坡度坡向tif生成

b3dm/data_3dtiles_manager.py

@@ -145,11 +145,7 @@ class MinIO3DTilesManager:
         visited = set()
         
         # 下载入口文件
-        entry_local_path = self.get_local_path(
+        entry_local_path = self.get_local_path(entry_bucket, entry_path, save_dir)
-            entry_bucket, entry_path,
-            entry_bucket, entry_dir,
-            save_dir
-        )
         
         success, result = self.download_file(entry_bucket, entry_path, entry_local_path)
         if not success:
@@ -352,39 +348,23 @@ class MinIO3DTilesManager:
         except Exception as e:
             return None
     
-    def get_local_path(self, bucket_name, object_name, base_bucket, base_object, save_dir):
+    def get_local_path(self, bucket_name, object_name, save_dir):
         """生成保持目录结构的本地路径"""
         clean_bucket = self.clean_filename(bucket_name)
-        bucket_dir = clean_bucket
         
-        if bucket_name == base_bucket and base_object:
+        path_parts = object_name.split('/')
-            base_dir = os.path.dirname(base_object)
+        cleaned_parts = []
+        for part in path_parts:
+            cleaned_part = self.clean_filename(part)
+            if cleaned_part:
+                cleaned_parts.append(cleaned_part)
         
-            if base_dir:
+        if cleaned_parts:
-                if object_name.startswith(base_dir):
+            cleaned_relative = '/'.join(cleaned_parts)
-                    relative_path = object_name[len(base_dir):].lstrip('/\\')
+            local_path = os.path.join(save_dir, clean_bucket, cleaned_relative)
-                else:
-                    relative_path = object_name
-            else:
-                relative_path = object_name
         else:
-            relative_path = object_name
+            local_path = os.path.join(save_dir, clean_bucket)
 
-        if relative_path:
-            path_parts = relative_path.split('/')
-            cleaned_parts = []
-            for part in path_parts:
-                cleaned_part = self.clean_filename(part)
-                if cleaned_part:
-                    cleaned_parts.append(cleaned_part)
-            
-            if cleaned_parts:
-                cleaned_relative = '/'.join(cleaned_parts)
-                local_path = os.path.join(save_dir, bucket_dir, cleaned_relative)
-            else:
-                local_path = os.path.join(save_dir, bucket_dir)
-        else:
-            local_path = os.path.join(save_dir, bucket_dir)
         
         return os.path.normpath(local_path)
     
@@ -437,11 +417,7 @@ class MinIO3DTilesManager:
                 print(f"下载文件:{file_id}")
                 visited.add(file_id)
                 
-                local_path = self.get_local_path(
+                local_path = self.get_local_path(file_bucket, file_path, save_dir)
-                    file_bucket, file_path,
-                    base_bucket, base_dir,
-                    save_dir
-                )
                 
                 self.download_file(file_bucket, file_path, local_path)
                 

b3dm/data_3dtiles_to_dem.py

@@ -778,7 +778,319 @@ def parse_tileset(tileset_path, region_coords=None, enable_enhancement=True, deb
     return []
 
