opencv实战 -- 车牌识别
2024-05-20 07:28:50
车牌识别
- 车牌提取
- 车牌处理提取各字符
- 模板匹配
- 识别车牌第一个中文
- 识别车牌第二字字母
- 字母或数字
- 将识别结果显示出来
- 车牌倾斜提取
- 直线拟合找斜率
- 字符分割方法
- 字符水平方向的切割
- 目的：去除车牌边框和铆钉的干扰
- 中间较为密集的地方就是车牌有字符的地方,从而很好的去除了牌边框及铆钉
- 字符垂直方向的切割
- 从直方图中可以看到很多波谷,这些就是字符分割区域的黑色点的个数等于0,我们就可以通过这些0点进行分割,过滤掉这些不需要的部分部分
车牌提取
任务：
- 将车牌中图片中提取出来
# 导入所需模块
import cv2
from matplotlib import pyplot as plt# 显示图片
def cv_show(name,img):cv2.imshow(name,img)cv2.waitKey()cv2.destroyAllWindows()# plt显示彩色图片
def plt_show0(img):b,g,r = cv2.split(img)img = cv2.merge([r, g, b])plt.imshow(img)plt.show()# plt显示灰度图片
def plt_show(img):plt.imshow(img,cmap='gray')plt.show()
# 加载图片
rawImage = cv2.imread("./image/test4.png")
plt_show0(rawImage)
# 高斯去噪
image = cv2.GaussianBlur(rawImage, (3, 3), 0)
# 预览效果
plt_show0(image)
# 灰度处理
gray_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
plt_show(gray_image)
# sobel算子边缘检测（做了一个y方向的检测）
Sobel_x = cv2.Sobel(gray_image, cv2.CV_16S, 1, 0)
# Sobel_y = cv2.Sobel(image, cv2.CV_16S, 0, 1)
absX = cv2.convertScaleAbs(Sobel_x) # 转回uint8
# absY = cv2.convertScaleAbs(Sobel_y)
# dst = cv2.addWeighted(absX, 0.5, absY, 0.5, 0)
image = absX
plt_show(image)
# 自适应阈值处理
ret, image = cv2.threshold(image, 0, 255, cv2.THRESH_OTSU)
plt_show(image)
# 闭运算,是白色部分练成整体
kernelX = cv2.getStructuringElement(cv2.MORPH_RECT, (17, 5))
print(kernelX)
# [[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]
# [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]
# [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]
# [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]
# [1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1]]
image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernelX,iterations = 3)
plt_show(image)
# 去除一些小的白点
kernelX = cv2.getStructuringElement(cv2.MORPH_RECT, (20, 1))
kernelY = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 19))# 膨胀，腐蚀
image = cv2.dilate(image, kernelX)
image = cv2.erode(image, kernelX)
# 腐蚀，膨胀
image = cv2.erode(image, kernelY)
image = cv2.dilate(image, kernelY)plt_show(image)
# 中值滤波去除噪点
image = cv2.medianBlur(image, 15)
plt_show(image)
# 轮廓检测
# cv2.RETR_EXTERNAL表示只检测外轮廓
# cv2.CHAIN_APPROX_SIMPLE压缩水平方向，垂直方向，对角线方向的元素，只保留该方向的终点坐标，例如一个矩形轮廓只需4个点来保存轮廓信息
contours, hierarchy = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# 绘制轮廓
image1 = rawImage.copy()
cv2.drawContours(image1, contours, -1, (0, 255, 0), 5)
plt_show0(image1)
len(contours)
# 11
# 筛选出车牌位置的轮廓
# 这里我只做了一个车牌的长宽比在3:1到4：1之间这样一个判断
for item in contours:# cv2.boundingRect用一个最小的矩形，把找到的形状包起来rect = cv2.boundingRect(item)x = rect[0]y = rect[1]weight = rect[2]height = rect[3]# 440mm×140mmif (weight > (height * 2.5)) and (weight < (height * 4)):image = rawImage[y:y + height, x:x + weight]
# cv_show('image',image)# 图像保存plt_show0(image)cv2.imwrite('./car_license/test4.png', image)
车牌处理提取各字符
# 导入所需模块
import cv2
from matplotlib import pyplot as plt# 显示图片
def cv_show(name,img):cv2.imshow(name,img)cv2.waitKey()cv2.destroyAllWindows()# plt显示彩色图片
def plt_show0(img):b,g,r = cv2.split(img)img = cv2.merge([r, g, b])plt.imshow(img)plt.show()# plt显示灰度图片
def plt_show(img):plt.imshow(img,cmap='gray')plt.show()
# 加载图片
rawImage = cv2.imread("./car_license/test4.png")
plt_show0(rawImage)
# 高斯去噪
image = cv2.GaussianBlur(rawImage, (3, 3), 0)
# 预览效果
plt_show0(image)
# 灰度处理
gray_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
plt_show(gray_image)
# 自适应阈值处理
ret, image = cv2.threshold(gray_image, 0, 255, cv2.THRESH_OTSU)
plt_show(image)
# 计算二值图像黑白点的个数，处理绿牌照问题，让车牌号码始终为白色
area_white = 0
area_black = 0
height, width = image.shape
print(image.shape)
# (33, 98)
for i in range(height):for j in range(width):if image[i, j] == 255:area_white += 1else:area_black += 1
if area_white>area_black:ret, image = cv2.threshold(gray_image, 0, 255, cv2.THRESH_OTSU | cv2.THRESH_BINARY_INV)plt_show(image)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (2, 2))
