783 lines
27 KiB
Python
783 lines
27 KiB
Python
#!/usr/bin/env python3
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"""
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高光谱图像地理变换工具
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功能：
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- 读取地理校正后的高光谱航带图像 (dat格式，11个波段：前3个光谱波段、列号、行号、航带号、后5个光谱波段)
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- 读取原始高光谱图像 (bil格式)
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- 通过行列号进行地理变换匹配
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- 保存变换后的高光谱图像
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文件命名格式：
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- 地理校正文件：2025_9_2_3_53_45_202592_35252_0_rad_rgbxyz_geo1.dat
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- 原始文件：2025_9_2_3_53_45_202592_35252_0_rad.bil
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依赖：
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- numpy
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- spectral (pip install spectral)
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- pathlib
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"""
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import numpy as np
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import os
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from pathlib import Path
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import re
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from typing import Tuple, Optional, List
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try:
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import spectral
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SPECTRAL_AVAILABLE = True
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except ImportError:
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SPECTRAL_AVAILABLE = False
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print("警告: spectral库不可用，请安装: pip install spectral")
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# 可选：GDAL库用于保存ENVI格式文件
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try:
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from osgeo import gdal
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GDAL_AVAILABLE = True
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except ImportError:
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GDAL_AVAILABLE = False
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print("警告: GDAL不可用，将使用numpy保存")
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def _format_geo_info_for_envi(geo_value):
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"""
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直接返回地理信息的原始格式（已经是从HDR文件直接复制的）
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"""
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if not geo_value:
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return None
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return geo_value
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def extract_file_key(filename: str) -> str:
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"""
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从文件名中提取用于匹配的关键字
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匹配规则：按"2025_9_2_3_53_45_202592_35252_0"这一部分完全相同进行匹配
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即匹配模式为：数字_数字_..._数字_rad
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示例：
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"2025_9_2_3_53_45_202592_35252_2_rad_rgbxyz_geo1.dat" -> "2025_9_2_3_53_45_202592_35252_2"
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"2025_9_2_3_53_45_202592_35252_9_rad.bil" -> "2025_9_2_3_53_45_202592_35252_9"
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"""
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# 提取_rad之前的部分作为匹配键
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if '_rad' in filename:
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key = filename.split('_rad')[0]
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return key
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else:
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# 如果没有_rad，尝试其他方式
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return filename
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def load_geo_corrected_dat(dat_file: str) -> Tuple[np.ndarray, dict]:
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"""
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读取地理校正后的dat文件
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dat文件包含11个波段的原始数据：
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- 波段0-2: 前三个光谱波段
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- 波段3: 列号
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- 波段4: 行号
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- 波段5: 航带号
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- 波段6-10: 后五个光谱波段
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使用spectral库读取dat文件
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参数:
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-----------
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dat_file : str
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dat文件路径
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返回:
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-----------
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data : np.ndarray
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图像数据 (pixels, 11)，每个像素11个波段
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metadata : dict
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元数据信息
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"""
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if not SPECTRAL_AVAILABLE:
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raise RuntimeError("需要spectral库来读取dat文件，请安装: pip install spectral")
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try:
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# 查找对应的头文件
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dat_path = Path(dat_file)
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hdr_file = dat_path.with_suffix('.hdr')
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if not hdr_file.exists():
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# 首先读取数据大小来推断图像尺寸
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data_temp = np.fromfile(dat_file, dtype=np.float32)
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if data_temp.size % 11 != 0:
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raise ValueError(f"数据大小 {data_temp.size} 不能被11整除，可能不是正确的dat文件格式")
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num_pixels = data_temp.size // 11
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# 尝试推断图像尺寸
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possible_rows = []
