python实现车牌识别的示例代码

import cv2
import numpy as np
 
 
# 形态学处理
def Process(img):
	# 高斯平滑
	gaussian = cv2.GaussianBlur(img, (3, 3), 0, 0, cv2.BORDER_DEFAULT)
	# 中值滤波
	median = cv2.medianBlur(gaussian, 5)
	# Sobel算子
	# 梯度方向: x
	sobel = cv2.Sobel(median, cv2.CV_8U, 1, 0, ksize=3)
	# 二值化
	ret, binary = cv2.threshold(sobel, 170, 255, cv2.THRESH_BINARY)
	# 核函数
	element1 = cv2.getStructuringElement(cv2.MORPH_RECT, (9, 1))
	element2 = cv2.getStructuringElement(cv2.MORPH_RECT, (9, 7))
	# 膨胀
	dilation = cv2.dilate(binary, element2, iterations=1)
	# 腐蚀
	erosion = cv2.erode(dilation, element1, iterations=1)
	# 膨胀
	dilation2 = cv2.dilate(erosion, element2, iterations=3)
	return dilation2
 
 
def GetRegion(img):
	regions = []
	# 查找轮廓
	_, contours, hierarchy = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
	for contour in contours:
		area = cv2.contourArea(contour)
		if (area < 2000):
			continue
		eps = 1e-3 * cv2.arcLength(contour, True)
		approx = cv2.approxPolyDP(contour, eps, True)
		rect = cv2.minAreaRect(contour)
		box = cv2.boxPoints(rect)
		box = np.int0(box)
		height = abs(box[0][1] - box[2][1])
		width = abs(box[0][0] - box[2][0])
		ratio =float(width) / float(height)
		if (ratio < 5 and ratio > 1.8):
			regions.append(box)
	return regions
 
 
def detect(img):
	# 灰度化
	gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
	prc = Process(gray)
	regions = GetRegion(prc)
	print('[INFO]:Detect %d license plates' % len(regions))
	for box in regions:
		cv2.drawContours(img, [box], 0, (0, 255, 0), 2)
	cv2.imshow('Result', img)
  #保存结果文件名
	cv2.imwrite('result2.jpg', img)
	cv2.waitKey(0)
	cv2.destroyAllWindows()
 
 
if __name__ == '__main__':
  #输入的参数为图片的路径
	img = cv2.imread('test2.jpg')
	detect(img)