# kernelX = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 1))
# kernelY = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 5))
# image = cv2.erode(image, kernelX)
# image = cv2.erode(image, kernelY)
image = cv2.dilate(image, kernel)
plt_show(image)
# 闭运算,是白色部分练成整体
# kernelX = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
# print(kernelX)
# image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernelX,iterations = 2)
# plt_show(image)
# 轮廓检测
# cv2.RETR_EXTERNAL表示只检测外轮廓
# cv2.CHAIN_APPROX_SIMPLE压缩水平方向，垂直方向，对角线方向的元素，只保留该方向的终点坐标，例如一个矩形轮廓只需4个点来保存轮廓信息
contours, hierarchy = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# 绘制轮廓
image1 = rawImage.copy()
cv2.drawContours(image1, contours, -1, (0, 0, 255), 1)
plt_show0(image1)
# 筛选出各个字符的位置的轮廓
words = []
for item in contours:# cv2.boundingRect用一个最小的矩形，把找到的形状包起来word = []rect = cv2.boundingRect(item)x = rect[0]y = rect[1]weight = rect[2]height = rect[3]word.append(x)word.append(y)word.append(weight)word.append(height)words.append(word)words = sorted(words,key=lambda s:s[0],reverse=False)print(words)
# [[3, 0, 63, 29], [5, 9, 11, 20], [22, 30, 16, 3], [29, 17, 4, 3], [33, 8, 12, 21], [46, 8, 11, 21], [56, 4, 29, 29], [58, 8, 11, 21], [82, 10, 11, 19], [90, 5, 6, 5]]i = 0
for word in words:if (word[3] > (word[2] * 1.8)) and (word[3] < (word[2] * 3.5)):i = i+1image = rawImage[word[1]:word[1] + word[3], word[0]:word[0] + word[2]]plt_show0(image)cv2.imwrite('./words/test2_'+str(i)+'.png', image)
模板匹配
# 导入所需模块
import cv2
from matplotlib import pyplot as plt
import os# 显示图片
def cv_show(name,img):cv2.imshow(name,img)cv2.waitKey()cv2.destroyAllWindows()# plt显示彩色图片
def plt_show0(img):b,g,r = cv2.split(img)img = cv2.merge([r, g, b])plt.imshow(img)plt.show()# plt显示灰度图片
def plt_show(img):plt.imshow(img,cmap='gray')plt.show()template = ['0','1','2','3','4','5','6','7','8','9','A','B','C','D','E','F','G','H','J','K','L','M','N','P','Q','R','S','T','U','V','W','X','Y','Z','藏','川','鄂','甘','赣','贵','桂','黑','沪','吉','冀','津','晋','京','辽','鲁','蒙','闽','宁','青','琼','陕','苏','皖','湘','新','渝','豫','粤','云','浙']template[33]
# 'Z'
# 读取一个文件夹下的所有图片，输入参数是文件名
def read_directory(directory_name):referImg_list = []for filename in os.listdir(directory_name):# print(filename) # 仅仅是为了测试# img = cv2.imread(directory_name + "/" + filename)referImg_list.append(directory_name + "/" + filename)return referImg_list
# 匹配中文
c_words = []
for i in range(34,64):c_word = read_directory('./refer1/'+ template[i])c_words.append(c_word)
c_words[1]
['./refer1/川/川_0.jpg','./refer1/川/川_1.jpg','./refer1/川/川_10.jpg','./refer1/川/川_100.jpg','./refer1/川/川_101.jpg','./refer1/川/川_102.jpg','./refer1/川/川_103.jpg','./refer1/川/川_104.jpg','./refer1/川/川_105.jpg','./refer1/川/川_106.jpg','./refer1/川/川_107.jpg','./refer1/川/川_108.jpg','./refer1/川/川_109.jpg','./refer1/川/川_11.jpg','./refer1/川/川_110.jpg','./refer1/川/川_111.jpg','./refer1/川/川_112.jpg','./refer1/川/川_113.jpg','./refer1/川/川_114.jpg','./refer1/川/川_115.jpg','./refer1/川/川_116.jpg','./refer1/川/川_117.jpg','./refer1/川/川_118.jpg','./refer1/川/川_119.jpg','./refer1/川/川_12.jpg','./refer1/川/川_120.jpg','./refer1/川/川_121.jpg','./refer1/川/川_122.jpg','./refer1/川/川_123.jpg','./refer1/川/川_124.jpg','./refer1/川/川_125.jpg','./refer1/川/川_126.jpg','./refer1/川/川_127.jpg','./refer1/川/川_128.jpg','./refer1/川/川_129.jpg','./refer1/川/川_13.jpg','./refer1/川/川_130.jpg','./refer1/川/川_131.jpg','./refer1/川/川_132.jpg','./refer1/川/川_133.jpg','./refer1/川/川_134.jpg','./refer1/川/川_135.jpg','./refer1/川/川_136.jpg','./refer1/川/川_137.jpg','./refer1/川/川_138.jpg','./refer1/川/川_139.jpg','./refer1/川/川_14.jpg','./refer1/川/川_140.jpg','./refer1/川/川_141.jpg','./refer1/川/川_142.jpg','./refer1/川/川_143.jpg','./refer1/川/川_144.jpg','./refer1/川/川_145.jpg','./refer1/川/川_146.jpg','./refer1/川/川_147.jpg','./refer1/川/川_