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parent faa2c610da
commit 688624aaad
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5
.vscode/settings.json vendored Normal file
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{
"python-envs.defaultEnvManager": "ms-python.python:conda",
"python-envs.defaultPackageManager": "ms-python.python:conda",
"python-envs.pythonProjects": []
}

370
Ai_tottle/ai_tottle_api copy.py Normal file
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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/<task_id>")
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)

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Ai_tottle/ai_tottle_api.py
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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):

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Ai_tottle/tiles copy.py Normal file
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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。")

569
Ai_tottle/tiles.py
View File

@ -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)
def __init__(self, tileset_path1, tileset_path2, resolution=1.0):
"""
初始化处理器
# 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"
]
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
# 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 _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
def adjust_z_in_transform(tileset_path, output_path=None, delta_z=0):
import json
import numpy as np
def set_analysis_area(self, bounds):
"""
设置分析区域
if not os.path.exists(tileset_path):
print(f"❌ tileset.json 文件不存在: {tileset_path}")
return
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
with open(tileset_path, 'r', encoding='utf-8') as f:
data = json.load(f)
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
root = data.get('root', {})
logger.info("开始在分析区域内采样高度值...")
# 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
]
# 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
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]}")
# 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
root['transform'] = transform.flatten().tolist()
data['root'] = root
# 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")
if output_path is None:
output_path = tileset_path.replace(".json", f"_adjusted_z{int(delta_z)}.json")
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)
with open(output_path, 'w', encoding='utf-8') as f:
json.dump(data, f, indent=2)
# 检查点是否在分析区域内
if not self.analysis_area.contains(point):
pbar.update(1)
continue
print(f"✅ 高度调整完成，输出文件: {output_path}")
# 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)
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
# 计算高度差异
if height1 is not None and height2 is not None:
self.height_difference_grid[i, j] = height2 - height1
def extract_vertices(gltf_bytes):
try:
with open("temp.glb", "wb") as f:
f.write(gltf_bytes)
pbar.update(1)
gltf = GLTF2().load("temp.glb")
# 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")
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
# 计算距离并找到最近的顶点
min_distance = float('inf')
closest_vertex = None
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
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)
# 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")
# 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'
})
return True
return differences
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
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
print(f"处理瓦片 {tile_info['url']} 时出错: {e}")
continue
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
return heights
os.makedirs(output_dir, exist_ok=True)
def get_tiles_in_region(tileset_json, tileset_base_url):
"""获取区域内的所有瓦片"""
tiles_in_region = []
# 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}")
# 去除 tileset.json 得到根路径
tileset_root_url = tileset_base_url.rsplit('/', 1)[0]
# 创建DataFrame
min_x, min_y, max_x, max_y = self.grid_bounds
rows, cols = self.height_difference_grid.shape
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
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]
})
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
})
df = pd.DataFrame(data)
df.to_csv(csv_path, index=False)
if 'children' in tile:
for child in tile['children']:
recursive_search(child, combined_transform)
# 生成热图
self._generate_heatmap(output_dir)
if 'root' in tileset_json:
recursive_search(tileset_json['root'])
logger.info(f"结果已导出到 {output_dir} 目录")
return tiles_in_region
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)
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
# 准备数据
data = self.height_difference_grid.copy()
valid_mask = ~np.isnan(data)
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
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
def parse_b3dm(b3dm_data: bytes):
"""
解析 b3dm 文件，返回 glb 二进制数据
"""
import struct
# 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
if b3dm_data[:4] != b'b3dm':
raise ValueError("不是有效的 b3dm 文件")
# 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)
# 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
# 添加颜色条
cbar = plt.colorbar()
cbar.set_label('高度变化 (米)', fontsize=12)
glb_start = 28 + ft_json_len + ft_bin_len + bt_json_len + bt_bin_len
glb_bytes = b3dm_data[glb_start:]
# 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)
return glb_bytes
# 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()
def point_in_region(lon, lat):
"""判断点是否在目标区域内"""
return region_polygon.contains(Point(lon, lat))
logger.info(f"热图已保存到: {heatmap_path}")
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')
sample_density = 20
args = parser.parse_args()
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)
processor = TilesetProcessor(args.tileset1, args.tileset2, args.resolution)
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)
# 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。")
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()

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Ai_tottle/tileset_model2.json Normal file
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Ai_tottle/tileset_model2_adjusted.json Normal file
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