hand_gesturer_ecognition/training.py

import os
import matplotlib.pyplot as plt
import matplotlib.font_manager as fm
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras import layers, models
import tensorflow as tf

# 设置字体
font_path = 'C:/Windows/Fonts/msyh.ttc'
prop = fm.FontProperties(fname=font_path)
plt.rcParams['font.family'] = prop.get_name()

# 设置数据目录
data_dir = 'D:/hand/archive'
train_dir = os.path.join(data_dir, 'leapGestRecog')
validation_dir = os.path.join(data_dir, 'validation')

# 检查目录路径是否存在
print("训练数据目录存在:", os.path.exists(train_dir))
print("验证数据目录存在:", os.path.exists(validation_dir))

if os.path.exists(train_dir) and os.path.exists(validation_dir):
    # 简单的标准化处理
    datagen = ImageDataGenerator(rescale=1./255)

    train_generator = datagen.flow_from_directory(train_dir, target_size=(150, 150), batch_size=32, class_mode='categorical')
    validation_generator = datagen.flow_from_directory(validation_dir, target_size=(150, 150), batch_size=32, class_mode='categorical')

    train_dataset = tf.data.Dataset.from_generator(
        lambda: train_generator,
        output_signature=(
            tf.TensorSpec(shape=(None, 150, 150, 3), dtype=tf.float32),
            tf.TensorSpec(shape=(None, 10), dtype=tf.float32)
        )
    ).repeat()

    validation_dataset = tf.data.Dataset.from_generator(
        lambda: validation_generator,
        output_signature=(
            tf.TensorSpec(shape=(None, 150, 150, 3), dtype=tf.float32),
            tf.TensorSpec(shape=(None, 10), dtype=tf.float32)
        )
    ).repeat()

    # 检查类别名称和数量
    train_classes = train_generator.class_indices
    validation_classes = validation_generator.class_indices

    print("训练数据集类别:", train_classes)
    print("验证数据集类别:", validation_classes)

    # 构建模型
    model = models.Sequential([
        layers.Input(shape=(150, 150, 3)),
        layers.Conv2D(32, (3, 3), activation='relu'),
        layers.MaxPooling2D((2, 2)),
        layers.Conv2D(64, (3, 3), activation='relu'),
        layers.MaxPooling2D((2, 2)),
        layers.Conv2D(128, (3, 3), activation='relu'),
        layers.MaxPooling2D((2, 2)),
        layers.Conv2D(128, (3, 3), activation='relu'),
        layers.MaxPooling2D((2, 2)),
        layers.Flatten(),
        layers.Dense(512, activation='relu'),
        layers.Dense(train_generator.num_classes, activation='softmax')
    ])

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

    steps_per_epoch = train_generator.samples // train_generator.batch_size
    validation_steps = validation_generator.samples // validation_generator.batch_size

    history = model.fit(train_dataset, steps_per_epoch=steps_per_epoch,
                        epochs=30, validation_data=validation_dataset,
                        validation_steps=validation_steps)

    validation_loss, validation_accuracy = model.evaluate(validation_dataset, steps=validation_steps)
    print(f"Validation Accuracy: {validation_accuracy * 100:.2f}%")

    # 保存模型
    model.save('hand_gesture_model.h5')
    print("模型已保存到 hand_gesture_model.h5")

    # 从文件加载模型
    loaded_model = load_model('hand_gesture_model.h5')
    print("模型已从 hand_gesture_model.h5 加载")

    # 可视化训练过程
    acc = history.history['accuracy']
    val_acc = history.history['val_accuracy']
    loss = history.history['loss']
    val_loss = history.history['val_loss']

    epochs = range(len(acc))

    plt.plot(epochs, acc, 'r', label='训练准确度')
    plt.plot(epochs, val_acc, 'b', label='验证准确度')
    plt.title('训练和验证准确度')
    plt.legend()

    plt.figure()

    plt.plot(epochs, loss, 'r', label='训练损失')
    plt.plot(epochs, val_loss, 'b', label='验证损失')
    plt.title('训练和验证损失')
    plt.legend()

    plt.show()
else:
    print(f"路径错误，请检查以下路径是否正确:\n训练数据目录: {train_dir}\n验证数据目录: {validation_dir}")