pantierra/efast-phenocam-validation
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refactored download and preselection.

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This commit is contained in:
Felix Delattre 2026-02-21 00:09:34 +01:00
parent 3919b8e871
commit ac0e687956
8 changed files with 206 additions and 164 deletions
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61
metrics_indices.py
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@ -6,6 +6,8 @@ from rasterio.warp import transform as transform_coords
from pathlib import Path
from datetime import datetime
from preselection import _sample_3x3
RED_BAND = 3
NIR_BAND = 4
BLUE_BAND = 1
@ -65,50 +67,6 @@ def _get_ndvi_value(ndvi_file, site_position):
return None
def _get_ndvi_from_original(input_file, site_position):
"""Calculate NDVI directly from original file without creating GeoTIFF."""
try:
with rasterio.open(input_file) as src:
if src.count < 4:
return None
red = src.read(RED_BAND).astype(np.float32)
nir = src.read(NIR_BAND).astype(np.float32)
lon, lat = site_position[1], site_position[0]
x, y = transform_coords("EPSG:4326", src.crs, [lon], [lat])
if not (
src.bounds.left <= x[0] <= src.bounds.right
and src.bounds.bottom <= y[0] <= src.bounds.top
):
return None
row, col = src.index(x[0], y[0])
if row < 0 or row >= src.height or col < 0 or col >= src.width:
return None
# Extract 3x3 window with boundary handling
r0, r1 = max(0, row - 1), min(src.height, row + 2)
c0, c1 = max(0, col - 1), min(src.width, col + 2)
red_window = red[r0:r1, c0:c1]
nir_window = nir[r0:r1, c0:c1]
# Calculate NDVI for each pixel in window
mask = (red_window > 0) & (nir_window > 0) & ~np.isnan(red_window) & ~np.isnan(nir_window)
if not np.any(mask):
return None
ndvi_window = np.zeros_like(red_window, dtype=np.float32)
ndvi_window[mask] = (nir_window[mask] - red_window[mask]) / (nir_window[mask] + red_window[mask])
# Return mean of valid NDVI values
valid_ndvi = ndvi_window[mask]
return float(np.mean(valid_ndvi)) if len(valid_ndvi) > 0 else None
except Exception as e:
return None
def _create_timeseries_for_dir(input_dir, output_dir, site_position, source_name, pattern="*.geotiff"):
print(f"[NDVI-{source_name}] Creating timeseries.json...")
timeseries = []
@ -138,13 +96,17 @@ def _create_timeseries_for_dir(input_dir, output_dir, site_position, source_name
f"[NDVI-{source_name}] Warning: Could not extract date from {filename}, using '{date_str}'"
)
ndvi_value = _get_ndvi_from_original(input_file, site_position)
ndvi_value, band_means = _sample_3x3(input_file, site_position)
blue_mean = band_means.get("b02") if band_means else None
if ndvi_value is None:
print(
f"[NDVI-{source_name}] Warning: Could not sample {filename} (outside bounds or nodata)"
)
timeseries.append({"date": date, "filename": filename, "ndvi": ndvi_value})
entry = {"date": date, "filename": filename, "ndvi": ndvi_value}
if blue_mean is not None:
entry["blue"] = blue_mean
timeseries.append(entry)
timeseries.sort(key=lambda x: x["date"])
output_dir.mkdir(parents=True, exist_ok=True)
@ -198,13 +160,6 @@ def generate_ndvi_raw(season, site_position, site_name):
pass
def create_ndvi_timeseries_raw(season, site_position, site_name):
for source in ["s2", "s3"]:
input_dir = Path(f"data/{site_name}/{season}/raw/{source}/")
output_dir = Path(f"data/{site_name}/{season}/raw/ndvi/{source}/")
_create_timeseries_for_dir(input_dir, output_dir, site_position, source.upper())
def _get_output_name_prepared(geotiff_file):
if geotiff_file.suffix == ".tif":
if "REFL" in geotiff_file.stem: