358 lines
11 KiB
Python
358 lines
11 KiB
Python
# coding: utf-8
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# The code is written by Linghui
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import numpy as np
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from skimage import data
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from matplotlib import pyplot as plt
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import time
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from PIL import Image
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import spectral
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import os
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import warnings
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warnings.filterwarnings('ignore')
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# 导入GLCM计算模块
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try:
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# 尝试相对导入
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from . import get_glcm
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except ImportError:
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try:
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# 尝试绝对导入
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import get_glcm
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except ImportError:
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# 如果都失败，尝试从当前目录导入
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import sys
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current_dir = os.path.dirname(os.path.abspath(__file__))
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if current_dir not in sys.path:
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sys.path.insert(0, current_dir)
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import get_glcm
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# 使用get_glcm模块进行GLCM计算
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class SpatialFeatureConfig:
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"""
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空间特征提取配置类
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"""
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def __init__(self):
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# GLCM参数
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self.nbit = 64 # gray levels
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self.mi = 0 # min gray
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self.ma = 255 # max gray
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self.slide_window = 7 # sliding window size
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# GLCM计算参数
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self.step = [2] # step distances
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self.angle = [0] # angles in radians
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# 数据处理参数
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self.band_index = 25 # 要处理的波段索引
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# 文件路径
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self.image_path = r"C:\Program Files\Spectronon3\_internal\examples\leaf_small.bip.hdr"
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# 输出设置
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self.output_dir = "output"
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self.save_dat = True # 是否保存为dat文件
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self.save_png = False # 是否保存为PNG文件
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def validate(self):
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"""验证配置参数的有效性"""
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if not os.path.exists(self.image_path):
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raise FileNotFoundError(f"图像文件不存在: {self.image_path}")
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if self.band_index < 0:
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raise ValueError("波段索引不能为负数")
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if self.nbit <= 0:
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raise ValueError("nbit必须大于0")
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if self.slide_window <= 0 or self.slide_window % 2 == 0:
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raise ValueError("slide_window必须为正奇数")
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return True
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def run_glcm_analysis(config):
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"""
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执行GLCM纹理特征分析
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参数:
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config: SpatialFeatureConfig对象
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"""
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start = time.time()
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print('---------------0. Parameter Setting-----------------')
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# 验证配置
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try:
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config.validate()
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print("配置验证通过")
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except Exception as e:
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print(f"配置验证失败: {e}")
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return
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print('-------------------1. Load Data---------------------')
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# 加载高光谱数据
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img, h, w = load_hyperspectral_data(config.image_path, config.band_index)
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# 提取纹理特征
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features_dict = extract_texture_features(img, config)
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print('---------------4. Display and Result----------------')
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print(f'使用的波段: {config.band_index}')
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print(f'GLCM参数: distances={config.step}, angles={[f"{a*180/np.pi:.0f}°" for a in config.angle]}')
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print(f'图像尺寸: {h}x{w}')
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print(f'处理时间: {time.time() - start:.2f} 秒')
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# 保存特征数据
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if config.save_dat:
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output_filename = f"spatial_features_band{config.band_index}"
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output_path = os.path.join(config.output_dir, output_filename)
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save_features_to_dat(features_dict, output_path, config)
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print('GLCM纹理特征分析完成！')
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return features_dict
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def load_hyperspectral_data(image_path, band_index=None):
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"""
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加载高光谱数据并可选地提取指定波段
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参数:
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- image_path: 高光谱图像文件路径
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- band_index: 要提取的波段索引，如果为None则返回整个图像
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返回:
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- img: 处理后的图像数据
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- h, w: 图像尺寸
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"""
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print(f'正在读取高光谱图像: {image_path}')
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try:
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# 使用spectral库读取ENVI格式
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img_obj = spectral.open_image(image_path)
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hyperspectral_data = img_obj.load()
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print(f'使用spectral库读取成功，数据形状: {hyperspectral_data.shape}')
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except Exception as e:
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print(f'spectral库读取失败: {e}')
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raise ValueError(f"无法读取图像文件: {image_path}")
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# 检查数据维度
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if len(hyperspectral_data.shape) != 3:
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raise ValueError(f"高光谱数据应该是3维的，当前形状: {hyperspectral_data.shape}")
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h, w, bands = hyperspectral_data.shape
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print(f'图像尺寸: {h}x{w}, 波段数: {bands}')
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# 如果指定了波段索引，提取该波段
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if band_index is not None:
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if band_index < 0 or band_index >= bands:
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raise ValueError(f"波段索引 {band_index} 超出范围 [0, {bands-1}]")
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print(f'提取波段 {band_index}...')
