标签:ash return single 改变 bat war encode ret 生成
1.使用PIL和opencv生成车牌图像数据
from PIL import ImageFont,Image,ImageDraw import cv2 import numpy as np import os from math import * #创建 生成车牌图像数据 的类 index = {"京": 0, "沪": 1, "津": 2, "渝": 3, "冀": 4, "晋": 5, "蒙": 6, "辽": 7, "吉": 8, "黑": 9, "苏": 10, "浙": 11, "皖": 12, "闽": 13, "赣": 14, "鲁": 15, "豫": 16, "鄂": 17, "湘": 18, "粤": 19, "桂": 20, "琼": 21, "川": 22, "贵": 23, "云": 24, "藏": 25, "陕": 26, "甘": 27, "青": 28, "宁": 29, "新": 30, "0": 31, "1": 32, "2": 33, "3": 34, "4": 35, "5": 36, "6": 37, "7": 38, "8": 39, "9": 40, "A": 41, "B": 42, "C": 43, "D": 44, "E": 45, "F": 46, "G": 47, "H": 48, "J": 49, "K": 50, "L": 51, "M": 52, "N": 53, "P": 54, "Q": 55, "R": 56, "S": 57, "T": 58, "U": 59, "V": 60, "W": 61, "X": 62, "Y": 63, "Z": 64} chars = ["京", "沪", "津", "渝", "冀", "晋", "蒙", "辽", "吉", "黑", "苏", "浙", "皖", "闽", "赣", "鲁", "豫", "鄂", "湘", "粤", "桂", "琼", "川", "贵", "云", "藏", "陕", "甘", "青", "宁", "新", "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" ] def r(val): return int(np.random.random()*val) def GenCh(f,val):#生成中文字符,f是中文字体对象, img=Image.new(‘RGB‘,(45,70),(255,255,255))#创建中文字符区域的全白画布,中文一般要方正一些,所以画布设置大一点,等下再resize draw=ImageDraw.Draw(img)#对画布创建画画对象 draw.text((0,3),val,(0,0,0),font=f)#画画对象画出规定字体的黑色的规定val【0】文字,在左上角位置是(0,3)的点开始 img = img.resize((23,70)) A=np.array(img)#图画转换成array格式 return A def GenCh1(f,val):#生成英文和数字字符,f是中文字体对象, img=Image.new(‘RGB‘,(23,70),(255,255,255)) draw=ImageDraw.Draw(img) draw.text((0,2),val,(0,0,0),font=f)#画画对象在左上角(0,2)出开始画出val,颜色全黑,字体是f A=np.array(img) return A def rot(img,angle,shape,max_angle):#透视畸变 size_o=[shape[1],shape[0]]#cv读的(h,w)换成[w,h] size=(shape[1]+int(shape[0]*cos((float(max_angle)/180)*3.14)),shape[0])#【变化后的w,h】 interval=abs(int(sin(float(angle)/180)*3.14)*shape[0])#h变换的绝对值 pts1=np.float32([[0,0],[0,size_o[1]],[size_o[0],0],[size_o[0],size_o[1]]])#(00,0h,w0,wh) if (angle>0): pts2=np.float32([[interval,0],[0,size[1]],[size[0],0],[size[0]-interval,size_o[1]]]) else: pts2 = np.float32([[0,0],[interval,size[1] ],[size[0]-interval,0 ],[size[0],size_o[1]]]) M=cv2.getPerspectiveTransform(pts1,pts2)#根据两幅图的四个坐标点计算透视矩阵 dst=cv2.warpPerspective(img,M,size)#img再M矩阵的变化下生成size大小的变化图 return dst def rotRandrom(img,factor,shape):#仿射畸变 pts1=np.float32([[0,0],[0,shape[0]],[shape[1],0],[shape[1],shape[0]]])#00,0h,w0,wh pts2=np.float32([[r(factor),r(factor)],[r(factor),shape[0]-r(factor)],[shape[1]-factor,r(factor)],[shape[1]-r(factor),shape[0]-r(factor)]]) M=cv2.getPerspectiveTransform(pts1,pts2) dst=cv2.warpPerspective(img,M,shape) return dst def tfactor(img):#添加饱和度光照的噪声 hsv=cv2.cvtColor(img,cv2.COLOR_BGR2HSV) hsv[:,:,0] = hsv[:,:,0]*(0.8+ np.random.random()*0.2) hsv[:,:,1] = hsv[:,:,1]*(0.3+ np.random.random()*0.7) hsv[:,:,2] = hsv[:,:,2]*(0.2+ np.random.random()*0.8) img=cv2.cvtColor(hsv,cv2.COLOR_HSV2BGR) return img def random_envirment(img,data_set):#添加自然环境噪声 index=r(len(data_set)) env=cv2.imread(data_set[index])#随机读出一张环境噪声图片 env=cv2.resize(env,(img.shape[1],img.shape[0]))#改变环境噪声图片的大下 