from osgeo import ogr import os import argparse from osgeo import ogr import os import argparse from osgeo import ogr, gdal import os import argparse import numpy as np from scipy.spatial import KDTree from tools.DotaOperator import DotaObj,readDotaFile,writerDotaFile,createDota from glob import glob from pathlib import Path import shutil MLCName="MLC" # M JLCName="JLC" # J MJLCName="MJLC" # JM 混合 NOLCName="NOLC" # 没有港口 def find_tif_files_pathlib(directory): path = Path(directory) # 使用rglob递归匹配所有.tif和.tiff文件 tif_files = list(path.rglob('*.tiff'))+list(path.rglob('*.tif')) # 将Path对象转换为字符串路径 return [str(file) for file in tif_files] def find_srcPath(srcFolder): root_path = Path(srcFolder) target_path = [folderpath for folderpath in root_path.rglob("*") if folderpath.is_dir() and folderpath.name=="0-原图"] tiff_files = [] for folderpath in target_path: tiff_files=tiff_files+find_tif_files_pathlib(folderpath) tiff_dict={} for filepath in tiff_files: rootname=Path(filepath).stem tiff_dict[rootname]=filepath return tiff_dict def read_tifInfo(path): dataset = gdal.Open(path) # 打开TIF文件 if dataset is None: print("无法打开文件,读取文件信息") return None, None, None cols = dataset.RasterXSize # 图像宽度 rows = dataset.RasterYSize # 图像高度 bands = dataset.RasterCount im_proj = dataset.GetProjection() # 获取投影信息 im_Geotrans = dataset.GetGeoTransform() # 获取仿射变换信息 # im_data = dataset.ReadAsArray(0, 0, cols, rows) # 读取栅格数据为NumPy数组 print("行数：", rows) print("列数：", cols) print("波段：", bands) x1=im_Geotrans[0]+im_Geotrans[1]*0 x2=im_Geotrans[0]+im_Geotrans[1]*cols y1=im_Geotrans[3]+im_Geotrans[5]*0 y2=im_Geotrans[3]+im_Geotrans[5]*rows xmin=min(x1,x2) xmax=max(x1,x2) ymin=min(y1,y2) ymax=max(y1,y2) geoExtend=[xmin,ymin,xmax,ymax] del dataset # 关闭数据集 return im_proj, im_Geotrans,geoExtend def getshapefileInfo(shp_path): """ 将Shapefile转换为DOTA格式 :param shp_path: Shapefile文件路径 """ geom_points=[] print("shapefile: ",shp_path) # 注册所有驱动 ogr.RegisterAll() # 打开Shapefile文件 driver = ogr.GetDriverByName('ESRI Shapefile') datasource = driver.Open(shp_path, 0) if datasource is None: print("无法打开Shapefile文件") return print("layer count: ",datasource.GetLayerCount()) for layerid in range(datasource.GetLayerCount()): print("layer id: ",layerid) # 获取图层 layer = datasource.GetLayer(layerid) layer_defn=layer.GetLayerDefn() field_count=layer_defn.GetFieldCount() print("field_count:", field_count) for i in range (field_count): field_defn=layer_defn.GetFieldDefn(i) field_name=field_defn.GetName() field_type=field_defn.GetType() field_type_name=field_defn.GetFieldTypeName(field_type) print("field_name:", field_name, field_type_name, field_type_name) for feature in layer: geom = feature.GetGeometryRef() if geom.GetGeometryName() == 'POINT': x=geom.GetX() y=geom.GetY() geom_points.append([x,y]) return np.array(geom_points) def getTiffsInfo(tiffnames,folderpath): """ 获取所有影像的几何信息 Args: tiff_paths: tiff列表 Returns: """ tiffdict={} for tiff_name in tiffnames: if tiff_name.endswith(".tiff"): tiff_path=os.path.join(folderpath,tiff_name) im_proj, im_Geotrans, geoExtend=read_tifInfo(tiff_path) tiffdict[tiff_name]={"geoExtend":geoExtend,"geoTrans":im_Geotrans,"imProj":im_proj} return tiffdict def getMJSignal(tiffpath,shipPortTree,outfolderPath): rootname=Path(tiffpath).stem portTxtpath=os.path.join(outfolderPath,rootname+".txt") im_proj, im_Geotrans, geoExtend = read_tifInfo(tiffpath) # geoExtend : [xmin,ymin,xmax,ymax] [xmin, ymin, xmax, ymax]=geoExtend center_x = (xmin + xmax) / 2.0 center_y = (ymin + ymax) / 2.0 center_point = [center_x, center_y] # 2. 