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img = hyperspectral_data[:, :, band_index].astype(np.float32)
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# 归一化到0-255范围
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img_min, img_max = img.min(), img.max()
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if img_max > img_min:
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img = np.uint8(255.0 * (img - img_min) / (img_max - img_min))
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else:
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img = np.zeros_like(img, dtype=np.uint8)
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print(f'提取的波段数据范围: [{img_min:.2f}, {img_max:.2f}] -> [0, 255]')
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else:
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# 如果没有指定波段，返回第一个波段作为示例
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print('未指定波段，使用第一个波段...')
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img = hyperspectral_data[:, :, 0].astype(np.float32)
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img_min, img_max = img.min(), img.max()
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if img_max > img_min:
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img = np.uint8(255.0 * (img - img_min) / (img_max - img_min))
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else:
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img = np.zeros_like(img, dtype=np.uint8)
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return img, h, w
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def save_features_to_dat(features_dict, output_path, config):
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"""
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将纹理特征保存为dat文件和对应的hdr文件
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参数:
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- features_dict: 包含各种纹理特征的字典
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- output_path: 输出文件路径（不含扩展名）
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- config: 配置对象
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"""
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try:
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# 确保输出目录存在
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output_dir = os.path.dirname(output_path)
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if output_dir and not os.path.exists(output_dir):
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os.makedirs(output_dir)
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# 准备要保存的特征
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feature_names = ['mean', 'variance', 'homogeneity', 'contrast',
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'dissimilarity', 'entropy', 'energy', 'correlation', 'Auto_correlation']
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# 将所有特征堆叠成多波段数据
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feature_list = []
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band_names = []
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for feature_name in feature_names:
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if feature_name in features_dict:
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feature_data = features_dict[feature_name]
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feature_list.append(feature_data)
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band_names.append(feature_name)
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if not feature_list:
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print("没有找到要保存的特征数据")
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return
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# 堆叠所有特征为多波段数组
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stacked_features = np.stack(feature_list, axis=-1)
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print(f"特征数据形状: {stacked_features.shape}")
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# 保存为dat文件（二进制格式）
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dat_path = f"{output_path}.dat"
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with open(dat_path, 'wb') as f:
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# 将数据转换为float32格式保存
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stacked_features.astype(np.float32).tofile(f)
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print(f"特征数据已保存到: {dat_path}")
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# 保存对应的hdr文件
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hdr_path = f"{output_path}.hdr"
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_save_hdr_file(hdr_path, stacked_features.shape, config, band_names)
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print(f"头文件已保存到: {hdr_path}")
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except Exception as e:
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print(f"保存dat文件时出错: {e}")
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def _save_hdr_file(hdr_path, data_shape, config, band_names):
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"""
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保存ENVI头文件
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参数:
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- hdr_path: 头文件路径
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- data_shape: 数据形状 (height, width, bands)
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- config: 配置对象
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- band_names: 波段名称列表
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"""
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height, width, bands = data_shape
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# 计算数据范围（为每个波段计算）
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data_range_info = f"Data ranges vary per band (normalized 0-1)"
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header_content = f"""ENVI
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description = {{Spatial Texture Features [Band: {config.band_index}, Window: {config.slide_window}, Step: {config.step}, Angles: {[f"{a*180/np.pi:.0f}°" for a in config.angle]}]}}
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samples = {width}
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lines = {height}
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bands = {bands}
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header offset = 0
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file type = ENVI Standard
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data type = 4
