【深度學習系列】車牌識別實踐（一）

　　小伙伴們，終于到了實戰部分了！今天給大家帶來的項目是用PaddlePaddle進行車牌識別。車牌識別其實屬于比較常見的圖像識別的項目了，目前也屬于比較成熟的應用，大多數老牌廠家能做到準確率99%+。傳統的方法需要對圖像進行多次預處理再用機器學習的分類算法進行分類識別，然而深度學習發展起來以后，我們可以通過用CNN來進行端對端的車牌識別。任何模型的訓練都離不開數據，在車牌識別中，除了晚上能下載到的一些包含車牌的數據是不夠的，本篇文章的主要目的是教大家如何批量生成車牌。

生成車牌數據

　　1.定義車牌數據所需字符

　　車牌中包括省份簡稱、大寫英文字母和數字，我們首先定義需要的字符和字典，方便后面使用

 1 index = {"京": 0, "滬": 1, "津": 2, "渝": 3, "冀": 4, "晉": 5, "蒙": 6, "遼": 7, "吉": 8, "黑": 9, "蘇": 10, "浙": 11, "皖": 12,
 2          "閩": 13, "贛": 14, "魯": 15, "豫": 16, "鄂": 17, "湘": 18, "粵": 19, "桂": 20, "瓊": 21, "川": 22, "貴": 23, "云": 24,
 3          "藏": 25, "陜": 26, "甘": 27, "青": 28, "寧": 29, "新": 30, "0": 31, "1": 32, "2": 33, "3": 34, "4": 35, "5": 36,
 4          "6": 37, "7": 38, "8": 39, "9": 40, "A": 41, "B": 42, "C": 43, "D": 44, "E": 45, "F": 46, "G": 47, "H": 48,
 5          "J": 49, "K": 50, "L": 51, "M": 52, "N": 53, "P": 54, "Q": 55, "R": 56, "S": 57, "T": 58, "U": 59, "V": 60,
 6          "W": 61, "X": 62, "Y": 63, "Z": 64};
 7 
 8 chars = ["京", "滬", "津", "渝", "冀", "晉", "蒙", "遼", "吉", "黑", "蘇", "浙", "皖", "閩", "贛", "魯", "豫", "鄂", "湘", "粵", "桂",
 9              "瓊", "川", "貴", "云", "藏", "陜", "甘", "青", "寧", "新", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "A",
10              "B", "C", "D", "E", "F", "G", "H", "J", "K", "L", "M", "N", "P", "Q", "R", "S", "T", "U", "V", "W", "X",
11              "Y", "Z"
12              ];

　　2.生成中英文字符

 1 def GenCh(f,val):
 2     """
 3     生成中文字符
 4     """
 5     img=Image.new("RGB", (45,70),(255,255,255))
 6     draw = ImageDraw.Draw(img)
 7     draw.text((0, 3),val,(0,0,0),font=f)
 8     img =  img.resize((23,70))
 9     A = np.array(img)
10     return A
11 
12 def GenCh1(f,val):
13     """
14     生成英文字符
15     """
16     img=Image.new("RGB", (23,70),(255,255,255))
17     draw = ImageDraw.Draw(img)
18     draw.text((0, 2),val.decode('utf-8'),(0,0,0),font=f)
19     A = np.array(img)
20     return A