计算能够覆盖整个矩形区域的最小半径（中心点到任一角点的最大距离） radius_to_corner = np.sqrt((xmax - center_x) ** 2 + (ymax - center_y) ** 2) MLCFlag=False JLCFlag=False ## MLC if MLCName in shipPortTree and not shipPortTree[MLCName] is None: # 3. 使用 query_ball_point 查找以中心点为圆心，radius_to_corner 为半径的圆内的所有点的索引 potential_indices = shipPortTree[MLCName].query_ball_point(center_point, r=radius_to_corner) # 4. 获取这些潜在点的实际坐标 # 假设你的 KDTree 是从 data_points 构建的：MLCTree = KDTree(data_points) potential_points = shipPortTree[MLCName].data[potential_indices] # 这是所有潜在点的坐标数组 # 5. 进行精确的矩形范围过滤 # 条件判断：x 坐标在 xmin 和 xmax 之间，且 y 坐标在 ymin 和 ymax 之间 x_in_range = (potential_points[:, 0] >= xmin) & (potential_points[:, 0] <= xmax) y_in_range = (potential_points[:, 1] >= ymin) & (potential_points[:, 1] <= ymax) within_rect_indices_mask = x_in_range & y_in_range # 6. 获取最终在矩形范围内的点的坐标（在原始 data_points 中的索引是 potential_indices[within_rect_indices_mask]） final_points = potential_points[within_rect_indices_mask] # final_points 就是你要的矩形范围内的点 # 如果你需要的是这些点在原始数据中的索引，而不是坐标本身： final_indices = np.array(potential_indices)[within_rect_indices_mask] if final_points.shape[0]>0: MLCFlag=True with open(portTxtpath,"w",encoding="utf-8") as f: for i in range(final_points.shape[0]): f.write("{}\t\t{},{}\n".format("MLC",final_points[i,0],final_points[i,1])) pass if JLCName in shipPortTree and not shipPortTree[JLCName] is None: # 3. 使用 query_ball_point 查找以中心点为圆心，radius_to_corner 为半径的圆内的所有点的索引 potential_indices = shipPortTree[JLCName].query_ball_point(center_point, r=radius_to_corner) # 4. 获取这些潜在点的实际坐标 # 假设你的 KDTree 是从 data_points 构建的：MLCTree = KDTree(data_points) potential_points = shipPortTree[JLCName].data[potential_indices] # 这是所有潜在点的坐标数组 # 5. 进行精确的矩形范围过滤 # 条件判断：x 坐标在 xmin 和 xmax 之间，且 y 坐标在 ymin 和 ymax 之间 x_in_range = (potential_points[:, 0] >= xmin) & (potential_points[:, 0] <= xmax) y_in_range = (potential_points[:, 1] >= ymin) & (potential_points[:, 1] <= ymax) within_rect_indices_mask = x_in_range & y_in_range # 6. 获取最终在矩形范围内的点的坐标（在原始 data_points 中的索引是 potential_indices[within_rect_indices_mask]） final_points = potential_points[within_rect_indices_mask] # final_points 就是你要的矩形范围内的点 # 如果你需要的是这些点在原始数据中的索引，而不是坐标本身： final_indices = np.array(potential_indices)[within_rect_indices_mask] if final_points.shape[0]>0: JLCFlag=True with open(portTxtpath,"a",encoding="utf-8") as f: for i in range(final_points.shape[0]): f.write("{}\t\t{},{}\n".format("JLC",final_points[i,0],final_points[i,1])) pass # 处理软件 return MLCFlag,JLCFlag def getTiffInPort(shipPortTree,srcFolderPath_0img,outTiffInfoFilePath,outfolderPath): tiffpaths=find_tif_files_pathlib(srcFolderPath_0img) tiffLCPort={ MLCName:[], JLCName:[], MJLCName:[], NOLCName:[] } for tiffpath in tiffpaths: MLCFlag,JLCFlag=getMJSignal(tiffpath,shipPortTree,outfolderPath) if MLCFlag and JLCFlag: tiffLCPort[MJLCName].append(tiffpath) elif MLCFlag: tiffLCPort[MLCName].append(tiffpath) elif JLCFlag: tiffLCPort[JLCName].append(tiffpath) else: tiffLCPort[NOLCName].append(tiffpath) # 输出文件 with open(outTiffInfoFilePath,'w',encoding="utf-8") as f: for k in tiffLCPort: for tiffpath in tiffLCPort[k]: f.write("{}\t\t{}\n".format(k,tiffpath)) def getMJSignal(geoExtend,shipPortTree): [xmin, ymin, xmax, ymax]=geoExtend center_x = (xmin + xmax) / 2.0 center_y = (ymin + ymax) / 2.0 center_point = [center_x, center_y] # 2. 