Added index then blend.
This commit is contained in:
Felix Delattre
parent
8e7fb1de18
commit
55d6facc4d
8 changed files with 661 additions and 68 deletions
110
preparation.py
110
preparation.py
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@ -1,4 +1,5 @@
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"""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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@ -16,6 +17,7 @@ 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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@ -40,6 +42,76 @@ 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 _get_itb_base_dir(season, site_name, cleaning_strategy):
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return Path(f"data/{site_name}/{season}/prepared_{cleaning_strategy}_itb")
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def _compute_gcc_from_refl_array(blue, green, red):
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total = red.astype(np.float32) + green.astype(np.float32) + red.astype(np.float32)
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mask = (total > 0) & np.isfinite(total)
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gcc = np.zeros_like(green, dtype=np.float32)
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gcc[mask] = green[mask].astype(np.float32) / total[mask]
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return gcc
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def _link_dist_cloud_from_prepared(src_s2_dir, dst_s2_dir):
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dst_s2_dir.mkdir(parents=True, exist_ok=True)
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for src in src_s2_dir.glob("*DIST_CLOUD.tif"):
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dst = dst_s2_dir / src.name
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if dst.exists():
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continue
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try:
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dst.symlink_to(src.resolve())
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except OSError:
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shutil.copy2(src, dst)
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def prepare_s2_gcc_for_itb(
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season, site_position, site_name, cleaning_strategy="aggressive"
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):
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base = _get_base_dir(season, site_name, cleaning_strategy)
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itb_s2 = _get_itb_base_dir(season, site_name, cleaning_strategy) / "s2"
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s2_prep = base / "s2"
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itb_s2.mkdir(parents=True, exist_ok=True)
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for refl in sorted(s2_prep.glob("*REFL.tif")):
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out = itb_s2 / refl.name.replace("_REFL.tif", "_GCC.tif")
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if out.exists():
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continue
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with rasterio.open(refl) as src:
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if src.count < 4:
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continue
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b, g, r = (src.read(i).astype(np.float32) for i in range(1, 4))
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gcc = _compute_gcc_from_refl_array(b, g, r)
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profile = src.profile.copy()
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profile.update({"count": 1, "dtype": "float32", "nodata": 0})
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with rasterio.open(out, "w", **profile) as dst:
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dst.write(gcc, 1)
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print(f"[S2-ITB] Saved {out.name}")
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_link_dist_cloud_from_prepared(s2_prep, itb_s2)
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def prepare_s3_gcc_for_itb(
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season, site_position, site_name, cleaning_strategy="aggressive"
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):
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base = _get_base_dir(season, site_name, cleaning_strategy)
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itb_s3 = _get_itb_base_dir(season, site_name, cleaning_strategy) / "s3"
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itb_s3.mkdir(parents=True, exist_ok=True)
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for comp in sorted((base / "s3").glob("composite_*.tif")):
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out = itb_s3 / comp.name
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if out.exists():
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continue
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with rasterio.open(comp) as src:
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if src.count < 4:
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continue
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b, g, r = (src.read(i).astype(np.float32) for i in range(1, 4))
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gcc = _compute_gcc_from_refl_array(b, g, r)
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profile = src.profile.copy()
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profile.update({"count": 1, "dtype": "float32", "nodata": 0})
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with rasterio.open(out, "w", **profile) as dst:
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dst.write(gcc, 1)
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print(f"[S3-ITB] Saved {out.name}")
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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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@ -90,7 +162,9 @@ def _rescale_dist_cloud_for_small_roi(s2_output_dir):
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print(f"[S2-PREP] Rescaled DIST_CLOUD for {dc_path.name} (max was {d_max})")
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def prepare_s2(season, site_position, site_name, cleaning_strategy="aggressive", date_range=None):
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def prepare_s2(
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season, site_position, site_name, cleaning_strategy="aggressive", date_range=None
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):
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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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@ -99,7 +173,9 @@ def prepare_s2(season, site_position, site_name, cleaning_strategy="aggressive",
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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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print(
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f"[S2-PREP] Starting preparation: {site_name} ({lat:.6f}, {lon:.6f}), {season}, strategy={cleaning_strategy}"
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)
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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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@ -113,7 +189,9 @@ def prepare_s2(season, site_position, site_name, cleaning_strategy="aggressive",
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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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print(
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f"[S2-PREP] Skipping {s2_file.name} (excluded by {cleaning_strategy})"
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)
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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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@ -136,14 +214,16 @@ def prepare_s2(season, site_position, site_name, cleaning_strategy="aggressive",
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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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print("[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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_rescale_dist_cloud_for_small_roi(s2_output_dir)
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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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def prepare_s3(
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season, site_position, site_name, cleaning_strategy="aggressive", date_range=None
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):
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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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@ -153,16 +233,22 @@ def prepare_s3(season, site_position, site_name, cleaning_strategy="aggressive",
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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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print(
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f"[S3-PREP] Starting preparation: {site_name} ({lat:.6f}, {lon:.6f}), {season}, strategy={cleaning_strategy}"
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)
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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(
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f"[S3-PREP] Skipping {s3_file.name} (excluded by {cleaning_strategy})"
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)
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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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print(
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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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)
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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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@ -187,7 +273,9 @@ def prepare_s3(season, site_position, site_name, cleaning_strategy="aggressive",
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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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print(
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f"[S3-PREP] Composite {date_str}: {len(s3_files)} acquisitions merged"
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)
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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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@ -212,7 +300,9 @@ def prepare_s3(season, site_position, site_name, cleaning_strategy="aggressive",
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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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print(
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f"[S3-PREP] Reprojecting {len(list(temp_composite_dir.glob('*.tif')))} composites to S2 grid ({width}×{height} px)..."
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)
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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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