　　3.對數據添加各種噪音和畸變，模糊處理

 1 def AddSmudginess(img, Smu):
 2     rows = r(Smu.shape[0] - 50)
 3     cols = r(Smu.shape[1] - 50)
 4     adder = Smu[rows:rows + 50, cols:cols + 50];
 5     adder = cv2.resize(adder, (50, 50));
 6     #adder = cv2.bitwise_not(adder)
 7     img = cv2.resize(img,(50,50))
 8     img = cv2.bitwise_not(img)
 9     img = cv2.bitwise_and(adder, img)
10     img = cv2.bitwise_not(img)
11     return img
12 
13 
14 def rot(img,angel,shape,max_angel):
15     """
16         添加放射畸變
17         img 輸入圖像
18         factor 畸變的參數
19         size 為圖片的目標尺寸
20     """
21     size_o = [shape[1],shape[0]]
22     size = (shape[1]+ int(shape[0]*cos((float(max_angel )/180) * 3.14)),shape[0])
23     interval = abs( int( sin((float(angel) /180) * 3.14)* shape[0]));
24     pts1 = np.float32([[0,0],[0,size_o[1]],[size_o[0],0],[size_o[0],size_o[1]]])
25     if(angel>0):
26         pts2 = np.float32([[interval,0],[0,size[1]  ],[size[0],0  ],[size[0]-interval,size_o[1]]])
27     else:
28         pts2 = np.float32([[0,0],[interval,size[1]  ],[size[0]-interval,0  ],[size[0],size_o[1]]])
29     M  = cv2.getPerspectiveTransform(pts1,pts2);
30     dst = cv2.warpPerspective(img,M,size);
31     return dst
32 
33 
34 def rotRandrom(img, factor, size):
35     """
36     添加透視畸變
37     """
38     shape = size;
39     pts1 = np.float32([[0, 0], [0, shape[0]], [shape[1], 0], [shape[1], shape[0]]])
40     pts2 = np.float32([[r(factor), r(factor)], [ r(factor), shape[0] - r(factor)], [shape[1] - r(factor),  r(factor)],
41                        [shape[1] - r(factor), shape[0] - r(factor)]])
42     M = cv2.getPerspectiveTransform(pts1, pts2);
43     dst = cv2.warpPerspective(img, M, size);
44     return dst
45 
46 def tfactor(img):
47     """
48     添加飽和度光照的噪聲
49     """
50     hsv = cv2.cvtColor(img,cv2.COLOR_BGR2HSV);
51     hsv[:,:,0] = hsv[:,:,0]*(0.8+ np.random.random()*0.2);
52     hsv[:,:,1] = hsv[:,:,1]*(0.3+ np.random.random()*0.7);
53     hsv[:,:,2] = hsv[:,:,2]*(0.2+ np.random.random()*0.8);
54 
55     img = cv2.cvtColor(hsv,cv2.COLOR_HSV2BGR);
56     return img
57 
58 def random_envirment(img,data_set):
59     """
60     添加自然環境的噪聲
61     """
62     index=r(len(data_set))
63     env = cv2.imread(data_set[index])
64     env = cv2.resize(env,(img.shape[1],img.shape[0]))
65     bak = (img==0);
66     bak = bak.astype(np.uint8)*255;
67     inv = cv2.bitwise_and(bak,env)
68     img = cv2.bitwise_or(inv,img)
69     return img
70 
71 def AddGauss(img, level):
72     """
73     添加高斯模糊
74     """
75     return cv2.blur(img, (level * 2 + 1, level * 2 + 1));
76 
77 def r(val):
78     return int(np.random.random() * val)
79 
80 def AddNoiseSingleChannel(single):
81     """
82     添加高斯噪聲
83     """
84     diff = 255-single.max();
85     noise = np.random.normal(0,1+r(6),single.shape);
86     noise = (noise - noise.min())/(noise.max()-noise.min())
87     noise= diff*noise;
88     noise= noise.astype(np.uint8)
89     dst = single + noise
90     return dst
91 
92 def addNoise(img,sdev = 0.5,avg=10):
93     img[:,:,0] =  AddNoiseSingleChannel(img[:,:,0]);
94     img[:,:,1] =  AddNoiseSingleChannel(img[:,:,1]);
95     img[:,:,2] =  AddNoiseSingleChannel(img[:,:,2]);
96     return img