 # ========== DEM生成函数 ==========
-def points_to_dem(points, output_dem_path, pixel_size=None, quality='medium'):
+def points_to_dem(points, output_dem_path, pixel_size=None, quality='high', min_resolution=10):
+    """
+    将点云转换为DEM，确保足够的网格分辨率用于坡度坡向计算
+    :param points: 点云数据 [(lon, lat, height), ...]
+    :param output_dem_path: 输出DEM文件路径
+    :param pixel_size: 像素大小（度），如果为None则自动计算
+    :param quality: 质量等级 'low'|'medium'|'high'|'terrain'（地形分析专用）
+    :param min_resolution: 最小网格分辨率（像素数），用于保证坡度计算
+    """
+    import time
+    import os
+    import numpy as np
+    from osgeo import gdal, osr
+    from scipy.interpolate import griddata
+    import warnings
+    
+    if len(points) == 0:
+        raise ValueError("无点云数据，无法生成DEM")
+    
+    start_time = time.time()
+    
+    # 转换为numpy数组
+    points_array = np.array(points)
+    lons = points_array[:, 0]
+    lats = points_array[:, 1]
+    heights = points_array[:, 2]
+    
+    # 计算范围
+    min_lon, max_lon = lons.min(), lons.max()
+    min_lat, max_lat = lats.min(), lats.max()
+    lon_range = max_lon - min_lon
+    lat_range = max_lat - min_lat
+    
+    print(f"[DEM生成] 点云范围:")
+    print(f"  经度: {min_lon:.6f}° ~ {max_lon:.6f}° (范围: {lon_range:.6f}°)")
+    print(f"  纬度: {min_lat:.6f}° ~ {max_lat:.6f}° (范围: {lat_range:.6f}°)")
+    print(f"  高程: {heights.min():.2f}m ~ {heights.max():.2f}m")
+    print(f"  点数: {len(points):,}")
+    
+    # 自动确定像素大小（优化版本）
+    if pixel_size is None:
+        # 根据数据量自动确定
+        if quality == 'terrain':  # 地形分析专用
+            # 确保足够的网格分辨率用于坡度计算
+            target_pixels = max(min_resolution, int(np.sqrt(len(points)) / 2))
+            
+            # 根据数据范围计算像素大小
+            lon_pixel = lon_range / target_pixels
+            lat_pixel = lat_range / target_pixels
+            
+            # 取较小的像素大小以保证分辨率
+            pixel_size = min(lon_pixel, lat_pixel)
+            
+            # 设置像素大小范围限制
+            pixel_size = max(pixel_size, 0.000001)  # 最小约0.1米
+            pixel_size = min(pixel_size, 0.0005)    # 最大约55米
+            
+        elif quality == 'high':
+            pixel_size = max(0.00001, lon_range / 100)  # 至少1.1米，最多100像素
+        elif quality == 'medium':
+            pixel_size = max(0.00002, lon_range / 50)   # 至少2.2米，最多50像素
+        else:  # low
+            pixel_size = max(0.00005, lon_range / 20)   # 至少5.6米，最多20像素
+    
+    # 计算网格尺寸
+    width = max(10, int(lon_range / pixel_size) + 1)
+    height = max(10, int(lat_range / pixel_size) + 1)
+    
+    # 确保网格大小符合最小分辨率要求
+    if width * height < min_resolution * min_resolution:
+        print(f"[DEM生成] 警告：网格分辨率不足 ({width}x{height})，自动调整...")
+        # 重新计算像素大小以满足最小分辨率
+        target_cells = min_resolution * min_resolution
+        target_pixel_size = np.sqrt(lon_range * lat_range / target_cells)
+        pixel_size = max(0.000001, min(pixel_size, target_pixel_size))
+        
+        width = max(min_resolution, int(lon_range / pixel_size) + 1)
+        height = max(min_resolution, int(lat_range / pixel_size) + 1)
+    
+    # 扩展数据范围以增加边缘像素（有助于坡度计算）
+    expand_factor = 1.1  # 扩展10%
+    min_lon_exp = min_lon - lon_range * (expand_factor - 1) / 2
+    max_lon_exp = max_lon + lon_range * (expand_factor - 1) / 2
+    min_lat_exp = min_lat - lat_range * (expand_factor - 1) / 2
+    max_lat_exp = max_lat + lat_range * (expand_factor - 1) / 2
+    
+    print(f"[DEM生成] 网格设置:")
+    print(f"  像素大小: {pixel_size:.6f}° (~{pixel_size*111320:.1f}米)")
+    print(f"  网格尺寸: {width} × {height}")
+    print(f"  总像素数: {width * height:,}")
+    print(f"  点云密度: {len(points)/(width*height):.2f} 点/像素")
+    
+    # 创建网格
+    x_grid = np.linspace(min_lon_exp, max_lon_exp, width)
+    y_grid = np.linspace(max_lat_exp, min_lat_exp, height)  # 纬度从上到下
+    xi, yi = np.meshgrid(x_grid, y_grid)
+    
+    # 插值 - 优化版本
+    print("[DEM生成] 开始插值计算...")
+    
+    # 检查数据分布
+    grid_points = np.column_stack([xi.flatten(), yi.flatten()])
+    data_points = np.column_stack([lons, lats])
+    
+    # 使用KD树加速最近邻查询
+    try:
+        from scipy.spatial import cKDTree
+        tree = cKDTree(data_points)
+        dists, _ = tree.query(grid_points, k=1)
+        max_dist = np.max(dists)
+        print(f"[DEM生成] 最大最近邻距离: {max_dist*111320:.1f}米")
+    except:
+        pass
+    
+    # 尝试IDW插值（适用于地形）
+    try:
+        if len(points) > 100:
+            print("[DEM生成] 使用IDW插值...")
+            zi = idw_interpolation(lons, lats, heights, xi, yi, power=2)
+        else:
+            # 数据太少，使用线性+最近邻
+            zi = griddata((lons, lats), heights, (xi, yi), method='linear', fill_value=np.nan)
+    except Exception as e:
+        print(f"[DEM生成] IDW插值失败: {e}，使用线性插值")
+        zi = griddata((lons, lats), heights, (xi, yi), method='linear', fill_value=np.nan)
+    
+    # 处理空白区域
+    nan_mask = np.isnan(zi)
+    if np.any(nan_mask):
+        nan_count = np.sum(nan_mask)
+        nan_percent = nan_count / (width * height) * 100
+        print(f"[DEM生成] 插值空白: {nan_count:,} 像素 ({nan_percent:.1f}%)")
+        
+        if nan_percent < 50:  # 空白区域少于50%
+            # 使用最近邻填充空白
+            zi_nn = griddata((lons, lats), heights, (xi, yi), method='nearest')
+            zi[nan_mask] = zi_nn[nan_mask]
+        else:
+            print("[DEM生成] 警告：空白区域过多，使用最近邻插值")
+            zi = griddata((lons, lats), heights, (xi, yi), method='nearest')
+    
+    # 平滑处理（可选，有助于坡度计算）
+    if quality in ['terrain', 'high']:
+        try:
+            from scipy.ndimage import gaussian_filter
+            sigma = 0.5  # 高斯滤波参数
+            zi = gaussian_filter(zi, sigma=sigma, mode='nearest')
+            print(f"[DEM生成] 应用高斯平滑 (sigma={sigma})")
+        except:
+            pass
+    
+    # 创建GeoTIFF
+    print("[DEM生成] 创建GeoTIFF文件...")
+    driver = gdal.GetDriverByName("GTiff")
+    
+    # 压缩选项
+    if quality in ['terrain', 'high']:
+        options = ["COMPRESS=DEFLATE", "PREDICTOR=3", "ZLEVEL=6", "TILED=YES", "BLOCKXSIZE=256", "BLOCKYSIZE=256"]
+    else:
+        options = ["COMPRESS=LZW", "TILED=YES"]
+    
+    dem_ds = driver.Create(output_dem_path, width, height, 1, gdal.GDT_Float32, options)
+    
+    if dem_ds is None:
+        raise RuntimeError(f"无法创建DEM文件: {output_dem_path}")
+    
+    # 设置投影和地理变换
+    srs = osr.SpatialReference()
+    srs.ImportFromEPSG(4326)  # WGS84
+    dem_ds.SetProjection(srs.ExportToWkt())
+    
+    # 注意：使用扩展后的范围
+    geotransform = [
+        min_lon_exp, pixel_size, 0,
+        max_lat_exp, 0, -pixel_size
+    ]
+    dem_ds.SetGeoTransform(geotransform)
+    
+    # 写入数据
+    band = dem_ds.GetRasterBand(1)
+    band.WriteArray(zi)
+    band.SetNoDataValue(-9999.0)
+    band.SetDescription("Elevation (meters)")
+    band.SetUnitType("meters")
+    
+    # 计算统计信息（处理可能的统计问题）
+    print("[DEM生成] 计算统计信息...")
+    band.FlushCache()
+    
+    try:
+        band.ComputeStatistics(False)
+        print("[DEM生成] 统计信息计算完成")
+    except RuntimeError as e:
+        print(f"[DEM生成] 警告：统计信息计算失败: {e}")
+        # 手动计算统计信息
+        valid_mask = zi != -9999.0
+        if np.any(valid_mask):
+            valid_data = zi[valid_mask]
+            stats = [
+                float(np.min(valid_data)),
+                float(np.max(valid_data)),
+                float(np.mean(valid_data)),
+                float(np.std(valid_data))
+            ]
+            band.SetStatistics(*stats)
+            print(f"[DEM生成] 手动设置统计信息:")
+            print(f"  最小值: {stats[0]:.2f}m")
+            print(f"  最大值: {stats[1]:.2f}m")
+            print(f"  平均值: {stats[2]:.2f}m")
+            print(f"  标准差: {stats[3]:.2f}m")
+    
+    # 构建金字塔
+    if quality in ['terrain', 'high']:
+        print("[DEM生成] 构建金字塔...")
+        gdal.SetConfigOption('COMPRESS_OVERVIEW', 'DEFLATE')
+        dem_ds.BuildOverviews("AVERAGE", [2, 4, 8, 16])
+    
+    dem_ds = None  # 关闭文件
+    
+    # 验证结果
+    elapsed_time = time.time() - start_time
+    file_size_mb = os.path.getsize(output_dem_path) / (1024 * 1024) if os.path.exists(output_dem_path) else 0
+    
+    # 重新打开验证
+    try:
+        ds = gdal.Open(output_dem_path, gdal.GA_ReadOnly)
+        if ds:
+            band = ds.GetRasterBand(1)
+            actual_width = ds.RasterXSize
+            actual_height = ds.RasterYSize
+            
+            print(f"[DEM生成] 验证结果:")
+            print(f"  实际尺寸: {actual_width} × {actual_height}")
+            print(f"  文件大小: {file_size_mb:.2f} MB")
+            print(f"  处理时间: {elapsed_time:.1f}秒")
+            
+            # 检查是否适合坡度计算
+            if actual_width >= 10 and actual_height >= 10:
+                print(f"[DEM生成] ✓ DEM分辨率适合坡度坡向计算")
+            else:
+                print(f"[DEM生成] ⚠ DEM分辨率较低，坡度计算可能不准确")
+            
+            ds = None
+    except:
+        print(f"[DEM生成] 完成! 文件: {output_dem_path}")
+    
+    return output_dem_path
+
+
+def idw_interpolation(x, y, z, xi, yi, power=2, radius=None):
+    """
+    反距离权重插值
+    """
+    import numpy as np
+    from scipy.spatial import cKDTree
+    
+    x = np.asarray(x)
+    y = np.asarray(y)
+    z = np.asarray(z)
+    xi = np.asarray(xi)
+    yi = np.asarray(yi)
+    
+    # 展平网格
+    xi_flat = xi.flatten()
+    yi_flat = yi.flatten()
+    
+    # 创建KD树
+    tree = cKDTree(np.column_stack([x, y]))
+    
+    # 设置搜索半径
+    if radius is None:
+        # 自动确定半径：平均点间距的3倍
+        from scipy.spatial.distance import pdist
+        if len(x) > 1:
+            distances = pdist(np.column_stack([x, y]))
+            radius = np.mean(distances) * 3
+        else:
+            radius = 0.01  # 默认值
+    
+    # 查询最近邻点
+    if len(z) > 50:  # 数据较多时，限制搜索点数
+        k = min(12, len(z))
+        dists, idxs = tree.query(np.column_stack([xi_flat, yi_flat]), k=k, distance_upper_bound=radius)
+    else:
+        dists, idxs = tree.query(np.column_stack([xi_flat, yi_flat]), k=len(z))
+    
+    # IDW插值
+    zi_flat = np.zeros(len(xi_flat))
+    
+    for i in range(len(xi_flat)):
+        valid_mask = idxs[i] < len(z)
+        if np.any(valid_mask):
+            valid_dists = dists[i][valid_mask]
+            valid_idx = idxs[i][valid_mask]
+            
+            # 避免除零
+            valid_dists = np.maximum(valid_dists, 1e-10)
+            
+            # 计算权重
+            weights = 1.0 / (valid_dists ** power)
+            weights = weights / np.sum(weights)
+            
+            # 加权平均
+            zi_flat[i] = np.sum(z[valid_idx] * weights)
+        else:
+            zi_flat[i] = np.nan
+    
+    # 恢复形状
+    zi = zi_flat.reshape(xi.shape)
+    
+    return zi
+
+def points_to_dem1(points, output_dem_path, pixel_size=None, quality='medium'):
     """
     将点云转换为DEM
     :param quality: 质量等级 'low'|'medium'|'high'