148.jpg','./refer1/川/川_149.jpg','./refer1/川/川_15.jpg','./refer1/川/川_150.jpg','./refer1/川/川_151.jpg','./refer1/川/川_152.jpg','./refer1/川/川_153.jpg','./refer1/川/川_154.jpg','./refer1/川/川_155.jpg','./refer1/川/川_156.jpg','./refer1/川/川_157.jpg','./refer1/川/川_158.jpg','./refer1/川/川_159.jpg','./refer1/川/川_16.jpg','./refer1/川/川_160.jpg','./refer1/川/川_161.jpg','./refer1/川/川_162.jpg','./refer1/川/川_163.jpg','./refer1/川/川_164.jpg','./refer1/川/川_165.jpg','./refer1/川/川_166.jpg','./refer1/川/川_167.jpg','./refer1/川/川_168.jpg','./refer1/川/川_169.jpg','./refer1/川/川_17.jpg','./refer1/川/川_170.jpg','./refer1/川/川_171.jpg','./refer1/川/川_172.jpg','./refer1/川/川_173.jpg','./refer1/川/川_174.jpg','./refer1/川/川_175.jpg','./refer1/川/川_176.jpg','./refer1/川/川_177.jpg','./refer1/川/川_178.jpg','./refer1/川/川_179.jpg','./refer1/川/川_18.jpg','./refer1/川/川_180.jpg','./refer1/川/川_181.jpg','./refer1/川/川_182.jpg','./refer1/川/川_183.jpg','./refer1/川/川_19.jpg','./refer1/川/川_2.jpg','./refer1/川/川_20.jpg','./refer1/川/川_21.jpg','./refer1/川/川_22.jpg','./refer1/川/川_23.jpg','./refer1/川/川_24.jpg','./refer1/川/川_25.jpg','./refer1/川/川_26.jpg','./refer1/川/川_27.jpg','./refer1/川/川_28.jpg','./refer1/川/川_29.jpg','./refer1/川/川_3.jpg','./refer1/川/川_30.jpg','./refer1/川/川_31.jpg','./refer1/川/川_32.jpg','./refer1/川/川_33.jpg','./refer1/川/川_34.jpg','./refer1/川/川_35.jpg','./refer1/川/川_36.jpg','./refer1/川/川_37.jpg','./refer1/川/川_38.jpg','./refer1/川/川_39.jpg','./refer1/川/川_4.jpg','./refer1/川/川_40.jpg','./refer1/川/川_41.jpg','./refer1/川/川_42.jpg','./refer1/川/川_43.jpg','./refer1/川/川_44.jpg','./refer1/川/川_45.jpg','./refer1/川/川_46.jpg','./refer1/川/川_47.jpg','./refer1/川/川_48.jpg','./refer1/川/川_49.jpg','./refer1/川/川_5.jpg','./refer1/川/川_50.jpg','./refer1/川/川_51.jpg','./refer1/川/川_52.jpg','./refer1/川/川_53.jpg','./refer1/川/川_54.jpg','./refer1/川/川_55.jpg','./refer1/川/川_56.jpg','./refer1/川/川_57.jpg','./refer1/川/川_58.jpg','./refer1/川/川_59.jpg','./refer1/川/川_6.jpg','./refer1/川/川_60.jpg','./refer1/川/川_61.jpg','./refer1/川/川_62.jpg','./refer1/川/川_63.jpg','./refer1/川/川_64.jpg','./refer1/川/川_65.jpg','./refer1/川/川_66.jpg','./refer1/川/川_67.jpg','./refer1/川/川_68.jpg','./refer1/川/川_69.jpg','./refer1/川/川_7.jpg','./refer1/川/川_70.jpg','./refer1/川/川_71.jpg','./refer1/川/川_72.jpg','./refer1/川/川_73.jpg','./refer1/川/川_74.jpg','./refer1/川/川_75.jpg','./refer1/川/川_76.jpg','./refer1/川/川_77.jpg','./refer1/川/川_78.jpg','./refer1/川/川_79.jpg','./refer1/川/川_8.jpg','./refer1/川/川_80.jpg','./refer1/川/川_81.jpg','./refer1/川/川_82.jpg','./refer1/川/川_83.jpg','./refer1/川/川_84.jpg','./refer1/川/川_85.jpg','./refer1/川/川_86.jpg','./refer1/川/川_87.jpg','./refer1/川/川_88.jpg','./refer1/川/川_89.jpg','./refer1/川/川_9.jpg','./refer1/川/川_90.jpg','./refer1/川/川_91.jpg','./refer1/川/川_92.jpg','./refer1/川/川_93.jpg','./refer1/川/川_94.jpg','./refer1/川/川_95.jpg','./refer1/川/川_96.jpg','./refer1/川/川_97.jpg','./refer1/川/川_98.jpg','./refer1/川/川_99.jpg']
# # 读取模板图片，用来读取文件夹中的所有模板图片，输入参数是文件名
# def read_directory(directory_name):
# referImg_list = []
# for filename in os.listdir(directory_name):
# # print(filename) # 仅仅是为了测试
# # img = cv2.imread(directory_name + "/" + filename)
# referImg_list.append(directory_name + "/" + filename)# return referImg_list# referImg_list = read_directory('./refer')# # 测试所用，查看模板长什么样
# # for i in referImg_list:
# # template = cv2.imread(i)
# # template = cv2.GaussianBlur(template, (3, 3), 0)
# # # 灰度处理
# # template = cv2.cvtColor(template, cv2.COLOR_RGB2GRAY)
# # # 自适应阈值处理
# # ret, template = cv2.threshold(template, 0, 255, cv2.THRESH_OTSU)
# # plt_show(template)
# referImg_list
识别车牌第一个中文
# 读取一个车牌字符
img = cv2.imread('./words/test4_1.png')
plt_show0(img)
# 灰度处理，二值化
# 高斯去噪
image = cv2.GaussianBlur(img, (3, 3), 0)
# 灰度处理
gray_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
plt_show(gray_image)
