调整缩放、多核运行、图标显示

降采样光谱.py

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+import numpy as np
+import spectral
+import spectral.io.envi as envi
+
+def downsample_bands_extract(data, factor=3, offset=0):
+    """抽取降采样：每 factor 个波段取第 (offset+1) 个波段"""
+    rows, cols, bands = data.shape
+    new_bands = bands // factor
+    indices = [offset + i * factor for i in range(new_bands)]
+    # 边界保护
+    indices = [idx for idx in indices if idx < bands]
+    return data[:, :, indices].astype(np.float32)
+
+def process_bsq_chunked(input_path, output_path, scale=10000, factor=3, offset=0, chunk_lines=500):
+    """
+    分块处理，抽取降采样，正确写入 BSQ 格式
+    BSQ 格式：每个波段的所有行数据连续存储
+    """
+    img = spectral.open_image(input_path)
+    hdr = img.metadata.copy()
+    n_rows, n_cols, n_bands = img.nrows, img.ncols, img.nbands
+    new_bands = n_bands // factor   # 抽取后的波段数
+
+    new_hdr = hdr.copy()
+    new_hdr['samples'] = n_cols
+    new_hdr['lines'] = n_rows
+    new_hdr['bands'] = new_bands
+    new_hdr['data type'] = 12
+    new_hdr['interleave'] = 'bsq'
+
+    # 波长抽取（优先使用原始 header 中的波长）
+    if 'wavelength' in hdr:
+        waves = np.array(hdr['wavelength'])
+        if len(waves) >= new_bands * factor:
+            extracted_waves = waves[offset::factor][:new_bands]
+            new_hdr['wavelength'] = extracted_waves.tolist()
+        else:
+            print("警告: 原始波长数量不足，跳过")
+    
+    # FWHM 处理
+    if 'fwhm' in hdr:
+        fwhm = np.array(hdr['fwhm'])
+        if len(fwhm) >= new_bands * factor:
+            new_hdr['fwhm'] = fwhm[offset::factor][:new_bands].tolist()
+
+    out_file = output_path + '.bsq'
+    
+    # 关键修改：BSQ 格式要求每个波段的所有行连续存储
+    # 先处理所有数据到内存缓冲区（按波段组织），再写入文件
+    # 对于大文件，我们按波段分批处理
+    
+    print(f"开始处理 {n_rows} 行 x {n_cols} 列 x {new_bands} 波段...")
+    
+    # 为每个波段创建一个临时文件（避免内存溢出）
+    import tempfile
+    import os
+    
+    temp_files = []
+    temp_band_data = []
+    
+    try:
+        # 初始化每个波段的临时文件
+        for b in range(new_bands):
+            fd, temp_path = tempfile.mkstemp(suffix=f'_band_{b}.tmp')
+            os.close(fd)
+            temp_files.append(temp_path)
+            temp_band_data.append(open(temp_path, 'wb'))
+        
+        # 第一遍：分块读取，按波段写入临时文件
+        for start_row in range(0, n_rows, chunk_lines):
+            end_row = min(start_row + chunk_lines, n_rows)
+            print(f"处理行 {start_row}-{end_row-1}...")
+            
+            chunk = img.read_subregion((start_row, end_row), (0, n_cols))
+            chunk_down = downsample_bands_extract(chunk, factor, offset)
+            chunk_scaled = chunk_down * scale
+            chunk_uint16 = np.clip(chunk_scaled, 0, 65535).astype(np.uint16)
+            
+            # 将每个波段的数据写入对应的临时文件
+            for b in range(new_bands):
+                band_data = chunk_uint16[:, :, b].tobytes()
+                temp_band_data[b].write(band_data)
+            
+            del chunk, chunk_down, chunk_scaled, chunk_uint16
+        
+        # 关闭所有临时文件
+        for f in temp_band_data:
+            f.close()
+        
+        # 第二遍：按 BSQ 格式合并（波段0全部数据 → 波段1全部数据 → ...）
+        print("合并为 BSQ 格式...")
+        with open(out_file, 'wb') as fout:
+            for b in range(new_bands):
+                with open(temp_files[b], 'rb') as fband:
+                    # 读取该波段的全部行数据并写入最终文件
+                    while True:
+                        data = fband.read(1024 * 1024)  # 1MB 块读取
+                        if not data:
+                            break
+                        fout.write(data)
+                print(f"  波段 {b+1}/{new_bands} 写入完成")
+        
+        print(f"BSQ 文件写入完成: {out_file}")
+        
+    finally:
+        # 清理临时文件
+        for temp_path in temp_files:
+            try:
+                os.remove(temp_path)
+            except:
+                pass
+    
+    # 写入头文件
+    envi.write_envi_header(output_path + '.hdr', new_hdr)
+    print(f"完成！输出: {out_file}")
+    print(f"  尺寸: {n_rows} 行 x {n_cols} 列 x {new_bands} 波段")
+
+if __name__ == '__main__':
+    input_base = r"D:\BaiduNetdiskDownload\yaobao\caijain.hdr"
+    output_base = r"D:\BaiduNetdiskDownload\yaobao\test"
+    process_bsq_chunked(input_base, output_base, scale=10000, factor=3, offset=0, chunk_lines=2000)