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Python深度学习~生成车牌

时间：2024-09-04 13:52:32浏览次数：6  
标签：img Python com self cv2 shape 深度 车牌 size

1.定义车牌数据所需字符

        车牌中包括省份简称、大写英文字母和数字，我们首先定义需要的字符和字典，方便后面使用

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"
             ];

2.生成中英文字符

def GenCh(f,val):
    """
    生成中文字符
    """
    img=Image.new("RGB", (45,70),(255,255,255))
    draw = ImageDraw.Draw(img)
    draw.text((0, 3),val,(0,0,0),font=f)
    img =  img.resize((23,70))
    A = np.array(img)
    return A

def GenCh1(f,val):
    """
    生成英文字符
    """
    img=Image.new("RGB", (23,70),(255,255,255))
    draw = ImageDraw.Draw(img)
    draw.text((0, 2),val.decode('utf-8'),(0,0,0),font=f)
    A = np.array(img)
    return A

　3.对数据添加各种噪音和畸变，模糊处理

def AddSmudginess(img, Smu):
    rows = r(Smu.shape[0] - 50)
    cols = r(Smu.shape[1] - 50)
    adder = Smu[rows:rows + 50, cols:cols + 50];
    adder = cv2.resize(adder, (50, 50));
    #adder = cv2.bitwise_not(adder)
    img = cv2.resize(img,(50,50))
    img = cv2.bitwise_not(img)
    img = cv2.bitwise_and(adder, img)
    img = cv2.bitwise_not(img)
    return img


def rot(img,angel,shape,max_angel):
    """
        添加放射畸变
        img 输入图像
        factor 畸变的参数
        size 为图片的目标尺寸
    """
    size_o = [shape[1],shape[0]]
    size = (shape[1]+ int(shape[0]*cos((float(max_angel )/180) * 3.14)),shape[0])
    interval = abs( int( sin((float(angel) /180) * 3.14)* shape[0]));
    pts1 = np.float32([[0,0],[0,size_o[1]],[size_o[0],0],[size_o[0],size_o[1]]])
    if(angel>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);
    dst = cv2.warpPerspective(img,M,size);
    return dst


def rotRandrom(img, factor, size):
    """
    添加透视畸变
    """
    shape = size;
    pts1 = np.float32([[0, 0], [0, shape[0]], [shape[1], 0], [shape[1], shape[0]]])
    pts2 = np.float32([[r(factor), r(factor)], [ r(factor), shape[0] - r(factor)], [shape[1] - r(factor),  r(factor)],
                       [shape[1] - r(factor), shape[0] - r(factor)]])
    M = cv2.getPerspectiveTransform(pts1, pts2);
    dst = cv2.warpPerspective(img, M, size);
    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):
    """
    添加自然环境的噪声
    """
    index=r(len(data_set))
    env = cv2.imread(data_set[index])
    env = cv2.resize(env,(img.shape[1],img.shape[0]))
    bak = (img==0);
    bak = bak.astype(np.uint8)*255;
    inv = cv2.bitwise_and(bak,env)
    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 r(val):
    return int(np.random.random() * val)

def AddNoiseSingleChannel(single):
    """
    添加高斯噪声
    """
    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):
    img[:,:,0] =  AddNoiseSingleChannel(img[:,:,0]);
    img[:,:,1] =  AddNoiseSingleChannel(img[:,:,1]);
    img[:,:,2] =  AddNoiseSingleChannel(img[:,:,2]);
    return img

4.加入背景图片，生成车牌字符串list和label，并存为图片格式，批量生成。

class GenPlate:

    def __init__(self,fontCh,fontEng,NoPlates):
        self.fontC =  ImageFont.truetype(fontCh,43,0);
        self.fontE =  ImageFont.truetype(fontEng,60,0);
        self.img=np.array(Image.new("RGB", (226,70),(255,255,255)))
        self.bg  = cv2.resize(cv2.imread("./images/template.bmp"),(226,70));
        self.smu = cv2.imread("./images/smu2.jpg");
        self.noplates_path = [];
        for parent,parent_folder,filenames in os.walk(NoPlates):
            for filename in filenames:
                path = parent+"/"+filename;
                self.noplates_path.append(path);


    def draw(self,val):
        offset= 2 ;
        self.img[0:70,offset+8:offset+8+23]= GenCh(self.fontC,val[0]);
        self.img[0:70,offset+8+23+6:offset+8+23+6+23]= GenCh1(self.fontE,val[1]);
        for i in range(5):
            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

