 16
+        h = w = r * 2 + 1
+        kernel = np.zeros((h, w), np.uint8)
+        cv2.circle(kernel, (r, r), r, 1, -1)
+
+        openingimg = cv2.morphologyEx(stretchedimg, cv2.MORPH_OPEN, kernel)
+
+        strtimg = cv2.absdiff(stretchedimg, openingimg)
+
+        #图像二值化
+        binaryimg = self.dobinaryzation(strtimg)
+
+        #canny边缘检测
+        canny = cv2.Canny(binaryimg, binaryimg.shape[0], binaryimg.shape[1])
+
+        '''消除小的区域，保留大块的区域，从而定位车牌'''
+        #进行闭运算
+        kernel = np.ones((5, 17), np.uint8)
+        closingimg = cv2.morphologyEx(canny, cv2.MORPH_CLOSE, kernel)
+
+        #进行开运算
+        openingimg = cv2.morphologyEx(closingimg, cv2.MORPH_OPEN, kernel)
+
+        #再次进行开运算
+        #kernel = np.ones((5, 5), np.uint8)
+        #openingimg=cv2.morphologyEx(openingimg,cv2.MORPH_CLOSE,kernel)
+        #openingimg = cv2.morphologyEx(openingimg, cv2.MORPH_OPEN, kernel)
+
+        #消除小区域，定位车牌位置
+        rect = self.locate_license(openingimg, img)
+
+        return rect, img
+    #图像分割函数
+    def cut_license(self, afterimg, rect):
+
+        #转换为宽度和高度
+        rect[2] = rect[2] - rect[0]
+        rect[3] = rect[3] - rect[1]
+        rect_copy = tuple(rect.copy())
+        #创建掩膜
+        mask = np.zeros(afterimg.shape[:2], np.uint8)
+        #创建背景模型  大小只能为13*5，行数只能为1，单通道浮点型
+        bgdModel = np.zeros((1, 65), np.float64)
+        #创建前景模型
+        fgdModel = np.zeros((1, 65), np.float64)
+        #分割图像
+        cv2.grabCut(afterimg, mask, rect_copy, bgdModel, fgdModel, 5, cv2.GC_INIT_WITH_RECT)
+        mask2 = np.where((mask == 2) | (mask == 0), 0, 1).astype('uint8')
+        img_show = afterimg * mask2[:, :, np.newaxis]
+
+        return img_show
+
+
+class Segmentation():
+    def __init__(self, cutimg):
+
+        #1、读取图像，并把图像转换为灰度图像并显示
+
+        img_gray = cv2.cvtColor(cutimg, cv2.COLOR_BGR2GRAY)  #转换了灰度化
+
+        #2、将灰度图像二值化，设定阈值是100
+        self.img_thre = img_gray
+        cv2.threshold(img_gray, 150
+                      , 255, cv2.THRESH_BINARY_INV, self.img_thre)
+
+        #cv2.waitKey(0)
+
+        #3、保存黑白图片
+        cv2.imwrite('thre_res.jpg', self.img_thre)
+
+        #4、分割字符
+        self.white = []               #记录每一列的白色像素总和
+        self.black = []               #黑色
+        self.height = self.img_thre.shape[0]
+        self.width = self.img_thre.shape[1]
+        self.white_max = 0
+        self.black_max = 0
+        #计算每一列的黑白色像素总和
+        for i in range(self.width):
+            white_count = 0                     #这一列白色总数
+            black_count = 0                     #这一列黑色总数
+            for j in range(self.height):
+                if self.img_thre[j][i] == 255:
+                    white_count += 1
+                if self.img_thre[j][i] == 0:
+                    black_count += 1
+            self.white_max = max(self.white_max, white_count)
+            self.black_max = max(self.black_max, black_count)
+            self.white.append(white_count)
+            self.black.append(black_count)
+
+        self.arg = False                #False表示白底黑字；True表示黑底白字
+        if self.black_max > self.white_max:
+            self.arg = True
+
+    def heibai(self):
+        return self.img_thre
+
+
+    def find_end(self, start_):
+        end_ = start_ + 1
+        for m in range(start_ + 1, self.width - 1):
+            if (self.black[m] if self.arg else self.white[m]) > (
+            0.85 * self.black_max if self.arg else 0.85 * self.white_max):
+                end_ = m
+                break
+        return end_
+
+    def display(self):
+        #img_list = []
+        n = 1
+        plt.figure()
+        img_num = 0
+        while n < self.width - 2:
+            n += 1
