Skip to content
GitLab
Commit 36353266 authored by Heisenberg5124's avatar Heisenberg5124
Browse files

Merge remote-tracking branch 'refs/remotes/origin/backend' into frontend 

# Conflicts:
#	BackendNew/main.py
parents fabaa42f 5cbbcb10
Show whitespace changes
Inline Side-by-side
BackendNew/main.py
View file @ 36353266
...@@ -6,6 +6,7 @@ ...@@ -6,6 +6,7 @@
import os import os
import json import json
import tempfile import tempfile
import time
from typing import Optional, Tuple from typing import Optional, Tuple
from fastapi import FastAPI, Query, HTTPException from fastapi import FastAPI, Query, HTTPException
...@@ -18,13 +19,25 @@ from rasterio.mask import mask ...@@ -18,13 +19,25 @@ from rasterio.mask import mask
from rasterio.enums import Resampling from rasterio.enums import Resampling
from rasterio.warp import calculate_default_transform, reproject from rasterio.warp import calculate_default_transform, reproject
from rasterio.io import MemoryFile from rasterio.io import MemoryFile
from rasterio.features import geometry_window
from rasterio.env import Env
from pystac_client import Client from pystac_client import Client
import planetary_computer as pc import planetary_computer as pc
import requests import requests
import numpy as np import numpy as np
from shapely.geometry import mapping from shapely.geometry import mapping, shape
from fastapi.middleware.cors import CORSMiddleware
# ------------------------------------------------------------------------------
# Optional GDAL/Rasterio HTTP tuning for faster COG range requests
# ------------------------------------------------------------------------------
GDAL_ENV = Env(
GDAL_DISABLE_READDIR_ON_OPEN='EMPTY_DIR',
GDAL_HTTP_MULTIRANGE='YES',
CPL_VSIL_CURL_ALLOWED_EXTENSIONS='.tif',
VSI_CACHE='TRUE',
CPL_VSIL_CURL_NON_CACHED=''
)
# --- CRS / AOI helpers -------------------------------------------------------- # --- CRS / AOI helpers --------------------------------------------------------
...@@ -40,7 +53,6 @@ def read_aoi_geom_wgs84(aoi_path: str) -> dict: ...@@ -40,7 +53,6 @@ def read_aoi_geom_wgs84(aoi_path: str) -> dict:
geom = gdf.unary_union geom = gdf.unary_union
return mapping(geom) # GeoJSON geometry dict return mapping(geom) # GeoJSON geometry dict
def buffer_aoi(aoi_path: str, buffer_meters: float, output_path: str) -> str: def buffer_aoi(aoi_path: str, buffer_meters: float, output_path: str) -> str:
""" """
Buffer the AOI by N meters (using EPSG:3857 for meter-based buffering), Buffer the AOI by N meters (using EPSG:3857 for meter-based buffering),
...@@ -56,7 +68,6 @@ def buffer_aoi(aoi_path: str, buffer_meters: float, output_path: str) -> str: ...@@ -56,7 +68,6 @@ def buffer_aoi(aoi_path: str, buffer_meters: float, output_path: str) -> str:
aoi_buffered_gdf.to_file(output_path, driver="GeoJSON") aoi_buffered_gdf.to_file(output_path, driver="GeoJSON")
return output_path return output_path
# --- High-quality asset selection / composition ------------------------------- # --- High-quality asset selection / composition -------------------------------
def _pick_fullres_asset_or_none(item): def _pick_fullres_asset_or_none(item):
...@@ -75,7 +86,6 @@ def _pick_fullres_asset_or_none(item): ...@@ -75,7 +86,6 @@ def _pick_fullres_asset_or_none(item):
return a return a
return None return None
def _scale_to_uint8(arr: np.ndarray) -> np.ndarray: def _scale_to_uint8(arr: np.ndarray) -> np.ndarray:
""" """
Simple 2–98 percentile contrast stretch to uint8. Simple 2–98 percentile contrast stretch to uint8.