# 计算二值图像黑白点的个数，处理绿牌照问题，让车牌号码始终为白色
# area_white = 0
# area_black = 0
# height, width = gray_image.shape
# print(gray_image.shape)
# for i in range(height):
# for j in range(width):
# if gray_image[i, j] == 255:
# area_white += 1
# else:
# area_black += 1
# if area_white<area_black:
# ret, gray_image = cv2.threshold(gray_image, 0, 255, cv2.THRESH_OTSU | cv2.THRESH_BINARY_INV)
# plt_show(gray_image)
# 自适应阈值处理
ret, image_ = cv2.threshold(gray_image, 0, 255, cv2.THRESH_OTSU)
plt_show(image_)
import numpy as np
best_score = []
for c_word in c_words:score = []for word in c_word:
# print(word)# fromfile()函数读回数据时需要用户指定元素类型，并对数组的形状进行适当的修改template_img=cv2.imdecode(np.fromfile(word,dtype=np.uint8),1)
# template_img = cv2.imread(word)
# print(template_img)
# cv_show('template_img',template_img)
# template_img = np.float32(template_img)
# plt_show0(template_img)
# print(word)template_img = cv2.cvtColor(template_img, cv2.COLOR_RGB2GRAY)ret, template_img = cv2.threshold(template_img, 0, 255, cv2.THRESH_OTSU)height, width = template_img.shapeimage = image_.copy()image = cv2.resize(image, (width, height))result = cv2.matchTemplate(image, template_img, cv2.TM_CCOEFF)score.append(result[0][0])best_score.append(max(score))print(best_score)
# [-459146.12, 1258738.6, 301110.5, 1197172.0, 187095.4, -263099.44, 624999.9, 74035.15, 52876.164, -289379.1, 24036.938, 335899.5, 806250.56, 763998.5, 186862.45, -603025.94, -263213.06, 1445831.5, 323525.0, 558358.44, 388600.25, -93354.36, -11898.9375, 190925.52, -6728.175, -301921.66, 31243.1, -51356.363, 535923.7, 41800.24]
print(max(best_score))
# 1445831.5
print(best_score.index(max(best_score)))
# 17
print(template[34])
# 藏
print(template[34+27])
# 豫
score = []
best_template = None
best_refer = None
for refer in referImg_list:
# refer = refer[0]
# print(refer)template = cv2.imread(refer)template = cv2.GaussianBlur(template, (3, 3), 0)# 灰度处理template = cv2.cvtColor(template, cv2.COLOR_RGB2GRAY)# 自适应阈值处理ret, template = cv2.threshold(template, 0, 255, cv2.THRESH_OTSU)height, width = template.shapeimage = image_.copy()image = cv2.resize(image, (width, height)) # 和模板一致
# plt_show(image)# TM_SQDIFF TM_CCOEFF# TM_SQDIFF：计算平方不同，计算出来的值越小，越相关# TM_CCORR：计算相关性，计算出来的值越大，越相关# TM_CCOEFF：计算相关系数，计算出来的值越大，越相关# TM_SQDIFF_NORMED：计算归一化平方不同，计算出来的值越接近0，越相关# TM_CCORR_NORMED：计算归一化相关性，计算出来的值越接近1，越相关# TM_CCOEFF_NORMED：计算归一化相关系数，计算出来的值越接近1，越相关result = cv2.matchTemplate(image, template, cv2.TM_CCOEFF)
# print(score)if score!=[] and result[0][0]>max(score):best_template = Nonebest_refer = Nonebest_template = templatebest_refer = referscore.append(result[0][0])print(score)
plt_show(best_template)
print(max(score))
print(best_refer)
识别车牌第二字字母
# 读取一个车牌字符
img = cv2.imread('./words/test1_5.png')
plt_show0(img)
# 灰度处理，二值化
# 高斯去噪
image = cv2.GaussianBlur(img, (3, 3), 0)
# 灰度处理
gray_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
plt_show(gray_image)
# 自适应阈值处理
ret, image_ = cv2.threshold(gray_image, 0, 255, cv2.THRESH_OTSU)
plt_show(image_)
# 字母模板列表
c_words = []
for i in range(10,34):c_word = read_directory('./refer1/'+ template[i])c_words.append(c_word)
c_words
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import numpy as np
best_score = []
for c_word in c_words:score = []for word in c_word:# fromfile()函数读回数据时需要用户指定元素类型，并对数组的形状进行适当的修改template_img=cv2.imdecode(np.fromfile(word,dtype=np.uint8),1)template_img = cv2.cvtColor(template_img, cv2.COLOR_RGB2GRAY)ret, template_img = cv2.threshold(template_img, 0, 255, cv2.THRESH_OTSU)height, width = template_img.shapeimage = image_.copy()image = cv2.resize(image, (width, height))result = cv2.matchTemplate(image, template_img, cv2.TM_CCOEFF)score.append(result[0][0])best_score.append(max(score))print(best_score)
# [-66376.36, 3963548.2, 3141550.2, 3175904.0, 2800020.5, 2178612.2, 4130253.8, 2096163.6, 2067158.1, 1239066.5, 1372073.9, 1149516.2, 1333215.4, 1757303.8, 2605298.0, 1990602.6, 4692668.0, 330763.06, 2393131.2, 391668.53, 607340.5, -365706.47, -278828.62, 1392899.8]
print(max(best_score))
