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在以往发布的图像识别文章中,基本原理均差不多,这个主要是添加了新的知识,就是对标签数字化,从而实现对标签的数字化展示,
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'] char_set = number + alphabet char_set_len = len(char_set) label_name_len = len(all_label_names[0]) # 将字符串数字化 def text2vec(text): vector = np.zeros([label_name_len, char_set_len]) for i, c in enumerate(text): idx = char_set.index(c) vector[i][idx] = 1.0 return vector all_labels = [text2vec(i) for i in all_label_names]
数据集来源
链接:https://pan.baidu.com/s/1ZX4tVslyAzjdqGklvBbYew
提取码:mdjn
数据集主要来源于灰度图像,如下图所示:
话不多说直接上代码
import tensorflow as tf from PIL import Image gpus = tf.config.list_physical_devices("GPU") if gpus: tf.config.experimental.set_memory_growth(gpus[0], True) #设置GPU显存用量按需使用 tf.config.set_visible_devices([gpus[0]],"GPU") import matplotlib.pyplot as plt # 支持中文 plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签 plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号 import os,PIL,random,pathlib # 设置随机种子尽可能使结果可以重现 import numpy as np np.random.seed(1) # 设置随机种子尽可能使结果可以重现 import tensorflow as tf tf.random.set_seed(1) data_dir = "H:\python_project\python辅助算法\data\captcha" data_dir = pathlib.Path(data_dir) all_image_paths = list(data_dir.glob('*')) all_image_paths = [str(path) for path in all_image_paths] # 打乱数据 random.shuffle(all_image_paths) # 获取数据标签 all_label_names = [path.split("\\")[5].split(".")[0] for path in all_image_paths] print(all_label_names) image_count = len(all_image_paths) print("图片总数为:",image_count) plt.figure(figsize=(10, 5)) for i in range(20): plt.subplot(5, 4, i + 1) plt.xticks([]) plt.yticks([]) plt.grid(False) # 显示图片 images = plt.imread(all_image_paths[i]) plt.imshow(images) # 显示标签 plt.xlabel(all_label_names[i]) plt.show() 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'] char_set = number + alphabet char_set_len = len(char_set) label_name_len = len(all_label_names[0]) # 将字符串数字化 def text2vec(text): vector = np.zeros([label_name_len, char_set_len]) for i, c in enumerate(text): idx = char_set.index(c) vector[i][idx] = 1.0 return vector all_labels = [text2vec(i) for i in all_label_names] def preprocess_image(image): image = tf.image.decode_jpeg(image, channels=1) image = tf.image.resize(image, [50, 200]) return image/255.0 def load_and_preprocess_image(path): image = tf.io.read_file(path) return preprocess_image(image) AUTOTUNE = tf.data.experimental.AUTOTUNE path_ds = tf.data.Dataset.from_tensor_slices(all_image_paths) image_ds = path_ds.map(load_and_preprocess_image, num_parallel_calls=AUTOTUNE) label_ds = tf.data.Dataset.from_tensor_slices(all_labels) image_label_ds = tf.data.Dataset.zip((image_ds, label_ds)) train_ds = image_label_ds.take(1000) # 前1000个batch val_ds = image_label_ds.skip(1000) # 跳过前1000,选取后面的 BATCH_SIZE = 16 train_ds = train_ds.batch(BATCH_SIZE) train_ds = train_ds.prefetch(buffer_size=AUTOTUNE) val_ds = val_ds.batch(BATCH_SIZE) val_ds = val_ds.prefetch(buffer_size=AUTOTUNE) from tensorflow.keras import datasets, layers, models model = models.Sequential([ layers.Conv2D(32, (3, 3), activation='relu', input_shape=(50, 200, 1)), # 卷积层1,卷积核3*3 layers.MaxPooling2D((2, 2)), # 池化层1,2*2采样 layers.Conv2D(64, (3, 3), activation='relu'), # 卷积层2,卷积核3*3 layers.MaxPooling2D((2, 2)), # 池化层2,2*2采样 layers.Flatten(), # Flatten层,连接卷积层与全连接层 layers.Dense(1000, activation='relu'), # 全连接层,特征进一步提取 layers.Dense(label_name_len * char_set_len), layers.Reshape([label_name_len, char_set_len]), layers.Softmax() # 输出层,输出预期结果 ]) # 打印网络结构 model.summary() model.compile(optimizer="adam", loss='categorical_crossentropy', metrics=['accuracy']) epochs = 20 history = model.fit( train_ds, validation_data=val_ds, epochs=epochs ) acc = history.history['accuracy'] val_acc = history.history['val_accuracy'] loss = history.history['loss'] val_loss = history.history['val_loss'] epochs_range = range(epochs) plt.figure(figsize=(12, 4)) plt.subplot(1, 2, 1) plt.plot(epochs_range, acc, label='Training Accuracy') plt.plot(epochs_range, val_acc, label='Validation Accuracy') plt.legend(loc='lower right') plt.title('Training and Validation Accuracy') plt.subplot(1, 2, 2) plt.plot(epochs_range, loss, label='Training Loss') plt.plot(epochs_range, val_loss, label='Validation Loss') plt.legend(loc='upper right') plt.title('Training and Validation Loss') plt.show() # 保存模型 model.save('model/12_model.h5') # 加载模型 new_model = tf.keras.models.load_model('model/12_model.h5') def vec2text(vec): """ 还原标签(向量->字符串) """ text = [] for i, c in enumerate(vec): text.append(char_set[c]) return "".join(text) plt.figure(figsize=(10, 8)) # 图形的宽为10高为8 import cv2 for images, labels in val_ds.take(1): for i in range(6): ax = plt.subplot(5, 2, i + 1) # 显示图片 # image = tf.image.decode_jpeg(images[i], channels=3) # plt.imshow(Image.fromarray(np.array(images[i]))) cv2.imshow("aa",np.array(images[i]*255)) cv2.waitKey() # 需要给图片增加一个维度 img_array = tf.expand_dims(images[i], 0) # 使用模型预测验证码 predictions = new_model.predict(img_array) print(vec2text(np.argmax(predictions, axis=2)[0]))
下面给出训练结果:
输出结果
本次验证码识别主要是根据这篇文章(深度学习100例-卷积神经网络(CNN)识别验证码 | 第12天)
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