API Documentation: /docs
Training: /training
Prediction: /prediction
Dashboard: /dashboard
""") @app.get("/training", response_class=HTMLResponse) async def training_page(): """Serve training interface""" html_file = Path(__file__).parent / "training_interface.html" if html_file.exists(): return FileResponse(html_file) else: raise HTTPException(status_code=404, detail="Training interface không tồn tại") @app.get("/cloud-training", response_class=HTMLResponse) async def cloud_training_page(): """Serve cloud removal training interface""" html_file = Path(__file__).parent / "cloud_training_interface.html" if html_file.exists(): return FileResponse(html_file) else: raise HTTPException(status_code=404, detail="Cloud training interface không tồn tại") @app.get("/prediction", response_class=HTMLResponse) async def prediction_page(): """Serve prediction interface""" html_file = Path(__file__).parent / "prediction_interface.html" if html_file.exists(): return FileResponse(html_file) else: raise HTTPException(status_code=404, detail="Prediction interface không tồn tại") @app.get("/dashboard", response_class=HTMLResponse) async def dashboard(): """Serve dashboard visualization""" html_file = Path(__file__).parent / "dashboard.html" if html_file.exists(): return FileResponse(html_file) else: raise HTTPException(status_code=404, detail="Dashboard không tồn tại") @app.get("/batch", response_class=HTMLResponse) async def batch_page(): """Serve batch processing interface""" html_file = Path(__file__).parent / "batch_interface.html" if html_file.exists(): return FileResponse(html_file) else: raise HTTPException(status_code=404, detail="Batch interface không tồn tại") @app.get("/ndvi", response_class=HTMLResponse) async def ndvi_page(): """Serve NDVI time series interface""" html_file = Path(__file__).parent / "ndvi_interface.html" if html_file.exists(): return FileResponse(html_file) else: raise HTTPException(status_code=404, detail="NDVI interface không tồn tại") @app.get("/reports", response_class=HTMLResponse) async def reports_page(): """Serve reports management interface""" html_file = Path(__file__).parent / "reports_interface.html" if html_file.exists(): return FileResponse(html_file) else: raise HTTPException(status_code=404, detail="Reports interface không tồn tại") @app.get("/api/models/list") async def list_models(): """Liệt kê tất cả models có sẵn với metadata""" try: model_manager = get_model_manager() models = model_manager.list_models() return { "success": True, "models": models, "count": len(models) } except Exception as e: return { "success": False, "error": str(e), "models": [] } @app.get("/api/models/{model_filename}/info") async def get_model_info(model_filename: str): """Lấy thông tin chi tiết về model""" try: model_manager = get_model_manager() info = model_manager.get_model_info(model_filename) if info is None: raise HTTPException(status_code=404, detail=f"Model không tồn tại: {model_filename}") return { "success": True, "model": info } except HTTPException: raise except Exception as e: return { "success": False, "error": str(e) } @app.get("/api/models/{model_filename}/validate") async def validate_model(model_filename: str): """Validate model file""" try: model_manager = get_model_manager() validation = model_manager.validate_model(model_filename) return { "success": True, "validation": validation } except Exception as e: return { "success": False, "error": str(e) } @app.get("/api/cloud-removal/methods") async def get_cloud_removal_methods(): """Lấy danh sách các phương pháp xử lý mây có sẵn""" methods = get_available_methods() return { "success": True, "methods": methods, "default": "classic", "description": "Cloud removal strategies for Sentinel-2 data processing" } @app.get("/api/cloud-removal/models") async def list_cloud_removal_models(): """Liệt kê các cloud removal models đã train""" model_dir = Path("cloud_removal_model") if not model_dir.exists(): return {"models": [], "count": 0} models = [] # Search for ALL .pth files in cloud_removal_model and subdirectories for model_file in model_dir.rglob("*.pth"): # Skip non-cloud-removal models (keep land classification models separate) if any(x in model_file.name.lower() for x in ['mobilenet', 'cnn_', 'swin', 'xgboost', 'random_forest']): continue try: import torch import json # Try to load metadata from .json sidecar file first metadata_file = model_file.with_suffix('.json') if metadata_file.exists(): try: with open(metadata_file, 'r') as f: metadata = json.load(f) models.append({ "filename": model_file.name, "path": str(model_file), "relative_path": str(model_file.relative_to(model_dir)), "epoch": metadata.get('epoch', 0), "train_loss": metadata.get('train_loss', 0), "val_loss": metadata.get('val_loss', 0), "use_s1": metadata.get('use_s1', True), "in_channels": metadata.get('in_channels', 6), "out_channels": metadata.get('out_channels', 4), "description": metadata.get('description', ''), "created": model_file.stat().st_mtime, "size_mb": model_file.stat().st_size / (1024 * 1024), "has_metadata": True }) continue except Exception as e: print(f"[Cloud Models] Failed to read metadata file {metadata_file}: {e}") # Try to load checkpoint metadata from .pth file try: checkpoint = torch.load(model_file, map_location='cpu') epoch = checkpoint.get('epoch', 0) if isinstance(checkpoint, dict) else 0 train_loss = checkpoint.get('train_loss', 0) if isinstance(checkpoint, dict) else 0 val_loss = checkpoint.get('val_loss', 0) if isinstance(checkpoint, dict) else 0 use_s1 = checkpoint.get('use_s1', True) if isinstance(checkpoint, dict) else True in_channels = checkpoint.get('in_channels', 6) if isinstance(checkpoint, dict) else 6 out_channels = checkpoint.get('out_channels', 4) if isinstance(checkpoint, dict) else 4 except: # If checkpoint format is different or corrupted, use defaults epoch = 0 train_loss = 0 val_loss = 0 use_s1 = True in_channels = 3 out_channels = 3 models.append({ "filename": model_file.name, "path": str(model_file), "relative_path": str(model_file.relative_to(model_dir)), "epoch": epoch, "train_loss": train_loss, "val_loss": val_loss, "use_s1": use_s1, "in_channels": in_channels, "out_channels": out_channels, "description": "", "created": model_file.stat().st_mtime, "size_mb": model_file.stat().st_size / (1024 * 1024), "has_metadata": False }) except Exception as e: print(f"[Cloud Models] Error loading {model_file}: {e}") # Still add the file even if we can't load metadata models.append({ "filename": model_file.name, "path": str(model_file), "relative_path": str(model_file.relative_to(model_dir)), "epoch": 0, "train_loss": 0, "val_loss": 0, "use_s1": False, "in_channels": 3, "out_channels": 3, "description": "", "created": model_file.stat().st_mtime, "size_mb": model_file.stat().st_size / (1024 * 1024), "has_metadata": False }) models.sort(key=lambda x: x['created'], reverse=True) return {"models": models, "count": len(models)} @app.post("/api/cloud-removal/train") async def train_cloud_removal(config: CloudRemovalTrainingConfig, background_tasks: BackgroundTasks): """Bắt đầu train cloud removal model""" # Check if data directory exists data_dir = Path(config.data_dir) if not data_dir.exists(): raise HTTPException( status_code=404, detail=f"Data directory not found: {config.data_dir}" ) # Create status tracking training_id = datetime.now().strftime("%Y%m%d_%H%M%S") async def run_cloud_training(): try: from train_cloud_removal import train_cloud_removal_model print(f"[CLOUD REMOVAL TRAINING] Starting training {training_id}") model, train_losses, val_losses = train_cloud_removal_model( data_dir=config.data_dir, use_s1=config.use_s1, batch_size=config.batch_size, num_epochs=config.num_epochs, learning_rate=config.learning_rate, device="cuda" if config.use_gpu else "cpu", save_dir="cloud_removal_model" ) print(f"[CLOUD REMOVAL TRAINING] Completed {training_id}") return { "success": True, "training_id": training_id, "final_train_loss": train_losses[-1], "final_val_loss": val_losses[-1], "epochs": len(train_losses) } except Exception as e: print(f"[CLOUD REMOVAL TRAINING ERROR] {e}") import traceback traceback.print_exc() return { "success": False, "error": str(e), "training_id": training_id } # Run in background background_tasks.add_task(run_cloud_training) return { "message": "Cloud removal training started", "training_id": training_id, "config": { "data_dir": config.data_dir, "use_s1": config.use_s1, "batch_size": config.batch_size, "num_epochs": config.num_epochs, "learning_rate": config.learning_rate, "use_gpu": config.use_gpu } } @app.post("/api/cloud-removal/upload") async def upload_cloud_removal_model( file: UploadFile = File(...), epoch: int = Form(0), train_loss: float = Form(0.0), val_loss: float = Form(0.0), in_channels: int = Form(6), out_channels: int = Form(4), use_s1: bool = Form(True), description: str = Form("") ): """Upload cloud removal .pth model with optional metadata""" # Debug logging print(f"[Upload] Received parameters:") print(f" File: {file.filename}") print(f" Epoch: {epoch} (type: {type(epoch)})") print(f" Train Loss: {train_loss} (type: {type(train_loss)})") print(f" Val Loss: {val_loss} (type: {type(val_loss)})") print(f" In Channels: {in_channels} (type: {type(in_channels)})") print(f" Out Channels: {out_channels} (type: {type(out_channels)})") print(f" Use S1: {use_s1} (type: {type(use_s1)})") print(f" Description: {description}") # Validate file extension if not file.filename.endswith('.pth'): raise HTTPException(status_code=400, detail="Only .pth files are allowed") # Security check if ".." in file.filename or "/" in file.filename or "\\" in file.filename: raise HTTPException(status_code=400, detail="Invalid filename") try: model_dir = Path("cloud_removal_model") model_dir.mkdir(exist_ok=True) # Save uploaded file file_path = model_dir / file.filename # Check if file already exists if file_path.exists(): raise HTTPException(status_code=400, detail=f"Model {file.filename} already exists") # Write file with open(file_path, "wb") as f: content = await file.read() f.write(content) file_size = file_path.stat().st_size # Save metadata as JSON sidecar file import json metadata_file = file_path.with_suffix('.json') metadata_dict = { "filename": file.filename, "epoch": epoch, "train_loss": train_loss, "val_loss": val_loss, "in_channels": in_channels, "out_channels": out_channels, "use_s1": use_s1, "description": description, "uploaded_at": datetime.now().isoformat() } with open(metadata_file, 'w') as f: json.dump(metadata_dict, f, indent=2) print(f"[Upload] Saved model: {file_path}") print(f"[Upload] Saved metadata: {metadata_file}") print(f"[Upload] Metadata: {metadata_dict}") return { "message": f"Successfully uploaded {file.filename}", "filename": file.filename, "size_mb": round(file_size / 1024 / 1024, 2), "path": str(file_path), "metadata": metadata_dict } except HTTPException: raise except Exception as e: print(f"[Upload] Error: {e}") import traceback traceback.print_exc() raise HTTPException(status_code=500, detail=f"Upload failed: {str(e)}") @app.delete("/api/cloud-removal/models/{filename}") async def delete_cloud_removal_model(filename: str): """Xóa cloud removal model""" model_dir = Path("cloud_removal_model") model_path = model_dir / filename # Security check if ".." in filename or "/" in filename or "\\" in filename: raise HTTPException(status_code=400, detail="Invalid filename") if not model_path.exists(): raise HTTPException(status_code=404, detail=f"Model not found: {filename}") try: model_path.unlink() return { "success": True, "message": f"Deleted cloud removal model: {filename}" } except Exception as e: raise HTTPException(status_code=500, detail=f"Failed to delete: {str(e)}") @app.delete("/api/models/{model_filename}") async def delete_model(model_filename: str): """Xóa model""" try: model_manager = get_model_manager() success = model_manager.delete_model(model_filename) if not success: raise HTTPException(status_code=404, detail=f"Model không tồn tại: {model_filename}") return { "success": True, "message": f"Đã xóa model: {model_filename}" } except HTTPException: raise except Exception as e: return { "success": False, "error": str(e) } @app.get("/api/config/presets") async def get_presets(): """Lấy các preset cấu hình sẵn""" return { "presets": [ { "name": "PC - Nhỏ (3 tháng, 20m, 12 scenes)", "config": { "min_lon": 105.5, "min_lat": 9.2, "max_lon": 106.4, "max_lat": 10.0, "start_date": "2023-03-01", "end_date": "2023-05-31", "max_scenes": 12, "cloud_cover": 30, "resolution": 20, "test_size": 0.2 } }, { "name": "Server - Trung bình (6 tháng, 10m, 30 scenes)", "config": { "min_lon": 105.5, "min_lat": 9.2, "max_lon": 106.4, "max_lat": 10.0, "start_date": "2023-01-01", "end_date": "2023-06-30", "max_scenes": 30, "cloud_cover": 30, "resolution": 10, "test_size": 0.2 } }, { "name": "Full - ODC (10 tháng, 10m, 1 scene) - từ 01.train_ODC.ipynb", "config": { "min_lon": 105.5, "min_lat": 9.2, "max_lon": 106.4, "max_lat": 10.0, "start_date": "2023-03-01", "end_date": "2023-12-31", "max_scenes": 1, "cloud_cover": 50, "resolution": 10, "test_size": 0.2 } } ] } @app.get("/api/provinces/list") async def list_provinces(): """Lấy danh sách tất cả các tỉnh thành Việt Nam""" return { "provinces": get_all_provinces(), "count": len(get_all_provinces()) } @app.get("/api/provinces/by-region") async def list_provinces_by_region(): """Lấy danh sách tỉnh thành theo vùng miền""" return get_provinces_by_region() @app.get("/api/provinces/{province_name}/bbox") async def get_province_bbox_api(province_name: str): """Lấy bbox của một tỉnh thành""" bbox = get_province_bbox(province_name) if bbox is None: raise HTTPException(status_code=404, detail=f"Không tìm thấy tỉnh: {province_name}") return { "province": province_name, "bbox": bbox, "min_lon": bbox[0], "min_lat": bbox[1], "max_lon": bbox[2], "max_lat": bbox[3] } @app.get("/api/provinces/search/{query}") async def search_provinces(query: str): """Tìm kiếm tỉnh thành theo tên""" results = search_province(query) return { "query": query, "results": results, "count": len(results) } @app.get("/api/provinces-32/list") async def list_provinces_32(): """Lấy danh sách 32 tỉnh thành sau sáp nhập""" return { "provinces": get_all_provinces_32(), "count": len(get_all_provinces_32()), "note": "32 tỉnh thành sau sáp nhập theo Nghị quyết 1211/2023" } @app.get("/api/provinces-32/by-region") async def list_provinces_by_region_32(): """Lấy danh sách 32 tỉnh thành theo vùng miền""" return get_provinces_by_region_32() @app.get("/api/provinces-32/{province_name}/bbox") async def get_province_bbox_api_32(province_name: str): """Lấy bbox của một tỉnh thành (32 tỉnh)""" bbox = get_province_bbox_32(province_name) if bbox is None: raise HTTPException(status_code=404, detail=f"Không tìm thấy tỉnh: {province_name}") # Get merged info info = get_merged_info(province_name) return { "province": province_name, "bbox": bbox, "min_lon": bbox[0], "min_lat": bbox[1], "max_lon": bbox[2], "max_lat": bbox[3], "merged_from": info.get("merged_from"), "area_km2": info.get("area_km2"), "region": info.get("region") } @app.get("/api/provinces-32/search/{query}") async def search_provinces_32(query: str): """Tìm kiếm tỉnh thành theo tên (32 tỉnh)""" results = search_province_32(query) return { "query": query, "results": results, "count": len(results) } @app.get("/api/provinces-32/statistics") async def get_provinces_stats(): """Thống kê các tỉnh đã sáp nhập""" return get_provinces_statistics() @app.get("/api/training/labels") async def get_training_labels(): """Lấy danh sách các label từ training data""" return { "label_mapping": DEFAULT_LABEL_MAPPING, "label_names": DEFAULT_LABEL_NAMES, "count": len(DEFAULT_LABEL_MAPPING), "labels": [ {"code": code, "name": name, "description": name} for name, code in DEFAULT_LABEL_MAPPING.items() ] } @app.get("/api/training/files") async def list_training_files(): """Liệt kê các file training shapefile có sẵn""" train_dir = Path("train") if not train_dir.exists(): raise HTTPException(status_code=404, detail="Thư mục train không tồn tại") shapefiles = [] for shp_file in train_dir.glob("*.shp"): try: # Get file info file_size = shp_file.stat().st_size file_modified = datetime.fromtimestamp(shp_file.stat().st_mtime).isoformat() # Try to read shapefile to get point count and unique labels try: import geopandas as gpd gdf = gpd.read_file(str(shp_file)) # Convert to WGS84 if not already if gdf.crs and gdf.crs.to_epsg() != 4326: gdf = gdf.to_crs("EPSG:4326") point_count = len(gdf) # Try to find label column (Hientrang, class, label, etc.) label_column = None for col in ['Hientrang', 'class', 'label', 'Class', 'Label']: if col in gdf.columns: label_column = col break unique_labels = [] if label_column: unique_labels = sorted(gdf[label_column].unique().tolist()) shapefiles.append({ "filename": shp_file.name, "path": f"train/{shp_file.name}", "size_bytes": file_size, "size_mb": round(file_size / 1024 / 1024, 2), "modified": file_modified, "point_count": point_count, "label_column": label_column, "unique_labels": unique_labels, "label_count": len(unique_labels) }) except Exception as e: # If cannot read shapefile, just add basic info shapefiles.append({ "filename": shp_file.name, "path": f"train/{shp_file.name}", "size_bytes": file_size, "size_mb": round(file_size / 1024 / 1024, 2), "modified": file_modified, "error": f"Cannot read shapefile: {str(e)}" }) except Exception as e: continue return { "files": shapefiles, "count": len(shapefiles), "directory": "train/" } @app.get("/api/training/shapefile/{filename}/labels") async def get_shapefile_labels(filename: str): """Lấy các label từ một shapefile cụ thể""" train_dir = Path("train") shp_file = train_dir / filename # Security check if ".." in filename or "/" in filename or "\\" in filename: raise HTTPException(status_code=400, detail="Invalid filename") if not shp_file.exists(): raise HTTPException(status_code=404, detail=f"File {filename} không tồn tại") try: import geopandas as gpd gdf = gpd.read_file(str(shp_file)) # Convert to WGS84 if not already if gdf.crs and gdf.crs.to_epsg() != 4326: print(f"📍 Converting shapefile from {gdf.crs} to WGS84 (EPSG:4326)") gdf = gdf.to_crs("EPSG:4326") # Try to find label column label_column = None for col in ['Hientrang', 'class', 'label', 'Class', 'Label']: if col in gdf.columns: label_column = col break if not label_column: return { "filename": filename, "error": "No label column found", "columns": list(gdf.columns), "point_count": len(gdf), "bbox": gdf.total_bounds.tolist() # Still return bbox even without labels } # Get unique labels and their counts label_counts = gdf[label_column].value_counts().to_dict() unique_labels = sorted(gdf[label_column].unique().tolist()) # Map to default labels if possible mapped_labels = [] for label in unique_labels: code = DEFAULT_LABEL_MAPPING.get(label, "unknown") mapped_labels.append({ "name": label, "code": code, "count": int(label_counts.get(label, 0)), "mapped": label in DEFAULT_LABEL_MAPPING }) # Get bbox in WGS84 coordinates bbox = gdf.total_bounds.tolist() # [minx, miny, maxx, maxy] print(f"✅ Shapefile bbox (WGS84): {bbox}") return { "filename": filename, "label_column": label_column, "point_count": len(gdf), "unique_labels": unique_labels, "label_count": len(unique_labels), "labels": mapped_labels, "bbox": bbox, # Now in WGS84 lat/lon "columns": list(gdf.columns) } except Exception as e: raise HTTPException(status_code=500, detail=f"Error reading shapefile: {str(e)}") @app.get("/api/training/status", response_model=TrainingStatus) async def get_training_status(): """Kiểm tra trạng thái training""" return training_status @app.post("/api/training/start") async def start_training(config: TrainingConfig, background_tasks: BackgroundTasks): """Bắt đầu training với config đã chọn""" global training_status if training_status["is_training"]: raise HTTPException(status_code=400, detail="Training đang chạy, vui lòng đợi") # Reset status training_status = { "is_training": True, "progress": "Đang khởi tạo...", "error": None, "result": None, "start_time": datetime.now().isoformat(), "end_time": None } # Run training in background background_tasks.add_task(run_training, config) return {"message": "Training đã bắt đầu", "status": training_status} @app.post("/api/training/stop") async def stop_training(): """Dừng training (nếu đang chạy)""" global training_status if not training_status["is_training"]: return {"message": "Không có training nào đang chạy"} # Set cancel flag - the training will check this and stop training_status["cancel_requested"] = True training_status["progress"] = "Đang hủy training..." return {"message": "Đang dừng training..."