Tensorflow车牌识别完整项目（含完整源代码及训练集）

PS D:\pycharm\pycharm_work> cd .\chepai\
PS D:\pycharm\pycharm_work\chepai> python train-license-province.py train

PS D:\pycharm\pycharm_work\chepai> python train-license-province.py predict

PS D:\pycharm\pycharm_work\chepai> python train-license-digits.py predict

def find_car_num_brod():
    watch_cascade = cv2.CascadeClassifier('D:\pycharm\pycharm_work\chepai\cascade.xml')
    # 先读取图片
    image = cv2.imread("D:\pycharm\pycharm_work\chepai\capture_img\car1.jpg")
    resize_h = 1000
    height = image.shape[0]
    scale = image.shape[1] / float(image.shape[0])
    image = cv2.resize(image, (int(scale * resize_h), resize_h))
    image_gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
    watches = watch_cascade.detectMultiScale(image_gray, 1.2, 2, minSize=(36, 9), maxSize=(36 * 40, 9 * 40))
    print("检测到车牌数", len(watches))
    for (x, y, w, h) in watches:
        cv2.rectangle(image, (x, y), (x + w, y + h), (0, 0, 255), 1)
        cut_img = image[y + 5:y - 5 + h, x + 8:x  + w]  # 裁剪坐标为[y0:y1, x0:x1]
        cut_gray = cv2.cvtColor(cut_img, cv2.COLOR_RGB2GRAY)
        cv2.imwrite("D:\pycharm\pycharm_work\chepai\\num_for_car.jpg", cut_gray)
        im = Image.open("D:\pycharm\pycharm_work\chepai\\num_for_car.jpg")
        size = 720, 180
        mmm = im.resize(size, Image.ANTIALIAS)
        mmm.save("D:\pycharm\pycharm_work\chepai\\num_for_car.jpg", "JPEG", quality=90)
        break

def cut_car_num_for_chart():
    # 1、读取图像，并把图像转换为灰度图像并显示
    img = cv2.imread("D:\pycharm\pycharm_work\chepai\\num_for_car.jpg")  # 读取图片
    img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)  # 转换了灰度化
    cv2.imshow('gray', img_gray)  # 显示图片
    cv2.waitKey(0)
    # 2、将灰度图像二值化，设定阈值是100   转换后 白底黑字 ---》 目标黑底白字
    img_thre = img_gray
    # 灰点  白点 加错
    # cv2.threshold(img_gray, 130, 255, cv2.THRESH_BINARY_INV, img_thre)
    # 二值化处理 自适应阈值   效果不理想
    # th3 = cv2.adaptiveThreshold(img_gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2)
    # 高斯除噪 二值化处理
    blur = cv2.GaussianBlur(img_gray, (5, 5), 0)
    ret3, th3 = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
    cv2.imshow('threshold', th3)
    cv2.imwrite('D:\pycharm\pycharm_work\chepai\wb_img.jpg', th3)
    cv2.waitKey(0)

n = 1
    start = 1
    end = 2
    temp = 1
    while n < width - 2:
        n += 1
        if (white[n] if arg else black[n]) > (0.05 * white_max if arg else 0.05 * black_max):
            # 上面这些判断用来辨别是白底黑字还是黑底白字
            # 0.05这个参数请多调整，对应上面的0.95
            start = n
            end = find_end(start, white, black, arg, white_max, black_max, width)
            n = end
            # 车牌框检测分割 二值化处理后 可以看到明显的左右边框  毕竟用的是网络开放资源 所以车牌框定位角度真的不准，
            # 所以我在这里截取单个字符时做处理，就当亡羊补牢吧
            # 思路就是从左开始检测匹配字符，若宽度（end - start）小与20则认为是左侧白条 pass掉  继续向右识别，否则说明是
            # 省份简称，剪切，压缩 保存，还有一个当后五位有数字 1 时，他的宽度也是很窄的，所以就直接认为是数字 1 不需要再
            # 做预测了（不然很窄的 1 截切  压缩后宽度是被拉伸的），
            # shutil.copy()函数是当检测到这个所谓的 1 时，从样本库中拷贝一张 1 的图片给当前temp下标下的字符
            if end - start > 5:  # 车牌左边白条移除
                print(" end - start" + str(end - start))
                if temp == 1 and end - start < 20:
                    pass
                elif temp > 3 and end - start < 20:
                    #  认为这个字符是数字1   copy 一个 32*40的 1 作为 temp.bmp
                    shutil.copy(
                        os.path.join("D:\pycharm\pycharm_work\chepai\fuzhi", "111.bmp"),  # 111.bmp 是一张 1 的样本图片
                        os.path.join("D:\pycharm\pycharm_work\chepai\\img_cut\\", str(temp) + '.bmp'))
                    pass
                else:
                    cj = th3[1:height, start:end]
                    cv2.imwrite("D:\pycharm\pycharm_work\chepai\img_cut_not_3240\\" + str(temp) + ".jpg", cj)
                    im = Image.open("D:\pycharm\pycharm_work\chepai\img_cut_not_3240\\" + str(temp) + ".jpg")
                    size = 32, 40
                    mmm = im.resize(size, Image.ANTIALIAS)
                    mmm.save("D:\pycharm\pycharm_work\chepai\img_cut\\" + str(temp) + ".bmp", quality=95)
                    temp = temp + 1

max1 = 0
            max2 = 0
            max3 = 0
            max1_index = 0
            max2_index = 0
            max3_index = 0
            for j in range(NUM_CLASSES):
                if result[0][j] > max1:
                    max1 = result[0][j]
                    max1_index = j
                    continue
                if (result[0][j] > max2) and (result[0][j] <= max1):
                    max2 = result[0][j]
                    max2_index = j
                    continue
                if (result[0][j] > max3) and (result[0][j] <= max2):
                    max3 = result[0][j]
                    max3_index = j
                    continue
            license_num = license_num + LETTERS_DIGITS[max1_index]
            print("概率：  [%s %0.2f%%]    [%s %0.2f%%]    [%s %0.2f%%]" % (
            LETTERS_DIGITS[max1_index], max1 * 100, LETTERS_DIGITS[max2_index], max2 * 100, LETTERS_DIGITS[max3_index],
            max3 * 100))
        print("车牌编号是: 【%s】" % license_num)