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import matplotlib.pyplot as plt
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# Common imports and settings
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import os, sys
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os.environ['USE_PYGEOS'] = '0'
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from IPython.display import Markdown
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import pandas as pd
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pd.set_option("display.max_rows", None)
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import xarray as xr
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# Datacube
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import datacube
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from datacube.utils.rio import configure_s3_access
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from datacube.utils import masking
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from datacube.utils.cog import write_cog
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# https://github.com/GeoscienceAustralia/dea-notebooks/tree/develop/Tools
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from dea_tools.plotting import display_map, rgb
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from dea_tools.datahandling import mostcommon_crs
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# EASI defaults
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easinotebooksrepo = '/home/jovyan/easi-notebooks'
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if easinotebooksrepo not in sys.path: sys.path.append(easinotebooksrepo)
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from easi_tools import EasiDefaults, xarray_object_size, notebook_utils, unset_cachingproxy
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from easi_tools.load_s2l2a import load_s2l2a_with_offset
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from dask.distributed import progress
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# Data tools
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import numpy as np
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from datetime import datetime
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# Datacube
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from datacube.utils import masking # https://github.com/opendatacube/datacube-core/blob/develop/datacube/utils/masking.py
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from odc.algo import enum_to_bool # https://github.com/opendatacube/odc-algo/blob/main/odc/algo/_masking.py
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from odc.algo import xr_reproject # https://github.com/opendatacube/odc-algo/blob/main/odc/algo/_warp.py
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from datacube.utils.geometry import GeoBox, box # https://github.com/opendatacube/datacube-core/blob/develop/datacube/utils/geometry/_base.py
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# Holoviews, Datashader and Bokeh
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import hvplot.pandas
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import hvplot.xarray
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import holoviews as hv
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import panel as pn
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import colorcet as cc
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import cartopy.crs as ccrs
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from datashader import reductions
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from holoviews import opts
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from utils import load_data_geo
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import rasterio
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import rioxarray
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# import geoviews as gv
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# from holoviews.operation.datashader import rasterize
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hv.extension('bokeh', logo=False)
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from deafrica_tools.bandindices import calculate_indices
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import accuracy_score, classification_report
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from sklearn.preprocessing import LabelEncoder
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from sklearn.pipeline import Pipeline
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from sklearn.ensemble import RandomForestClassifier
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from sklearn.impute import SimpleImputer
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from sklearn.preprocessing import StandardScaler
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from sklearn.model_selection import GridSearchCV
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import accuracy_score
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from shapely.geometry import Point, Polygon
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import geopandas as gpd
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from pyproj import CRS
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from matplotlib.colors import ListedColormap
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from holoviews import opts
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from datashader import reductions
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from bokeh.models.tickers import FixedTicker
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from rioxarray.merge import merge_arrays
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from sklearn.preprocessing import PolynomialFeatures
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from sklearn.linear_model import LinearRegression
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from sklearn.ensemble import RandomForestRegressor
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import mean_squared_error, r2_score
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import joblib
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def load_data(dc, date_range, longtitude_range, latitude_range):
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product = 's2_l2a'
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query = {
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'product': product, # Product name
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'x': longtitude_range, # "x" axis bounds
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'y': latitude_range, # "y" axis bounds
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'time': date_range, # Any parsable date strings
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}
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native_crs = notebook_utils.mostcommon_crs(dc, query)
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print(f'Most common native CRS: {native_crs}')
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measurements = ['blue', 'green', 'red', 'nir', 'scl']
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load_params = {
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'measurements': measurements, # Selected measurement or alias names
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'output_crs': native_crs, # Target EPSG code
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'resolution': (-10, 10), # Target resolution
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'group_by': 'solar_day', # Scene grouping
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'dask_chunks': {'x': 2048, 'y': 2048}, # Dask chunks
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}
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data = load_s2l2a_with_offset(
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dc,
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query | load_params # Combine the two dicts that contain our search and load parameters
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)
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return data
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def mask_clean(data):
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flag_name = 'scl'
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flag_desc = masking.describe_variable_flags(data[flag_name]) # Pandas dataframe
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display(flag_desc)
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display(flag_desc.loc['qa'].values[1])
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# Create a "data quality" Mask layer
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flags_def = flag_desc.loc['qa'].values[1]
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good_pixel_flags = [flags_def[str(i)] for i in [2, 4, 5, 6]] # To pass strings to enum_to_bool()
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# enum_to_bool calculates the pixel-wise "or" of each set of pixels given by good_pixel_flags
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# 1 = good data
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# 0 = "bad" data
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good_pixel_mask = enum_to_bool(data[flag_name], good_pixel_flags)
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data_layer_names = [x for x in data.data_vars if x != 'scl']
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# Apply good pixel mask to blue, green, red and nir.