计算能够覆盖整个矩形区域的最小半径（中心点到任一角点的最大距离） radius_to_corner = np.sqrt((xmax - center_x) ** 2 + (ymax - center_y) ** 2) MLCFlag=False JLCFlag=False ## MLC if MLCName in shipPortTree and not shipPortTree[MLCName] is None: # 3. 使用 query_ball_point 查找以中心点为圆心，radius_to_corner 为半径的圆内的所有点的索引 potential_indices = shipPortTree[MLCName].query_ball_point(center_point, r=radius_to_corner) # 4. 获取这些潜在点的实际坐标 # 假设你的 KDTree 是从 data_points 构建的：MLCTree = KDTree(data_points) potential_points = shipPortTree[MLCName].data[potential_indices] # 这是所有潜在点的坐标数组 # 5. 进行精确的矩形范围过滤 # 条件判断：x 坐标在 xmin 和 xmax 之间，且 y 坐标在 ymin 和 ymax 之间 x_in_range = (potential_points[:, 0] >= xmin) & (potential_points[:, 0] <= xmax) y_in_range = (potential_points[:, 1] >= ymin) & (potential_points[:, 1] <= ymax) within_rect_indices_mask = x_in_range & y_in_range # 6. 获取最终在矩形范围内的点的坐标（在原始 data_points 中的索引是 potential_indices[within_rect_indices_mask]） final_points = potential_points[within_rect_indices_mask] # final_points 就是你要的矩形范围内的点 # 如果你需要的是这些点在原始数据中的索引，而不是坐标本身： final_indices = np.array(potential_indices)[within_rect_indices_mask] if final_points.shape[0]>0: MLCFlag=True # with open(portTxtpath,"w",encoding="utf-8") as f: # for i in range(final_points.shape[0]): # f.write("{}\t\t{},{}\n".format("MLC",final_points[i,0],final_points[i,1])) # pass if JLCName in shipPortTree and not shipPortTree[JLCName] is None: # 3. 使用 query_ball_point 查找以中心点为圆心，radius_to_corner 为半径的圆内的所有点的索引 potential_indices = shipPortTree[JLCName].query_ball_point(center_point, r=radius_to_corner) # 4. 获取这些潜在点的实际坐标 # 假设你的 KDTree 是从 data_points 构建的：MLCTree = KDTree(data_points) potential_points = shipPortTree[JLCName].data[potential_indices] # 这是所有潜在点的坐标数组 # 5. 进行精确的矩形范围过滤 # 条件判断：x 坐标在 xmin 和 xmax 之间，且 y 坐标在 ymin 和 ymax 之间 x_in_range = (potential_points[:, 0] >= xmin) & (potential_points[:, 0] <= xmax) y_in_range = (potential_points[:, 1] >= ymin) & (potential_points[:, 1] <= ymax) within_rect_indices_mask = x_in_range & y_in_range # 6. 获取最终在矩形范围内的点的坐标（在原始 data_points 中的索引是 potential_indices[within_rect_indices_mask]） final_points = potential_points[within_rect_indices_mask] # final_points 就是你要的矩形范围内的点 # 如果你需要的是这些点在原始数据中的索引，而不是坐标本身： final_indices = np.array(potential_indices)[within_rect_indices_mask] if final_points.shape[0]>0: JLCFlag=True # with open(portTxtpath,"a",encoding="utf-8") as f: # for i in range(final_points.shape[0]): # f.write("{}\t\t{},{}\n".format("JLC",final_points[i,0],final_points[i,1])) # pass # 处理软件 if MLCFlag and JLCFlag: return "mix_airport" # tiffLCPort[MJLCName].append(tiffpath) elif MLCFlag: return "civil_harbor" # tiffLCPort[MLCName].append(tiffpath) elif JLCFlag: return "military_harbor" # tiffLCPort[JLCName].append(tiffpath) else: return "no_harbor" # tiffLCPort[NOLCName].append(tiffpath) # return MLCFlag,JLCFlag def shapefile_to_dota(shp_path, output_path, shipPortTree,difficulty_value=1): """ 