function.py

import cv2
import numpy as np

import os
import json

SZ = 20  # 训练图片长宽
MAX_WIDTH = 1000  # 原始图片最大宽度
Min_Area = 2000  # 车牌区域允许最大面积
PROVINCE_START = 1000


# 读取图片文件
def imreadex(filename):
    return cv2.imdecode(np.fromfile(filename, dtype=np.uint8), cv2.IMREAD_COLOR)


def point_limit(point):
    if point[0] < 0:
        point[0] = 0
    if point[1] < 0:
        point[1] = 0


# 图像去噪灰度处理
def gray_guss(image):
    image = cv2.GaussianBlur(image, (3, 3), 0)
    gray_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
    return gray_image

# 根据设定的阈值和图片直方图，找出波峰，用于分隔字符
def find_waves(threshold, histogram):
    up_point = -1  # 上升点
    is_peak = False
    if histogram[0] > threshold:
        up_point = 0
        is_peak = True
    wave_peaks = []
    for i, x in enumerate(histogram):
        if is_peak and x < threshold:
            if i - up_point > 2:
                is_peak = False
                wave_peaks.append((up_point, i))
        elif not is_peak and x >= threshold:
            is_peak = True
            up_point = i
    if is_peak and up_point != -1 and i - up_point > 4:
        wave_peaks.append((up_point, i))
    return wave_peaks


# 根据找出的波峰，分隔图片，从而得到逐个字符图片
def seperate_card(img, waves):
    part_cards = []
    for wave in waves:
        part_cards.append(img[:, wave[0]:wave[1]])
    return part_cards
# 准备模板
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']
# 读取一个文件夹下的所有图片，输入参数是文件名，返回文件地址列表
def read_directory(directory_name):
    referImg_list = []
    for filename in os.listdir(directory_name):
        referImg_list.append(directory_name + "/" + filename)
    return referImg_list

# 中文模板列表（只匹配车牌的第一个字符）
def get_chinese_words_list():
    chinese_words_list = []
    for i in range(34,64):
        c_word = read_directory('./refer1/'+ template[i])
        chinese_words_list.append(c_word)
    return chinese_words_list
chinese_words_list = get_chinese_words_list()

# 英文模板列表（只匹配车牌的第二个字符）
def get_eng_words_list():
    eng_words_list = []
    for i in range(10,34):
        e_word = read_directory('./refer1/'+ template[i])
        eng_words_list.append(e_word)
    return eng_words_list
eng_words_list = get_eng_words_list()

# 英文数字模板列表（匹配车牌后面的字符）
def get_eng_num_words_list():
    eng_num_words_list = []
    for i in range(0,34):
        word = read_directory('./refer1/'+ template[i])
        eng_num_words_list.append(word)
    return eng_num_words_list
eng_num_words_list = get_eng_num_words_list()


# 读取一个模板地址与图片进行匹配，返回得分
def template_score(template,image):
    template_img=cv2.imdecode(np.fromfile(template,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)
    image_ = image.copy()
    height, width = image_.shape
    template_img = cv2.resize(template_img, (width, height))
    result = cv2.matchTemplate(image_, template_img, cv2.TM_CCOEFF)#相关系数匹配，返回值愈大，匹配值越高
    return result[0][0]

def template_matching(word_images):
    results = []
    for index,word_image in enumerate(word_images):
        if index == 0:
            best_score = []
            for chinese_words in chinese_words_list:
                score = []
                for chinese_word in chinese_words:
                    result = template_score(chinese_word,word_image)
                    score.append(result)
                best_score.append(max(score))
            i = best_score.index(max(best_score))
            # print(template[34+i])
            r = template[34+i]
            results.append(r)
            continue
        if index==1:
            best_score = []
            for eng_word_list in eng_words_list:
                score = []
                for eng_word in eng_word_list:
                    result = template_score(eng_word,word_image)
                    score.append(result)
                best_score.append(max(score))
            i = best_score.index(max(best_score))
            # print(template[10+i])
            r = template[10+i]
            results.append(r)
            continue
        else:
            best_score = []
            for eng_num_word_list in eng_num_words_list:
                score = []
                for eng_num_word in eng_num_word_list:
                    result = template_score(eng_num_word,word_image)
                    score.append(result)
                best_score.append(max(score))
            i = best_score.index(max(best_score))
            # print(template[i])
            r = template[i]
            results.append(r)
            continue
    return results


class CardPredictor:
    def __init__(self):
        # 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
        else:
            raise RuntimeError('没有设置有效配置参数')