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for rows in range(1000, int(np.sqrt(num_pixels)) + 1000, 100):
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if num_pixels % rows == 0:
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cols = num_pixels // rows
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if cols > 0 and cols < 10000:
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possible_rows.append((rows, cols))
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if not possible_rows:
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rows = int(np.sqrt(num_pixels))
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cols = (num_pixels + rows - 1) // rows
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else:
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rows, cols = possible_rows[0]
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# 创建临时的头文件
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hdr_content = f"""ENVI
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description = {{
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高光谱地理校正数据 - 11波段格式
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前3个光谱波段、列号、行号、航带号、后5个光谱波段}}
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samples = {cols}
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lines = {rows}
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bands = 11
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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 = {{
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波段1, 波段2, 波段3, 列号, 行号, 航带号, 波段7, 波段8, 波段9, 波段10, 波段11}}
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"""
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with open(hdr_file, 'w', encoding='utf-8') as f:
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f.write(hdr_content)
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# 使用spectral读取
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image_data = spectral.open_image(str(hdr_file))
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data = image_data.load()
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# 如果数据是3D的，需要重新整形为2D (pixels, bands)
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if data.ndim == 3:
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data_reshaped = data.reshape(-1, data.shape[2])
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else:
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data_reshaped = data
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# 确保是11波段
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if data_reshaped.shape[1] != 11:
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raise ValueError(f"数据波段数 {data_reshaped.shape[1]} 不等于期望的11波段")
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# 检查坐标是否全部为0（第4、5波段为列号、行号）
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all_coords_zero = (data_reshaped[:, 3].max() == 0 and data_reshaped[:, 4].max() == 0)
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# 如果坐标都为0，显示警告
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if all_coords_zero:
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print(f"警告: dat文件中的所有行列号都是0，可能需要重新解释数据格式")
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# 读取HDR文件中的地理信息（直接复制原始格式）
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geo_info = {}
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try:
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# 直接读取HDR文件内容，保留原始格式
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with open(str(hdr_file), 'r', encoding='utf-8', errors='ignore') as f:
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hdr_content = f.read()
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# 提取map info行
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import re
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map_info_match = re.search(r'^map info\s*=\s*(.+)$', hdr_content, re.MULTILINE | re.IGNORECASE)
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if map_info_match:
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geo_info['map_info'] = map_info_match.group(1).strip()
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# 提取coordinate system string行
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coord_sys_match = re.search(r'^coordinate system string\s*=\s*(.+)$', hdr_content, re.MULTILINE | re.IGNORECASE)
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if coord_sys_match:
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geo_info['coordinate_system_string'] = coord_sys_match.group(1).strip()
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# 提取projection info行（如果有）
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proj_info_match = re.search(r'^projection info\s*=\s*(.+)$', hdr_content, re.MULTILINE | re.IGNORECASE)
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if proj_info_match:
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geo_info['projection_info'] = proj_info_match.group(1).strip()
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except Exception as e:
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print(f"警告: 无法读取geo文件的HDR地理信息: {e}")
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metadata = {
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'file_path': dat_file,
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'hdr_file': str(hdr_file),
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'data_type': str(data_reshaped.dtype),
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'bands': 11,
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'pixels': len(data_reshaped),
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'lines': image_data.nrows,
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'samples': image_data.ncols,
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'band_names': ['band1', 'band2', 'band3', 'column', 'row', 'strip', 'band7', 'band8', 'band9', 'band10', 'band11'],
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'col_idx': 3,
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'row_idx': 4,
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'strip_idx': 5,
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'all_coords_zero': all_coords_zero,
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'wavelengths': getattr(image_data.bands, 'centers', None),
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'interleave': getattr(image_data, 'interleave', 'unknown'),
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'geo_info': geo_info
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}
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return data_reshaped, metadata
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except Exception as e:
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print(f"spectral读取失败: {e}")
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print("回退到numpy直接读取...")
