hoàn thành chức năng phân lô trên ảnh predict
This commit is contained in:
Victor Phan
+291
-1
@@ -224,6 +224,9 @@ class PredictionConfig(BaseModel):
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# Cloud removal strategy
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cloud_removal_method: str = "classic"
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cloud_removal_model: Optional[str] = None # Optional: .pth model filename for deep learning cloud removal
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# Shapefile overlay for visualization
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shapefile_overlay: Optional[str] = None # Path to shapefile for overlaying boundaries
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class TrainingStatus(BaseModel):
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@@ -286,6 +289,7 @@ class PredictionWithNDVIConfig(BaseModel):
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export_classification: bool = True # Export classification raster
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cloud_removal_method: str = "classic"
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cloud_removal_model: Optional[str] = None # Optional: .pth model filename for deep learning cloud removal
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shapefile_overlay: Optional[str] = None # Path to shapefile for overlaying boundaries
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class CloudRemovalTrainingConfig(BaseModel):
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@@ -1228,6 +1232,87 @@ async def list_training_files():
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}
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@app.get("/api/overlay/shapefiles")
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async def list_overlay_shapefiles():
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"""Liệt kê các shapefile có sẵn cho overlay trên prediction"""
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overlay_dirs = ["region", "ChauThanh", "ThuanHoa"]
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shapefiles = []
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for overlay_dir in overlay_dirs:
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dir_path = Path(overlay_dir)
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if not dir_path.exists():
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continue
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# Find all .shp files in this directory and subdirectories
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for shp_file in dir_path.rglob("*.shp"):
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try:
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file_size = shp_file.stat().st_size
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file_modified = datetime.fromtimestamp(shp_file.stat().st_mtime).isoformat()
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# Try to read shapefile to get feature count and bbox
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import geopandas as gpd
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gdf = gpd.read_file(str(shp_file))
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feature_count = len(gdf)
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# Calculate bbox (always in EPSG:4326 for consistency)
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bbox = None
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if not gdf.empty and gdf.crs:
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try:
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# Reproject to EPSG:4326 if needed
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if gdf.crs != "EPSG:4326":
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gdf_4326 = gdf.to_crs("EPSG:4326")
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else:
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gdf_4326 = gdf
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# Get total bounds [minx, miny, maxx, maxy]
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bounds = gdf_4326.total_bounds
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if len(bounds) == 4:
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bbox = [
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float(bounds[0]), # min_lon
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float(bounds[1]), # min_lat
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float(bounds[2]), # max_lon
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float(bounds[3]) # max_lat
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]
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except Exception as bbox_error:
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print(f"[WARNING] Cannot calculate bbox for {shp_file}: {bbox_error}")
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# Get relative path from workspace root
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relative_path = str(shp_file)
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shapefiles.append({
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"filename": shp_file.name,
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"path": relative_path,
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"directory": overlay_dir,
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"size_bytes": file_size,
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"size_mb": round(file_size / 1024 / 1024, 2),
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"modified": file_modified,
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"feature_count": feature_count,
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"crs": str(gdf.crs) if gdf.crs else "Unknown",
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"bbox": bbox # [min_lon, min_lat, max_lon, max_lat] in EPSG:4326
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})
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except Exception as e:
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# If cannot read shapefile, just add basic info
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file_size = shp_file.stat().st_size
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file_modified = datetime.fromtimestamp(shp_file.stat().st_mtime).isoformat()
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relative_path = str(shp_file)
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shapefiles.append({
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"filename": shp_file.name,
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"path": relative_path,
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"directory": overlay_dir,
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"size_bytes": file_size,
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"size_mb": round(file_size / 1024 / 1024, 2),
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"modified": file_modified,
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"error": f"Cannot read shapefile: {str(e)}"
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})
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return {
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"shapefiles": shapefiles,
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"count": len(shapefiles),
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"directories": overlay_dirs
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}
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@app.get("/api/training/shapefile/{filename}/labels")
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async def get_shapefile_labels(filename: str):
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"""Lấy các label từ một shapefile cụ thể"""
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@@ -1729,6 +1814,85 @@ def update_progress(message: str):
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print(f"[PROGRESS] {message}")
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def rasterize_shapefile_overlay(shapefile_path, reference_raster, boundary_value=255):
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"""
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Rasterize shapefile boundaries to overlay on prediction result.
