jeonghyo.k
3946ff6a25
- prediction/image/ FastAPI 서버 Docker 환경 구성 - Dockerfile: PyTorch 2.1 + CUDA 12.1 기반 GPU 이미지 - docker-compose.yml: GPU 할당 + 데이터 볼륨 마운트 - requirements.txt: 서버 의존성 목록 - .env.example: 환경변수 템플릿 - DOCKER_USAGE.md: 빌드/실행/API 사용법 문서 - Dockerfile에 .dockerignore 제외 폴더 mkdir -p 추가 - .gitignore: prediction/image 결과물 및 모델 가중치(.pth) 제외 추가 - dbInsert_csv.py, dbInsert_shp.py 삭제 (미사용 DB 로직) - api.py: dbInsert import 및 주석 처리된 DB 호출 코드 제거 - aerialRouter.ts: req.params 타입 오류 수정
97 lines
2.9 KiB
Python
97 lines
2.9 KiB
Python
import csv
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from datetime import datetime
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from pathlib import Path
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import geopandas as gpd
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import json
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def get_metadata(camTy: str, fileId: str):
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# CSV 파일 경로 설정
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# base_dir = "mx15hdi" if pollId == "1" else "starsafire"
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if camTy == "mx15hdi":
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csv_path = f"{camTy}/Metadata/CSV/{fileId}/mx15hdi_interpolation.csv"
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elif camTy == "starsafire":
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csv_path = f"{camTy}/Metadata/CSV/{fileId}/Metadata_Extracted.csv"
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try:
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# CSV 파일 읽기
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with open(csv_path, 'r', newline='', encoding='utf-8-sig') as csvfile:
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reader = csv.reader(csvfile)
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next(reader, None)
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row = next(reader, None)
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return ','.join(row)
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except FileNotFoundError:
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print(f"CSV file not found: {csv_path}")
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raise
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except ValueError as e:
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print(f"Value error: {str(e)}")
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raise
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except Exception as e:
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print(f"Error processing CSV: {e}")
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raise
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def get_oil_type(camTy: str, fileId: str):
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# Shapefile 경로 설정
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path = f"{camTy}/Polygon/Shp/{fileId}"
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shp_file = list(Path(path).glob("*.shp"))
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if not shp_file:
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return []
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shp_path = f"{camTy}/Polygon/Shp/{fileId}/{shp_file[0].name}"
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print(shp_path)
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# if camTy == "mx15hdi":
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# fileSub = f"{Path(fileName).stem}_gsd"
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# elif camTy == "starsafire":
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# fileSub = f"{Path(fileName).stem}"
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# shp_path = f"{camTy}/Polygon/Shp/{fileId}/{fileSub}.shp"
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# 두께 정보
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class_thickness_mm = {
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1: 1.0, # Black oil (Emulsion)
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2: 0.1, # Brown oil (Crude)
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3: 0.0003, # Rainbow oil (Slick)
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4: 0.0001 # Silver oil (Slick)
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}
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# 알고리즘 정보
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algorithm = {
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1: "검정",
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2: "갈색",
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3: "무지개",
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4: "은색"
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}
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try:
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# Shapefile 읽기
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gdf = gpd.read_file(shp_path)
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if gdf.crs != "epsg:4326":
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gdf = gdf.to_crs("epsg:4326")
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# 데이터 준비
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data = []
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for _, row in gdf.iterrows():
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class_id = row.get('class_id', None)
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area_m2 = row.get('area_m2', None)
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volume_m3 = row.get('volume_m3', None)
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note = row.get('note', None)
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thickness_m = class_thickness_mm.get(class_id, 0) / 1000.0
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geom_wkt = row.geometry.wkt if row.geometry else None
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result = {
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"classId": algorithm.get(class_id, 0),
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"area": area_m2,
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"volume": volume_m3,
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"note": note,
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"thickness": thickness_m,
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"wkt": geom_wkt
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}
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data.append(result)
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return data
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except FileNotFoundError:
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print(f"Shapefile not found: {shp_path}")
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raise
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except Exception as e:
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print(f"Error processing shapefile or database: {str(e)}")
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raise |