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interleave = bip
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byte order = 0
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band names = {{{', '.join(band_names)}}}
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data range = {{{data_range_info}}}
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GLCM_params = {{nbit: {config.nbit}, window: {config.slide_window}, step: {config.step}, angles: {config.angle}}}
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"""
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with open(hdr_path, 'w', encoding='utf-8') as f:
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f.write(header_content)
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def extract_texture_features(img, config):
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"""
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提取纹理特征
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参数:
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- img: 输入图像
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- config: 配置对象
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返回:
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- features_dict: 包含各种纹理特征的字典
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"""
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print('------------------2. 计算GLCM特征---------------------')
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img = img.squeeze()
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# 使用get_glcm计算GLCM特征
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glcm = get_glcm.calcu_glcm(img, config.mi, config.ma, config.nbit,
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config.slide_window, config.step, config.angle)
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print('GLCM计算完成！')
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print('-----------------3. 提取纹理特征-------------------')
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# 提取纹理特征
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# 使用最后一个step和angle的特征
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i, j = len(config.step)-1, len(config.angle)-1
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glcm_cut = glcm[:, :, i, j, :, :]
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features_dict = {
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'image_shape': img.shape,
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'mean': get_glcm.calcu_glcm_mean(glcm_cut, config.nbit),
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'variance': get_glcm.calcu_glcm_variance(glcm_cut, config.nbit),
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'homogeneity': get_glcm.calcu_glcm_homogeneity(glcm_cut, config.nbit),
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'contrast': get_glcm.calcu_glcm_contrast(glcm_cut, config.nbit),
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'dissimilarity': get_glcm.calcu_glcm_dissimilarity(glcm_cut, config.nbit),
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'entropy': get_glcm.calcu_glcm_entropy(glcm_cut, config.nbit),
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'energy': get_glcm.calcu_glcm_energy(glcm_cut, config.nbit),
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'correlation': get_glcm.calcu_glcm_correlation(glcm_cut, config.nbit),
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'Auto_correlation': get_glcm.calcu_glcm_Auto_correlation(glcm_cut, config.nbit)
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}
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return features_dict
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def main():
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"""主函数：执行空间特征提取流程"""
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start = time.time()
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print('---------------0. Parameter Setting-----------------')
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# 创建配置对象
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config = SpatialFeatureConfig()
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# 可以在这里修改配置参数
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# config.band_index = 30 # 修改波段索引
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# config.step = [2, 4, 8] # 修改步长
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# config.angle = [0, np.pi/4, np.pi/2, np.pi*3/4] # 修改角度
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# 验证配置
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try:
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config.validate()
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print("配置验证通过")
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except Exception as e:
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print(f"配置验证失败: {e}")
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return
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print('-------------------1. Load Data---------------------')
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# 加载高光谱数据
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img, h, w = load_hyperspectral_data(config.image_path, config.band_index)
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# 提取纹理特征
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features_dict = extract_texture_features(img, config)
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print('---------------4. Display and Result----------------')
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print(f'使用的波段: {config.band_index}')
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print(f'GLCM参数: distances={config.step}, angles={[f"{a*180/np.pi:.0f}°" for a in config.angle]}')
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print(f'图像尺寸: {h}x{w}')
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print(f'处理时间: {time.time() - start:.2f} 秒')
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# 保存特征数据
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if config.save_dat:
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output_filename = f"spatial_features_band{config.band_index}"
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output_path = os.path.join(config.output_dir, output_filename)
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save_features_to_dat(features_dict, output_path, config)
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print('处理完成！')
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if __name__ == '__main__':
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main()
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