...@@ -90,7 +100,6 @@ def _scale_to_uint8(arr: np.ndarray) -> np.ndarray: ...@@ -90,7 +100,6 @@ def _scale_to_uint8(arr: np.ndarray) -> np.ndarray:
scaled = (arr.astype("float32") - p2) * (255.0 / (p98 - p2)) scaled = (arr.astype("float32") - p2) * (255.0 / (p98 - p2))
return np.clip(scaled, 0, 255).astype("uint8") return np.clip(scaled, 0, 255).astype("uint8")
def _build_true_color_from_bands(item, out_path: str, rgb_uint8: bool) -> str: def _build_true_color_from_bands(item, out_path: str, rgb_uint8: bool) -> str:
""" """
Build a 10 m true-color GeoTIFF from Sentinel-2 L2A bands (B04,B03,B02). Build a 10 m true-color GeoTIFF from Sentinel-2 L2A bands (B04,B03,B02).
...@@ -104,6 +113,7 @@ def _build_true_color_from_bands(item, out_path: str, rgb_uint8: bool) -> str: ...@@ -104,6 +113,7 @@ def _build_true_color_from_bands(item, out_path: str, rgb_uint8: bool) -> str:
raise RuntimeError(f"Item lacks required band {bn} to build RGB.") raise RuntimeError(f"Item lacks required band {bn} to build RGB.")
bands[bn] = pc.sign(a.href) bands[bn] = pc.sign(a.href)
with GDAL_ENV:
with rasterio.open(bands["B04"]) as rsrc, \ with rasterio.open(bands["B04"]) as rsrc, \
rasterio.open(bands["B03"]) as gsrc, \ rasterio.open(bands["B03"]) as gsrc, \
rasterio.open(bands["B02"]) as bsrc: rasterio.open(bands["B02"]) as bsrc:
...@@ -135,7 +145,6 @@ def _build_true_color_from_bands(item, out_path: str, rgb_uint8: bool) -> str: ...@@ -135,7 +145,6 @@ def _build_true_color_from_bands(item, out_path: str, rgb_uint8: bool) -> str:
return out_path return out_path
def download_best_image(aoi_geojson: str, def download_best_image(aoi_geojson: str,
collection: str, collection: str,
date_range: Tuple[str, str], date_range: Tuple[str, str],
...@@ -181,15 +190,13 @@ def download_best_image(aoi_geojson: str, ...@@ -181,15 +190,13 @@ def download_best_image(aoi_geojson: str,
except Exception: except Exception:
return None return None
# --- Post-processing: resampling & overviews ---------------------------------- # --- Post-processing: resampling & overviews ----------------------------------
def _pixel_size_from_transform(transform) -> Tuple[float, float]: def _pixel_size_from_transform(transform) -> Tuple[float, float]:
# (pixel width, pixel height) in CRS units (usually meters) # (pixel width, pixel height) in CRS units (usually meters)
return abs(transform.a), abs(transform.e) return abs(transform.a), abs(transform.e)
def reproject_to_resolution(src_path: str, dst_path: str, target_res_m: float, force_upsample: bool=False) -> str:
def reproject_to_resolution(src_path: str, dst_path: str, target_res_m: float, force_upsample: bool = False) -> str:
""" """
Reproject/resample to a clean target pixel size (meters). Reproject/resample to a clean target pixel size (meters).
If target_res is finer than native and force_upsample=False, clamp to native. If target_res is finer than native and force_upsample=False, clamp to native.
...@@ -227,7 +234,6 @@ def reproject_to_resolution(src_path: str, dst_path: str, target_res_m: float, f ...@@ -227,7 +234,6 @@ def reproject_to_resolution(src_path: str, dst_path: str, target_res_m: float, f
) )
return dst_path return dst_path
def add_overviews_inplace(tif_path: str, levels=(2, 4, 8, 16)): def add_overviews_inplace(tif_path: str, levels=(2, 4, 8, 16)):
""" """
Build internal overviews to improve on-screen clarity at multiple zooms. Build internal overviews to improve on-screen clarity at multiple zooms.
