我的数据集是D:\wjd这个路径下有八个文件夹,分别是Al,Ag,Au,Cu,Mo,Cu,V,W这8个文件夹,每个文件夹下面是300张1000*1000的格式为png的ovito三色图,现在想用一种无监督方法对这个路径下D:\wjd下的8个文件夹的图片进行特征提取,然后归为两大类,一类是FCC,一类是BCC,然后把分类结果放到两个文件夹里,一个叫FCC,另外一个叫BCC可以根据这些描述,给我写程序吗
时间: 2024-02-21 08:01:50 浏览: 35
好的,根据你的描述,我可以为你提供一个基于深度学习的无监督图像分类的程序。程序采用自编码器(Autoencoder)来进行特征提取和图像分类。具体实现如下:
1. 导入所需的库和模块:
```python
import os
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.keras.layers import Input, Dense, Conv2D, MaxPooling2D, Flatten, UpSampling2D
from tensorflow.keras.models import Model
```
2. 定义自编码器模型
```python
def autoencoder(input_shape):
input_img = Input(shape=input_shape)
x = Conv2D(32, (3, 3), activation='relu', padding='same')(input_img)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(16, (3, 3), activation='relu', padding='same')(x)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(x)
encoded = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(8, (3, 3), activation='relu', padding='same')(encoded)
x = UpSampling2D((2, 2))(x)
x = Conv2D(16, (3, 3), activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
x = Conv2D(32, (3, 3), activation='relu')(x)
x = UpSampling2D((2, 2))(x)
decoded = Conv2D(3, (3, 3), activation='sigmoid', padding='same')(x)
autoencoder = Model(input_img, decoded)
autoencoder.compile(optimizer='adam', loss='mse')
return autoencoder
```
3. 加载数据集
```python
def load_data(data_path):
data = []
for foldername in os.listdir(data_path):
folderpath = os.path.join(data_path, foldername)
for filename in os.listdir(folderpath):
filepath = os.path.join(folderpath, filename)
img = plt.imread(filepath)
data.append(img)
data = np.array(data)
data = data.astype('float32') / 255.
return data
```
4. 训练自编码器模型
```python
def train_model(data):
autoencoder_model = autoencoder(data.shape[1:])
autoencoder_model.fit(data, data, epochs=50, batch_size=32, shuffle=True)
return autoencoder_model
```
5. 对数据进行特征提取
```python
def extract_features(model, data):
features = model.predict(data)
return features
```
6. 对提取的特征进行聚类
```python
def cluster_features(features):
from sklearn.cluster import KMeans
kmeans = KMeans(n_clusters=2, random_state=0)
labels = kmeans.fit_predict(features)
return labels
```
7. 对分类结果进行可视化
```python
def visualize_results(data, labels, save_path):
folder_fcc = os.path.join(save_path, 'FCC')
folder_bcc = os.path.join(save_path, 'BCC')
os.makedirs(folder_fcc, exist_ok=True)
os.makedirs(folder_bcc, exist_ok=True)
for i in range(len(data)):
img = data[i]
label = labels[i]
if label == 0:
plt.imsave(os.path.join(folder_fcc, f'img_{i}.png'), img)
else:
plt.imsave(os.path.join(folder_bcc, f'img_{i}.png'), img)
```
8. 运行程序
```python
data_path = 'D:/wjd'
save_path = 'D:/results'
data = load_data(data_path)
model = train_model(data)
features = extract_features(model, data)
labels = cluster_features(features)
visualize_results(data, labels, save_path)
```
请将以上代码保存为一个Python文件,然后运行即可。程序会自动对数据集进行特征提取和聚类,并将分类结果保存在两个文件夹中。注意,程序中的自编码器模型是针对1000x1000x3的图像进行设计的,如果你的图像尺寸不同,需要相应地修改模型结构。