bak=(img==0) bak=bak.astype(np.uint8)*255#图片的黑色部分变成白色部分 inv=cv2.bitwise_and(bak,env)#图片的非黑部分和噪声求and, img=cv2.bitwise_or(inv,img) return img def AddGauss(img,level):#添加高斯模糊 return cv2.blur(img,(level*2+1,level*2+1)) def AddNoiseSingleChannel(single):#高斯噪声 diff=255-single.max() noise=np.random.normal(0,1+r(6),single.shape) noise = (noise - noise.min())/(noise.max()-noise.min()) noise= diff*noise noise= noise.astype(np.uint8) dst = single + noise return dst def addNoise(img,sdev = 0.5,avg=10):#每个通道随机添加高斯噪声 img[:,:,0] = AddNoiseSingleChannel(img[:,:,0]) img[:,:,1] = AddNoiseSingleChannel(img[:,:,1]) img[:,:,2] = AddNoiseSingleChannel(img[:,:,2]) return img class GenPlate:#生成车牌图像数据 的类 def __init__(self,fontCh,fontEng,NoPlates): self.fontC=ImageFont.truetype(fontCh,43,0)#创建中文字体对象,fontCh是字体文件地址,43是字体大小,规定文字字体 self.fontE=ImageFont.truetype(fontEng,60,0) self.img=np.array(Image.new(‘RGB‘,(226,70),(255,255,255)))#创建(226,70)大小的全白图像,并转换成array格式 self.bg=cv2.resize(cv2.imread(‘./input_data/images/template.bmp‘),(226,70))#读取出背景模板图 self.smu=cv2.imread(‘./input_data/images/smu2.jpg‘)#******************************** self.noplates_path=[] for parent,parent_folder,filenames in os.walk(NoPlates):#NoPlates的上级目录,NoPlates的子目录(没有),NoPlates的子文件 for filename in filenames: path=parent+‘/‘+filename self.noplates_path.append(path)#环境噪声图片 def draw(self,val): offset=2 self.img[0:70,offset+8:offset+8+23]=GenCh(self.fontC,val[0])#再self.img画布上画出中文字 self.img[0:79,offset+8+23+6:offset+8+23+6+23]=GenCh1(self.fontE,val[1])#英文字 for i in range(5):#画出5个数字 base=offset+8+23+6+23+17+ i*23+ i*6 self.img[0:70,base:base+23]=GenCh1(self.fontE,val[i+2]) return self.img#画出背景白色,文字黑色的文字内容 def generate(self,text): if len(text)==7: fg=self.draw(text)#调用draw函数,画出这7个字符) fg=cv2.bitwise_not(fg)#图像的array按位取反:黑色背景,白色文字 com=cv2.bitwise_or(fg,self.bg)#再与背景图片取或,则生成背景是背景图片,前景是白色文字的图片array格式 com=rot(com,r(60)-30,com.shape,30)#透视畸变效果(img,angle,shape,max_angle) com = rotRandrom(com,10,(com.shape[1],com.shape[0]))#仿射畸变 com = tfactor(com)#添加饱和度光照的噪声 com = random_envirment(com,self.noplates_path)#环境图片的噪声 com = AddGauss(com, 1+r(4))#高斯模糊 com = addNoise(com)#高斯噪声 return com def genPlateString(self,pos,val):#随机生成(中文 英文 数字*5)的字符 #pos!=-1时,读取出val值就是车牌值************************pos=-1时,val随机,主要是生成车牌 plateStr=‘‘ if (pos!=-1):# plateStr+=val#读出你想要生成的车牌号 else:#随机生成车牌号 for cpos in range(7): if cpos==0: plateStr+=chars[r(31)] elif cpos==1: plateStr+=chars[41+r(24)] else: plateStr+=chars[31+r(10)] return plateStr def genBatch(self,batchSize,size):#生成batch——size个车牌数据 for i in range(batchSize): plateStr=self.genPlateString(-1,-1) print(plateStr) img=self.generate(plateStr) img=cv2.resize(img,size) filename=str(plateStr)+‘.jpg‘ cv2.imencode(‘.jpg‘,img)[1].tofile(filename)#此处解决中文名乱码 if __name__==‘__main__‘: G=GenPlate("./input_data/font/platech.ttf",‘./input_data/font/platechar.ttf‘,"./input_data/NoPlates") G.genBatch(10,(224,224))
2.使用keras生成resnet34模型
标签:ash return single 改变 bat war encode ret 生成
原文地址:https://www.cnblogs.com/Turing-dz/p/13288055.html