车牌字符分割

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


def preprocess(gray):
    # # 直方图均衡化
    #equ = cv2.equalizeHist(gray)
    # 高斯平滑
    gaussian = cv2.GaussianBlur(gray, (3, 3), 0, 0, cv2.BORDER_DEFAULT)
    # 中值滤波
    median = cv2.medianBlur(gaussian, 5)
    # Sobel算子，X方向求梯度
    sobel = cv2.Sobel(median, cv2.CV_8U, 1, 0, ksize=3)
    # 二值化
    ret, binary = cv2.threshold(sobel, 170, 255, cv2.THRESH_BINARY)
    #cv2.imshow("binary",binary)
    # 膨胀和腐蚀操作的核函数
    element1 = cv2.getStructuringElement(cv2.MORPH_RECT, (9, 1))
    element2 = cv2.getStructuringElement(cv2.MORPH_RECT, (8, 6))
    # 膨胀2次，让轮廓突出
    dilation = cv2.dilate(binary, element2, iterations=2)
    # 腐蚀一次，去掉细节
    erosion = cv2.erode(dilation, element1, iterations=1)
    # 再3次膨胀，让轮廓明显一些
    dilation2 = cv2.dilate(erosion, element2, iterations=3)
    cv2.imshow('dilation2', dilation2)
    cv2.waitKey(0)
    return dilation2


def findPlateNumberRegion(img):
    region = []
    # 查找轮廓
    binary,contours, hierarchy = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

    # 筛选面积小的
    for i in range(len(contours)):
        cnt = contours[i]
        # 计算该轮廓的面积
        area = cv2.contourArea(cnt)

        # 面积小的都筛选掉
        if (area < 2000):
            continue

        # 轮廓近似，作用很小
        epsilon = 0.001 * cv2.arcLength(cnt, True)
        approx = cv2.approxPolyDP(cnt, epsilon, True)

        # 找到最小的矩形，该矩形可能有方向
        rect = cv2.minAreaRect(cnt)
        print("rect is: ")
        print(rect)

        # box是四个点的坐标
        box = cv2.boxPoints(rect)
        box = np.int0(box)

        # 计算高和宽
        height = abs(box[0][1] - box[2][1])
        width = abs(box[0][0] - box[2][0])
        # 车牌正常情况下长高比在2.7-5之间
        ratio = float(width) / float(height)
        print(ratio)
        if (ratio > 5 or ratio < 2):
            continue
        region.append(box)

    return region


def detect(img):
    # 转化成灰度图
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    # 形态学变换的预处理
    dilation = preprocess(gray)

    # 查找车牌区域
    region = findPlateNumberRegion(dilation)

    # 用绿线画出这些找到的轮廓
    for box in region:
        cv2.drawContours(img, [box], 0, (0, 255, 0), 2)
    ys = [box[0, 1], box[1, 1], box[2, 1], box[3, 1]]
    xs = [box[0, 0], box[1, 0], box[2, 0], box[3, 0]]
    ys_sorted_index = np.argsort(ys)
    xs_sorted_index = np.argsort(xs)

    x1 = box[xs_sorted_index[0], 0]
    x2 = box[xs_sorted_index[3], 0]

    y1 = box[ys_sorted_index[0], 1]
    y2 = box[ys_sorted_index[3], 1]

    img_org2 = img.copy()
    img_plate = img_org2[y1:y2, x1:x2]
    cv2.imshow('dingwei', img_plate)
    cv2.imwrite('dingwei.jpg', img_plate)

    cv2.namedWindow('img', cv2.WINDOW_NORMAL)
    cv2.imshow('img', img)

    # 带轮廓的图片
    cv2.imwrite('contours.png', img)

    cv2.waitKey(0)
    cv2.destroyAllWindows()


'''在imagePath中放入你要测试的图片'''

imagePath = '1.jpg'
im3 = Image.open(imagePath)
im3.show(imagePath)
img = cv2.imread(imagePath)
detect(img)


