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增加模块；增加主调用命令

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zhanghuilai
2026-01-07 16:36:47 +08:00
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# coding: utf-8
# The code is written by Linghui
import numpy as np
from skimage import data
from matplotlib import pyplot as plt
import get_glcm
import time
from PIL import Image
import spectral
import os
import warnings
warnings.filterwarnings('ignore')
# 使用get_glcm模块进行GLCM计算
def main():
pass
def load_hyperspectral_data(image_path, band_index=None):
"""
加载高光谱数据并可选地提取指定波段
参数:
- image_path: 高光谱图像文件路径
- band_index: 要提取的波段索引，如果为None则返回整个图像
返回:
- img: 处理后的图像数据
- h, w: 图像尺寸
"""
print(f'正在读取高光谱图像: {image_path}')
try:
# 使用spectral库读取ENVI格式
img_obj = spectral.open_image(image_path)
hyperspectral_data = img_obj.load()
print(f'使用spectral库读取成功，数据形状: {hyperspectral_data.shape}')
except Exception as e:
print(f'spectral库读取失败: {e}')
raise ValueError(f"无法读取图像文件: {image_path}")
# 检查数据维度
if len(hyperspectral_data.shape) != 3:
raise ValueError(f"高光谱数据应该是3维的，当前形状: {hyperspectral_data.shape}")
h, w, bands = hyperspectral_data.shape
print(f'图像尺寸: {h}x{w}, 波段数: {bands}')
# 如果指定了波段索引，提取该波段
if band_index is not None:
if band_index < 0 or band_index >= bands:
raise ValueError(f"波段索引 {band_index} 超出范围 [0, {bands-1}]")
print(f'提取波段 {band_index}...')
img = hyperspectral_data[:, :, band_index].astype(np.float32)
# 归一化到0-255范围
img_min, img_max = img.min(), img.max()
if img_max > img_min:
img = np.uint8(255.0 * (img - img_min) / (img_max - img_min))
else:
img = np.zeros_like(img, dtype=np.uint8)
print(f'提取的波段数据范围: [{img_min:.2f}, {img_max:.2f}] -> [0, 255]')
else:
# 如果没有指定波段，返回第一个波段作为示例
print('未指定波段，使用第一个波段...')
img = hyperspectral_data[:, :, 0].astype(np.float32)
img_min, img_max = img.min(), img.max()
if img_max > img_min:
img = np.uint8(255.0 * (img - img_min) / (img_max - img_min))
else:
img = np.zeros_like(img, dtype=np.uint8)
return img, h, w
if __name__ == '__main__':
main()
start = time.time()
print('---------------0. Parameter Setting-----------------')
nbit = 64 # gray levels
mi, ma = 0, 255 # max gray and min gray
slide_window = 7 # sliding window
# step = [2, 4, 8, 16] # step
# angle = [0, np.pi/4, np.pi/2, np.pi*3/4] # angle or direction
step = [2]
angle = [0]
# 指定波段索引（可以修改）
band_index = 25 # 要处理的波段索引，从0开始
print('-------------------1. Load Data---------------------')
# 修改为高光谱图像路径
image_path = r"C:\Program Files\Spectronon3\_internal\examples\leaf_small.bip.hdr"
# image_path = r"./test.tif" # 备用的TIFF图像路径
# 检查文件是否存在
if not os.path.exists(image_path):
print(f"警告: 指定的图像文件不存在: {image_path}")
print("请确保文件路径正确，或使用其他可用的图像文件")
exit(1)
# 加载高光谱数据
img, h, w = load_hyperspectral_data(image_path, band_index)
print('------------------2. 计算GLCM特征---------------------')
img = img.squeeze()
# 使用get_glcm计算GLCM特征
glcm = get_glcm.calcu_glcm(img, mi, ma, nbit, slide_window, step, angle)
print('GLCM计算完成！')
print('-----------------3. 提取纹理特征-------------------')
# 提取纹理特征用于显示
# 只计算最后一个step和angle的特征用于显示
i, j = len(step)-1, len(angle)-1 # 使用最后一个配置
glcm_cut = glcm[:, :, i, j, :, :]
mean = get_glcm.calcu_glcm_mean(glcm_cut, nbit)
variance = get_glcm.calcu_glcm_variance(glcm_cut, nbit)
homogeneity = get_glcm.calcu_glcm_homogeneity(glcm_cut, nbit)
contrast = get_glcm.calcu_glcm_contrast(glcm_cut, nbit)
dissimilarity = get_glcm.calcu_glcm_dissimilarity(glcm_cut, nbit)
entropy = get_glcm.calcu_glcm_entropy(glcm_cut, nbit)
energy = get_glcm.calcu_glcm_energy(glcm_cut, nbit)
correlation = get_glcm.calcu_glcm_correlation(glcm_cut, nbit)
Auto_correlation = get_glcm.calcu_glcm_Auto_correlation(glcm_cut, nbit)
print('---------------4. Display and Result----------------')
print(f'使用的波段: {band_index}')
print(f'GLCM参数: distances={step}, angles={[f"{a*180/np.pi:.0f}°" for a in angle]}')
plt.figure(figsize=(10, 4.5))
font = {'family' : 'Times New Roman',
'weight' : 'normal',
'size' : 12,
}
plt.subplot(2,5,1)
plt.tick_params(labelbottom=False, labelleft=False)
plt.axis('off')
plt.imshow(img, cmap ='gray')
plt.title(f'Band {band_index}', font)
plt.subplot(2,5,2)
plt.tick_params(labelbottom=False, labelleft=False)
plt.axis('off')
plt.imshow(mean, cmap ='gray')
plt.title('Mean', font)
plt.subplot(2,5,3)
plt.tick_params(labelbottom=False, labelleft=False)
plt.axis('off')
plt.imshow(variance, cmap ='gray')
plt.title('Variance', font)
plt.subplot(2,5,4)
plt.tick_params(labelbottom=False, labelleft=False)
plt.axis('off')
plt.imshow(homogeneity, cmap ='gray')
plt.title('Homogeneity', font)
plt.subplot(2,5,5)
plt.tick_params(labelbottom=False, labelleft=False)
plt.axis('off')
plt.imshow(contrast, cmap ='gray')
plt.title('Contrast', font)
plt.subplot(2,5,6)
plt.tick_params(labelbottom=False, labelleft=False)
plt.axis('off')
plt.imshow(dissimilarity, cmap ='gray')
plt.title('Dissimilarity', font)
plt.subplot(2,5,7)
plt.tick_params(labelbottom=False, labelleft=False)
plt.axis('off')
plt.imshow(entropy, cmap ='gray')
plt.title('Entropy', font)
plt.subplot(2,5,8)
plt.tick_params(labelbottom=False, labelleft=False)
plt.axis('off')
plt.imshow(energy, cmap ='gray')
plt.title('Energy', font)
plt.subplot(2,5,9)
plt.tick_params(labelbottom=False, labelleft=False)
plt.axis('off')
plt.imshow(correlation, cmap ='gray')
plt.title('Correlation', font)
plt.subplot(2,5,10)
plt.tick_params(labelbottom=False, labelleft=False)
plt.axis('off')
plt.imshow(Auto_correlation, cmap ='gray')
plt.title('Auto Correlation', font)
plt.tight_layout(pad=0.5)
# 创建输出文件名，包含波段信息
output_filename = f'GLCM_Features_Band{band_index}.png'
plt.savefig(output_filename
, format='png'
, bbox_inches = 'tight'
, pad_inches = 0
, dpi=300)
print(f'结果图像已保存到: {output_filename}')
end = time.time()
print('Code run time:', end - start)