    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

    def predict(self, car_pic):
        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:
            resize_rate = MAX_WIDTH / pic_width
            img = cv2.resize(img, (MAX_WIDTH, int(pic_hight * resize_rate)), interpolation=cv2.INTER_AREA)

        blur = self.cfg["blur"]
        # 高斯去噪
        if blur > 0:
            img = cv2.GaussianBlur(img, (blur, blur), 0)  # 图片分辨率调整
        oldimg = img
        gray_image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  #灰度处理
        Sobel_x = cv2.Sobel(gray_image, cv2.CV_16S, 1, 0)   #sobel算子边缘检测
        absX = cv2.convertScaleAbs(Sobel_x)   #转回uint8
        image = absX
        ret, image = cv2.threshold(image, 0, 255, cv2.THRESH_OTSU)  #自适应阈值处理
        kernelX = cv2.getStructuringElement(cv2.MORPH_RECT, (14, 5))  #闭运算，白色部分练成整体
        image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernelX, iterations=1)
        kernelX = cv2.getStructuringElement(cv2.MORPH_RECT, (20, 1))  #去除小白点
        kernelY = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 19))
        image = cv2.dilate(image, kernelX)  #膨胀
        image = cv2.erode(image, kernelX)   #腐蚀
        image = cv2.erode(image, kernelY)   #腐蚀
        image = cv2.dilate(image, kernelY)  #膨胀
        image = cv2.medianBlur(image, 15)  #中值滤波去除噪点
        contours, hierarchy = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)#轮廓检测
        # 一一排除不是车牌的矩形区域
        car_contours = []  #筛选车牌位置的轮廓
        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)




        print("精确定位")
        card_imgs = []
        # 矩形区域可能是倾斜的矩形，需要矫正，以便使用颜色定位
        for rect in car_contours:
            if rect[2] > -1 and rect[2] < 1:  # 创造角度，使得左、高、右、低拿到正确的值
                angle = 1
            else:
                angle = rect[2]
            rect = (rect[0], (rect[1][0] + 5, rect[1][1] + 5), angle)  # 扩大范围，避免车牌边缘被排除

            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变
                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)
            elif left_point[1] > right_point[1]:  # 负角度

                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变
                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.waitKey(0)
        # 开始使用颜色定位，排除不是车牌的矩形，目前只识别蓝、绿、黄车牌
        colors = []
        for card_index, card_img in enumerate(card_imgs):
                    green = yello = blue = black = white = 0
                    card_img_hsv = cv2.cvtColor(card_img, cv2.COLOR_BGR2HSV)
                    # 有转换失败的可能，原因来自于上面矫正矩形出错
                    if card_img_hsv is None:
                        continue
                    row_num, col_num = card_img_hsv.shape[:2]
                    card_img_count = row_num * col_num

                    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
                                yello += 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"

