基于图像处理和深度学习的齿轮表面缺陷检测的python代码

基于图像处理和深度学习进行齿轮表面缺陷检测的Python代码通常涉及以下几个步骤：

1. **数据预处理**：

import cv2
from sklearn.model_selection import train_test_split

# 加载齿轮图像数据集
data = ... # 图像文件路径列表，标签列表
X, y = data['images'], data['labels']

# 数据增强，如旋转、缩放等
augmenter = imgaug.augmenters.Sequential([
    imgaug.augmenters.Fliplr(0.5),  # 水平翻转
    imgaug.augmenters.RandomCrop((img_height, img_width))  # 随机裁剪
])
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)
X_train = augmenter(images=X_train)

# 转换为灰度图或预训练模型所需的输入格式
X_train_gray = np.array([cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) for img in X_train])

2. **特征提取**：

from tensorflow.keras.applications.vgg16 import VGG16, preprocess_input

# 使用预训练模型提取特征
vgg16 = VGG16(weights='imagenet', include_top=False, input_shape=(img_height, img_width, 1))
features = vgg16.predict(X_train_gray)
features = features.reshape((-1, features.shape[-1]))

3. **构建深度学习模型**：

from tensorflow.keras.layers import Dense, GlobalAveragePooling2D
from tensorflow.keras.models import Model

base_model = vgg16
x = base_model.output
x = GlobalAveragePooling2D()(x)
predictions = Dense(num_classes, activation='softmax')(x)

model = Model(inputs=base_model.input, outputs=predictions)
for layer in base_model.layers:
    layer.trainable = False

# 冻结基础模型层，只训练添加的自定义层
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

4. **模型训练**：

history = model.fit(X_train, y_train, validation_data=(X_val, y_val), epochs=num_epochs, batch_size=batch_size)

5. **评估与预测**：

score = model.evaluate(X_test_gray, y_test)
print(f"Test accuracy: {score[1]}")

def predict_defect(image_path):
    image = ...  # 读取测试图像并转换为适当格式
    prediction = model.predict(image)
    return prediction