# 对minAreaRect获得的最小外接矩形，用纵横比进行判断
def verifySizes(RotatedRect):
    error = 0.4
    aspect = 4.7272
    min = 15 * aspect * 15
    max = 125 * aspect * 125
    rmin = aspect - aspect * error
    rmax = aspect + aspect * error
    height,width = RotatedRect[1]
    if height==0 or width==0:
        return False
    area = height * width
    r = width/height
    if r < 1:
        r = height/width
    if (area < min or area > max) or (r < rmin or r > rmax):
        return False
    else:
        return True

依据定义的验证函数，使用for循环一次遍历轮廓大点集中的点集集合

# 对minAreaRect获得的最小外接矩形，用纵横比进行判断
save = []  #存储合理轮廓
rectall = []  #存储对应的在最小面积矩形

for contour in contours:
    rect = cv2.minAreaRect(contour)
    if verifySizes(rect):
        save.append(contour)
        rectall.append(rect)

下图为经过验证函数处理后得到的合理轮廓的点集，画成的轮廓图

cv2.drawContours(initial_car,save,-1,(0,0,255),2)

八、对合理矩形（区域），进行floodFill泛洪处理 & 综合后续

为了进一步提高效果，因为刚得到的合理的矩形所包括的车牌区域可能并不完整，所以我们使用泛洪处理来将得到的区域更为完整

step 1 : 利用合理矩形的中心点（rect[0]）为中心，生成十个周围的随机种子点

Step 2：对生成的十个随机种子点，依次使用cv2.floodFill泛洪算法进行处理

生成的随机种子点图片（种子点：黄色，以矩形中心作圆：红色）

关于

cv2.floodFill(img,mask,(seed_x,seed_y),newvalue(b,g,r),(loDiff,loDiff,loDiff),(upDiff,upDiff,upDiff),flag)

的详细信息，参见https://blog.csdn.net/qq_37385726/article/details/82313004

我们使用floodFill算法，对掩码层进行处理，则我们可以通过掩码层中被标记为255的像素点来判断是不是目标区域。
再将目标区域的点都保存成一个新点集，使用minAreaRect函数来提取最小面积矩形。
对最小面积矩形再次应用verifySize函数进行处理，得到合理的矩形
对得到的合理矩形的区域，在原图上进行图像切割
对切割后的图像，进行高斯模糊，去噪，直方图均衡化处理
保存

#Step7  得到矩形中心附近随机数点
for step,rect in enumerate(rectall):
    x,y = rect[0]   #x:列数，y:行数
    x = int(x)
    y = int(y)

    cv2.circle(initial_car,(x,y),3,(0,255,0),2)

    width, height = rect[1]
    minimum = width if width<height else height
    minimum = 0.5*minimum

    h,w=initial_car.shape[:2]  #600,


    mask = np.zeros((h + 2, w + 2), dtype=np.uint8)

    for i in range(10):
        seed_x = int(x+0.5*(np.random.random_integers(0,100000)%int(minimum)-(minimum/2)))
        seed_y = int(y+0.5*(np.random.random_integers(0,100000)%int(minimum)-(minimum/2)))

        cv2.circle(initial_car,(seed_x,seed_y),1,(0,255,255))

        loDiff = 7.95
        upDiff = 30
        Connectivity = 4
        flag = Connectivity + (255<<8) + cv2.FLOODFILL_MASK_ONLY
        cv2.floodFill(initial_car,mask,(seed_x,seed_y),(255,0,0),(loDiff,loDiff,loDiff),(upDiff,upDiff,upDiff),flag)

    # cv2.imshow(str(step),mask)

    points = []
    row,column = mask.shape

    for i in range(row):
        for j in range(column):
            if mask[i][j]==255:
                points.append((j,i))   #点应该输入点坐标（列，行）
    points = np.asarray(points)
    new_rect = cv2.minAreaRect(points)

    if verifySizes(new_rect):
        # 宽,高
        x,y = new_rect[0]
        new_width, new_height = new_rect[1]
        angel = new_rect[2]
        point1 = cv2.boxPoints(new_rect)[0]
        point2 = cv2.boxPoints(new_rect)[1]
        point3 = cv2.boxPoints(new_rect)[2]
        point4 = cv2.boxPoints(new_rect)[3]

        # cv2.line(initial_car,tuple(point1),tuple(point2),(255,255,255),2)
        # cv2.line(initial_car, tuple(point2), tuple(point3), (255, 255, 255), 2)
        # cv2.line(initial_car, tuple(point3), tuple(point4), (255, 255, 255), 2)
        # cv2.line(initial_car, tuple(point4), tuple(point1), (255, 255, 255), 2)  #width
        rotate = cv2.getRotationMatrix2D((x,y),90+angel,1)
        res = cv2.warpAffine(initial_car,rotate,initial_car.shape[:2])

        #img,(列，行)，（中心）
        res = cv2.getRectSubPix(res,(int(new_height),int(new_width)),(x,y))

        #img,(列，行)
        res = cv2.resize(res,(105,25),interpolation=cv2.INTER_AREA)
        res = cv2.cvtColor(res,cv2.COLOR_BGR2GRAY)
        res = cv2.GaussianBlur(res,(3,3),0)
        res = cv2.equalizeHist(res)
        path = './Image/Sample/sample_'+str(step)+'.jpg'
        cv2.imwrite(path,res)

九、完整代码

import cv2
import numpy as np
from matplotlib import pyplot as plt


# Step1  读入灰度图

initial_car = cv2.imread(r'F:\ml_summer\Opencv\Image\car.jpg')   #(600, 800, 3)  行，列，通道数


gray_car = cv2.cvtColor(initial_car,cv2.COLOR_BGR2GRAY)