b3dm/earthwork_api.py

@@ -14,33 +14,34 @@ earthwork_bp = Blueprint("earthwork", url_prefix="")
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
-# 全局变量
-_calculator_point_cloud = None
-_calculator_3d_tiles = None
-_data_source_3d_tiles = None
-
 # 初始化函数
-async def init_app():
+def init_app(url, type = "3dtiles"):
     """初始化应用"""
-    global _data_source_3d_tiles, _calculator_3d_tiles, _calculator_point_cloud
+    data_source = None
+    calculator_3d_tiles = None
+    calculator_point_cloud = None
     
     try:
-        # 配置数据源
-        tileset_path = "./data/3dtiles/tileset.json"  
-        
         # 初始化数据源
-        _data_source_3d_tiles = TilesetDataSource(tileset_path)
+        data_source = TilesetDataSource(url)
-        await _data_source_3d_tiles.initialize()
+        data_source.dowload_map_data(url)
         
-        # 初始化计算器-3dTiles
+        if type == "3dtiles" :
-        _calculator_3d_tiles = EarthworkCalculator3dTiles(_data_source_3d_tiles)
+            # 初始化计算器-3dTiles
-
+            calculator_3d_tiles = EarthworkCalculator3dTiles(data_source)
-        # 初始化计算器-点云
+        elif type == "pointcloud" :
-        point_cloud_path = "./data/pointCloud/simulated_points.laz"
+            # 初始化计算器-点云
-        _calculator_point_cloud = EarthworkCalculatorPointCloud(point_cloud_path)
+            calculator_point_cloud = EarthworkCalculatorPointCloud(data_source.tileset_path)
+        else :
+            logger.info(f"不支持的3d地图数据格式:{type}")
+            raise
         
         logger.info("土方量计算器初始化完成")
-        
+        return {
+            "data_source":data_source,
+            "calculator_3d_tiles":calculator_3d_tiles,
+            "calculator_point_cloud":calculator_point_cloud
+        }
     except ImportError as e:
         logger.error(f"依赖库缺失: {str(e)}")
         raise
@@ -56,12 +57,26 @@ async def calc_earthwork(request: Request):
     
     请求参数示例:
     {
-        "polygonCoords": [[120.1, 30.1], [120.2, 30.1], [120.2, 30.2], [120.1, 30.2]],
+        "polygonCoords": [
-        "designElevation": 50.0,
+            [
+                115.70440468338526,
+                30.77363140345639
+            ],
+            [
+                115.70443054007985,
+                30.773510462589584
+            ],
+            [
+                115.70459702429197,
+                30.77360789911405
+            ]
+        ],
+        "designElevation": 100,
         "algorithm": "tin",
-        "resolution": 1.0,
+        "resolution": 1,
         "crs": "EPSG:4326",
-        "interpolationMethod": "linear"
+        "interpolationMethod": "linear",
+        "url": "http://222.212.85.86:9000/300bdf2b-a150-406e-be63-d28bd29b409f/model/hbgldk/yzk/20260113/3D/terra_b3dms/tileset.json"
     }
     """
     try:
@@ -73,11 +88,14 @@ async def calc_earthwork(request: Request):
         # 2. 提取参数
         polygon_coords = data.get("polygonCoords")
         design_elevation = data.get("designElevation")
+        url = data.get("url")
         
         if not polygon_coords:
             return _error_response("多边形坐标不能为空", 400)
         if design_elevation is None:
             return _error_response("设计高程不能为空", 400)
+        if url is None:
+            return _error_response("地图不能为空", 400)
         
         # 3. 可选参数
         algorithm = data.get("algorithm", "tin")
@@ -102,13 +120,13 @@ async def calc_earthwork(request: Request):
             return _error_response("分辨率必须在0-100米之间", 400)
         
         # 5. 确保计算器已初始化
-        if _calculator_3d_tiles is None:
+        app_info = init_app(url)
-            await init_app()
+        calculator_3d_tiles = app_info.get("calculator_3d_tiles")
         
         # 6. 执行计算
         algorithm_type = AlgorithmType(algorithm)
         
-        result = await _calculator_3d_tiles.calculate(
+        result = await calculator_3d_tiles.calculate(
             polygon_coords=polygon_coords,
             design_elevation=design_elevation,
             algorithm=algorithm_type,
@@ -144,6 +162,10 @@ async def validate_earthwork(request: Request):
         if not polygon_coords:
             return _error_response("多边形坐标不能为空", 400)
         
+        url = data.get("url")
+        if url is None:
+            return _error_response("地图不能为空", 400)
+        
         # 3. 参数验证
         if len(polygon_coords) < 3:
             return _error_response("多边形至少需要3个点", 400)
@@ -153,11 +175,11 @@ async def validate_earthwork(request: Request):
             polygon_coords.append(polygon_coords[0])
         
         # 4. 确保计算器已初始化
-        if _calculator_3d_tiles is None:
+        app_info = init_app(url)
-            await init_app()
+        calculator_3d_tiles = app_info.get("calculator_3d_tiles")
         
         # 5. 执行验证
-        validation_result = await _calculator_3d_tiles.validate(polygon_coords)
+        validation_result = await calculator_3d_tiles.validate(polygon_coords)
         
         # 6. 返回结果
         return _success_response(validation_result)
@@ -242,9 +264,7 @@ async def batch_calc_earthwork(request: Request):
         if len(calculations) > 100:
             return _error_response("批量计算数量超过限制（最多100个）", 400)
         
-        # 2. 确保计算器已初始化
+        
-        if _calculator_3d_tiles is None:
-            await init_app()
         
         # 3. 执行批量计算
         results = []
@@ -255,8 +275,9 @@ async def batch_calc_earthwork(request: Request):
                 # 提取参数
                 polygon_coords = calc_data.get("polygonCoords")
                 design_elevation = calc_data.get("designElevation")
+                url = calc_data.get("url")
                 
-                if not polygon_coords or design_elevation is None:
+                if not polygon_coords or design_elevation is None or url is None:
                     errors.append({
                         "index": i,
                         "error": "缺少必要参数"
@@ -281,10 +302,14 @@ async def batch_calc_earthwork(request: Request):
                 crs = calc_data.get("crs", "EPSG:4326")
                 interpolation_method = calc_data.get("interpolationMethod", "linear")
 
+                # 2. 确保计算器已初始化
+                app_info = init_app(url)
+                calculator_3d_tiles = app_info.get("calculator_3d_tiles")
+                
                 # 执行计算
                 algorithm_type = AlgorithmType(algorithm)
                 
