From 688624aaadf470ed83d22712a9ac8f4b9f5448ae Mon Sep 17 00:00:00 2001 From: yooooger <761181201@qq.com> Date: Fri, 25 Jul 2025 10:24:47 +0800 Subject: [PATCH] yoooooger --- .vscode/settings.json | 5 + Ai_tottle/ai_tottle_api copy.py | 370 +++++ Ai_tottle/ai_tottle_api.py | 74 - Ai_tottle/temp.glb | Bin 0 -> 92956 bytes Ai_tottle/tiles copy.py | 234 +++ Ai_tottle/tiles.py | 643 +++++--- Ai_tottle/tileset_model2.json | 1 + Ai_tottle/tileset_model2_adjusted.json | 2000 ++++++++++++++++++++++++ 8 files changed, 3008 insertions(+), 319 deletions(-) create mode 100644 .vscode/settings.json create mode 100644 Ai_tottle/ai_tottle_api copy.py create mode 100644 Ai_tottle/temp.glb create mode 100644 Ai_tottle/tiles copy.py create mode 100644 Ai_tottle/tileset_model2.json create mode 100644 Ai_tottle/tileset_model2_adjusted.json diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 0000000..a8c2003 --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,5 @@ +{ + "python-envs.defaultEnvManager": "ms-python.python:conda", + "python-envs.defaultPackageManager": "ms-python.python:conda", + "python-envs.pythonProjects": [] +} \ No newline at end of file diff --git a/Ai_tottle/ai_tottle_api copy.py b/Ai_tottle/ai_tottle_api copy.py new file mode 100644 index 0000000..12d14cd --- /dev/null +++ b/Ai_tottle/ai_tottle_api copy.py @@ -0,0 +1,370 @@ +from sanic import Sanic, json, Blueprint,response +from sanic.exceptions import Unauthorized +from sanic.response import json as json_response +from sanic_cors import CORS +import numpy as np +import logging +import uuid +import os,traceback +import asyncio +from ai_image import process_images # 你实现的图片处理函数 +from queue import Queue +from map_find import map_process_images +from yolo_train import auto_train +import torch +from yolo_photo import map_process_images_with_progress # 引入你的处理函数 +from tiles import TilesetProcessor +# 日志配置 +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') +logger = logging.getLogger(__name__) + +###################################################################################验证中间件和管理件############################################################################################## + +async def token_and_resource_check(request): + # --- Token 验证 --- + token = request.headers.get('X-API-Token') + expected_token = request.app.config.get("VALID_TOKEN") + if not token or token != expected_token: + logger.warning(f"Unauthorized request with token: {token}") + raise Unauthorized("Invalid token") + + # --- GPU 使用率检查 --- + try: + if torch.cuda.is_available(): + num_gpus = torch.cuda.device_count() + max_usage_ratio = request.app.config.get("MAX_GPU_USAGE", 0.9) # 默认90% + + for i in range(num_gpus): + used = torch.cuda.memory_reserved(i) + total = torch.cuda.max_memory_reserved(i) + ratio = used / total if total else 0 + + logger.info(f"GPU {i} Usage: {ratio:.2%}") + + if ratio > max_usage_ratio: + logger.warning(f"GPU {i} usage too high: {ratio:.2%}") + return json_response({ + "status": "error", + "message": f"GPU resource busy (GPU {i} at {ratio:.2%}). Try later." + }, status=503) + except Exception as e: + logger.error(f"GPU check failed: {e}") + + return None # 允许请求继续 + +################################################################################################################################################################################################## +#创建Sanic应用 +app = Sanic("ai_Service_v2") +CORS(app) # 允许跨域请求 +task_progress = {} + +@app.middleware("request") +async def global_middleware(request): + result = await token_and_resource_check(request) + if result: + return result + +# 配置Token和最大GPU使用率 +app.config.update({ + "VALID_TOKEN": "Beidou_b8609e96-bfec-4485-8c64-6d4f662ee44a", + "MAX_GPU_USAGE": 0.9 +}) + +######################################################################地图切割相关的API######################################################################################################## +#创建地图的蓝图 +map_tile_blueprint = Blueprint('map', url_prefix='/map/') +app.blueprint(map_tile_blueprint) + +# @map_tile_blueprint.post("/compare_tilesets") +# async def compare_tilesets(request): +# ''' +# 接口：/map/compare_tilesets +# 输入 JSON: +# { +# "tileset1": "path/to/tileset1/tileset.json", +# "tileset2": "path/to/tileset2/tileset.json", +# "bounds": [500000, 3000000, 500100, 3000100], +# "resolution": 1.0, +# "output": "results" +# } +# 输出 JSON: +# { +# "success": true, +# "message": "分析完成", +# "data": { +# "csv_path": "results/height_differences.csv", +# "heatmap_path": "results/height_difference_heatmap.png", +# "summary": { +# "mean": 0.28, +# "max": 1.10, +# "min": -0.85, +# "std": 0.23 +# } +# } +# } +# ''' +# try: +# body = request.json + +# # 参数提取与验证 +# tileset1 = body.get("tileset1") +# tileset2 = body.get("tileset2") +# bounds = body.get("bounds") +# resolution = body.get("resolution", 1.0) +# output = body.get("output", "results") + +# if not all([tileset1, tileset2, bounds]): +# return response.json({"success": False, "message": "参数不完整"}, status=400) + +# processor = TilesetProcessor(tileset1, tileset2, resolution) + +# if