本文主要实现了两个工作：1.验证码生成 2.Pytorch识别验证码
一. 验证码生成
方法1. 利用PIL库的ImageDraw实现绘图，此法参考博客实现：
#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Tue Mar 27 15:45:04 2018 @author: lps """ from PIL import Image, ImageDraw, ImageFont, ImageFilter import random import cv2 import numpy as np import matplotlib.pyplot as plt path = \'/media/lps/python-3.5.2.amd64/Lib/site-packages/matplotlib/mpl-data/fonts/ttf/\' # 选择字体 data_path = \'/home/lps/yanzm/\' # random chr def rndChar(): return chr(random.randint(65, 90)) # 随机字母 def rndInt(): return str(random.randint(0,9)) # 随机数字 def rndColor(): return (random.randint(64, 255), random.randint(64, 255), random.randint(64, 255)) # 随机颜色 def rndColor2(): return (random.randint(32, 127), random.randint(32, 127), random.randint(32, 127)) # 随机颜色 def gaussian_noise(): # 高斯噪声 mu = 125 sigma = 20 return tuple((np.random.normal(mu, sigma, 3).astype(int))) def rotate(x, angle): # 旋转 M_rotate = cv2.getRotationMatrix2D((x.shape[0]/2, x.shape[1]/2), angle, 1) x = cv2.warpAffine(x, M_rotate, (x.shape[0], x.shape[1])) return x width = 180 * 4 height = 180 def gen_image(num): for l in range(num): image = Image.new(\'RGB\', (width, height), (255, 255, 255)) # 先生成一张大图 font = ImageFont.truetype(path+\'cmb10.ttf\', 36) draw = ImageDraw.Draw(image) # 新的画板 for x in range(0,width): for y in range(0,height): draw.point((x, y), fill=rndColor()) label = [] for t in range(4): # 每一张验证码4个数字 numb = rndInt() draw.text((180 * t + 60+10, 60+10), numb, font=font, fill=rndColor2()) label.append(numb) with open(data_path+"label.txt","a") as f: for s in label: f.write(s + \' \') f.writelines("\n") # 写入label img = image.filter(ImageFilter.GaussianBlur(radius=0.5)) img = np.array(img) img1 = np.array([]) for i in range(0,4): img0 = img[:, 180*i: 180*i+180] # 提取含有验证码的小图 angle = random.randint(-45, 45) img0 = rotate(img0, angle) # 对小图随机旋转 if img1.any(): img1 = np.concatenate((img1, img0[60:120, 60:120, :]), axis=1) else: img1 = img0[60:120, 60:120, :] plt.imsave(data_path+\'src/\' + str(l)+\'.jpg\', img1) # 保存结果 if __name__==\'__main__\': gen_image(100)
结果大致：
方法2. 利用更专业的库：captcha
#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Sun Mar 25 19:06:46 2018 @author: lps """ from captcha.image import ImageCaptcha import numpy as np #import matplotlib.pyplot as plt from PIL import Image import random import cv2 number = [\'0\',\'1\',\'2\',\'3\',\'4\',\'5\',\'6\',\'7\',\'8\',\'9\'] alphabet = [\'a\',\'b\',\'c\',\'d\',\'e\',\'f\',\'g\',\'h\',\'i\',\'j\',\'k\',\'l\',\'m\',\'n\',\'o\',\'p\',\'q\',\'r\',\'s\',\'t\',\'u\',\'v\',\'w\',\'x\',\'y\',\'z\'] ALPHABET = [\'A\',\'B\',\'C\',\'D\',\'E\',\'F\',\'G\',\'H\',\'I\',\'J\',\'K\',\'L\',\'M\',\'N\',\'O\',\'P\',\'Q\',\'R\',\'S\',\'T\',\'U\',\'V\',\'W\',\'X\',\'Y\',\'Z\'] data_path = \'/home/lps/yanzm/\' def random_captcha_text(char_set=number,captcha_size=4): # 可以设置只用来生成数字 captcha_text = [] for i in range(captcha_size): c = random.choice(char_set) captcha_text.append(c) return captcha_text def gen_capthcha_text_and_image(m): image = ImageCaptcha() captcha_text = random_captcha_text() # 生成数字 captcha_text = \' \'.join(captcha_text) # 生成标签 captcha = image.generate(captcha_text) # image.write(captcha_text,captcha_text+\'.jpg\') captcha_image = Image.open(captcha) captcha_image = np.array(captcha_image) with open(data_path+"label.txt","a") as f: # 写入标签 f.write(captcha_text) f.writelines("\n") cv2.imwrite(data_path + \'/src/\'+\'%.4d.jpg\'%m, captcha_image) # 保存 # return captcha_text,captcha_image if __name__ == \'__main__\': for m in range(0,5000): # text,image = gen_capthcha_text_and_image() gen_capthcha_text_and_image(m) # f = plt.figure() # ax = f.add_subplot(212) # ax.text(0.1,0.1,text,ha=\'center\',va=\'center\',transform=ax.transAxes) # plt.imshow(image) # plt.show() #
结果大致：
二. pytorch实现
对于一张验证码来说作为一张单一的图片，每输入一张图片，得到四个数字作为输出，只有4个数字同时预测正确才表示预测正确。所以在每一张图上是四个多二分类器：因为验证码上面的数字为0-9，类似于mnist，只不过此时一张图片对应于4个数字。所以思路很简单，实现如下：
