235 lines
8.7 KiB
Python
235 lines
8.7 KiB
Python
import os
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import logging
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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from matplotlib.colors import LinearSegmentedColormap
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from shapely.geometry import Polygon, Point
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from tqdm import tqdm
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from py3dtiles.tileset import TileSet
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import requests
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# 日志配置
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logger = logging.getLogger("TilesetProcessor")
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logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
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class TilesetProcessor:
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"""3D Tiles数据集处理器，用于加载、分析和比较两个3D Tiles模型"""
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def __init__(self, tileset_path1, tileset_path2, resolution=1.0, polygon_points=None):
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self.tileset1 = self._load_tileset(tileset_path1)
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self.tileset2 = self._load_tileset(tileset_path2)
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self.resolution = resolution
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self.analysis_area = None
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self.height_difference_grid = None
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self.grid_bounds = None
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if polygon_points:
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self.set_analysis_area(polygon_points=polygon_points)
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def _load_tileset(self, path_or_url):
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try:
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logger.info(f"加载3D Tiles数据集: {path_or_url}")
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if path_or_url.startswith("http://") or path_or_url.startswith("https://"):
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resp = requests.get(path_or_url)
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resp.raise_for_status()
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tileset_json = resp.json()
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tileset = TileSet.from_dict(tileset_json)
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else:
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tileset = TileSet.from_file(path_or_url)
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logger.info(f"成功加载，包含 {len(tileset.root.children)} 个根瓦片")
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return tileset
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except Exception as e:
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logger.error(f"加载数据集失败（路径: {path_or_url}）: {e}")
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raise
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def set_analysis_area(self, bounds=None, polygon_points=None):
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if polygon_points:
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self.analysis_area = Polygon(polygon_points)
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min_x = min(p[0] for p in polygon_points)
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min_y = min(p[1] for p in polygon_points)
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max_x = max(p[0] for p in polygon_points)
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max_y = max(p[1] for p in polygon_points)
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self.grid_bounds = (min_x, min_y, max_x, max_y)
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logger.info(f"设置多边形分析区域: {polygon_points}")
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elif bounds:
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min_x, min_y, max_x, max_y = bounds
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self.analysis_area = Polygon([
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(min_x, min_y),
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(max_x, min_y),
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(max_x, max_y),
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(min_x, max_y)
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])
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self.grid_bounds = bounds
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logger.info(f"设置矩形分析区域: {bounds}")
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else:
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logger.error("请提供 bounds 或 polygon_points")
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return False
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logger.info(f"分析区域面积: {self.analysis_area.area:.2f} 平方米")
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return True
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def sample_heights(self):
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if self.analysis_area is None:
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logger.error("请先设置分析区域")
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return False
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logger.info("开始在分析区域内采样高度值...")
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min_x, min_y, max_x, max_y = self.grid_bounds
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rows = int((max_y - min_y) / self.resolution) + 1
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cols = int((max_x - min_x) / self.resolution) + 1
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self.height_difference_grid = np.full((rows, cols), np.nan, dtype=np.float32)
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total_points = rows * cols
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logger.info(f"创建了 {rows}x{cols}={total_points} 个采样点")
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with tqdm(total=total_points, desc="采样高度点") as pbar:
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for i in range(rows):
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for j in range(cols):
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x = min_x + j * self.resolution
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y = min_y + i * self.resolution
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point = Point(x, y)
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if not self.analysis_area.contains(point):
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pbar.update(1)
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continue
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height1 = self._sample_height_at_point(self.tileset1, x, y)
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height2 = self._sample_height_at_point(self.tileset2, x, y)
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if height1 is not None and height2 is not None:
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self.height_difference_grid[i, j] = height2 - height1
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pbar.update(1)
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valid_differences = self.height_difference_grid[~np.isnan(self.height_difference_grid)]
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if len(valid_differences) > 0:
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logger.info("高度变化统计:")
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logger.info(f" 平均变化: {np.mean(valid_differences):.2f}m")
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logger.info(f" 最大上升: {np.max(valid_differences):.2f}m")
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logger.info(f" 最大下降: {np.min(valid_differences):.2f}m")