-                result = await _calculator_3d_tiles.calculate(
+                result = await calculator_3d_tiles.calculate(
                     polygon_coords=polygon_coords,
                     design_elevation=design_elevation,
                     algorithm=algorithm_type,
@@ -338,19 +363,22 @@ async def calc_earthwork_point_cloud(request: Request):
         polygon_coords = data.get("polygonCoords")  # 计算区域多边形坐标
         design_elev = data.get("designElevation")  # 设计高程
         crs = data.get("crs", "EPSG:4326")  # 坐标系，默认WGS84
+        url = data.get("url")
+        if url is None:
+            return _error_response("地图不能为空", 400)
 
         # 2. 确保计算器已初始化
-        if _calculator_point_cloud is None:
+        app_info = init_app(url)
-            await init_app()
+        calculator_point_cloud = app_info.get("calculator_point_cloud")
 
-        result = _calculator_point_cloud.calculate_earthwork(polygon_coords=polygon_coords, design_elev=design_elev, crs=crs)
+        result = calculator_point_cloud.calculate_earthwork(polygon_coords=polygon_coords, design_elev=design_elev, crs=crs)
             
         # 3. 处理结果
         if not result["success"]:
             return _error_response(result["error"], 400)
         
         # 4. 格式化结果
-        formatted_result = _calculator_point_cloud.format_result(result)
+        formatted_result = calculator_point_cloud.format_result(result)
         
         # 5. 返回成功响应
         return _success_response(formatted_result)

b3dm/earthwork_calculator_3d_tiles.py

@@ -8,6 +8,7 @@ import logging
 from enum import Enum
 from abc import ABC, abstractmethod
 import math
+from pyproj import Geod
 
 logger = logging.getLogger(__name__)
 
@@ -37,13 +38,13 @@ class EarthworkResult3dTiles:
         """转换为字典"""
         return {
             "volume": {
-                "cut": round(self.cut_volume, 3),
+                "cut": round(self.cut_volume, 8),
-                "fill": round(self.fill_volume, 3),
+                "fill": round(self.fill_volume, 8),
-                "net": round(self.net_volume, 3),
+                "net": round(self.net_volume, 8),
                 "unit": "m³"
             },
             "area": {
-                "value": round(self.area, 3),
+                "value": round(self.area, 8),
                 "unit": "m²"
             },
             "elevation": {
@@ -94,67 +95,375 @@ class TerrainDataSource(ABC):
         pass
 
 class GeometryUtils:
-    """几何计算工具类"""
+    """地理空间几何计算工具类（支持经纬度坐标）"""
     
-    @staticmethod
+    def __init__(self, source_crs: str = "EPSG:4326", target_crs: str = "EPSG:3857"):
-    def calculate_polygon_area(polygon_coords: List[List[float]]) -> float:
+        """
-        """计算多边形面积（平面面积）"""
+        初始化
-        polygon_np = np.array(polygon_coords)
+        Args:
-        x = polygon_np[:, 0]
+            source_crs: 源坐标系（通常是EPSG:4326）
-        y = polygon_np[:, 1]
+            target_crs: 目标投影坐标系（用于平面计算）
-        return 0.5 * np.abs(np.dot(x, np.roll(y, 1)) - np.dot(y, np.roll(x, 1)))
+        """
+        self.source_crs = source_crs
+        self.target_crs = target_crs
+        self.geod = Geod(ellps="WGS84")
         
-    @staticmethod
+        # 创建坐标转换器
-    def is_point_in_polygon(point: np.ndarray, polygon: np.ndarray) -> bool:
+        self.transformer_to_proj = Transformer.from_crs(
-        """判断点是否在多边形内"""
+            source_crs, target_crs, always_xy=True
-        from matplotlib.path import Path
+        )
-        path = Path(polygon)
+        self.transformer_to_geo = Transformer.from_crs(
-        return path.contains_point(point)
+            target_crs, source_crs, always_xy=True
+        )
+    
+    def calculate_polygon_area(self, polygon_coords: List[List[float]]) -> float:
+        """
+        计算多边形的地面实际面积（平方米）
+        
+        Args:
+            polygon_coords: 经纬度坐标列表 [[lon1, lat1], ...]
+            
+        Returns:
+            面积（平方米）
+        """
+        if len(polygon_coords) < 3:
+            return 0.0
+        
+        # 确保多边形闭合
+        closed_coords = self._ensure_closed_polygon(polygon_coords)
+        
+        # 提取经纬度
+        lons = [coord[0] for coord in closed_coords]
+        lats = [coord[1] for coord in closed_coords]
+        
+        # 使用测地线计算面积
+        area, _ = self.geod.polygon_area_perimeter(lons, lats)
+        return abs(area)
+    
+    def is_point_in_polygon(self, point: Tuple[float, float], 
+                           polygon_coords: List[List[float]], 
+                           use_spherical: bool = True) -> bool:
+        """
+        判断点是否在多边形内（支持地球表面判断）
+        
+        Args:
+            point: 点坐标 (lon, lat)
+            polygon_coords: 多边形顶点坐标
+            use_spherical: 是否使用球面算法
+            
+        Returns:
+            是否在多边形内
+        """
+        if len(polygon_coords) < 3:
+            return False
+        
+        if use_spherical:
+            # 方法1：球面射线法（更准确）
+            return self._is_point_in_polygon_spherical(point, polygon_coords)
+        else:
+            # 方法2：投影到平面后判断（更快）
+            return self._is_point_in_polygon_planar(point, polygon_coords)
+    
+    def calculate_triangle_area(self, points: np.ndarray) -> float:
+        """
+        计算三角形的地面面积（平方米）
+        
+        Args:
+            points: 3×2数组，每行是[lon, lat]
+            
+        Returns:
+            三角形地面面积（平方米）
+        """
+        if points.shape != (3, 2):
+            raise ValueError("需要3个点的坐标")
+        
+        # 转换为球面坐标计算
+        lons = points[:, 0]
+        lats = points[:, 1]
+        
+        # 使用球面三角形面积公式
+        R = 6378137.0  # WGS84地球半径（米）
+        
+        # 转换为弧度
+        lon_rad = np.radians(lons)
+        lat_rad = np.radians(lats)
+        
+        # 计算球面三角形的面积
+        # 使用L'Huilier公式
+        a = self._spherical_distance(lon_rad[0], lat_rad[0], lon_rad[1], lat_rad[1])
+        b = self._spherical_distance(lon_rad[1], lat_rad[1], lon_rad[2], lat_rad[2])
+        c = self._spherical_distance(lon_rad[2], lat_rad[2], lon_rad[0], lat_rad[0])
         
-    @staticmethod
-    def calculate_triangle_area(points: np.ndarray) -> float:
-        """计算三角形面积"""
-        a = np.linalg.norm(points[0] - points[1])
-        b = np.linalg.norm(points[1] - points[2])
-        c = np.linalg.norm(points[2] - points[0])
         s = (a + b + c) / 2
-        return np.sqrt(s * (s - a) * (s - b) * (s - c))
         
-    @staticmethod
+        # 防止数值误差
-    def create_grid(polygon: np.ndarray, resolution: float) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+        tan_e2 = np.tan(s/2) * np.tan((s-a)/2) * np.tan((s-b)/2) * np.tan((s-c)/2)
-        """创建规则格网"""
+        tan_e2 = max(tan_e2, 0)  # 避免负值
-        x_min, y_min = polygon.min(axis=0)
-        x_max, y_max = polygon.max(axis=0)
         
-        # 扩展一个格网单元
+        if tan_e2 > 0:
-        x_min -= resolution
+            E = 4 * np.arctan(np.sqrt(tan_e2))
-        x_max += resolution
+        else:
-        y_min -= resolution
+            E = 0
-        y_max += resolution
         
-        x_grid = np.arange(x_min, x_max + resolution, resolution)
+        area = R * R * E
-        y_grid = np.arange(y_min, y_max + resolution, resolution)
+        return area
-        xx, yy = np.meshgrid(x_grid, y_grid)
     
-        return xx, yy, x_grid, y_grid
+    def create_grid(self, polygon_coords: List[List[float]], 
+                   resolution_m: float, 
+                   use_projection: bool = True) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+        """
+        创建规则格网（地面距离为单位的网格）
         