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# 备用方案：使用numpy直接读取
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try:
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data = np.fromfile(dat_file, dtype=np.float32)
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if data.size % 11 != 0:
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raise ValueError(f"数据大小 {data.size} 不能被11整除，可能不是正确的dat文件格式")
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num_pixels = data.size // 11
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data_reshaped = data.reshape(num_pixels, 11)
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print(f"numpy直接读取成功: {dat_file}")
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print(f"数据形状: {data_reshaped.shape} (pixels, bands)")
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# 分析坐标信息
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all_coords_zero = (data_reshaped[:, 3].max() == 0 and data_reshaped[:, 4].max() == 0)
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metadata = {
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'file_path': dat_file,
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'data_type': 'float32',
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'bands': 11,
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'pixels': num_pixels,
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'band_names': ['band1', 'band2', 'band3', 'column', 'row', 'strip', 'band7', 'band8', 'band9', 'band10', 'band11'],
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'col_idx': 3,
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'row_idx': 4,
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'strip_idx': 5,
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'all_coords_zero': all_coords_zero
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}
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return data_reshaped, metadata
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except Exception as e2:
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raise RuntimeError(f"读取dat文件失败: spectral错误: {e}, numpy错误: {e2}")
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def load_original_bil(bil_file: str) -> Tuple[np.ndarray, dict]:
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"""
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读取原始高光谱bil文件
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使用spectral库通过hdr头文件读取ENVI格式的高光谱数据
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参数:
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-----------
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bil_file : str
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bil文件路径
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返回:
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-----------
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data : np.ndarray
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高光谱数据 (lines, samples, bands)
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metadata : dict
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元数据信息
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"""
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if not SPECTRAL_AVAILABLE:
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raise RuntimeError("需要spectral库来读取bil文件，请安装: pip install spectral")
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try:
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# 查找对应的头文件
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bil_path = Path(bil_file)
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hdr_file = bil_path.with_suffix('.hdr')
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if not hdr_file.exists():
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hdr_file = bil_path.parent / f"{bil_path.name}.hdr"
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if not hdr_file.exists():
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hdr_file = bil_path.parent / f"{bil_path.stem}.hdr"
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if not hdr_file.exists():
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possible_hdrs = list(bil_path.parent.glob(f"{bil_path.stem}*.hdr"))
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if possible_hdrs:
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hdr_file = possible_hdrs[0]
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else:
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raise FileNotFoundError(f"未找到对应的头文件")
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# 使用spectral读取ENVI格式文件
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image_data = spectral.open_image(str(hdr_file))
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data = image_data.load()
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# 获取元数据
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metadata = {
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'file_path': bil_file,
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'hdr_file': str(hdr_file),
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'lines': image_data.nrows,
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'samples': image_data.ncols,
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'bands': image_data.nbands,
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'wavelengths': image_data.bands.centers if hasattr(image_data.bands, 'centers') else None,
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'data_type': str(data.dtype),
