教你使用TensorFlow2对识别验证码
验证码是教使根据随机字符生成一幅图片，然后在图片中加入干扰象素，用T验证用户必须手动填入，识别防止有人利用机器人自动批量注册、教使灌水、用T验证发垃圾广告等等 。识别
数据集来源：https://www.kaggle.com/fournierp/captcha-version-2-images
图片是教使5个字母的单词，可以包含数字。用T验证这些图像应用了噪声(模糊和一条线)。识别它们是教使200 x 50 PNG。我们的用T验证任务是尝试制作光学字符识别算法的模型。
在数据集中存在的识别验证码png图片，对应的教使标签就是图片的名字。
import os import numpy as np import pandas as pd import cv2 import matplotlib.pyplot as plt import seaborn as sns # imgaug 图片数据增强 import imgaug.augmenters as iaa import tensorflow as tf # Conv2D MaxPooling2D Dropout Flatten Dense BN GAP from tensorflow.keras.layers import Conv2D,用T验证 MaxPooling2D, Dropout, Flatten, Dense, Layer, BatchNormalization, GlobalAveragePooling2D from tensorflow.keras.optimizers import Adam from tensorflow.keras import Model, Input from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau # 图片处理器 from tensorflow.keras.preprocessing.image import ImageDataGenerator import plotly.express as px import plotly.graph_objects as go import plotly.offline as pyo pyo.init_notebook_mode()对数据进行一个简单的分析，统计图像中大约出现了什么样的识别符号。
# 数据路径 DIR = ../input/captcha-version-2-images/samples/samples # 存储验证码的标签 captcha_list = [] characters = { } for captcha in os.listdir(DIR): captcha_list.append(captcha) # 每张验证码的captcha_code captcha_code = captcha.split(".")[0] for i in captcha_code: # 遍历captcha_code characters[i] = characters.get(i, 0) +1 symbols = list(characters.keys()) len_symbols = len(symbols) print(f图像中只使用了{ len_symbols}符号) plt.bar(*zip(*characters.items())) plt.title(Frequency of symbols) plt.show() 
如何提取图像的数据建立X，y??
# 如何提取图像 建立 model X 的云服务器提供商shape 1070 * 50 * 200 * 1 # y的shape 5 * 1070 * 19 for i, captcha in enumerate(captcha_list): captcha_code = captcha.split(.)[0] # cv2.IMREAD_GRAYSCALE 灰度图 captcha_cv2 = cv2.imread(os.path.join(DIR, captcha),cv2.IMREAD_GRAYSCALE) # 缩放 captcha_cv2 = captcha_cv2 / 255.0 # print(captcha_cv2.shape) (50, 200) # 将captcha_cv2的(50, 200) 切换成(50, 200, 1) captcha_cv2 = np.reshape(captcha_cv2, img_shape) # (5,19) targs = np.zeros((len_captcha, len_symbols)) for a, b in enumerate(captcha_code): targs[a, symbols.index(b)] = 1 X[i] = captcha_cv2 y[:, i] = targs print("shape of X:", X.shape) print("shape of y:", y.shape)输出如下
print("shape of X:", X.shape) print("shape of y:", y.shape)通过Numpy中random 随机选择数据，划分训练集和测试集
# 生成随机数 from numpy.random import default_rng rng = default_rng(seed=1) test_numbers = rng.choice(1070, size=int(1070*0.3), replace=False) X_test = X[test_numbers] X_full = np.delete(X, test_numbers,0) y_test = y[:,test_numbers] y_full = np.delete(y, test_numbers,1) val_numbers = rng.choice(int(1070*0.7), size=int(1070*0.3), replace=False) X_val = X_full[val_numbers] X_train = np.delete(X_full, val_numbers,0) y_val = y_full[:,val_numbers] y_train = np.delete(y_full, val_numbers,1)在此验证码数据中，容易出现过拟合的现象，你可能会想到添加更多的新数据、 添加正则项等， 但这里使用数据增强的方法，特别是对于机器视觉的任务，数据增强技术尤为重要。
常用的数据增强操作：imgaug库。imgaug是提供了各种图像增强操作的python库 https://github.com/aleju/imgaug。
imgaug几乎包含了所有主流的数据增强的图像处理操作， 增强方法详见github
# Sequential(C, R) 尺寸增加了5倍， # 选取一系列子增强器C作用于每张图片的位置,第二个参数表示是否对每个batch的图片应用不同顺序的Augmenter list # rotate=(-8, 8) 旋转 # iaa.CropAndPad 截取(crop)或者填充(pad),填充时,被填充区域为黑色。 # px: 想要crop(negative values)的或者pad(positive values)的像素点。亿华云计算 # (top, right, bottom, left) # 当pad_mode=constant的时候选择填充的值 aug =iaa.Sequential([iaa.CropAndPad( px=((0, 10), (0, 35), (0, 10), (0, 35)), pad_mode=[edge], pad_cval=1 ),iaa.Rotate(rotate=(-8,8))]) X_aug_train = None y_aug_train = y_train for i in range(40): X_aug = aug(images = X_train) if X_aug_train is not None: X_aug_train = np.concatenate([X_aug_train, X_aug], axis = 0) y_aug_train = np.concatenate([y_aug_train, y_train], axis = 1) else: X_aug_train = X_aug让我们看看一些数据增强的训练图像。
fig, ax = plt.subplots(nrows=2, ncols =5, figsize = (16,16)) for i in range(10): index = np.random.randint(X_aug_train.shape[0]) ax[i//5][i%5].imshow(X_aug_train[index],cmap=gray) 
这次使用函数式API创建模型，函数式API是创建模型的另一种方式，它具有更多的灵活性，包括创建更为复杂的模型。
需要定义inputs和outputs