...@@ -236,7 +242,6 @@ def add_overviews_inplace(tif_path: str, levels=(2, 4, 8, 16)): ...@@ -236,7 +242,6 @@ def add_overviews_inplace(tif_path: str, levels=(2, 4, 8, 16)):
ds.build_overviews(levels, Resampling.average) ds.build_overviews(levels, Resampling.average)
ds.update_tags(ns="rio_overview", resampling="average") ds.update_tags(ns="rio_overview", resampling="average")
# --- NDVI helpers ------------------------------------------------------------- # --- NDVI helpers -------------------------------------------------------------
def collection_for_year(year: int): def collection_for_year(year: int):
...@@ -263,7 +268,6 @@ def collection_for_year(year: int): ...@@ -263,7 +268,6 @@ def collection_for_year(year: int):
"native_res_m": 30.0, "native_res_m": 30.0,
} }
def _fetch_least_cloudy_item(collection: str, geometry: dict, date_range: tuple, max_cloud: int): def _fetch_least_cloudy_item(collection: str, geometry: dict, date_range: tuple, max_cloud: int):
cat = Client.open("https://planetarycomputer.microsoft.com/api/stac/v1") cat = Client.open("https://planetarycomputer.microsoft.com/api/stac/v1")
search = cat.search( search = cat.search(
...@@ -278,15 +282,15 @@ def _fetch_least_cloudy_item(collection: str, geometry: dict, date_range: tuple, ...@@ -278,15 +282,15 @@ def _fetch_least_cloudy_item(collection: str, geometry: dict, date_range: tuple,
items.sort(key=lambda x: x.properties.get("eo:cloud_cover", 100)) items.sort(key=lambda x: x.properties.get("eo:cloud_cover", 100))
return items[0] return items[0]
def _read_band_to_array_signed(asset_href: str) -> tuple: def _read_band_to_array_signed(asset_href: str) -> tuple:
"""Open a single-band raster and return (array, profile).""" """Open a single-band raster and return (array, profile)."""
with rasterio.open(pc.sign(asset_href)) as src: href = pc.sign(asset_href)
with GDAL_ENV:
with rasterio.open(href) as src:
arr = src.read(1) arr = src.read(1)
profile = src.profile.copy() profile = src.profile.copy()
return arr, profile return arr, profile
def _scale_reflectance(arr: np.ndarray, scale_info) -> np.ndarray: def _scale_reflectance(arr: np.ndarray, scale_info) -> np.ndarray:
"""Return reflectance in 0..1 range where possible.""" """Return reflectance in 0..1 range where possible."""
kind, params = scale_info kind, params = scale_info
...@@ -301,7 +305,6 @@ def _scale_reflectance(arr: np.ndarray, scale_info) -> np.ndarray: ...@@ -301,7 +305,6 @@ def _scale_reflectance(arr: np.ndarray, scale_info) -> np.ndarray:
else: else:
return arr return arr
def _compute_ndvi(nir: np.ndarray, red: np.ndarray, nodata_mask: np.ndarray = None) -> np.ndarray: def _compute_ndvi(nir: np.ndarray, red: np.ndarray, nodata_mask: np.ndarray = None) -> np.ndarray:
denom = (nir + red) denom = (nir + red)
ndvi = np.where(denom != 0, (nir - red) / denom, np.nan).astype("float32") ndvi = np.where(denom != 0, (nir - red) / denom, np.nan).astype("float32")
...@@ -309,7 +312,6 @@ def _compute_ndvi(nir: np.ndarray, red: np.ndarray, nodata_mask: np.ndarray = No ...@@ -309,7 +312,6 @@ def _compute_ndvi(nir: np.ndarray, red: np.ndarray, nodata_mask: np.ndarray = No
ndvi = np.where(nodata_mask, np.nan, ndvi) ndvi = np.where(nodata_mask, np.nan, ndvi)
return ndvi return ndvi
def _write_float_geotiff(path: str, arr: np.ndarray, profile, nodata=-9999.0): def _write_float_geotiff(path: str, arr: np.ndarray, profile, nodata=-9999.0):
""" """
Write a single-band float32 GeoTIFF. Write a single-band float32 GeoTIFF.