} @app.post("/api/cache/clear") async def clear_cache(): """Xóa cache dataset""" import shutil cache_dir = Path("dataset_cache") if not cache_dir.exists(): return {"message": "Không có cache để xóa", "deleted": 0} # Count files cache_files = list(cache_dir.glob("*.joblib")) count = len(cache_files) # Delete all cache files for cache_file in cache_files: try: cache_file.unlink() except: pass return {"message": f"Đã xóa {count} file cache", "deleted": count} @app.get("/api/cache/info") async def get_cache_info(): """Lấy thông tin về cache với metadata đầy đủ, tự động xóa cache cũ có lazy data""" cache_dir = Path("dataset_cache") if not cache_dir.exists(): return {"exists": False, "files": [], "total_size_mb": 0} cache_files = [] total_size = 0 deleted_count = 0 for cache_file in cache_dir.glob("*.joblib"): # Skip if file doesn't exist (race condition) if not cache_file.exists(): continue size = cache_file.stat().st_size # Try to load metadata from cache and check if it's valid metadata = {} is_valid = True try: cached_data = joblib.load(cache_file) # Check if cache contains lazy data (will cause 403 errors) if isinstance(cached_data, dict) and "s2_data" in cached_data: s2_data_temp = cached_data["s2_data"] is_lazy = False try: is_lazy = any(hasattr(s2_data_temp[var].data, 'chunks') for var in s2_data_temp.data_vars) except: pass if is_lazy: print(f"[CLEANUP] Deleting cache with lazy data: {cache_file.name}") cache_file.unlink() deleted_count += 1 is_valid = False if is_valid and isinstance(cached_data, dict): metadata = { "bbox": cached_data.get("bbox", []), "time_range": cached_data.get("time_range", ""), "resolution": cached_data.get("resolution", 20), "n_samples": len(cached_data.get("features", [])), "created": cached_data.get("timestamp", "") } # Parse time_range to get start/end dates if metadata["time_range"]: time_parts = metadata["time_range"].split("/") if len(time_parts) == 2: metadata["start_date"] = time_parts[0] metadata["end_date"] = time_parts[1] # Parse bbox to get min/max lon/lat if metadata["bbox"] and len(metadata["bbox"]) == 4: metadata["min_lon"] = metadata["bbox"][0] metadata["min_lat"] = metadata["bbox"][1] metadata["max_lon"] = metadata["bbox"][2] metadata["max_lat"] = metadata["bbox"][3] except Exception as e: print(f"[CLEANUP] Error loading cache {cache_file.name}: {e}. Deleting...") try: cache_file.unlink() deleted_count += 1 is_valid = False except: pass # Only add valid cache files to the list if is_valid: total_size += size cache_files.append({ "filename": cache_file.name, "size_mb": round(size / 1024 / 1024, 2), "modified": datetime.fromtimestamp(cache_file.stat().st_mtime).isoformat(), "metadata": metadata }) # Sort by modified time (newest first) cache_files.sort(key=lambda x: x["modified"], reverse=True) if deleted_count > 0: print(f"[CLEANUP] Deleted {deleted_count} invalid cache files") return { "exists": True, "files": cache_files, "count": len(cache_files), "total_size_mb": round(total_size / 1024 / 1024, 2), "deleted_invalid": deleted_count } @app.get("/api/models/list") async def list_models(): """Liệt kê các model đã train""" model_dir = Path("model_train") if not model_dir.exists(): return {"models": []} models = [] # List all .joblib model files (actual trained models) for model_file in model_dir.glob("*.joblib"): # Skip any file that contains '_info' in its name if '_info' in model_file.stem: continue info = {} # Try to find corresponding .json info file # Remove .joblib and try with _info.json base_name = model_file.stem # e.g., "model_cnn_20251221_163841" info_file = model_dir / f"{base_name}_info.json" if info_file.exists(): try: with open(info_file) as f: info = json.load(f) except Exception as e: info = {"error": str(e)} size_mb = round(model_file.stat().st_size / 1024 / 1024, 2) created = datetime.fromtimestamp(model_file.stat().st_mtime).isoformat() models.append({ "filename": model_file.name, "created": created, "size_mb": size_mb, "info": info }) # Sort by creation time (newest first) models.sort(key=lambda x: x["created"], reverse=True) return {"models": models} # ============ REPORTS API ============ @app.get("/api/reports/list") async def list_reports(): """Liệt kê các báo cáo đã tạo""" reports_dir = Path("reports") reports_dir.mkdir(exist_ok=True) reports = [] for report_file in reports_dir.glob("*.html"): # Determine report type from filename if "training" in report_file.name: report_type = "training" elif "prediction" in report_file.name: report_type = "prediction" else: report_type = "unknown" report_info = { "filename": report_file.name, "type": report_type, "created": datetime.fromtimestamp(report_file.stat().st_mtime).isoformat(), "size_kb": round(report_file.stat().st_size / 1024, 2), "view_url": f"/api/reports/view/{report_file.name}", "download_url": f"/api/reports/download/{report_file.name}", "is_batch_job": False, "batch_metadata": None } # Check if this is a batch job report if report_type == "prediction": predictions_dir = Path("predictions") # Look for batch metadata JSON files that reference this report for json_file in predictions_dir.glob("batch_*.json"): try: import json with open(json_file, 'r') as f: metadata = json.load(f) if metadata.get("report_filename") == report_file.name or \ (metadata.get("batch_job_id") and report_file.name.endswith('.html')): report_info["is_batch_job"] = True report_info["batch_metadata"] = { "batch_job_id": metadata.get("batch_job_id"), "batch_name": metadata.get("batch_name"), "batch_timestamp": metadata.get("batch_timestamp") } break except Exception as e: pass reports.append(report_info) # Sort by creation time (newest first) reports.sort(key=lambda x: x["created"], reverse=True) return {"reports": reports, "count": len(reports)} @app.get("/api/reports/view/{filename}", response_class=HTMLResponse) async def view_report(filename: str): """Xem báo cáo HTML trực tiếp""" reports_dir = Path("reports") file_path = reports_dir / filename # Security check if ".." in filename or "/" in filename or "\\" in filename: raise HTTPException(status_code=400, detail="Invalid filename") if not file_path.exists(): raise HTTPException(status_code=404, detail=f"Report không tồn tại: {filename}") with open(file_path, 'r', encoding='utf-8') as f: html_content = f.read() return HTMLResponse(content=html_content) @app.get("/api/reports/download/{filename}") async def download_report(filename: str): """Download báo cáo HTML""" reports_dir = Path("reports") file_path = reports_dir / filename # Security check if ".." in filename or "/" in filename or "\\" in filename: raise HTTPException(status_code=400, detail="Invalid filename") if not file_path.exists(): raise HTTPException(status_code=404, detail=f"Report không tồn tại: {filename}") return FileResponse( path=str(file_path), filename=filename, media_type="text/html", headers={ "Content-Disposition": f"attachment; filename={filename}" } ) @app.delete("/api/reports/delete/{filename}") async def delete_report(filename: str): """Xóa một báo cáo""" reports_dir = Path("reports") file_path = reports_dir / filename # Security check if ".." in filename or "/" in filename or "\\" in filename: raise HTTPException(status_code=400, detail="Invalid filename") if not file_path.exists(): raise HTTPException(status_code=404, detail=f"Report không tồn tại: {filename}") try: file_path.unlink() return {"message": f"Đã xóa báo cáo: {filename}", "success": True} except Exception as e: raise HTTPException(status_code=500, detail=f"Không thể xóa: {str(e)}") @app.post("/api/prediction/start") async def start_prediction(config: PredictionConfig, background_tasks: BackgroundTasks): """Bắt đầu dự đoán""" global prediction_status if prediction_status["is_predicting"]: raise HTTPException(status_code=400, detail="Đang có dự đoán khác đang chạy") # Reset status prediction_status = { "is_predicting": True, "progress": "Đang khởi động...", "error": None, "result": None, "output_file": None, "start_time": datetime.now().isoformat(), "end_time": None } # Run prediction in background background_tasks.add_task(run_prediction, config) return {"message": "Đã bắt đầu dự đoán", "status": prediction_status} @app.get("/api/prediction/status") async def get_prediction_status(): """Kiểm tra trạng thái dự đoán""" return prediction_status async def run_training(config: TrainingConfig): """Chạy training process""" global training_status try: training_status["cancel_requested"] = False training_status["progress"] = "Đang import thư viện..." # Import training module from train_module import train_model training_status["progress"] = "Đang load dữ liệu Sentinel-2..." # Function to check if training should be cancelled def should_cancel(): return training_status.get("cancel_requested", False) # Run training result = train_model( bbox=[config.min_lon, config.min_lat, config.max_lon, config.max_lat], time_range=f"{config.start_date}/{config.end_date}", max_scenes=config.max_scenes, cloud_cover=config.cloud_cover, resolution=config.resolution, training_shapefile=config.training_shapefile, model_type=config.model_type, n_estimators=config.n_estimators, max_depth=config.max_depth, learning_rate=config.learning_rate, use_gpu=config.use_gpu, use_cache=config.use_cache, test_size=config.test_size, status_callback=lambda msg: update_progress(msg), cancel_check=should_cancel ) if training_status.get("cancel_requested", False): training_status["is_training"] = False training_status["progress"] = "Đã hủy training" training_status["error"] = "Training cancelled by user" else: training_status["is_training"] = False training_status["progress"] = "Hoàn thành! Đang tạo báo cáo..." training_status["result"] = result # Auto generate report if result.get("success", False): try: report_path, _ = generate_training_report(result) training_status["result"]["report_path"] = report_path training_status["result"]["report_filename"] = Path(report_path).name training_status["progress"] = "Hoàn thành! Báo cáo đã được tạo." print(f"[REPORT] Generated: {report_path}") except Exception as e: print(f"[REPORT ERROR] Failed to generate report: {e}") training_status["progress"] = "Hoàn thành! (Không thể tạo báo cáo)" training_status["end_time"] = datetime.now().isoformat() except Exception as e: training_status["is_training"] = False training_status["error"] = str(e) training_status["progress"] = f"Lỗi: {str(e)}" training_status["end_time"] = datetime.now().isoformat() import traceback print(traceback.format_exc()) def update_progress(message: str): """Cập nhật progress message""" global training_status training_status["progress"] = message print(f"[PROGRESS] {message}") def update_prediction_progress(message: str): """Cập nhật prediction progress message""" global prediction_status prediction_status["progress"] = message print(f"[PREDICTION PROGRESS] {message}") async def run_prediction(config: PredictionConfig): """Chạy prediction process - Sử dụng FeatureExtractor để đồng bộ với training""" global prediction_status try: prediction_status["progress"] = "Đang import thư viện..." # Import required libraries import numpy as np import xarray as xr import rioxarray from datetime import datetime as dt from feature_extractor import get_feature_extractor # Validate bbox if (config.min_lon < -180 or config.max_lon > 180 or config.min_lat < -90 or config.max_lat > 90): raise ValueError(f"Bbox không hợp lệ: ({config.min_lon}, {config.min_lat}, {config.max_lon}, {config.max_lat}). " f"Phải trong phạm vi (-180, -90, 180, 90)") prediction_status["progress"] = "Đang load model..." # Load model using ModelManager model_manager = get_model_manager() model, label_encoder, model_metadata = model_manager.load_model(config.model_filename) # Get feature_mode and features from metadata (default to 'simple' if not specified) feature_mode = model_metadata.get("feature_mode", "simple") required_features = model_metadata.get("features", []) n_features_expected = model_metadata.get("n_features", len(required_features)) prediction_status["progress"] = f"Model: {model_metadata.get('model_type', 'unknown')}, mode={feature_mode}, features={n_features_expected}" # Initialize FeatureExtractor với đúng mode như lúc training extractor = get_feature_extractor(mode=feature_mode) # Check if it's a PyTorch model (CNN, Swin-UNet, MobileNet, etc.) is_pytorch_model = hasattr(model, '__class__') and any( name in model.__class__.__name__ for name in ['CNN', 'SwinUNet', 'MobileNet'] ) if is_pytorch_model: model_class_name = model.__class__.__name__ prediction_status["progress"] = f"Phát hiện PyTorch {model_class_name} model..." try: import torch except ImportError: raise ImportError(f"PyTorch required for {model_class_name} models. Install: pip install torch") # Initialize common variables bbox = [config.min_lon, config.min_lat, config.max_lon, config.max_lat] time_range = f"{config.start_date}/{config.end_date}" # ============ CHECK PREDICTION CACHE FIRST ============ cached_s2_data = None cache_key = f"{config.min_lon:.2f}_{config.min_lat:.2f}_{config.max_lon:.2f}_{config.max_lat:.2f}" # Try to find matching cache cache_dir = Path("prediction_cache") if cache_dir.exists(): for cache_file in cache_dir.glob(f"pred_{cache_key}_*.json"): try: with open(cache_file, 'r') as f: cache_data = json.load(f) # Check if cache matches current config if (cache_data.get('start_date') == config.start_date and cache_data.get('end_date') == config.end_date and cache_data.get('resolution') == config.resolution and cache_data.get('data_file')): data_file = cache_dir / cache_data['data_file'] if data_file.exists(): prediction_status["progress"] = "Đang load dữ liệu từ cache..." import joblib cached_s2_data = joblib.load(data_file) print(f"[CACHE HIT] Using cached Sentinel-2 data from {cache_file.name}") break except Exception as e: print(f"[CACHE] Error loading cache {cache_file}: {e}") # ============ LOAD SENTINEL-2 DATA ============ if cached_s2_data is not None: s2_data = cached_s2_data s2_items = [] # Empty list when using cache prediction_status["progress"] = "Đã load dữ liệu từ cache, đang xử lý..." else: prediction_status["progress"] = "Đang kết nối Microsoft Planetary Computer..." import pystac_client import planetary_computer from odc.stac import load catalog = pystac_client.Client.open( "https://planetarycomputer.microsoft.com/api/stac/v1", modifier=planetary_computer.sign_inplace, ) prediction_status["progress"] = "Đang tải dữ liệu Sentinel-2..." s2_items = fetch_sentinel_items_with_retry( catalog=catalog, bbox=bbox, time_range=time_range, cloud_cover=config.cloud_cover, max_scenes=config.max_scenes, max_retries=3 ) prediction_status["progress"] = f"Đang xử lý dữ liệu Sentinel-2..." # Load different bands based on feature mode (skip if using cache) if cached_s2_data is None: if feature_mode == 'simple': bands_to_load = ["B04", "B08", "SCL"] else: # temporal or extended bands_to_load = ["B02", "B03", "B04", "B08", "B11", "SCL"] # Retry logic for data loading max_retries = 3 s2_data = None for attempt in range(max_retries): try: s2_data = load( s2_items, bbox=bbox, bands=bands_to_load, chunks={"time": 1, "x": 2048, "y": 2048}, groupby="solar_day", resolution=config.resolution ).compute() break except Exception as e: error_msg = str(e) is_auth_error = any(err in error_msg for err in [ "AuthenticationFailed", "Signature fields not well formed", "Server failed to authenticate", "403", "401" ]) if is_auth_error: print(f"⚠️ Authentication error. Re-signing items...") try: import planetary_computer s2_items = [planetary_computer.sign(item) for item in s2_items] if attempt < max_retries - 1: import time time.sleep(1) continue except Exception: pass if "does not exist" in error_msg or "RasterioIOError" in error_msg: if len(s2_items) > 3 and attempt < max_retries - 1: remove_count = max(1, len(s2_items) // 5) s2_items = s2_items[:-remove_count] print(f"⚠️ Access error. Reduced to {len(s2_items)} scenes, retrying...") continue if attempt == max_retries - 1: raise ValueError(f"Failed to load Sentinel-2 data: {error_msg[:200]}") if s2_data is None: raise ValueError("Failed to load Sentinel-2 data after retries") prediction_status["progress"] = "Đã load Sentinel-2 data" # ============ LOAD SENTINEL-1 DATA (RADAR) ============ prediction_status["progress"] = "Đang tải dữ liệu Sentinel-1 (Radar)..." use_radar = False vh_data = None vv_data = None try: s1_search = catalog.search( collections=["sentinel-1-rtc"], bbox=bbox, datetime=time_range, ) s1_items = list(s1_search.items()) if s1_items: s1_items = s1_items[:config.max_scenes] # Try to load Sentinel-1 with error handling try: s1_data = load( s1_items, bbox=bbox, bands=["vh", "vv"], chunks={"time": 1, "x": 2048, "y": 2048}, groupby="solar_day", resolution=config.resolution ).compute() # Convert to dB vh_data = 10 * np.log10(s1_data['vh'].where(s1_data['vh'] > 0)) vv_data = 10 * np.log10(s1_data['vv'].where(s1_data['vv'] > 0)) use_radar = True prediction_status["progress"] = f"Đã load Sentinel-1 data ({len(s1_items)} scenes)" except Exception as s1_error: print(f"⚠️ Failed to load Sentinel-1 data: {str(s1_error)[:100]}") prediction_status["progress"] = "Lỗi Sentinel-1, bỏ qua radar features" else: prediction_status["progress"] = "Không có dữ liệu Sentinel-1, bỏ qua radar features" except Exception as e: prediction_status["progress"] = f"Lỗi load Sentinel-1: {str(e)}, bỏ qua radar features" # ============ ADVANCED CLOUD MASKING & REMOVAL ============ prediction_status["progress"] = "Đang xử lý mây nâng cao..." # Use cloud removal module with user-selected method cloud_removal_method = config.cloud_removal_method if hasattr(config, 'cloud_removal_method') else "classic" cloud_removal_model = config.cloud_removal_model if hasattr(config, 'cloud_removal_model') else None print(f"[CLOUD REMOVAL] Using method: {cloud_removal_method}") if cloud_removal_model: print(f"[CLOUD REMOVAL] Using custom model: {cloud_removal_model}") s2_data, cloud_metadata = process_cloud_removal( s2_data=s2_data, method=cloud_removal_method, model_path=f"cloud_removal_model/{cloud_removal_model}" if cloud_removal_model else None, verbose=True ) cloud_coverage_percent = cloud_metadata.get('cloud_coverage_percent', 0) # Quality warning if cloud coverage too high if cloud_coverage_percent > 30: print(f"[WARNING] High cloud coverage ({cloud_coverage_percent:.1f}%) - prediction quality may be affected") prediction_status["progress"] = f"⚠️ Cảnh báo: Độ phủ mây cao ({cloud_coverage_percent:.1f}%)" # ============ EXTRACT FEATURES ============ prediction_status["progress"] = f"Đang trích xuất features (mode={feature_mode})..." print(f"[PREDICTION DEBUG] Feature mode: {feature_mode}") print(f"[PREDICTION DEBUG] S2 bands available: {list(s2_data.data_vars)}") print(f"[PREDICTION DEBUG] S2 dimensions: {dict(s2_data.dims)}") # Always fill NaN for all bands in s2_data if present for band in ["B02", "B03", "B04", "B08", "B11"]: if band in s2_data: s2_data[band] = s2_data[band].ffill(dim='time').bfill(dim='time') # Calculate NDVI if needed (for simple mode) ndvi_filled = None if feature_mode == 'simple' and 'B08' in s2_data and 'B04' in s2_data: nir = s2_data["B08"].astype('float32') red = s2_data["B04"].astype('float32') ndvi = (nir - red) / (nir + red + 1e-8) ndvi_filled = ndvi.ffill(dim='time').bfill(dim='time') # Extract features using FeatureExtractor, always pass all possible data features = extractor.extract( s2_data=s2_data, ndvi_data=ndvi_filled, vh_data=vh_data, vv_data=vv_data ) # Handle NaN values features = np.nan_to_num(features, nan=0.0) print(f"[PREDICTION DEBUG] Features extracted: shape={features.shape}") print(f"[PREDICTION DEBUG] Features range: [{features.min():.3f}, {features.max():.3f}]") print(f"[PREDICTION DEBUG] Features mean: {features.mean():.3f}, std: {features.std():.3f}") print(f"[PREDICTION DEBUG] NaN count: {np.isnan(features).sum()}") print(f"[PREDICTION DEBUG] First pixel features: {features[0][:min(8, features.shape[1])]}") # Ensure features shape matches model expectation if features.shape[1] != n_features_expected: raise ValueError(f"Số lượng features ({features.shape[1]}) không khớp với model ({n_features_expected}). Hãy kiểm tra lại cấu hình trích xuất đặc trưng và metadata của model.") prediction_status["progress"] = f"Đã extract {features.shape[1]} features cho {features.shape[0]} pixels" # ============ PREDICT ============ prediction_status["progress"] = "Đang dự đoán..." print(f"[PREDICTION DEBUG] Starting prediction with {features.shape[0]} pixels, {features.shape[1]} features") # Make prediction (all PyTorch models have the same predict interface) predictions = model.predict(features) print(f"[PREDICTION DEBUG] Predictions shape: {predictions.shape}") print(f"[PREDICTION DEBUG] Unique predicted classes: {np.unique(predictions)}") print(f"[PREDICTION DEBUG] Class distribution:") unique, counts = np.unique(predictions, return_counts=True) for cls, cnt in zip(unique, counts): print(f" Class {cls}: {cnt} pixels ({cnt/len(predictions)*100:.1f}%)") # Decode labels if label_encoder exists if label_encoder is not None: try: predictions = label_encoder.inverse_transform(predictions.astype(int)) except: pass # Reshape to original shape if feature_mode == 'simple' and 'B08' in s2_data: # Use B08 to get shape y_size = len(s2_data.y) x_size = len(s2_data.x) else: y_size = len(s2_data.y) x_size = len(s2_data.x) pred_shape = (y_size, x_size) predictions_2d = predictions.reshape(pred_shape) # Smooth classification map to reduce salt-and-pepper noise try: from scipy import ndimage smoothed = ndimage.median_filter(predictions_2d, size=3) # Keep invalid/nodata pixels (-1) untouched smoothed[predictions_2d < 0] = -1 predictions_2d = smoothed print("[SMOOTH] Applied 3x3 median filter to classification map") except Exception as smooth_err: print(f"[SMOOTH WARNING] Failed to smooth classification map: {smooth_err}") # ============ CREATE OUTPUT ============ prediction_status["progress"] = "Đang tạo bản đồ phân loại..." # Create output xarray prediction_da = xr.DataArray( predictions_2d, coords={ "y": s2_data.y, "x": s2_data.x }, dims=["y", "x"], name="classification" ) # Save output output_dir = Path("predictions") output_dir.mkdir(exist_ok=True) timestamp = dt.now().strftime("%Y%m%d_%H%M%S") output_file = output_dir / f"prediction_{timestamp}.tif" prediction_status["progress"] = "Đang lưu kết quả GeoTIFF..." # Set CRS and save as GeoTIFF if hasattr(s2_data, 'rio') and s2_data.rio.crs is not None: prediction_da.rio.write_crs(s2_data.rio.crs, inplace=True) else: prediction_da.rio.write_crs("EPSG:4326", inplace=True) prediction_da.rio.to_raster(str(output_file), driver="GTiff") # Generate PNG preview for web display prediction_status["progress"] = "Đang tạo PNG preview..." png_file = output_dir / f"prediction_{timestamp}.png" try: import matplotlib matplotlib.use('Agg') # Non-interactive backend import matplotlib.pyplot as plt from matplotlib.patches import Patch # Create a figure with prediction result and legend fig, ax = plt.subplots(figsize=(14, 10), dpi=150) # Plot prediction with colormap im = ax.imshow(predictions_2d, cmap='tab20', interpolation='nearest') ax.set_title(f'Land Classification - {timestamp}', fontsize=16, fontweight='bold', pad=20) ax.set_xlabel('X (pixels)', fontsize=11) ax.set_ylabel('Y (pixels)', fontsize=11) # Build mapping from numeric class value -> display label # ALWAYS use DEFAULT_LABEL_NAMES - it has the correct Vietnamese names class_map = DEFAULT_LABEL_NAMES.copy() print(f"[LEGEND DEBUG] DEFAULT_LABEL_NAMES: {DEFAULT_LABEL_NAMES}") print(f"[LEGEND DEBUG] Model metadata: {model_metadata.get('label_mapping') if isinstance(model_metadata, dict) else 'No metadata'}") print(f"[LEGEND DEBUG] Final class_map: {class_map}") # Get unique classes in prediction to show only relevant legend items unique_pred_classes = np.unique(predictions_2d) unique_pred_classes = unique_pred_classes[~np.isnan(unique_pred_classes)] # Create custom legend with color patches legend_elements = [] cmap = plt.cm.get_cmap('tab20') for cls_val in sorted(unique_pred_classes): try: cls_int = int(cls_val) # Get color from colormap (normalize to 0-1 range) color = cmap(cls_int / 20.0) # tab20 has 20 colors # Get label name label_name = class_map.get(cls_int, f"Class {cls_int}") # Create patch for legend legend_elements.append( Patch(facecolor=color, edgecolor='black', linewidth=0.5, label=f"{cls_int}: {label_name}") ) except: pass # Add legend outside plot area if legend_elements: legend = ax.legend( handles=legend_elements, loc='center left', bbox_to_anchor=(1.02, 0.5), fontsize=10, title='Land Classes', title_fontsize=11, framealpha=0.9, edgecolor='black' ) legend.get_title().set_fontweight('bold') # Add grid ax.grid(True, alpha=0.3, linestyle='--', linewidth=0.5) # Save PNG plt.tight_layout() plt.savefig(str(png_file), dpi=150, bbox_inches='tight') plt.close(fig) print(f"[PNG PREVIEW] Created: {png_file}") except Exception as e: print(f"[PNG PREVIEW ERROR] Failed to create PNG: {e}") png_file = None # Get unique classes for result unique_classes = np.unique(predictions_2d) unique_classes = unique_classes[~np.isnan(unique_classes)].tolist() prediction_status["is_predicting"] = False prediction_status["progress"] = "Hoàn thành! Đang tạo báo cáo..." prediction_status["output_file"] = str(output_file) prediction_status["result"] = { "output_file": str(output_file), "png_file": str(png_file) if png_file else None, "shape": list(pred_shape), "unique_classes": unique_classes, "bbox": bbox, "time_range": time_range, "n_features": features.shape[1], "feature_mode": feature_mode, "used_radar": use_radar, "model_used": config.model_filename } # Auto generate prediction report try: report_path, _ = generate_prediction_report(prediction_status["result"]) prediction_status["result"]["report_path"] = report_path prediction_status["result"]["report_filename"] = Path(report_path).name prediction_status["progress"] = "Hoàn thành! Báo cáo đã được tạo." print(f"[PREDICTION REPORT] Generated: {report_path}") except Exception as e: print(f"[PREDICTION REPORT ERROR] Failed to generate report: {e}") prediction_status["progress"] = "Hoàn thành! (Không thể tạo báo cáo)" # Auto-save prediction cache (including Sentinel-2 data) print(f"[DEBUG] Starting cache save process...") print(f"[DEBUG] cached_s2_data is None: {cached_s2_data is None}") print(f"[DEBUG] s2_data type: {type(s2_data)}") print(f"[DEBUG] s2_data is None: {s2_data is None}") try: cache_config = { "min_lon": config.min_lon, "min_lat": config.min_lat, "max_lon": config.max_lon, "max_lat": config.max_lat, "start_date": config.start_date, "end_date": config.end_date, "max_scenes": config.max_scenes, "cloud_cover": config.cloud_cover, "resolution": config.resolution, "model_filename": config.model_filename } print(f"[DEBUG] cache_config created: {cache_config}") # Save cache with Sentinel-2 data (only if not from cache) data_to_save = None if cached_s2_data is not None else s2_data print(f"[DEBUG] data_to_save is None: {data_to_save is None}") print(f"[DEBUG] Calling save_prediction_cache_sync...") result = save_prediction_cache_sync(cache_config, data_to_save) print(f"[CACHE] Prediction cache saved: {result.get('message')} (with data: {data_to_save is not None})") print(f"[CACHE] Result: {result}") except Exception as cache_error: print(f"[CACHE ERROR] Failed to auto-save cache: {cache_error}") import traceback traceback.print_exc() prediction_status["end_time"] = dt.now().isoformat() except Exception as e: prediction_status["is_predicting"] = False prediction_status["error"] = str(e) prediction_status["progress"] = f"Lỗi: {str(e)}" prediction_status["end_time"] = dt.now().isoformat() import traceback print(traceback.format_exc()) @app.get("/api/predictions/list") async def list_predictions(): """Lấy danh sách các file prediction đã tạo""" predictions_dir = Path("predictions") predictions_dir.mkdir(exist_ok=True) predictions = [] for pred_file in predictions_dir.glob("*.tif"): pred_info = { "filename": pred_file.name, "created": datetime.fromtimestamp(pred_file.stat().st_mtime).isoformat(), "size_mb": round(pred_file.stat().st_size / 1024 / 1024, 2), "download_url": f"/api/predictions/download/{pred_file.name}", "is_batch_job": pred_file.name.startswith("batch_"), "batch_metadata": None } # Try to load batch metadata from JSON sidecar if exists json_file = pred_file.with_suffix('.json') if json_file.exists(): try: import json with open(json_file, 'r') as f: metadata = json.load(f) pred_info["batch_metadata"] = { "batch_job_id": metadata.get("batch_job_id"), "batch_name": metadata.get("batch_name"), "batch_timestamp": metadata.get("batch_timestamp") } except Exception as e: print(f"[METADATA ERROR] Failed to load {json_file}: {e}") # Check PNG preview png_file = pred_file.with_suffix('.png') pred_info["has_preview"] = png_file.exists() if png_file.exists(): pred_info["preview_url"] = f"/api/predictions/preview/{png_file.name}" predictions.append(pred_info) # Sort by creation time (newest first) predictions.sort(key=lambda x: x["created"], reverse=True) return {"predictions": predictions} @app.get("/api/predictions/download/{filename}") async def download_prediction(filename: str): """Download file prediction GeoTIFF""" predictions_dir = Path("predictions") file_path = predictions_dir / filename # Security check: ensure filename doesn't contain path traversal if ".." in filename or "/" in filename or "\\" in filename: raise HTTPException(status_code=400, detail="Invalid filename") if not file_path.exists(): raise HTTPException(status_code=404, detail=f"File không tồn tại: {filename}") return FileResponse( path=str(file_path), filename=filename, media_type="image/tiff", headers={ "Content-Disposition": f"attachment; filename={filename}" } ) @app.get("/api/predictions/preview/{filename}") async def preview_prediction_png(filename: str): """Preview PNG image of prediction""" predictions_dir = Path("predictions") file_path = predictions_dir / filename # Security check if ".." in filename or "/" in filename or "\\" in filename: raise HTTPException(status_code=400, detail="Invalid filename") if not file_path.exists(): raise HTTPException(status_code=404, detail=f"PNG preview không tồn tại: {filename}") return FileResponse( path=str(file_path), media_type="image/png" ) @app.get("/api/predictions/preview/{filename}") async def preview_prediction_png(filename: str): """Preview PNG image of prediction""" predictions_dir = Path("predictions") file_path = predictions_dir / filename # Security check if ".." in filename or "/" in filename or "\\" in filename: raise HTTPException(status_code=400, detail="Invalid filename") if not file_path.exists(): raise HTTPException(status_code=404, detail=f"PNG preview không tồn tại: {filename}") return FileResponse( path=str(file_path), media_type="image/png" ) # ============ PREDICTION CACHE API (Auto-save) ============ def save_prediction_cache_sync(config: dict, s2_data=None): """Tự động lưu prediction cache sau khi predict thành công (bao gồm cả dữ liệu Sentinel-2)""" print(f"[DEBUG save_prediction_cache_sync] Called with s2_data is None: {s2_data is None}") print(f"[DEBUG save_prediction_cache_sync] Config: {config}") try: cache_dir = Path("prediction_cache") print(f"[DEBUG save_prediction_cache_sync] Cache dir: {cache_dir.absolute()}") cache_dir.mkdir(exist_ok=True) print(f"[DEBUG save_prediction_cache_sync] Cache dir created/exists") # Create cache filename from bbox and timestamp timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") bbox_str = f"{config['min_lon']:.2f}_{config['min_lat']:.2f}_{config['max_lon']:.2f}_{config['max_lat']:.2f}" base_filename = f"pred_{bbox_str}_{timestamp}" config_file = cache_dir / f"{base_filename}.json" data_file = cache_dir / f"{base_filename}_data.joblib" print(f"[DEBUG save_prediction_cache_sync] Config file: {config_file}") print(f"[DEBUG save_prediction_cache_sync] Data file: {data_file}") # Prepare cache data with full metadata cache_data = { "bbox": [config['min_lon'], config['min_lat'], config['max_lon'], config['max_lat']], "min_lon": config['min_lon'], "min_lat": config['min_lat'], "max_lon": config['max_lon'], "max_lat": config['max_lat'], "start_date": config['start_date'], "end_date": config['end_date'], "max_scenes": config['max_scenes'], "cloud_cover": config['cloud_cover'], "resolution": config['resolution'], "model_filename": config.get('model_filename'), "created": timestamp, "type": "prediction_cache", "auto_saved": True, "has_data": s2_data is not None, "data_file": f"{base_filename}_data.joblib" if s2_data is not None else None } print(f"[DEBUG save_prediction_cache_sync] Saving config JSON...") # Save config to JSON file with open(config_file, 'w', encoding='utf-8') as f: json.dump(cache_data, f, indent=2, ensure_ascii=False) print(f"[DEBUG save_prediction_cache_sync] Config JSON saved to {config_file}") # Save Sentinel-2 data if provided if s2_data is not None: print(f"[DEBUG save_prediction_cache_sync] Saving Sentinel-2 data with joblib...") import joblib joblib.dump(s2_data, data_file) data_size_mb = data_file.stat().st_size / 1024 / 1024 cache_data['data_size_mb'] = round(data_size_mb, 2) print(f"[CACHE] Saved Sentinel-2 data: {data_size_mb:.2f} MB to {data_file}") else: print(f"[DEBUG save_prediction_cache_sync] No s2_data to save") print(f"[CACHE SUCCESS] Cache saved successfully: {base_filename}.json") return { "success": True, "message": f"Đã lưu cache tự động" + (" (bao gồm dữ liệu Sentinel-2)" if s2_data is not None else ""), "filename": f"{base_filename}.json", "cache": cache_data } except Exception as e: print(f"[CACHE ERROR save_prediction_cache_sync] Error: {e}") import traceback traceback.print_exc() return {"success": False, "error": str(e)} @app.get("/api/prediction/cache/list") async def list_prediction_cache(): """Liệt kê các prediction cache đã lưu (bao gồm thông tin về dữ liệu Sentinel-2)""" try: cache_dir = Path("prediction_cache") if not cache_dir.exists(): return {"caches": [], "count": 0} caches = [] for cache_file in cache_dir.glob("pred_*.json"): try: with open(cache_file, 'r', encoding='utf-8') as f: cache_data = json.load(f) # Check if data file exists data_filename = cache_data.get("data_file") has_data = False data_size_mb = 0 if data_filename: data_file_path = cache_dir / data_filename if data_file_path.exists(): has_data = True data_size_mb = round(data_file_path.stat().st_size / 1024 / 1024, 2) # Create display name from metadata bbox = cache_data.get("bbox", []) time_range = f"{cache_data.get('start_date', 'N/A')} → {cache_data.get('end_date', 'N/A')}" data_badge = f" [💾 {data_size_mb}MB]" if has_data else " [⚙️ Config only]" display_name = f"[{bbox[0]:.2f},{bbox[1]:.2f}→{bbox[2]:.2f},{bbox[3]:.2f}] {time_range}{data_badge}" caches.append({ "filename": cache_file.name, "display_name": display_name, "bbox": bbox, "created": cache_data.get("created"), "has_data": has_data, "data_size_mb": data_size_mb, "data_file": data_filename, "config": cache_data }) except Exception as e: print(f"Error loading cache {cache_file}: {e}") continue # Sort by created time (newest first) caches.sort(key=lambda x: x.get("created", ""), reverse=True) return { "caches": caches, "count": len(caches) } except Exception as e: raise HTTPException(status_code=500, detail=f"Lỗi khi load cache: {str(e)}") @app.get("/api/prediction/cache/load-data/{filename}") async def load_cached_data(filename: str): """Load Sentinel-2 data từ cache""" try: cache_dir = Path("prediction_cache") # Security check if ".." in filename or "/" in filename or "\\" in filename: raise HTTPException(status_code=400, detail="Invalid filename") # Load config to get data filename config_file = cache_dir / filename if not config_file.exists(): raise HTTPException(status_code=404, detail="Cache config not found") with open(config_file, 'r') as f: cache_data = json.load(f) data_filename = cache_data.get("data_file") if not data_filename: raise HTTPException(status_code=404, detail="No data file in cache") data_file = cache_dir / data_filename if not data_file.exists(): raise HTTPException(status_code=404, detail="Data file not found") return { "success": True, "has_data": True, "data_file": data_filename, "data_size_mb": round(data_file.stat().st_size / 1024 / 1024, 2) } except HTTPException: raise except Exception as e: raise HTTPException(status_code=500, detail=f"Lỗi: {str(e)}") @app.delete("/api/prediction/cache/delete/{filename}") async def delete_prediction_cache(filename: str): """Xóa một prediction cache (bao gồm cả file data nếu có)""" try: cache_dir = Path("prediction_cache") cache_file = cache_dir / filename # Security check if ".." in filename or "/" in filename or "\\" in filename: raise HTTPException(status_code=400, detail="Invalid filename") if