#函数式API模型创建 captcha = Input(shape=(50,200,channels)) x = Conv2D(32, (5,5),padding=valid,activation=relu)(captcha) x = MaxPooling2D((2,2),padding=same)(x) x = Conv2D(64, (3,3),padding=same,activation=relu)(x) x = MaxPooling2D((2,2),padding=same)(x) x = Conv2D(128, (3,3),padding=same,activation=relu)(x) maxpool = MaxPooling2D((2,2),padding=same)(x) outputs = [] for i in range(5): x = Conv2D(256, (3,3),padding=same,activation=relu)(maxpool) x = MaxPooling2D((2,2),padding=same)(x) x = Flatten()(x) x = Dropout(0.5)(x) x = BatchNormalization()(x) x = Dense(64, activation=relu)(x) x = Dropout(0.5)(x) x = BatchNormalization()(x) x = Dense(len_symbols , activation=softmax , name=fchar_{ i+1})(x) outputs.append(x) model = Model(inputs = captcha , outputs=outputs) # ReduceLROnPlateau更新学习率 reduce_lr = ReduceLROnPlateau(patience =3, factor = 0.5,verbose = 1) model.compile(loss=categorical_crossentropy, optimizer=Adam(learning_rate=0.0005), metrics=["accuracy"]) # EarlyStopping用于提前停止训练的callbacks。具体地,可以达到当训练集上的loss不在减小 earlystopping = EarlyStopping(monitor ="val_loss", mode ="min", patience = 10, min_delta = 1e-4, restore_best_weights = True) history = model.fit(X_train, [y_train[i] for i in range(5)], batch_size=32, epochs=30, verbose=1, validation_data = (X_val, [y_val[i] for i in range(5)]), callbacks =[earlystopping,reduce_lr]) 
下面对model进行一个测试和评估。
score = model.evaluate(X_test,[y_test[0], y_test[1], y_test[2], y_test[3], y_test[4]],verbose=1) metrics = [loss,char_1_loss, char_2_loss, char_3_loss, char_4_loss, char_5_loss, char_1_acc, char_2_acc, char_3_acc, char_4_acc, char_5_acc] for i,j in zip(metrics, score): print(f{ i}: { j})具体输出如下：
11/11 [==============================] - 0s 11ms/step - loss: 0.7246 - char_1_loss: 0.0682 - char_2_loss: 0.1066 - char_3_loss: 0.2730 - char_4_loss: 0.2636 - char_5_loss: 0.0132 - char_1_accuracy: 0.9844 - char_2_accuracy: 0.9657 - char_3_accuracy: 0.9408 - char_4_accuracy: 0.9626 - char_5_accuracy: 0.9938 loss: 0.7246273756027222 char_1_loss: 0.06818050146102905 char_2_loss: 0.10664034634828568 char_3_loss: 0.27299806475639343 char_4_loss: 0.26359987258911133 char_5_loss: 0.013208594173192978 char_1_acc: 0.9844236969947815 char_2_acc: 0.9657320976257324 char_3_acc: 0.940809965133667 char_4_acc: 0.9626168012619019 char_5_acc: 0.9937694668769836字母1到字母5的精确值都大于
绘制loss和score
metrics_df = pd.DataFrame(history.history) columns = [col for col in metrics_df.columns if loss in col and len(col)>8] fig = px.line(metrics_df, y = columns) fig.show()
下面预测所有的数据
actual_pred = [] for i in range(X_test.shape[0]): actual = .join([symbols[i] for i in (np.argmax(y_test[:, i],axis=1))]) pred = predict(X_test[i]) actual_pred.append((actual, pred)) print(actal_pred[:10])输出如下：
[(n4b4m, n4b4m), (42nxy, 42nxy), (25257, 25277), (cewnm, cewnm), (w46ep, w46ep), (cdcb3, edcb3), (8gf7n, 8gf7n), (nny5e, nny5e), (gm2c2, gm2c2), (g7fmc, g7fmc)] sameCount = 0 diffCount = 0 letterDiff = { i:0 for i in range(5)} incorrectness = { i:0 for i in range(1,6)} for real, pred in actual_pred: # 预测和输出相同 if real == pred: sameCount += 1 else: # 失败 diffCount += 1 # 遍历 incorrectnessPoint = 0 for i in range(5): if real[i] != pred[i]: letterDiff[i] += 1 incorrectnessPoint += 1 incorrectness[incorrectnessPoint] += 1 x = [True predicted, False predicted] y = [sameCount, diffCount] fig = go.Figure(data=[go.Bar(x = x, y = y)]) fig.show()在预测数据中，一共有287个数据预测正确。
在这里，我们可以看到出现错误到底是哪一个index。
x1 = ["Character " + str(x) for x in range(1, 6)] fig = go.Figure(data=[go.Bar(x = x1, y = list(letterDiff.values()))]) fig.show() 
为了计算每个单词的错误数，绘制相关的条形图。
x2 = [str(x) + " incorrect" for x in incorrectness.keys()] y2 = list(incorrectness.values()) fig = go.Figure(data=[go.Bar(x = x2, y = y2)]) fig.show() 
下面绘制错误的验证码图像，并标准正确和错误的区别。亿华云
fig, ax = plt.subplots(nrows = 8, ncols=4,figsize = (16,20)) count = 0 for i, (actual , pred) in enumerate(actual_pred): if actual != pred: img = X_test[i] try: ax[count//4][count%4].imshow(img, cmap = gray) ax[count//4][count%4].title.set_text(pred + - + actual) count += 1 except: pass