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logger.info(f" 变化标准差: {np.std(valid_differences):.2f}m")
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else:
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logger.warning("未找到有效的高度差异数据")
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return True
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def _sample_height_at_point(self, tileset, x, y, max_depth=3):
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def find_tile(tile, depth=0):
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bbox = tile.bounding_volume.box
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min_x_tile = bbox[0] - bbox[3]
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max_x_tile = bbox[0] + bbox[3]
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min_y_tile = bbox[1] - bbox[4]
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max_y_tile = bbox[1] + bbox[4]
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if not (min_x_tile <= x <= max_x_tile and min_y_tile <= y <= max_y_tile):
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return None
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if (tile.content is not None and depth >= max_depth) or not tile.children:
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return tile
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for child in tile.children:
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result = find_tile(child, depth + 1)
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if result is not None:
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return result
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return None
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tile = find_tile(tileset.root)
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if tile is None or tile.content is None:
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return None
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try:
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# 简化模拟返回瓦片中心高度加随机偏移
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return tile.bounding_volume.box[2] + np.random.uniform(-0.5, 0.5)
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except Exception as e:
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logger.warning(f"获取瓦片高度失败: {e}")
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return None
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def export_results(self, output_dir="results"):
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if self.height_difference_grid is None:
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logger.error("请先采样高度值")
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return
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os.makedirs(output_dir, exist_ok=True)
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csv_path = os.path.join(output_dir, "height_differences.csv")
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logger.info(f"导出CSV文件: {csv_path}")
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min_x, min_y, max_x, max_y = self.grid_bounds
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rows, cols = self.height_difference_grid.shape
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data = []
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for i in range(rows):
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for j in range(cols):
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if not np.isnan(self.height_difference_grid[i, j]):
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x = min_x + j * self.resolution
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y = min_y + i * self.resolution
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data.append({
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'x': x,
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'y': y,
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'height_difference': self.height_difference_grid[i, j]
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})
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df = pd.DataFrame(data)
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df.to_csv(csv_path, index=False)
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self._generate_heatmap(output_dir)
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logger.info(f"结果已导出到 {output_dir} 目录")
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def _generate_heatmap(self, output_dir):
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colors = [(0.0, 0.0, 1.0), (1.0, 1.0, 1.0), (1.0, 0.0, 0.0)]
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cmap = LinearSegmentedColormap.from_list('height_diff_cmap', colors, N=256)
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data = self.height_difference_grid.copy()
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valid_mask = ~np.isnan(data)
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if not np.any(valid_mask):
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logger.warning("没有有效的高度差异数据，无法生成热图")
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return
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data[~valid_mask] = 0
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plt.figure(figsize=(12, 10))
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plt.imshow(data, cmap=cmap, origin='lower',
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extent=[self.grid_bounds[0], self.grid_bounds[2],
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self.grid_bounds[1], self.grid_bounds[3]],
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alpha=0.9)
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cbar = plt.colorbar()
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cbar.set_label('高度变化 (米)', fontsize=12)
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plt.title('两个3D Tiles模型的高度变化分布', fontsize=16)
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plt.xlabel('X坐标 (米)', fontsize=12)
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plt.ylabel('Y坐标 (米)', fontsize=12)
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heatmap_path = os.path.join(output_dir, "height_difference_heatmap.png")
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plt.savefig(heatmap_path, dpi=300, bbox_inches='tight')
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plt.close()
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logger.info(f"热图已保存到: {heatmap_path}")
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if __name__ == "__main__":
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tileset1_url = "http://8.137.54.85:9000/300bdf2b-a150-406e-be63-d28bd29b409f/dszh/1748398014403562192_OUT/B3DM/tileset.json"
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tileset2_url = "http://8.137.54.85:9000/300bdf2b-a150-406e-be63-d28bd29b409f/dszh/1748325943733189898_OUT/B3DM/tileset.json"
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polygon_coords = [
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(102.2232, 29.3841),
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(102.2261, 29.3845),
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(102.2263, 29.3821),
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(102.2231, 29.3818)
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]
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resolution = 0.5
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output_dir = "output_results"
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processor = TilesetProcessor(tileset1_url, tileset2_url, resolution, polygon_coords)
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if processor.sample_heights():
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processor.export_results(output_dir)
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print("分析完成！结果已导出到指定目录。")
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else:
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print("高度采样失败，无法完成分析。")
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