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Args:
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shapefile_path: Path to shapefile
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reference_raster: xarray DataArray to match dimensions and CRS
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boundary_value: Value to use for boundaries (default 255 for white)
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Returns:
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numpy array with boundaries, same shape as reference_raster
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"""
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try:
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import geopandas as gpd
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from rasterio.features import rasterize
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import numpy as np
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# Read shapefile
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gdf = gpd.read_file(shapefile_path)
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print(f"[OVERLAY] Loaded shapefile with {len(gdf)} features, CRS: {gdf.crs}")
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# Ensure CRS matches
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target_crs = reference_raster.rio.crs
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if gdf.crs != target_crs:
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print(f"[OVERLAY] Reprojecting from {gdf.crs} to {target_crs}")
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gdf = gdf.to_crs(target_crs)
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# Get raster dimensions and transform
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height, width = reference_raster.shape
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transform = reference_raster.rio.transform()
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print(f"[OVERLAY] Raster dimensions: {height}x{width}")
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print(f"[OVERLAY] Transform: {transform}")
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# Calculate appropriate buffer size based on pixel resolution
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# Get pixel size from transform (transform[0] is x resolution)
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pixel_size = abs(transform[0]) # in CRS units
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# Very thin boundary - only 0.2 pixels wide for 1px line
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buffer_distance = pixel_size * 0.2
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print(f"[OVERLAY] Pixel size: {pixel_size}, Buffer distance: {buffer_distance} (thin 1px line)")
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# Create boundary geometries with minimal buffering
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boundary_geoms = []
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for idx, geom in enumerate(gdf.geometry):
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if geom is not None and geom.is_valid:
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# Get boundary of each polygon
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boundary = geom.boundary
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if boundary is not None:
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# Minimal buffer for 1-pixel thin line
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buffered = boundary.buffer(buffer_distance)
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boundary_geoms.append((buffered, boundary_value))
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if not boundary_geoms:
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print(f"[WARNING] No valid boundary geometries found in {shapefile_path}")
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return np.zeros((height, width), dtype=np.uint8)
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print(f"[OVERLAY] Rasterizing {len(boundary_geoms)} boundaries...")
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# Rasterize boundaries
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boundary_mask = rasterize(
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shapes=boundary_geoms,
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out_shape=(height, width),
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transform=transform,
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fill=0, # Background
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dtype=np.uint8
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)
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boundary_count = np.count_nonzero(boundary_mask)
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print(f"[OVERLAY] Boundary pixels: {boundary_count} / {height*width} ({boundary_count/(height*width)*100:.2f}%)")
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if boundary_count == 0:
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print(f"[OVERLAY WARNING] No boundary pixels were rasterized! Check CRS and geometry overlap.")
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return boundary_mask
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except Exception as e:
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print(f"[ERROR] Failed to rasterize shapefile {shapefile_path}: {e}")
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return None
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def update_prediction_progress(message: str):
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"""Cập nhật prediction progress message"""
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global prediction_status
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@@ -2083,6 +2247,33 @@ async def run_prediction(config: PredictionConfig):
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prediction_da.rio.to_raster(str(output_file), driver="GTiff")
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# ============ SHAPEFILE OVERLAY ============
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overlay_mask = None
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if config.shapefile_overlay:
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prediction_status["progress"] = f"Đang overlay shapefile: {config.shapefile_overlay}..."
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print(f"[OVERLAY] Shapefile overlay requested: {config.shapefile_overlay}")
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# Validate shapefile path exists
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shapefile_path = Path(config.shapefile_overlay)
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if not shapefile_path.exists():
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print(f"[OVERLAY ERROR] Shapefile not found: {shapefile_path}")
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print(f"[OVERLAY ERROR] Absolute path: {shapefile_path.absolute()}")
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print(f"[OVERLAY ERROR] Current working directory: {Path.cwd()}")
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else:
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print(f"[OVERLAY] Shapefile exists: {shapefile_path.absolute()}")
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try:
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overlay_mask = rasterize_shapefile_overlay(str(shapefile_path), prediction_da)
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if overlay_mask is not None and np.count_nonzero(overlay_mask) > 0:
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print(f"[OVERLAY] Successfully rasterized shapefile boundaries ({np.count_nonzero(overlay_mask)} pixels)")
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else:
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print(f"[OVERLAY WARNING] Shapefile rasterized but no boundary pixels found")
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except Exception as overlay_error:
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print(f"[OVERLAY ERROR] Exception: {overlay_error}")
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import traceback
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traceback.print_exc()
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else:
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print(f"[OVERLAY] No shapefile overlay requested")
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# Generate PNG preview for web display
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prediction_status["progress"] = "Đang tạo PNG preview..."