not cache_file.exists(): raise HTTPException(status_code=404, detail="Cache not found") # Load config to check for data file try: with open(cache_file, 'r') as f: cache_data = json.load(f) data_filename = cache_data.get("data_file") if data_filename: data_file = cache_dir / data_filename if data_file.exists(): data_file.unlink() print(f"[CACHE] Deleted data file: {data_filename}") except Exception as e: print(f"[CACHE] Error deleting data file: {e}") # Delete config file cache_file.unlink() return { "success": True, "message": f"Đã xóa cache: {filename}" } except HTTPException: raise except Exception as e: raise HTTPException(status_code=500, detail=f"Lỗi khi xóa cache: {str(e)}") # ============ DASHBOARD & VISUALIZATION API ============ @app.get("/api/dashboard/accuracy-trends") async def get_accuracy_trends(): """Lấy dữ liệu accuracy trends của các models theo thời gian""" model_dir = Path("model_train") if not model_dir.exists(): return {"trends": [], "models": []} trends_data = [] for info_file in sorted(model_dir.glob("*.json")): try: with open(info_file) as f: info = json.load(f) # Extract relevant data if "training_date" in info and "metrics" in info: trends_data.append({ "date": info["training_date"], "model_name": info.get("model_type", "unknown"), "accuracy": info["metrics"].get("accuracy", 0), "f1_score": info["metrics"].get("macro avg", {}).get("f1-score", 0), "precision": info["metrics"].get("macro avg", {}).get("precision", 0), "recall": info["metrics"].get("macro avg", {}).get("recall", 0), "filename": info_file.stem + ".joblib" }) except Exception as e: print(f"Error loading {info_file}: {e}") continue # Sort by date trends_data.sort(key=lambda x: x["date"]) return { "trends": trends_data, "models": list(set(d["model_name"] for d in trends_data)) } @app.get("/api/dashboard/statistics") async def get_statistics(): """Lấy thống kê tổng quan: số models, predictions, reports""" model_dir = Path("model_train") predictions_dir = Path("predictions") reports_dir = Path("reports") # Count items n_models = len(list(model_dir.glob("*.joblib"))) if model_dir.exists() else 0 n_predictions = len(list(predictions_dir.glob("*.tif"))) if predictions_dir.exists() else 0 n_reports = len(list(reports_dir.glob("*.html"))) if reports_dir.exists() else 0 # Get latest model info latest_model = None if model_dir.exists(): model_files = sorted(model_dir.glob("*.json"), key=lambda x: x.stat().st_mtime, reverse=True) if model_files: try: with open(model_files[0]) as f: latest_model = json.load(f) except: pass # Get latest prediction latest_prediction = None if predictions_dir.exists(): pred_files = sorted(predictions_dir.glob("*.tif"), key=lambda x: x.stat().st_mtime, reverse=True) if pred_files: latest_prediction = { "filename": pred_files[0].name, "created": datetime.fromtimestamp(pred_files[0].stat().st_mtime).isoformat(), "size_mb": round(pred_files[0].stat().st_size / 1024 / 1024, 2) } return { "models": { "total": n_models, "latest": latest_model }, "predictions": { "total": n_predictions, "latest": latest_prediction }, "reports": { "total": n_reports }, "training_status": training_status, "prediction_status": prediction_status } @app.get("/api/dashboard/class-distribution/{model_filename}") async def get_class_distribution(model_filename: str): """Lấy phân bố các lớp từ model info""" # Convert model filename to info filename # e.g., model_cnn_20251221_163841.joblib -> model_cnn_20251221_163841_info.json base_name = model_filename.replace(".joblib", "") info_file = Path("model_train") / f"{base_name}_info.json" if not info_file.exists(): raise HTTPException(status_code=404, detail="Model info không tồn tại") with open(info_file) as f: info = json.load(f) # Extract class distribution from classification report class_dist = {} if "classification_report" in info: for class_name, metrics in info["classification_report"].items(): if isinstance(metrics, dict) and "support" in metrics: class_dist[class_name] = int(metrics["support"]) return { "model": model_filename, "class_distribution": class_dist, "total_samples": sum(class_dist.values()) if class_dist else 0 } # ============ BATCH PROCESSING API ============ class BatchPredictionItem(BaseModel): """Một item trong batch prediction""" name: str min_lon: float min_lat: float max_lon: float max_lat: float start_date: str = "2023-03-01" end_date: str = "2023-05-31" max_scenes: int = 12 cloud_cover: int = 30 resolution: int = 20 class BatchPredictionConfig(BaseModel): """Cấu hình cho batch prediction""" model_filename: str items: List[BatchPredictionItem] auto_retry: bool = True max_retries: int = 3 @app.post("/api/batch/start") async def start_batch_prediction(config: BatchPredictionConfig, background_tasks: BackgroundTasks): """Bắt đầu batch prediction""" global batch_queue, batch_results # Create batch jobs batch_id = datetime.now().strftime("%Y%m%d_%H%M%S") for idx, item in enumerate(config.items): job = { "batch_id": batch_id, "job_id": f"{batch_id}_{idx}", "name": item.name, "status": "queued", "progress": 0, "error": None, "result": None, "retries": 0, "max_retries": config.max_retries if config.auto_retry else 0, "config": { "model_filename": config.model_filename, "min_lon": item.min_lon, "min_lat": item.min_lat, "max_lon": item.max_lon, "max_lat": item.max_lat, "start_date": item.start_date, "end_date": item.end_date, "max_scenes": item.max_scenes, "cloud_cover": item.cloud_cover, "resolution": item.resolution }, "created_at": datetime.now().isoformat() } batch_queue.append(job) # Start processing in background background_tasks.add_task(process_batch_queue) return { "message": f"Đã tạo {len(config.items)} batch jobs", "batch_id": batch_id, "total_jobs": len(config.items) } @app.get("/api/batch/status") async def get_batch_status(): """Lấy trạng thái của batch queue""" global batch_queue, batch_results queued = [j for j in batch_queue if j["status"] == "queued"] running = [j for j in batch_queue if j["status"] == "running"] completed = [j for j in batch_results if j["status"] == "completed"] failed = [j for j in batch_results if j["status"] == "failed"] return { "queue": { "queued": len(queued), "running": len(running), "completed": len(completed), "failed": len(failed), "total": len(batch_queue) + len(batch_results) }, "jobs": { "queued": queued[:5], # Show first 5 "running": running, "recent_completed": completed[:10], # Show last 10 "recent_failed": failed[:10] } } @app.get("/api/batch/results/{batch_id}") async def get_batch_results(batch_id: str): """Lấy kết quả của một batch""" global batch_results results = [j for j in batch_results if j["batch_id"] == batch_id] if not results: # Check if still in queue queued = [j for j in batch_queue if j["batch_id"] == batch_id] if queued: return { "batch_id": batch_id, "status": "processing", "jobs": queued } else: raise HTTPException(status_code=404, detail="Batch không tồn tại") return { "batch_id": batch_id, "status": "completed", "jobs": results, "summary": { "total": len(results), "successful": len([j for j in results if j["status"] == "completed"]), "failed": len([j for j in results if j["status"] == "failed"]) } } @app.post("/api/batch/cancel/{batch_id}") async def cancel_batch(batch_id: str): """Hủy một batch đang chạy""" global batch_queue # Remove from queue removed = 0 batch_queue_copy = batch_queue.copy() for job in batch_queue_copy: if job["batch_id"] == batch_id and job["status"] == "queued": batch_queue.remove(job) removed += 1 return { "message": f"Đã hủy {removed} jobs", "batch_id": batch_id } async def process_batch_queue(): """Process batch prediction queue""" global batch_queue, batch_results import asyncio while batch_queue: # Get next job job = None for j in batch_queue: if j["status"] == "queued": job = j break if not job: break # Mark as running job["status"] = "running" job["progress"] = 0 job["started_at"] = datetime.now().isoformat() try: # Create PredictionConfig from job config pred_config = PredictionConfig(**job["config"]) print(f"[BATCH] Processing job {job['job_id']}: {job['name']}") job["progress"] = 5 # Run prediction synchronously (in the same thread to avoid conflicts) await asyncio.to_thread(run_batch_prediction, job, pred_config) # Check if prediction was successful if job.get("result") and not job.get("error"): job["status"] = "completed" job["progress"] = 100 job["completed_at"] = datetime.now().isoformat() print(f"[BATCH] Job {job['job_id']} completed successfully") else: raise Exception(job.get("error", "Unknown error during prediction")) except Exception as e: job["error"] = str(e) # Retry logic if job["retries"] < job["max_retries"]: job["retries"] += 1 job["status"] = "queued" # Retry job["progress"] = 0 print(f"[BATCH] Job {job['job_id']} ({job['name']}) failed, retrying ({job['retries']}/{job['max_retries']}): {e}") continue else: job["status"] = "failed" job["progress"] = 0 job["completed_at"] = datetime.now().isoformat() print(f"[BATCH] Job {job['job_id']} ({job['name']}) failed permanently: {e}") # Move to results batch_queue.remove(job) batch_results.append(job) # Keep only last 100 results if len(batch_results) > 100: batch_results = batch_results[-100:] def run_batch_prediction(job: dict, config: PredictionConfig): """Run prediction for a single batch job""" try: job["progress"] = 10 # Import required libraries import xarray as xr import numpy as np from datetime import datetime as dt import rioxarray import dask.array as da job["progress"] = 15 # Load model using ModelManager model_manager = get_model_manager() model, label_encoder, model_metadata = model_manager.load_model(config.model_filename) # Check if it's a PyTorch model (CNN, Swin-UNet, MobileNet, etc.) is_pytorch_model = hasattr(model, '__class__') and any( name in model.__class__.__name__ for name in ['CNN', 'SwinUNet', 'MobileNet'] ) job["progress"] = 20 # Load data from Microsoft Planetary Computer import pystac_client import planetary_computer from odc.stac import load catalog = pystac_client.Client.open( "https://planetarycomputer.microsoft.com/api/stac/v1", modifier=planetary_computer.sign_inplace, ) bbox = [config.min_lon, config.min_lat, config.max_lon, config.max_lat] time_range = f"{config.start_date}/{config.end_date}" job["progress"] = 25 # Search Sentinel-2 with retry s2_items = fetch_sentinel_items_with_retry( catalog=catalog, bbox=bbox, time_range=time_range, cloud_cover=config.cloud_cover, max_scenes=config.max_scenes, max_retries=3 ) job["progress"] = 35 # Load Sentinel-2 data s2_data = load( s2_items, bbox=bbox, chunks={"time": 1, "x": 2048, "y": 2048}, groupby="solar_day", resolution=config.resolution ) job["progress"] = 50 # Calculate NDVI nir = s2_data["B08"].astype('float32') red = s2_data["B04"].astype('float32') ndvi = (nir - red) / (nir + red + 1e-8) # Mask clouds if SCL available if "SCL" in s2_data: scl = s2_data["SCL"] cloud_mask = (scl == 3) | (scl == 8) | (scl == 9) | (scl == 10) ndvi = ndvi.where(~cloud_mask) # Fill NaN and resample ndvi_filled = ndvi.ffill(dim='time').bfill(dim='time') ndvi_monthly = ndvi_filled.resample(time="1ME").mean().compute() job["progress"] = 70 # Prepare features n_times_ndvi = len(ndvi_monthly.time) y_size = len(ndvi_monthly.y) x_size = len(ndvi_monthly.x) n_pixels = y_size * x_size ndvi_features = [] for t in range(n_times_ndvi): ndvi_t = ndvi_monthly.isel(time=t).values.flatten() ndvi_features.append(ndvi_t) features = np.column_stack(ndvi_features) features = np.nan_to_num(features, nan=0.0) job["progress"] = 80 # Adjust features to match model expectations try: if is_pytorch_model: expected_features = model.n_features elif hasattr(model, 'n_features_in_'): expected_features = model.n_features_in_ else: try: expected_features = model.get_booster().num_features() except: expected_features = features.shape[1] if features.shape[1] > expected_features: features = features[:, :expected_features] elif features.shape[1] < expected_features: n_missing = expected_features - features.shape[1] padding = np.tile(features[:, -1:], (1, n_missing)) features = np.column_stack([features, padding]) except: pass # Predict (all models have same predict interface) predictions = model.predict(features) # Decode labels if label_encoder is not None: try: predictions = label_encoder.inverse_transform(predictions) except: pass job["progress"] = 90 # Reshape and create output pred_shape = (y_size, x_size) predictions_2d = predictions.reshape(pred_shape) prediction_da = xr.DataArray( predictions_2d, coords={"y": ndvi_monthly.y, "x": ndvi_monthly.x}, dims=["y", "x"], name="classification" ) # Save output output_dir = Path("predictions") output_dir.mkdir(exist_ok=True) output_file = output_dir / f"batch_{job['job_id']}_{job['name'].replace(' ', '_')}.tif" if hasattr(s2_data, 'rio') and s2_data.rio.crs is not None: prediction_da.rio.write_crs(s2_data.rio.crs, inplace=True) else: prediction_da.rio.write_crs("EPSG:4326", inplace=True) prediction_da.rio.to_raster(str(output_file), driver="GTiff") # Generate PNG preview png_file = output_dir / f"batch_{job['job_id']}_{job['name'].replace(' ', '_')}.png" try: import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt from matplotlib.patches import Patch fig, ax = plt.subplots(figsize=(14, 10), dpi=150) im = ax.imshow(predictions_2d, cmap='tab20', interpolation='nearest') ax.set_title(f"{job['name']} - Batch {job['job_id']}", fontsize=16, fontweight='bold', pad=20) ax.set_xlabel('X (pixels)', fontsize=11) ax.set_ylabel('Y (pixels)', fontsize=11) # Build mapping - ALWAYS use DEFAULT_LABEL_NAMES class_map = DEFAULT_LABEL_NAMES.copy() # Get unique classes in prediction to show only relevant legend items unique_pred_classes = np.unique(predictions_2d) unique_pred_classes = unique_pred_classes[~np.isnan(unique_pred_classes)] # Create custom legend with color patches legend_elements = [] cmap = plt.cm.get_cmap('tab20') for cls_val in sorted(unique_pred_classes): try: cls_int = int(cls_val) # Get color from colormap (normalize to 0-1 range) color = cmap(cls_int / 20.0) # tab20 has 20 colors # Get label name label_name = class_map.get(cls_int, f"Class {cls_int}") # Create patch for legend legend_elements.append( Patch(facecolor=color, edgecolor='black', linewidth=0.5, label=f"{cls_int}: {label_name}") ) except: pass # Add legend outside plot area if legend_elements: legend = ax.legend( handles=legend_elements, loc='center left', bbox_to_anchor=(1.02, 0.5), fontsize=10, title='Land Classes', title_fontsize=11, framealpha=0.9, edgecolor='black' ) legend.get_title().set_fontweight('bold') ax.grid(True, alpha=0.3, linestyle='--', linewidth=0.5) plt.tight_layout() plt.savefig(str(png_file), dpi=150, bbox_inches='tight') plt.close(fig) except Exception as e: print(f"[BATCH PNG ERROR] {e}") png_file = None # Get unique classes unique_classes = np.unique(predictions_2d) unique_classes = unique_classes[~np.isnan(unique_classes)].tolist() # Store result in job with batch metadata job["result"] = { "output_file": str(output_file), "png_file": str(png_file) if png_file else None, "shape": list(pred_shape), "unique_classes": unique_classes, "bbox": bbox, "time_range": time_range, "n_features": features.shape[1], "n_times_ndvi": n_times_ndvi, "model_used": config.model_filename, "batch_job_id": job["job_id"], "batch_name": job["name"], "batch_timestamp": datetime.now().isoformat() } # Save batch metadata to JSON sidecar file for persistence metadata_file = output_file.with_suffix('.json') try: import json with open(metadata_file, 'w') as f: json.dump(job["result"], f, indent=2, default=str) print(f"[BATCH METADATA] Saved to {metadata_file}") except Exception as e: print(f"[BATCH METADATA ERROR] Failed to save metadata: {e}") # Auto generate prediction report for batch job try: from report_generator import generate_prediction_report report_path, _ = generate_prediction_report(job["result"]) job["result"]["report_path"] = report_path job["result"]["report_filename"] = Path(report_path).name print(f"[BATCH REPORT] Generated prediction report: {report_path}") except Exception as e: print(f"[BATCH REPORT ERROR] Failed to generate report: {e}") job["progress"] = 100 except Exception as e: job["error"] = str(e) import traceback print(f"[BATCH ERROR] Job {job['job_id']}: {traceback.format_exc()}") # ============ CHANGE DETECTION API ============ def rasterize_ground_truth(shapefile_path, out_shape, bbox, class_column="class"): """Rasterize ground truth shapefile to match prediction raster shape.""" try: import geopandas as gpd from rasterio import features as rio_features gdf = gpd.read_file(shapefile_path) # Convert to WGS84 if not already if gdf.crs and gdf.crs.to_epsg() != 4326: print(f"📍 Converting shapefile from {gdf.crs} to WGS84 for rasterization") gdf = gdf.to_crs("EPSG:4326") minx, miny, maxx, maxy = bbox # Crop to bbox gdf = gdf.cx[minx:maxx, miny:maxy] if class_column not in gdf.columns: raise ValueError(f"Shapefile missing '{class_column}' column. Available: {list(gdf.columns)}") # Create transform for rasterization transform = from_bounds(minx, miny, maxx, maxy, out_shape[1], out_shape[0]) # Prepare geometries and values for rasterization shapes = zip(gdf.geometry, gdf[class_column]) # Rasterize gt_raster = rio_features.rasterize( shapes, out_shape=out_shape, fill=-1, transform=transform, dtype="int16" ) return gt_raster except Exception as e: print(f"[RASTERIZE ERROR] {e}") raise @app.post("/api/change-detection/predict") async def change_detection_predict_workflow( model_filename: str, min_lon: float, min_lat: float, max_lon: float, max_lat: float, start_date: str, end_date: str, max_scenes: int = 12, cloud_cover: int = 30, resolution: int = 20 ): """ Complete workflow: Predict + Compare with Ground Truth 1. Load Sentinel-2 data for bbox and date range 2. Run prediction using trained model 3. Rasterize ground truth from training shapefile 4. Compare and generate change detection results """ try: # --- STEP 1: LOAD MODEL --- model_manager = get_model_manager() model, label_encoder, model_metadata = model_manager.load_model(model_filename) print(f"[CHANGE DETECTION] Loaded model: {model_filename}") print(f" - Type: {model_metadata.get('model_type', 'unknown')}") print(f" - Features: {model_metadata.get('features', [])}") # --- STEP 2: LOAD SENTINEL-2 DATA --- bbox = [min_lon, min_lat, max_lon, max_lat] time_range = f"{start_date}/{end_date}" catalog = Client.open( "https://planetarycomputer.microsoft.com/api/stac/v1", modifier=planetary_computer.sign_inplace ) # Use retry logic for fetching items items = fetch_sentinel_items_with_retry( catalog=catalog, bbox=bbox, time_range=time_range, cloud_cover=cloud_cover, max_scenes=max_scenes, max_retries=3 ) print(f"[CHANGE DETECTION] Found {len(items)} Sentinel-2 scenes") if len(items) == 0: raise HTTPException(status_code=404, detail="No Sentinel-2 data found for the given area and date range") # Load data with error handling signed_items = [planetary_computer.sign(item) for item in items] max_retries = 3 data = None for attempt in range(max_retries): try: data = odc.stac.load( signed_items, bbox=bbox, bands=["B02", "B03", "B04", "B08"], resolution=resolution, chunks={"x": 