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refactor: reorganize project structure by moving core modules and update import paths in API server

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Victor Phan
2026-07-18 01:24:30 +07:00
parent abab846884
commit a82b2f6fa5
155 changed files with 25 additions and 370 deletions
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scripts/extraction/extract_fusion_32ch.py
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import os
import gc
import json
import joblib
import numpy as np
import pandas as pd
import geopandas as gpd
import xarray as xr
from tqdm import tqdm
from joblib import Parallel, delayed
import pystac_client
import planetary_computer
import odc.stac
from shapely.geometry import Point, shape
from pyproj import Transformer
import warnings
warnings.filterwarnings('ignore')
from core.cloud_removal import DeepInpaintingStrategy
def get_s2_items(bbox, time_range):
catalog = pystac_client.Client.open(
"https://planetarycomputer.microsoft.com/api/stac/v1",
modifier=planetary_computer.sign_inplace,
)
search = catalog.search(
collections=["sentinel-2-l2a"],
bbox=bbox,
datetime=time_range,
)
items = list(search.items())
print(f"Found {len(items)} Sentinel-2 scenes")
return items
def get_s1_items(bbox, time_range):
catalog = pystac_client.Client.open(
"https://planetarycomputer.microsoft.com/api/stac/v1",
modifier=planetary_computer.sign_inplace,
)
search = catalog.search(
collections=["sentinel-1-rtc"],
bbox=bbox,
datetime=time_range,
)
items = list(search.items())
print(f"Found {len(items)} Sentinel-1 scenes")
return items
def process_point_s1_s2(idx, row, s2_items_dicts, s1_items_dicts, patch_size=16):
try:
import pystac
import odc.stac
import planetary_computer
from shapely.geometry import Point, shape
from pyproj import Transformer
s2_items = [pystac.Item.from_dict(d) for d in s2_items_dicts]
s1_items = [pystac.Item.from_dict(d) for d in s1_items_dicts]
x_coord = row['geometry'].x
y_coord = row['geometry'].y
transformer = Transformer.from_crs("epsg:32648", "epsg:4326", always_xy=True)
lon, lat = transformer.transform(x_coord, y_coord)
point = Point(lon, lat)
# --- SENTINEL-2 ---
filtered_s2 = [item for item in s2_items if shape(item.geometry).contains(point)]
if not filtered_s2: return None
filtered_s2 = [planetary_computer.sign(item) for item in filtered_s2][:10]
patch_s2 = odc.stac.load(
filtered_s2,
bands=["B02", "B03", "B04", "B08", "SCL"],
x=(x_coord - 100, x_coord + 100),
y=(y_coord - 100, y_coord + 100),
crs="EPSG:32648",
resolution=10,
patch_url=planetary_computer.sign,
fail_on_error=False
).compute()
b2_sums = patch_s2["B02"].sum(dim=["x", "y"])
valid_times = b2_sums > 0
patch_s2 = patch_s2.isel(time=valid_times)
if len(patch_s2.time) == 0: return None
patch_s2 = patch_s2.isel(time=slice(0, min(4, len(patch_s2.time))))
if "SCL" not in patch_s2 or "B02" not in patch_s2: return None
if patch_s2.dims['x'] < patch_size or patch_s2.dims['y'] < patch_size: return None
patch_s2 = patch_s2.isel(x=slice(0, patch_size), y=slice(0, patch_size))
# --- SENTINEL-1 ---
filtered_s1 = [item for item in s1_items if shape(item.geometry).contains(point)]
if not filtered_s1: return None
filtered_s1 = [planetary_computer.sign(item) for item in filtered_s1][:10]
patch_s1 = odc.stac.load(
filtered_s1,
bands=["vv", "vh"],
x=(x_coord - 100, x_coord + 100),
y=(y_coord - 100, y_coord + 100),
crs="EPSG:32648",
resolution=10,
patch_url=planetary_computer.sign,
fail_on_error=False
).compute()
vv_sums = patch_s1["vv"].sum(dim=["x", "y"])
valid_s1_times = vv_sums > 0
patch_s1 = patch_s1.isel(time=valid_s1_times)
if len(patch_s1.time) == 0: return None
# take up to 4 timesteps to match S2
patch_s1 = patch_s1.isel(time=slice(0, min(4, len(patch_s1.time))))
if patch_s1.dims['x'] < patch_size or patch_s1.dims['y'] < patch_size: return None
patch_s1 = patch_s1.isel(x=slice(0, patch_size), y=slice(0, patch_size))
return {
'patch_s2': patch_s2,
'patch_s1': patch_s1,
'label': row['HT_code'] - 1
}
except Exception as e:
return None
def extract_fusion_patches(s2_items, s1_items, gdf, patch_size=16):
print(f"Extracting S1+S2 Fusion patches for {len(gdf)} points using 8 parallel jobs...")
