python实现车牌识别的示例代码

import cv2import 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)