python3+opencv

# -*- coding: utf-8 -*-# [e-mail] sysimport cv2import numpy as npdef 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)    # 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, (9, 7))    # 膨胀一次，让轮廓突出    dilation = cv2.dilate(binary, element2, iterations=1)    # 腐蚀一次，去掉细节    erosion = cv2.erode(dilation, element1, iterations=1)    # 再次膨胀，让轮廓明显一些    dilation2 = cv2.dilate(erosion, element2, iterations=3)    cv2.imshow('dilation2', dilation2)    cv2.waitKey(0)    return dilation2def findPlateNumberRegion(img):    region = []    # 查找轮廓    _a,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: ",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 regiondef 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('number plate', img_plate)    # cv2.imwrite('number_plate.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()if __name__ == '__main__':    imagePath = 'c:/cp/10.jpg'    img = cv2.imread(imagePath)    detect(img)

import cv2from PIL import Imageimport numpy as npimage=cv2.imread('c:/1.jpg')#高斯模糊# image=cv2.GaussianBlur(image, (5,5), 1.5);# cv2.imshow('GaussianBlur',image)# cv2.waitKey(0)# cv2.destroyAllWindows()#灰度操作gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)#获取长宽h, w= gray.shape[:2]#滤波去掉噪gray = cv2.blur(gray, (5,5))  # 进行滤波去掉噪声mask = np.zeros((h+2, w+2), np.uint8)       #掩码长和宽都比输入图像多两个像素点，泛洪填充不会超出掩码的非零边缘cv2.floodFill(gray, mask, (w-1,h-1), (255,255,255), (2,2,2),(3,3,3),8)#定义结构元素kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(3,3))#开闭运算，先开运算去除背景噪声，再继续闭运算填充目标内的孔洞gray = cv2.morphologyEx(gray, cv2.MORPH_OPEN, kernel)gray = cv2.morphologyEx(gray, cv2.MORPH_CLOSE, kernel)ret, binary = cv2.threshold(gray,250,255,cv2.THRESH_OTSU+cv2.THRESH_BINARY)# kernel = np.ones((30,30),np.uint8)# erosion = cv2.erode(binary,kernel,iterations = 1)# dilation = cv2.dilate(erosion,kernel,iterations = 1)#找到轮廓tim,contours, hierarchy = cv2.findContours(binary,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)#计算轮廓面积for i in contours:    area = cv2.contourArea(i)    if area<2000 and area>600:        print(area)        #图像的质心，面积等        M = cv2.moments(i)        print(M)        #轮廓重心        cx = int(M['m10'] / M['m00'])        cy = int(M['m01'] / M['m00'])        print(cx,cy)        #轮廓周长        perimeter = cv2.arcLength(i, True)        print(perimeter)cv2.drawContours(image,contours,-1,(0,0,255),3)cv2.imshow('dilation',binary)cv2.waitKey(0)cv2.destroyAllWindows()cv2.imshow('image',image)cv2.waitKey(0)cv2.destroyAllWindows()