将Shapefile转换为DOTA格式 :param shp_path: Shapefile文件路径 :param output_path: 输出目录 :param class_field: 类别字段名 :param difficulty_value: 难度默认字段 """ # 注册所有驱动 ogr.RegisterAll() # 打开Shapefile文件 driver = ogr.GetDriverByName('ESRI Shapefile') datasource = driver.Open(shp_path, 0) if datasource is None: print("无法打开Shapefile文件") return # 获取图层 layer = datasource.GetLayer() output_file = output_path with open(output_file, 'w',encoding="utf-8") as f: # 写入DOTA格式头信息(可选) # f.write('imagesource:unknown\n') # f.write('gsd:1.0\n') # 遍历所有要素 for feature in layer: # 获取几何对象 geom = feature.GetGeometryRef() if geom is None: continue # 获取类别和难度 try: class_name = 'unknown' except Exception as e: class_name="MLC" print(e) difficulty = difficulty_value # 处理不同类型的几何图形 if geom.GetGeometryName() == 'POLYGON': # 获取多边形外环 ring = geom.GetGeometryRef(0) # 获取所有点 points = [] for i in range(ring.GetPointCount()): points.append(ring.GetPoint(i)) # 确保有足够的点(至少4个) if len(points) >= 4: # 取前4个点作为DOTA格式的四个角点 # 注意: DOTA要求按顺序排列(顺时针或逆时针) x1, y1 = points[0][0], points[0][1] x2, y2 = points[1][0], points[1][1] x3, y3 = points[2][0], points[2][1] x4, y4 = points[3][0], points[3][1] xmin = min(x1, x2,x3,x4) xmax = max(x1, x2,x3,x4) ymin = min(y1, y2,y3,y4) ymax = max(y1, y2,y3,y4) # [xmin, ymin, xmax, ymax] = geoExtend geoExtend = [xmin, ymin, xmax, ymax] class_name=getMJSignal(geoExtend, shipPortTree) # 写入DOTA格式行 line = f"{x1} {y1} {x2} {y2} {x3} {y3} {x4} {y4} {class_name} {difficulty}\n" f.write(line) # 释放资源 datasource.Destroy() print("转换完毕") def PortShapeProces(shp_path, output_path, MLCPath,JLCPath,JMLCPath,difficulty_value=1): shipPort={ MLCName:getshapefileInfo(MLCPath), JLCName:getshapefileInfo(JLCPath), MJLCName:getshapefileInfo(JMLCPath), # 舰船不区分 居民一体 } shipPortTree={ MLCName:KDTree(shipPort[MLCName]), JLCName:KDTree(shipPort[JLCName]), MJLCName:KDTree(shipPort[MJLCName]), } shapefile_to_dota(shp_path, output_path,shipPortTree, difficulty) def getParams(): parser = argparse.ArgumentParser() parser.add_argument('-i','--infile',type=str,default=r'D:\港口\港口\Geo_bc2-sm-org-vv-20231016t135315-008424-0020e8-01.military_harbor.shp', help='输入shapefile文件') parser.add_argument('-o', '--outfile',type=str,default=r'D:\港口\港口dota\Geo_bc2-sm-org-vv-20231016t135315-008424-0020e8-01.military_harbor.txt', help='输出geojson文件') parser.add_argument('-m', '--mLC',type=str,help=r'MLC', default=r'D:\TYSAR-德清院\目标点位信息更新\0828目标点位\港口（民船）.shp') parser.add_argument('-j', '--jLC',type=str,help=r'JLC' ,default=r'D:\TYSAR-德清院\目标点位信息更新\0828目标点位\军港.shp') parser.add_argument('-jm', '--jmlc',type=str,help=r'JMLC', default=r'D:\TYSAR-德清院\目标点位信息更新\0828目标点位\军民一体港口.shp') parser.add_argument('-d', '--difficulty',type=int,default=1, help='输出geojson文件') args = parser.parse_args() return args if __name__ == '__main__': try: parser = getParams() inFilePath=parser.infile outpath=parser.outfile mLCPath=parser.mLC jLCPath=parser.jLC jmLCPath=parser.jmlc difficulty=parser.difficulty print('infile=',inFilePath) print('outfile=',outpath) print('mLCPath=',mLCPath) print('jLCPath=',jLCPath) print('jmLCPath=',jmLCPath) print('difficulty=',difficulty) PortShapeProces(inFilePath, outpath, mLCPath, jLCPath, jmLCPath, difficulty_value=difficulty) exit(2) except Exception as e: print(e) exit(3)