not processor.set_analysis_area(bounds): +# return response.json({"success": False, "message": "设置分析区域失败"}, status=400) + +# if not processor.sample_heights(): +# return response.json({"success": False, "message": "高度采样失败"}, status=500) + +# processor.export_results(output) + +# # 汇总统计结果 +# valid_differences = processor.height_difference_grid[~np.isnan(processor.height_difference_grid)] +# summary = { +# "mean": float(np.mean(valid_differences)), +# "max": float(np.max(valid_differences)), +# "min": float(np.min(valid_differences)), +# "std": float(np.std(valid_differences)) +# } + +# return response.json({ +# "success": True, +# "message": "分析完成", +# "data": { +# "csv_path": os.path.join(output, "height_differences.csv"), +# "heatmap_path": os.path.join(output, "height_difference_heatmap.png"), +# "summary": summary +# } +# }) + +# except Exception as e: +# traceback.print_exc() +# return response.json({"success": False, "message": str(e)}, status=500) + +#语义识别 +@map_tile_blueprint.post("/uav") +async def process_handler(request): + """ + 接口：/map/uav + 输入 JSON: + { + "urls": [ + "http://example.com/img1.jpg", + "http://example.com/img2.jpg" + ], + "yaml_name": "config", + "bucket_name": "300bdf2b-a150-406e-be63-d28bd29b409f", + "bucket_directory": "2025/seg" + "model_path": "deeplabv3plus_best.pth" + } + 输出 JSON: + { + "code": 200, + "msg": "success", + "data": [ + "http://minio.example.com/uav-results/2025/seg/result1.png", + "http://minio.example.com/uav-results/2025/seg/result2.png" + ] + } + + """ + try: + body = request.json + urls = body.get("urls", []) + yaml_name = body.get("yaml_name") + bucket_name = body.get("bucket_name") + bucket_directory = body.get("bucket_directory") + model_path = os.path.join("map", "checkpoints", body.get("model_path")) + + # 校验参数 + if not urls or not isinstance(urls, list): + return json({"code": 400, "msg": "Missing or invalid 'urls'"}) + if not all([yaml_name, bucket_name, bucket_directory]): + return json({"code": 400, "msg": "Missing required parameters"}) + + # 调用图像处理函数 + result = map_process_images(urls, yaml_name, bucket_name, bucket_directory,model_path) + return json(result) + + except Exception as e: + return json({"code": 500, "msg": f"Server error: {str(e)}"}) + +######################################################################yolo相关的API######################################################################################################## +#创建yolo的蓝图 +yolo_tile_blueprint = Blueprint('yolo', url_prefix='/yolo/') +app.blueprint(yolo_tile_blueprint) + +# YOLO URL APT +# 存储任务进度和结果（内存示例，可用 Redis 或 DB 持久化） + +@yolo_tile_blueprint.post("/process_images") +async def process_images(request): + """ + { + "urls": [ + "http://example.com/image1.jpg", + "http://example.com/image2.jpg", + "http://example.com/image3.jpg" + ], + "yaml_name": "your_minio_config", + "bucket_name": "my-bucket", + "bucket_directory": "2025/uav-results", + "model_path": "deeplabv3plus_best.pth" +} + """ + data = request.json + urls = data.get("urls") + yaml_name = data.get("yaml_name") + bucket_name = data.get("bucket_name") + bucket_directory = data.get("bucket_directory") + uav_model_path = data.get("uav_model_path") + + if not urls or not yaml_name or not bucket_name or not uav_model_path: + return response.json({"code": 400, "msg": "Missing parameters"}, status=400) + + task_id = str(uuid.uuid4()) + task_progress[task_id] = {"status": "pending", "progress": 0, "result": None} + + # 启动后台任务 + asyncio.create_task(run_image_processing(task_id, urls, yaml_name, bucket_name, bucket_directory, uav_model_path)) + + return response.json({"code": 200, "msg": "Task started", "task_id": task_id}) + +@yolo_tile_blueprint.get("/task_status/") +async def task_status(request, task_id): + progress = task_progress.get(task_id) + if not progress: + return response.json({"code": 404, "msg": "Task not found"}, status=404) + return response.json({"code": 200, "msg": "Task status", "data": progress}) + +async def run_image_processing(task_id, urls, yaml_name, bucket_name, bucket_directory, uav_model_path): + + + try: + task_progress[task_id]["status"] = "running" + task_progress[task_id]["progress"] = 10 # 开始进度 + + # 下载、推理、上传阶段分别更新进度 + def progress_callback(stage, percent): + task_progress[task_id]["status"] = stage + task_progress[task_id]["progress"] = percent + + result = await asyncio.to_thread( + map_process_images_with_progress, + urls, yaml_name, bucket_name, bucket_directory, uav_model_path, progress_callback + ) + + task_progress[task_id]["status"] = "completed" + task_progress[task_id]["progress"] = 100 + task_progress[task_id]["result"] = result + + except Exception as e: + task_progress[task_id]["status"] = "failed" + task_progress[task_id]["progress"] = 100 + task_progress[task_id]["result"] = str(e) + +# YOLO检测API +@yolo_tile_blueprint.post("/picture") +async def yolo_detect_api(request): + try: + + detect_data = request.json + + # 解析必要字段 + image_list = detect_data.get("image_list") + yolo_model = detect_data.get("yolo_model", "best.pt") + class_filter = detect_data.get("class", None) + minio_info = detect_data.get("minio", None) + + if not image_list: + return json_response({"status": "error", "message": "image_list is required"}, status=400) + + if not minio_info: + return json_response({"status": "error", "message": "MinIO information is required"}, status=400) + + # 创建临时文件夹 + input_folder = f"./temp_input_{str(uuid.uuid4())}" + output_folder = f"./temp_output_{str(uuid.uuid4())}" + + # 执行图像处理 + result = await asyncio.to_thread( + process_images, + yolo_model=yolo_model, + image_list=image_list, + class_filter=class_filter, + input_folder=input_folder, + output_folder=output_folder, + minio_info=minio_info + ) + + # 返回处理结果 + return json_response(result) + + except Exception as e: + logger.error(f"Error occurred while processing request: {str(e)}", exc_info=True) + return json_response({ + "status": "error", + "message": f"Internal server error: {str(e)}" + }, status=500) + +# YOLO自动训练 +@yolo_tile_blueprint.post("/train") +async def yolo_train_api(request): + """ + 自动训练模型 + 输入 JSON: + { + "db_host": str, + "db_database": str, + "db_user": str, + "db_password": str, + "db_port": int, + "model_id": int, + "img_path": str, + "label_path": str, + "new_path": str, + "split_list": List[float], + "class_names": Optional[List[str]], + "project_name": str + } + 输出 JSON: + { + "base_metrics": Dict[str, float], + "best_model_path": str, + "final_metrics": Dict[str, float] + } + + """ + try: + # 修改为直接访问 request.json 而不是调用它 + data = request.json + + + if not data: + return json_response({"status": "error", "message": "data is required"}, status=400) + + # 执行图像处理 + result = await asyncio.to_thread( + auto_train, + data + ) + # 返回处理结果 + return json_response(result) + + except Exception as e: + logger.error(f"Error occurred while processing request: {str(e)}", exc_info=True) + return json_response({ + "status": "error", + "message": f"Internal server error: {str(e)}" + }, status=500) + +if __name__ == '__main__': + app.run(host="0.0.0.0", port=12366, debug=True,workers=1) diff --git a/Ai_tottle/ai_tottle_api.py b/Ai_tottle/ai_tottle_api.py index ed733fb..a86b9d4 100644 --- a/Ai_tottle/ai_tottle_api.py +++ b/Ai_tottle/ai_tottle_api.py @@ -75,80 +75,6 @@ app.config.update({ map_tile_blueprint = Blueprint('map', url_prefix='/map/') app.blueprint(map_tile_blueprint) -@map_tile_blueprint.post("/compare_tilesets") -async def compare_tilesets(request): - ''' - 接口：/map/compare_tilesets - 输入 JSON: - { - "tileset1": "path/to/tileset1/tileset.json", - "tileset2": "path/to/tileset2/tileset.json", - "bounds": [500000, 3000000, 500100, 3000100], - "resolution": 1.0, - "output": "results" - } - 输出 JSON: - { - "success": true, - "message": "分析完成", - "data": { - "csv_path": "results/height_differences.csv", - "heatmap_path": "results/height_difference_heatmap.png", - "summary": { - "mean": 0.28, - "max": 1.10, - "min": -0.85, - "std": 0.23 - } - } - } - ''' - try: - body = request.json - - # 参数提取与验证 - tileset1 = body.get("tileset1") - tileset2 = body.get("tileset2") - bounds = body.get("bounds") - resolution = body.get("resolution", 1.0) - output = body.get("output", "results") - - if not all([tileset1, tileset2, bounds]): - return response.json({"success": False, "message": "参数不完整"}, status=400) - - processor = TilesetProcessor(tileset1, tileset2, resolution) - - if not processor.set_analysis_area(bounds): - return response.json({"success": False, "message": "设置分析区域失败"}, status=400) - - if not processor.sample_heights(): - return response.json({"success": False, "message": "高度采样失败"}, status=500) - - processor.export_results(output) - - # 汇总统计结果 - valid_differences = processor.height_difference_grid[~np.isnan(processor.height_difference_grid)] - summary = { - "mean": float(np.mean(valid_differences)), - "max": float(np.max(valid_differences)), - "min": float(np.min(valid_differences)), - "std": float(np.std(valid_differences)) - } - - return response.json({ - "success": True, - "message": "分析完成", - "data": { - "csv_path": os.path.join(output, "height_differences.csv"), - "heatmap_path": os.path.join(output, "height_difference_heatmap.png"), - "summary": summary - } - }) - - except Exception as e: - traceback.print_exc() - return response.json({"success": False, "message": str(e)}, status=500) - #语义识别 @map_tile_blueprint.post("/uav") async def process_handler(request): diff --git a/Ai_tottle/temp.glb b/Ai_tottle/temp.glb new file mode 100644 index 0000000000000000000000000000000000000000..a60447e7cd53d6fb8b2bfd7d397474970b8dfef2 GIT binary patch literal 92956 zcma&O1$b1uxA$L}+RW756}P$pv&&9!C{WxfP@p&!TnZFFKyi0>cc0+y?(VLIw(ocG zJNMqqbN~PMd3pLdD_P0T-aE3glASr7yEm$4VPzUPPK<~7W#F&W47*b~U%nC`2 ziiwRV8`2}SKQ4@jijOZJ-Wt*Lo`{D34QKtmCZr*&Qm0Bv$dIz8rQ_QFU3%<)myQw> z{;TxZnAq~+;o%YSQ4!