-    @staticmethod
+        Args:
-    def interpolate_grid(xx: np.ndarray, yy: np.ndarray, 
+            polygon_coords: 多边形坐标
-                        points: np.ndarray, method: str = 'linear') -> np.ndarray:
+            resolution_m: 网格分辨率（米）
-        """格网插值"""
+            use_projection: 是否使用投影坐标系
-        from scipy.interpolate import LinearNDInterpolator, CloughTocher2DInterpolator
             
-        grid_points = np.column_stack([xx.ravel(), yy.ravel()])
+        Returns:
+            xx, yy: 网格坐标
+            grid_coords_geo: 网格点的地理坐标
+        """
+        if use_projection:
+            # 方法1：投影到平面坐标系创建网格
+            return self._create_grid_projected(polygon_coords, resolution_m)
+        else:
+            # 方法2：直接在经纬度上创建近似网格（小区域可用）
+            return self._create_grid_geographic(polygon_coords, resolution_m)
     
+    def interpolate_grid(self, xx: np.ndarray, yy: np.ndarray,
+                        points: np.ndarray, 
+                        method: str = 'linear',
+                        return_geo: bool = False) -> np.ndarray:
+        """
+        格网插值
+        
+        Args:
+            xx, yy: 网格坐标（投影坐标系）
+            points: 已知点，每行是[lon, lat, elevation]或[x_proj, y_proj, elevation]
+            method: 插值方法 'linear' 或 'cubic'
+            return_geo: 是否返回地理坐标
+            
+        Returns:
+            插值后的高程网格
+        """
+        # 确保points是投影坐标
+        if points.shape[1] != 3:
+            raise ValueError("points应为3列: x, y, z")
+        
+        # 如果输入是地理坐标，转换为投影坐标
+        if np.max(np.abs(points[:, 0])) > 180:  # 粗略判断
+            # 已经是投影坐标
+            points_proj = points
+        else:
+            # 转换为投影坐标
+            x_proj, y_proj = self.transformer_to_proj.transform(
+                points[:, 0], points[:, 1]
+            )
+            points_proj = np.column_stack([x_proj, y_proj, points[:, 2]])
+        
+        # 创建插值器
         if method == 'linear':
-            interpolator = LinearNDInterpolator(points[:, :2], points[:, 2])
+            interpolator = LinearNDInterpolator(
+                points_proj[:, :2], 
+                points_proj[:, 2],
+                fill_value=np.nan
+            )
         elif method == 'cubic':
-            interpolator = CloughTocher2DInterpolator(points[:, :2], points[:, 2])
+            interpolator = CloughTocher2DInterpolator(
+                points_proj[:, :2], 
+                points_proj[:, 2],
+                fill_value=np.nan
+            )
         else:
             raise ValueError(f"不支持的插值方法: {method}")
         
+        # 插值
+        grid_points = np.column_stack([xx.ravel(), yy.ravel()])
         elevations = interpolator(grid_points)
-        return elevations.reshape(xx.shape)
+        result = elevations.reshape(xx.shape)
+        
+        if return_geo:
+            # 如果需要，将网格点转回地理坐标
+            lon_grid, lat_grid = self.transformer_to_geo.transform(
+                xx.ravel(), yy.ravel()
+            )
+            lon_grid = lon_grid.reshape(xx.shape)
+            lat_grid = lat_grid.reshape(xx.shape)
+            return result, lon_grid, lat_grid
+        
+        return result
+    
+    # ============ 私有方法 ============
+    
+    def _ensure_closed_polygon(self, coords: List[List[float]]) -> List[List[float]]:
+        """确保多边形闭合"""
+        if len(coords) >= 3:
+            # 使用 numpy 比较
+            if not np.array_equal(coords[0], coords[-1]):
+                return coords + [coords[0]]
+        return coords
+    
+    def _spherical_distance(self, lon1_rad: float, lat1_rad: float, 
+                           lon2_rad: float, lat2_rad: float) -> float:
+        """计算球面两点间角距离"""
+        dlon = lon2_rad - lon1_rad
+        dlat = lat2_rad - lat1_rad
+        
+        a = np.sin(dlat/2)**2 + np.cos(lat1_rad) * np.cos(lat2_rad) * np.sin(dlon/2)**2
+        return 2 * np.arcsin(np.sqrt(a))
+    
+    def _is_point_in_polygon_spherical(self, point: Tuple[float, float], 
+                                      polygon_coords: List[List[float]]) -> bool:
+        """球面射线法判断点是否在多边形内"""
+        lon_p, lat_p = point
+        closed_polygon = self._ensure_closed_polygon(polygon_coords)
+        
+        # 将多边形的边转换为球面大圆弧
+        crossings = 0
+        n = len(closed_polygon) - 1
+        
+        for i in range(n):
+            lon1, lat1 = closed_polygon[i]
+            lon2, lat2 = closed_polygon[i + 1]
+            
+            # 检查射线是否与边相交（近似算法）
+            # 简化：使用平面近似，对小区域足够准确
+            if ((lat1 > lat_p) != (lat2 > lat_p)) and \
+               (lon_p < (lon2 - lon1) * (lat_p - lat1) / (lat2 - lat1) + lon1):
+                crossings += 1
+        
+        return crossings % 2 == 1
+    
+    def _is_point_in_polygon_planar(self, point: Tuple[float, float], 
+                                   polygon_coords: List[List[float]]) -> bool:
+        """投影到平面后判断"""
+        # 转换为投影坐标
+        point_proj = np.array(self.transformer_to_proj.transform(point[0], point[1])).reshape(1, 2)
+        polygon_proj = np.array([
+            self.transformer_to_proj.transform(lon, lat) 
+            for lon, lat in polygon_coords
+        ])
+        
+        # 使用平面方法判断
+        path = Path(polygon_proj)
+        return path.contains_point(point_proj[0])
+    
+    def _create_grid_projected(self, polygon_coords: List[List[float]], 
+                              resolution_m: float) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+        """在投影坐标系中创建网格"""
+        # 将多边形转换为投影坐标
+        polygon_proj = []
+        for lon, lat in polygon_coords:
+            x, y = self.transformer_to_proj.transform(lon, lat)
+            polygon_proj.append([x, y])
+        
+        polygon_proj = np.array(polygon_proj)
+        
+        # 计算边界框
+        x_min, y_min = polygon_proj.min(axis=0)
+        x_max, y_max = polygon_proj.max(axis=0)
+        
+        # 扩展半个网格
+        x_min -= resolution_m / 2
+        x_max += resolution_m / 2
+        y_min -= resolution_m / 2
+        y_max += resolution_m / 2
+        
+        # 创建网格
+        x_grid = np.arange(x_min, x_max + resolution_m, resolution_m)
+        y_grid = np.arange(y_min, y_max + resolution_m, resolution_m)
+        xx, yy = np.meshgrid(x_grid, y_grid)
+        
+        # 将网格点转回地理坐标
+        grid_coords_geo = []
+        for x, y in zip(xx.ravel(), yy.ravel()):
+            lon, lat = self.transformer_to_geo.transform(x, y)
+            grid_coords_geo.append([lon, lat])
+        grid_coords_geo = np.array(grid_coords_geo).reshape(xx.shape[0], xx.shape[1], 2)
+        
+        return xx, yy, grid_coords_geo
+    
+    def _create_grid_geographic(self, polygon_coords: List[List[float]], 
+                               resolution_m: float) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+        """在经纬度坐标系中创建近似网格"""
+        # 计算中心点
+        lons = [coord[0] for coord in polygon_coords]
+        lats = [coord[1] for coord in polygon_coords]
+        center_lon = np.mean(lons)
+        center_lat = np.mean(lats)
+        
+        # 计算经纬度到米的换算系数
+        lat_rad = np.radians(center_lat)
+        meters_per_degree_lon = 111319.9 * np.cos(lat_rad)
+        meters_per_degree_lat = 111000.0
+        
+        # 计算边界框（米）
+        x_min_m, x_max_m, y_min_m, y_max_m = 1e9, -1e9, 1e9, -1e9
+        for lon, lat in polygon_coords:
+            x_m = (lon - center_lon) * meters_per_degree_lon
+            y_m = (lat - center_lat) * meters_per_degree_lat
+            x_min_m = min(x_min_m, x_m)
+            x_max_m = max(x_max_m, x_m)
+            y_min_m = min(y_min_m, y_m)
+            y_max_m = max(y_max_m, y_m)
+        
+        # 扩展半个网格
+        x_min_m -= resolution_m / 2
+        x_max_m += resolution_m / 2
+        y_min_m -= resolution_m / 2
+        y_max_m += resolution_m / 2
+        
+        # 创建网格（米）
+        x_grid_m = np.arange(x_min_m, x_max_m + resolution_m, resolution_m)
+        y_grid_m = np.arange(y_min_m, y_max_m + resolution_m, resolution_m)
+        
+        # 转换为经纬度
+        x_grid_lon = center_lon + x_grid_m / meters_per_degree_lon
+        y_grid_lat = center_lat + y_grid_m / meters_per_degree_lat
+        
+        xx, yy = np.meshgrid(x_grid_lon, y_grid_lat)
+        
+        # 网格坐标（经纬度）
+        grid_coords_geo = np.dstack([xx, yy])
+        
+        return xx, yy, grid_coords_geo
+    
+    def get_polygon_bounds(self, polygon_coords: List[List[float]], 
+                          in_meters: bool = False) -> dict:
+        """
+        获取多边形边界信息
+        
+        Args:
+            polygon_coords: 多边形坐标
+            in_meters: 是否返回米为单位
+            
+        Returns:
+            边界信息字典
+        """
+        lons = [coord[0] for coord in polygon_coords]
+        lats = [coord[1] for coord in polygon_coords]
+        
+        bounds = {
+            'min_lon': min(lons),
+            'max_lon': max(lons),
+            'min_lat': min(lats),
+            'max_lat': max(lats),
+            'center_lon': (min(lons) + max(lons)) / 2,
+            'center_lat': (min(lats) + max(lats)) / 2
+        }
+        
+        if in_meters:
+            # 计算实际尺寸（米）
+            center_lat = bounds['center_lat']
+            lat_rad = np.radians(center_lat)
+            meters_per_degree_lon = 111319.9 * np.cos(lat_rad)
+            meters_per_degree_lat = 111000.0
+            
+            bounds['width_m'] = (bounds['max_lon'] - bounds['min_lon']) * meters_per_degree_lon
+            bounds['height_m'] = (bounds['max_lat'] - bounds['min_lat']) * meters_per_degree_lat
+            bounds['area_m2'] = self.calculate_polygon_area(polygon_coords)
+        
+        return bounds
 