...@@ -342,7 +344,6 @@ def _write_float_geotiff(path: str, arr: np.ndarray, profile, nodata=-9999.0): ...@@ -342,7 +344,6 @@ def _write_float_geotiff(path: str, arr: np.ndarray, profile, nodata=-9999.0):
with rasterio.open(path, "w", **clean) as dst: with rasterio.open(path, "w", **clean) as dst:
dst.write(arr_out, 1) dst.write(arr_out, 1)
def _resample_match(src_arr, src_profile, ref_profile, resampling=Resampling.bilinear): def _resample_match(src_arr, src_profile, ref_profile, resampling=Resampling.bilinear):
"""Resample a single-band array to the reference profile's grid.""" """Resample a single-band array to the reference profile's grid."""
with MemoryFile() as mem_src: with MemoryFile() as mem_src:
...@@ -372,7 +373,6 @@ def _resample_match(src_arr, src_profile, ref_profile, resampling=Resampling.bil ...@@ -372,7 +373,6 @@ def _resample_match(src_arr, src_profile, ref_profile, resampling=Resampling.bil
) )
return dst_ds.read(1) return dst_ds.read(1)
def _summarize_ndvi(ndvi: np.ndarray, pixel_size_m: float) -> dict: def _summarize_ndvi(ndvi: np.ndarray, pixel_size_m: float) -> dict:
valid = np.isfinite(ndvi) valid = np.isfinite(ndvi)
if not np.any(valid): if not np.any(valid):
...@@ -392,6 +392,103 @@ def _summarize_ndvi(ndvi: np.ndarray, pixel_size_m: float) -> dict: ...@@ -392,6 +392,103 @@ def _summarize_ndvi(ndvi: np.ndarray, pixel_size_m: float) -> dict:
"area_gt_0_6_m2": float(np.sum(v > 0.6) * area_per_pixel_m2), "area_gt_0_6_m2": float(np.sum(v > 0.6) * area_per_pixel_m2),
} }
# --- FAST in-memory NDVI metric helpers --------------------------------------
def _read_band_over_aoi(asset_href: str, aoi_geom_wgs84: dict):
"""
Read a single-band raster only over the AOI window, returning (array, profile).
Uses HTTP range requests against COGs and returns float32 with NaNs for nodata.
"""
href = pc.sign(asset_href)
with GDAL_ENV:
with rasterio.open(href) as src:
# Reproject AOI to raster CRS and compute minimal read window
aoi_geom = shape(aoi_geom_wgs84)
aoi = gpd.GeoSeries([aoi_geom], crs="EPSG:4326").to_crs(src.crs)
geom = aoi.iloc[0]
# Compute the read window; if AOI is completely outside, bail early
try:
win = geometry_window(src, [mapping(geom)], pad_x=0, pad_y=0, north_up=True, pixel_precision=3)
except ValueError:
# No overlap
return np.full((0, 0), np.nan, dtype="float32"), {
"height": 0, "width": 0, "count": 1, "dtype": "float32",
"crs": src.crs, "transform": src.transform
}
# Windowed read as float32 masked array; fill mask with NaN
m = src.read(1, window=win, boundless=False, masked=True, out_dtype="float32")
arr = m.filled(np.nan)
transform = src.window_transform(win)
prof = {
"height": arr.shape[0],
"width": arr.shape[1],
"count": 1,
"dtype": "float32",
"crs": src.crs,
"transform": transform
}
return arr, prof
def _ndvi_metric_from_arrays(nir_arr, nir_prof, red_arr, red_prof, scale_info, metric: str) -> float:
# scale to reflectance
nir = _scale_reflectance(nir_arr, scale_info)
red = _scale_reflectance(red_arr, scale_info)
# align grids if needed
same_grid = (
red_prof["transform"] == nir_prof["transform"] and
red_prof["crs"] == nir_prof["crs"] and
red_arr.shape == nir_arr.shape
)
if not same_grid:
nir = _resample_match(nir, nir_prof, red_prof, resampling=Resampling.bilinear)
ndvi = _compute_ndvi(nir, red)
valid = np.isfinite(ndvi)
if not np.any(valid):
return float("nan")
v = ndvi[valid]
metric = (metric or "mean").lower()
if metric == "mean":
return float(np.nanmean(v))
if metric == "median":
return float(np.nanmedian(v))
if metric.startswith("p"):
try:
q = float(metric[1:])
except Exception:
q = 50.0
return float(np.nanpercentile(v, q))
# default to mean
return float(np.nanmean(v))
def _ndvi_metric_for_year(year: int, geometry_wgs84: dict, max_cloud: int, metric: str) -> Tuple[float, dict]:
"""Fast, in-memory NDVI metric for a year over AOI."""
