Victor Phan
492 lines
16 KiB
Python
492 lines
16 KiB
Python
"""
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API Server for Land Classification Model Training
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Cho phép chọn dữ liệu và cấu hình training qua giao diện web
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"""
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from fastapi import FastAPI, BackgroundTasks, HTTPException
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from fastapi.middleware.cors import CORSMiddleware
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from fastapi.staticfiles import StaticFiles
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from fastapi.responses import HTMLResponse, FileResponse
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from pydantic import BaseModel
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from typing import Optional, List
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import uvicorn
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import joblib
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import json
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from datetime import datetime
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from pathlib import Path
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import sys
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app = FastAPI(title="Land Classification Training API", version="1.0.0")
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# Enable CORS
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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# Global training status
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training_status = {
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"is_training": False,
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"progress": "",
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"error": None,
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"result": None,
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"start_time": None,
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"end_time": None,
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"cancel_requested": False
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}
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# Global prediction status
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prediction_status = {
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"is_predicting": False,
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"progress": "",
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"error": None,
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"result": None,
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"output_file": None,
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"start_time": None,
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"end_time": None
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}
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class TrainingConfig(BaseModel):
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"""Cấu hình training"""
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# Khu vực (bbox)
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min_lon: float = 105.6
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min_lat: float = 9.3
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max_lon: float = 106.2
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max_lat: float = 9.8
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# Thời gian
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start_date: str = "2023-03-01"
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end_date: str = "2023-05-31"
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# Dữ liệu
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max_scenes: int = 12
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cloud_cover: int = 30
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resolution: int = 20 # 10m hoặc 20m
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# Model parameters
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n_estimators: int = 100
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max_depth: int = 20
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learning_rate: float = 0.1
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use_gpu: bool = True
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# Training data
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training_shapefile: str = "train/ST_training data_updated_1130points_new.shp"
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class PredictionConfig(BaseModel):
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"""Cấu hình dự đoán"""
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# Model to use
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model_filename: str
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# Khu vực (bbox)
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min_lon: float = 105.6
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min_lat: float = 9.3
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max_lon: float = 106.2
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max_lat: float = 9.8
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# Thời gian
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start_date: str = "2023-03-01"
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end_date: str = "2023-05-31"
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# Dữ liệu
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max_scenes: int = 12
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cloud_cover: int = 30
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resolution: int = 20
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class TrainingStatus(BaseModel):
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"""Trạng thái training"""
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is_training: bool
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progress: str
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error: Optional[str]
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result: Optional[dict]
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start_time: Optional[str]
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end_time: Optional[str]
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@app.get("/", response_class=HTMLResponse)
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async def root():
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"""Serve giao diện web"""
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html_file = Path(__file__).parent / "training_interface.html"
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if html_file.exists():
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return FileResponse(html_file)
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else:
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return HTMLResponse("""
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<html>
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<head><title>Training Interface</title></head>
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<body>
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<h1>Land Classification Training API</h1>
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<p>API Documentation: <a href="/docs">/docs</a></p>
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<p>Training Interface: Tạo file training_interface.html</p>
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</body>
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</html>
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""")
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@app.get("/api/config/presets")
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async def get_presets():
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"""Lấy các preset cấu hình sẵn"""
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return {
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"presets": [
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{
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"name": "PC - Nhỏ (3 tháng, 20m, 12 scenes)",
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"config": {
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"min_lon": 105.6, "min_lat": 9.3, "max_lon": 106.2, "max_lat": 9.8,
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"start_date": "2023-03-01", "end_date": "2023-05-31",
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"max_scenes": 12, "cloud_cover": 30, "resolution": 20
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}
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},
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{
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"name": "Server - Trung bình (6 tháng, 10m, 30 scenes)",
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"config": {
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"min_lon": 105.5, "min_lat": 9.2, "max_lon": 106.4, "max_lat": 10.0,
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"start_date": "2023-01-01", "end_date": "2023-06-30",
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"max_scenes": 30, "cloud_cover": 30, "resolution": 10
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}
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},
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{
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"name": "Full - Lớn (1 năm, 10m, 60 scenes)",
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"config": {
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"min_lon": 105.5, "min_lat": 9.2, "max_lon": 106.4, "max_lat": 10.0,
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"start_date": "2022-09-01", "end_date": "2023-10-01",
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"max_scenes": 60, "cloud_cover": 50, "resolution": 10
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}
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}
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]
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}
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@app.get("/api/training/status", response_model=TrainingStatus)
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async def get_training_status():
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"""Kiểm tra trạng thái training"""
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return training_status
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@app.post("/api/training/start")
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async def start_training(config: TrainingConfig, background_tasks: BackgroundTasks):
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"""Bắt đầu training với config đã chọn"""
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global training_status
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if training_status["is_training"]:
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raise HTTPException(status_code=400, detail="Training đang chạy, vui lòng đợi")
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# Reset status
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training_status = {
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"is_training": True,
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"progress": "Đang khởi tạo...",
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"error": None,
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"result": None,
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"start_time": datetime.now().isoformat(),
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"end_time": None
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}
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# Run training in background
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background_tasks.add_task(run_training, config)
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return {"message": "Training đã bắt đầu", "status": training_status}
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@app.post("/api/training/stop")
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async def stop_training():
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"""Dừng training (nếu đang chạy)"""
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global training_status
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if not training_status["is_training"]:
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return {"message": "Không có training nào đang chạy"}
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# Set cancel flag - the training will check this and stop
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training_status["cancel_requested"] = True
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training_status["progress"] = "Đang hủy training..."
