222 lines
7.6 KiB
Python
222 lines
7.6 KiB
Python
import json
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import shutil
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import importlib.util
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from pathlib import Path
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from datetime import datetime, timedelta
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import numpy as np
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import rasterio
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from rasterio.warp import Resampling
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from scipy import ndimage
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RESOLUTION_RATIO = 21
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try:
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import efast as efast_fusion
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except ImportError:
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import site
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efast_fusion = None
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for site_pkg in site.getsitepackages():
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candidate = Path(site_pkg) / "efast" / "efast.py"
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if candidate.exists():
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spec = importlib.util.spec_from_file_location(
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"efast_fusion_module", candidate
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)
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efast_fusion = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(efast_fusion)
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break
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if efast_fusion is None:
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raise ImportError(
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"efast package not found. Install with: pip install git+https://github.com/DHI-GRAS/efast.git"
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)
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def _load_clouds(clouds_file):
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clouds = {"s2": set(), "s3": set()}
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if clouds_file.exists():
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clouds_data = json.loads(clouds_file.read_text())
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clouds["s2"] = set(clouds_data.get("s2", []))
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clouds["s3"] = set(clouds_data.get("s3", []))
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return clouds
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def _reproject_to_target(
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data, src_transform, src_crs, target_bounds, target_crs, width, height, resampling
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):
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dst_transform = rasterio.transform.from_bounds(
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target_bounds.left,
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target_bounds.bottom,
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target_bounds.right,
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target_bounds.top,
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width,
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height,
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)
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reprojected, _ = rasterio.warp.reproject(
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source=data,
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destination=np.zeros((data.shape[0], height, width), dtype=data.dtype),
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src_transform=src_transform,
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src_crs=src_crs,
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dst_transform=dst_transform,
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dst_crs=target_crs,
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resampling=resampling,
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)
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return reprojected, dst_transform
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def prepare_s2(season, site_position, site_name, date_range=None):
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s2_dir = Path(f"data/{site_name}/{season}/raw/s2/")
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s3_dir = Path(f"data/{site_name}/{season}/raw/s3/")
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s2_output_dir = Path(f"data/{site_name}/{season}/prepared/s2/")
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clouds_file = Path(f"data/{site_name}/{season}/clouds.json")
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clouds = _load_clouds(clouds_file)
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s2_output_dir.mkdir(parents=True, exist_ok=True)
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s3_files = [f for f in s3_dir.glob("*.geotiff") if f.name not in clouds["s3"]]
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if not s3_files:
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raise ValueError("No non-cloud S3 files found for reference bounds")
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with rasterio.open(s3_files[0]) as s3_ref:
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target_bounds = s3_ref.bounds
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target_crs = s3_ref.crs
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s3_width = s3_ref.width
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s3_height = s3_ref.height
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s2_width = s3_width * RESOLUTION_RATIO
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s2_height = s3_height * RESOLUTION_RATIO
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for s2_file in s2_dir.glob("*.geotiff"):
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if s2_file.name in clouds["s2"]:
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continue
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date_str = s2_file.name.split("_")[0]
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refl_dst = s2_output_dir / f"S2A_MSIL2A_{date_str}_REFL.tif"
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if not refl_dst.exists():
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with rasterio.open(s2_file) as src:
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data = src.read().astype("float32") / 10000.0
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reprojected_data, dst_transform = _reproject_to_target(
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data,
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src.transform,
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src.crs,
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target_bounds,
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target_crs,
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s2_width,
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s2_height,
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Resampling.cubic,
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)
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profile = src.profile.copy()
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profile.update(
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{
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"dtype": "float32",
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"nodata": 0,
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"width": s2_width,
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"height": s2_height,
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"transform": dst_transform,
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"crs": target_crs,
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}
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)
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with rasterio.open(refl_dst, "w", **profile) as dst_file:
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dst_file.write(reprojected_data)
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dist_cloud_dst = s2_output_dir / f"S2A_MSIL2A_{date_str}_DIST_CLOUD.tif"
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if not dist_cloud_dst.exists():
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with rasterio.open(refl_dst) as src:
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s2_hr = src.read(1)
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mask = s2_hr == 0
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distance_to_cloud_hr = np.clip(
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ndimage.distance_transform_edt(~mask), 0, 255
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).astype("float32")
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distance_to_cloud_lr, lr_transform = _reproject_to_target(
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distance_to_cloud_hr[np.newaxis, :, :],
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src.transform,
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src.crs,
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target_bounds,
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target_crs,
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s3_width,
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s3_height,
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Resampling.average,
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)
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distance_to_cloud_lr = distance_to_cloud_lr[0]
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profile = src.profile.copy()
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profile.update(
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{
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"count": 1,
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"dtype": "float32",
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"width": s3_width,
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"height": s3_height,
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"transform": lr_transform,
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}
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)
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with rasterio.open(dist_cloud_dst, "w", **profile) as dst:
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dst.write(distance_to_cloud_lr, 1)
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def prepare_s3(season, site_position, site_name, date_range=None):
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s3_dir = Path(f"data/{site_name}/{season}/raw/s3/")
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s3_preprocessed_dir = Path(f"data/{site_name}/{season}/prepared/s3/")
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clouds_file = Path(f"data/{site_name}/{season}/clouds.json")
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clouds = _load_clouds(clouds_file)
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s3_preprocessed_dir.mkdir(parents=True, exist_ok=True)
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for s3_file in s3_dir.glob("*.geotiff"):
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if s3_file.name in clouds["s3"]:
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continue
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date_str = s3_file.name.split("_")[0]
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output_path = s3_preprocessed_dir / f"composite_{date_str}.tif"
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if output_path.exists():
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continue
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shutil.copy2(s3_file, output_path)
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def run_efast(season, site_position, site_name, date_range=None):
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lat, lon = site_position
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datetime_range = date_range or f"{season}-01-01/{season}-12-31"
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efast_base_dir = Path(f"data/{site_name}/{season}/prepared/")
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s2_output_dir = efast_base_dir / "s2"
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s3_output_dir = efast_base_dir / "s3"
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fusion_output_dir = efast_base_dir / "fusion"
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fusion_output_dir.mkdir(parents=True, exist_ok=True)
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print(f"[EFAST] Starting fusion: {site_name} ({lat:.6f}, {lon:.6f}), {season}")
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start_str, end_str = datetime_range.split("/")
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start_date = datetime.strptime(start_str, "%Y-%m-%d")
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end_date = datetime.strptime(end_str, "%Y-%m-%d")
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current_date = start_date
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while current_date <= end_date:
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date_str = current_date.strftime("%Y%m%d")
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output_file = fusion_output_dir / f"REFL_{date_str}.tif"
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if output_file.exists():
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print(f"[EFAST] Skipping {date_str} (exists)")
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current_date += timedelta(days=1)
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continue
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try:
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efast_fusion.fusion(
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current_date,
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s3_output_dir,
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s2_output_dir,
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fusion_output_dir,
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product="REFL",
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max_days=30,
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date_position=2,
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minimum_acquisition_importance=0.0,
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ratio=RESOLUTION_RATIO,
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)
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if output_file.exists():
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print(f"[EFAST] Saved: {output_file}")
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else:
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print(f"[EFAST] No output for {date_str} (insufficient nearby data)")
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except Exception as e:
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print(f"[EFAST] Error processing {date_str}: {e}")
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current_date += timedelta(days=1)
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print("[EFAST] Completed")
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