#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Fri Mar 30 15:46:09 2018 @author: lps """ import torch import torch.nn as nn import torch.nn.functional as F from torch.autograd import Variable import torch.optim as optim import torchvision.models as models import torchvision from torch.utils.data import Dataset, DataLoader from torchvision import transforms, utils import matplotlib.pyplot as plt from PIL import Image #import pandas as pd import numpy as np import os import copy, time file_path = \'/home/lps/yanzm\' BATCH_SIZE = 16 EPOCH = 10 # Load data class dataset(Dataset): def __init__(self, root_dir, label_file, transform=None): self.root_dir = root_dir self.label = np.loadtxt(label_file) self.transform = transform def __getitem__(self, idx): img_name = os.path.join(self.root_dir,\'%.4d.jpg\'%idx) image = Image.open(img_name) labels = self.label[idx,:] # sample = image if self.transform: image = self.transform(image) return image, labels def __len__(self): return (self.label.shape[0]) data = dataset(file_path+\'/src\', file_path+\'/label.txt\',transform=transforms.ToTensor()) dataloader = DataLoader(data, batch_size=BATCH_SIZE, shuffle=True, num_workers=4, drop_last=True) dataset_size = len(data) # Conv network class ConvNet(nn.Module): def __init__(self): super(ConvNet, self).__init__() self.conv =nn.Sequential( nn.Conv2d(3, 32, kernel_size=4, stride=1, padding=2), # in:(bs,3,60,160) nn.BatchNorm2d(32), nn.LeakyReLU(0.2, inplace=True), nn.MaxPool2d(kernel_size=2), # out:(bs,32,30,80) nn.Conv2d(32, 64, kernel_size=4, stride=1, padding=2), nn.BatchNorm2d(64), nn.LeakyReLU(0.2, inplace=True), nn.MaxPool2d(kernel_size=2), # out:(bs,64,15,40) nn.Conv2d(64, 64, kernel_size=3 ,stride=1, padding=1), nn.BatchNorm2d(64), nn.LeakyReLU(0.2, inplace=True), nn.MaxPool2d(kernel_size=2) # out:(bs,64,7,20) ) self.fc1 = nn.Linear(64*7*20, 500) self.fc2 = nn.Linear(500,40) def forward(self, x): x = self.conv(x) x = x.view(x.size(0), -1) # reshape to (batch_size, 64 * 7 * 30) output = self.fc1(x) output = self.fc2(output) return output # Train the net class nCrossEntropyLoss(torch.nn.Module): def __init__(self, n=4): super(nCrossEntropyLoss, self).__init__() self.n = n self.total_loss = 0 self.loss = nn.CrossEntropyLoss() def forward(self, output, label): output_t = output[:,0:10] label = Variable(torch.LongTensor(label.data.cpu().numpy())).cuda() label_t = label[:,0] for i in range(1, self.n): output_t = torch.cat((output_t, output[:,10*i:10*i+10]), 0) # 损失的思路是将一张图平均剪切为4张小图即4个多分类，然后再用多分类交叉熵方损失 label_t = torch.cat((label_t, label[:,i]), 0) self.total_loss = self.loss(output_t, label_t) return self.total_loss def equal(np1,np2): n = 0 for i in range(np1.shape[0]): if (np1[i,:]==np2[i,:]).all(): n += 1 return n net = ConvNet().cuda() optimizer = torch.optim.Adam(net.parameters(), lr=0.001) #loss_func = nn.CrossEntropyLoss() loss_func = nCrossEntropyLoss() best_model_wts = copy.deepcopy(net.state_dict()) best_acc = 0.0 since = time.time() for epoch in range(EPOCH): running_loss=0.0 running_corrects=0 for step,(inputs,label) in enumerate(dataloader): pred = torch.LongTensor(BATCH_SIZE,1).zero_() inputs = Variable(inputs).cuda() # (bs, 3, 60, 240) label = Variable(label).cuda() # (bs, 4) optimizer.zero_grad() output = net(inputs) # (bs, 40) loss = loss_func(output, label) for i in range(4): pre = F.log_softmax(output[:,10*i:10*i+10], dim=1) # (bs, 10) pred = torch.cat((pred, pre.data.max(1, keepdim=True)[1].cpu()), dim=1) # loss.backward() optimizer.step() running_loss += loss.data[0] * inputs.size()[0] running_corrects += equal(pred.numpy()[:,1:], label.data.cpu().numpy().astype(int)) epoch_loss = running_loss / dataset_size epoch_acc = running_corrects / dataset_size if epoch_acc > best_acc: best_acc = epoch_acc best_model_wts = copy.deepcopy(net.state_dict()) if epoch == EPOCH-1: torch.save(best_model_wts, file_path+\'/best_model_wts.pkl\') print() time_elapsed = time.time() - since print(\'Training complete in {:.0f}m {:.0f}s\'.format( time_elapsed // 60, time_elapsed % 60)) print(\'Train Loss:{:.4f} Acc: {:.4f}\'.format(epoch_loss, epoch_acc))
随机生成5000张图片拿来训练，准确率也会有97%左右。