 class EarthworkCalculator3dTiles:
     """土方量计算器"""
@@ -167,6 +476,7 @@ class EarthworkCalculator3dTiles:
             data_source: 地形数据源
         """
         self.data_source = data_source
+        self.geometryUtils = GeometryUtils()
         self._transformer_cache = {}
         
     async def calculate(self,
@@ -183,7 +493,7 @@ class EarthworkCalculator3dTiles:
             polygon_coords: 多边形坐标
             design_elevation: 设计高程
             algorithm: 计算算法
-            resolution: 格网分辨率（米）
+            resolution: 格网分辨率(米)
             target_crs: 目标坐标系
             interpolation_method: 插值方法
             
@@ -195,7 +505,7 @@ class EarthworkCalculator3dTiles:
             points = await self.data_source.get_points_in_polygon(polygon_coords)
             
             if points.size == 0:
-                raise ValueError("区域内没有找到高程数据")
+                raise ValueError("区域内没有找到顶点数据")
             
             # 2. 坐标转换
             points = await self._transform_coordinates(points, target_crs)
@@ -250,10 +560,10 @@ class EarthworkCalculator3dTiles:
         polygon_np = np.array(polygon_coords)
         
         # 创建格网
-        xx, yy, x_grid, y_grid = GeometryUtils.create_grid(polygon_np, resolution)
+        xx, yy, x_grid, y_grid = self.geometryUtils.create_grid(polygon_np, resolution)
         
         # 插值
-        natural_elevations = GeometryUtils.interpolate_grid(xx, yy, points, interpolation_method)
+        natural_elevations = self.geometryUtils.interpolate_grid(xx, yy, points, interpolation_method)
         
         # 初始化挖填量
         cut_volume = 0.0
@@ -273,7 +583,7 @@ class EarthworkCalculator3dTiles:
                 
                 # 检查格网中心点是否在多边形内
                 cell_center = cell_corners.mean(axis=0)
-                if not GeometryUtils.is_point_in_polygon(cell_center, polygon_np):
+                if not self.geometryUtils.is_point_in_polygon(cell_center, polygon_np):
                     continue
                 
                 # 获取格网四个角点的高程
@@ -301,7 +611,7 @@ class EarthworkCalculator3dTiles:
                     cut_volume += abs(height_diff) * cell_area
         
         # 计算统计信息
-        area = GeometryUtils.calculate_polygon_area(polygon_coords)
+        area = self.geometryUtils.calculate_polygon_area(polygon_coords)
         mask = ~np.isnan(natural_elevations)
         valid_elevations = natural_elevations[mask]
         
@@ -342,14 +652,16 @@ class EarthworkCalculator3dTiles:
             triangle_center = triangle_points.mean(axis=0)[:2]
             
             # 检查三角形中心是否在多边形内
-            if not GeometryUtils.is_point_in_polygon(triangle_center, polygon_np):
+            if not self.geometryUtils.is_point_in_polygon(triangle_center, polygon_np):
                 continue
             
             # 计算三角形面积
-            area = GeometryUtils.calculate_triangle_area(triangle_points[:, :2])
+            area = self.geometryUtils.calculate_triangle_area(triangle_points[:, :2])
+            if  math.isnan(area) :
+                continue
             total_area += area
             
-            # 计算平均高程（使用三个顶点的高程）
+            # 计算平均高程(使用三个顶点的高程)
             avg_elevation = triangle_points[:, 2].mean()
             
             # 计算挖填量
@@ -358,9 +670,13 @@ class EarthworkCalculator3dTiles:
                 fill_volume += height_diff * area
             else:
                 cut_volume += abs(height_diff) * area
+            # if  math.isnan(cut_volume) :
+            #     print("cut_volume变为nan")
+            # if  math.isnan(fill_volume) :
+            #     print("fill_volume变为nan")
 
         # 计算统计信息
-        area = GeometryUtils.calculate_polygon_area(polygon_coords)
+        area = self.geometryUtils.calculate_polygon_area(polygon_coords)
         
         return EarthworkResult3dTiles(
             cut_volume=cut_volume,
@@ -397,11 +713,11 @@ class EarthworkCalculator3dTiles:
             triangle_points = points[simplex]
             triangle_center = triangle_points.mean(axis=0)[:2]
             
-            if not GeometryUtils.is_point_in_polygon(triangle_center, polygon_np):
+            if not self.geometryUtils.is_point_in_polygon(triangle_center, polygon_np):
                 continue
             
             # 计算三角形面积
-            area = GeometryUtils.calculate_triangle_area(triangle_points[:, :2])
+            area = self.geometryUtils.calculate_triangle_area(triangle_points[:, :2])
             total_area += area
             
             # 对于每个三角形，计算三棱柱体积
@@ -418,7 +734,7 @@ class EarthworkCalculator3dTiles:
                 # 计算边的平均挖填高度
                 avg_height = (abs(height_i) + abs(height_j)) / 2
                 
