464 lines
16 KiB
Python
464 lines
16 KiB
Python
import os
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import numpy as np
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import matplotlib.pyplot as plt
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from pathlib import Path
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import glob
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class BilToPngConverter:
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"""
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将BIL/BSQ/BIP文件转换为PNG图像的工具类
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"""
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def __init__(self, bil_folder, output_folder=None):
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"""
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初始化转换器
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参数:
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bil_folder: BIL文件所在的文件夹路径
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output_folder: 输出PNG的文件夹路径，如果为None则创建在bil_folder下的'png_output'文件夹
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"""
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self.bil_folder = Path(bil_folder)
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if output_folder is None:
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self.output_folder = self.bil_folder / 'png_output'
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else:
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self.output_folder = Path(output_folder)
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# 创建输出文件夹
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self.output_folder.mkdir(parents=True, exist_ok=True)
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def find_bil_files(self):
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"""查找文件夹中的所有BIL文件"""
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bil_files = []
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# 使用glob查找所有BIL文件
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for ext in ['.bil', '.BIL']:
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bil_files.extend(glob.glob(str(self.bil_folder / f'*{ext}')))
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# 去重并排序
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bil_files = sorted(list(set(bil_files)))
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print(f"找到 {len(bil_files)} 个BIL文件")
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return [Path(f) for f in bil_files]
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def read_envi_header(self, header_path):
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"""读取ENVI头文件获取图像尺寸和格式信息"""
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rows, cols, bands = None, None, None
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data_type = None
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interleave = 'bsq' # 默认值
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byte_order = 0 # 默认小端字节序
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try:
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with open(header_path, 'r') as f:
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content = f.read()
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# 解析关键参数
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lines = content.split('\n')
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for line in lines:
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line = line.strip()
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if '=' in line:
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key, value = line.split('=', 1)
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key = key.strip()
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value = value.strip()
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if key.lower() == 'samples':
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cols = int(value)
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elif key.lower() == 'lines':
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rows = int(value)
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elif key.lower() == 'bands':
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bands = int(value)
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elif key.lower() == 'data type':
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data_type = int(value)
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elif key.lower() == 'interleave':
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interleave = value.lower() # bsq, bil, bip
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elif key.lower() == 'byte order':
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byte_order = int(value)
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print(f"从头文件读取: {rows}x{cols}x{bands}, interleave={interleave}, data_type={data_type}")
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except Exception as e:
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print(f"读取头文件时出错: {e}")
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return rows, cols, bands, data_type, interleave, byte_order
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def get_numpy_dtype(self, envi_data_type):
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"""将ENVI数据类型转换为numpy数据类型"""
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dtype_map = {
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1: np.uint8, # 8-bit byte
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2: np.int16, # 16-bit signed integer
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3: np.int32, # 32-bit signed long integer
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4: np.float32, # 32-bit floating point
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5: np.float64, # 64-bit double precision floating point
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12: np.uint16, # 16-bit unsigned integer
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13: np.uint32, # 32-bit unsigned long integer
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14: np.int64, # 64-bit signed long integer
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15: np.uint64, # 64-bit unsigned long integer
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}
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return dtype_map.get(envi_data_type, np.float32)
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def read_envi_file(self, bil_path, rows=None, cols=None, bands=None):
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"""
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读取ENVI格式文件（支持BSQ, BIL, BIP格式）
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参数:
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bil_path: 数据文件路径
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rows: 图像行数（高度）
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cols: 图像列数（宽度）
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bands: 波段数
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返回:
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图像数据数组，形状为(rows, cols, bands)
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"""
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# 尝试读取同名的头文件(.hdr)获取尺寸信息
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header_path = bil_path.with_suffix('.hdr')
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if header_path.exists():
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print(f"正在读取头文件: {header_path}")
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rows, cols, bands, data_type, interleave, byte_order = self.read_envi_header(header_path)
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if data_type is None:
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data_type = 4 # 默认float32
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dtype = self.get_numpy_dtype(data_type)
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else:
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# 如果没有头文件，使用默认值或参数值
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if rows is None or cols is None or bands is None:
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raise ValueError(f"找不到头文件 {header_path}，请手动指定rows, cols, bands参数")
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print(f"未找到头文件，使用手动指定的尺寸: {rows}x{cols}x{bands}")
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dtype = np.float32
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interleave = 'bsq' # 默认
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# 计算文件大小并验证
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expected_size = rows * cols * bands * np.dtype(dtype).itemsize
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actual_size = bil_path.stat().st_size
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if expected_size != actual_size:
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print(f"警告: 文件大小不匹配。预期: {expected_size}字节，实际: {actual_size}字节")
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print(f"这可能是由于数据压缩或头部信息导致")
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# 读取二进制数据
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print(f"正在读取数据文件: {bil_path.name}, 尺寸: {rows}x{cols}x{bands}, 格式: {interleave}")
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try:
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# 读取原始数据
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raw_data = np.fromfile(bil_path, dtype=dtype)
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# 根据数据交织方式重塑数据
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if interleave == 'bsq':
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# BSQ: Band Sequential (波段顺序)
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# 存储顺序: 波段1的所有行所有列 -> 波段2的所有行所有列 -> ...
