217 lines
8.5 KiB
Python
217 lines
8.5 KiB
Python
"""Data preparation: S2/S3 preprocessing for fusion."""
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import json
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import shutil
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from pathlib import Path
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from collections import defaultdict
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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 rasterio.vrt import WarpedVRT
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from rasterio import shutil as rio_shutil
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RESOLUTION_RATIO = 21
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def _import_distance_to_clouds():
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"""Lazy import of efast.distance_to_clouds."""
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try:
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from efast.s2_processing import distance_to_clouds
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return distance_to_clouds
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except ImportError:
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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_excluded(season, site_name, cleaning_strategy):
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"""Load excluded filenames from NDVI timeseries (excluded_aggressive / excluded_nonaggressive)."""
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base = Path(f"data/{site_name}/{season}/raw/preselection")
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key = f"excluded_{cleaning_strategy}"
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clouds = {"s2": set(), "s3": set()}
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for source in ["s2", "s3"]:
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ts_file = base / f"{source}_preselection.json"
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if ts_file.exists():
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data = json.loads(ts_file.read_text())
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clouds[source] = {e["filename"] for e in data if e.get(key)}
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return clouds
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def _get_base_dir(season, site_name, cleaning_strategy):
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return Path(f"data/{site_name}/{season}/prepared_{cleaning_strategy}/")
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def _reproject_raster_to_target(
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src_path,
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dst_path,
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target_bounds,
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target_crs,
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width,
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height,
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resampling=Resampling.bilinear,
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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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with rasterio.open(src_path) as src:
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vrt_options = {
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"transform": dst_transform,
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"height": height,
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"width": width,
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"crs": target_crs,
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"resampling": resampling,
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}
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with WarpedVRT(src, **vrt_options) as vrt:
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profile = vrt.profile.copy()
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profile.update({"dtype": "float32", "nodata": 0, "driver": "GTiff"})
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rio_shutil.copy(vrt, dst_path, **profile)
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def prepare_s2(season, site_position, site_name, cleaning_strategy="aggressive", date_range=None):
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lat, lon = site_position
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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 = _get_base_dir(season, site_name, cleaning_strategy) / "s2"
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clouds = _load_excluded(season, site_name, cleaning_strategy)
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s2_output_dir.mkdir(parents=True, exist_ok=True)
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print(f"[S2-PREP] Starting preparation: {site_name} ({lat:.6f}, {lon:.6f}), {season}, strategy={cleaning_strategy}")
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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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s2_width = s3_ref.width * RESOLUTION_RATIO
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s2_height = s3_ref.height * RESOLUTION_RATIO
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for s2_file in sorted(s2_dir.glob("*.geotiff")):
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if s2_file.name in clouds["s2"]:
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print(f"[S2-PREP] Skipping {s2_file.name} (excluded by {cleaning_strategy})")
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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 refl_dst.exists():
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print(f"[S2-PREP] Skipping {s2_file.name} (exists)")
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continue
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print(f"[S2-PREP] Processing {s2_file.name}...")
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temp_normalized = s2_output_dir / f"temp_{s2_file.name}"
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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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profile = src.profile.copy()
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profile.update({"dtype": "float32", "nodata": 0})
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with rasterio.open(temp_normalized, "w", **profile) as dst:
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dst.write(data)
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_reproject_raster_to_target(
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temp_normalized, refl_dst, target_bounds, target_crs, s2_width, s2_height
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)
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temp_normalized.unlink()
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print(f"[S2-PREP] Saved: {refl_dst}")
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print(f"[S2-PREP] Computing distance-to-clouds...")