    def generate(self,text):
        if len(text) == 9:
            fg = self.draw(text.decode(encoding="utf-8"));
            fg = cv2.bitwise_not(fg);
            com = cv2.bitwise_or(fg,self.bg);
            com = rot(com,r(60)-30,com.shape,30);
            com = rotRandrom(com,10,(com.shape[1],com.shape[0]));
            com = tfactor(com)
            com = random_envirment(com,self.noplates_path);
            com = AddGauss(com, 1+r(4));
            com = addNoise(com);
            return com

    def genPlateString(self,pos,val):
        '''
    生成车牌String,存为图片
        生成车牌list,存为label
        '''
        plateStr = "";
        plateList=[]
        box = [0,0,0,0,0,0,0];
        if(pos!=-1):
            box[pos]=1;
        for unit,cpos in zip(box,range(len(box))):
            if unit == 1:
                plateStr += val
                #print plateStr
                plateList.append(val)
            else:
                if cpos == 0:
                    plateStr += chars[r(31)]
                    plateList.append(plateStr)
                elif cpos == 1:
                    plateStr += chars[41+r(24)]
                    plateList.append(plateStr)
                else:
                    plateStr += chars[31 + r(34)]
                    plateList.append(plateStr)
        plate = [plateList[0]]
        b = [plateList[i][-1] for i in range(len(plateList))]
        plate.extend(b[1:7])
        return plateStr,plate

    # 将生成的车牌图片写入文件夹，对应的label写入label.txt
    def genBatch(self, batchSize,pos,charRange, outputPath,size):
        if (not os.path.exists(outputPath)):
            os.mkdir(outputPath)
    outfile = open('label.txt','w')
        for i in xrange(batchSize):
                plateStr,plate = G.genPlateString(-1,-1)
                print plateStr,plate
        img =  G.generate(plateStr);
                img = cv2.resize(img,size);
                cv2.imwrite(outputPath + "/" + str(i).zfill(2) + ".jpg", img);
        outfile.write(str(plate)+"\n")
G = GenPlate("./font/platech.ttf",'./font/platechar.ttf',"./NoPlates")

　5.完整代码：

#coding=utf-8
"""
   genPlate.py:生成随机车牌
"""

__author__ = "Huxiaoman"
__copyright__ = "Copyright (c) 2017 "

import PIL
from PIL import ImageFont
from PIL import Image
from PIL import ImageDraw
import cv2;
import numpy as np;
import os;
from math import *
import sys


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 AddSmudginess(img, Smu):
    rows = r(Smu.shape[0] - 50)
    cols = r(Smu.shape[1] - 50)
    adder = Smu[rows:rows + 50, cols:cols + 50];
    adder = cv2.resize(adder, (50, 50));
    #adder = cv2.bitwise_not(adder)
    img = cv2.resize(img,(50,50))
    img = cv2.bitwise_not(img)
    img = cv2.bitwise_and(adder, img)
    img = cv2.bitwise_not(img)
    return img

def rot(img,angel,shape,max_angel):
    """
        添加放射畸变
        img 输入图像
        factor 畸变的参数
        size 为图片的目标尺寸
    """
    size_o = [shape[1],shape[0]]
    size = (shape[1]+ int(shape[0]*cos((float(max_angel )/180) * 3.14)),shape[0])
    interval = abs( int( sin((float(angel) /180) * 3.14)* shape[0]));
    pts1 = np.float32([[0,0],[0,size_o[1]],[size_o[0],0],[size_o[0],size_o[1]]])
    if(angel>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);
    dst = cv2.warpPerspective(img,M,size);
    return dst

def rotRandrom(img, factor, size):
    """
    添加透视畸变
    """
    shape = size;
    pts1 = np.float32([[0, 0], [0, shape[0]], [shape[1], 0], [shape[1], shape[0]]])
    pts2 = np.float32([[r(factor), r(factor)], [ r(factor), shape[0] - r(factor)], [shape[1] - r(factor),  r(factor)],
                       [shape[1] - r(factor), shape[0] - r(factor)]])
    M = cv2.getPerspectiveTransform(pts1, pts2);
    dst = cv2.warpPerspective(img, M, size);
    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):
    """
    添加自然环境的噪声
    """
    index=r(len(data_set))
    env = cv2.imread(data_set[index])
    env = cv2.resize(env,(img.shape[1],img.shape[0]))
    bak = (img==0);
    bak = bak.astype(np.uint8)*255;
    inv = cv2.bitwise_and(bak,env)
    img = cv2.bitwise_or(inv,img)
    return img

def GenCh(f,val):
    """
    生成中文字符
    """
    img=Image.new("RGB", (45,70),(255,255,255))
    draw = ImageDraw.Draw(img)
    draw.text((0, 3),val,(0,0,0),font=f)
    img =  img.resize((23,70))
    A = np.array(img)
    return A

def GenCh1(f,val):
    """
    生成英文字符
    """
    img=Image.new("RGB", (23,70),(255,255,255))
    draw = ImageDraw.Draw(img)
    draw.text((0, 2),val.decode('utf-8'),(0,0,0),font=f)
    A = np.array(img)
    return A

def AddGauss(img, level):
    """
    添加高斯模糊
    """
    return cv2.blur(img, (level * 2 + 1, level * 2 + 1));

def r(val):
    return int(np.random.random() * val)

def AddNoiseSingleChannel(single):
    """
    添加高斯噪声
    """
    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):
    img[:,:,0] =  AddNoiseSingleChannel(img[:,:,0]);
    img[:,:,1] =  AddNoiseSingleChannel(img[:,:,1]);
    img[:,:,2] =  AddNoiseSingleChannel(img[:,:,2]);
    return img


class GenPlate:

    def __init__(self,fontCh,fontEng,NoPlates):
        self.fontC =  ImageFont.truetype(fontCh,43,0);
        self.fontE =  ImageFont.truetype(fontEng,60,0);
        self.img=np.array(Image.new("RGB", (226,70),(255,255,255)))
        self.bg  = cv2.resize(cv2.imread("./images/template.bmp"),(226,70));
        self.smu = cv2.imread("./images/smu2.jpg");
        self.noplates_path = [];
        for parent,parent_folder,filenames in os.walk(NoPlates):
            for filename in filenames:
                path = parent+"/"+filename;
                self.noplates_path.append(path);


    def draw(self,val):
        offset= 2 ;
        self.img[0:70,offset+8:offset+8+23]= GenCh(self.fontC,val[0]);
        self.img[0:70,offset+8+23+6:offset+8+23+6+23]= GenCh1(self.fontE,val[1]);
        for i in range(5):
            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

    def generate(self,text):
        if len(text) == 9:
            fg = self.draw(text.decode(encoding="utf-8"));
            fg = cv2.bitwise_not(fg);
            com = cv2.bitwise_or(fg,self.bg);
            com = rot(com,r(60)-30,com.shape,30);
            com = rotRandrom(com,10,(com.shape[1],com.shape[0]));
            com = tfactor(com)
            com = random_envirment(com,self.noplates_path);
            com = AddGauss(com, 1+r(4));
            com = addNoise(com);
            return com

    def genPlateString(self,pos,val):
        '''
    生成车牌String,存为图片
        生成车牌list,存为label
        '''
        plateStr = "";
        plateList=[]
        box = [0,0,0,0,0,0,0];
        if(pos!=-1):
            box[pos]=1;
        for unit,cpos in zip(box,range(len(box))):
            if unit == 1:
                plateStr += val
                #print plateStr
                plateList.append(val)
            else:
                if cpos == 0:
                    plateStr += chars[r(31)]
                    plateList.append(plateStr)
                elif cpos == 1:
                    plateStr += chars[41+r(24)]
                    plateList.append(plateStr)
                else:
                    plateStr += chars[31 + r(34)]
                    plateList.append(plateStr)
        plate = [plateList[0]]
        b = [plateList[i][-1] for i in range(len(plateList))]
        plate.extend(b[1:7])
        return plateStr,plate

    # 将生成的车牌图片写入文件夹，对应的label写入label.txt
    def genBatch(self, batchSize,pos,charRange, outputPath,size):
        if (not os.path.exists(outputPath)):
            os.mkdir(outputPath)
    outfile = open('label.txt','w')
        for i in xrange(batchSize):
                plateStr,plate = G.genPlateString(-1,-1)
                print plateStr,plate
        img =  G.generate(plateStr);
                img = cv2.resize(img,size);
                cv2.imwrite(outputPath + "/" + str(i).zfill(2) + ".jpg", img);
        outfile.write(str(plate)+"\n")
G = GenPlate("./font/platech.ttf",'./font/platechar.ttf',"./NoPlates")
#G.genBatch(100,2,range(31,65),"./plate_100",(272,72))

if __name__=='__main__':
    G.genBatch(int(sys.argv[1]),2,range(31,65),sys.argv[2],(272,72))

6.运行py文件时加生成数量和保存路径即可，如：

        

标签：img,Python,com,self,cv2,shape,深度,车牌,size
From： https://blog.csdn.net/cxl0406/article/details/141891119

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