cfg = collection_for_year(year)
date_range = year_to_range(year)
item = _fetch_least_cloudy_item(cfg["collection"], geometry_wgs84, date_range, max_cloud)
if item is None:
raise HTTPException(status_code=404, detail=f"No imagery for year={year}")
red_href = item.assets[cfg["red"]].href
nir_href = item.assets[cfg["nir"]].href
# Read only AOI window for each band
red_arr, red_prof = _read_band_over_aoi(red_href, geometry_wgs84)
nir_arr, nir_prof = _read_band_over_aoi(nir_href, geometry_wgs84)
value = _ndvi_metric_from_arrays(nir_arr, nir_prof, red_arr, red_prof, cfg["scale"], metric)
meta = {
"collection": cfg["collection"],
"native_res_m": cfg["native_res_m"],
"cloud_cover": item.properties.get("eo:cloud_cover"),
"item_id": item.id
}
return value, meta
# --- API setup ---------------------------------------------------------------- # --- API setup ----------------------------------------------------------------
...@@ -402,18 +499,7 @@ An API that returns a satellite image file for a given year and computes NDVI & ...@@ -402,18 +499,7 @@ An API that returns a satellite image file for a given year and computes NDVI &
- Swagger UI: `/docs` - Swagger UI: `/docs`
- ReDoc: `/redoc` - ReDoc: `/redoc`
""", """,
version="1.3.0" version="1.4.0"
)
app.add_middleware(
CORSMiddleware,
allow_origins=[
"http://localhost:5173",
"http://127.0.0.1:5173",
], # use ["*"] for local dev only
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
) )
# Defaults; override via env or query params # Defaults; override via env or query params
...@@ -422,28 +508,21 @@ BUFFER_METERS_DEFAULT = float(os.environ.get("BUFFER_METERS", "200")) ...@@ -422,28 +508,21 @@ BUFFER_METERS_DEFAULT = float(os.environ.get("BUFFER_METERS", "200"))
COLLECTION_DEFAULT = os.environ.get("COLLECTION", "sentinel-2-l2a") COLLECTION_DEFAULT = os.environ.get("COLLECTION", "sentinel-2-l2a")
MAX_CLOUD_DEFAULT = int(os.environ.get("MAX_CLOUD", "10")) MAX_CLOUD_DEFAULT = int(os.environ.get("MAX_CLOUD", "10"))
# Map a year -> date window (customize as needed; here June–Aug) # Map a year -> date window (customize as needed; here June–Aug)
def year_to_range(year: int) -> Tuple[str, str]: def year_to_range(year: int) -> Tuple[str, str]:
return (f"{year}-06-01", f"{year}-08-31") return (f"{year}-06-01", f"{year}-08-31")
# --- Download Endpoint -------------------------------------------------------- # --- Download Endpoint --------------------------------------------------------
class DownloadParams(BaseModel): class DownloadParams(BaseModel):
year: int = Field(..., ge=2015, le=2100, description="Year to fetch imagery for") year: int = Field(..., ge=2015, le=2100, description="Year to fetch imagery for")
collection: Optional[str] = Field(default=COLLECTION_DEFAULT, description="STAC collection (e.g., sentinel-2-l2a)") collection: Optional[str] = Field(default=COLLECTION_DEFAULT, description="STAC collection (e.g., sentinel-2-l2a)")
max_cloud: Optional[int] = Field(default=MAX_CLOUD_DEFAULT, ge=0, le=100, description="Max cloud cover percent") max_cloud: Optional[int] = Field(default=MAX_CLOUD_DEFAULT, ge=0, le=100, description="Max cloud cover percent")
buffer_meters: Optional[float] = Field(default=BUFFER_METERS_DEFAULT, ge=0, buffer_meters: Optional[float] = Field(default=BUFFER_METERS_DEFAULT, ge=0, description="Buffer to apply to AOI (meters)")
description="Buffer to apply to AOI (meters)")
clip_to_aoi: Optional[bool] = Field(default=False, description="If true, clip the image to buffered AOI") clip_to_aoi: Optional[bool] = Field(default=False, description="If true, clip the image to buffered AOI")
