第十四周周总结

2020.05.23

车牌模板匹配

将之前切割的车牌字符图篇一张一张的与已知数据集进行对比，获取相似度，取相似度最高的图篇标签为该车牌字符

# 导入所需模块
import cv2
from matplotlib import pyplot as plt
import os

def chepai_shibie():
    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']
    resultChepai = ''

    # 读取一个文件夹下的所有图片，输入参数是文件名
    def read_directory(directory_name):
        referImg_list = []
        for filename in os.listdir(directory_name):
            # print(filename)  # 仅仅是为了测试
            # img = cv2.imread(directory_name + "/" + filename)
            referImg_list.append(directory_name + "/" + filename)

        return referImg_list

    # 匹配中文
    c_words = []
    for i in range(34, 64):
        c_word = read_directory('refer1/' + template[i])
        c_words.append(c_word)

    # 读取一个车牌字符
    img = cv2.imread('chepai/test2_1.jpg')
    # plt_show0(img)

    # 灰度处理，二值化
    # 高斯去噪
    image = cv2.GaussianBlur(img, (1, 1), 0)
    # plt_show(image)
    # 灰度处理
    gray_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
    # plt_show(gray_image)

    # 自适应阈值处理
    ret, image_ = cv2.threshold(gray_image, 0, 255, cv2.THRESH_OTSU)
    # plt_show(image_)

    import numpy as np

    best_score = []
    for c_word in c_words:
        # print('c_word:',c_word)
        score = []
        for word in c_word:
            # print('word:', word)
            #         print(word)
            # fromfile()函数读回数据时需要用户指定元素类型，并对数组的形状进行适当的修改
            template_img = cv2.imdecode(np.fromfile(word, dtype=np.uint8), 1)
            #         template_img = cv2.imread(word)
            #         print(template_img)
            #         cv_show('template_img',template_img)
            #         template_img = np.float32(template_img)
            #         plt_show0(template_img)
            #         print(word)
            template_img = cv2.cvtColor(template_img, cv2.COLOR_RGB2GRAY)
            ret, template_img = cv2.threshold(template_img, 0, 255, cv2.THRESH_OTSU)
            height, width = template_img.shape
            image = image_.copy()
            image = cv2.resize(image, (width, height))
            result = cv2.matchTemplate(image, template_img, cv2.TM_CCOEFF)  # matchTemplate可更改参数 ↓↓↓  匹配方法
            score.append(result[0][0])
        best_score.append(max(score))

    print(best_score)
    print(max(best_score))
    print(best_score.index(max(best_score)))
    flag1 = 34 + best_score.index(max(best_score))
    # print(template[34])
    print(template[int(flag1)])  # 车牌第一次汉字
    resultChepai += template[int(flag1)]
    '''
    score = []
    best_template = None
    best_refer = None
    for refer in referImg_list:
        #     refer = refer[0]
        #     print(refer)
        template = cv2.imread(refer)

        template = cv2.GaussianBlur(template, (3, 3), 0)
        # 灰度处理
        template = cv2.cvtColor(template, cv2.COLOR_RGB2GRAY)
        # 自适应阈值处理
        ret, template = cv2.threshold(template, 0, 255, cv2.THRESH_OTSU)

        height, width = template.shape
        image = image_.copy()
        image = cv2.resize(image, (width, height))  # 和模板一致
        #     plt_show(image)
        # TM_SQDIFF TM_CCOEFF
        # TM_SQDIFF：计算平方不同，计算出来的值越小，越相关
        # TM_CCORR：计算相关性，计算出来的值越大，越相关
        # TM_CCOEFF：计算相关系数，计算出来的值越大，越相关
        # TM_SQDIFF_NORMED：计算归一化平方不同，计算出来的值越接近0，越相关
        # TM_CCORR_NORMED：计算归一化相关性，计算出来的值越接近1，越相关
        # TM_CCOEFF_NORMED：计算归一化相关系数，计算出来的值越接近1，越相关
        result = cv2.matchTemplate(image, template, cv2.TM_CCOEFF)
        #     print(score)
        if score != [] and result[0][0] > max(score):
            best_template = None
            best_refer = None
            best_template = template
            best_refer = refer
        score.append(result[0][0])

    print(score)
    plt_show(best_template)
    print(max(score))
    print(best_refer)
    '''
    # 读取一个车牌字符
    img = cv2.imread('chepai/test2_2.jpg')
    # plt_show0(img)
    # 灰度处理，二值化
    # 高斯去噪
    image = cv2.GaussianBlur(img, (3, 3), 0)
    # 灰度处理
    gray_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
    # plt_show(gray_image)
    # 自适应阈值处理
    ret, image_ = cv2.threshold(gray_image, 0, 255, cv2.THRESH_OTSU)
    # plt_show(image_)
    # 字母模板列表
    # 匹配中文
    c_words = []
    for i in range(10, 34):
        c_word = read_directory('./refer1/' + template[i])
        c_words.append(c_word)

    import numpy as np

    best_score = []
    for c_word in c_words:
        score = []
        for word in c_word:
            # fromfile()函数读回数据时需要用户指定元素类型，并对数组的形状进行适当的修改
            template_img = cv2.imdecode(np.fromfile(word, dtype=np.uint8), 1)
            template_img = cv2.cvtColor(template_img, cv2.COLOR_RGB2GRAY)
            ret, template_img = cv2.threshold(template_img, 0, 255, cv2.THRESH_OTSU)
            height, width = template_img.shape
            image = image_.copy()
            image = cv2.resize(image, (width, height))
            result = cv2.matchTemplate(image, template_img, cv2.TM_CCOEFF)
            score.append(result[0][0])
        best_score.append(max(score))

    print(best_score)
    print(max(best_score))
    print(best_score.index(max(best_score)))

    flag2 = 10 + int(best_score.index(max(best_score)))
    print(template[int(flag2)])
    resultChepai += template[int(flag2)]

    chepais = ['test2_3', 'test2_4', 'test2_5', 'test2_6', 'test2_7']
    for chepai in chepais:
        # 读取一个车牌字符
        img = cv2.imread('chepai/' + chepai + '.jpg')
        # plt_show0(img)
        # 灰度处理，二值化
        # 高斯去噪
        image = cv2.GaussianBlur(img, (3, 3), 0)
        # 灰度处理
        gray_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
        # plt_show(gray_image)
        # 自适应阈值处理
        ret, image_ = cv2.threshold(gray_image, 0, 255, cv2.THRESH_OTSU)
        # plt_show(image_)
        # 字母数字模板列表
        c_words = []
        for i in range(0, 34):
            c_word = read_directory('refer1/' + template[i])
            c_words.append(c_word)
        import numpy as np

        best_score = []
        for c_word in c_words:
            score = []
            for word in c_word:
                # fromfile()函数读回数据时需要用户指定元素类型，并对数组的形状进行适当的修改
                template_img = cv2.imdecode(np.fromfile(word, dtype=np.uint8), 1)
                template_img = cv2.cvtColor(template_img, cv2.COLOR_RGB2GRAY)
                ret, template_img = cv2.threshold(template_img, 0, 255, cv2.THRESH_OTSU)
                height, width = template_img.shape
                image = image_.copy()
                image = cv2.resize(image, (width, height))
                result = cv2.matchTemplate(image, template_img, cv2.TM_CCOEFF)
                score.append(result[0][0])
            best_score.append(max(score))

        print(best_score)
        print(max(best_score))
        flag3 = 0 + best_score.index(max(best_score))
        print(template[int(flag3)])
        resultChepai += template[int(flag3)]

    print('车牌识别结果为:', resultChepai