s2_items_dicts = [item.to_dict() for item in s2_items]
s1_items_dicts = [item.to_dict() for item in s1_items]
results = Parallel(n_jobs=8, backend="loky")(
delayed(process_point_s1_s2)(idx, row, s2_items_dicts, s1_items_dicts, patch_size)
for idx, row in tqdm(gdf.iterrows(), total=len(gdf), desc="Downloading S1+S2 Patches")
)
X = []
y = []
cloud_remover = DeepInpaintingStrategy(model_path="cloud_removal_model/cloud_removal_unet_best.pth")
if cloud_remover.model is None:
print("Warning: Could not load DeepInpainting model.")
print("Applying Cloud Removal & Merging Sentinel-1...")
valid_results = [r for r in results if r is not None]
print(f"Valid points extracted: {len(valid_results)}/{len(gdf)}")
for res in tqdm(valid_results, desc="Processing Fusion Features"):
try:
patch_s2 = res['patch_s2']
patch_s1 = res['patch_s1']
label = res['label']
# --- PROCESS S2 ---
patch_cloud_mask = patch_s2["SCL"].isin([3, 8, 9, 10])
clean_patch, _ = cloud_remover.remove_clouds(patch_s2, patch_cloud_mask)
b4 = clean_patch["B04"].values
b8 = clean_patch["B08"].values
b3 = clean_patch["B03"].values
b2 = clean_patch["B02"].values
ndvi = (b8 - b4) / (b8 + b4 + 1e-6)
ndwi = (b3 - b8) / (b3 + b8 + 1e-6)
b2 = np.clip(b2 / 10000.0, 0, 1)
b3 = np.clip(b3 / 10000.0, 0, 1)
b4 = np.clip(b4 / 10000.0, 0, 1)
b8 = np.clip(b8 / 10000.0, 0, 1)
features_t_s2 = np.stack([b2, b3, b4, b8, ndvi, ndwi], axis=1) # (time, 6, 16, 16)
t_len = features_t_s2.shape[0]
if t_len < 4:
pad = np.zeros((4 - t_len, 6, 16, 16))
features_t_s2 = np.concatenate([features_t_s2, pad], axis=0)
# --- PROCESS S1 ---
vv = patch_s1["vv"].values
vh = patch_s1["vh"].values
vv = np.clip(vv, 0, 1.0)
vh = np.clip(vh, 0, 1.0)
features_t_s1 = np.stack([vv, vh], axis=1) # (time, 2, 16, 16)
t_len_s1 = features_t_s1.shape[0]
if t_len_s1 < 4:
pad_s1 = np.zeros((4 - t_len_s1, 2, 16, 16))
features_t_s1 = np.concatenate([features_t_s1, pad_s1], axis=0)
# --- MERGE S1 and S2 ---
features_t = np.concatenate([features_t_s2, features_t_s1], axis=1) # (4, 8, 16, 16)
features = features_t.reshape(32, 16, 16)
features = np.nan_to_num(features, nan=0.0)
X.append(features)
y.append(label)
except Exception as e:
pass
return np.array(X), np.array(y)
def main():
print("🚀 BẮT ĐẦU TRÍCH XUẤT FUSION S1 + S2 (32-CHANNELS)")
cache_file = "dataset_cache/training_data_fusion_32ch.joblib"
bbox = [105.5, 9.2, 106.3, 10.0]
time_range = "2023-01-01/2023-04-30"
s2_items = get_s2_items(bbox, time_range)
s1_items = get_s1_items(bbox, time_range)
gdf = gpd.read_file("train/ST_training_data_updated_1130points_new.shp")
gdf = gdf.to_crs("EPSG:32648")
X, y = extract_fusion_patches(s2_items, s1_items, gdf, patch_size=16)
print(f"Final extracted shape: X={X.shape}, y={y.shape}")
os.makedirs('dataset_cache', exist_ok=True)
joblib.dump({'X': X, 'y': y}, cache_file)
print(f"Saved 32-channel Fusion cache to {cache_file}")
if __name__ == "__main__":
main()