　　4.加入背景圖片，生成車牌字符串list和label，并存為圖片格式，批量生成。

 1 class GenPlate:
 2 
 3     def __init__(self,fontCh,fontEng,NoPlates):
 4         self.fontC =  ImageFont.truetype(fontCh,43,0);
 5         self.fontE =  ImageFont.truetype(fontEng,60,0);
 6         self.img=np.array(Image.new("RGB", (226,70),(255,255,255)))
 7         self.bg  = cv2.resize(cv2.imread("./images/template.bmp"),(226,70));
 8         self.smu = cv2.imread("./images/smu2.jpg");
 9         self.noplates_path = [];
10         for parent,parent_folder,filenames in os.walk(NoPlates):
11             for filename in filenames:
12                 path = parent+"/"+filename;
13                 self.noplates_path.append(path);
14 
15 
16     def draw(self,val):
17         offset= 2 ;
18         self.img[0:70,offset+8:offset+8+23]= GenCh(self.fontC,val[0]);
19         self.img[0:70,offset+8+23+6:offset+8+23+6+23]= GenCh1(self.fontE,val[1]);
20         for i in range(5):
21             base = offset+8+23+6+23+17 +i*23 + i*6 ;
22             self.img[0:70, base  : base+23]= GenCh1(self.fontE,val[i+2]);
23         return self.img
24     
25     def generate(self,text):
26         if len(text) == 9:
27             fg = self.draw(text.decode(encoding="utf-8"));
28             fg = cv2.bitwise_not(fg);
29             com = cv2.bitwise_or(fg,self.bg);
30             com = rot(com,r(60)-30,com.shape,30);
31             com = rotRandrom(com,10,(com.shape[1],com.shape[0]));
32             com = tfactor(com)
33             com = random_envirment(com,self.noplates_path);
34             com = AddGauss(com, 1+r(4));
35             com = addNoise(com);
36             return com
37 
38     def genPlateString(self,pos,val):
39         '''
40     生成車牌String,存為圖片
41         生成車牌list,存為label
42         '''
43         plateStr = "";
44         plateList=[]
45         box = [0,0,0,0,0,0,0];
46         if(pos!=-1):
47             box[pos]=1;
48         for unit,cpos in zip(box,range(len(box))):
49             if unit == 1:
50                 plateStr += val
51                 #print plateStr
52                 plateList.append(val)
53             else:
54                 if cpos == 0:
55                     plateStr += chars[r(31)]
56                     plateList.append(plateStr)
57                 elif cpos == 1:
58                     plateStr += chars[41+r(24)]
59                     plateList.append(plateStr)
60                 else:
61                     plateStr += chars[31 + r(34)]
62                     plateList.append(plateStr)
63         plate = [plateList[0]]
64         b = [plateList[i][-1] for i in range(len(plateList))]
65         plate.extend(b[1:7])
66         return plateStr,plate
67 
68     # 將生成的車牌圖片寫入文件夾，對應的label寫入label.txt
69     def genBatch(self, batchSize,pos,charRange, outputPath,size):
70         if (not os.path.exists(outputPath)):
71             os.mkdir(outputPath)
72     outfile = open('label.txt','w')
73         for i in xrange(batchSize):
74                 plateStr,plate = G.genPlateString(-1,-1)
75                 print plateStr,plate
76         img =  G.generate(plateStr);
77                 img = cv2.resize(img,size);
78                 cv2.imwrite(outputPath + "/" + str(i).zfill(2) + ".jpg", img);
79         outfile.write(str(plate)+"\n")
80 G = GenPlate("./font/platech.ttf",'./font/platechar.ttf',"./NoPlates")