# 4692668.0
print(best_score.index(max(best_score)))
# 16
print(template[10])
# A
print(template[10+16])
# S
字母或数字
# 读取一个车牌字符
img = cv2.imread('./words/test1_4.png')
plt_show0(img)
# 灰度处理，二值化
# 高斯去噪
image = cv2.GaussianBlur(img, (3, 3), 0)
# 灰度处理
gray_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
plt_show(gray_image)
# 自适应阈值处理
ret, image_ = cv2.threshold(gray_image, 0, 255, cv2.THRESH_OTSU)
plt_show(image_)
# 字母数字模板列表
c_words = []
for i in range(0,34):c_word = read_directory('./refer1/'+ template[i])c_words.append(c_word)
import numpy as np
best_score = []
for c_word in c_words:score = []for word in c_word:# fromfile()函数读回数据时需要用户指定元素类型，并对数组的形状进行适当的修改template_img=cv2.imdecode(np.fromfile(word,dtype=np.uint8),1)template_img = cv2.cvtColor(template_img, cv2.COLOR_RGB2GRAY)ret, template_img = cv2.threshold(template_img, 0, 255, cv2.THRESH_OTSU)height, width = template_img.shapeimage = image_.copy()image = cv2.resize(image, (width, height))result = cv2.matchTemplate(image, template_img, cv2.TM_CCOEFF)score.append(result[0][0])best_score.append(max(score))print(best_score)
# [2963695.5, 2303256.0, 803304.8, 2365068.5, 434013.8, -83176.92, 1380545.8, 1889097.6, 2604892.5, 1897710.5, -122162.6, 2407313.5, 1802338.0, 263166.38, 1908248.9, 1174134.0, 1311924.5, 674854.2, 1245533.2, 992409.5, 2245806.5, 1212642.0, 1236842.2, 428578.5]
print(max(best_score))
# 2963695.5
print(best_score.index(max(best_score)))
# 0
print(template[0])
# 0
print(template[0+4])
# 4
将识别结果显示出来
# 导入所需模块
import cv2
from matplotlib import pyplot as plt
import os# 显示图片
def cv_show(name,img):cv2.imshow(name,img)cv2.waitKey()cv2.destroyAllWindows()# plt显示彩色图片
def plt_show0(img):b,g,r = cv2.split(img)img = cv2.merge([r, g, b])plt.imshow(img)plt.show()# plt显示灰度图片
def plt_show(img):plt.imshow(img,cmap='gray')plt.show()# 读取一个车牌字符
img = cv2.imread('./image/test1.png')
plt_show0(img)
height,weight = img.shape[0:2]
print(height)
# 500
print(weight)
# 900
list_ = ['豫','A','0','4','S','8','9']
image = img.copy()
cv2.rectangle(image, (int(0.2*weight), int(0.75*height)), (int(weight*0.8), int(height*0.95)), (0, 255, 0), 5)
from PIL import ImageFont# fontpath = './font/simsun.ttc'
# font = ImageFont.truetype(fontpath, 30)cv2.putText(image, "".join(list_), (int(0.2*weight)+30, int(0.75*height)+80), cv2.FONT_HERSHEY_COMPLEX, 3, (0, 255, 0), 12)
plt_show0(image)
车牌倾斜提取
# 导入所需模块
import cv2
from matplotlib import pyplot as plt# 显示图片
def cv_show(name,img):cv2.imshow(name,img)cv2.waitKey()cv2.destroyAllWindows()# plt显示彩色图片
def plt_show0(img):b,g,r = cv2.split(img)img = cv2.merge([r, g, b])plt.imshow(img)plt.show()# plt显示灰度图片
def plt_show(img):plt.imshow(img,cmap='gray')plt.show()
# 加载图片
rawImage = cv2.imread("./image/test3.png")
plt_show0(rawImage)
# 高斯去噪
image = cv2.GaussianBlur(rawImage, (3, 3), 0)
# 预览效果
plt_show0(image)
# 灰度处理
gray_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
plt_show(gray_image)
# sobel算子边缘检测（做了一个y方向的检测）
Sobel_x = cv2.Sobel(gray_image, cv2.CV_16S, 1, 0)
# Sobel_y = cv2.Sobel(image, cv2.CV_16S, 0, 1)
absX = cv2.convertScaleAbs(Sobel_x) # 转回uint8
# absY = cv2.convertScaleAbs(Sobel_y)
# dst = cv2.addWeighted(absX, 0.5, absY, 0.5, 0)
image = absX
plt_show(image)
# 自适应阈值处理
ret, image = cv2.threshold(image, 0, 255, cv2.THRESH_OTSU)
plt_show(image)
# 闭运算,是白色部分练成整体
kernelX = cv2.getStructuringElement(cv2.MORPH_RECT, (14, 5))
print(kernelX)
# [[1 1 1 1 1 1 1 1 1 1 1 1 1 1]
# [1 1 1 1 1 1 1 1 1 1 1 1 1 1]
# [1 1 1 1 1 1 1 1 1 1 1 1 1 1]
# [1 1 1 1 1 1 1 1 1 1 1 1 1 1]
# [1 1 1 1 1 1 1 1 1 1 1 1 1 1]]
image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernelX,iterations = 1)
plt_show(image)
# 去除一些小的白点
kernelX = cv2.getStructuringElement(cv2.MORPH_RECT, (20, 1))
