python opencv车牌图像校正

合集 - OpenCV(2)

1.OpenCV--图片处理操作2024-06-03

2.python opencv车牌图像校正2024-12-16

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车牌图片

代码

# -*- coding: UTF-8 -*-


import cv2
import numpy as np


# 预处理
def imgProcess(path):
    img = cv2.imread(path)
    # 统一规定大小
    img = cv2.resize(img, (640, 480))
    # 高斯模糊
    img_Gas = cv2.GaussianBlur(img, (5, 5), 0)
    # RGB通道分离
    img_B = cv2.split(img_Gas)[0]
    img_G = cv2.split(img_Gas)[1]
    img_R = cv2.split(img_Gas)[2]
    # 读取灰度图和HSV空间图
    img_gray = cv2.cvtColor(img_Gas, cv2.COLOR_BGR2GRAY)
    img_HSV = cv2.cvtColor(img_Gas, cv2.COLOR_BGR2HSV)

    return img, img_Gas, img_B, img_G, img_R, img_gray, img_HSV


# 初步识别
def preIdentification(img_gray, img_HSV, img_B, img_R):
    """
    图像二值化
    :param img_gray:
    :param img_HSV:
    :param img_B:
    :param img_R:
    :return:
    """
    for i in range(480):
        for j in range(640):
            # 普通蓝色车牌，同时排除透明反光物质的干扰
            if ((img_HSV[:, :, 0][i, j]-115)**2 < 15**2) and (img_B[i, j] > 70) and (img_R[i, j] < 40):
                img_gray[i, j] = 255
            else:
                img_gray[i, j] = 0
    # 定义核
    kernel_small = np.ones((3, 3))
    kernel_big = np.ones((7, 7))

    img_gray = cv2.GaussianBlur(img_gray, (5, 5), 0)                      # 高斯平滑
    img_di = cv2.dilate(img_gray, kernel_small, iterations=5)             # 腐蚀5次
    img_close = cv2.morphologyEx(img_di, cv2.MORPH_CLOSE, kernel_big)     # 闭操作
    img_close = cv2.GaussianBlur(img_close, (5, 5), 0)                    # 高斯平滑
    _, img_bin = cv2.threshold(img_close, 100, 255, cv2.THRESH_BINARY)    # 二值化

    return img_bin


# 定位
def fixPosition(img, img_bin):
    """
    对二值化后的图片进行车牌定位，根据车牌区域的大致长宽比进行筛选，获得车牌最可能得区域
    :param img:
    :param img_bin:
    :return:
    """
    # 检测所有外轮廓，只留矩形的四个顶点
    contours, _ = cv2.findContours(img_bin, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
    # print(f'contours={contours}')
    # 形状及大小筛选校验
    det_x_max = 0
    det_y_max = 0
    num = 0
    for i in range(len(contours)):
        # print('i=', contours[i])
        x_min = np.min(contours[i][:, :, 0])
        x_max = np.max(contours[i][:, :, 0])
        y_min = np.min(contours[i][:, :, 1])
        y_max = np.max(contours[i][:, :, 1])
        det_x = x_max - x_min
        det_y = y_max - y_min
        print(f'det_x={det_x}, det_y={det_y}')
        if (det_x / det_y > 1.8) and (det_x > det_x_max) and (det_y > det_y_max):
            det_y_max = det_y
            det_x_max = det_x
            num = i
    # 获取最可疑区域轮廓点集
    points = np.array(contours[num][:, 0])
    print(f'points={points}')

    return points


# img_lic_canny = cv2.Canny(img_lic_bin, 100, 200)


def findVertices(points):
    """
    # 获取最小外接矩阵，中心点坐标，宽高，旋转角度
    :param points:
    :return:
    """

    rect = cv2.minAreaRect(points)   # 生成最小外接矩形，返回最小外接矩形的中心（x，y），（宽度，高度），旋转角度
    print(f'rect={rect}')
    # 获取矩形四个顶点，浮点型
    box = cv2.boxPoints(rect)
    print(f'box={box}')
    # 取整
    box = np.int0(box)
    # 获取四个顶点坐标
    left_point_x = np.min(box[:, 0])
    right_point_x = np.max(box[:, 0])
    top_point_y = np.min(box[:, 1])
    bottom_point_y = np.max(box[:, 1])

