Image Classification

<form action="/upload" method="post" enctype="multipart/form-data">
    <input type="file" name="image">
    <input type="submit" value="Upload and Classify">
</form>

<h2>Result:</h2>
<div id="result"></div>

<script>
    const form = document.querySelector('form');
    form.addEventListener('submit', (event) => {
        event.preventDefault();
        const formData = new FormData(form);
        fetch('/classify', {
            method: 'POST',
            body: formData
        })
        .then(response => response.json())
        .then(data => {
            const resultDiv = document.getElementById('result');
            resultDiv.innerHTML = `Predicted label: ${data.label}`;
        })
        .catch(error => console.error(error));
    });
</script>

导入必要的包

import os import random import numpy as np import pandas as pd import tensorflow as tf import matplotlib.pyplot as plt from tensorflow.keras.models import Sequential, Model from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D, BatchNormalization, GlobalAveragePooling2D from tensorflow.keras.preprocessing.image import ImageDataGenerator from tensorflow.keras.preprocessing import image from tensorflow.keras.applications.vgg16 import VGG16 from sklearn.metrics import accuracy_score

设置参数

train_dir = 'D:/BS/class/archive/seg_train/seg_train' # 训练集目录 val_dir = 'D:/BS/class/archive/seg_test/seg_test' #目录 classes = os.listdir(train_dir) # 获取类别列表 batch_size = 64 # 批大小 IMG_HEIGHT = 150 # 图像高度 IMG_WIDTH = 150 # 图像宽度 epochs = 10 # 训练轮数

创建图像生成器

train_image_generator = ImageDataGenerator( rescale=1./255, # 归一化 horizontal_flip=True # 水平翻转 )

val_image_generator = ImageDataGenerator( rescale=1./255 # 归一化 )

train_data_gen = train_image_generator.flow_from_directory( batch_size=batch_size, # 批大小 directory=train_dir, # 训练集目录 shuffle=True, # 是否打乱数据 target_size=(IMG_HEIGHT, IMG_WIDTH), # 图像大小 class_mode='categorical' # 分类方式 )

val_data_gen = val_image_generator.flow_from_directory( batch_size=batch_size, # 批大小 directory=val_dir, # 验证集目录 shuffle=True, # 是否打乱数据 target_size=(IMG_HEIGHT, IMG_WIDTH), # 图像大小 class_mode='categorical' # 分类方式 )

total_train = len(train_data_gen) # 训练集样本数 total_val = len(val_data_gen) # 验证集样本数

print("总训练数据批次数：", total_train) print("总验证数据批次数： ", total_val)

加载 VGG16 模型并进行微调

base_model = VGG16(weights='imagenet', include_top=False, input_shape=(IMG_HEIGHT, IMG_WIDTH, 3))

for layer in base_model.layers: layer.trainable = False

model = Sequential() model.add(base_model) model.add(Flatten()) model.add(Dense(1024, activation='relu')) model.add(Dropout(0.3)) model.add(Dense(512, activation='relu')) model.add(Dropout(0.3)) model.add(Dense(64, activation='relu')) model.add(Dense(len(classes), activation='softmax'))

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

model.summary()

训练模型

history = model.fit( train_data_gen, # 训练集数据生成器 steps_per_epoch=total_train, # 每轮迭代的步数 epochs=epochs, # 训练轮数 validation_data=val_data_gen, # 验证集数据生成器 validation_steps=total_val # 验证集每轮迭代的步数 )

绘制训练过程中的准确率和损失值

acc = history.history['accuracy'] val_acc = history.history['val_accuracy'] loss = history.history['loss'] val_loss = history.history['val_loss']

plt.figure(figsize=(8, 8)) plt.subplot(2, 1, 1) plt.plot(acc, label='Training Accuracy') plt.plot(val_acc, label='Validation Accuracy') plt.legend(loc='lower right') plt.ylabel('Accuracy') plt.ylim([min(plt.ylim()), 1]) plt.title('Training and Validation Accuracy')

plt.subplot(2, 1, 2) plt.plot(loss, label='Training Loss') plt.plot(val_loss, label='Validation Loss') plt.legend(loc='upper right') plt.ylabel('Cross Entropy') plt.ylim([0, 1.0]) plt.title('Training and Validation Loss') plt.xlabel('epoch')

plt.show()

设置图像目录和类别

img_dir = 'D:/BS/class/archive/seg_train/seg_train' classes = os.listdir(img_dir)

构建图像路径列表

img_paths = [] for class_name in classes: class_dir = os.path.join(img_dir, class_name) img_names = [img_name for img_name in os.listdir(class_dir) if img_name.endswith('.jpg')] class_paths = [os.path.join(class_dir, img_name) for img_name in img_names] img_paths.extend(class_paths)

随机选择一部分图像路径

selected_img_paths = random.sample(img_paths, 10)

加载测试集数据生成器

test_image_generator = ImageDataGenerator(rescale=1. / 255) test_data_gen = test_image_generator.flow_from_directory( batch_size=1, directory=val_dir, shuffle=False, target_size=(IMG_HEIGHT, IMG_WIDTH), class_mode='categorical' )

获取测试集样本数

total_test = len(test_data_gen) print("Total testing data batches: ", total_test)

进行预测并计算准确率

y_pred = model.predict(test_data_gen) y_pred_class = np.argmax(y_pred, axis=1) y_true_class = test_data_gen.classes class_names = list(test_data_gen.class_indices.keys()) accuracy = accuracy_score(y_true_class, y_pred_class) print('Accuracy: {:.2f}%'.format(accuracy * 100))

随机选择一些测试样本进行展示

for i in range(5): rand_num = random.randint(0, total_test - 1) img, label = test_data_gen[rand_num] img = img[0] label_name = class_names[np.argmax(label)] pred_name = class_names[y_pred_class[rand_num]] plt.title('True Label: {}, Predicted Label: {}'.format(label_name, pred_name)) plt.imshow(img) plt.show()