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'interleave': getattr(image_data, 'interleave', 'unknown')
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}
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return data, metadata
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except Exception as e:
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raise RuntimeError(f"读取高光谱文件失败: {bil_file}, 错误: {e}")
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def perform_geometric_transform(original_data: np.ndarray,
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geo_data: np.ndarray,
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geo_metadata: dict,
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output_shape: Optional[Tuple[int, int]] = None,
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chunk_size: Optional[int] = None) -> np.ndarray:
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"""
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向量化版本的地理变换
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核心逻辑：
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1. geo_data[:, 3]和geo_data[:, 4]是原始bil文件中的列号和行号（geo从0开始，bil从1开始，需减1）
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2. geo像素中"非地理参考"的波段（前3个和后5个）不全为0的像素才需要替换（输出保持0）
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3. 输出尺寸为geo图像的尺寸（geo_metadata['lines'], geo_metadata['samples']）
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4. 支持可选分块以降低峰值内存
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参数:
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-----------
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original_data : np.ndarray
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原始高光谱数据 (orig_lines, orig_samples, bands)
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geo_data : np.ndarray
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地理校正数据 (pixels, 11) - geo图像中所有像素的序列
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geo_metadata : dict
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地理校正数据的元数据
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output_shape : tuple, optional
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输出图像的形状 (lines, samples)，如果不指定则尝试从geo_metadata推断
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chunk_size : int, optional
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分块处理时的块大小，默认一次性处理所有像素
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返回:
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-----------
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transformed_data : np.ndarray
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变换后的高光谱数据，尺寸为geo图像的尺寸
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"""
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try:
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# 原始BIL尺寸
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orig_lines, orig_samples, bands = original_data.shape
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# 输出尺寸：使用geo尺寸
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if output_shape is None:
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if 'lines' in geo_metadata and 'samples' in geo_metadata:
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lines_out, samples_out = int(geo_metadata['lines']), int(geo_metadata['samples'])
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else:
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# 兜底：按geo展平像素数推断一个尽量方的尺寸
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total = len(geo_data)
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lines_out = int(np.sqrt(total))
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samples_out = (total + lines_out - 1) // lines_out
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else:
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lines_out, samples_out = output_shape
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# 验证输出图像尺寸是否与geo_data像素数量匹配
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expected_pixels = lines_out * samples_out
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if expected_pixels != len(geo_data):
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print(f"警告: 输出图像尺寸({lines_out}x{samples_out}={expected_pixels})与geo数据像素数({len(geo_data)})不匹配")
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print("将调整输出尺寸以匹配geo数据像素数")
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lines_out = len(geo_data) // samples_out
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if lines_out * samples_out < len(geo_data):
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lines_out += 1
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# 预分配输出（忽略值=0）
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out = np.zeros((lines_out, samples_out, bands), dtype=original_data.dtype)
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print(f"开始地理变换: 原始bil尺寸 {orig_lines}x{orig_samples}x{bands}")
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print(f"输出geo图像尺寸: {lines_out}x{samples_out}x{bands}")
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print(f"处理 {len(geo_data)} 个geo像素")
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# 获取坐标索引（从metadata中读取，默认为3、4、5）
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col_idx = geo_metadata.get('col_idx', 3)
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row_idx = geo_metadata.get('row_idx', 4)
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# 有效掩码：
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# 1) geo像素的"非地理参考"波段（前col_idx个和后面的）不全为0
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# 对于11波段，检查波段0-2和6-10是否不全为0