-                # 计算边的面积（假设边宽度为resolution）
+                # 计算边的面积(假设边宽度为resolution)
                 edge_area = edge_length * resolution
                 
                 if height_i > 0 or height_j > 0:
@@ -426,7 +742,7 @@ class EarthworkCalculator3dTiles:
                 else:
                     cut_volume += avg_height * edge_area
         
-        area = GeometryUtils.calculate_polygon_area(polygon_coords)
+        area = self.geometryUtils.calculate_polygon_area(polygon_coords)
         
         return EarthworkResult3dTiles(
             cut_volume=cut_volume,
@@ -477,7 +793,7 @@ class EarthworkCalculator3dTiles:
             
             validation_result = {
                 "polygon_valid": len(polygon_coords) >= 3,
-                "area": GeometryUtils.calculate_polygon_area(polygon_coords),
+                "area": self.geometryUtils.calculate_polygon_area(polygon_coords),
                 "points_available": points.size > 0,
                 "points_count": points.shape[0] if points.size > 0 else 0,
                 "data_quality": "good" if points.shape[0] > 100 else "poor",

b3dm/terrain_api.py

@@ -174,7 +174,7 @@ async def preload_3dtiles(request: Request):
         
         # 创建并启动线程
         script_dir = os.path.dirname(os.path.abspath(__file__))
-        thread1 = threading.Thread(target=TerrainCalculator.preload_3dtiles, args=(vector.url))
+        thread1 = threading.Thread(target=TerrainCalculator.preload_3dtiles, args=(vector.url,))
         # 启动线程
         thread1.start()
         url_prefix = extract_and_rebuild_url(vector.url)

b3dm/terrain_calculator.py

@@ -3,58 +3,39 @@ from typing import List, Tuple, Dict, Any
 import logging
 import os
 import uuid
-from b3dm.data_3dtiles_manager import MinIO3DTilesManager
 import b3dm.data_3dtiles_to_dem as data_3dtiles_to_dem
 import b3dm.slope_aspect_img as slope_aspect_img
 import b3dm.slope_aspect_tif as slope_aspect_tif
+from b3dm.tileset_data_source import TilesetDataSource
 
 logger = logging.getLogger(__name__)
 
-ENDPOINT_URL = "222.212.85.86:9000"
+_data_source = None
-ACCESS_KEY = "WuRenJi"
-SECRET_KEY = "WRJ@2024"
 
 
 class TerrainCalculator:
     """地形坡度和坡向计算器"""
 
-    def preload_3dtiles(url) :
+    def preload_3dtiles(url: str) :
         # 下载3dtiles地图数据
-        manager = MinIO3DTilesManager(
+        _data_source = TilesetDataSource(url)
-            endpoint_url=ENDPOINT_URL,
+        _data_source.dowload_map_data(url)
-            access_key=ACCESS_KEY,
+        
-            secret_key=SECRET_KEY,
+        if not _data_source.tileset_path :
-            secure=False
-        )
-        script_dir = os.path.dirname(os.path.abspath(__file__))
-        success, entry_local_path = manager.download_full_tileset(
-            tileset_url=url,
-            save_dir=f"data_3dtiles",
-            region_filter=None
-        )
-        if not success :
             logger.info(f"下载地图数据失败: {url}")
             return "下载地图数据失败", None
 
     def generate_slopeAspect_3d_overlook(region_coords, url, overall_3d_png_name, minio_sub_path) :
         # 下载3dtiles地图数据
-        manager = MinIO3DTilesManager(
+        _data_source = TilesetDataSource(url)
-            endpoint_url=ENDPOINT_URL,
+        _data_source.dowload_map_data(url)
-            access_key=ACCESS_KEY,
+        
-            secret_key=SECRET_KEY,
+        if not _data_source.tileset_path :
-            secure=False
-        )
-        script_dir = os.path.dirname(os.path.abspath(__file__))
-        success, entry_local_path = manager.download_full_tileset(
-            tileset_url=url,
-            save_dir=f"data_3dtiles",
-            region_filter=None
-        )
-        if not success :
             logger.info(f"下载地图数据失败: {url},{region_coords}")
             return "下载地图数据失败", None
         
-        tileset_path = entry_local_path
+        tileset_path = _data_source.tileset_path
+        script_dir = os.path.dirname(os.path.abspath(__file__))
         dem_path = os.path.join(script_dir, f"o_dem_{uuid.uuid4().hex[:8]}.tif")
         data_3dtiles_to_dem.generate_dem(tileset_path, dem_path, region_coords)
 
@@ -66,8 +47,8 @@ class TerrainCalculator:
         slope_aspect_img.read_slope_aspect_by_dem(dem_path, overall_3d_png_path)
         logger.info(f"生成成功: {url},{region_coords},{overall_3d_png_path}")
 
-        entry_bucket, _ = manager.parse_minio_url(url);
+        entry_bucket, _ = _data_source.parse_minio_url(url);
-        success, minio_path = manager.upload_file(entry_bucket, f"{minio_sub_path}/{overall_3d_png_name}", overall_3d_png_path)
+        success, minio_path = _data_source.upload_file(entry_bucket, f"{minio_sub_path}/{overall_3d_png_name}", overall_3d_png_path)
         if success :
             return "生成成功", minio_path
         else :
@@ -75,36 +56,28 @@ class TerrainCalculator:
 
     def generate_slopeAspect_tif(region_coords, url, slope_aspect_tif_name, minio_sub_path) :
         # 下载3dtiles地图数据
-        manager = MinIO3DTilesManager(
+        _data_source = TilesetDataSource(url)
-            endpoint_url=ENDPOINT_URL,
+        _data_source.dowload_map_data(url)
-            access_key=ACCESS_KEY,
+        
-            secret_key=SECRET_KEY,
+        if not _data_source.tileset_path :
-            secure=False
-        )
-        script_dir = os.path.dirname(os.path.abspath(__file__))
-        success, entry_local_path = manager.download_full_tileset(
-            tileset_url=url,
-            save_dir=f"data_3dtiles",
-            region_filter=None
-        )
-        if not success :
             logger.info(f"下载地图数据失败: {url},{region_coords}")
             return "下载地图数据失败", None
         
-        tileset_path = entry_local_path
+        tileset_path = _data_source.tileset_path
+        script_dir = os.path.dirname(os.path.abspath(__file__))
         dem_path = os.path.join(script_dir, f"o_dem_{uuid.uuid4().hex[:8]}.tif")
         data_3dtiles_to_dem.generate_dem(tileset_path, dem_path, region_coords)
 
         if not os.path.exists(dem_path) :
-            logger.info(f"生成坡度坡向俯视图失败: {url},{region_coords}")
+            logger.info(f"生成坡度坡向tif失败: {url},{region_coords}")
-            return "生成坡度坡向俯视图失败", None
+            return "生成坡度坡向tif失败", None
         
         slope_aspect_tif_path = os.path.join(script_dir, slope_aspect_tif_name)
         slope_aspect_tif.create_slope_aspect(dem_path, 'combined', slope_aspect_tif_path)
         logger.info(f"生成成功: {url},{region_coords},{slope_aspect_tif_path}")
 
-        entry_bucket, _ = manager.parse_minio_url(url);
+        entry_bucket, _ = _data_source.parse_minio_url(url);
-        success, minio_path = manager.upload_file(entry_bucket, f"{minio_sub_path}/{slope_aspect_tif_name}", slope_aspect_tif_path)
+        success, minio_path = _data_source.upload_file(entry_bucket, f"{minio_sub_path}/{slope_aspect_tif_name}", slope_aspect_tif_path)
         if success :
             return "生成成功", minio_path
         else :

b3dm/tileset_data_source.py

@@ -5,148 +5,71 @@ import asyncio
 from concurrent.futures import ThreadPoolExecutor
 import logging
 import os
+from pathlib import Path
+from b3dm.data_3dtiles_manager import MinIO3DTilesManager
+import b3dm.data_3dtiles_to_dem as data_3dtiles_to_dem
 
 logger = logging.getLogger(__name__)
 