　　完整代碼：

  1 #coding=utf-8
  2 """ 
  3    genPlate.py:生成隨機車牌
  4 """
  5 
  6 __author__ = "Huxiaoman"
  7 __copyright__ = "Copyright (c) 2017 "
  8 
  9 import PIL
 10 from PIL import ImageFont
 11 from PIL import Image
 12 from PIL import ImageDraw
 13 import cv2;
 14 import numpy as np;
 15 import os;
 16 from math import *
 17 import sys
 18 
 19 
 20 index = {"京": 0, "滬": 1, "津": 2, "渝": 3, "冀": 4, "晉": 5, "蒙": 6, "遼": 7, "吉": 8, "黑": 9, "蘇": 10, "浙": 11, "皖": 12,
 21          "閩": 13, "贛": 14, "魯": 15, "豫": 16, "鄂": 17, "湘": 18, "粵": 19, "桂": 20, "瓊": 21, "川": 22, "貴": 23, "云": 24,
 22          "藏": 25, "陜": 26, "甘": 27, "青": 28, "寧": 29, "新": 30, "0": 31, "1": 32, "2": 33, "3": 34, "4": 35, "5": 36,
 23          "6": 37, "7": 38, "8": 39, "9": 40, "A": 41, "B": 42, "C": 43, "D": 44, "E": 45, "F": 46, "G": 47, "H": 48,
 24          "J": 49, "K": 50, "L": 51, "M": 52, "N": 53, "P": 54, "Q": 55, "R": 56, "S": 57, "T": 58, "U": 59, "V": 60,
 25          "W": 61, "X": 62, "Y": 63, "Z": 64};
 26 
 27 chars = ["京", "滬", "津", "渝", "冀", "晉", "蒙", "遼", "吉", "黑", "蘇", "浙", "皖", "閩", "贛", "魯", "豫", "鄂", "湘", "粵", "桂",
 28              "瓊", "川", "貴", "云", "藏", "陜", "甘", "青", "寧", "新", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "A",
 29              "B", "C", "D", "E", "F", "G", "H", "J", "K", "L", "M", "N", "P", "Q", "R", "S", "T", "U", "V", "W", "X",
 30              "Y", "Z"
 31              ];
 32 
 33 def AddSmudginess(img, Smu):
 34     rows = r(Smu.shape[0] - 50)
 35     cols = r(Smu.shape[1] - 50)
 36     adder = Smu[rows:rows + 50, cols:cols + 50];
 37     adder = cv2.resize(adder, (50, 50));
 38     #adder = cv2.bitwise_not(adder)
 39     img = cv2.resize(img,(50,50))
 40     img = cv2.bitwise_not(img)
 41     img = cv2.bitwise_and(adder, img)
 42     img = cv2.bitwise_not(img)
 43     return img
 44 
 45 def rot(img,angel,shape,max_angel):
 46     """ 
 47         添加放射畸變
 48         img 輸入圖像
 49         factor 畸變的參數
 50         size 為圖片的目標尺寸
 51     """
 52     size_o = [shape[1],shape[0]]
 53     size = (shape[1]+ int(shape[0]*cos((float(max_angel )/180) * 3.14)),shape[0])
 54     interval = abs( int( sin((float(angel) /180) * 3.14)* shape[0]));
 55     pts1 = np.float32([[0,0],[0,size_o[1]],[size_o[0],0],[size_o[0],size_o[1]]])
 56     if(angel>0):
 57         pts2 = np.float32([[interval,0],[0,size[1]  ],[size[0],0  ],[size[0]-interval,size_o[1]]])
 58     else:
 59         pts2 = np.float32([[0,0],[interval,size[1]  ],[size[0]-interval,0  ],[size[0],size_o[1]]])
 60     M  = cv2.getPerspectiveTransform(pts1,pts2);
 61     dst = cv2.warpPerspective(img,M,size);
 62     return dst
 63 
 64 def rotRandrom(img, factor, size):
 65     """
 66     添加透視畸變
 67     """
 68     shape = size;
 69     pts1 = np.float32([[0, 0], [0, shape[0]], [shape[1], 0], [shape[1], shape[0]]])
 70     pts2 = np.float32([[r(factor), r(factor)], [ r(factor), shape[0] - r(factor)], [shape[1] - r(factor),  r(factor)],
 71                        [shape[1] - r(factor), shape[0] - r(factor)]])
 72     M = cv2.getPerspectiveTransform(pts1, pts2);
 73     dst = cv2.warpPerspective(img, M, size);
