• Web和Internet开发

• 科学计算和统计

• 人工智能

• 教育

• 桌面界面开发

• 软件开发

• 后端开发

• 网络接口：能方便进行系统维护和管理，Linux下标志性语言之一，是很多系统管理员理想的编程工具。

系统编程：提供API（Application Programming Interface应用程序编程接口），能方便进行系统维护和管理，Linux下标志性语言之一，是很多系统管理员理想的编程工具。

图形处理：有PIL、Tkinter等图形库支持，能方便进行图形处理。

数学处理：NumPy扩展提供大量与许多标准数学库的接口。

文本处理：python提供的re模块能支持正则表达式，还提供SGML，XML分析模块，许多程序员利用python进行XML程序的开发。

数据库编程：程序员可通过遵循Python DB-API（数据库应用程序编程接口）规范的模块与Microsoft SQL Server，Oracle，Sybase，DB2，MySQL、SQLite等数据库通信。python自带有一个Gadfly模块，提供了一个完整的SQL环境。

网络编程：提供丰富的模块支持sockets编程，能方便快速地开发分布式应用程序。很多大规模软件开发计划例如Zope，Mnet 及BitTorrent. Google都在广泛地使用它。

Web编程：应用的开发语言，支持最新的XML技术。

多媒体应用：Python的PyOpenGL模块封装了“OpenGL应用程序编程接口”，能进行二维和三维图像处理。PyGame模块可用于编写游戏软件。

五、程序及步骤

算法：

5.1车牌类型识别及保存

class PlateRecognition():
    client_id = 'LR6w0HKc8Yzjd8q6FPTkrgpR'
    client_secret = 'UyKYwPp38XkPAwPxl9pYpW0WsccfnBc9'
    def __init__(self):
        self.MAX_WIDTH = 1000  # 原始图片最大宽度
        self.Min_Area = 2000  # 车牌区域允许最大面积
        self.PROVINCE_START = 1000

        # 省份代码保存在provinces.json中
        with open('provinces.json', 'r', encoding='utf-8') as f:
            self.provinces = json.load(f)

        # 车牌类型保存在cardtype.json中，便于调整
        with open('cardtype.json', 'r', encoding='utf-8') as f:
            self.cardtype = json.load(f)

        # 字母所代表的地区保存在Prefecture.json中，便于更新
        with open('Prefecture.json', 'r', encoding='utf-8') as f:
            self.Prefecture = json.load(f)

        # 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

    def __del__(self):
        pass

5.2读取识别图片

    def __imreadex(self, filename):
        return cv2.imdecode(np.fromfile(filename, dtype=np.uint8), cv2.IMREAD_COLOR)

    def __point_limit(self, point):
        if point[0] < 0:
            point[0] = 0
        if point[1] < 0:
            point[1] = 0


    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

5.3图片预处理

    def pretreatment(self, car_pic):
        if type(car_pic) == type(""):
            img = self.__imreadex(car_pic)
        else:
            img = car_pic
        pic_hight, pic_width = img.shape[:2]

        if pic_width > self.MAX_WIDTH:
            resize_rate = self.MAX_WIDTH / pic_width
            img = cv2.resize(img, (self.MAX_WIDTH, int(pic_hight * resize_rate)),
                             interpolation=cv2.INTER_AREA)  # 图片分辨率调整
        # cv2.imshow('Image', img)

        blur = self.cfg["blur"]

5.4排除不属于车牌的矩形区域

car_contours = []
        for cnt in contours:
            # 框选 生成最小外接矩形 返回值（中心(x,y), (宽,高), 旋转角度）
            rect = cv2.minAreaRect(cnt)
            # print('宽高:',rect[1])
            area_width, area_height = rect[1]
            # 选择宽大于高的区域
            if area_width < area_height:
                area_width, area_height = area_height, area_width
            wh_ratio = area_width / area_height
            # print('宽高比：',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)
            # 框出所有可能的矩形
            # oldimg = cv2.drawContours(img, [box], 0, (0, 0, 255), 2)
            # cv2.imshow("Test",oldimg )
            # print(car_contours)

5.5颜色定位

 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
                        yellow += 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"
            # print('黄：{:<6}绿：{:<6}蓝：{:<6}'.format(yellow,green,blue))

            limit1 = limit2 = 0
            if yellow * 2 >= card_img_count:
                color = "yellow"
                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:
                color = "bw"
            # print(color)
            colors.append(color)
            # print(blue, green, yellow, black, white, card_img_count)
            if limit1 == 0:
                continue

5.6缩小非车牌边界

xl, xr, yh, yl = self.accurate_place(card_img_hsv, limit1, limit2, color)
            if yl == yh and xl == xr:
                continue
            need_accurate = False
            if yl >= yh:
                yl = 0
                yh = row_num
                need_accurate = True
            if xl >= xr:
                xl = 0
                xr = col_num
                need_accurate = True
            card_imgs[card_index] = card_img[yl:yh, xl:xr] \
                if color != "green" or yl < (yh - yl) // 4 else card_img[yl - (yh - yl) // 4:yh, xl:xr]
            if need_accurate:  # 可能x或y方向未缩小，需要再试一次
                card_img = card_imgs[card_index]
                card_img_hsv = cv2.cvtColor(card_img, cv2.COLOR_BGR2HSV)
                xl, xr, yh, yl = self.accurate_place(card_img_hsv, limit1, limit2, color)
                if yl == yh and xl == xr:
                    continue
                if yl >= yh:
                    yl = 0
                    yh = row_num
                if xl >= xr:
                    xl = 0
                    xr = col_num
            card_imgs[card_index] = card_img[yl:yh, xl:xr] \
                if color != "green" or yl < (yh - yl) // 4 else card_img[yl - (yh - yl) // 4:yh, xl:xr]
        # cv2.imshow("result", card_imgs[0])
        # cv2.imwrite('1.jpg', card_imgs[0])
        # print('颜色识别结果：' + colors[0])
        return card_imgs, colors

5.7测试

if __name__ == '__main__':
    c = PlateRecognition()
    # card_imgs, colors = c.pretreatment('./2.jpg')
    result = c.get_license_plate('./2.jpg')
    # cv2.imshow('card', card_imgs)

    cv2.waitKey(0)

六、LabVIEW OCR实现车牌识别

6.1OCR