Helper functions for ndvi calculation.
This commit is contained in:
Felix Delattre
parent
0806bf3876
commit
0a14711851
1 changed files with 87 additions and 137 deletions
182
ndvi.py
182
ndvi.py
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@ -6,6 +6,68 @@ from pathlib import Path
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from datetime import datetime
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def _calculate_and_write_ndvi(input_file, output_file):
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"""Calculate NDVI from red and NIR bands and write to output file."""
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with rasterio.open(input_file) as src:
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red = src.read(3).astype(np.float32)
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nir = src.read(4).astype(np.float32)
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mask = (red > 0) & (nir > 0)
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ndvi = np.zeros_like(red, dtype=np.float32)
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ndvi[mask] = (nir[mask] - red[mask]) / (nir[mask] + red[mask])
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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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"nodata": 0,
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"compress": "lzw",
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}
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)
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with rasterio.open(output_file, "w", **profile) as dst:
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dst.write(ndvi, 1)
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dst.set_band_description(1, "NDVI")
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def _create_timeseries_for_dir(output_dir, site_position, source_name):
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"""Create timeseries.json for NDVI files in the given directory."""
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print(f"[NDVI-{source_name}] Creating timeseries.json...")
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timeseries = []
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ndvi_files = sorted(output_dir.glob("*.geotiff"))
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for ndvi_file in ndvi_files:
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filename = ndvi_file.name
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date_str = filename.split("_")[0]
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try:
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date = datetime.strptime(date_str, "%Y%m%d").isoformat()
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except ValueError:
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date = date_str
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ndvi_value = None
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try:
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with rasterio.open(ndvi_file) as src:
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lon, lat = site_position[1], site_position[0]
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x, y = transform_coords("EPSG:4326", src.crs, [lon], [lat])
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samples = list(src.sample([(x[0], y[0])]))
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if samples and len(samples) > 0:
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value = float(samples[0][0])
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if value != 0 and not np.isnan(value):
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ndvi_value = value
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except Exception as e:
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print(f"[NDVI-{source_name}] Warning: Could not sample {filename}: {e}")
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timeseries.append({"date": date, "filename": filename, "ndvi": ndvi_value})
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timeseries.sort(key=lambda x: x["date"])
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timeseries_file = output_dir / "timeseries.json"
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with open(timeseries_file, "w") as f:
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json.dump(timeseries, f, indent=2)
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print(f"[NDVI-{source_name}] Saved: {timeseries_file} ({len(timeseries)} entries)")
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def generate_ndvi(year, site_position, site_name):
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for source in ["s2", "s3"]:
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input_dir = Path(f"data/{site_name}/{year}/{source}/")
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@ -26,28 +88,7 @@ def generate_ndvi(year, site_position, site_name):
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print(f"[NDVI-{source.upper()}] Skipping {geotiff_file.name} (exists)")
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continue
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with rasterio.open(geotiff_file) as src:
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red = src.read(3).astype(np.float32)
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nir = src.read(4).astype(np.float32)
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mask = (red > 0) & (nir > 0)
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ndvi = np.zeros_like(red, dtype=np.float32)
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ndvi[mask] = (nir[mask] - red[mask]) / (nir[mask] + red[mask])
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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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"nodata": 0,
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"compress": "lzw",
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}
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)
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with rasterio.open(output_file, "w", **profile) as dst:
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dst.write(ndvi, 1)
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dst.set_band_description(1, "NDVI")
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_calculate_and_write_ndvi(geotiff_file, output_file)
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print(f"[NDVI-{source.upper()}] Saved: {output_file}")
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print(f"[NDVI-{source.upper()}] Completed")
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@ -56,44 +97,7 @@ def generate_ndvi(year, site_position, site_name):
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def create_ndvi_timeseries(year, site_position, site_name):
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for source in ["s2", "s3"]:
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output_dir = Path(f"data/{site_name}/{year}/ndvi/{source}/")
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print(f"[NDVI-{source.upper()}] Creating timeseries.json...")