+ENDPOINT_URL = "222.212.85.86:9000"
+ACCESS_KEY = "WuRenJi"
+SECRET_KEY = "WRJ@2024"
+
 class TilesetDataSource:
     """使用py3dtiles库的数据源"""
     
-    def __init__(self, tileset_path: str, cache_size: int = 1000):
+    def __init__(self, url: str, cache_size: int = 1000):
-        self.tileset_path = os.path.abspath(tileset_path)
+        self.url = url
-        self.tileset_dir = os.path.dirname(self.tileset_path)
+        self.tileset_path = None
-        self.cache_size = cache_size
+        self.tileset_dir = None
-        self._tileset = None
-        self._point_cache = {}
-        self._executor = ThreadPoolExecutor(max_workers=4)
         self._crs = "EPSG:4979"
 
-    async def initialize(self):
+    def parse_minio_url(self, url):
-        """初始化"""
+        manager = MinIO3DTilesManager(
-        try:
+            endpoint_url=ENDPOINT_URL,
-            # 尝试导入py3dtiles
+            access_key=ACCESS_KEY,
-            try:
+            secret_key=SECRET_KEY,
-                import py3dtiles
+            secure=False
-                from py3dtiles.tileset import TileSet
+        )
-            except ImportError:
+        return manager.parse_minio_url(url)
-                logger.warning("py3dtiles未安装，将使用简化数据源")
-                raise ImportError("请安装py3dtiles: pip install py3dtiles")
     
-            loop = asyncio.get_event_loop()
+    def upload_file(self, bucket_name, object_name, file_path):
-            self._tileset = await loop.run_in_executor(
+        manager = MinIO3DTilesManager(
-                self._executor,
+            endpoint_url=ENDPOINT_URL,
-                TileSet.from_file,
+            access_key=ACCESS_KEY,
-                self.tileset_path
+            secret_key=SECRET_KEY,
-            )
+            secure=False
+        )
+        flag, path = manager.upload_file(bucket_name, object_name, file_path)
+        if flag :
+            os.remove(file_path)
+        return flag, path
         
-            logger.info(f"py3dtiles数据源初始化完成: {self.tileset_path}")
 
-        except Exception as e:
+    def dowload_map_data(self, url: str) :
-            logger.error(f"py3dtiles初始化失败: {str(e)}")
+        # 下载3dtiles地图数据
-            # 回退到简化数据源
+        manager = MinIO3DTilesManager(
-            self._tileset = None
+            endpoint_url=ENDPOINT_URL,
+            access_key=ACCESS_KEY,
+            secret_key=SECRET_KEY,
+            secure=False
+        )
+        success, tileset_path = manager.download_full_tileset(
+            tileset_url=url,
+            save_dir=f"data_3dtiles",
+            region_filter=None
+        )
+        if success :
+            self.tileset_path = os.path.abspath(tileset_path)
+            self.tileset_dir = os.path.dirname(tileset_path)
     
-    async def get_points_in_polygon(self, 
+    async def get_points_in_polygon(self, polygon_coords, z_range=None):
-                                   polygon_coords: List[List[float]],
+        """获取多边形内的点数据"""
-                                   z_range: Optional[Tuple[float, float]] = None) -> np.ndarray:
+        points = data_3dtiles_to_dem.parse_tileset(self.tileset_path, polygon_coords)
-        """获取点数据"""
+        return np.array(points)
-        if self._tileset is None:
-            # 使用简化数据源
-            return await self._get_simulated_points(polygon_coords, z_range)
-        
-        try:
-            # 使用py3dtiles API获取数据
-            points = []
-            polygon_np = np.array(polygon_coords)
-            
-            # 遍历tileset中的所有tile
-            for tile in self._tileset.root_tile.traverse():
-                tile_points = self._extract_tile_points(tile)
-                if tile_points.size > 0:
-                    # 筛选多边形内的点
-                    points_in_polygon = self._filter_points_by_polygon(tile_points, polygon_np)
-                    if points_in_polygon.size > 0:
-                        points.append(points_in_polygon)
-            
-            if points:
-                all_points = np.vstack(points)
-                if z_range:
-                    mask = (all_points[:, 2] >= z_range[0]) & (all_points[:, 2] <= z_range[1])
-                    all_points = all_points[mask]
-                return all_points
-            
-            return np.array([])
-            
-        except Exception as e:
-            logger.error(f"py3dtiles获取数据失败: {str(e)}")
-            return await self._get_simulated_points(polygon_coords, z_range)
-    
-    def _extract_tile_points(self, tile) -> np.ndarray:
-        """从tile提取点数据"""
-        try:
-            if hasattr(tile, 'content') and tile.content:
-                # 尝试获取点数据
-                if hasattr(tile.content, 'points'):
-                    return tile.content.points.positions
-                elif hasattr(tile.content, 'body'):
-                    # 处理其他格式
-                    return np.array([])
-            return np.array([])
-        except:
-            return np.array([])
-    
-    def _filter_points_by_polygon(self, points: np.ndarray, polygon: np.ndarray) -> np.ndarray:
-        """筛选多边形内的点"""
-        from matplotlib.path import Path
-        if points.size == 0:
-            return points
-        
-        path = Path(polygon[:, :2])
-        mask = path.contains_points(points[:, :2])
-        return points[mask]
-    
-    async def _get_simulated_points(self, 
-                                   polygon_coords: List[List[float]],
-                                   z_range: Optional[Tuple[float, float]] = None) -> np.ndarray:
-        """获取模拟点数据"""
-        # 同上一个类的生成模拟数据方法
-        polygon_np = np.array(polygon_coords)
-        
-        if polygon_np.shape[0] < 3:
-            return np.array([])
-        
-        x_min, y_min = polygon_np.min(axis=0)
-        x_max, y_max = polygon_np.max(axis=0)
-        
-        grid_size = 100
-        x = np.linspace(x_min, x_max, grid_size)
-        y = np.linspace(y_min, y_max, grid_size)
-        xx, yy = np.meshgrid(x, y)
-        
-        points = np.column_stack([xx.ravel(), yy.ravel()])
-        
-        np.random.seed(42)
-        base_elevation = 50.0
-        terrain_variation = 10.0
-        z = base_elevation + terrain_variation * np.sin(0.1 * xx).ravel() * np.cos(0.1 * yy).ravel()
-        
-        points = np.column_stack([points[:, 0], points[:, 1], z])
-        
-        from matplotlib.path import Path
-        path = Path(polygon_np[:, :2])
-        mask = path.contains_points(points[:, :2])
-        points = points[mask]
-        
-        if z_range:
-            mask = (points[:, 2] >= z_range[0]) & (points[:, 2] <= z_range[1])
-            points = points[mask]
-        
-        logger.info(f"生成 {points.shape[0]} 个模拟点")
-        return points
     
     async def get_data_bounds(self) -> Dict[str, List[float]]:
         """获取数据边界"""
-        if self._tileset is None:
-            await self.initialize()
         
         bounds = {
             "min": [float('inf'), float('inf'), float('inf')],
@@ -165,3 +88,18 @@ class TilesetDataSource:
     
     def get_crs(self) -> str:
         return self._crs
+
+async def main() : 
+    SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))
+    SCRIPT_PAR_DIR = os.path.dirname(SCRIPT_DIR)
+    tileset_path = os.path.join(SCRIPT_PAR_DIR, "data/3dtiles/tileset.json")
+    data_source_3d_tiles = TilesetDataSource(tileset_path)
+    # tileSet = TileSet()
+    # path = Path(tileset_path)
+    # tileset_data = tileSet.from_file(path)
+    print("====================================================")
+
+
+if __name__ == "__main__":
+    loop = asyncio.get_event_loop()
+    loop.run_until_complete(main())