kernelY = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 19))# 膨胀，腐蚀
image = cv2.dilate(image, kernelX)
image = cv2.erode(image, kernelX)
# 腐蚀，膨胀
image = cv2.erode(image, kernelY)
image = cv2.dilate(image, kernelY)plt_show(image)
# 中值滤波去除噪点
image = cv2.medianBlur(image, 15)
plt_show(image)
# 轮廓检测
# cv2.RETR_EXTERNAL表示只检测外轮廓
# cv2.CHAIN_APPROX_SIMPLE压缩水平方向，垂直方向，对角线方向的元素，只保留该方向的终点坐标，例如一个矩形轮廓只需4个点来保存轮廓信息
contours, hierarchy = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# 绘制轮廓
image1 = rawImage.copy()
cv2.drawContours(image1, contours, -1, (0, 255, 0), 5)
plt_show0(image1)
# 筛选出车牌位置的轮廓
# 这里我只做了一个车牌的长宽比在3:1到4：1之间这样一个判断
for index,item in enumerate(contours):# cv2.boundingRect用一个最小的矩形，把找到的形状包起来rect = cv2.boundingRect(item)x = rect[0]y = rect[1]weight = rect[2]height = rect[3]# 440mm×140mmif (weight > (height * 2.5)) and (weight < (height * 4)):print(index)# 1image2 = rawImage.copy()cv2.drawContours(image2, contours, 1, (0, 0, 255), 5)plt_show0(image2)
contours[1]
array([[[153, 156]],[[152, 157]],[[151, 157]],[[148, 160]],[[148, 161]],[[147, 162]],[[147, 164]],[[146, 165]],[[146, 171]],[[145, 172]],[[145, 180]],[[144, 181]],[[144, 195]],[[145, 196]],[[145, 197]],[[148, 200]],[[149, 200]],[[150, 201]],[[153, 201]],[[154, 200]],[[169, 200]],[[170, 201]],[[176, 201]],[[177, 202]],[[179, 202]],[[180, 203]],[[181, 203]],[[182, 204]],[[183, 204]],[[184, 205]],[[185, 205]],[[186, 206]],[[187, 206]],[[189, 208]],[[190, 208]],[[191, 209]],[[192, 209]],[[193, 210]],[[226, 210]],[[227, 211]],[[231, 211]],[[232, 212]],[[233, 212]],[[234, 213]],[[236, 213]],[[237, 214]],[[239, 214]],[[240, 215]],[[243, 215]],[[244, 216]],[[255, 216]],[[256, 217]],[[258, 217]],[[259, 218]],[[261, 218]],[[262, 219]],[[264, 219]],[[265, 220]],[[267, 220]],[[268, 221]],[[278, 221]],[[279, 222]],[[280, 222]],[[281, 223]],[[282, 223]],[[284, 225]],[[285, 225]],[[286, 226]],[[287, 226]],[[288, 227]],[[289, 227]],[[290, 228]],[[291, 228]],[[292, 229]],[[297, 229]],[[298, 228]],[[307, 228]],[[308, 229]],[[312, 229]],[[313, 230]],[[317, 230]],[[318, 231]],[[325, 231]],[[327, 233]],[[328, 233]],[[329, 234]],[[332, 234]],[[336, 230]],[[336, 229]],[[337, 228]],[[337, 226]],[[338, 225]],[[338, 221]],[[339, 220]],[[339, 217]],[[340, 216]],[[340, 211]],[[341, 210]],[[341, 200]],[[340, 199]],[[340, 198]],[[336, 194]],[[335, 194]],[[334, 193]],[[333, 194]],[[313, 194]],[[312, 193]],[[309, 193]],[[305, 189]],[[304, 189]],[[300, 185]],[[298, 185]],[[297, 184]],[[295, 184]],[[294, 183]],[[293, 183]],[[292, 184]],[[288, 184]],[[287, 185]],[[263, 185]],[[262, 184]],[[260, 184]],[[259, 183]],[[257, 183]],[[256, 182]],[[254, 182]],[[253, 181]],[[251, 181]],[[250, 180]],[[244, 180]],[[243, 179]],[[238, 179]],[[237, 178]],[[234, 178]],[[233, 177]],[[230, 177]],[[229, 176]],[[227, 176]],[[226, 175]],[[219, 175]],[[218, 174]],[[204, 174]],[[203, 173]],[[202, 173]],[[199, 170]],[[198, 170]],[[196, 168]],[[195, 168]],[[193, 166]],[[192, 166]],[[191, 165]],[[190, 165]],[[189, 164]],[[188, 164]],[[187, 165]],[[174, 165]],[[173, 164]],[[169, 164]],[[168, 163]],[[166, 163]],[[165, 162]],[[164, 162]],[[163, 161]],[[160, 161]],[[155, 156]]], dtype=int32)
直线拟合找斜率
直线拟合fitline
参数:
InputArray Points: 待拟合的直线的集合，必须是矩阵形式；
distType: 距离类型。fitline为距离最小化函数，拟合直线时，要使输入点到拟合直线的距离和最小化。这里的** 距离**的类型有以下几种：
cv2.DIST_USER : User defined distance
cv2.DIST_L1: distance = |x1-x2| + |y1-y2|
cv2.DIST_L2: 欧式距离，此时与最小二乘法相同
cv2.DIST_C:distance = max(|x1-x2|,|y1-y2|)
cv2.DIST_L12:L1-L2 metric: distance = 2(sqrt(1+x*x/2) - 1))
cv2.DIST_FAIR:distance = c^2(|x|/c-log(1+|x|/c)), c = 1.3998
cv2.DIST_WELSCH: distance = c2/2(1-exp(-(x/c)2)), c = 2.9846
cv2.DIST_HUBER:distance = |x|<c ? x^2/2 : c(|x|-c/2), c=1.345
param： 距离参数，跟所选的距离类型有关，值可以设置为0。
reps, aeps： 第5/6个参数用于表示拟合直线所需要的径向和角度精度，通常情况下两个值均被设定为1e-2.