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data = raw_data.reshape(bands, rows, cols)
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# 转换为标准格式: (rows, cols, bands)
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data = np.transpose(data, (1, 2, 0))
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elif interleave == 'bil':
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# BIL: Band Interleaved by Line (按行交织)
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# 存储顺序: 行1的所有波段 -> 行2的所有波段 -> ...
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data = raw_data.reshape(rows, bands, cols)
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# 转换为标准格式: (rows, cols, bands)
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data = np.transpose(data, (0, 2, 1))
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elif interleave == 'bip':
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# BIP: Band Interleaved by Pixel (按像素交织)
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# 存储顺序: 像素1的所有波段 -> 像素2的所有波段 -> ...
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data = raw_data.reshape(rows, cols, bands)
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# 已经是标准格式，无需转置
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else:
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print(f"未知的交织格式: {interleave}，尝试BSQ格式")
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data = raw_data.reshape(bands, rows, cols)
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data = np.transpose(data, (1, 2, 0))
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print(f"成功读取，数据形状: {data.shape}, 数据类型: {data.dtype}")
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return data
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except Exception as e:
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print(f"读取数据文件时出错: {e}")
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print(f"原始数据大小: {len(raw_data)}, 期望大小: {rows * cols * bands}")
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raise
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def normalize_image(self, image_data, percentile_low=2, percentile_high=98):
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"""
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归一化图像数据
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参数:
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image_data: 输入图像数据
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percentile_low: 低百分位数
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percentile_high: 高百分位数
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返回:
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归一化后的图像数据 (0-1范围)
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"""
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# 如果有多波段，分别归一化每个波段
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if len(image_data.shape) == 3 and image_data.shape[2] > 1:
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normalized = np.zeros_like(image_data, dtype=np.float32)
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for band in range(image_data.shape[2]):
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band_data = image_data[:, :, band]
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# 去除异常值
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low = np.percentile(band_data, percentile_low)
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high = np.percentile(band_data, percentile_high)
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# 截断
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band_clipped = np.clip(band_data, low, high)
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# 归一化到0-1
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if high > low:
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normalized[:, :, band] = (band_clipped - low) / (high - low)
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else:
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normalized[:, :, band] = band_data - band_data.min()
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if normalized[:, :, band].max() > 0:
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normalized[:, :, band] /= normalized[:, :, band].max()
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else:
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# 单波段图像
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if len(image_data.shape) == 3:
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image_data = image_data.squeeze()
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# 去除异常值
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low = np.percentile(image_data, percentile_low)
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high = np.percentile(image_data, percentile_high)
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# 截断
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clipped = np.clip(image_data, low, high)
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# 归一化到0-1
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if high > low:
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normalized = (clipped - low) / (high - low)
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else:
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normalized = image_data - image_data.min()
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if normalized.max() > 0:
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normalized /= normalized.max()
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return np.clip(normalized, 0, 1)
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def apply_colormap(self, image_data, colormap='viridis'):
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"""
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应用颜色映射
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参数:
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image_data: 归一化的图像数据 (0-1范围)
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colormap: 颜色映射名称
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返回:
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RGB图像数据
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"""
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# 获取颜色映射
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cmap = plt.cm.get_cmap(colormap)
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# 应用颜色映射
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colored = cmap(image_data)
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# 转换为RGB (移除alpha通道)
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if colored.shape[2] == 4:
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colored = colored[:, :, :3]
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return colored
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def extract_title_from_filename(self, filename):
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"""
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从文件名提取标题（以下划线分割的第二个元素）
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参数:
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filename: 文件名
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返回:
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提取的标题
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"""
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# 获取文件名（不带扩展名）
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stem = Path(filename).stem
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# 以下划线分割
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parts = stem.split('_')
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if len(parts) >= 2:
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return parts[1]
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else:
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# 如果没有足够的下划线分割，返回文件名本身
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return stem
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def envi_to_png(self, bil_path, output_name=None,
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rows=None, cols=None, bands=None,
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colormap='viridis', dpi=150):
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"""
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将ENVI格式文件转换为PNG
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参数:
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bil_path: 数据文件路径
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output_name: 输出文件名，如果为None则使用BIL文件名
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rows: 图像行数
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cols: 图像列数
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bands: 波段数
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colormap: 颜色映射名称
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dpi: 输出图像DPI
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返回:
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输出文件路径
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"""
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try:
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# 读取ENVI文件
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image_data = self.read_envi_file(bil_path, rows, cols, bands)
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# 提取标题
