113 lines
3.8 KiB
Python
113 lines
3.8 KiB
Python
import numpy as np
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import os
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from glob import glob
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from pathlib import Path
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from multiprocessing import Pool
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########################################################
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# 函数区
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########################################################
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spacetySliceEnvPathExEPath=r"D:\ProgramData\miniconda3\envs\spacetySliceEnv\python.exe"
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def find_tif_files_pathlib(directory):
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"""
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使用pathlib.Path.rglob递归查找指定目录下所有.tif和.tiff文件
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参数:
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directory (str): 要搜索的根目录路径
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返回:
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list: 包含所有找到的.tif/.tiff文件完整路径的列表
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"""
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path = Path(directory)
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# 使用rglob递归匹配所有.tif和.tiff文件
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tif_files = list(path.rglob('*.bin'))
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# 将Path对象转换为字符串路径
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return [str(file) for file in tif_files]
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def find_txt_files_pathlib(directory):
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"""
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使用pathlib.Path.rglob递归查找指定目录下所有.tif和.tiff文件
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参数:
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directory (str): 要搜索的根目录路径
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返回:
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list: 包含所有找到的.tif/.tiff文件完整路径的列表
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"""
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path = Path(directory)
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# 使用rglob递归匹配所有.tif和.tiff文件
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tif_files = list(path.rglob('*.txt'))
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# 将Path对象转换为字符串路径
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return [str(file) for file in tif_files]
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def matchTiff_Txt(tiffpaths,txtPaths):
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match_results={}
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tiffID=0
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for tid in range(len(tiffpaths)):
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tiffID=tiffID+1
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match_results[tiffID]={"tiff":tiffpaths[tid],"txt":[]}
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rootname=Path(tiffpaths[tid]).stem
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for txtpath in txtPaths:
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txtrootname=Path(txtpath).stem
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if txtrootname.startswith(rootname):
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match_results[tiffID]["txt"].append(txtpath)
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return match_results
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def sliceTiFFAndTxt(match_meta,outfolderpath):
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resultTxt=""
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programpath = r"R:\TYSAR-德清院\A-预处理-未标注\A0-算法版本\AA\SpacetySliceDataTools\tools\DataSampleSliceTrainDataset.py"
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tiffPath=match_meta["tiff"]
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for txtpath in match_meta["txt"]:
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cmdtxt = r"{} {} -i {} -l {} -o {}".format(spacetySliceEnvPathExEPath, programpath,
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tiffPath,
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txtpath,
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outfolderpath
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)
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if os.system(cmdtxt) == 2:
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resultTxt="{}\nsucess: {}".format(resultTxt,txtpath)
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print("sucess:", cmdtxt)
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else:
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resultTxt = "{}\nfailed: {}".format(resultTxt, txtpath)
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print("failed:", cmdtxt)
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if __name__ == '__main__':
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async_results = []
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with Pool() as pool:
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# 聚束模式
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preFolderPath = r"D:\Annotation_Y\预处理\聚束模式"
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outfolderPath=r"D:\Annotation_Y\切片样本\聚束模式"
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tiffPaths = find_tif_files_pathlib(preFolderPath)
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txtPaths = find_txt_files_pathlib(preFolderPath)
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match_results=matchTiff_Txt(tiffPaths,txtPaths)
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for tiffID in match_results:
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match_meta=match_results[tiffID]
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async_results.append(
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pool.apply_async(sliceTiFFAndTxt, (match_meta,outfolderPath,))
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)
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# 聚束模式
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preFolderPath = r"D:\Annotation_Y\预处理\条带模式"
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outfolderPath=r"D:\Annotation_Y\切片样本\条带模式"
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tiffPaths = find_tif_files_pathlib(preFolderPath)
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txtPaths = find_txt_files_pathlib(preFolderPath)
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match_results=matchTiff_Txt(tiffPaths,txtPaths)
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for tiffID in match_results:
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match_meta=match_results[tiffID]
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async_results.append(
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pool.apply_async(sliceTiFFAndTxt, (match_meta,outfolderPath,))
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)
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for async_result in async_results:
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print(async_result.get())
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