目录:
1.读取图像
2.图像预处理(降噪)
3.定位到ROI(感兴趣区域)图像
4.对ROI图像进行车牌号文字识别提取
# 准备工作:导入相关库、定义需全局使用的函数、设置相关参数
import cv2
import imutils
import numpy as np
import pytesseract
import re
from matplotlib import pyplot as plt
# %matplotlib inline
def imshow(img):
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
plt.imshow(img_rgb)
1.读取图像
img = cv2.imread('LicensePlate001.jpg',cv2.IMREAD_COLOR)
img = cv2.resize(img, (600,400))
imshow(img)
2.图像预处理(降噪)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# 使用双边滤波(模糊)从图像中删除不需要的细节。
# gray = cv2.bilateralFilter(gray, 13, 15, 15)
imshow(gray)
3.定位到ROI(感兴趣区域)图像
# 3-1.边缘检测
edged = cv2.Canny(gray, 30, 200)
imshow(edged)
# 3-2.在图像上寻找轮廓,并定位ROI
contours = cv2.findContours(edged.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
contours = imutils.grab_contours(contours)
contours = sorted(contours, key = cv2.contourArea, reverse = True)[:10]
ROI = None
for c in contours:
peri = cv2.arcLength(c, True)
approx = cv2.approxPolyDP(c, 0.018 * peri, True)
if len(approx) == 4:
ROI = approx
cv2.drawContours(img, [ROI], -1, (0, 0, 255), 2)
break
imshow(img)
# 3-3.从主图像裁剪出ROI(感兴趣区域)图像
y0,y1 = np.min(ROI[:,:,1]),np.max(ROI[:,:,1])
x0,x1 = np.min(ROI[:,:,0]),np.max(ROI[:,:,0])
Cropped = gray[y0:y1,x0:x1]
imshow(Cropped)
4.对ROI图像进行车牌号文字识别提取
text = pytesseract.image_to_string(Cropped)
text = re.sub('[^a-zA-Z\d ]','',text)
img2 = img.copy()
cv2.putText(img2,text,(x0-50,y0),cv2.FONT_HERSHEY_SIMPLEX,1.2, (255, 255, 255), 2)
imshow(img2)
附1:Tesseract文字识别可参考https://www.jianshu.com/p/24352fead613
最后
[1].代码截止
2020-08-29
调试无误。
[2].如需全部代码及相关文件
,留言邮箱。
[3].过程中有任何问题,欢迎交流!Q597966823