# Step2  高斯模糊处理
blur_car = cv2.GaussianBlur(gray_car,(5,5),0)



#Step3  Sobel计算水平导数
sobel_car = cv2.Sobel(blur_car,cv2.CV_16S,1,0)
sobel_car = cv2.convertScaleAbs(sobel_car)  #转回uint8

#Step4  Otsu大津算法自适应阈值二值化
_, otsu_car = cv2.threshold(sobel_car,0,255,cv2.THRESH_OTSU|cv2.THRESH_BINARY)



#Step5  闭操作
kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(8,8))
close_car = cv2.morphologyEx(otsu_car,cv2.MORPH_CLOSE,kernel)
# cv2.imshow('sss',close_car)

#Step6  提取外部轮廓
img, contours, hierarchy = cv2.findContours(close_car,cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)





save = []  #存储合理轮廓
rectall = []  #存储对应的在最小面积矩形

# 对minAreaRect获得的最小外接矩形，用纵横比进行判断
def verifySizes(RotatedRect):
    error = 0.4
    aspect = 4.7272
    min = 15 * aspect * 15
    max = 125 * aspect * 125
    rmin = aspect - aspect * error
    rmax = aspect + aspect * error
    height,width = RotatedRect[1]
    if height==0 or width==0:
        return False
    area = height * width
    r = width/height
    if r < 1:
        r = height/width
    if (area < min or area > max) or (r < rmin or r > rmax):
        return False
    else:
        return True



for contour in contours:
    rect = cv2.minAreaRect(contour)
    if verifySizes(rect):
        save.append(contour)
        rectall.append(rect)

# cv2.drawContours(initial_car,save,-1,(0,0,255),2)


#Step7  得到矩形中心附近随机数点
for step,rect in enumerate(rectall):
    x,y = rect[0]   #x:列数，y:行数
    x = int(x)
    y = int(y)

    cv2.circle(initial_car,(x,y),3,(0,255,0),2)

    width, height = rect[1]
    minimum = width if width<height else height
    minimum = 0.5*minimum

    h,w=initial_car.shape[:2]  #600,


    mask = np.zeros((h + 2, w + 2), dtype=np.uint8)

    for i in range(10):
        seed_x = int(x+0.5*(np.random.random_integers(0,100000)%int(minimum)-(minimum/2)))
        seed_y = int(y+0.5*(np.random.random_integers(0,100000)%int(minimum)-(minimum/2)))

        cv2.circle(initial_car,(seed_x,seed_y),1,(0,255,255))

        loDiff = 7.95
        upDiff = 30
        Connectivity = 4
        flag = Connectivity + (255<<8) + cv2.FLOODFILL_MASK_ONLY
        cv2.floodFill(initial_car,mask,(seed_x,seed_y),(255,0,0),(loDiff,loDiff,loDiff),(upDiff,upDiff,upDiff),flag)

    # cv2.imshow(str(step),mask)

    points = []
    row,column = mask.shape

    for i in range(row):
        for j in range(column):
            if mask[i][j]==255:
                points.append((j,i))   #点应该输入点坐标（列，行）
    points = np.asarray(points)
    new_rect = cv2.minAreaRect(points)

    if verifySizes(new_rect):
        # 宽,高
        x,y = new_rect[0]
        new_width, new_height = new_rect[1]
        angel = new_rect[2]
        point1 = cv2.boxPoints(new_rect)[0]
        point2 = cv2.boxPoints(new_rect)[1]
        point3 = cv2.boxPoints(new_rect)[2]
        point4 = cv2.boxPoints(new_rect)[3]

        # cv2.line(initial_car,tuple(point1),tuple(point2),(255,255,255),2)
        # cv2.line(initial_car, tuple(point2), tuple(point3), (255, 255, 255), 2)
        # cv2.line(initial_car, tuple(point3), tuple(point4), (255, 255, 255), 2)
        # cv2.line(initial_car, tuple(point4), tuple(point1), (255, 255, 255), 2)  #width
        rotate = cv2.getRotationMatrix2D((x,y),90+angel,1)
        res = cv2.warpAffine(initial_car,rotate,initial_car.shape[:2])

        #img,(列，行)，（中心）
        res = cv2.getRectSubPix(res,(int(new_height),int(new_width)),(x,y))

        #img,(列，行)
        res = cv2.resize(res,(105,25),interpolation=cv2.INTER_AREA)
        res = cv2.cvtColor(res,cv2.COLOR_BGR2GRAY)
        res = cv2.GaussianBlur(res,(3,3),0)
        res = cv2.equalizeHist(res)

        # 绘制直方图
        # hist = cv2.calcHist([res],[0],None,[256],[0,255])
        # plt.plot(hist,'r')
        # plt.hist(res.ravel(), 256, [0, 256],color='r')
        # plt.show()

        path = './Image/Sample/sample_'+str(step)+'.jpg'
        cv2.imwrite(path,res)

# cv2.imshow('now',initial_car)
cv2.waitKey(0)

Opencv之python下车牌识别

目录

一、读入原始图片，灰度处理

二、高斯模糊处理，去噪

三、Sobel滤波，边缘检测

四、 Otsu大津算法自适应阈值二值化处理

五、形态学操作，闭操作

六、轮廓提取

七、根据contours信息，构建外界矩形，并判断该轮廓是否合理

八、对合理矩形（区域），进行floodFill泛洪处理 & 综合后续

九、完整代码

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