rgb_uint8: Optional[bool] = Field(default=False, rgb_uint8: Optional[bool] = Field(default=False, description="If true, write 8-bit RGB with a simple stretch (preview-friendly)")
description="If true, write 8-bit RGB with a simple stretch (preview-friendly)") target_res_m: Optional[float] = Field(default=10.0, ge=0, description="Reproject/resample clipped output to this pixel size in meters")
target_res_m: Optional[float] = Field(default=10.0, ge=0, build_overviews: Optional[bool] = Field(default=True, description="If true, add internal overviews to the output GeoTIFF")
description="Reproject/resample clipped output to this pixel size in meters")
build_overviews: Optional[bool] = Field(default=True,
description="If true, add internal overviews to the output GeoTIFF")
@app.get( @app.get(
"/download", "/download",
...@@ -463,8 +542,7 @@ def download_imagery( ...@@ -463,8 +542,7 @@ def download_imagery(
buffer_meters: float = Query(BUFFER_METERS_DEFAULT, ge=0, description="Buffer in meters to apply to AOI"), buffer_meters: float = Query(BUFFER_METERS_DEFAULT, ge=0, description="Buffer in meters to apply to AOI"),
clip_to_aoi: bool = Query(False, description="If true, clip the image to buffered AOI"), clip_to_aoi: bool = Query(False, description="If true, clip the image to buffered AOI"),
rgb_uint8: bool = Query(False, description="If true, write 8-bit RGB with a simple stretch (preview-friendly)"), rgb_uint8: bool = Query(False, description="If true, write 8-bit RGB with a simple stretch (preview-friendly)"),
target_res_m: float = Query(10.0, ge=0, target_res_m: float = Query(10.0, ge=0, description="Reproject/resample clipped output to this pixel size in meters"),
description="Reproject/resample clipped output to this pixel size in meters"),
build_overviews: bool = Query(True, description="If true, add internal overviews to the output GeoTIFF") build_overviews: bool = Query(True, description="If true, add internal overviews to the output GeoTIFF")
): ):
""" """
...@@ -487,12 +565,12 @@ def download_imagery( ...@@ -487,12 +565,12 @@ def download_imagery(
raw_tif = os.path.join(workdir, f"imagery_{year}.tif") raw_tif = os.path.join(workdir, f"imagery_{year}.tif")
out_path = download_best_image(aoi_path, collection, date_range, max_cloud, raw_tif, rgb_uint8=rgb_uint8) out_path = download_best_image(aoi_path, collection, date_range, max_cloud, raw_tif, rgb_uint8=rgb_uint8)
if not out_path: if not out_path:
raise HTTPException(status_code=404, raise HTTPException(status_code=404, detail=f"No imagery found for year={year}, range={date_range}, max_cloud={max_cloud}")
detail=f"No imagery found for year={year}, range={date_range}, max_cloud={max_cloud}")
# Optional: clip to AOI (must reproject AOI to raster CRS before masking) # Optional: clip to AOI (must reproject AOI to raster CRS before masking)
final_path = out_path final_path = out_path
if clip_to_aoi: if clip_to_aoi:
with GDAL_ENV:
with rasterio.open(out_path) as src: with rasterio.open(out_path) as src:
geoms = gpd.read_file(aoi_path).to_crs(src.crs) geoms = gpd.read_file(aoi_path).to_crs(src.crs)
shapes = [mapping(geom) for geom in geoms.geometry] shapes = [mapping(geom) for geom in geoms.geometry]
...@@ -531,7 +609,6 @@ def download_imagery( ...@@ -531,7 +609,6 @@ def download_imagery(
filename=os.path.basename(final_path) filename=os.path.basename(final_path)
) )
@app.post( @app.post(
"/download", "/download",
response_class=FileResponse, response_class=FileResponse,
...@@ -549,8 +626,7 @@ def download_imagery_post(params: DownloadParams): ...@@ -549,8 +626,7 @@ def download_imagery_post(params: DownloadParams):