#进一步切割
img1 = Image.open("dingwei.jpg")
#车牌大小统一成440*140
new_img = img1.resize((440,140),Image.BILINEAR)  
#车牌再次定位截取统一成([30,390],[20,390])
region = new_img.crop((30, 23, 416, 110))
region.save("dingwei1.jpg")



#对车脾进行倾斜矫正

'''imga = cv2.imread("dingwei1.jpg", 0)
imge = cv2.GaussianBlur(imga,(3,3),0)
edges = cv2.Canny(imge, 50, 150, apertureSize = 3)

lines = cv2.HoughLines(edges,1,np.pi/180,118)
for l in lines[0]:
    theta = l[1]

jiao = -(1.57079-theta)*180/3.14159
pil_im = Image.open('dingwei1.jpg')
pil_im = pil_im.rotate(jiao)
pil_im.save('300.jpg')'''



im = Image.open('dingwei1.jpg')
im.show('dingwei1.jpg')
#灰度化
im = im.convert('L')
im.save('001.png')
#自己写的二值化
pixload = im.load()
for x in range(im.size[0]):
    for y in range(im.size[1]):
        if pixload[x,y]<125:
            pixload[x,y] = 255
        else:
            pixload[x,y] = 0
#去掉车牌的上下的圆孔
im.save('003.png')
#im.show('003.png')
pixload2 = im.load()
print(im.size)
for x in range(im.size[1]):
    count =0
    for y in range(im.size[0]):
        if pixload2[y,x]==0:
            count=count+1
    if count<50:
        for y in range(im.size[0]):
            pixload2[y, x] =255
im.save('004.png')
#im.show('004.png')
pixload3 = im.load()
#去掉车牌上的圆点
for x in range(im.size[0]):
    count =0
    for y in range(im.size[1]):
        if pixload3[x,y]==0:
            count=count+1
    if count<0:
        for y in range(im.size[1]):
            pixload2[x, y] =255
im.save('005.png')
#im.show('005.png')
img = cv2.imread("005.png")  # 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  
img_thre = img_gray  
cv2.threshold(img_gray, 140, 255, cv2.THRESH_BINARY_INV, img_thre)  
cv2.imshow('threshold', img_thre)  
cv2.waitKey(0)  
  
# 3、保存黑白图片  
cv2.imwrite('132.png', img_thre)  
  
# 4、分割字符  
white = []  # 记录每一列的白色像素总和  
black = []  # ..........黑色.......  
height = img_thre.shape[0]
width = img_thre.shape[1]
white_max = 0
black_max = 0
# 计算每一列的黑白色像素总和
for i in range(width):
    s = 0  # 这一列白色总数
    t = 0  # 这一列黑色总数
    for j in range(height):
        if img_thre[j][i] == 255:
            s += 1
        if img_thre[j][i] == 0:
            t += 1
    white_max = max(white_max, s)
    black_max = max(black_max, t)
    white.append(s)
    black.append(t)
    #print(s)
    #print(t)

arg = False  # False表示白底黑字；True表示黑底白字
if black_max > white_max:
    arg = True
  
# 分割图像  
def find_end(start_):  
    end_ = start_+1  
    for m in range(start_+1, width-1):  
        if (black[m] if arg else white[m]) > (0.95 * black_max if arg else 0.95 * white_max):  # 0.95这个参数请多调整，对应下面的0.05  
            end_ = m  
            break  
    return end_  
  
n = 1  
start = 1  
end = 2
aaa =15
bb = 0
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)  
        n = end  
        if end-start > 9:
            aaa += 1
            bb = bb+1
            cj = img_thre[1:height, start:end]
            cv2.imwrite(str(aaa)+'.jpg',cj)
            # cv2.imshow('caijian', cj)
            cv2.waitKey(0)



#显示分割后的图像
aaa = aaa-bb
b = []
for i in range(bb):
    aaa += 1
    img = cv2.imread(str(aaa)+'.jpg',0)
    b.append(img)
for c in range(bb):
    print(c)
    c += 1
    plt.subplot(1,bb,c)
    plt.imshow(b[c-1],'gray')

plt.show()