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non_geo_bands = np.concatenate([np.arange(col_idx), np.arange(col_idx+3, geo_data.shape[1])])
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non_zero_mask = ~(np.all(geo_data[:, non_geo_bands] == 0, axis=1))
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# 2) 索引合法（geo从0，BIL从1，因此索引时要减1）
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src_cols = geo_data[:, col_idx].astype(np.int64) - 1
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src_rows = geo_data[:, row_idx].astype(np.int64) - 1
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in_bounds = (src_rows >= 0) & (src_rows < orig_lines) & (src_cols >= 0) & (src_cols < orig_samples)
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valid = non_zero_mask & in_bounds
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if not np.any(valid):
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print("地理变换完成: 没有有效像素需要处理")
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return out
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# 有效条目
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valid_idx = np.flatnonzero(valid)
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# 计算原始BIL扁平索引，并批量取光谱
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src_rows_v = src_rows[valid]
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src_cols_v = src_cols[valid]
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src_lin = src_rows_v * orig_samples + src_cols_v # (N_valid,)
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orig_flat = original_data.reshape(-1, bands)
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if chunk_size is None or len(valid_idx) <= chunk_size:
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# 一次性
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spectra = orig_flat[src_lin] # (N_valid, bands)
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# 计算输出位置（geo是按行优先展平）
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out_rows = valid_idx // samples_out
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out_cols = valid_idx % samples_out
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out[out_rows, out_cols, :] = spectra
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else:
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# 分块，降低峰值内存
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for s in range(0, len(valid_idx), chunk_size):
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e = min(s + chunk_size, len(valid_idx))
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src_lin_chunk = src_lin[s:e]
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spectra = orig_flat[src_lin_chunk]
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idx_chunk = valid_idx[s:e]
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out_rows = idx_chunk // samples_out
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out_cols = idx_chunk % samples_out
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out[out_rows, out_cols, :] = spectra
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valid_count = len(valid_idx)
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skipped_count = len(geo_data) - valid_count
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print(f"地理变换完成: 成功处理 {valid_count} 个像素，跳过 {skipped_count} 个像素（无效或全0）")
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return out
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except Exception as e:
|
||
raise RuntimeError(f"地理变换失败: {e}")
|
||
|
||
|
||
def fill_nan_with_nearest(data: np.ndarray) -> np.ndarray:
|
||
"""
|
||
用最近邻值填充NaN值
|
||
|
||
参数:
|
||
-----------
|
||
data : np.ndarray
|
||
包含NaN的数组
|
||
|
||
返回:
|
||
-----------
|
||
filled_data : np.ndarray
|
||
填充后的数组
|
||
"""
|
||
# 简单实现：使用前向填充
|
||
filled_data = data.copy()
|
||
|
||
# 对于每个波段
|
||
for band in range(data.shape[2]):
|
||
band_data = data[:, :, band]
|
||
|
||
# 找到非NaN值的掩码
|
||
valid_mask = ~np.isnan(band_data)
|
||
|
||
if np.any(valid_mask):
|
||
# 使用最近邻插值（这里使用简单的行方向填充）
|
||
for i in range(band_data.shape[0]):
|
||
row_data = band_data[i, :]
|
||
valid_indices = np.where(~np.isnan(row_data))[0]
|
||
|
||
if len(valid_indices) > 0:
|
||
# 对每一行，用有效值填充
|
||
for j in range(len(row_data)):
|
||
if np.isnan(row_data[j]):
|
||
# 找到最近的有效值
|
||
distances = np.abs(valid_indices - j)
|
||
nearest_idx = valid_indices[np.argmin(distances)]
|
||
row_data[j] = row_data[nearest_idx]
|
||
|
||
filled_data[:, :, band] = band_data
|
||
|
||
return filled_data
|
||
|
||
|
||
def save_transformed_data(data: np.ndarray, output_file: str, wavelengths: Optional[np.ndarray] = None,
|
||
geo_info: Optional[dict] = None):
|
||
"""
|
||
保存变换后的高光谱数据为ENVI BIL格式
|
||
|
||
参数:
|
||
-----------
|
||
data : np.ndarray
|
||
要保存的数据 (lines, samples, bands)
|
||
output_file : str
|
||
输出文件路径 (.bil)
|
||
wavelengths : np.ndarray, optional
|
||
波长信息
|
||
"""
|
||
lines, samples, bands = data.shape
|
||
|
||
# 确保输出目录存在
|
||
output_path = Path(output_file)
|
||
output_path.parent.mkdir(parents=True, exist_ok=True)
|
||
|
||
# 输出文件路径
|
||
bil_file = str(output_path.with_suffix('.dat'))
|
||
hdr_file = str(output_path.with_suffix('.hdr'))
|
||
|
||
try:
|
||
if GDAL_AVAILABLE:
|
||
# 使用GDAL保存ENVI格式文件
|
||
save_with_gdal_envi(data, bil_file, wavelengths, geo_info)
|
||
else:
|
||
# 回退到numpy保存
|
||
save_with_numpy_envi(data, bil_file, hdr_file, wavelengths, geo_info)
|
||
|
||
print(f"✅ 成功保存地理变换结果:")
|
||
print(f" 数据文件: {bil_file}")
|
||
print(f" 头文件: {hdr_file}")
|
||
print(f" 数据尺寸: {lines} x {samples} x {bands}")