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title = self.extract_title_from_filename(bil_path.name)
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# 如果有多波段，选择前三个波段用于RGB显示
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if len(image_data.shape) == 3 and image_data.shape[2] >= 3:
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# 取前三个波段
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rgb_data = image_data[:, :, :3]
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# 分别归一化每个波段
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normalized_r = self.normalize_image(rgb_data[:, :, 0])
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normalized_g = self.normalize_image(rgb_data[:, :, 1])
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normalized_b = self.normalize_image(rgb_data[:, :, 2])
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# 组合为RGB
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normalized_rgb = np.stack([normalized_r, normalized_g, normalized_b], axis=2)
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# 用于显示
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display_image = normalized_rgb
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print(f"使用RGB合成（波段1,2,3）")
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elif len(image_data.shape) == 3 and image_data.shape[2] == 1:
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# 单波段，使用颜色映射
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single_band = image_data.squeeze()
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normalized = self.normalize_image(single_band)
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display_image = self.apply_colormap(normalized, colormap)
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print(f"使用单波段 + 颜色映射")
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else:
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# 单波段（二维数组）
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normalized = self.normalize_image(image_data)
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display_image = self.apply_colormap(normalized, colormap)
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print(f"使用单波段 + 颜色映射")
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# 创建输出文件名
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if output_name is None:
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output_name = bil_path.stem + '.png'
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else:
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if not output_name.endswith('.png'):
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output_name += '.png'
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output_path = self.output_folder / output_name
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# 创建图形 - 修复关键问题：确保每次创建新图形
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plt.figure(figsize=(10, 8), dpi=100)
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# 显示图像
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plt.imshow(display_image)
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plt.title(f'{title}', fontsize=16, fontweight='bold')
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plt.axis('off')
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# 调整布局
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plt.tight_layout()
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# 保存图像
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plt.savefig(output_path, dpi=dpi, bbox_inches='tight', pad_inches=0.1)
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plt.close() # 关闭当前图形
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print(f"已保存: {output_path}")
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return output_path
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except Exception as e:
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print(f"转换文件 {bil_path.name} 时出错: {e}")
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import traceback
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traceback.print_exc()
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return None
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def batch_convert(self, rows=None, cols=None, bands=None, colormap='viridis'):
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"""
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批量转换所有ENVI格式文件
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参数:
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rows: 图像行数
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cols: 图像列数
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bands: 波段数
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colormap: 颜色映射名称
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返回:
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成功转换的文件列表
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"""
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# 查找所有数据文件
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bil_files = self.find_bil_files()
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if not bil_files:
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print("未找到BIL文件")
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return []
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successful_conversions = []
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print(f"\n开始批量转换 {len(bil_files)} 个文件...")
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print("=" * 50)
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for i, bil_path in enumerate(bil_files, 1):
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print(f"\n处理文件 {i}/{len(bil_files)}: {bil_path.name}")
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print("-" * 30)
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# 生成输出文件名
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output_name = f"{bil_path.stem}.png"
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# 转换文件
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output_path = self.envi_to_png(
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bil_path,
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output_name=output_name,
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rows=rows,
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cols=cols,
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bands=bands,
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colormap=colormap
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)
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if output_path:
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successful_conversions.append(output_path)
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print("\n" + "=" * 50)
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print(f"批量转换完成！")
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print(f"成功转换: {len(successful_conversions)}/{len(bil_files)} 个文件")
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print(f"输出文件夹: {self.output_folder}")
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return successful_conversions
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def main():
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"""主函数：使用示例"""
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# 设置BIL文件夹路径
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bil_folder = r"E:\code\spectronon\single_classsfication\output"
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# 检查文件夹是否存在
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if not os.path.exists(bil_folder):
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print(f"错误: 文件夹 '{bil_folder}' 不存在")
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return
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# 创建转换器
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converter = BilToPngConverter(bil_folder)
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# 批量转换
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# 注意：如果BIL文件没有头文件，需要手动指定rows, cols, bands参数
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# 例如：converter.batch_convert(rows=512, cols=512, bands=1)
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# 自动尝试转换（如果有头文件）
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successful = converter.batch_convert()
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if not successful:
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print("\n自动转换失败，可能需要手动指定图像尺寸。")
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# 尝试手动指定
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try:
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rows = int(input("请输入图像行数（高度）: "))
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cols = int(input("请输入图像列数（宽度）: "))
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bands = int(input("请输入波段数（单波段图像请输入1）: "))
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# 重新尝试转换
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converter.batch_convert(rows=rows, cols=cols, bands=bands)
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except ValueError:
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print("请输入有效的数字")
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# 如果直接运行此脚本
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if __name__ == "__main__":
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main() |