output ：
- 对于二维直线，输出output为4维，前两维代表拟合出的直线的方向，后两位代表直线上的一点。（即通常说的点斜式直线）
- 其中(vx, vy) 是直线的方向向量，(x, y) 是直线上的一个点。
- 斜率k = vy / vx
- 截距b = y - k * x
cnt = contours[1]
image3 = rawImage.copy()h, w = image3.shape[:2]
[vx, vy, x, y] = cv2.fitLine(cnt, cv2.DIST_L2, 0, 0.01, 0.01)
print([vx, vy, x, y])
# [array([0.9724686], dtype=float32), array([0.23303394], dtype=float32), array([241.13939], dtype=float32), array([196.93939], dtype=float32)]k = vy/vx
b = y-k*xprint(k,b)
# [0.23963133] [139.15485]lefty = b
righty = k*w+bimg = cv2.line(image3, (w, righty), (0, lefty), (0, 255, 0), 2)plt_show0(img)print((w, righty))
# (500, array([258.97052], dtype=float32))
print((0, lefty))
# (0, array([139.15485], dtype=float32))
import math
a = math.atan(k)
a
# 0.23519635569664088
a = math.degrees(a)
a
# 13.475758538275219
image4 = rawImage.copy()
# 图像旋转
h,w = image1.shape[:2]
print(h,w)
#第一个参数旋转中心，第二个参数旋转角度，第三个参数：缩放比例
M = cv2.getRotationMatrix2D((w/2,h/2),a,0.8)
#第三个参数：变换后的图像大小
dst = cv2.warpAffine(image4,M,(int(w*1.1),int(h*1.1)))
plt_show0(dst)
# 300 500
# 高斯去噪
image = cv2.GaussianBlur(dst, (3, 3), 0)
# 预览效果
plt_show0(image)
# 灰度处理
gray_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
plt_show(gray_image)
# sobel算子边缘检测（做了一个y方向的检测）
Sobel_x = cv2.Sobel(gray_image, cv2.CV_16S, 1, 0)
# Sobel_y = cv2.Sobel(image, cv2.CV_16S, 0, 1)
absX = cv2.convertScaleAbs(Sobel_x) # 转回uint8
# absY = cv2.convertScaleAbs(Sobel_y)
# dst = cv2.addWeighted(absX, 0.5, absY, 0.5, 0)
image = absX
plt_show(image)
# 自适应阈值处理
ret, image = cv2.threshold(image, 0, 255, cv2.THRESH_OTSU)
plt_show(image)
# 闭运算,是白色部分练成整体
kernelX = cv2.getStructuringElement(cv2.MORPH_RECT, (14, 5))
print(kernelX)
image = cv2.morphologyEx(image, cv2.MORPH_CLOSE, kernelX,iterations = 1)
plt_show(image)
# [[1 1 1 1 1 1 1 1 1 1 1 1 1 1]
# [1 1 1 1 1 1 1 1 1 1 1 1 1 1]
# [1 1 1 1 1 1 1 1 1 1 1 1 1 1]
# [1 1 1 1 1 1 1 1 1 1 1 1 1 1]
# [1 1 1 1 1 1 1 1 1 1 1 1 1 1]]
# 去除一些小的白点
kernelX = cv2.getStructuringElement(cv2.MORPH_RECT, (20, 1))
kernelY = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 19))# 膨胀，腐蚀
image = cv2.dilate(image, kernelX)
image = cv2.erode(image, kernelX)
# 腐蚀，膨胀
image = cv2.erode(image, kernelY)
image = cv2.dilate(image, kernelY)plt_show(image)
# 中值滤波去除噪点
image = cv2.medianBlur(image, 15)
plt_show(image)
# 轮廓检测
# cv2.RETR_EXTERNAL表示只检测外轮廓
# cv2.CHAIN_APPROX_SIMPLE压缩水平方向，垂直方向，对角线方向的元素，只保留该方向的终点坐标，例如一个矩形轮廓只需4个点来保存轮廓信息
contours, hierarchy = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# 绘制轮廓
image1 = dst.copy()
cv2.drawContours(image1, contours, -1, (0, 255, 0), 5)
plt_show0(image1)
c = None
i = None
# 筛选出车牌位置的轮廓
# 这里我只做了一个车牌的长宽比在3:1到4：1之间这样一个判断
for item in contours:# cv2.boundingRect用一个最小的矩形，把找到的形状包起来rect = cv2.boundingRect(item)x = rect[0]y = rect[1]weight = rect[2]height = rect[3]# 440mm×140mmif (weight > (height * 2.5)) and (weight < (height * 5)):c=recti = itemimage = dst[y:y + height, x:x + weight]
# cv_show('image',image)# 图像保存plt_show0(image)cv2.imwrite('./car_license/test3.png', image)
字符分割方法
# 导入所需模块
import cv2
from matplotlib import pyplot as plt# 显示图片
def cv_show(name,img):cv2.imshow(name,img)cv2.waitKey()cv2.destroyAllWindows()# plt显示彩色图片
def plt_show0(img):b,g,r = cv2.split(img)img = cv2.merge([r, g, b])plt.imshow(img)plt.show()# plt显示灰度图片
def plt_show(img):plt.imshow(img,cmap='gray')plt.show()
# 加载图片
rawImage = cv2.imread("./car_license/test4.png")
plt_show0(rawImage)
# 灰度处理
image = rawImage.copy()
gray_image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)
plt_show(gray_image)
字符水平方向的切割
目的：去除车牌边框和铆钉的干扰
# 自适应阈值处理(二值化)
ret, image = cv2.threshold(gray_image, 0, 255, cv2.THRESH_OTSU)