%W(XsLIWg}xFp^1!0h>Jub9;#eKM0ELx_;{oyME%Vc_uo$w 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0000000..7aedeab --- /dev/null +++ b/Ai_tottle/tiles copy.py @@ -0,0 +1,234 @@ +import os +import logging +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +from matplotlib.colors import LinearSegmentedColormap +from shapely.geometry import Polygon, Point +from tqdm import tqdm +from py3dtiles.tileset import TileSet +import requests + +# 日志配置 +logger = logging.getLogger("TilesetProcessor") +logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') + + +class TilesetProcessor: + """3D Tiles数据集处理器，用于加载、分析和比较两个3D Tiles模型""" + + def __init__(self, tileset_path1, tileset_path2, resolution=1.0, polygon_points=None): + self.tileset1 = self._load_tileset(tileset_path1) + self.tileset2 = self._load_tileset(tileset_path2) + self.resolution = resolution + self.analysis_area = None + self.height_difference_grid = None + self.grid_bounds = None + + if polygon_points: + self.set_analysis_area(polygon_points=polygon_points) + + def _load_tileset(self, path_or_url): + try: + logger.info(f"加载3D Tiles数据集: {path_or_url}") + if path_or_url.startswith("http://") or path_or_url.startswith("https://"): + resp = requests.get(path_or_url) + resp.raise_for_status() + tileset_json = resp.json() + tileset = TileSet.from_dict(tileset_json) + else: + tileset = TileSet.from_file(path_or_url) + logger.info(f"成功加载，包含 {len(tileset.root.children)} 个根瓦片") + return tileset + except Exception as e: + logger.error(f"加载数据集失败（路径: {path_or_url}）: {e}") + raise + + def set_analysis_area(self, bounds=None, polygon_points=None): + if polygon_points: + self.analysis_area = Polygon(polygon_points) + min_x = min(p[0] for p in polygon_points) + min_y = min(p[1] for p in polygon_points) + max_x = max(p[0] for p in polygon_points) + max_y = max(p[1] for p in polygon_points) + self.grid_bounds = (min_x, min_y, max_x, max_y) + logger.info(f"设置多边形分析区域: {polygon_points}") + elif bounds: + min_x, min_y, max_x, max_y = bounds + self.analysis_area = Polygon([ + (min_x, min_y), + (max_x, min_y), + (max_x, max_y), + (min_x, max_y) + ]) + self.grid_bounds = bounds + logger.info(f"设置矩形分析区域: {bounds}") + else: + logger.error("请提供 bounds 或 polygon_points") + return False + + logger.info(f"分析区域面积: {self.analysis_area.area:.2f} 平方米") + return True + + def sample_heights(self): + if self.analysis_area is None: + logger.error("请先设置分析区域") + return False + + logger.info("开始在分析区域内采样高度值...") + + min_x, min_y, max_x, max_y = self.grid_bounds + rows = int((max_y - min_y) / self.resolution) + 1 + cols = int((max_x - min_x) / self.resolution) + 1 + + self.height_difference_grid = np.full((rows, cols), np.nan, dtype=np.float32) + + total_points = rows * cols + logger.info(f"创建了 {rows}x{cols}={total_points} 个采样点") + + with tqdm(total=total_points, desc="采样高度点") as pbar: + for i in range(rows): + for j in range(cols): + x = min_x + j * self.resolution + y = min_y + i * self.resolution + point = Point(x, y) + + if not self.analysis_area.contains(point): + pbar.update(1) + continue + + height1 = self._sample_height_at_point(self.tileset1, x, y) + height2 = self._sample_height_at_point(self.tileset2, x, y) + + if height1 is not None and height2 is not None: + self.height_difference_grid[i, j] = height2 - height1 + + pbar.update(1) + + valid_differences = self.height_difference_grid[~np.isnan(self.height_difference_grid)] + if len(valid_differences) > 0: + logger.info("高度变化统计:") + logger.info(f" 平均变化: {np.mean(valid_differences):.2f}m") + logger.info(f" 最大上升: {np.max(valid_differences):.2f}m") + logger.info(f" 最大下降: {np.min(valid_differences):.2f}m") + logger.info(f" 变化标准差: {np.std(valid_differences):.2f}m") + else: + logger.warning("未找到有效的高度差异数据") + + return True + + def _sample_height_at_point(self, tileset, x, y, max_depth=3): + def find_tile(tile, depth=0): + bbox = tile.bounding_volume.box + min_x_tile = bbox[0] - bbox[3] + max_x_tile = bbox[0] + bbox[3] + min_y_tile = bbox[1] - bbox[4] + max_y_tile = bbox[1] + bbox[4] + + if not (min_x_tile <= x <= max_x_tile and min_y_tile <= y <= max_y_tile): + return None + + if (tile.content is not None and depth >= max_depth) or not tile.children: + return tile + + for child in tile.children: + result = find_tile(child, depth + 1) + if result is not None: + return result + + return None + + tile = find_tile(tileset.root) + if tile is None or tile.content is None: + return None + + try: + # 简化模拟返回瓦片中心高度加随机偏移 + return tile.bounding_volume.box[2] + np.random.uniform(-0.5, 0.5) + except Exception as e: + logger.warning(f"获取瓦片高度失败: {e}") + return None + + def export_results(self, output_dir="results"): + if self.height_difference_grid is None: + logger.error("请先采样高度值") + return + + os.makedirs(output_dir, exist_ok=True) + + csv_path = os.path.join(output_dir, "height_differences.csv") + logger.info(f"导出CSV文件: {csv_path}") + + min_x, min_y, max_x, max_y = self.grid_bounds + rows, cols = self.height_difference_grid.shape + + data = [] + for i in range(rows): + for j in range(cols): + if not np.isnan(self.height_difference_grid[i, j]): + x = min_x + j * self.resolution + y = min_y + i * self.resolution + data.append({ + 'x': x, + 'y': y, + 'height_difference': self.height_difference_grid[i, j] + }) + + df = pd.DataFrame(data) + df.to_csv(csv_path, index=False) + + self._generate_heatmap(output_dir) + + logger.info(f"结果已导出到 {output_dir} 目录") + + def _generate_heatmap(self, output_dir): + colors = [(0.0, 0.0, 1.0), (1.0, 1.0, 1.0), (1.0, 0.0, 0.0)] + cmap = LinearSegmentedColormap.from_list('height_diff_cmap', colors, N=256) + + data = self.height_difference_grid.copy() + valid_mask = ~np.isnan(data) + + if not np.any(valid_mask): + logger.warning("没有有效的高度差异数据，无法生成热图") + return + + data[~valid_mask] = 0 + + plt.figure(figsize=(12, 10)) + plt.imshow(data, cmap=cmap, origin='lower', + extent=[self.grid_bounds[0], self.grid_bounds[2], + self.grid_bounds[1], self.grid_bounds[3]], + alpha=0.9) + + cbar = plt.colorbar() + cbar.set_label('高度变化 (米)', fontsize=12) + + plt.title('两个3D Tiles模型的高度变化分布', fontsize=16) + plt.xlabel('X坐标 (米)', fontsize=12) + plt.ylabel('Y坐标 (米)', fontsize=12) + + heatmap_path = os.path.join(output_dir, "height_difference_heatmap.png") + plt.savefig(heatmap_path, dpi=300, bbox_inches='tight') + plt.close() + + logger.info(f"热图已保存到: {heatmap_path}") + + +if __name__ == "__main__": + tileset1_url = "http://8.137.54.85:9000/300bdf2b-a150-406e-be63-d28bd29b409f/dszh/1748398014403562192_OUT/B3DM/tileset.json" + tileset2_url = "http://8.137.54.85:9000/300bdf2b-a150-406e-be63-d28bd29b409f/dszh/1748325943733189898_OUT/B3DM/tileset.json" + polygon_coords = [ + (102.2232, 29.3841), + (102.2261, 29.3845), + (102.2263, 29.3821), + (102.2231, 29.3818) + ] + resolution = 0.5 + output_dir = "output_results" + + processor = TilesetProcessor(tileset1_url, tileset2_url, resolution, polygon_coords) + if processor.sample_heights(): + processor.export_results(output_dir) + print("分析完成！结果已导出到指定目录。") + else: + print("高度采样失败，无法完成分析。") diff --git a/Ai_tottle/tiles.py b/Ai_tottle/tiles.py index 1584373..f6ceba0 100644 --- a/Ai_tottle/tiles.py +++ b/Ai_tottle/tiles.py @@ -1,265 +1,418 @@ import os +import requests import numpy as np -import pandas as pd -import py3dtiles -from py3dtiles import Tileset, BoundingVolumeBox +from py3dtiles.tileset.content import B3dm +from pyproj import Proj, Transformer from shapely.geometry import Polygon, Point -import matplotlib.pyplot as plt -from matplotlib.colors import LinearSegmentedColormap -import argparse -from tqdm import tqdm -import logging +from shapely.ops import unary_union +import math +from urllib.parse import urljoin +from pygltflib import GLTF2 -# 配置日志 -logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s') -logger = logging.getLogger(__name__) +# 目标区域经纬度坐标（转换为多边形） +region_coords = [ + [102.22321717600258, 29.384100779345513], + [102.22612442019208, 29.384506810595088], + [102.22638603372953, 29.382061071072794], + [102.22311237980807, 29.38186133280733], + [102.22321717600258, 29.384100779345513] # 闭合多边形 +] -class TilesetProcessor: - """3D Tiles数据集处理器，用于加载、分析和比较两个3D Tiles模型""" +# 创建多边形对象 +region_polygon = Polygon(region_coords) + +# 两个3D Tiles模型的URL +tileset_urls = [ + "http://8.137.54.85:9000/300bdf2b-a150-406e-be63-d28bd29b409f/dszh/1748398014403562192_OUT/B3DM/tileset.json", + "http://8.137.54.85:9000/300bdf2b-a150-406e-be63-d28bd29b409f/dszh/1748325943733189898_OUT/B3DM/tileset.json" +] + +# 坐标系转换 +wgs84 = Proj(init='epsg:4326') # WGS84经纬度 +web_mercator = Proj(init='epsg:3857') # Web墨卡托投影 + +def adjust_z_in_transform(tileset_path, output_path=None, delta_z=0): + import json + import numpy as np + + if not os.path.exists(tileset_path): + print(f"❌ tileset.json 文件不存在: {tileset_path}") + return + + with open(tileset_path, 'r', encoding='utf-8') as f: + data = json.load(f) + + root = data.get('root', {}) + + # 插入默认 transform + if 'transform' not in root: + print("⚠️ 未找到 transform 字段，使用单位矩阵") + root['transform'] = [ + 1, 0, 0, 0, + 0, 1, 0, 0, + 0, 0, 1, 0, + 0, 0, 0, 1 + ] + + transform = np.array(root['transform']).reshape(4, 4) + print(f"原始 Z 平移: {transform[3, 2]}") + transform[3, 2] += delta_z + print(f"修正后 Z 平移: {transform[3, 2]}") + + root['transform'] = transform.flatten().tolist() + data['root'] = root + + if output_path is None: + output_path = tileset_path.replace(".json", f"_adjusted_z{int(delta_z)}.json") + + with open(output_path, 'w', encoding='utf-8') as f: + json.dump(data, f, indent=2) + + print(f"✅ 高度调整完成，输出文件: {output_path}") + +def download_tileset(tileset_url): + """下载tileset.json数据""" + try: + response = requests.get(tileset_url) + response.raise_for_status() + return response.json() + except Exception as e: + print(f"下载tileset失败: {e}") + return None + +def extract_vertices(gltf_bytes): + try: + with open("temp.glb", "wb") as f: + f.write(gltf_bytes) + + gltf = GLTF2().load("temp.glb") + + for mesh in gltf.meshes: + for primitive in mesh.primitives: + if not hasattr(primitive.attributes, "POSITION"): + continue + + accessor_idx = primitive.attributes.POSITION + accessor = gltf.accessors[accessor_idx] + buffer_view = gltf.bufferViews[accessor.bufferView] + buffer = gltf.buffers[buffer_view.buffer] + + byte_offset = (buffer_view.byteOffset or 0) + (accessor.byteOffset or 0) + byte_length = accessor.count * 3 * 4 # 3 floats per vertex + + data_bytes = gltf.binary_blob()[byte_offset: byte_offset + byte_length] + vertices = np.frombuffer(data_bytes, dtype=np.float32).reshape((accessor.count, 3)) + + return vertices + + except Exception as e: + print(f"提取顶点数据失败: {e}") + + return np.array([]) + +def find_closest_vertex(vertices, lon, lat): + """找到离目标点最近的顶点""" + if not vertices: + return None - def __init__(self, tileset_path1, tileset_path2, resolution=1.0): - """ - 初始化处理器 - - 参数: - tileset_path1: 第一个3D Tiles数据集路径 - tileset_path2: 第二个3D Tiles数据集路径 - resolution: 分析网格的分辨率（米） - """ - self.tileset1 = self._load_tileset(tileset_path1) - self.tileset2 = self._load_tileset(tileset_path2) - self.resolution = resolution - self.analysis_area = None - self.height_difference_grid = None - self.grid_bounds = None - - def _load_tileset(self, path): - """加载3D Tiles数据集""" - try: - logger.info(f"加载3D Tiles数据集: {path}") - tileset = Tileset.from_file(path) - logger.info(f"成功加载，包含 {len(tileset.root.children)} 个根瓦片") - return tileset - except Exception as e: - logger.error(f"加载数据集失败: {e}") - raise + # 计算距离并找到最近的顶点 + min_distance = float('inf') + closest_vertex = None - def set_analysis_area(self, bounds): - """ - 设置分析区域 - - 参数: - bounds: 分析区域边界元组 (min_x, min_y, max_x, max_y) - """ - min_x, min_y, max_x, max_y = bounds - self.analysis_area = Polygon([ - (min_x, min_y), - (max_x, min_y), - (max_x, max_y), - (min_x, max_y) - ]) - self.grid_bounds = bounds - logger.info(f"设置分析区域: {bounds}") - logger.info(f"分析区域面积: {self.analysis_area.area:.2f} 平方米") - return True + for vertex in vertices: + v_lon, v_lat, v_z = vertex + # 计算经纬度距离（简化为平面距离） + distance = math.hypot(v_lon - lon, v_lat - lat) + if distance < min_distance: + min_distance = distance + closest_vertex = vertex - def sample_heights(self): - """在分析区域内采样两个模型的高度值并计算差异""" - if self.analysis_area is None: - logger.error("请先设置分析区域") - return False + return closest_vertex + +def compare_heights(heights1, heights2, tolerance=0.5): + """比较两个高度数据集，找出差异""" + # 找到所有点的并集 + all_points = set(heights1.keys()).union(set(heights2.keys())) + differences = [] + + for point in all_points: + h1 = heights1.get(point, None) + h2 = heights2.get(point, None) - logger.info("开始在分析区域内采样高度值...") - - # 创建网格 - min_x, min_y, max_x, max_y = self.grid_bounds - rows = int((max_y - min_y) / self.resolution) + 1 - cols = int((max_x - min_x) / self.resolution) + 1 - - # 初始化高度差异网格 - self.height_difference_grid = np.zeros((rows, cols), dtype=np.float32) - self.height_difference_grid[:] = np.nan # 初始化为NaN，表示未采样 - - # 对每个网格点进行采样 - total_points = rows * cols - logger.info(f"创建了 {rows}x{cols}={total_points} 个采样点") - - with tqdm(total=total_points, desc="采样高度点") as pbar: - for i in range(rows): - for j in range(cols): - # 计算当前点的坐标 - x = min_x + j * self.resolution - y = min_y + i * self.resolution - point = Point(x, y) - - # 检查点是否在分析区域内 - if not self.analysis_area.contains(point): - pbar.update(1) - continue - - # 采样两个模型的高度 - height1 = self._sample_height_at_point(self.tileset1, x, y) - height2 = self._sample_height_at_point(self.tileset2, x, y) - - # 计算高度差异 - if height1 is not None and height2 is not None: - self.height_difference_grid[i, j] = height2 - height1 - - pbar.update(1) - - # 统计结果 - valid_differences = self.height_difference_grid[~np.isnan(self.height_difference_grid)] - if len(valid_differences) > 0: - logger.info(f"高度变化统计:") - logger.info(f" 平均变化: {np.mean(valid_differences):.2f}m") - logger.info(f" 最大上升: {np.max(valid_differences):.2f}m") - logger.info(f" 最大下降: {np.min(valid_differences):.2f}m") - logger.info(f" 变化标准差: {np.std(valid_differences):.2f}m") + # 检查是否有一个模型在该点没有数据 + if h1 is None or h2 is None: + differences.append({ + 'point': point, + 'height1': h1, + 'height2': h2, + 'difference': None, + 'type': 'missing_data' + }) else: - logger.warning("未找到有效的高度差异数据") - - return True + # 检查高度差异是否超过容忍度 + diff = abs(h1 - h2) + if diff > tolerance: + differences.append({ + 'point': point, + 'height1': h1, + 'height2': h2, + 'difference': diff, + 'type': 'height_difference' + }) - def _sample_height_at_point(self, tileset, x, y, max_depth=3): - """在指定点采样3D Tiles模型的高度值""" - # 找到包含该点的瓦片 - def find_tile(tile, depth=0): - # 检查点是否在瓦片边界框内 - bbox = tile.bounding_volume.box - min_x_tile = bbox[0] - bbox[3] - max_x_tile = bbox[0] + bbox[3] - min_y_tile = bbox[1] - bbox[4] - max_y_tile = bbox[1] + bbox[4] - - if not (min_x_tile <= x <= max_x_tile and min_y_tile <= y <= max_y_tile): - return None - - # 如果瓦片有内容且深度足够，或者没有子瓦片，就使用这个瓦片 - if (tile.content is not None and depth >= max_depth) or not tile.children: - return tile - - # 否则递归查找子瓦片 - for child in tile.children: - result = find_tile(child, depth + 1) - if result is not None: - return result - + return differences + +def get_b3dm_from_tile_json(json_url): + try: + response = requests.get(json_url) + response.raise_for_status() + data = response.json() + + # 递归查找 b3dm uri + def find_b3dm_uri(node): + if 'content' in node and 'uri' in node['content']: + uri = node['content']['uri'] + if uri.endswith('.b3dm'): + return uri + if 'children' in node: + for child in