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

                    if limit1 == 0:
                        continue
                    # 以上为确定车牌颜色
                    # 以下为根据车牌颜色再定位，缩小边缘非车牌边界
                    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]
                    # 以上为车牌定位
                    # 以下为识别车牌中的字符
                    predict_result = []
                    word_images = []
                    roi = None
                    card_color = None
                    for i, color in enumerate(colors):
                        if color in ("blue", "yello", "green"):
                            card_img = card_imgs[i]  # 定位的车牌
                            gray_img = cv2.cvtColor(card_img, cv2.COLOR_BGR2GRAY)
                            # 黄、绿车牌字符比背景暗、与蓝车牌刚好相反，所以黄、绿车牌需要反向
                            if color == "green" or color == "yello":
                                gray_img = cv2.bitwise_not(gray_img)
                            ret, gray_img = cv2.threshold(gray_img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
                            # 查找水平直方图波峰
                            x_histogram = np.sum(gray_img, axis=1)
                            x_min = np.min(x_histogram)
                            x_average = np.sum(x_histogram) / x_histogram.shape[0]
                            x_threshold = (x_min + x_average) / 2
                            wave_peaks = find_waves(x_threshold, x_histogram)
                            if len(wave_peaks) == 0:
                                print("peak less 0:")
                                continue
                            # 认为水平方向，最大的波峰为车牌区域
                            wave = max(wave_peaks, key=lambda x: x[1] - x[0])
                            gray_img = gray_img[wave[0]:wave[1]]
                            # 查找垂直直方图波峰
                            row_num, col_num = gray_img.shape[:2]
                            # 去掉车牌上下边缘1个像素，避免白边影响阈值判断
                            gray_img = gray_img[1:row_num - 1]
                            y_histogram = np.sum(gray_img, axis=0)
                            y_min = np.min(y_histogram)
                            y_average = np.sum(y_histogram) / y_histogram.shape[0]
                            y_threshold = (y_min + y_average) / 5  # U和0要求阈值偏小，否则U和0会被分成两半

                            wave_peaks = find_waves(y_threshold, y_histogram)

                            # for wave in wave_peaks:
                            #	cv2.line(card_img, pt1=(wave[0], 5), pt2=(wave[1], 5), color=(0, 0, 255), thickness=2)
                            # 车牌字符数应大于6
                            if len(wave_peaks) <= 6:
                                print("peak less 1:", len(wave_peaks))
                                continue

                            wave = max(wave_peaks, key=lambda x: x[1] - x[0])
                            max_wave_dis = wave[1] - wave[0]
                            # 判断是否是左侧车牌边缘
                            if wave_peaks[0][1] - wave_peaks[0][0] < max_wave_dis / 3 and wave_peaks[0][0] == 0:
                                wave_peaks.pop(0)

                            # 组合分离汉字
                            cur_dis = 0
                            for i, wave in enumerate(wave_peaks):
                                if wave[1] - wave[0] + cur_dis > max_wave_dis * 0.6:
                                    break
                                else:
                                    cur_dis += wave[1] - wave[0]
                            if i > 0:
                                wave = (wave_peaks[0][0], wave_peaks[i][1])
                                wave_peaks = wave_peaks[i + 1:]
                                wave_peaks.insert(0, wave)

                            # 去除车牌上的分隔点
                            point = wave_peaks[2]
                            if point[1] - point[0] < max_wave_dis / 3:
                                point_img = gray_img[:, point[0]:point[1]]
                                if np.mean(point_img) < 255 / 5:
                                    wave_peaks.pop(2)

                            if len(wave_peaks) <= 6:
                                print("peak less 2:", len(wave_peaks))
                                continue
                            part_cards = seperate_card(gray_img, wave_peaks)
                            for i, part_card in enumerate(part_cards):
                                # 可能是固定车牌的铆钉
                                if np.mean(part_card) < 255 / 5:
                                    print("a point")
                                    continue
                                part_card_old = part_card
                                w = abs(part_card.shape[1] - SZ) // 2

                                part_card = cv2.copyMakeBorder(part_card, 0, 0, w, w, cv2.BORDER_CONSTANT,
                                                               value=[0, 0, 0])
                                part_card = cv2.resize(part_card, (SZ, SZ), interpolation=cv2.INTER_AREA)
                                word_images.append(part_card)
                            word_images_ = word_images.copy()
                            predict_result = template_matching(word_images_)
                            roi = card_img
                            card_color = color
                            print(predict_result)
                            break

                    return predict_result, roi, card_color  # 识别到的字符、定位的车牌图像、车牌颜色