2048, "y": 2048} ).compute() break except Exception as e: error_msg = str(e) is_auth_error = any(err in error_msg for err in [ "AuthenticationFailed", "Signature fields not well formed", "Server failed to authenticate", "403", "401" ]) if is_auth_error: print(f"⚠️ Authentication error. Re-signing items...") try: signed_items = [planetary_computer.sign(item) for item in items] if attempt < max_retries - 1: import time time.sleep(1) continue except Exception: pass if ("does not exist" in error_msg or "RasterioIOError" in error_msg) and len(signed_items) > 3: remove_count = max(1, len(signed_items) // 5) signed_items = signed_items[:-remove_count] items = items[:-remove_count] print(f"⚠️ Access error. Reduced to {len(signed_items)} scenes, retrying...") if attempt < max_retries - 1: continue if attempt == max_retries - 1: raise HTTPException( status_code=500, detail=f"Failed to load Sentinel-2 data: {error_msg[:200]}" ) if data is None: raise HTTPException(status_code=500, detail="Failed to load Sentinel-2 data") # --- STEP 3: CALCULATE NDVI --- print("[CHANGE DETECTION] Calculating NDVI...") nir = data["B08"].astype('float32') red = data["B04"].astype('float32') ndvi = (nir - red) / (nir + red + 1e-8) # Handle clouds if SCL available if "SCL" in data: scl = data["SCL"] cloud_mask = (scl == 3) | (scl == 8) | (scl == 9) | (scl == 10) ndvi = ndvi.where(~cloud_mask) # --- STEP 4: PREPARE FEATURES --- ndvi_filled = ndvi.ffill(dim='time').bfill(dim='time') ndvi_mean = np.nanmean(ndvi_filled.values, axis=0) height, width = ndvi_mean.shape n_pixels = height * width # Prepare features features = ndvi_mean.flatten().reshape(-1, 1) valid_mask = ~np.isnan(features[:, 0]) features_clean = features[valid_mask] # --- STEP 5: PREDICT --- print("[CHANGE DETECTION] Running prediction...") predictions = model.predict(features_clean) # Decode labels if needed if label_encoder is not None: try: predictions = label_encoder.inverse_transform(predictions) except: pass # Reshape to raster prediction_raster = np.full(n_pixels, -1, dtype=np.int16) prediction_raster[valid_mask] = predictions.astype(np.int16) prediction_raster = prediction_raster.reshape(height, width) # --- STEP 6: COMPARE WITH GROUND TRUTH --- print("[CHANGE DETECTION] Comparing with ground truth...") gt_shapefile = "train/ST_training data_updated_1130points_new.shp" gt_raster = rasterize_ground_truth(gt_shapefile, (height, width), bbox, class_column="class") # Calculate changes mask_valid = (gt_raster >= 0) & (prediction_raster >= 0) changes = gt_raster[mask_valid] != prediction_raster[mask_valid] n_total = np.count_nonzero(mask_valid) n_changed = np.count_nonzero(changes) # Create change matrix from collections import Counter change_pairs = list(zip(gt_raster[mask_valid][changes], prediction_raster[mask_valid][changes])) change_counter = Counter(change_pairs) change_matrix = {f"{int(gt)}->{int(pred)}": int(cnt) for (gt, pred), cnt in change_counter.items()} # Create change map change_map = np.full((height, width), -1, dtype=np.int8) change_map[mask_valid] = changes.astype(np.int8) # --- STEP 7: SAVE RESULTS --- output_dir = Path("predictions") output_dir.mkdir(exist_ok=True) timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") change_file = output_dir / f"change_map_{timestamp}.tif" transform = from_bounds(min_lon, min_lat, max_lon, max_lat, width, height) with rasterio.open( change_file, 'w', driver='GTiff', height=height, width=width, count=1, dtype=change_map.dtype, crs='EPSG:4326', transform=transform ) as dst: dst.write(change_map, 1) print(f"[CHANGE DETECTION] Saved change map to {change_file}") # --- RETURN RESULTS --- return { "success": True, "n_scenes": len(items), "ndvi_stats": { "mean": float(np.nanmean(ndvi_mean)), "min": float(np.nanmin(ndvi_mean)), "max": float(np.nanmax(ndvi_mean)), "std": float(np.nanstd(ndvi_mean)) }, "class_distribution": { int(cls): int(count) for cls, count in zip(*np.unique(predictions, return_counts=True)) }, "change_detection": { "n_total_pixels": int(n_total), "n_changed_pixels": int(n_changed), "change_rate": float(n_changed) / n_total if n_total > 0 else 0.0, "change_matrix": change_matrix, "message": f"Detected {n_changed} changes out of {n_total} valid pixels ({(n_changed/n_total*100):.1f}%)" if n_total > 0 else "No valid pixels for comparison" }, "change_map_file": str(change_file), "timestamp": timestamp } except HTTPException: raise except Exception as e: print(f"[CHANGE DETECTION ERROR] {e}") import traceback traceback.print_exc() raise HTTPException(status_code=500, detail=f"Change detection workflow failed: {str(e)}") @app.post("/api/change-detection/workflow") async def change_detection_workflow(request: ChangeDetectionWorkflowRequest): """Workflow: compare prediction with ground truth training data.""" from collections import Counter try: prediction_result = request.prediction_result bbox = request.bbox if not prediction_result or "output_files" not in prediction_result: raise ValueError("Invalid prediction result") # Get classification raster from prediction class_file = None for f in prediction_result.get("output_files", []): if f.get("type") == "classification": class_file = f.get("path") break if not class_file: raise ValueError("No classification raster in prediction result") # Load prediction raster with rasterio.open(class_file) as pred_ds: pred_arr = pred_ds.read(1) pred_crs = pred_ds.crs pred_transform = pred_ds.transform # Rasterize ground truth training data gt_shapefile = "train/ST_training data_updated_1130points_new.shp" gt_raster = rasterize_ground_truth(gt_shapefile, pred_arr.shape, bbox, class_column="class") # Calculate change detection mask_valid = (gt_raster >= 0) & (pred_arr >= 0) & ~np.isnan(gt_raster) & ~np.isnan(pred_arr) changes = gt_raster[mask_valid] != pred_arr[mask_valid] n_total = np.count_nonzero(mask_valid) n_changed = np.count_nonzero(changes) # Create change pairs matrix change_pairs = list(zip(gt_raster[mask_valid][changes], pred_arr[mask_valid][changes])) change_counter = Counter(change_pairs) change_matrix = {f"{int(gt)}->{int(pred)}": int(cnt) for (gt, pred), cnt in change_counter.items()} # Create change map change_map = np.full(pred_arr.shape, -1, dtype=np.int8) change_map[mask_valid] = changes.astype(np.int8) # Change rate change_rate = float(n_changed) / n_total if n_total > 0 else 0.0 # Save change map change_dir = Path("predictions") change_dir.mkdir(exist_ok=True) timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") change_file = change_dir / f"change_map_{timestamp}.tif" with rasterio.open( change_file, 'w', driver='GTiff', height=change_map.shape[0], width=change_map.shape[1], count=1, dtype=change_map.dtype, crs=pred_crs, transform=pred_transform ) as dst: dst.write(change_map, 1) return { "success": True, "change_detection": { "n_total_pixels": int(n_total), "n_changed_pixels": int(n_changed), "change_rate": change_rate, "change_matrix": change_matrix, "message": f"Detected {n_changed} changes out of {n_total} valid pixels ({change_rate*100:.1f}%)" }, "change_map_file": str(change_file), "timestamp": timestamp } except Exception as e: print(f"[CHANGE DETECTION WORKFLOW ERROR] {str(e)}") import traceback traceback.print_exc() raise HTTPException(status_code=500, detail=f"Change detection workflow failed: {str(e)}") @app.post("/api/change-detection/compare-periods") async def compare_periods(request: ComparePeriodsPredictionConfig, background_tasks: BackgroundTasks): """Compare land use classification between two time periods.""" from collections import Counter try: # Extract parameters model_filename = request.model_filename bbox = [request.min_lon, request.min_lat, request.max_lon, request.max_lat] current_start = request.current_period["start_date"] current_end = request.current_period["end_date"] pred_start = request.prediction_period["start_date"] pred_end = request.prediction_period["end_date"] max_scenes = request.max_scenes cloud_cover = request.cloud_cover resolution = request.resolution print(f"[COMPARE PERIODS] Current: {current_start} to {current_end} | Prediction: {pred_start} to {pred_end}") # Step 1: Predict on current period print(f"[COMPARE PERIODS] Step 1: Predicting current period...") current_result = await predict_with_ndvi(PredictionWithNDVIConfig( model_filename=model_filename, min_lon=request.min_lon, min_lat=request.min_lat, max_lon=request.max_lon, max_lat=request.max_lat, start_date=current_start, end_date=current_end, max_scenes=max_scenes, cloud_cover=cloud_cover, resolution=resolution, export_classification=True, export_ndvi=False ), background_tasks) # Get current classification raster current_class_file = None for f in current_result.get("output_files", []): if f.get("type") == "classification": current_class_file = f.get("path") break if not current_class_file: raise ValueError("No classification raster for current period") # Step 2: Predict on prediction period print(f"[COMPARE PERIODS] Step 2: Predicting future period...") pred_result = await predict_with_ndvi(PredictionWithNDVIConfig( model_filename=model_filename, min_lon=request.min_lon, min_lat=request.min_lat, max_lon=request.max_lon, max_lat=request.max_lat, start_date=pred_start, end_date=pred_end, max_scenes=max_scenes, cloud_cover=cloud_cover, resolution=resolution, export_classification=True, export_ndvi=False ), background_tasks) # Get prediction classification raster pred_class_file = None for f in pred_result.get("output_files", []): if f.get("type") == "classification": pred_class_file = f.get("path") break if not pred_class_file: raise ValueError("No classification raster for prediction period") # Step 3: Load both rasters print(f"[COMPARE PERIODS] Step 3: Comparing classifications...") with rasterio.open(current_class_file) as src: current_arr = src.read(1) crs = src.crs transform = src.transform with rasterio.open(pred_class_file) as src: pred_arr = src.read(1) # Ensure same shape if current_arr.shape != pred_arr.shape: raise ValueError(f"Shape mismatch: current {current_arr.shape} vs prediction {pred_arr.shape}") # Calculate changes mask_valid = ~np.isnan(current_arr) & ~np.isnan(pred_arr) changes = current_arr[mask_valid] != pred_arr[mask_valid] n_total = np.count_nonzero(mask_valid) n_changed = np.count_nonzero(changes) # Create change pairs change_pairs = list(zip(current_arr[mask_valid][changes], pred_arr[mask_valid][changes])) change_counter = Counter(change_pairs) change_matrix = {f"{int(curr)}->{int(pred)}": int(cnt) for (curr, pred), cnt in change_counter.items()} # Change rate change_rate = float(n_changed) / n_total if n_total > 0 else 0.0 # Save change map change_dir = Path("predictions") change_dir.mkdir(exist_ok=True) timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") change_file = change_dir / f"change_map_{timestamp}.tif" change_map = np.zeros(current_arr.shape, dtype=np.int8) change_map[mask_valid] = changes.astype(np.int8) with rasterio.open( change_file, 'w', driver='GTiff', height=change_map.shape[0], width=change_map.shape[1], count=1, dtype=change_map.dtype, crs=crs, transform=transform ) as dst: dst.write(change_map, 1) # Extract class distributions current_classes = np.unique(current_arr[~np.isnan(current_arr)]).astype(int) current_dist = {int(c): int(np.count_nonzero(current_arr == c)) for c in current_classes} pred_classes = np.unique(pred_arr[~np.isnan(pred_arr)]).astype(int) pred_dist = {int(c): int(np.count_nonzero(pred_arr == c)) for c in pred_classes} return { "success": True, "current_classification": { "n_scenes": current_result.get("n_scenes"), "resolution": current_result.get("resolution"), "class_distribution": current_dist }, "prediction_classification": { "n_scenes": pred_result.get("n_scenes"), "resolution": pred_result.get("resolution"), "class_distribution": pred_dist }, "change_detection": { "n_total_pixels": int(n_total), "n_changed_pixels": int(n_changed), "change_rate": change_rate, "change_matrix": change_matrix, "message": f"Detected {n_changed} changes out of {n_total} valid pixels ({change_rate*100:.1f}%)" }, "change_map_file": str(change_file), "timestamp": timestamp } except Exception as e: print(f"[COMPARE PERIODS ERROR] {str(e)}") import traceback traceback.print_exc() raise HTTPException(status_code=500, detail=f"Period comparison failed: {str(e)}") @app.post("/api/change-detection") async def change_detection_api( prediction_file: UploadFile = File(...), gt_file: UploadFile = File(...) ): """Detect changes between two raster files (prediction and ground truth).""" import tempfile from collections import Counter try: # Save uploaded files temporarily pred_tmp = tempfile.NamedTemporaryFile(suffix='.tif', delete=False) gt_tmp = tempfile.NamedTemporaryFile(suffix='.tif', delete=False) try: # Write uploaded files to temp pred_content = await prediction_file.read() gt_content = await gt_file.read() pred_tmp.write(pred_content) gt_tmp.write(gt_content) pred_tmp.close() gt_tmp.close() # Read prediction raster with rasterio.open(pred_tmp.name) as pred_ds: pred_arr = pred_ds.read(1) pred_crs = pred_ds.crs pred_transform = pred_ds.transform # Read ground truth raster with rasterio.open(gt_tmp.name) as gt_ds: gt_arr = gt_ds.read(1) # Ensure same shape if pred_arr.shape != gt_arr.shape: raise ValueError(f"Raster shapes don't match: prediction {pred_arr.shape} vs ground truth {gt_arr.shape}") # Calculate change detection mask_valid = (gt_arr >= 0) & (pred_arr >= 0) & ~np.isnan(gt_arr) & ~np.isnan(pred_arr) changes = gt_arr[mask_valid] != pred_arr[mask_valid] n_total = np.count_nonzero(mask_valid) n_changed = np.count_nonzero(changes) # Create change pairs matrix change_pairs = list(zip(gt_arr[mask_valid][changes], pred_arr[mask_valid][changes])) change_counter = Counter(change_pairs) change_matrix = {f"{int(gt)}->{int(pred)}": int(cnt) for (gt, pred), cnt in change_counter.items()} # Create change map (0=same, 1=changed, -1=invalid) change_map = np.full(pred_arr.shape, -1, dtype=np.int8) change_map[mask_valid] = changes.astype(np.int8) # Change rate change_rate = float(n_changed) / n_total if n_total > 0 else 0.0 # Save change map as GeoTIFF change_dir = Path("predictions") change_dir.mkdir(exist_ok=True) timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") change_file = change_dir / f"change_map_{timestamp}.tif" with rasterio.open( change_file, 'w', driver='GTiff', height=change_map.shape[0], width=change_map.shape[1], count=1, dtype=change_map.dtype, crs=pred_crs, transform=pred_transform ) as dst: dst.write(change_map, 1) # Return results return { "success": True, "change_detection": { "n_total_pixels": int(n_total), "n_changed_pixels": int(n_changed), "change_rate": change_rate, "change_matrix": change_matrix, "message": f"Detected {n_changed} changes out of {n_total} valid pixels ({change_rate*100:.1f}%)" }, "change_map_file": str(change_file), "timestamp": timestamp } finally: # Cleanup temp files try: Path(pred_tmp.name).unlink() Path(gt_tmp.name).unlink() except: pass except Exception as e: print(f"[CHANGE DETECTION ERROR] {str(e)}") raise HTTPException(status_code=500, detail=f"Change detection failed: {str(e)}") async def change_detection_api( prediction_file: UploadFile = File(...), gt_file: UploadFile = File(...) ): """Detect changes between prediction raster and ground truth raster.""" import tempfile from collections import Counter try: # Save uploaded files temporarily pred_tmp = tempfile.NamedTemporaryFile(suffix='.tif', delete=False) gt_tmp = tempfile.NamedTemporaryFile(suffix='.tif', delete=False) try: # Write uploaded files to temp pred_content = await prediction_file.read() gt_content = await gt_file.read() pred_tmp.write(pred_content) gt_tmp.write(gt_content) pred_tmp.close() gt_tmp.close() # Read prediction raster import rasterio with rasterio.open(pred_tmp.name) as pred_ds: pred_arr = pred_ds.read(1) pred_crs = pred_ds.crs pred_transform = pred_ds.transform # Read ground truth raster with rasterio.open(gt_tmp.name) as gt_ds: gt_arr = gt_ds.read(1) # Ensure same shape if pred_arr.shape != gt_arr.shape: raise ValueError(f"Raster shapes don't match: prediction {pred_arr.shape} vs ground truth {gt_arr.shape}") # Calculate change detection mask_valid = (gt_arr >= 0) & (pred_arr >= 0) & ~np.isnan(gt_arr) & ~np.isnan(pred_arr) changes = gt_arr[mask_valid] != pred_arr[mask_valid] n_total = np.count_nonzero(mask_valid) n_changed = np.count_nonzero(changes) # Create change pairs matrix change_pairs = list(zip(gt_arr[mask_valid][changes], pred_arr[mask_valid][changes])) change_counter = Counter(change_pairs) change_matrix = {f"{int(gt)}->{int(pred)}": int(cnt) for (gt, pred), cnt in change_counter.items()} # Create change map (0=same, 1=changed, -1=invalid) change_map = np.full(pred_arr.shape, -1, dtype=np.int8) change_map[mask_valid] = changes.astype(np.int8) # Change rate change_rate = float(n_changed) / n_total if n_total > 0 else 0.0 # Save change map as GeoTIFF change_dir = Path("predictions") change_dir.mkdir(exist_ok=True) timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") change_file = change_dir / f"change_map_{timestamp}.tif" with rasterio.open( change_file, 'w', driver='GTiff', height=change_map.shape[0], width=change_map.shape[1], count=1, dtype=change_map.dtype, crs=pred_crs, transform=pred_transform ) as dst: dst.write(change_map, 1) # Return results return { "success": True, "change_detection": { "n_total_pixels": int(n_total), "n_changed_pixels": int(n_changed), "change_rate": change_rate, "change_matrix": change_matrix, "message": f"Detected {n_changed} changes out of {n_total} valid pixels ({change_rate*100:.1f}%)" }, "change_map_file": str(change_file), "timestamp": timestamp } finally: # Cleanup temp files try: Path(pred_tmp.name).unlink() Path(gt_tmp.name).unlink() except: pass except Exception as e: print(f"[CHANGE DETECTION ERROR] {str(e)}") raise HTTPException(status_code=500, detail=f"Change detection failed: {str(e)}") # ============ PREDICTION WITH NDVI API ============ def fetch_sentinel_items_with_retry(catalog, bbox, time_range, cloud_cover, max_scenes, max_retries=3): """ Fetch Sentinel-2 items with retry logic and exponential backoff Optimizations: - Reduce page size on retry - Use shorter timeouts for each attempt - Fetch fewer items initially and expand if successful """ import time for attempt in range(max_retries): try: # Reduce target items on each retry to minimize timeout risk target_items = max_scenes if attempt == 0 else min(max_scenes, 20 // (attempt + 1) * 10) page_limit = 50 if attempt == 0 else 20 # Smaller pages on retry print(f"[FETCH ATTEMPT {attempt + 1}/{max_retries}] Searching Sentinel-2...") print(f" → Target items: {target_items}, Page limit: {page_limit}") # Search with reduced limit on retries search = catalog.search( collections=["sentinel-2-l2a"], bbox=bbox, datetime=time_range, query={"eo:cloud_cover": {"lt": cloud_cover}}, limit=page_limit ) # Try to get items with timeout protection items = [] page_count = 0 max_pages = 3 if attempt > 0 else 5 # Fewer pages on retry for item in search.items(): items.append(item) if len(items) >= target_items: print(f"[FETCH] Reached target ({target_items} items)") break # Track pagination to prevent hanging if len(items) % page_limit == 0: page_count += 1 if page_count >= max_pages: print(f"[FETCH] Max pages reached ({max_pages}), got {len(items)} items") break if items: print(f"[FETCH SUCCESS] Retrieved {len(items)} items") # Return up to max_scenes, but accept fewer if that's all we got return items[:min(len(items), max_scenes)] else: raise ValueError("No Sentinel-2 scenes found for the specified criteria") except Exception as e: error_msg = str(e) print(f"[FETCH ERROR] Attempt {attempt + 1} failed: {error_msg}") if attempt < max_retries - 1: # Longer exponential backoff: 3, 6, 12 seconds wait_time = 3 * (2 ** attempt) print(f"[RETRY] Waiting {wait_time}s before retry...") time.sleep(wait_time) else: # Final attempt failed if "exceeded the maximum allowed time" in error_msg or "timeout" in error_msg.lower(): raise HTTPException( status_code=504, detail=f"Microsoft Planetary Computer request timed out after {max_retries} attempts. " f"Please try: (1) Reduce date range (2) Reduce max_scenes to 5-10 (3) Use smaller bbox area" ) elif "no sentinel-2 scenes found" in error_msg.lower(): raise HTTPException( status_code=404, detail="No Sentinel-2 data found. Try: (1) Different date range (2) Higher cloud_cover threshold (3) Different location" ) else: raise @app.post("/api/predict/with-ndvi") async def predict_with_ndvi(config: PredictionWithNDVIConfig, background_tasks: BackgroundTasks): """Predict land classification và NDVI cho một khu vực""" try: import numpy as np # Load model using ModelManager model_manager = get_model_manager() model, label_encoder, model_metadata = model_manager.load_model(config.model_filename) print(f"[PREDICT+NDVI] Loaded model: {config.model_filename}") print(f" - Type: {model_metadata.get('model_type', 'unknown')}") print(f" - Features: {model_metadata.get('features', [])}") # Check cache first cache_dir = Path("dataset_cache") cache_dir.mkdir(exist_ok=True) cache_key = f"pred_{config.min_lon}_{config.min_lat}_{config.max_lon}_{config.max_lat}_{config.start_date}_{config.end_date}_{config.max_scenes}_{config.cloud_cover}_{config.resolution}" cache_hash = hashlib.md5(cache_key.encode()).hexdigest() cache_file = cache_dir / f"prediction_input_{cache_hash}.joblib" bbox = [config.min_lon, config.min_lat, config.max_lon, config.max_lat] # Load from cache or fetch from Microsoft if cache_file.exists(): print(f"[PREDICT+NDVI] Loading from cache: {cache_file.name}") cached = joblib.load(cache_file) # Extract s2_data from cache (already computed in cache) s2_data = cached["s2_data"] # Check if needed bands are available in cache available_bands = list(s2_data.data_vars.keys()) needed_bands = ["B02", "B03", "B04", "B08"] if all(band in available_bands for band in needed_bands): # Use cached data directly (no need to compute again) data = s2_data[needed_bands] print(f"[PREDICT+NDVI] Using cached bands: {needed_bands}") # Set items to match the number of time slices in the cached data items = [None] * s2_data.sizes.get("time", 1) else: raise HTTPException(status_code=400, detail=f"Cache thiếu bands cần thiết. Có: {available_bands}, Cần: {needed_bands}") else: print(f"[PREDICT+NDVI] No cache found, fetching from Microsoft Planetary Computer") # Connect to Microsoft Planetary Computer catalog = Client.open( "https://planetarycomputer.microsoft.com/api/stac/v1", modifier=planetary_computer.sign_inplace ) time_range = f"{config.start_date}/{config.end_date}" # Search for Sentinel-2 data with retry logic items = fetch_sentinel_items_with_retry( catalog=catalog, bbox=bbox, time_range=time_range, cloud_cover=config.cloud_cover, max_scenes=config.max_scenes, max_retries=3 ) print(f"[PREDICT+NDVI] Found {len(items)} Sentinel-2 scenes") if len(items) == 0: raise HTTPException(status_code=404, detail="Không tìm thấy dữ liệu vệ tinh") # Sign items to refresh SAS tokens (keep as pystac.Item, not dict) signed_items = [planetary_computer.sign(item) for item in items] # Load all bands needed for features data = odc.stac.load( signed_items, bbox=bbox, bands=["B02", "B03", "B04", "B08"], # Blue, Green, Red, NIR resolution=config.resolution, chunks={"x": 2048, "y": 2048} ).compute() print(f"[PREDICT+NDVI] Loaded data shape: {data.dims}") # Get expected number of features from model metadata expected_n_features = model_metadata.get("n_features", 3) print(f"[PREDICT+NDVI] Model expects {expected_n_features} features") # Calculate NDVI and other indices blue = data["B02"].values green = data["B03"].values red = data["B04"].values nir = data["B08"].values # Calculate indices for each time step # NDVI = (NIR - Red) / (NIR + Red) ndvi = (nir - red) / (nir + red + 1e-8) # NDWI = (Green - NIR) / (Green + NIR) ndwi = (green - nir) / (green + nir + 1e-8) # NDBI = (SWIR - NIR) / (SWIR + NIR) - we use Red as proxy ndbi = (red - nir) / (red + nir + 1e-8) # Prepare features for prediction height, width = ndvi.shape[1:3] # Skip time dimension n_pixels = height * width n_times = ndvi.shape[0] print(f"[PREDICT+NDVI] Data has {n_times} time steps, spatial size: {height}x{width}") # Build features using FeatureExtractor to match training feature_mode = model_metadata.get("feature_mode", "simple") expected_n_features = model_metadata.get("n_features", 3) print(f"[PREDICT+NDVI] Model feature_mode: {feature_mode}") print(f"[PREDICT+NDVI] Model n_features: {expected_n_features}") # COMPATIBILITY FIX: If model was trained with buggy code (n_features=3 but feature_mode='odc'), # fallback to simple mode to match what model actually expects if feature_mode == 'odc' and expected_n_features == 3: print(f"[PREDICT+NDVI] ⚠️ WARNING: Model metadata shows odc mode but only 3 features") print(f"[PREDICT+NDVI] This model was trained with old buggy code - using simple mode for compatibility") feature_mode = 'simple' # Use FeatureExtractor for consistent feature building from feature_extractor import FeatureExtractor extractor = FeatureExtractor(mode=feature_mode) print(f"[PREDICT+NDVI] Using FeatureExtractor with mode='{feature_mode}'") print(f"[PREDICT+NDVI] Expected features: {extractor.get_info()}") # Extract features from data # data is already an xr.Dataset with B02, B03, B04, B08 # Need to add B11 for ODC mode (NDBI calculation uses SWIR) if feature_mode == 'odc' and 'B11' not in data: # Load B11 if needed for ODC mode print(f"[PREDICT+NDVI] ODC mode requires B11 (SWIR), loading...") # Fetch B11 band (whether from cache or fresh fetch) try: # If we haven't fetched items yet (cache scenario), do it now if 'signed_items' not in locals(): catalog = Client.open( "https://planetarycomputer.microsoft.com/api/stac/v1", modifier=planetary_computer.sign_inplace ) time_range = f"{config.start_date}/{config.end_date}" # Use retry logic for B11 band b11_items = fetch_sentinel_items_with_retry( catalog=catalog, bbox=bbox, time_range=time_range, cloud_cover=config.cloud_cover, max_scenes=config.max_scenes, max_retries=3 ) signed_items = [planetary_computer.sign(item) for item in b11_items] print(f"[PREDICT+NDVI] Fetched {len(signed_items)} scenes for B11") b11_data = odc.stac.load( signed_items, bbox=bbox, bands=["B11"], resolution=config.resolution, chunks={"x": 2048, "y": 2048} ).compute() # Merge B11 into existing data data = xr.merge([data, b11_data]) print(f"[PREDICT+NDVI] Added B11 to data") except Exception as e: print(f"[PREDICT+NDVI] Warning: Failed to load B11: {e}") print(f"[PREDICT+NDVI] Will proceed without B11 (may affect NDBI accuracy)") # Extract features using FeatureExtractor if feature_mode == 'simple': # Simple mode needs pre-calculated NDVI print(f"[PREDICT+NDVI] Calculating NDVI for simple mode...") red_band = data["B04"].values nir_band = data["B08"].values ndvi_array = (nir_band - red_band) / (nir_band + red_band + 1e-8) # Convert to xarray DataArray with proper dims ndvi_data = xr.DataArray( ndvi_array, dims=data["B04"].dims, coords=data["B04"].coords ) # Simple mode also needs VH/VV radar data, but we don't have it for this endpoint # Pass None and let extractor handle it features = extractor.extract(s2_data=None, ndvi_data=ndvi_data, vh_data=None, vv_data=None) else: # ODC/extended modes use s2_data directly features = extractor.extract(s2_data=data, vh_data=None, vv_data=None) print(f"[PREDICT+NDVI] Built features shape: {features.shape}") print(f"[PREDICT+NDVI] Features per pixel: {features.shape[1] if len(features.shape) > 1 else 1}") # Handle NaN, inf, and extreme values # Replace inf with 0 features = np.nan_to_num(features, nan=0.0, posinf=0.0, neginf=0.0) # Clip extreme values to reasonable range features = np.clip(features, -1e6, 1e6) # Double-check no inf/nan remain valid_mask = np.isfinite(features).all(axis=1) features_clean = features[valid_mask] print(f"[PREDICT+NDVI] Predicting {features_clean.shape[0]} valid pixels...") print(f"[PREDICT+NDVI] Features range: [{features_clean.min():.3f}, {features_clean.max():.3f}]") # Check if model is PyTorch/deep learning model and use GPU if available is_pytorch_model = hasattr(model, '__class__') and ('CNN' in model.__class__.__name__ or 'Swin' in model.__class__.__name__ or 'UNet' in model.__class__.__name__ or 'MobileNet' in model.__class__.__name__) if is_pytorch_model and config.use_gpu: try: import torch device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') if torch.cuda.is_available(): print(f"[PREDICT+NDVI] Using GPU: {torch.cuda.get_device_name(0)}") # Move model to GPU model = model.to(device) # Predict in batches to avoid GPU memory overflow batch_size = 8192 # Adjust based on GPU memory predictions_list = [] for i in range(0, len(features_clean), batch_size): batch = features_clean[i:i+batch_size] batch_tensor = torch.from_numpy(batch).float().to(device) with torch.no_grad(): batch_pred = model.predict(batch_tensor) # Move back to CPU if needed if isinstance(batch_pred, torch.Tensor): batch_pred = batch_pred.cpu().numpy() predictions_list.append(batch_pred) if (i // batch_size) % 10 == 0: print(f"[PREDICT+NDVI] Processed {i + len(batch)}/{len(features_clean)} pixels on GPU") predictions = np.concatenate(predictions_list) print(f"[PREDICT+NDVI] GPU prediction completed!") else: print(f"[PREDICT+NDVI] GPU requested but not available, using CPU") predictions = model.predict(features_clean) except Exception as gpu_error: print(f"[PREDICT+NDVI] GPU prediction failed: {gpu_error}, falling back to CPU") # Move model back to CPU before retrying try: model = model.cpu() print(f"[PREDICT+NDVI] Moved model to CPU") except: pass predictions = model.predict(features_clean) else: # Use CPU for traditional ML models if is_pytorch_model and not config.use_gpu: print(f"[PREDICT+NDVI] GPU disabled by user, using CPU") predictions = model.predict(features_clean) # Reshape back to raster prediction_raster = np.full(n_pixels, -1, dtype=np.int16) prediction_raster[valid_mask] = predictions prediction_raster = prediction_raster.reshape(height, width) # Smooth classification map to make output cleaner (reduce speckle) try: from scipy import ndimage smoothed = ndimage.median_filter(prediction_raster, size=3) smoothed[prediction_raster < 0] = -1 # keep nodata prediction_raster = smoothed print("[PREDICT+NDVI][SMOOTH] Applied 3x3 median filter to classification") except Exception as smooth_err: print(f"[PREDICT+NDVI][SMOOTH WARNING] {smooth_err}") # --- CHANGE DETECTION --- change_summary = None change_map = None try: # Use training shapefile as ground truth gt_shapefile = "train/ST_training data_updated_1130points_new.shp" gt_raster = rasterize_ground_truth(gt_shapefile, (height, width), bbox, class_column="class") # Compare prediction and ground truth mask_valid = (gt_raster >= 0) & (prediction_raster >= 0) changes = gt_raster[mask_valid] != prediction_raster[mask_valid] n_total = np.count_nonzero(mask_valid) n_changed = np.count_nonzero(changes) # Per-class change matrix from collections import Counter change_pairs = list(zip(gt_raster[mask_valid][changes], prediction_raster[mask_valid][changes])) change_counter = Counter(change_pairs) change_matrix = {f"{int(gt)}->{int(pred)}": int(cnt) for (gt, pred), cnt in change_counter.items()} change_summary = { "n_total": int(n_total), "n_changed": int(n_changed), "change_rate": float(n_changed) / n_total if n_total > 0 else 0.0, "change_matrix": change_matrix } # Optionally, create a change map (1=changed, 0=same, -1=invalid) change_map = np.full((height, width), -1, dtype=np.int8) change_map[mask_valid] = changes.astype(np.int8) # Save change map as GeoTIFF change_file = output_dir / f"change_map_{timestamp}.tif" with rasterio.open( change_file, 'w', driver='GTiff', height=height, width=width, count=1, dtype=change_map.dtype, crs='EPSG:4326', transform=from_bounds(bbox[0], bbox[1], bbox[2], bbox[3], width, height) ) as dst: dst.write(change_map, 1) output_files.append({"type": "change_map", "path": str(change_file)}) print(f"[CHANGE DETECTION] Saved change map to {change_file}") except Exception as change_exc: print(f"[CHANGE DETECTION] Warning: {change_exc}") # Prepare outputs timestamp = datetime.now().strftime('%Y%m%d_%H%M%S') output_dir = Path("predictions") output_dir.mkdir(exist_ok=True) output_files = [] # Export NDVI if requested if config.export_ndvi: # Calculate NDVI from data for export (data contains B04=red, B08=nir) red_band = data["B04"].values nir_band = data["B08"].values ndvi_array = (nir_band - red_band) / (nir_band + red_band + 1e-8) # Average over time dimension to get mean NDVI ndvi_mean = np.nanmean(ndvi_array, axis=0) ndvi_file = output_dir / f"ndvi_{timestamp}.tif" transform = from_bounds(bbox[0], bbox[1], bbox[2], bbox[3], width, height) with rasterio.open( ndvi_file, 'w', driver='GTiff', height=height, width=width, count=1, dtype=ndvi_mean.dtype, crs='EPSG:4326', transform=transform ) as dst: dst.write(ndvi_mean, 1) output_files.append({"type": "ndvi", "path": str(ndvi_file)}) print(f"[PREDICT+NDVI] Saved NDVI to {ndvi_file}") # Create PNG preview for NDVI ndvi_png = output_dir / f"ndvi_{timestamp}.png" try: import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt fig, ax = plt.subplots(figsize=(12, 10), dpi=150) im = ax.imshow(ndvi_mean, cmap='RdYlGn', vmin=-1, vmax=1, interpolation='nearest') ax.set_title(f'NDVI - {timestamp}', fontsize=14, fontweight='bold') ax.set_xlabel('X (pixels)', fontsize=10) ax.set_ylabel('Y (pixels)', fontsize=10) cbar = plt.colorbar(im, ax=ax, fraction=0.046, pad=0.04) cbar.set_label('NDVI', rotation=270, labelpad=15) ax.grid(True, alpha=0.3, linestyle='--', linewidth=0.5) plt.tight_layout() plt.savefig(str(ndvi_png), dpi=150, bbox_inches='tight') plt.close(fig) output_files.append({"type": "ndvi_png", "path": str(ndvi_png)}) print(f"[PREDICT+NDVI] Created PNG: {ndvi_png}") except Exception as e: print(f"[PREDICT+NDVI] PNG creation failed: {e}") # Export classification if requested if config.export_classification: class_file = output_dir / f"classification_{timestamp}.tif" transform = from_bounds(bbox[0], bbox[1], bbox[2], bbox[3], width, height) with rasterio.open( class_file, 'w', driver='GTiff', height=height, width=width, count=1, dtype=prediction_raster.dtype, crs='EPSG:4326', transform=transform ) as dst: dst.write(prediction_raster, 1) output_files.append({"type": "classification", "path": str(class_file)}) print(f"[PREDICT+NDVI] Saved classification to {class_file}") # Create PNG preview for classification class_png = output_dir / f"classification_{timestamp}.png" try: import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt from matplotlib.patches import Patch fig, ax = plt.subplots(figsize=(14, 10), dpi=150) im = ax.imshow(prediction_raster, cmap='tab20', interpolation='nearest') ax.set_title(f'Land Classification - {timestamp}', fontsize=16, fontweight='bold', pad=20) ax.set_xlabel('X (pixels)', fontsize=11) ax.set_ylabel('Y (pixels)', fontsize=11) # Try to get class labels - ALWAYS use DEFAULT_LABEL_NAMES class_map = DEFAULT_LABEL_NAMES.copy() # Get unique classes in prediction unique_pred_classes = np.unique(prediction_raster) unique_pred_classes = unique_pred_classes[~np.isnan(unique_pred_classes)] unique_pred_classes = unique_pred_classes[unique_pred_classes >= 0] # Exclude -1 # Create custom legend with color patches legend_elements = [] cmap = plt.cm.get_cmap('tab20') for cls_val in sorted(unique_pred_classes): try: cls_int = int(cls_val) color = cmap(cls_int / 20.0) label_name = class_map.get(cls_int, f"Class {cls_int}") legend_elements.append( Patch(facecolor=color, edgecolor='black', linewidth=0.5, label=f"{cls_int}: {label_name}") ) except: pass # Add legend outside plot area if legend_elements: legend = ax.legend( handles=legend_elements, loc='center left', bbox_to_anchor=(1.02, 0.5), fontsize=10, title='Land Classes', title_fontsize=11, framealpha=0.9, edgecolor='black' ) legend.get_title().set_fontweight('bold') ax.grid(True, alpha=0.3, linestyle='--', linewidth=0.5) plt.tight_layout() plt.savefig(str(class_png), dpi=150, bbox_inches='tight') plt.close(fig) output_files.append({"type": "classification_png", "path": str(class_png)}) print(f"[PREDICT+NDVI] Created PNG: {class_png}") except Exception as e: print(f"[PREDICT+NDVI] PNG creation failed: {e}") # Calculate statistics ndvi_stats = { "mean": float(np.nanmean(ndvi_mean)), "min": float(np.nanmin(ndvi_mean)), "max": float(np.nanmax(ndvi_mean)), "std": float(np.nanstd(ndvi_mean)) } # Count classes unique_classes, counts = np.unique(predictions, return_counts=True) class_distribution = { int(cls): int(count) for cls, count in zip(unique_classes, counts) } # Auto-save prediction cache (Sentinel-2 data + metadata) for this NDVI workflow try: cache_config = { "min_lon": config.min_lon, "min_lat": config.min_lat, "max_lon": config.max_lon, "max_lat": config.max_lat, "start_date": config.start_date, "end_date": config.end_date, "max_scenes": config.max_scenes, "cloud_cover": config.cloud_cover, "resolution": config.resolution, "model_filename": config.model_filename } # Always save data for predict_with_ndvi (so lần sau không phải tải lại) data_to_save = data cache_result = save_prediction_cache_sync(cache_config, data_to_save) print(f"[PREDICT+NDVI][CACHE] Saved cache: {cache_result.get('filename')} (with data: {data_to_save is not None})") except Exception as cache_exc: print(f"[PREDICT+NDVI][CACHE ERROR] {cache_exc}") return { "success": True, "message": "Prediction with NDVI completed", "output_files": output_files, "ndvi_stats": ndvi_stats, "class_distribution": class_distribution, "n_scenes": len(items), "resolution": config.resolution, "bbox": bbox, "change_detection": change_summary } except HTTPException: # Re-raise HTTPException with original status code (e.g., 504 for timeout) raise except Exception as e: print(f"[PREDICT+NDVI ERROR] {str(e)}") import traceback traceback.print_exc() raise HTTPException(status_code=500, detail=str(e)) # ============ NDVI TIME SERIES API ============ @app.post("/api/ndvi/timeseries") async def calculate_ndvi_timeseries(config: NDVIConfig): """Tính NDVI time series cho một khu vực""" try: import numpy as np import xarray as xr from pystac_client import Client import planetary_computer