node['children']: + result = find_b3dm_uri(child) + if result: + return result return None - - # 找到包含该点的最详细瓦片 - tile = find_tile(tileset.root) - if tile is None or tile.content is None: + + root = data.get('root', {}) + b3dm_uri = find_b3dm_uri(root) + if not b3dm_uri: + print(f"{json_url} 中找不到 content.uri") return None - - # 从瓦片内容中获取高度 + + base_url = os.path.dirname(json_url) + full_b3dm_url = urljoin(base_url + '/', b3dm_uri) + return full_b3dm_url + + except Exception as e: + print(f"解析 JSON {json_url} 时出错: {e}") + return None + + +def get_heights_in_region(tileset_url, sample_density=10): + """获取区域内的高度数据""" + tileset_json = download_tileset(tileset_url) + if not tileset_json: + return {} + + tiles_in_region = get_tiles_in_region(tileset_json, tileset_url) + if not tiles_in_region: + print(f"在{tileset_url}中未找到区域内的瓦片") + return {} + + min_lon, min_lat = min(p[0] for p in region_coords), min(p[1] for p in region_coords) + max_lon, max_lat = max(p[0] for p in region_coords), max(p[1] for p in region_coords) + lon_steps = np.linspace(min_lon, max_lon, sample_density) + lat_steps = np.linspace(min_lat, max_lat, sample_density) + + heights = {} + + for tile_info in tiles_in_region: try: - # 这里是简化的模拟实现，实际应该解析瓦片内容 - # 例如，使用py3dtiles中的TileContent.get_vertices()获取顶点 - # 然后找到最近的顶点或三角形来计算高度 - # 这里为了示例，我们返回瓦片中心的高度加上一个随机偏移 - return tile.bounding_volume.box[2] + np.random.uniform(-0.5, 0.5) + response = requests.get(tile_info['url']) + response.raise_for_status() + b3dm_data = response.content + + # ✅ 尝试解析为 b3dm + try: + gltf_bytes = parse_b3dm(b3dm_data) + except Exception: + # 可能 tile_info['url'] 是 JSON，不是真 b3dm + print(f"尝试从 {tile_info['url']} 获取真实 b3dm 地址...") + actual_b3dm_url = get_b3dm_from_tile_json(tile_info['url']) + if not actual_b3dm_url: + print(f"跳过：无法从 {tile_info['url']} 获取有效 b3dm") + continue + response = requests.get(actual_b3dm_url) + response.raise_for_status() + b3dm_data = response.content + gltf_bytes = parse_b3dm(b3dm_data) + + # ✅ 模拟解析 glb + vertices = extract_vertices(gltf_bytes) + if not vertices.size: + continue + + # ✅ 应用变换 + transformed_vertices = [transform_point(v, tile_info['transform']) for v in vertices] + transformer = Transformer.from_crs("EPSG:3857", "EPSG:4326", always_xy=True) + wgs84_vertices = [] + for x, y, z in transformed_vertices: + lon, lat = transformer.transform(x, y) + wgs84_vertices.append((lon, lat, z)) + + for lon in lon_steps: + for lat in lat_steps: + if point_in_region(lon, lat): + closest_vertex = find_closest_vertex(wgs84_vertices, lon, lat) + if closest_vertex: + key = (round(lon, 6), round(lat, 6)) + heights[key] = closest_vertex[2] + except Exception as e: - logger.warning(f"获取瓦片高度失败: {e}") - return None - - def export_results(self, output_dir="results"): - """导出分析结果""" - if self.height_difference_grid is None: - logger.error("请先采样高度值") - return - - os.makedirs(output_dir, exist_ok=True) - - # 导出CSV文件 - csv_path = os.path.join(output_dir, "height_differences.csv") - logger.info(f"导出CSV文件: {csv_path}") - - # 创建DataFrame - min_x, min_y, max_x, max_y = self.grid_bounds - rows, cols = self.height_difference_grid.shape - - data = [] - for i in range(rows): - for j in range(cols): - if not np.isnan(self.height_difference_grid[i, j]): - x = min_x + j * self.resolution - y = min_y + i * self.resolution - data.append({ - 'x': x, - 'y': y, - 'height_difference': self.height_difference_grid[i, j] - }) - - df = pd.DataFrame(data) - df.to_csv(csv_path, index=False) - - # 生成热图 - self._generate_heatmap(output_dir) - - logger.info(f"结果已导出到 {output_dir} 目录") - - def _generate_heatmap(self, output_dir): - """生成高度变化热图""" - # 创建自定义颜色映射 - colors = [(0.0, 0.0, 1.0), (1.0, 1.0, 1.0), (1.0, 0.0, 0.0)] # 蓝-白-红 - cmap = LinearSegmentedColormap.from_list('height_diff_cmap', colors, N=256) - - # 准备数据 - data = self.height_difference_grid.copy() - valid_mask = ~np.isnan(data) - - if not np.any(valid_mask): - logger.warning("没有有效的高度差异数据，无法生成热图") - return - - # 设置NaN值为0以便绘图，但在颜色映射中标记为透明 - data[~valid_mask] = 0 - - # 创建图形 - plt.figure(figsize=(12, 10)) - plt.imshow(data, cmap=cmap, origin='lower', - extent=[self.grid_bounds[0], self.grid_bounds[2], - self.grid_bounds[1], self.grid_bounds[3]], - alpha=0.9) - - # 添加颜色条 - cbar = plt.colorbar() - cbar.set_label('高度变化 (米)', fontsize=12) - - # 设置标题和坐标轴 - plt.title('两个3D Tiles模型的高度变化分布', fontsize=16) - plt.xlabel('X坐标 (米)', fontsize=12) - plt.ylabel('Y坐标 (米)', fontsize=12) - - # 保存图形 - heatmap_path = os.path.join(output_dir, "height_difference_heatmap.png") - plt.savefig(heatmap_path, dpi=300, bbox_inches='tight') - plt.close() - - logger.info(f"热图已保存到: {heatmap_path}") + print(f"处理瓦片 {tile_info['url']} 时出错: {e}") + continue + + return heights + +def get_tiles_in_region(tileset_json, tileset_base_url): + """获取区域内的所有瓦片""" + tiles_in_region = [] + + # 去除 tileset.json 得到根路径 + tileset_root_url = tileset_base_url.rsplit('/', 1)[0] + + def recursive_search(tile, parent_transform=None): + tile_transform = tile.get('transform', [1,0,0,0, 0,1,0,0, 0,0,1,0, 0,0,0,1]) + combined_transform = multiply_matrices(parent_transform, tile_transform) if