 74     return dst
 75 
 76 def tfactor(img):
 77     """
 78     添加飽和度光照的噪聲
 79     """
 80     hsv = cv2.cvtColor(img,cv2.COLOR_BGR2HSV);
 81     hsv[:,:,0] = hsv[:,:,0]*(0.8+ np.random.random()*0.2);
 82     hsv[:,:,1] = hsv[:,:,1]*(0.3+ np.random.random()*0.7);
 83     hsv[:,:,2] = hsv[:,:,2]*(0.2+ np.random.random()*0.8);
 84 
 85     img = cv2.cvtColor(hsv,cv2.COLOR_HSV2BGR);
 86     return img
 87 
 88 def random_envirment(img,data_set):
 89     """
 90     添加自然環境的噪聲    
 91     """
 92     index=r(len(data_set))
 93     env = cv2.imread(data_set[index])
 94     env = cv2.resize(env,(img.shape[1],img.shape[0]))
 95     bak = (img==0);
 96     bak = bak.astype(np.uint8)*255;
 97     inv = cv2.bitwise_and(bak,env)
 98     img = cv2.bitwise_or(inv,img)
 99     return img
100 
101 def GenCh(f,val):
102     """
103     生成中文字符
104     """
105     img=Image.new("RGB", (45,70),(255,255,255))
106     draw = ImageDraw.Draw(img)
107     draw.text((0, 3),val,(0,0,0),font=f)
108     img =  img.resize((23,70))
109     A = np.array(img)
110     return A
111 
112 def GenCh1(f,val):
113     """
114     生成英文字符
115     """
116     img=Image.new("RGB", (23,70),(255,255,255))
117     draw = ImageDraw.Draw(img)
118     draw.text((0, 2),val.decode('utf-8'),(0,0,0),font=f)
119     A = np.array(img)
120     return A
121 
122 def AddGauss(img, level):
123     """
124     添加高斯模糊
125     """ 
126     return cv2.blur(img, (level * 2 + 1, level * 2 + 1));
127 
128 def r(val):
129     return int(np.random.random() * val)
130 
131 def AddNoiseSingleChannel(single):
132     """
133     添加高斯噪聲
134     """
135     diff = 255-single.max();
136     noise = np.random.normal(0,1+r(6),single.shape);
137     noise = (noise - noise.min())/(noise.max()-noise.min())
138     noise= diff*noise;
139     noise= noise.astype(np.uint8)
140     dst = single + noise
141     return dst
142 
143 def addNoise(img,sdev = 0.5,avg=10):
144     img[:,:,0] =  AddNoiseSingleChannel(img[:,:,0]);
145     img[:,:,1] =  AddNoiseSingleChannel(img[:,:,1]);
146     img[:,:,2] =  AddNoiseSingleChannel(img[:,:,2]);
147     return img
148 
149 
150 class GenPlate:
151 
152     def __init__(self,fontCh,fontEng,NoPlates):
153         self.fontC =  ImageFont.truetype(fontCh,43,0);
154         self.fontE =  ImageFont.truetype(fontEng,60,0);
155         self.img=np.array(Image.new("RGB", (226,70),(255,255,255)))
156         self.bg  = cv2.resize(cv2.imread("./images/template.bmp"),(226,70));
157         self.smu = cv2.imread("./images/smu2.jpg");
158         self.noplates_path = [];
159         for parent,parent_folder,filenames in os.walk(NoPlates):
160             for filename in filenames:
161                 path = parent+"/"+filename;
162                 self.noplates_path.append(path);
163 
164 
165     def draw(self,val):
166         offset= 2 ;
167         self.img[0:70,offset+8:offset+8+23]= GenCh(self.fontC,val[0]);
168         self.img[0:70,offset+8+23+6:offset+8+23+6+23]= GenCh1(self.fontE,val[1]);
169         for i in range(5):
170             base = offset+8+23+6+23+17 +i*23 + i*6 ;
171             self.img[0:70, base  : base+23]= GenCh1(self.fontE,val[i+2]);
