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