parent_transform else tile_transform + + if 'boundingVolume' in tile and is_bounding_volume_intersects_region(tile['boundingVolume']): + if 'content' in tile and 'uri' in tile['content']: + # 修复URL拼接 + tile_url = urljoin(tileset_root_url + '/', tile['content']['uri']) + tiles_in_region.append({ + 'url': tile_url, + 'transform': combined_transform + }) + + if 'children' in tile: + for child in tile['children']: + recursive_search(child, combined_transform) + + if 'root' in tileset_json: + recursive_search(tileset_json['root']) + + return tiles_in_region + +def is_bounding_volume_intersects_region(bounding_volume): + """检查边界体是否与区域相交""" + # 简化实现，实际需要根据不同边界体类型实现 + if 'region' in bounding_volume: + # region格式: [west, south, east, north, minHeight, maxHeight] + region = bounding_volume['region'] + bv_polygon = Polygon([ + [region[0], region[1]], + [region[2], region[1]], + [region[2], region[3]], + [region[0], region[3]], + [region[0], region[1]] + ]) + return region_polygon.intersects(bv_polygon) + elif 'box' in bounding_volume: + # 对于box类型，需要转换到经纬度后再判断 + # 这里简化处理，返回True让更细致的检查在后续进行 + return True + elif 'sphere' in bounding_volume: + # 对于sphere类型，简化处理 + return True + return False + +def multiply_matrices(a, b): + """计算两个4x4矩阵的乘积""" + result = [0.0] * 16 + for i in range(4): + for j in range(4): + result[i*4 + j] = a[i*4 + 0] * b[0*4 + j] + \ + a[i*4 + 1] * b[1*4 + j] + \ + a[i*4 + 2] * b[2*4 + j] + \ + a[i*4 + 3] * b[3*4 + j] + return result + +def parse_b3dm(b3dm_data: bytes): + """ + 解析 b3dm 文件，返回 glb 二进制数据 + """ + import struct + + if b3dm_data[:4] != b'b3dm': + raise ValueError("不是有效的 b3dm 文件") + + # 读取 header（28 字节） + header = struct.unpack('<4sIIIIII', b3dm_data[:28]) + _, version, byte_length, ft_json_len, ft_bin_len, bt_json_len, bt_bin_len = header + + glb_start = 28 + ft_json_len + ft_bin_len + bt_json_len + bt_bin_len + glb_bytes = b3dm_data[glb_start:] + + return glb_bytes + +def point_in_region(lon, lat): + """判断点是否在目标区域内""" + return region_polygon.contains(Point(lon, lat)) + +def transform_point(point, matrix): + """应用变换矩阵到点""" + x, y, z = point + x_out = x * matrix[0] + y * matrix[4] + z * matrix[8] + matrix[12] + y_out = x * matrix[1] + y * matrix[5] + z * matrix[9] + matrix[13] + z_out = x * matrix[2] + y * matrix[6] + z * matrix[10] + matrix[14] + return (x_out, y_out, z_out) def main(): - parser = argparse.ArgumentParser(description='分析两个3D Tiles模型指定区域的高度变化') - parser.add_argument('--tileset1', required=True, help='第一个3D Tiles数据集路径') - parser.add_argument('--tileset2', required=True, help='第二个3D Tiles数据集路径') - parser.add_argument('--bounds', required=True, type=float, nargs=4, - help='分析区域边界 [min_x, min_y, max_x, max_y]') - parser.add_argument('--resolution', type=float, default=1.0, help='采样分辨率（米）') - parser.add_argument('--output', default='results', help='输出目录') - - args = parser.parse_args() - - processor = TilesetProcessor(args.tileset1, args.tileset2, args.resolution) - - # 设置分析区域 - if processor.set_analysis_area(args.bounds): - if processor.sample_heights(): - processor.export_results(args.output) - print("分析完成！结果已导出到指定目录。") - else: - print("高度采样失败，无法完成分析。") + sample_density = 20 + + print("正在从第一个3D Tiles模型提取区域高度数据...") + heights1 = get_heights_in_region(tileset_urls[0], sample_density) + + print("正在从第二个3D Tiles模型提取区域高度数据...") + heights2 = get_heights_in_region(tileset_urls[1], sample_density) + + if not heights1 or not heights2: + print("无法获取足够的高度数据进行比较") + return + + # 计算平均高度 + avg1 = np.mean(list(heights1.values())) + avg2 = np.mean(list(heights2.values())) + + print(f"\n模型1 平均高度: {avg1:.2f} 米") + print(f"模型2 平均高度: {avg2:.2f} 米") + + delta = avg1 - avg2 + print(f"高度差: {delta:.2f} 米") + + # 🔧 自动统一高度 + if abs(delta) > 0.5: + print("\n⚙️ 正在统一高度基准（修改模型2的 transform）...") + # tileset_urls[1] 是远程 URL，下载后调整 + try: + ts2_url = tileset_urls[1] + response = requests.get(ts2_url) + response.raise_for_status() + with open("tileset_model2.json", "w", encoding="utf-8") as f: + f.write(response.text) + + adjust_z_in_transform("tileset_model2.json", "tileset_model2_adjusted.json", delta_z=delta) + except Exception as e: + print(f"❌ 调整高度失败: {e}") else: - print("设置分析区域失败，无法进行分析。") + print("\n✅ 高度差异在容忍范围内，无需调整") + + # 🔍 差异分析 + print("\n正在分析详细差异点...") + differences = compare_heights(heights1, heights2, 0.5) + + if differences: + print(f"共发现 {len(differences)} 处显著高度差异:") + for i, diff in enumerate(differences[:10], 1): # 仅显示前10条 + lon, lat = diff['point'] + print(f"\n位置 {i}: 经度 {lon}, 纬度 {lat}") + print(f"模型1高度: {diff['height1']:.2f}米") + print(f"模型2高度: {diff['height2']:.2f}米") + if diff['difference'] is not None: + print(f"差异: {diff['difference']:.2f}米") + else: + print("差异: 一个模型在该位置没有数据") + else: + print("两个模型在指定区域高度基本一致 ✅") + if __name__ == "__main__": - main() \ No newline at end of file + main() diff --git a/Ai_tottle/tileset_model2.json b/Ai_tottle/tileset_model2.json new file mode 100644 index 0000000..efa8690 --- /dev/null +++ b/Ai_tottle/tileset_model2.json @@ -0,0 +1 @@ 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