plt_show(image)
image.shape # 47行，170列
rows = image.shape[0]
cols = image.shape[1]
print(rows,cols)
# 33 98
# 二值统计,统计没每一行的黑值（0）的个数
hd = []
for row in range(rows):res = 0for col in range(cols):if image[row][col] == 0:res = res+1hd.append(res)
len(hd)
max(hd)
# 62
# 画出柱状图
y = [y for y in range(rows)]
x = hd
plt.barh(y,x,color='black',height=1)
# 设置x，y轴标签
plt.xlabel('0_number')
plt.ylabel('row')
# 设置刻度
plt.xticks([x for x in range(0,130,5)])
plt.yticks([y for y in range(0,rows,1)])plt.show()
中间较为密集的地方就是车牌有字符的地方,从而很好的去除了牌边框及铆钉
从图中可以明显看出车牌字符区域的投影值和车牌边框及铆钉区域的投影值之间明显有一个波谷，找到此处波谷，就可以得到车牌的字符区域，去除车牌边框及铆钉。
x = range(int(rows/2),2,-1)
x = [*x]
x
# [16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3]
# 定义一个算法,找到波谷,定位车牌字符的行数区域
# 我的思路;对于一个车牌,中间位置肯定是有均匀的黑色点的,所以我将图片垂直分为两部分,找波谷
mean = sum(hd[0:int(rows/2)])/(int(rows/2)+1)
mean
region = []
for i in range(int(rows/2),2,-1): # 0,1行肯定是边框,直接不考虑,直接从第二行开始if hd[i]<mean:region.append(i)break
for i in range(int(rows/2),rows): # 0,1行肯定是边框,直接不考虑,直接从第二行开始if hd[i]<mean:region.append(i)break
region
# [8, 27]
image1 = image[region[0]:region[1],:] # 使用行区间
plt_show(image1)
字符垂直方向的切割
image11 = image1.copy()
image11.shape # 47行，170列
rows = image11.shape[0]
cols = image11.shape[1]
print(rows,cols)
# 19 98
cols
# 98
# 二值统计,统计没每一列的黑值（0）的个数
hd1 = []
for col in range(cols):res = 0for row in range(rows):if image11[row][col] == 0:res = res+1hd1.append(res)
len(hd1)
max(hd1)
# 18
# 画出柱状图
y = hd1 # 点个数
x = [x for x in range(cols)] # 列数
plt.bar(x,y,color='black',width=1)
# 设置x，y轴标签
plt.xlabel('col')
plt.ylabel('0_number')
# 设置刻度
plt.xticks([x for x in range(0,cols,10)])
plt.yticks([y for y in range(0,max(hd1)+5,5)])plt.show()
mean = sum(hd1)/len(hd1)
mean
# 6.448979591836735
# 简单的筛选
for i in range(cols):if hd1[i] < mean/4:hd1[i] = 0
# 画出柱状图
y = hd1 # 点个数
x = [x for x in range(cols)] # 列数
plt.bar(x,y,color='black',width=1)
# 设置x，y轴标签
plt.xlabel('col')
plt.ylabel('0_number')
# 设置刻度
plt.xticks([x for x in range(0,cols,10)])
plt.yticks([y for y in range(0,max(hd1)+5,5)])plt.show()
从直方图中可以看到很多波谷,这些就是字符分割区域的黑色点的个数等于0,我们就可以通过这些0点进行分割,过滤掉这些不需要的部分部分
# 找所有不为0的区间(列数)
region1 = []
reg = []
for i in range(cols-1):if hd1[i]==0 and hd1[i+1] != 0:reg.append(i)if hd1[i]!=0 and hd1[i+1] == 0:reg.append(i+2)if len(reg) == 2:if (reg[1]-reg[0])>5: # 限定区间长度要大于5(可以更大),过滤掉不需要的点region1.append(reg) reg = []else:reg = []
region1
# [[4, 15], [17, 27], [33, 45], [45, 56], [57, 69], [69, 81], [81, 93]]
# 测试
image2 = image1[:,region1[0][0]:region1[0][1]]
plt_show(image2)
# 为了使字符之间还是存在空格,定义一个2像素白色的区域
import numpy as np
white = []
for i in range(rows*2):white.append(255)
white = np.array(white)
white = white.reshape(rows,2)
white.shape
# (19, 2)
# 遍历所有区域,保存字符图片到列表
p = []
for r in region1:r = image1[:,r[0]:r[1]]plt_show(r)p.append(r)p.append(white)
# 将字符图片列表拼接为一张图
image2 = np.hstack(p)
plt_show(image2)
# 将分割好的字符图片保存到文件夹
print(region)
# [8, 27]
print(region1)
# [[4, 15], [17, 27], [33, 45], [45, 56], [57, 69], [69, 81], [81, 93]]
plt_show0(rawImage)
v_image = rawImage[region[0]:region[1],:]
plt_show0(v_image)
i = 1
for reg in region1:h_image = v_image[:,reg[0]:reg[1]]plt_show0(h_image)cv2.imwrite('./words/test4_'+str(i)+'.png', h_image)i = i+1
word_images = []
for i in range(1,8):word = cv2.imread('./words/test4_'+str(i)+'.png',0)ret, word = cv2.threshold(word, 0, 255, cv2.THRESH_OTSU | cv2.THRESH_BINARY_INV)word_images.append(word)
word_images
plt.imshow(word_images[0],cmap='gray')
for i,j in enumerate(word_images): plt.subplot(1,8,i+1)plt.imshow(word_images[i],cmap='gray')
plt.show()
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