|
||
print(f" 保存方式: {'GDAL' if GDAL_AVAILABLE else 'NumPy'}")
|
||
|
||
except Exception as e:
|
||
raise RuntimeError(f"保存文件失败: {output_file}, 错误: {e}")
|
||
|
||
|
||
def save_with_gdal_envi(data: np.ndarray, bil_file: str, wavelengths: Optional[np.ndarray] = None,
|
||
geo_info: Optional[dict] = None):
|
||
"""
|
||
使用GDAL保存ENVI BIL格式文件
|
||
"""
|
||
lines, samples, bands = data.shape
|
||
hdr_file = bil_file.replace('.dat', '.hdr')
|
||
|
||
# 创建GDAL ENVI驱动
|
||
driver = gdal.GetDriverByName('ENVI')
|
||
|
||
# 创建数据集，GDAL ENVI默认使用BSQ格式，data type = 12 (uint16)
|
||
dataset = driver.Create(bil_file, samples, lines, bands, gdal.GDT_UInt16,
|
||
options=['INTERLEAVE=BSQ'])
|
||
|
||
if dataset is None:
|
||
raise RuntimeError(f"无法创建ENVI数据集: {bil_file}")
|
||
|
||
try:
|
||
# 设置元数据
|
||
metadata = dataset.GetMetadata()
|
||
metadata['DESCRIPTION'] = 'Geometrically transformed hyperspectral data'
|
||
metadata['SENSOR_TYPE'] = 'Hyperspectral'
|
||
metadata['DATA_UNITS'] = 'Reflectance'
|
||
metadata['PROCESSING_ALGORITHM'] = 'Geometric Transformation'
|
||
metadata['CREATION_DATE'] = str(np.datetime64('now'))
|
||
|
||
# 添加波长信息到元数据
|
||
if wavelengths is not None and len(wavelengths) == bands:
|
||
metadata['wavelength_units'] = 'nm'
|
||
for i, wl in enumerate(wavelengths):
|
||
metadata[f'wavelength_{i+1}'] = str(wl)
|
||
|
||
dataset.SetMetadata(metadata)
|
||
|
||
# 写入数据
|
||
for band_idx in range(bands):
|
||
band = dataset.GetRasterBand(band_idx + 1)
|
||
band_data = data[:, :, band_idx].astype(np.float32)
|
||
band.WriteArray(band_data)
|
||
band.SetNoDataValue(0.0) # 设置NoData值
|
||
|
||
# 设置波段描述
|
||
if wavelengths is not None and band_idx < len(wavelengths):
|
||
band.SetDescription(f'{wavelengths[band_idx]:.1f} nm')
|
||
else:
|
||
band.SetDescription(f'Band {band_idx + 1}')
|
||
|
||
finally:
|
||
# 关闭数据集
|
||
dataset = None
|
||
|
||
# 在数据集完全关闭后，覆盖GDAL自动创建的HDR文件
|
||
import time
|
||
time.sleep(0.1) # 短暂等待确保GDAL完成写入
|
||
create_envi_header(hdr_file, lines, samples, bands, wavelengths, geo_info)
|
||
|
||
|
||
def save_with_numpy_envi(data: np.ndarray, bil_file: str, hdr_file: str,
|
||
wavelengths: Optional[np.ndarray] = None, geo_info: Optional[dict] = None):
|
||
"""
|
||
使用numpy保存ENVI BSQ格式文件（GDAL不可用时的回退方案）
|
||
"""
|
||
lines, samples, bands = data.shape
|
||
|
||
# 保存二进制数据 - BSQ格式：按波段顺序存储，data type = 12 (uint16)
|
||
with open(bil_file, 'wb') as f:
|
||
# BSQ格式：对于每个波段，存储所有像素
|
||
for band_idx in range(bands):
|
||
# 转换为uint16，裁剪到有效范围
|
||
band_data = np.clip(data[:, :, band_idx], 0, 65535).astype(np.uint16)
|
||
band_data.tofile(f)
|
||
|
||
# 创建HDR头文件
|
||
create_envi_header(hdr_file, lines, samples, bands, wavelengths, geo_info)
|
||
|
||
|
||
def create_envi_header(hdr_file: str, lines: int, samples: int, bands: int,
|
||
wavelengths: Optional[np.ndarray] = None, geo_info: Optional[dict] = None):
|
||
"""
|
||
创建ENVI格式的HDR头文件
|
||
"""
|
||
with open(hdr_file, 'w', encoding='utf-8') as f:
|
||
f.write("ENVI\n")
|
||
f.write("description = {\n")
|
||
f.write(" Geometrically transformed hyperspectral data\n")
|
||
f.write(" Processed with Python geometric transformation}\n")
|
||
f.write(f"samples = {samples}\n")
|
||
f.write(f"lines = {lines}\n")
|
||
f.write(f"bands = {bands}\n")
|
||
f.write("header offset = 0\n")
|
||
f.write("file type = ENVI Standard\n")
|
||
f.write("data type = 12\n") # uint16
|
||
f.write("interleave = bsq\n")
|
||
f.write("sensor type = Hyperspectral\n")
|
||
f.write("byte order = 0\n") # little-endian
|
||
f.write("data ignore value = 0\n")
|
||
|
||
# 添加地理参考信息（直接使用从原始HDR复制的格式）
|
||
if geo_info:
|
||
if geo_info.get('map_info'):
|
||
f.write(f"map info = {geo_info['map_info']}\n")
|
||
if geo_info.get('coordinate_system_string'):
|
||
f.write(f"coordinate system string = {geo_info['coordinate_system_string']}\n")
|
||
if geo_info.get('projection_info'):
|
||
f.write(f"projection info = {geo_info['projection_info']}\n")
|
||
|
||
# 添加波长信息
|
||
if wavelengths is not None and len(wavelengths) == bands:
|
||
f.write("wavelength units = nm\n")
|
||
f.write("wavelength = { ")
|
||
for i, wl in enumerate(wavelengths):
|
||
f.write(f"{wl}")
|
||
if i < len(wavelengths) - 1:
|
||
f.write(",")
|
||
f.write(" }\n")
|
||
|
||
|
||
|
||
|
||
def process_file_pair(geo_dat_file: str, bil_file: str, output_dir: str) -> bool:
|
||
"""
|
||
处理一对匹配的文件
|
||
|
||
参数:
|
||
-----------
|
||
geo_dat_file : str
|
||
地理校正dat文件路径
|
||
bil_file : str
|
||
原始bil文件路径
|
||
output_dir : str
|
||
输出目录
|
||
|
||
返回:
|
||
-----------
|
||
success : bool
|
||
处理是否成功
|
||
"""
|
||
try:
|
||
# 读取地理校正数据
|
||
geo_data, geo_metadata = load_geo_corrected_dat(geo_dat_file)
|
||
|
||
# 读取原始高光谱数据
|
||
original_data, orig_metadata = load_original_bil(bil_file)
|
||
|
||
# 执行地理变换
|
||
transformed_data = perform_geometric_transform(original_data, geo_data, geo_metadata)
|
||
|
||
# 生成输出文件名
|
||
bil_name = Path(bil_file).stem
|
||
output_file = Path(output_dir) / f"{bil_name}_geo_corrected.dat"
|
||
|
||
# 保存结果
|
||
wavelengths = orig_metadata.get('wavelengths')
|
||
geo_info = geo_metadata.get('geo_info')
|
||
save_transformed_data(transformed_data, str(output_file), wavelengths, geo_info)
|
||
|
||
return True
|
||
|
||
except Exception as e:
|
||
print(f"处理失败: {e}")
|
||
return False
|
||
|
||
|
||
def find_matching_files(geo_dir: str, bil_dir: str) -> List[Tuple[str, str]]:
|
||
"""
|
||
查找匹配的文件对
|
||
|
||
参数:
|
||
-----------
|
||
geo_dir : str
|
||
地理校正文件目录
|
||
bil_dir : str
|
||
原始bil文件目录
|
||
|
||
返回:
|
||
-----------
|
||
file_pairs : List[Tuple[str, str]]
|
||
匹配的文件对列表 [(geo_file, bil_file), ...]