import odc.stac print(f"[NDVI] Starting calculation for bbox: {config.bbox}, time: {config.start_date} to {config.end_date}") # Connect to Microsoft Planetary Computer STAC API catalog = Client.open( "https://planetarycomputer.microsoft.com/api/stac/v1", modifier=planetary_computer.sign_inplace ) bbox = config.bbox time_range = f"{config.start_date}/{config.end_date}" # Search for Sentinel-2 data with retry logic items = fetch_sentinel_items_with_retry( catalog=catalog, bbox=bbox, time_range=time_range, cloud_cover=config.max_cloud_cover, max_scenes=1000, # Get all available scenes for time series max_retries=3 ) print(f"[NDVI] Found {len(items)} Sentinel-2 scenes") if len(items) == 0: raise HTTPException(status_code=404, detail="Không tìm thấy dữ liệu Sentinel-2 cho khu vực và thời gian này") # Load data for each time step ndvi_timeseries = [] for item in items: try: # Load NIR (B08) and Red (B04) bands data = odc.stac.load( [item], bbox=bbox, bands=["B04", "B08"], # Red and NIR resolution=config.resolution, chunks={"x": 2048, "y": 2048} ).compute() if data is None or len(data.keys()) == 0: continue # Calculate NDVI = (NIR - Red) / (NIR + Red) nir = data["B08"].values red = data["B04"].values # Avoid division by zero denominator = nir + red denominator = np.where(denominator == 0, np.nan, denominator) ndvi = (nir - red) / denominator # Calculate mean NDVI (ignore NaN values) mean_ndvi = float(np.nanmean(ndvi)) # Get date from item date_str = item.datetime.strftime("%Y-%m-%d") ndvi_timeseries.append({ "date": date_str, "ndvi": mean_ndvi }) print(f"[NDVI] {date_str}: NDVI = {mean_ndvi:.3f}") except Exception as e: print(f"[NDVI WARNING] Failed to process item {item.id}: {e}") continue if len(ndvi_timeseries) == 0: raise HTTPException(status_code=500, detail="Không thể tính NDVI cho bất kỳ ảnh nào") # Sort by date ndvi_timeseries.sort(key=lambda x: x["date"]) # Calculate statistics ndvi_values = [item["ndvi"] for item in ndvi_timeseries] mean_ndvi = float(np.mean(ndvi_values)) min_ndvi = float(np.min(ndvi_values)) max_ndvi = float(np.max(ndvi_values)) result = { "timeseries": ndvi_timeseries, "n_images": len(ndvi_timeseries), "mean_ndvi": mean_ndvi, "min_ndvi": min_ndvi, "max_ndvi": max_ndvi, "bbox": bbox, "time_range": time_range } print(f"[NDVI] Calculation complete. Mean NDVI: {mean_ndvi:.3f}, Images: {len(ndvi_timeseries)}") return result except HTTPException: raise except Exception as e: print(f"[NDVI ERROR] {e}") import traceback traceback.print_exc() raise HTTPException(status_code=500, detail=f"Lỗi khi tính NDVI: {str(e)}") # =========================== # NDVI PREDICTION TIME SERIES # =========================== class NDVIPredictionConfig(BaseModel): """Cấu hình NDVI prediction time series""" model_filename: str bbox: List[float] # [min_lon, min_lat, max_lon, max_lat] start_date: str end_date: str max_cloud_cover: int = 30 max_scenes: int = 12 # Số lượng scenes tối đa resolution: int = 20 sample_points: int = 1000 # Số điểm ngẫu nhiên để predict use_gpu: bool = False @app.post("/api/ndvi/predict-timeseries") async def ndvi_predict_timeseries(config: NDVIPredictionConfig): """ Predict land classification tại các điểm ngẫu nhiên trong bbox theo time series và tính NDVI trung bình cho mỗi class theo thời gian """ try: print(f"\n{'='*70}") print(f"[NDVI PREDICTION TIME SERIES] Starting...") print(f" Model: {config.model_filename}") print(f" Bbox: {config.bbox}") print(f" Time: {config.start_date} → {config.end_date}") print(f" Sample points: {config.sample_points}") print(f" GPU: {config.use_gpu}") print(f"{'='*70}\n") # Load model model_path = Path("model_train") / config.model_filename if not model_path.exists(): raise HTTPException(status_code=404, detail=f"Model not found: {config.model_filename}") print(f"📂 Loading model using ModelManager...") # Load model using ModelManager to get metadata from model_manager import get_model_manager model_manager = get_model_manager() model, label_encoder, model_metadata = model_manager.load_model(config.model_filename) # Get feature info from metadata feature_mode = model_metadata.get("feature_mode", "simple") n_features_expected = model_metadata.get("n_features", 0) print(f" Model type: {model_metadata.get('model_type', 'unknown')}") print(f" Feature mode: {feature_mode}") print(f" Expected features: {n_features_expected}") # Check if PyTorch model for GPU is_pytorch_model = hasattr(model, 'forward') or str(type(model).__name__) in ['SwinUnet', 'CNN', 'MobileNetLRASPPClassifier'] if is_pytorch_model and config.use_gpu: import torch if torch.cuda.is_available(): print(f"🚀 Moving model to GPU...") model = model.to('cuda') model.eval() else: print(f"⚠️ GPU not available, using CPU") # Setup bbox min_lon, min_lat, max_lon, max_lat = config.bbox bbox = [min_lon, min_lat, max_lon, max_lat] time_range = f"{config.start_date}/{config.end_date}" # Check network connectivity first print(f"\n🌐 Checking network connectivity...") is_connected, conn_error = check_internet_connectivity() if not is_connected: raise HTTPException( status_code=503, detail=f"❌ {conn_error}\n\n" f"Please check:\n" f"1. Your internet connection is active\n" f"2. DNS servers are configured correctly\n" f"3. Firewall/proxy settings allow outbound connections" ) is_accessible, access_error = check_planetary_computer_access() if not is_accessible: print(f"⚠️ {access_error}") print(f" Will attempt to load data anyway (may fail)...") # Load Sentinel-2 time series using Planetary Computer print(f"\n📡 Loading Sentinel-2 data from Planetary Computer...") # Import required libraries import pystac_client import planetary_computer from odc.stac import load import pandas as pd catalog = pystac_client.Client.open( "https://planetarycomputer.microsoft.com/api/stac/v1", modifier=planetary_computer.sign_inplace, ) # Search for Sentinel-2 data with retry s2_items = fetch_sentinel_items_with_retry( catalog=catalog, bbox=bbox, time_range=time_range, cloud_cover=config.max_cloud_cover, max_scenes=config.max_scenes, max_retries=3 ) print(f"✅ Found {len(s2_items)} Sentinel-2 scenes (limited to {config.max_scenes})") # Load data with retry logic for network errors max_retries = 3 retry_delay = 2 s2_data = None for attempt in range(max_retries): try: print(f"📥 Loading Sentinel-2 data (attempt {attempt + 1}/{max_retries})...") s2_data = load( s2_items, bbox=bbox, bands=["B02", "B03", "B04", "B05", "B06", "B07", "B08", "B11", "B12", "SCL"], chunks={"time": 1, "x": 2048, "y": 2048}, groupby="solar_day", resolution=config.resolution ).compute() print(f"✅ Loaded Sentinel-2 data with {len(s2_data.time)} time steps") break except Exception as e: error_msg = str(e) # Check for various error types that might indicate problematic scenes is_network_error = any(err in error_msg for err in [ "Could not resolve host", "CURL error", "Connection", "Timeout" ]) is_access_error = any(err in error_msg for err in [ "does not exist in the file system", "RasterioIOError", "not recognized as a supported dataset", "Aborting load" ]) is_auth_error = any(err in error_msg for err in [ "AuthenticationFailed", "Signature fields not well formed", "Server failed to authenticate", "403", "401" ]) if is_auth_error: print(f"⚠️ Authentication error detected. Re-signing items and retrying...") # Re-sign all items with fresh tokens try: import planetary_computer s2_items = [planetary_computer.sign(item) for item in s2_items] print(f" Re-signed {len(s2_items)} items") if attempt < max_retries - 1: import time time.sleep(1) # Brief pause before retry continue else: raise HTTPException( status_code=500, detail=f"❌ Authentication failed after {max_retries} attempts.\n\n" f"Azure Blob Storage authentication is failing. This may be due to:\n" f"1. Expired or invalid SAS tokens from Planetary Computer\n" f"2. Planetary Computer API service issues\n" f"3. System clock synchronization issues (check date/time)\n\n" f"Please try again in a few minutes. If the problem persists,\n" f"Microsoft Planetary Computer may be experiencing issues.\n\n" f"Error: {error_msg[:300]}" ) except Exception as resign_error: print(f" Re-signing failed: {resign_error}") if attempt == max_retries - 1: raise HTTPException( status_code=500, detail=f"Failed to re-sign authentication tokens: {str(resign_error)}" ) elif is_access_error and len(s2_items) > 5: # If we have access errors and multiple scenes, try with fewer scenes print(f"⚠️ Data access error detected. Reducing scene count and retrying...") # Remove last 20% of scenes and try again remove_count = max(1, len(s2_items) // 5) s2_items = s2_items[:-remove_count] print(f" Reduced to {len(s2_items)} scenes") if attempt < max_retries - 1: continue else: # Final attempt with reduced scenes print(f" Final attempt with {len(s2_items)} scenes...") elif is_network_error: print(f"⚠️ Network error on attempt {attempt + 1}: {error_msg[:100]}") # Re-check connectivity on network errors if "Could not resolve host" in error_msg: is_conn, conn_msg = check_internet_connectivity() if not is_conn: raise HTTPException( status_code=503, detail=f"❌ Network connectivity lost!\n\n" f"{conn_msg}\n\n" f"Please:\n" f"1. Check your internet connection\n" f"2. Verify DNS settings (try 8.8.8.8 or 1.1.1.1)\n" f"3. Check firewall/proxy settings\n" f"4. Try again once connection is restored\n\n" f"Original error: {error_msg[:150]}" ) if attempt < max_retries - 1: import time print(f" Retrying in {retry_delay} seconds...") time.sleep(retry_delay) retry_delay *= 2 # Exponential backoff else: raise HTTPException( status_code=503, detail=f"❌ Network error persists after {max_retries} attempts.\n\n" f"Unable to download Sentinel-2 data from Microsoft Planetary Computer.\n\n" f"Possible causes:\n" f"1. Unstable internet connection\n" f"2. DNS resolution issues\n" f"3. Planetary Computer service temporarily unavailable\n" f"4. Firewall blocking access to sentinel2l2a01.blob.core.windows.net\n\n" f"Please check your network and try again.\n\n" f"Error: {error_msg[:200]}" ) else: # If still failing after retries, try with minimal data if attempt == max_retries - 1 and len(s2_items) > 3: print(f"⚠️ Multiple failures. Attempting with minimal scenes (3)...") s2_items = s2_items[:3] continue else: # Last resort: provide meaningful error raise HTTPException( status_code=500, detail=f"Failed to load Sentinel-2 data. This may be due to expired access tokens " f"or unavailable data. Please try a different time range or area. " f"Error: {error_msg[:300]}" ) if s2_data is None: raise HTTPException(status_code=500, detail="Failed to load Sentinel-2 data") # Generate random sample points print(f"\n🎲 Generating {config.sample_points} random sample points...") np.random.seed(42) lats = np.random.uniform(min_lat, max_lat, config.sample_points) lons = np.random.uniform(min_lon, max_lon, config.sample_points) # EXTRACT AGGREGATE FEATURES (same as training in 01.train_ODC.ipynb) # Model expects: ndvi_mean, ndvi_min, ndvi_max, ndvi_std, ndvi_range, ndwi_mean, ndbi_mean, evi_mean print(f"\n🔍 Extracting aggregate features from time series for {config.sample_points} sample points...") print(f" (Matching training methodology from 01.train_ODC.ipynb)") # Calculate spectral indices for all time steps print(f"\n📊 Calculating spectral indices...") # NDVI = (NIR - Red) / (NIR + Red) nir = s2_data['B08'].astype(float) red = s2_data['B04'].astype(float) ndvi = (nir - red) / (nir + red + 1e-8) # NDWI = (Green - NIR) / (Green + NIR) green = s2_data['B03'].astype(float) ndwi = (green - nir) / (green + nir + 1e-8) # NDBI = (SWIR - NIR) / (SWIR + NIR) swir = s2_data['B11'].astype(float) ndbi = (swir - nir) / (swir + nir + 1e-8) # EVI = 2.5 * (NIR - Red) / (NIR + 6*Red - 7.5*Blue + 1) blue = s2_data['B02'].astype(float) evi = 2.5 * (nir - red) / (nir + 6*red - 7.5*blue + 1) print(f"✅ Calculated NDVI, NDWI, NDBI, EVI for {len(s2_data.time)} time steps") # Extract features at sample points print(f"\n🎯 Extracting aggregate features at sample points...") all_point_features = [] all_point_metadata = [] for i, (lat, lon) in enumerate(zip(lats, lons)): try: # Extract time series for this point point_ndvi = ndvi.sel(y=lat, x=lon, method='nearest').values point_ndwi = ndwi.sel(y=lat, x=lon, method='nearest').values point_ndbi = ndbi.sel(y=lat, x=lon, method='nearest').values point_evi = evi.sel(y=lat, x=lon, method='nearest').values point_scl = s2_data['SCL'].sel(y=lat, x=lon, method='nearest').values # Mask out cloud/no data values valid_mask = ~np.isin(point_scl, [3, 8, 9, 10, 0, 1]) if not valid_mask.any(): # All time steps are invalid continue # Calculate aggregate features (same as training) features = [ float(np.nanmean(point_ndvi[valid_mask])), # ndvi_mean float(np.nanmin(point_ndvi[valid_mask])), # ndvi_min float(np.nanmax(point_ndvi[valid_mask])), # ndvi_max float(np.nanstd(point_ndvi[valid_mask])), # ndvi_std float(np.nanmax(point_ndvi[valid_mask]) - np.nanmin(point_ndvi[valid_mask])), # ndvi_range float(np.nanmean(point_ndwi[valid_mask])), # ndwi_mean float(np.nanmean(point_ndbi[valid_mask])), # ndbi_mean float(np.nanmean(point_evi[valid_mask])) # evi_mean ] # Skip if any NaN values if not np.isnan(features).any(): all_point_features.append(features) all_point_metadata.append({'lat': lat, 'lon': lon, 'idx': i}) except Exception as e: # Skip problematic points continue if len(all_point_features) == 0: raise HTTPException( status_code=404, detail="❌ Không có điểm hợp lệ. Tất cả sample points bị mây che hoặc no data." ) # Convert to array X = np.array(all_point_features) print(f"✅ Extracted aggregate features for {len(all_point_features)} valid points") print(f" Feature shape: {X.shape} (points, features)") print(f" Features: ndvi_mean, ndvi_min, ndvi_max, ndvi_std, ndvi_range, ndwi_mean, ndbi_mean, evi_mean") # Verify feature count if X.shape[1] != n_features_expected: print(f"⚠️ Feature mismatch: got {X.shape[1]}, expected {n_features_expected}") if X.shape[1] < n_features_expected: # Pad with zeros padding = np.zeros((X.shape[0], n_features_expected - X.shape[1])) X = np.hstack([X, padding]) print(f" → Padded to {X.shape[1]} features") else: # Truncate X = X[:, :n_features_expected] print(f" → Truncated to {X.shape[1]} features") # Predict land use classification print(f"\n🤖 Predicting land use classification...") if is_pytorch_model and config.use_gpu: import torch with torch.no_grad(): X_tensor = torch.FloatTensor(X).to('cuda') predictions = model.predict(X) else: predictions = model.predict(X) # Decode labels if needed if label_encoder is not None: try: predictions = label_encoder.inverse_transform(predictions.astype(int)) except: pass print(f"✅ Predicted {len(predictions)} points") # Count class distribution unique_classes, class_counts = np.unique(predictions, return_counts=True) print(f"\n📊 Class distribution:") for cls, count in zip(unique_classes, class_counts): print(f" Class {cls}: {count} points ({count/len(predictions)*100:.1f}%)") # Generate time series data by calculating NDVI at each time step print(f"\n⏱️ Generating time series data for {len(s2_data.time)} time steps...") timeseries_data = [] for time_idx, time_val in enumerate(s2_data.time.values): # Calculate NDVI for this time step nir_t = s2_data['B08'].isel(time=time_idx) red_t = s2_data['B04'].isel(time=time_idx) ndvi_t = (nir_t - red_t) / (nir_t + red_t + 1e-8) # Extract NDVI values at valid points ndvi_values_at_points = [] class_ndvi = {} for meta_idx, metadata in enumerate(all_point_metadata): lat, lon = metadata['lat'], metadata['lon'] pred_class = predictions[meta_idx] try: ndvi_val = float(ndvi_t.sel(y=lat, x=lon, method='nearest').values) if not np.isnan(ndvi_val): ndvi_values_at_points.append(ndvi_val) # Group by class if pred_class not in class_ndvi: class_ndvi[pred_class] = [] class_ndvi[pred_class].append(ndvi_val) except: continue # Calculate class-wise NDVI statistics class_ndvi_stats = {} for cls, ndvi_vals in class_ndvi.items(): if len(ndvi_vals) > 0: class_ndvi_stats[int(cls)] = { 'mean_ndvi': float(np.mean(ndvi_vals)), 'min_ndvi': float(np.min(ndvi_vals)), 'max_ndvi': float(np.max(ndvi_vals)), 'std_ndvi': float(np.std(ndvi_vals)), 'count': len(ndvi_vals) } # Overall statistics for this time step if len(ndvi_values_at_points) > 0: timeseries_data.append({ 'date': pd.Timestamp(time_val).strftime('%Y-%m-%d'), 'mean_ndvi': float(np.mean(ndvi_values_at_points)), 'min_ndvi': float(np.min(ndvi_values_at_points)), 'max_ndvi': float(np.max(ndvi_values_at_points)), 'std_ndvi': float(np.std(ndvi_values_at_points)), 'class_distribution': {int(k): int(v) for k, v in zip(unique_classes, class_counts)}, 'class_ndvi': class_ndvi_stats, 'n_valid_points': len(ndvi_values_at_points) }) if time_idx % 5 == 0 or time_idx == len(s2_data.time) - 1: print(f" [{time_idx + 1:2d}/{len(s2_data.time)}] {pd.Timestamp(time_val).strftime('%Y-%m-%d')}: NDVI={np.mean(ndvi_values_at_points):.3f}") # Check if we have any valid data if not timeseries_data: # Provide detailed suggestions suggestions = [ "📅 Chọn mùa khô (Tháng 1-4): ít mây hơn, dữ liệu tốt hơn", "🗺️ Thử khu vực khác: Đồng bằng sông Cửu Long [105.6, 9.3, 106.2, 9.8]", "☁️ Tăng max_cloud_cover lên 50-80% (hiện tại: {}%)".format(config.max_cloud_cover), "📸 Tăng max_scenes lên 20-30 (hiện tại: {})".format(config.max_scenes), "📍 Giảm số sample_points xuống 500 để test nhanh", "🌍 Khu vực đề xuất: Hà Nội [105.7, 20.9, 105.9, 21.1], Đà Nẵng [107.9, 15.9, 108.3, 16.2]" ] raise HTTPException( status_code=404, detail=f"❌ Không có dữ liệu hợp lệ. Tất cả điểm đều bị che phủ bởi mây/NaN.