    left_point_y = box[:, 1][np.where(box[:, 0] == left_point_x)][0]
    right_point_y = box[:, 1][np.where(box[:, 0] == right_point_x)][0]
    top_point_x = box[:, 0][np.where(box[:, 1] == top_point_y)][0]
    bottom_point_x = box[:, 0][np.where(box[:, 1] == bottom_point_y)][0]
    # 上下左右四个点坐标
    vertices = np.array([[top_point_x, top_point_y], [bottom_point_x, bottom_point_y], [left_point_x, left_point_y],
                         [right_point_x, right_point_y]])
    print(f'vertices={vertices}')
    return vertices, rect


def tiltCorrection(vertices, rect):
    # 畸变情况1
    if rect[2] > -45:  # 倾斜角度
        new_right_point_x = vertices[0, 0]
        new_right_point_y = int(vertices[1, 1] - (vertices[0, 0] - vertices[1, 0]) / (vertices[3, 0] - vertices[1, 0]) * (vertices[1, 1] - vertices[3, 1]))
        new_left_point_x = vertices[1, 0]
        new_left_point_y = int(vertices[0, 1] + (vertices[0, 0] - vertices[1, 0]) / (vertices[0, 0] - vertices[2, 0]) * (vertices[2, 1] - vertices[0, 1]))
        # 校正后的四个顶点坐标
        point_set_1 = np.float32([[440, 0], [0, 0], [0, 140], [440, 140]])
    # 畸变情况2
    elif rect[2] < -45:
        new_right_point_x = vertices[1, 0]
        new_right_point_y = int(vertices[0, 1] + (vertices[1, 0] - vertices[0, 0]) / (vertices[3, 0] - vertices[0, 0]) * (vertices[3, 1] - vertices[0, 1]))
        new_left_point_x = vertices[0, 0]
        new_left_point_y = int(vertices[1, 1] - (vertices[1, 0] - vertices[0, 0]) / (vertices[1, 0] - vertices[2, 0]) * (vertices[1, 1] - vertices[2, 1]))
        # 校正后的四个顶点坐标
        point_set_1 = np.float32([[0, 0], [0, 140], [440, 140], [440, 0]])

    # 校正前平行四边形四个顶点坐标
    new_box = np.array([(vertices[0, 0], vertices[0, 1]), (new_left_point_x, new_left_point_y), (vertices[1, 0], vertices[1, 1]), (new_right_point_x, new_right_point_y)])
    point_set_0 = np.float32(new_box)
    return point_set_0, point_set_1, new_box


def transform(img, point_set_0, point_set_1):
    # 变换矩阵
    mat = cv2.getPerspectiveTransform(point_set_0, point_set_1)
    # 投影变换
    lic = cv2.warpPerspective(img, mat, (440, 140))
    return lic


def main():

    # 图片路径
    path = "./car_license.png"

    # 图像预处理
    img, img_Gas, img_B, img_G, img_R, img_gray, img_HSV = imgProcess(path)
    # 初步识别
    img_bin = preIdentification(img_gray, img_HSV, img_B, img_R)
    # print('img_bin=', img_bin)
    points = fixPosition(img, img_bin)
    vertices, rect = findVertices(points)
    point_set_0, point_set_1, new_box = tiltCorrection(vertices, rect)
    img_draw = cv2.drawContours(img.copy(), [new_box], -1, (0, 0, 255), 3)
    lic = transform(img, point_set_0, point_set_1)
    # 原图上框出车牌
    cv2.namedWindow("Image")
    cv2.imshow("Image", img_draw)
    # 二值化图像
    cv2.namedWindow("Image_Bin")
    cv2.imshow("Image_Bin", img_bin)     # 二值化后的图片
    # 显示校正后的车牌
    cv2.namedWindow("Lic")
    cv2.imshow("Lic", lic)
    # 暂停、关闭窗口
    cv2.waitKey(0)
    cv2.destroyAllWindows()


if __name__ == '__main__':
    main()