|
||
"""
|
||
geo_files = {}
|
||
bil_files = {}
|
||
|
||
# 收集地理校正文件
|
||
for file_path in Path(geo_dir).glob('*_rad_rgbxyz_geo_angles_registered.bip'):
|
||
key = extract_file_key(file_path.name)
|
||
geo_files[key] = str(file_path)
|
||
|
||
# 收集bil文件
|
||
for file_path in Path(bil_dir).glob('*_rad.bil'):
|
||
key = extract_file_key(file_path.name)
|
||
bil_files[key] = str(file_path)
|
||
|
||
# 找到匹配的对
|
||
matching_pairs = []
|
||
for key in geo_files.keys():
|
||
if key in bil_files:
|
||
matching_pairs.append((geo_files[key], bil_files[key]))
|
||
|
||
return matching_pairs
|
||
|
||
|
||
def batch_process(geo_dir: str, bil_dir: str, output_dir: str) -> dict:
|
||
"""
|
||
批量处理所有匹配的文件对
|
||
|
||
参数:
|
||
-----------
|
||
geo_dir : str
|
||
地理校正文件目录
|
||
bil_dir : str
|
||
原始bil文件目录
|
||
output_dir : str
|
||
输出目录
|
||
|
||
返回:
|
||
-----------
|
||
results : dict
|
||
处理结果统计
|
||
"""
|
||
# 确保输出目录存在
|
||
Path(output_dir).mkdir(parents=True, exist_ok=True)
|
||
|
||
# 查找匹配的文件对
|
||
file_pairs = find_matching_files(geo_dir, bil_dir)
|
||
|
||
if not file_pairs:
|
||
return {'total': 0, 'success': 0, 'failed': 0}
|
||
|
||
# 处理每一对文件
|
||
results = {'total': len(file_pairs), 'success': 0, 'failed': 0}
|
||
|
||
for geo_file, bil_file in file_pairs:
|
||
success = process_file_pair(geo_file, bil_file, output_dir)
|
||
if success:
|
||
results['success'] += 1
|
||
else:
|
||
results['failed'] += 1
|
||
|
||
return results
|
||
|
||
|
||
def main():
|
||
"""
|
||
主函数 - 示例用法
|
||
"""
|
||
# 示例路径（需要根据实际情况修改）
|
||
geo_corrected_dir = r"D:\BaiduNetdiskDownload\20250902\_3_52_52\316\jiaozhen" # 地理校正dat文件目录
|
||
original_bil_dir = r"D:\BaiduNetdiskDownload\20250902\_3_52_52\Geoout\Radout" # 原始bil文件目录
|
||
output_dir = r"D:\BaiduNetdiskDownload\20250902\_3_52_52\316\cube" # 输出目录
|
||
|
||
# 检查依赖
|
||
if not SPECTRAL_AVAILABLE:
|
||
print("错误: 需要安装spectral库")
|
||
return
|
||
|
||
# 批量处理
|
||
results = batch_process(geo_corrected_dir, original_bil_dir, output_dir)
|
||
|
||
if results['success'] > 0:
|
||
print(f"处理完成！成功处理了 {results['success']} 对文件")
|
||
else:
|
||
print("未成功处理任何文件，请检查文件路径和格式")
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main() |