163 lines
5.1 KiB
Python
163 lines
5.1 KiB
Python
import os
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import pandas as pd
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from pathlib import Path
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import numpy as np
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def process_single_bil_background(full_bil_path):
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"""
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处理单个BIL文件，提取滤纸背景的平均光谱（不需要mask）
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参数:
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full_bil_path: BIL文件路径
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返回:
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mean_spectrum: 归一化的平均光谱数组
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"""
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try:
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from plantcv import plantcv as pcv
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import cv2
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except ImportError:
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raise ImportError("需要导入plantcv和cv2库：from plantcv import plantcv as pcv; import cv2")
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# 设置 PlantCV 的全局参数
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pcv.params.debug = "None"
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pcv.params.dpi = 100
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pcv.params.text_size = 1
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pcv.params.text_thickness = 1
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# 读取光谱图像（ENVI 格式）
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spectral_array = pcv.readimage(filename=full_bil_path, mode='envi')
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bath_100 = spectral_array.array_data[:, :, 100]
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# 使用阈值提取滤纸区域（背景区域，阈值>5500）
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bath_over = pcv.threshold.binary(gray_img=bath_100, threshold=5500, object_type='light')
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# 对滤纸掩膜进行形态学操作
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bath_erode = pcv.erode(gray_img=bath_over, ksize=3, i=4)
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dilate_img = pcv.dilate(gray_img=bath_erode, ksize=3, i=4)
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# 填充大的孔洞
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dilate_img = pcv.fill(bin_img=dilate_img, size=10000)
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# 使用cv2.connectedComponents找到连通区域
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num1, labeled_spectral_mask = cv2.connectedComponents(dilate_img.astype(np.uint8))
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print(f" 检测到 {num1 - 1} 个滤纸区域")
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# 找到最大面积的对象
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max_area = 0
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max_label = 0
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for label in range(1, num1):
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obj_mask = labeled_spectral_mask == label
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area = np.sum(obj_mask)
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if area > max_area:
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max_area = area
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max_label = label
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# 提取最大面积对象的光谱
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if max_label > 0:
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max_obj_mask = labeled_spectral_mask == max_label
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spectral_values = spectral_array.array_data[max_obj_mask, :]
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mean_spectrum = np.mean(spectral_values, axis=0) / 10000 # 归一化
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return mean_spectrum
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else:
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print(f" 警告: 未找到有效的滤纸区域")
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n_bands = spectral_array.array_data.shape[-1]
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return np.zeros(n_bands)
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def batch_process_bil_background(bil_folder, output_csv_path):
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"""
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批量处理文件夹中的BIL文件，提取滤纸背景光谱并保存到CSV
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参数:
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bil_folder: 包含.bil文件的文件夹路径
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output_csv_path: 输出CSV文件路径（宽格式：每行一个文件，每列一个波段）
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返回:
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df: 包含所有文件光谱的DataFrame
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"""
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bil_folder = Path(bil_folder)
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if not bil_folder.exists():
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raise ValueError(f"文件夹不存在: {bil_folder}")
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# 查找所有.bil文件
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bil_files = []
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for ext in ['.bil', '.BIL']:
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bil_files.extend(list(bil_folder.glob(f'*{ext}')))
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if len(bil_files) == 0:
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print(f"警告: 在 {bil_folder} 中未找到任何.bil文件")
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return None
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print(f"找到 {len(bil_files)} 个BIL文件")
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# 确保输出目录存在
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output_path = Path(output_csv_path)
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output_path.parent.mkdir(parents=True, exist_ok=True)
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# 存储所有结果
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all_spectra = []
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filenames = []
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failed_files = []
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# 处理每个文件
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for i, bil_path in enumerate(bil_files, 1):
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print(f"\n[{i}/{len(bil_files)}] 处理文件: {bil_path.name}")
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print(f"{'=' * 60}")
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try:
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spectrum = process_single_bil_background(str(bil_path))
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if spectrum is not None and len(spectrum) > 0 and np.sum(spectrum) > 0:
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all_spectra.append(spectrum)
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filenames.append(bil_path.stem)
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print(f" ✓ 成功提取光谱 ({len(spectrum)} 个波段)")
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else:
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failed_files.append(bil_path.name)
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print(f" ✗ 未能提取有效光谱")
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except Exception as e:
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failed_files.append(bil_path.name)
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print(f" ✗ 处理失败: {str(e)}")
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import traceback
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traceback.print_exc()
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continue
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# 合并结果
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if len(all_spectra) == 0:
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print("\n没有有效的结果可以保存")
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return None
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# 创建宽格式DataFrame
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n_bands = len(all_spectra[0])
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column_names = [f'band_{i}' for i in range(n_bands)]
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df = pd.DataFrame(all_spectra, columns=column_names, index=filenames)
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df.index.name = 'filename'
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df = df.reset_index()
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# 保存到CSV
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df.to_csv(output_csv_path, index=False)
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# 打印统计信息
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print(f"\n{'=' * 60}")
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print(f"批量处理完成！")
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print(f" 总共文件数: {len(bil_files)}")
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print(f" 成功处理: {len(all_spectra)}")
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print(f" 失败文件: {len(failed_files)}")
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if failed_files:
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print(f" 失败列表: {', '.join(failed_files)}")
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print(f" 光谱波段数: {n_bands}")
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print(f" 输出文件: {output_csv_path}")
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print(f"{'=' * 60}")
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return df
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bil_folder = r"D:\Data\Traindata-09\whitebord"
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output_csv = r"D:\Data\Traindata-09\whitebord\filter_paper_spectra.csv"
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batch_process_bil_background(bil_folder, output_csv)
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