172         return self.img
173     
174     def generate(self,text):
175         if len(text) == 9:
176             fg = self.draw(text.decode(encoding="utf-8"));
177             fg = cv2.bitwise_not(fg);
178             com = cv2.bitwise_or(fg,self.bg);
179             com = rot(com,r(60)-30,com.shape,30);
180             com = rotRandrom(com,10,(com.shape[1],com.shape[0]));
181             com = tfactor(com)
182             com = random_envirment(com,self.noplates_path);
183             com = AddGauss(com, 1+r(4));
184             com = addNoise(com);
185             return com
186 
187     def genPlateString(self,pos,val):
188         '''
189     生成車牌String,存為圖片
190         生成車牌list,存為label
191         '''
192         plateStr = "";
193         plateList=[]
194         box = [0,0,0,0,0,0,0];
195         if(pos!=-1):
196             box[pos]=1;
197         for unit,cpos in zip(box,range(len(box))):
198             if unit == 1:
199                 plateStr += val
200                 #print plateStr
201                 plateList.append(val)
202             else:
203                 if cpos == 0:
204                     plateStr += chars[r(31)]
205                     plateList.append(plateStr)
206                 elif cpos == 1:
207                     plateStr += chars[41+r(24)]
208                     plateList.append(plateStr)
209                 else:
210                     plateStr += chars[31 + r(34)]
211                     plateList.append(plateStr)
212         plate = [plateList[0]]
213         b = [plateList[i][-1] for i in range(len(plateList))]
214         plate.extend(b[1:7])
215         return plateStr,plate
216 
217     # 將生成的車牌圖片寫入文件夾，對應的label寫入label.txt
218     def genBatch(self, batchSize,pos,charRange, outputPath,size):
219         if (not os.path.exists(outputPath)):
220             os.mkdir(outputPath)
221     outfile = open('label.txt','w')
222         for i in xrange(batchSize):
223                 plateStr,plate = G.genPlateString(-1,-1)
224                 print plateStr,plate
225         img =  G.generate(plateStr);
226                 img = cv2.resize(img,size);
227                 cv2.imwrite(outputPath + "/" + str(i).zfill(2) + ".jpg", img);
228         outfile.write(str(plate)+"\n")
229 G = GenPlate("./font/platech.ttf",'./font/platechar.ttf',"./NoPlates")
230 #G.genBatch(100,2,range(31,65),"./plate_100",(272,72))
231 
232 if __name__=='__main__':
233     G.genBatch(int(sys.argv[1]),2,range(31,65),sys.argv[2],(272,72))

View Code

　　運行時加生成數量和保存路徑即可，如：

1 python genPlate.py 100 ./plate_100

　　顯示結果：

　　上圖即為生成的車牌數據，有清晰的有模糊的，有比較方正的，也有一些比較傾斜，生成完大量的車牌樣張后就可以進行車牌識別了。下一小節將會講如何用端對端的CNN進行車牌識別，不需要通過傳統的ocr先對字符進行分割處理后再識別。

參考資料：

1.原來做的車牌識別項目：https://github.com/huxiaoman7/mxnet-cnn-plate-recognition

posted @ 2018-02-26 10:42 Charlotte77 閱讀(27238) 評論(18) 收藏舉報

刷新頁面返回頂部

Charlotte77

數學系的數據挖掘民工(公眾號:CharlotteDataMining，深度學習技術交流qq群:339120614)最新深度學習免費學習視頻請移步我的B站：https://www.bilibili.com/video/av75414647

【深度學習系列】車牌識別實踐（一）

作者：Charlotte77

出處：http://www.rzrgm.cn/charlotte77/

本文以學習、研究和分享為主，如需轉載，請聯系本人，標明作者和出處，非商業用途！

關注【Charlotte數據挖掘】回復 '資料' 獲取深度學習優質資料

公告