build_overviews=params.build_overviews, build_overviews=params.build_overviews,
) )
# --- NDVI Endpoints (original, file-based) -----------------------------------
# --- NDVI Endpoints -----------------------------------------------------------
@app.get("/ndvi", summary="Compute NDVI for a given year; returns stats and writes a GeoTIFF") @app.get("/ndvi", summary="Compute NDVI for a given year; returns stats and writes a GeoTIFF")
def ndvi_year( def ndvi_year(
...@@ -558,8 +634,7 @@ def ndvi_year( ...@@ -558,8 +634,7 @@ def ndvi_year(
max_cloud: int = Query(MAX_CLOUD_DEFAULT, ge=0, le=100), max_cloud: int = Query(MAX_CLOUD_DEFAULT, ge=0, le=100),
buffer_meters: float = Query(BUFFER_METERS_DEFAULT, ge=0), buffer_meters: float = Query(BUFFER_METERS_DEFAULT, ge=0),
clip_to_aoi: bool = Query(True, description="Clip NDVI to AOI"), clip_to_aoi: bool = Query(True, description="Clip NDVI to AOI"),
res_for_compare_m: float = Query(30.0, ge=0, res_for_compare_m: float = Query(30.0, ge=0, description="Optional resampling resolution (m) for cross-year/sensor comparability"),
description="Optional resampling resolution (m) for cross-year/sensor comparability"),
): ):
if not os.path.exists(AOI_PATH): if not os.path.exists(AOI_PATH):
raise HTTPException(status_code=500, detail=f"AOI file not found at {AOI_PATH}") raise HTTPException(status_code=500, detail=f"AOI file not found at {AOI_PATH}")
...@@ -589,14 +664,14 @@ def ndvi_year( ...@@ -589,14 +664,14 @@ def ndvi_year(
nir = _scale_reflectance(nir, cfg["scale"]) nir = _scale_reflectance(nir, cfg["scale"])
# Ensure bands are on identical grid (usually true, but safeguard) # Ensure bands are on identical grid (usually true, but safeguard)
same_grid = (red_prof["transform"] == nir_prof["transform"]) and (red_prof["crs"] == nir_prof["crs"]) and ( same_grid = (red_prof["transform"] == nir_prof["transform"]) and (red_prof["crs"] == nir_prof["crs"]) and (red.shape == nir.shape)
red.shape == nir.shape)
if not same_grid: if not same_grid:
nir = _resample_match(nir, nir_prof, red_prof, resampling=Resampling.bilinear) nir = _resample_match(nir, nir_prof, red_prof, resampling=Resampling.bilinear)
ndvi = _compute_ndvi(nir, red) ndvi = _compute_ndvi(nir, red)
# Base profile from red band # Base profile from red band
with GDAL_ENV:
with rasterio.open(pc.sign(red_href)) as ref: with rasterio.open(pc.sign(red_href)) as ref:
base_profile = ref.profile.copy() base_profile = ref.profile.copy()
base_profile.update(count=1, dtype="float32") base_profile.update(count=1, dtype="float32")
...@@ -607,6 +682,7 @@ def ndvi_year( ...@@ -607,6 +682,7 @@ def ndvi_year(
final_ndvi = tmp_ndvi final_ndvi = tmp_ndvi
if clip_to_aoi: if clip_to_aoi:
with GDAL_ENV:
with rasterio.open(tmp_ndvi) as src: with rasterio.open(tmp_ndvi) as src:
geoms = gpd.read_file(aoi_path).to_crs(src.crs) geoms = gpd.read_file(aoi_path).to_crs(src.crs)
shapes = [mapping(geom) for geom in geoms.geometry] shapes = [mapping(geom) for geom in geoms.geometry]
...@@ -620,6 +696,7 @@ def ndvi_year( ...@@ -620,6 +696,7 @@ def ndvi_year(
# Optionally resample to a standard comparison grid (e.g., 30 m) # Optionally resample to a standard comparison grid (e.g., 30 m)
if res_for_compare_m and res_for_compare_m > 0: if res_for_compare_m and res_for_compare_m > 0:
with GDAL_ENV:
with rasterio.open(final_ndvi) as src: with rasterio.open(final_ndvi) as src:
dst_transform, width, height = calculate_default_transform( dst_transform, width, height = calculate_default_transform(
src.crs, src.crs, src.width, src.height, *src.bounds, resolution=res_for_compare_m src.crs, src.crs, src.width, src.height, *src.bounds, resolution=res_for_compare_m
