【深度學(xué)習(xí)系列】用PaddlePaddle進(jìn)行車牌識(shí)別（一）
作者：Charlotte77
小伙伴們，終于到了實(shí)戰(zhàn)部分了！今天給大家?guī)淼捻?xiàng)目是用PaddlePaddle進(jìn)行車牌識(shí)別。車牌識(shí)別其實(shí)屬于比較常見的圖像識(shí)別的項(xiàng)目了，目前也屬于比較成熟的應(yīng)用，大多數(shù)老牌廠家能做到準(zhǔn)確率99%+。
小伙伴們，終于到了實(shí)戰(zhàn)部分了！今天給大家?guī)淼捻?xiàng)目是用PaddlePaddle進(jìn)行車牌識(shí)別。車牌識(shí)別其實(shí)屬于比較常見的圖像識(shí)別的項(xiàng)目了，目前也屬于比較成熟的應(yīng)用，大多數(shù)老牌廠家能做到準(zhǔn)確率99%+。傳統(tǒng)的方法需要對(duì)圖像進(jìn)行多次預(yù)處理再用機(jī)器學(xué)習(xí)的分類算法進(jìn)行分類識(shí)別，然而深度學(xué)習(xí)發(fā)展起來以后，我們可以通過用CNN來進(jìn)行端對(duì)端的車牌識(shí)別。任何模型的訓(xùn)練都離不開數(shù)據(jù)，在車牌識(shí)別中，除了晚上能下載到的一些包含車牌的數(shù)據(jù)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(shù)據(jù)
1.定義車牌數(shù)據(jù)所需字符
車牌中包括省份簡(jiǎn)稱、大寫英文字母和數(shù)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(duì)數(shù)據(jù)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(shù) 19 size 為圖片的目標(biāo)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(huán)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(duì)應(yīng)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：
View Code
運(yùn)行時(shí)加生成數(shù)量和保存路徑即可，如：
1 python genPlate.py 100 ./plate_100
顯示結(jié)果：
上圖即為生成的車牌數(shù)據(jù)，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(jìn)行車牌識(shí)別了。下一小節(jié)將會(huì)講如何用端對(duì)端的CNN進(jìn)行車牌識(shí)別，不需要通過傳統(tǒng)的ocr先對(duì)字符進(jìn)行分割處理后再識(shí)別。
參考資料：
1.原來做的車牌識(shí)別項(xiàng)目：https://github.com/huxiaoman7/mxnet-cnn-plate-recognition
責(zé)任編輯：張燕妮
來源：
www.cnblogs.com