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return {"message": "Đang dừng training..."}
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@app.get("/api/models/list")
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async def list_models():
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"""Liệt kê các model đã train"""
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model_dir = Path("model_train")
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if not model_dir.exists():
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return {"models": []}
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models = []
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for model_file in model_dir.glob("*.joblib"):
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info_file = model_file.with_suffix('.json')
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info = {}
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if info_file.exists():
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with open(info_file) as f:
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info = json.load(f)
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models.append({
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"filename": model_file.name,
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"created": datetime.fromtimestamp(model_file.stat().st_mtime).isoformat(),
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"size_mb": round(model_file.stat().st_size / 1024 / 1024, 2),
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"info": info
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})
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# Sort by creation time (newest first)
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models.sort(key=lambda x: x["created"], reverse=True)
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return {"models": models}
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@app.post("/api/prediction/start")
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async def start_prediction(config: PredictionConfig, background_tasks: BackgroundTasks):
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"""Bắt đầu dự đoán"""
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global prediction_status
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if prediction_status["is_predicting"]:
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raise HTTPException(status_code=400, detail="Đang có dự đoán khác đang chạy")
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# Reset status
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prediction_status = {
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"is_predicting": True,
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"progress": "Đang khởi động...",
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"error": None,
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"result": None,
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"output_file": None,
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"start_time": datetime.now().isoformat(),
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"end_time": None
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}
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# Run prediction in background
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background_tasks.add_task(run_prediction, config)
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return {"message": "Đã bắt đầu dự đoán", "status": prediction_status}
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@app.get("/api/prediction/status")
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async def get_prediction_status():
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"""Kiểm tra trạng thái dự đoán"""
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return prediction_status
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async def run_training(config: TrainingConfig):
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"""Chạy training process"""
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global training_status
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try:
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training_status["cancel_requested"] = False
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training_status["progress"] = "Đang import thư viện..."
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# Import training module
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from train_module import train_model
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training_status["progress"] = "Đang load dữ liệu Sentinel-2..."
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# Function to check if training should be cancelled
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def should_cancel():
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return training_status.get("cancel_requested", False)
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# Run training
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result = train_model(
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bbox=[config.min_lon, config.min_lat, config.max_lon, config.max_lat],
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time_range=f"{config.start_date}/{config.end_date}",
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max_scenes=config.max_scenes,
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cloud_cover=config.cloud_cover,
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resolution=config.resolution,
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training_shapefile=config.training_shapefile,
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n_estimators=config.n_estimators,
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max_depth=config.max_depth,
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learning_rate=config.learning_rate,
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use_gpu=config.use_gpu,
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status_callback=lambda msg: update_progress(msg),
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cancel_check=should_cancel
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)
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if training_status.get("cancel_requested", False):
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training_status["is_training"] = False
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training_status["progress"] = "Đã hủy training"
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training_status["error"] = "Training cancelled by user"
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else:
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training_status["is_training"] = False
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training_status["progress"] = "Hoàn thành!"
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training_status["result"] = result
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training_status["end_time"] = datetime.now().isoformat()
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except Exception as e:
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training_status["is_training"] = False
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training_status["error"] = str(e)
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training_status["progress"] = f"Lỗi: {str(e)}"
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training_status["end_time"] = datetime.now().isoformat()
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import traceback
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print(traceback.format_exc())
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def update_progress(message: str):
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"""Cập nhật progress message"""
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global training_status
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training_status["progress"] = message
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print(f"[PROGRESS] {message}")
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def update_prediction_progress(message: str):
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"""Cập nhật prediction progress message"""
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global prediction_status
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prediction_status["progress"] = message
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print(f"[PREDICTION PROGRESS] {message}")
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async def run_prediction(config: PredictionConfig):
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"""Chạy prediction process"""
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global prediction_status
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try:
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prediction_status["progress"] = "Đang import thư viện..."
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# Import required libraries
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import xarray as xr
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import numpy as np
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from datetime import datetime as dt
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import rioxarray
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prediction_status["progress"] = "Đang load model..."
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# Load model
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model_path = Path("model_train") / config.model_filename
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if not model_path.exists():
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raise FileNotFoundError(f"Model không tồn tại: {config.model_filename}")
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model_data = joblib.load(model_path)
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# Extract model from dict (models are saved as {'model': xgb_model, 'label_encoder': encoder})
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if isinstance(model_data, dict):
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model = model_data.get('model')
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label_encoder = model_data.get('label_encoder')
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else:
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model = model_data
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label_encoder = None
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prediction_status["progress"] = "Đang kết nối Microsoft Planetary Computer..."
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# Import and use Microsoft Planetary Computer STAC API
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import pystac_client
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import planetary_computer
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catalog = pystac_client.Client.open(
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"https://planetarycomputer.microsoft.com/api/stac/v1",
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modifier=planetary_computer.sign_inplace,
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)
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bbox = [config.min_lon, config.min_lat, config.max_lon, config.max_lat]
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time_range = f"{config.start_date}/{config.end_date}"
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prediction_status["progress"] = "Đang tải dữ liệu Sentinel-2..."
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# Search Sentinel-2 data
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search = catalog.search(
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collections=["sentinel-2-l2a"],
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bbox=bbox,
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datetime=time_range,
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query={"eo:cloud_cover": {"lt": config.cloud_cover}}
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)
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items = list(search.items()) # Changed from items_as_dicts() to items()
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if not items:
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raise ValueError("Không tìm thấy dữ liệu Sentinel-2 cho khu vực và thời gian này")
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items = items[:config.max_scenes]
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prediction_status["progress"] = f"Đang xử lý {len(items)} scenes Sentinel-2..."
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# Load and process Sentinel-2 data (simplified)
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# Note: This is a simplified version. Full implementation would need more processing
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from odc.stac import load
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s2_data = load(
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items,
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bbox=bbox,
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chunks={"time": 1, "x": 2048, "y": 2048},
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groupby="solar_day",
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resolution=config.resolution
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)
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prediction_status["progress"] = "Đang tính toán các chỉ số..."
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# Calculate NDVI using Sentinel-2 band names
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# B08 = NIR, B04 = Red
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nir = s2_data["B08"] # NIR band
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red = s2_data["B04"] # Red band
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ndvi = (nir - red) / (nir + red + 1e-8) # Add small value to avoid division by zero
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# Resample to monthly
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ndvi_monthly = ndvi.resample(time="1M").mean()
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prediction_status["progress"] = "Đang dự đoán..."
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# Prepare features for prediction
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features_list = []
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for t in range(len(ndvi_monthly.time)):
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ndvi_t = ndvi_monthly.isel(time=t).values
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features_list.append(ndvi_t.flatten())
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# Stack features
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features = np.column_stack(features_list)
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# Make prediction
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predictions = model.predict(features)
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# Reshape to original shape
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pred_shape = ndvi_monthly.isel(time=0).shape
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predictions_2d = predictions.reshape(pred_shape)
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# Create output xarray
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prediction_da = xr.DataArray(
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predictions_2d,
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coords={
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"y": ndvi_monthly.y,
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"x": ndvi_monthly.x
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},
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dims=["y", "x"],
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name="classification"
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)
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# Save output
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output_dir = Path("predictions")
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output_dir.mkdir(exist_ok=True)
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timestamp = dt.now().strftime("%Y%m%d_%H%M%S")
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output_file = output_dir / f"prediction_{timestamp}.tif"
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prediction_status["progress"] = "Đang lưu kết quả..."
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# Save as GeoTIFF
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prediction_da.rio.write_crs(s2_data.rio.crs, inplace=True)
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prediction_da.rio.to_raster(output_file, driver="GTiff")
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prediction_status["is_predicting"] = False
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prediction_status["progress"] = "Hoàn thành!"
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prediction_status["output_file"] = str(output_file)
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prediction_status["result"] = {
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"output_file": str(output_file),
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"shape": pred_shape,
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"unique_classes": np.unique(predictions).tolist(),
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"bbox": bbox,
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"time_range": time_range
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}
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prediction_status["end_time"] = dt.now().isoformat()
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except Exception as e:
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prediction_status["is_predicting"] = False
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prediction_status["error"] = str(e)
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prediction_status["progress"] = f"Lỗi: {str(e)}"
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prediction_status["end_time"] = dt.now().isoformat()
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import traceback
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print(traceback.format_exc())
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if __name__ == "__main__":
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print("=" * 70)
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print("🚀 LAND CLASSIFICATION TRAINING API SERVER")
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print("=" * 70)
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print("\n📍 Endpoints:")
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print(" - Web Interface: http://localhost:8000")
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print(" - API Docs: http://localhost:8000/docs")
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print(" - Start Training: POST http://localhost:8000/api/training/start")
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print(" - Check Status: GET http://localhost:8000/api/training/status")
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print("\n" + "=" * 70)
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uvicorn.run(app, host="0.0.0.0", port=8000, log_level="info")
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