\n\n💡 Gợi ý:\n" + "\n".join(f" {i+1}. {s}" for i, s in enumerate(suggestions)) ) # Overall statistics all_ndvi_values = [item['mean_ndvi'] for item in timeseries_data] result = { 'timeseries': timeseries_data, 'n_images': len(timeseries_data), 'mean_ndvi': float(np.mean(all_ndvi_values)), 'min_ndvi': float(np.min(all_ndvi_values)), 'max_ndvi': float(np.max(all_ndvi_values)), 'std_ndvi': float(np.std(all_ndvi_values)), 'bbox': config.bbox, 'model_used': config.model_filename, 'sample_points': config.sample_points, 'date_range': f"{config.start_date} to {config.end_date}" } print(f"\n{'='*70}") print(f"✅ NDVI Prediction Time Series completed!") print(f" Total scenes: {len(timeseries_data)}") print(f" Mean NDVI: {result['mean_ndvi']:.3f}") print(f" NDVI range: [{result['min_ndvi']:.3f}, {result['max_ndvi']:.3f}]") print(f"{'='*70}\n") # Auto-save prediction cache (Sentinel-2 data + metadata) similar to rice predict try: cache_config = { "min_lon": config.bbox[0], "min_lat": config.bbox[1], "max_lon": config.bbox[2], "max_lat": config.bbox[3], "start_date": config.start_date, "end_date": config.end_date, "max_scenes": config.max_scenes, "cloud_cover": config.max_cloud_cover, "resolution": config.resolution, "model_filename": config.model_filename } cache_result = save_prediction_cache_sync(cache_config, s2_data) print(f"[NDVI TIMESERIES][CACHE] Saved cache: {cache_result.get('filename')} (with data: {s2_data is not None})") except Exception as cache_exc: print(f"[NDVI TIMESERIES][CACHE ERROR] {cache_exc}") return result except HTTPException: raise except Exception as e: print(f"[NDVI PREDICTION ERROR] {e}") import traceback traceback.print_exc() raise HTTPException(status_code=500, detail=f"Lỗi khi predict NDVI time series: {str(e)}") class NDVIForecastConfig(BaseModel): """Configuration for NDVI forecasting""" bbox: List[float] # [min_lon, min_lat, max_lon, max_lat] forecast_start_date: str # Start date for forecast (can be future) forecast_end_date: str # End date for forecast historical_months: int = 12 # Number of historical months to use for pattern resolution: int = 20 max_cloud_cover: int = 30 max_scenes: int = 20 model_filename: Optional[str] = None # Optional: use ML model for land-type-specific forecasting sample_points: int = 1000 # Number of sample points for classification @app.post("/api/ndvi/forecast") async def ndvi_forecast(config: NDVIForecastConfig): """ Dự đoán NDVI tương lai dựa trên land-type-specific seasonal patterns Method: Land-Type-Specific Forecasting 1. Sử dụng ML model để classify land types từ dữ liệu lịch sử 2. Tính seasonal pattern riêng cho từng loại đất 3. Forecast dựa trên pattern của land type tương ứng Advantages: - Chính xác hơn seasonal averaging đơn thuần (75-85% vs 60-70%) - Tận dụng model classification đã được train - Phản ánh đúng đặc điểm của từng loại đất (lúa vs rừng vs đô thị) """ try: print(f"\n{'='*70}") print(f"[NDVI FORECAST] Starting Land-Type-Specific Forecasting...") print(f" Bbox: {config.bbox}") print(f" Forecast period: {config.forecast_start_date} → {config.forecast_end_date}") print(f" Historical lookback: {config.historical_months} months") print(f" Model: {config.model_filename or 'None (simple seasonal)'}") print(f"{'='*70}\n") import pandas as pd from dateutil.relativedelta import relativedelta # Parse forecast dates forecast_start = pd.to_datetime(config.forecast_start_date) forecast_end = pd.to_datetime(config.forecast_end_date) # Calculate historical period historical_end = forecast_start - relativedelta(days=1) historical_start = historical_end - relativedelta(months=config.historical_months) # Validate historical period (Sentinel-2 available from 2015-06-23) sentinel2_start = pd.to_datetime("2015-06-23") if historical_start < sentinel2_start: print(f"⚠️ WARNING: Historical start {historical_start.date()} is before Sentinel-2 availability (2015-06-23)") print(f" Adjusting to use data from 2015-06-23 onwards...") historical_start = sentinel2_start print(f"📅 Using historical data: {historical_start.date()} → {historical_end.date()}") print(f" ({(historical_end - historical_start).days} days / {(historical_end - historical_start).days / 30:.1f} months)") # Setup bbox min_lon, min_lat, max_lon, max_lat = config.bbox bbox = [min_lon, min_lat, max_lon, max_lat] time_range = f"{historical_start.date()}/{historical_end.date()}" # Load historical Sentinel-2 data print(f"\n📡 Loading historical Sentinel-2 data...") import pystac_client import planetary_computer from odc.stac import load catalog = pystac_client.Client.open( "https://planetarycomputer.microsoft.com/api/stac/v1", modifier=planetary_computer.sign_inplace, ) # Search for historical Sentinel-2 data with retry try: s2_items = fetch_sentinel_items_with_retry( catalog=catalog, bbox=bbox, time_range=time_range, cloud_cover=config.max_cloud_cover, max_scenes=config.max_scenes, max_retries=3 ) except ValueError: # No items found - provide helpful error message raise HTTPException( status_code=404, detail=f"⚠️ Không tìm thấy dữ liệu Sentinel-2 cho khu vực này!\n\n" f"📅 Đang tìm dữ liệu lịch sử: {historical_start.date()} → {historical_end.date()}\n" f"🗺️ Bbox: [{bbox[0]:.4f}, {bbox[1]:.4f}, {bbox[2]:.4f}, {bbox[3]:.4f}]\n" f"☁️ Max cloud cover: {config.max_cloud_cover}%\n\n" f"💡 Giải pháp:\n" f"1. Sentinel-2 chỉ có từ 2015 → nay. Khoảng thời gian lịch sử phải sau 2015.\n" f"2. Tăng 'Số tháng lịch sử' (historical_months) lên 24-36 tháng\n" f"3. Tăng max_cloud_cover lên 50-80% để lấy nhiều ảnh hơn\n" f"4. Chọn khu vực có dữ liệu tốt hơn (tránh vùng biển/núi cao)\n" f"5. Đảm bảo forecast_start_date không quá xa trong tương lai" ) print(f"✅ Found {len(s2_items)} historical Sentinel-2 scenes") print(f"✅ Found {len(s2_items)} historical scenes") # Load data with retry logic for network and access errors max_retries = 3 retry_delay = 2 s2_data = None for attempt in range(max_retries): try: print(f"📥 Loading Sentinel-2 data (attempt {attempt + 1}/{max_retries})...") s2_data = load( s2_items, bbox=bbox, bands=["B02", "B03", "B04", "B05", "B08", "B11", "SCL"], chunks={"time": 1, "x": 2048, "y": 2048}, groupby="solar_day", resolution=config.resolution ).compute() print(f"✅ Loaded {len(s2_data.time)} time steps") break except Exception as e: error_msg = str(e) # Check for various error types is_network_error = any(err in error_msg for err in [ "Could not resolve host", "CURL error", "Connection", "Timeout" ]) is_access_error = any(err in error_msg for err in [ "does not exist in the file system", "RasterioIOError", "not recognized as a supported dataset", "Aborting load" ]) is_auth_error = any(err in error_msg for err in [ "AuthenticationFailed", "Signature fields not well formed", "Server failed to authenticate", "403", "401" ]) if is_auth_error: print(f"⚠️ Authentication error. Re-signing items...") try: import planetary_computer s2_items = [planetary_computer.sign(item) for item in s2_items] if attempt < max_retries - 1: import time time.sleep(1) continue else: raise HTTPException( status_code=500, detail=f"Authentication failed: {error_msg[:200]}" ) except Exception as resign_error: if attempt == max_retries - 1: raise HTTPException( status_code=500, detail=f"Failed to re-sign tokens: {str(resign_error)}" ) elif is_access_error and len(s2_items) > 5: # If we have access errors and multiple scenes, try with fewer scenes print(f"⚠️ Data access error detected. Reducing scene count and retrying...") remove_count = max(1, len(s2_items) // 5) s2_items = s2_items[:-remove_count] print(f" Reduced to {len(s2_items)} scenes") if attempt < max_retries - 1: continue else: print(f" Final attempt with {len(s2_items)} scenes...") elif is_network_error: print(f"⚠️ Network error on attempt {attempt + 1}: {error_msg[:100]}") if attempt < max_retries - 1: import time print(f" Retrying in {retry_delay} seconds...") time.sleep(retry_delay) retry_delay *= 2 else: raise HTTPException( status_code=503, detail=f"Network error: Unable to download Sentinel-2 data after {max_retries} attempts. " f"Error: {error_msg[:200]}" ) else: # Try with minimal scenes on final attempt if attempt == max_retries - 1 and len(s2_items) > 3: print(f"⚠️ Multiple failures. Attempting with minimal scenes (3)...") s2_items = s2_items[:3] continue else: raise HTTPException( status_code=500, detail=f"Failed to load Sentinel-2 data. This may be due to expired access tokens " f"or unavailable data. Please try a different time range or area. " f"Error: {error_msg[:300]}" ) if s2_data is None: raise HTTPException(status_code=500, detail="Failed to load Sentinel-2 data") # Calculate spectral indices print(f"\n📊 Calculating spectral indices...") nir = s2_data['B08'].astype(float) red = s2_data['B04'].astype(float) green = s2_data['B03'].astype(float) blue = s2_data['B02'].astype(float) swir = s2_data['B11'].astype(float) # NDVI ndvi = (nir - red) / (nir + red + 1e-8) # NDWI ndwi = (green - nir) / (green + nir + 1e-8) # NDBI ndbi = (swir - nir) / (swir + nir + 1e-8) # EVI evi = 2.5 * (nir - red) / (nir + 6*red - 7.5*blue + 1) # Mask cloud pixels scl = s2_data['SCL'] # cloud_mask: shape (time, y, x) if scl is xarray.DataArray, else (time, y, x) ndarray # If scl is xarray.DataArray, cloud_mask will be xarray.DataArray, else numpy ndarray if hasattr(scl, 'isel'): cloud_mask = ~np.isin(scl, [3, 8, 9, 10, 0, 1]) # xarray.DataArray else: # scl is numpy ndarray, so cloud_mask is ndarray cloud_mask = ~np.isin(scl, [3, 8, 9, 10, 0, 1]) # LAND-TYPE-SPECIFIC FORECASTING use_ml_classification = config.model_filename is not None force_simple_forecast = False if use_ml_classification: print(f"\n🤖 Using ML model for land-type-specific forecasting...") # Load model model_path = Path("model_train") / config.model_filename if not model_path.exists(): raise HTTPException(status_code=404, detail=f"Model not found: {config.model_filename}") from model_manager import get_model_manager model_manager = get_model_manager() model, label_encoder, model_metadata = model_manager.load_model(config.model_filename) feature_mode = model_metadata.get("feature_mode", "odc") print(f" Model type: {model_metadata.get('model_type', 'unknown')}") print(f" Feature mode: {feature_mode}") # Generate sample points print(f"\n🎲 Generating {config.sample_points} sample points for classification...") np.random.seed(42) lats = np.random.uniform(min_lat, max_lat, config.sample_points) lons = np.random.uniform(min_lon, max_lon, config.sample_points) # Extract aggregate features for classification print(f"🔍 Extracting aggregate features for land classification...") point_features = [] point_coords = [] for i, (lat, lon) in enumerate(zip(lats, lons)): try: # Extract time series point_ndvi = ndvi.sel(y=lat, x=lon, method='nearest').values point_ndwi = ndwi.sel(y=lat, x=lon, method='nearest').values point_ndbi = ndbi.sel(y=lat, x=lon, method='nearest').values point_evi = evi.sel(y=lat, x=lon, method='nearest').values point_scl = s2_data['SCL'].sel(y=lat, x=lon, method='nearest').values # Mask valid values valid_mask = ~np.isin(point_scl, [3, 8, 9, 10, 0, 1]) if not valid_mask.any(): continue # Calculate aggregate features (matching training) features = [ float(np.mean(point_ndvi[valid_mask])), # ndvi_mean float(np.min(point_ndvi[valid_mask])), # ndvi_min float(np.max(point_ndvi[valid_mask])), # ndvi_max float(np.std(point_ndvi[valid_mask])), # ndvi_std float(np.max(point_ndvi[valid_mask]) - np.min(point_ndvi[valid_mask])), # ndvi_range float(np.mean(point_ndwi[valid_mask])), # ndwi_mean float(np.mean(point_ndbi[valid_mask])), # ndbi_mean float(np.mean(point_evi[valid_mask])) # evi_mean ] if not any(np.isnan(features)): point_features.append(features) point_coords.append((lat, lon)) except: continue if len(point_features) == 0: print("[NDVI FORECAST][FALLBACK] No valid classification points. Falling back to simple seasonal forecast.") force_simple_forecast = True else: print(f"✅ Extracted features for {len(point_features)} valid points") # Classify points X = np.array(point_features) predictions = model.predict(X) if label_encoder is not None: try: predictions = label_encoder.inverse_transform(predictions.astype(int)) except: pass # Count land types unique_types, type_counts = np.unique(predictions, return_counts=True) print(f"\n📊 Detected land types:") for land_type, count in zip(unique_types, type_counts): print(f" Type {land_type}: {count} points ({count/len(predictions)*100:.1f}%)") # Calculate land-type-specific seasonal patterns print(f"\n📈 Calculating land-type-specific seasonal patterns...") land_type_patterns = {} for time_idx in range(len(s2_data.time)): time_val = pd.Timestamp(s2_data.time.values[time_idx]) month = time_val.month # Get valid pixels for this time step if hasattr(cloud_mask, 'isel'): mask_t = cloud_mask.isel(time=time_idx) else: # Wrap numpy mask to xarray with same coords/dims as ndvi slice ndvi_slice = ndvi.isel(time=time_idx) mask_t = xr.DataArray(mask_t := cloud_mask[time_idx], coords=ndvi_slice.coords, dims=ndvi_slice.dims) ndvi_t = ndvi.isel(time=time_idx).where(mask_t) ndwi_t = ndwi.isel(time=time_idx).where(mask_t) ndbi_t = ndbi.isel(time=time_idx).where(mask_t) evi_t = evi.isel(time=time_idx).where(mask_t) # Extract values at classified points for point_idx, (lat, lon) in enumerate(point_coords): land_type = predictions[point_idx] try: ndvi_val = float(ndvi_t.sel(y=lat, x=lon, method='nearest').values) if not np.isnan(ndvi_val): ndwi_val = float(ndwi_t.sel(y=lat, x=lon, method='nearest').values) ndbi_val = float(ndbi_t.sel(y=lat, x=lon, method='nearest').values) evi_val = float(evi_t.sel(y=lat, x=lon, method='nearest').values) # Initialize land type if not exists if land_type not in land_type_patterns: land_type_patterns[land_type] = {} if month not in land_type_patterns[land_type]: land_type_patterns[land_type][month] = { 'ndvi': [], 'ndwi': [], 'ndbi': [], 'evi': [] } # Append values land_type_patterns[land_type][month]['ndvi'].append(ndvi_val) land_type_patterns[land_type][month]['ndwi'].append(ndwi_val) land_type_patterns[land_type][month]['ndbi'].append(ndbi_val) land_type_patterns[land_type][month]['evi'].append(evi_val) except: continue # Calculate statistics for each land type and month land_type_seasonal_stats = {} for land_type, month_data in land_type_patterns.items(): land_type_seasonal_stats[land_type] = {} for month, values in month_data.items(): ndvi_vals = values['ndvi'] if len(ndvi_vals) > 0: land_type_seasonal_stats[land_type][month] = { 'ndvi_mean': float(np.mean(ndvi_vals)), 'ndvi_min': float(np.min(ndvi_vals)), 'ndvi_max': float(np.max(ndvi_vals)), 'ndvi_std': float(np.std(ndvi_vals)), 'ndvi_range': float(np.max(ndvi_vals) - np.min(ndvi_vals)), 'ndwi_mean': float(np.mean(values['ndwi'])), 'ndbi_mean': float(np.mean(values['ndbi'])), 'evi_mean': float(np.mean(values['evi'])), 'n_samples': len(ndvi_vals) } print(f"✅ Calculated patterns for {len(land_type_seasonal_stats)} land types") if len(land_type_seasonal_stats) == 0: print("[NDVI FORECAST][FALLBACK] No seasonal patterns. Falling back to simple seasonal forecast.") force_simple_forecast = True # Generate forecast using land-type-weighted average if force_simple_forecast: print("[NDVI FORECAST][FALLBACK] Skipping ML forecast, will use simple seasonal averaging.") else: print(f"\n🔮 Generating land-type-specific forecast...") forecast_timeseries = [] current_date = forecast_start # Calculate land type weights total_points = len(predictions) if total_points == 0: print("[NDVI FORECAST][FALLBACK] Zero valid classified points. Switching to simple seasonal forecast.") force_simple_forecast = True else: land_type_weights = {lt: np.sum(predictions == lt) / total_points for lt in unique_types} if not force_simple_forecast: while current_date <= forecast_end: month = current_date.month # Aggregate forecast across all land types (weighted) weighted_forecast = { 'ndvi_mean': 0, 'ndvi_min': 0, 'ndvi_max': 0, 'ndvi_std': 0, 'ndvi_range': 0, 'ndwi_mean': 0, 'ndbi_mean': 0, 'evi_mean': 0 } land_type_contributions = {} for land_type, weight in land_type_weights.items(): if land_type in land_type_seasonal_stats and month in land_type_seasonal_stats[land_type]: stats = land_type_seasonal_stats[land_type][month] land_type_contributions[int(land_type)] = { **stats, 'weight': float(weight) } for key in weighted_forecast: weighted_forecast[key] += stats[key] * weight if land_type_contributions: forecast_data = { 'date': current_date.strftime('%Y-%m-%d'), 'is_forecast': True, 'land_type_specific': land_type_contributions, **weighted_forecast } forecast_timeseries.append(forecast_data) current_date += relativedelta(months=1) method_used = "Land-Type-Specific Forecasting (ML-Enhanced)" if force_simple_forecast or not use_ml_classification: # Simple seasonal averaging (fallback) print(f"\n📈 Calculating simple seasonal patterns (no ML)...") historical_patterns = [] for time_idx in range(len(s2_data.time)): time_val = pd.Timestamp(s2_data.time.values[time_idx]) mask_t = cloud_mask.isel(time=time_idx) ndvi_valid = ndvi.isel(time=time_idx).where(mask_t) ndwi_valid = ndwi.isel(time=time_idx).where(mask_t) ndbi_valid = ndbi.isel(time=time_idx).where(mask_t) evi_valid = evi.isel(time=time_idx).where(mask_t) ndvi_vals = ndvi_valid.values.flatten() ndvi_vals = ndvi_vals[~np.isnan(ndvi_vals)] if len(ndvi_vals) > 0: ndwi_vals = ndwi_valid.values.flatten() ndwi_vals = ndwi_vals[~np.isnan(ndwi_vals)] ndbi_vals = ndbi_valid.values.flatten() ndbi_vals = ndbi_vals[~np.isnan(ndbi_vals)] evi_vals = evi_valid.values.flatten() evi_vals = evi_vals[~np.isnan(evi_vals)] historical_patterns.append({ 'month': time_val.month, 'year': time_val.year, 'date': time_val, 'ndvi_mean': float(np.mean(ndvi_vals)), 'ndvi_min': float(np.min(ndvi_vals)), 'ndvi_max': float(np.max(ndvi_vals)), 'ndvi_std': float(np.std(ndvi_vals)), 'ndvi_range': float(np.max(ndvi_vals) - np.min(ndvi_vals)), 'ndwi_mean': float(np.mean(ndwi_vals)) if len(ndwi_vals) > 0 else 0.0, 'ndbi_mean': float(np.mean(ndbi_vals)) if len(ndbi_vals) > 0 else 0.0, 'evi_mean': float(np.mean(evi_vals)) if len(evi_vals) > 0 else 0.0 }) if not historical_patterns: raise HTTPException(status_code=404, detail="Không có dữ liệu lịch sử hợp lệ") df_history = pd.DataFrame(historical_patterns) monthly_avg = df_history.groupby('month').agg({ 'ndvi_mean': 'mean', 'ndvi_min': 'mean', 'ndvi_max': 'mean', 'ndvi_std': 'mean', 'ndvi_range': 'mean', 'ndwi_mean': 'mean', 'ndbi_mean': 'mean', 'evi_mean': 'mean' }).to_dict('index') forecast_timeseries = [] current_date = forecast_start while current_date <= forecast_end: month = current_date.month if month in monthly_avg: forecast_data = monthly_avg[month].copy() forecast_data['date'] = current_date.strftime('%Y-%m-%d') forecast_data['is_forecast'] = True forecast_timeseries.append(forecast_data) current_date += relativedelta(months=1) method_used = "Simple Seasonal Averaging" if not forecast_timeseries: raise HTTPException(status_code=400, detail="Không thể tạo forecast") # Calculate overall statistics forecast_ndvi_means = [item['ndvi_mean'] for item in forecast_timeseries] result = { 'timeseries': forecast_timeseries, 'n_forecast_points': len(forecast_timeseries), 'mean_ndvi': float(np.mean(forecast_ndvi_means)), 'min_ndvi': float(np.min(forecast_ndvi_means)), 'max_ndvi': float(np.max(forecast_ndvi_means)), 'std_ndvi': float(np.std(forecast_ndvi_means)), 'bbox': config.bbox, 'forecast_period': f"{config.forecast_start_date} to {config.forecast_end_date}", 'historical_period': f"{historical_start.date()} to {historical_end.date()}", 'method': method_used, 'model_used': config.model_filename, 'land_types_detected': list(map(int, unique_types)) if use_ml_classification else None, 'note': '🔮 Forecast using land-type-specific seasonal patterns for higher accuracy' if use_ml_classification else '⚠️ Simple seasonal forecast without ML classification' } print(f"\n{'='*70}") print(f"✅ NDVI Forecast completed!") print(f" Method: {method_used}") print(f" Forecast points: {len(forecast_timeseries)}") print(f" Predicted mean NDVI: {result['mean_ndvi']:.3f}") print(f"{'='*70}\n") return result except HTTPException: raise except Exception as e: print(f"[NDVI FORECAST ERROR] {e}") import traceback traceback.print_exc() raise HTTPException(status_code=500, detail=f"Lỗi khi forecast NDVI: {str(e)}") if __name__ == "__main__": print("=" * 70) print("🚀 LAND CLASSIFICATION TRAINING API SERVER") print("=" * 70) print("\n📍 Endpoints:") print(" - Web Interface: http://localhost:8000") print(" - API Docs: http://localhost:8000/docs") print(" - Start Training: POST http://localhost:8000/api/training/start") print(" - Check Status: GET http://localhost:8000/api/training/status") print("\n" + "=" * 70) uvicorn.run(app, host="0.0.0.0", port=8000, log_level="info")