步骤：
读入图片——转灰度图——高斯过滤——均值过滤——sobel提取边缘——图像二值化——膨胀——腐蚀——膨胀——提取轮廓——遍历所有轮廓——对轮廓拟合多边形——求出最小矩形——对矩形进行筛选——在图像中截取矩形框——通过连通区域分割提取车牌字符
代码如下：
import numpy as npimport cv2import skimageimport pytesseractfrom PIL import Imagedef image_threshold(image):image = cv2.GaussianBlur(image,(3,3),0,0,cv2.BORDER_DEFAULT)image = cv2.medianBlur(image,5)image = cv2.Sobel(image,cv2.CV_8U,1,0,ksize=3)#image = cv2.Canny(image,50,150)image = cv2.threshold(image,150,255,cv2.THRESH_TOZERO_INV)image = cv2.threshold(image[1],120,255,cv2.THRESH_BINARY)return image[1]def image_thresh(image):element1 = cv2.getStructuringElement(cv2.MORPH_RECT,(9,1))element2 = cv2.getStructuringElement(cv2.MORPH_RECT,(11,3))dilation = cv2.dilate(image,element2,iterations=1)erosion = cv2.erode(dilation,element1,iterations=1)image = cv2.dilate(erosion,element2,iterations=2)return imagedef get_car_cart(image):regoins = []contours,hierarchy = cv2.findContours(image,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)# img = cv2.drawContours(image,contours,-1,(0,255,0),2)# see_image(img)for contour in contours:area = cv2.contourArea(contour)if (area<2000):continue#contour = cv2.convexHull(contour)eps = 1e-3 * cv2.arcLength(contour,True)approx = cv2.approxPolyDP(contour,eps,True)rect = cv2.minAreaRect(approx)box = cv2.boxPoints(rect)box = np.int0(box)#print(box)height = abs(box[0][1] - box[2][1])width = abs(box[0][0] - box[2][0])raio = float(width)/float(height)#print(raio)if (raio<5 and raio>1.8):regoins.append(box)return regoinsdef get_rect(image,regoins):old_image = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)img_list = []for index,box in enumerate(regoins):if not (box[0][1]>0 and box[2][1]>0 and box[0][0]>0 and box[2][0]>0):continueimg = np.zeros((abs(box[0][1] - box[2][1]),abs(box[0][0]-box[2][0])))#cv2.drawContours(image,[box],0,(0,255,0),2)if (box[0][1]>box[2][1] and box[0][0]>box[2][0]):img[:,:] = old_image[box[2][1]:box[0][1],box[2][0]:box[0][0]]img_list.append(img)if (box[2][1]>box[0][1] and box[0][0]>box[2][0]):img[:,:] = old_image[box[0][1]:box[2][1],box[2][0]:box[0][0]]img_list.append(img)if (box[0][1]>box[2][1] and box[2][0]>box[0][0]):img[:,:] = old_image[box[2][1]:box[0][1],box[0][0]:box[2][0]]img_list.append(img)if (box[2][1]>box[0][1] and box[2][0]>box[0][0]):img[:,:] = old_image[box[0][1]:box[2][1],box[0][0]:box[2][0]]img_list.append(img)return img_listdef see_image():cv2.waitKey(0)cv2.destroyAllWindows()def remove_back(image):label_list = skimage.measure.label(image)reg_list = skimage.measure.regionprops(label_list)labels = []for reg in reg_list:if reg.area<50000:labels.append(reg.label)current = np.in1d(label_list,list(labels)).reshape(label_list.shape)current = np.asarray(current).astype(np.uint8)image = image*currentreturn imagedef see_cart(image_list):for image in image_list:image = cv2.threshold(image,127,255,cv2.THRESH_BINARY)cv2.imwrite('car_test.jpg',image[1].astype(np.uint8))image = Image.open('car_test.jpg')code = pytesseract.image_to_string(image)print(code)def main():path = 'car1.jpg'image = cv2.imread(path)gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)image_thre = image_threshold(gray)image_thre = image_thresh(image_thre)regoins = get_car_cart(image_thre)image_list = get_rect(image,regoins)see_cart(image_list)for i,image in enumerate(image_list):cv2.imshow('test1'+str(i),image.astype(np.uint8))h,w = image.shapeimage = cv2.threshold(image,150,255,cv2.THRESH_BINARY)#创建连通区域列表labels = skimage.measure.label(image[1],connectivity=2)#image = skimage.color.label2rgb(labels)#获取连通区域属性labels = skimage.measure.regionprops(labels)#切割合适的连通区域for index,label in enumerate(labels):radio = label.area/(h*w)if (radio>0.007 and radio<0.02):print(radio)box = label.bboximg = np.zeros((box[2]-box[0]+9,box[3]-box[1]+2))if (box[0]-7<0):a = box[0]else:a = box[0] -7img[:box[2]+2-a,:] = image[1][a:box[2]+2,box[1]-1:box[3]+1]cv2.imshow('test'+str(index),img.astype(np.uint8))see_image()if __name__=='__main__':main()效果图如下分割效果复现性不太好：