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timeseries = []
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ndvi_files = sorted(output_dir.glob("*.geotiff"))
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for ndvi_file in ndvi_files:
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filename = ndvi_file.name
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date_str = filename.split("_")[0]
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try:
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date = datetime.strptime(date_str, "%Y%m%d").isoformat()
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except ValueError:
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date = date_str
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ndvi_value = None
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try:
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with rasterio.open(ndvi_file) as src:
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lon, lat = site_position[1], site_position[0]
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x, y = transform_coords("EPSG:4326", src.crs, [lon], [lat])
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samples = list(src.sample([(x[0], y[0])]))
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if samples and len(samples) > 0:
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value = float(samples[0][0])
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if value != 0 and not np.isnan(value):
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ndvi_value = value
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except Exception as e:
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print(
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f"[NDVI-{source.upper()}] Warning: Could not sample {filename}: {e}"
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)
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timeseries.append({"date": date, "filename": filename, "ndvi": ndvi_value})
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timeseries.sort(key=lambda x: x["date"])
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timeseries_file = output_dir / "timeseries.json"
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with open(timeseries_file, "w") as f:
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json.dump(timeseries, f, indent=2)
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print(
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f"[NDVI-{source.upper()}] Saved: {timeseries_file} ({len(timeseries)} entries)"
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)
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_create_timeseries_for_dir(output_dir, site_position, source.upper())
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def generate_ndvi_fusion(year, site_position, site_name):
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@ -116,28 +120,7 @@ def generate_ndvi_fusion(year, site_position, site_name):
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print(f"[NDVI-FUSION] Skipping {geotiff_file.name} (exists)")
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continue
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with rasterio.open(geotiff_file) as src:
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red = src.read(3).astype(np.float32)
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nir = src.read(4).astype(np.float32)
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mask = (red > 0) & (nir > 0)
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ndvi = np.zeros_like(red, dtype=np.float32)
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ndvi[mask] = (nir[mask] - red[mask]) / (nir[mask] + red[mask])
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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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"nodata": 0,
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"compress": "lzw",
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}
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)
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with rasterio.open(output_file, "w", **profile) as dst:
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dst.write(ndvi, 1)
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dst.set_band_description(1, "NDVI")
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_calculate_and_write_ndvi(geotiff_file, output_file)
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print(f"[NDVI-FUSION] Saved: {output_file}")
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print(f"[NDVI-FUSION] Completed")
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@ -145,37 +128,4 @@ def generate_ndvi_fusion(year, site_position, site_name):
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def create_ndvi_timeseries_fusion(year, site_position, site_name):
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output_dir = Path(f"data/{site_name}/{year}/ndvi/fusion/")
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print(f"[NDVI-FUSION] Creating timeseries.json...")
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timeseries = []
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ndvi_files = sorted(output_dir.glob("*.geotiff"))
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for ndvi_file in ndvi_files:
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filename = ndvi_file.name
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date_str = filename.split("_")[0]
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try:
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date = datetime.strptime(date_str, "%Y%m%d").isoformat()
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except ValueError:
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date = date_str
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ndvi_value = None
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try:
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with rasterio.open(ndvi_file) as src:
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lon, lat = site_position[1], site_position[0]
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x, y = transform_coords("EPSG:4326", src.crs, [lon], [lat])
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samples = list(src.sample([(x[0], y[0])]))
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if samples and len(samples) > 0:
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value = float(samples[0][0])
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if value != 0 and not np.isnan(value):
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ndvi_value = value
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except Exception as e:
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print(f"[NDVI-FUSION] Warning: Could not sample {filename}: {e}")
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timeseries.append({"date": date, "filename": filename, "ndvi": ndvi_value})
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timeseries.sort(key=lambda x: x["date"])
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timeseries_file = output_dir / "timeseries.json"
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with open(timeseries_file, "w") as f:
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json.dump(timeseries, f, indent=2)
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print(f"[NDVI-FUSION] Saved: {timeseries_file} ({len(timeseries)} entries)")
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_create_timeseries_for_dir(output_dir, site_position, "FUSION")
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