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distance_to_clouds = _import_distance_to_clouds()
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distance_to_clouds(s2_output_dir, ratio=RESOLUTION_RATIO)
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print("[S2-PREP] Completed")
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def prepare_s3(season, site_position, site_name, cleaning_strategy="aggressive", date_range=None):
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lat, lon = site_position
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s3_dir = Path(f"data/{site_name}/{season}/raw/s3/")
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base_dir = _get_base_dir(season, site_name, cleaning_strategy)
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s2_prepared_dir = base_dir / "s2"
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s3_preprocessed_dir = base_dir / "s3"
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clouds = _load_excluded(season, site_name, cleaning_strategy)
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s3_preprocessed_dir.mkdir(parents=True, exist_ok=True)
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print(f"[S3-PREP] Starting preparation: {site_name} ({lat:.6f}, {lon:.6f}), {season}, strategy={cleaning_strategy}")
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s3_by_date = defaultdict(list)
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for s3_file in s3_dir.glob("*.geotiff"):
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if s3_file.name not in clouds["s3"]:
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s3_by_date[s3_file.name.split("_")[0]].append(s3_file)
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else:
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print(f"[S3-PREP] Skipping {s3_file.name} (excluded by {cleaning_strategy})")
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print(f"[S3-PREP] Found {sum(len(v) for v in s3_by_date.values())} acquisitions across {len(s3_by_date)} dates")
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temp_composite_dir = s3_preprocessed_dir / "temp_composites"
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if temp_composite_dir.exists():
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shutil.rmtree(temp_composite_dir)
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temp_composite_dir.mkdir()
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for date_str, s3_files in sorted(s3_by_date.items()):
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composite_path = temp_composite_dir / f"composite_{date_str}.tif"
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if len(s3_files) == 1:
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shutil.copy(s3_files[0], composite_path)
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print(f"[S3-PREP] Composite {date_str}: 1 acquisition")
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else:
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s3_stack = []
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for s3_file in s3_files:
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with rasterio.open(s3_file) as src:
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data = src.read()
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data[:, np.abs(np.nanmean(data, axis=0)) >= 5] = np.nan
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s3_stack.append(data)
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composite = np.nanmean(np.array(s3_stack), axis=0).astype("float32")
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with rasterio.open(s3_files[0]) as src:
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profile = src.profile.copy()
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profile.update({"count": composite.shape[0], "dtype": "float32"})
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with rasterio.open(composite_path, "w", **profile) as dst:
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dst.write(composite)
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print(f"[S3-PREP] Composite {date_str}: {len(s3_files)} acquisitions merged")
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# Reproject S3 to match S2 REFL bounds (full coverage) instead of DIST_CLOUD bounds
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# This ensures fusion covers the same area as S2 and dimensions match
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sen2_ref_paths = list(s2_prepared_dir.glob("*REFL.tif"))
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if len(sen2_ref_paths) == 0:
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raise ValueError(f"No REFL files found in {s2_prepared_dir}")
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# Get bounds from REFL file (full coverage, matches S2)
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# Use integer division to match distance_to_clouds logic exactly
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with rasterio.open(sen2_ref_paths[0]) as s2_ref:
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target_bounds = s2_ref.bounds
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target_crs = s2_ref.crs
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# Use integer division matching distance_to_clouds: s2_height // ratio, s2_width // ratio
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width = s2_ref.width // RESOLUTION_RATIO
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height = s2_ref.height // RESOLUTION_RATIO
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s3_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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print(f"[S3-PREP] Reprojecting {len(list(temp_composite_dir.glob('*.tif')))} composites to S2 grid ({width}×{height} px)...")
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# Reproject each S3 composite to match S2 REFL bounds
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sen3_paths = sorted(temp_composite_dir.glob("*.tif"))
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for sen3_path in sen3_paths:
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vrt_options = {
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"transform": s3_transform,
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"height": height,
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"width": width,
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"crs": target_crs,
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"resampling": Resampling.cubic,
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}
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with rasterio.open(sen3_path) as s3_src:
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with WarpedVRT(s3_src, **vrt_options) as vrt:
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name = sen3_path.name
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outfile = s3_preprocessed_dir / name
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profile = vrt.profile.copy()
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profile.update({"dtype": "float32", "nodata": 0, "driver": "GTiff"})
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rio_shutil.copy(vrt, outfile, **profile)
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print(f"[S3-PREP] Saved: {outfile}")
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shutil.rmtree(temp_composite_dir)
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print("[S3-PREP] Completed")
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