+            if (self.white[n] if self.arg else self.black[n]) > (
+            0.15 * self.white_max if self.arg else 0.15 * self.black_max):
+                #上面这些判断用来辨别是白底黑字还是黑底白字
+
+                start = n
+                end = self.find_end(start)
+                n = end
+
+                if end - start > 5:
+                    cj = self.img_thre[1:self.height, start:end]
+                    img_num += 1
+                    cj = cv2.cvtColor(cj, cv2.COLOR_RGB2BGR)
+
+                    plt.figure(2)
+                    plt.subplot(2, 4, img_num)
+                    plt.title('{}'.format(img_num))
+                    plt.imshow(cj)
+        plt.show()
+        return self.img_thre
+
+
+if __name__ == '__main__':
+
+    def select_image():
+        # 弹出文件选择对话框，限制文件类型为图片
+        file_path = filedialog.askopenfilename()
+        if file_path:
+            # 将文件路径显示在标签上
+            img = cv2.imread(file_path)
+            img1 = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+            # 绘图
+            plt.figure(1)
+            plt.suptitle('车牌识别', fontproperties=font)
+            plt.subplot(2, 3, 1)
+            plt.title('原始图像', fontproperties=font)
+            plt.imshow(img1)
+
+            # 预处理图像
+            license = Get_license()
+            rect, afterimg = license.find_license(img)
+            afterimg = cv2.cvtColor(afterimg, cv2.COLOR_RGB2BGR)
+
+            plt.subplot(2, 3, 2)
+            plt.title('预处理后图像', fontproperties=font)
+            plt.imshow(afterimg)
+
+            # 车牌号打框
+            cv2.rectangle(afterimg, (rect[0], rect[1]), (rect[2], rect[3]), (0, 255, 0), 1)
+            x1, y1, x2, y2 = int(rect[0]), int(rect[1]), int(rect[2]), int(rect[3])
+
+            plt.subplot(2, 3, 3)
+            plt.title('车牌框出', fontproperties=font)
+            plt.imshow(afterimg)
+
+            # 背景去除
+            cutimg = license.cut_license(afterimg, rect)
+            plt.subplot(2, 3, 4)
+            plt.title('车牌背景去除', fontproperties=font)
+            plt.imshow(cutimg)
+            # print(int(_rect[0]), int(_rect[3]), int(_rect[2]), int(_rect[1]))
+
+            # 开始分割车牌
+            # cutimg = cutimg[140:165, 151:240]
+            cutimg = cutimg[y1 + 3:y2 - 3, x1 - 1:x2 - 3]
+            # cutimg = cutimg[int(_rect[0]):int(_rect[3]),int(_rect[2]):int(_rect[1])]
+
+            height, width = cutimg.shape[:2]
+            cutimg1 = cv2.resize(cutimg, (2 * width, 2 * height), interpolation=cv2.INTER_CUBIC)
+            plt.subplot(2, 3, 5)
+            plt.title('分割车牌与背景', fontproperties=font)
+            plt.imshow(cutimg)
+
+            # 字符切割
+            seg = Segmentation(cutimg)
+            plt.subplot(2, 3, 6)
+            img_hei = seg.heibai()
+            img_hei = cv2.cvtColor(img_hei, cv2.COLOR_RGB2BGR)
+            plt.title('车牌二值化处理', fontproperties=font)
+            plt.imshow(img_hei)
+            seg.display()
+            plt.show()
+
+            # 打印车牌
+            ocr = ddddocr.DdddOcr()
+            with open('thre_res.jpg', 'rb') as f:
+                image = f.read()
+            res = ocr.classification(image)
+            print(res)
+
+
+
+
+    # 创建根窗口
+    root = tk.Tk()
+    root.title('车牌识别程序')
+    root.geometry('600x450')
+    # 创建一个标签用于显示图片路径
+    label = tk.Label(root, text='未选择图片')
+    label.pack(pady=20)  # 使用pack布局管理器，并添加一些垂直填充
+
+
+    def exit_app():
+        # 退出应用程序的函数
+        root.destroy()
+    # 创建一个按钮，点击时会调用select_image函数
+    button = tk.Button(root, text='选择图片', command=select_image)
+    button.pack()
+    button_exit = tk.Button(root, text='退出', command=exit_app)
+    button_exit.pack(pady=30)
+    # 启动事件循环
+    root.mainloop()
+
+
+
+
+
+
+
+
+