彩色图像使用view_as_blocks分好块后提取每个块的纹理特征和光谱特征的代码
时间: 2024-02-11 18:06:06 浏览: 47
以下是使用scikit-image库中的view_as_blocks函数对彩色图像进行分块,并提取每个块的纹理特征和光谱特征的代码:
```python
from skimage.util import view_as_blocks
from skimage.feature import greycomatrix, greycoprops
from skimage.color import rgb2gray
import numpy as np
def extract_texture_and_spectrum_features(image, block_shape):
# Convert image to grayscale
gray_image = rgb2gray(image)
# Split the grayscale image into blocks
blocks = view_as_blocks(gray_image, block_shape)
# Initialize arrays to store texture and spectrum features
texture_features = np.zeros((blocks.shape[0], blocks.shape[1], 4))
spectrum_features = np.zeros((blocks.shape[0], blocks.shape[1], 3))
# Iterate over each block and extract texture and spectrum features
for i in range(blocks.shape[0]):
for j in range(blocks.shape[1]):
block = blocks[i, j]
# Texture features
glcm = greycomatrix(block, [1], [0, np.pi/4, np.pi/2, 3*np.pi/4], levels=256, symmetric=True, normed=True)
contrast = greycoprops(glcm, 'contrast').ravel()
dissimilarity = greycoprops(glcm, 'dissimilarity').ravel()
homogeneity = greycoprops(glcm, 'homogeneity').ravel()
energy = greycoprops(glcm, 'energy').ravel()
texture_features[i, j] = np.concatenate([contrast, dissimilarity, homogeneity, energy])
# Spectrum features
spectrum_features[i, j, 0] = np.mean(block[:, :, 0])
spectrum_features[i, j, 1] = np.mean(block[:, :, 1])
spectrum_features[i, j, 2] = np.mean(block[:, :, 2])
# Flatten the texture and spectrum feature arrays
texture_features = texture_features.reshape(-1, texture_features.shape[-1])
spectrum_features = spectrum_features.reshape(-1, spectrum_features.shape[-1])
# Return the flattened texture and spectrum feature arrays
return texture_features, spectrum_features
```
其中,`image`为输入的彩色图像,`block_shape`为分块的大小。函数会返回两个数组,分别为纹理特征和光谱特征。纹理特征包括对比度、不相似度、同质性和能量,共4种特征;光谱特征包括红、绿、蓝三个通道的均值。
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