拆分港口工具

SplitPortRasterTools/SplitShipPortRasterTools.py

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+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
+
+"""
+1. 港口，5000x5000，军港与民港切片，
+	a. 预标注框给他们
+	b. 重叠率25%重复
+	c. 军民2000m 以内，影像抽出来
+	d. 原影像如果只有 1 个港口，-》 港口类型
+	               多 个港口，-》 远近，如果港口距离1000m ,单独拉出来
+
+"""
+
+
+MLCName="MLC"
+JLCName="JLC"
+MJLCName="MJLC"
+NOLCName="NOLC"
+
+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):
+    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]
+
+        MLCFlag=True
+    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]
+        JLCFlag=True
+    # 处理软件
+    return MLCFlag,JLCFlag
+
+
+
+
+def getTiffInPort(shipPortTree,srcFolderPath_0img,outTiffInfoFilePath):
+    tiffpaths=find_tif_files_pathlib(srcFolderPath_0img)
+    tiffLCPort={
+        MLCName:[],
+        JLCName:[],
+        MJLCName:[],
+        NOLCName:[]
+    }
+    for tiffpath in tiffpaths:
+        MLCFlag,JLCFlag=getMJSignal(tiffpath,shipPortTree)
+
+        if MLCFlag and JLCFlag:
+            tiffLCPort[MJLCName].append(tiffpath)
+        elif MLCFlag:
+            tiffLCPort[MJLCName].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("{} {}\n".format(k,tiffpath))
+
+
+def SpliteProcess(srcfolderpath,outfolderpath,MLCPath,JLCPath,JMLCPath):
+    shipPort={
+        MLCName:getshapefileInfo(MLCPath),
+        JLCName:getshapefileInfo(JLCPath),
+        # "JMLC":getshapefileInfo(JMLCPath), # 舰船不区分 居民一体
+    }
+
+    shipPortTree={
+        MLCName:KDTree(shipPort[MLCName]),
+        JLCName:KDTree(shipPort[JLCName]),
+        # "JMLC":KDTree(shipPort["JMLC"]),
+    }
+    srcFolderPath_0img=os.path.join(srcfolderpath,"0-原图") # 0-原图 文件路径
+    outTiffInfoFilePath=os.path.join(srcfolderpath,"JMPort.txt")
+    return True
+    pass
+
+
+def getParams():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-s','--srcfolder',type=str,default=r'R:\TYSAR-德清院\TYSAR-条带模式(SM)\港口\20250903-不分类', help='输入shapefile文件')
+    parser.add_argument('-o', '--outfolder',type=str,default=r'D:\TYSAR-德清院\TYSAR-条带模式(SM)\港口\20250903-不分类', 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'MJLC', 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')
+    args = parser.parse_args()
+    return args
+
+if __name__ == '__main__':
+    try:
+        parser = getParams()
+        srcfolder=parser.srcfolder
+        outfolder=parser.outfolder
+        mLCPath=parser.mLC
+        jLCPath=parser.jLC
+        jmLCPath=parser.jmLC
+        print('srcfolder=',srcfolder)
+        print('outfolder=',outfolder)
+        print('mLCPath=',mLCPath)
+        print('jLCPath=',jLCPath)
+        print('jmLCPath=',jmLCPath)
+        SpliteProcess(srcfolder,outfolder,mLCPath,jLCPath,jmLCPath)
+        exit(2)
+    except Exception as e:
+        print(e)
+        exit(3)
+
+
+
+
+