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png_file = output_dir / f"prediction_{timestamp}.png"
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@@ -2097,6 +2288,42 @@ async def run_prediction(config: PredictionConfig):
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# Plot prediction with colormap
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im = ax.imshow(predictions_2d, cmap='tab20', interpolation='nearest')
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# Overlay shapefile boundaries if available
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if overlay_mask is not None and np.count_nonzero(overlay_mask) > 0:
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print(f"[PNG OVERLAY] Overlaying {np.count_nonzero(overlay_mask)} boundary pixels")
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# Create a mask for boundaries (where overlay_mask > 0)
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boundary_mask = overlay_mask > 0
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# Method: Direct overlay with high-contrast colors
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# Create RGBA overlay image
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overlay_rgba = np.zeros((*predictions_2d.shape, 4))
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overlay_rgba[boundary_mask, 0] = 1.0 # Red = 1.0 (white)
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overlay_rgba[boundary_mask, 1] = 1.0 # Green = 1.0 (white)
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overlay_rgba[boundary_mask, 2] = 1.0 # Blue = 1.0 (white)
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overlay_rgba[boundary_mask, 3] = 1.0 # Alpha = 1.0 (fully opaque)
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# Overlay on top of prediction
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ax.imshow(overlay_rgba, interpolation='nearest')
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# Also add a black outline for better contrast
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from scipy import ndimage
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boundary_dilated = ndimage.binary_dilation(boundary_mask, iterations=1)
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boundary_outline = boundary_dilated & ~boundary_mask
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outline_rgba = np.zeros((*predictions_2d.shape, 4))
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outline_rgba[boundary_outline, 0] = 0.0 # Black outline
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outline_rgba[boundary_outline, 1] = 0.0
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outline_rgba[boundary_outline, 2] = 0.0
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outline_rgba[boundary_outline, 3] = 0.8
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ax.imshow(outline_rgba, interpolation='nearest')
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print(f"[PNG OVERLAY] Added shapefile boundaries to visualization (direct overlay method)")
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else:
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print(f"[PNG OVERLAY] No overlay mask or empty mask (pixels: {np.count_nonzero(overlay_mask) if overlay_mask is not None else 0})")
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ax.set_title(f'Land Classification - {timestamp}', fontsize=16, fontweight='bold', pad=20)
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ax.set_xlabel('X (pixels)', fontsize=11)
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ax.set_ylabel('Y (pixels)', fontsize=11)
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@@ -2176,7 +2403,9 @@ async def run_prediction(config: PredictionConfig):
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"n_features": features.shape[1],
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"feature_mode": feature_mode,
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"used_radar": use_radar,
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"model_used": config.model_filename
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"model_used": config.model_filename,
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"shapefile_overlay": config.shapefile_overlay,
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"overlay_applied": overlay_mask is not None
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}
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# Auto generate prediction report
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@@ -4355,8 +4584,69 @@ async def predict_with_ndvi(config: PredictionWithNDVIConfig, background_tasks:
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import matplotlib.pyplot as plt
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from matplotlib.patches import Patch
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# ============ SHAPEFILE OVERLAY ============
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overlay_mask = None
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if config.shapefile_overlay:
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print(f"[OVERLAY] Shapefile overlay requested: {config.shapefile_overlay}")
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# Validate shapefile path exists
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shapefile_path = Path(config.shapefile_overlay)
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if not shapefile_path.exists():
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print(f"[OVERLAY ERROR] Shapefile not found: {shapefile_path}")
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print(f"[OVERLAY ERROR] Absolute path: {shapefile_path.absolute()}")
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else:
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print(f"[OVERLAY] Shapefile exists: {shapefile_path.absolute()}")
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try:
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# Import rioxarray for rio accessor
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import rioxarray
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# Create temporary DataArray for rasterization
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temp_da = xr.DataArray(
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prediction_raster,
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coords={
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"y": np.linspace(bbox[3], bbox[1], height),
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"x": np.linspace(bbox[0], bbox[2], width)
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},
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dims=["y", "x"]
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)
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temp_da.rio.write_crs("EPSG:4326", inplace=True)
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temp_da.rio.write_transform(transform, inplace=True)
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overlay_mask = rasterize_shapefile_overlay(str(shapefile_path), temp_da)
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if overlay_mask is not None and np.count_nonzero(overlay_mask) > 0:
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print(f"[OVERLAY] Successfully rasterized shapefile boundaries ({np.count_nonzero(overlay_mask)} pixels)")
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else:
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print(f"[OVERLAY WARNING] Shapefile rasterized but no boundary pixels found")
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except Exception as overlay_error:
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print(f"[OVERLAY ERROR] Exception: {overlay_error}")
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import traceback
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traceback.print_exc()
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else:
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print(f"[OVERLAY] No shapefile overlay requested")
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fig, ax = plt.subplots(figsize=(14, 10), dpi=150)
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im = ax.imshow(prediction_raster, cmap='tab20', interpolation='nearest')
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# Overlay shapefile boundaries if available
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if overlay_mask is not None and np.count_nonzero(overlay_mask) > 0:
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print(f"[PNG OVERLAY] Overlaying {np.count_nonzero(overlay_mask)} boundary pixels (1px thin line)")
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# Create a mask for boundaries
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boundary_mask = overlay_mask > 0
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# Create RGBA overlay image - thin 1px white line only
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overlay_rgba = np.zeros((*prediction_raster.shape, 4))
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overlay_rgba[boundary_mask, 0] = 1.0 # White (R=1)
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overlay_rgba[boundary_mask, 1] = 1.0 # White (G=1)
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overlay_rgba[boundary_mask, 2] = 1.0 # White (B=1)
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overlay_rgba[boundary_mask, 3] = 1.0 # Fully opaque
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ax.imshow(overlay_rgba, interpolation='nearest')
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print(f"[PNG OVERLAY] Added thin 1px shapefile boundaries to visualization")
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else:
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print(f"[PNG OVERLAY] No overlay mask or empty mask")
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ax.set_title(f'Land Classification - {timestamp}', fontsize=16, fontweight='bold', pad=20)
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ax.set_xlabel('X (pixels)', fontsize=11)
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ax.set_ylabel('Y (pixels)', fontsize=11)
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