...@@ -653,7 +730,6 @@ def ndvi_year( ...@@ -653,7 +730,6 @@ def ndvi_year(
"stats": stats "stats": stats
} }
@app.get("/ndvi-change", summary="Compute NDVI change (end - start); returns stats and paths to GeoTIFFs") @app.get("/ndvi-change", summary="Compute NDVI change (end - start); returns stats and paths to GeoTIFFs")
def ndvi_change( def ndvi_change(
start_year: int = Query(..., ge=2013), start_year: int = Query(..., ge=2013),
...@@ -686,10 +762,10 @@ def ndvi_change( ...@@ -686,10 +762,10 @@ def ndvi_change(
ndvi_end_path = os.path.join(end["workdir"], end["ndvi_tif"]) ndvi_end_path = os.path.join(end["workdir"], end["ndvi_tif"])
# Align and difference # Align and difference
with GDAL_ENV:
with rasterio.open(ndvi_end_path) as end_ds, rasterio.open(ndvi_start_path) as start_ds: with rasterio.open(ndvi_end_path) as end_ds, rasterio.open(ndvi_start_path) as start_ds:
# If shapes differ (shouldn't if same res), resample start to end grid # If shapes differ (shouldn't if same res), resample start to end grid
if (start_ds.transform != end_ds.transform) or (start_ds.width != end_ds.width) or ( if (start_ds.transform != end_ds.transform) or (start_ds.width != end_ds.width) or (start_ds.height != end_ds.height):
start_ds.height != end_ds.height):
arr_s = _resample_match(start_ds.read(1), start_ds.profile, end_ds.profile, resampling=Resampling.bilinear) arr_s = _resample_match(start_ds.read(1), start_ds.profile, end_ds.profile, resampling=Resampling.bilinear)
arr_e = end_ds.read(1) arr_e = end_ds.read(1)
profile = end_ds.profile.copy() profile = end_ds.profile.copy()
...@@ -734,5 +810,42 @@ def ndvi_change( ...@@ -734,5 +810,42 @@ def ndvi_change(
"change_stats": change_stats "change_stats": change_stats
} }
# --- FAST NDVI change VALUE endpoint (in-memory, no file writes) --------------
@app.get("/ndvi-change/value", summary="Fast NDVI change value (in-memory, no file writes)")
def ndvi_change_value(
start_year: int = Query(..., ge=2013),
end_year: int = Query(..., ge=2013),
metric: str = Query("mean", description="NDVI summary to compare: mean | median | p05 | p95"),
max_cloud: int = Query(MAX_CLOUD_DEFAULT, ge=0, le=100),
buffer_meters: float = Query(BUFFER_METERS_DEFAULT, ge=0, description="Optional AOI buffer (m)")
):
if end_year < start_year:
raise HTTPException(status_code=422, detail="end_year must be >= start_year")
if not os.path.exists(AOI_PATH):
raise HTTPException(status_code=500, detail=f"AOI file not found at {AOI_PATH}")
t0 = time.time()
# prepare (buffered) AOI once
with tempfile.TemporaryDirectory(prefix="aoi_") as tmp:
aoi_path = AOI_PATH
if buffer_meters and buffer_meters > 0:
aoi_path = os.path.join(tmp, "aoi_buffered.geojson")
buffer_aoi(AOI_PATH, buffer_meters, aoi_path)
geom_wgs84 = read_aoi_geom_wgs84(aoi_path)
v_start, meta_start = _ndvi_metric_for_year(start_year, geom_wgs84, max_cloud, metric)
v_end, meta_end = _ndvi_metric_for_year(end_year, geom_wgs84, max_cloud, metric)
elapsed_s = time.time() - t0
change_value = None if (np.isnan(v_start) or np.isnan(v_end)) else float(v_end - v_start)
return {
"metric": metric,
"start": {"year": start_year, "value": v_start, **meta_start},
"end": {"year": end_year, "value": v_end, **meta_end},
"change_value": change_value,
"elapsed_seconds": round(elapsed_s, 3)
}
# Run: # Run:
# uvicorn main:app --host 0.0.0.0 --port 8000 # uvicorn main:app --host 0.0.0.0 --port 8000
Supports Markdown
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment

Dies ist die Gitlab-Instanz des Transferportals der Hochschule für Technik Stuttgart. Hier geht es zurück zum Portal