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result = data[data_layer_names].where(good_pixel_mask).persist()
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return result
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def fill_nan(ndvi, time_split):
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rs = []
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for times in time_split:
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tmp = ndvi.sel(time=times)
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fill_ds = tmp.sel(time=times).bfill(dim='time')
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fill_ds = fill_ds.sel(time=times).ffill(dim='time')
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rs.append(fill_ds)
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merged_ndvi = xr.concat([i for i in rs], dim="time")
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fill_m = merged_ndvi.bfill(dim="time")
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fill_m = fill_m.ffill(dim="time")
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return fill_m
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def load_train_data(train_path):
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train = load_data_geo(train_path)
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return train
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def load_sen1(name_vh, name_vv):
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dsvv = rioxarray.open_rasterio(name_vv)
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dsvh = rioxarray.open_rasterio(name_vh)
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return dsvh, dsvv
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def get_data_sen1_and_sen2(train, average_ndvi, dsvh, dsvv):
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loaded_datasets = {}
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for idx, point in train.iterrows():
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key = f"point_{idx + 1}"
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try:
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ndvi_data = average_ndvi.sel(x=point.geometry.x, y=point.geometry.y, method='nearest').values
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vh_data = dsvh.sel(x=point.geometry.x, y=point.geometry.y, method='nearest').values
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vv_data = dsvv.sel(x=point.geometry.x, y=point.geometry.y, method='nearest').values
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loaded_datasets[key] = {
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"data": np.concatenate((ndvi_data, vh_data, vv_data)),
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"label": point.HT_code
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}
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except Exception as e:
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# loaded_datasets[key] = None
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print(e)
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return loaded_datasets
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def split_train_data(train, label_mapping, datasets):
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label_encoder = LabelEncoder()
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# Fit and transform the labels
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labels = train.Hientrang.values
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numeric_labels = label_encoder.fit_transform([label_mapping[label] for label in labels])
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X = []
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x_new = []
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lb_new = []
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for k, v in datasets.items():
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X.append(v)
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for i in range(len(X)):
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if X[i] is not None:
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x_new.append(X[i]["data"])
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lb_new.append(numeric_labels[i])
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X_train, X_temp, y_train, y_temp= train_test_split(x_new, lb_new, test_size=0.4, random_state=42)
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X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)
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return X_train, X_val, X_test, y_train, y_val, y_test
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def train_with_rf(X_train, X_val, y_train, y_val):
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# Takes 1-2 minutes to complete
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# Tạo RandomForestClassifier mặc định để sử dụng làm mô hình ban đầu trong pipeline
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base_model = RandomForestClassifier(random_state=42, n_jobs=-1)
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# Tạo pipeline
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pipeline = Pipeline([
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# ('imputer', SimpleImputer(strategy='mean')),
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('scaler', StandardScaler()),
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('classifier', base_model),
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])
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# Thiết lập các tham số bạn muốn tối ưu hóa
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param_grid = {
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'classifier__n_estimators': [100, 300, 500, 700, 1000],
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'classifier__max_depth': [6, 8, 10, 15, 20],
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'classifier__criterion': ['gini', 'entropy'],
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}
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# Sử dụng GridSearchCV để tìm bộ tham số tốt nhất
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grid_search = GridSearchCV(pipeline, param_grid, cv=5, scoring='accuracy', n_jobs=-1)
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grid_search.fit(X_train, y_train)
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# In ra bộ tham số tốt nhất
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best_params = grid_search.best_params_
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print("Best Parameters:", best_params)
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# Dự đoán trên tập kiểm tra
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y_pred = grid_search.predict(X_val)
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# Đánh giá kết quả
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accuracy = accuracy_score(y_val, y_pred)
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print(f"Accuracy: {round(accuracy, 2)*100} %")
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return grid_search
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def save_model(name_file, grid_search):
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dir_save_model = "model_train"
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if not os.path.exists(dir_save_model):
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os.mkdir(dir_save_model)
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joblib.dump(grid_search, os.path.join(dir_save_model, name_file))
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print("Done!")
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def predict(model, data_crs, ndvi, vh, vv):
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data_predict = []
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for i in range(ndvi.shape[1]):
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ndvi_tmp = ndvi.isel(y=i).values
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vh_data = vh.sel(y=ndvi.y.values[i], method='nearest').values
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vv_data = vv.sel(y=ndvi.y.values[i], method='nearest').values
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all_tmp = np.concatenate((ndvi_tmp, vh_data, vv_data), axis=0)
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data_predict.extend(all_tmp.T)
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y_pred = model.predict(data_predict)
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final_label = y_pred.reshape(ndvi.y.shape[0], ndvi.x.shape[0])
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final_xarray_save = xr.DataArray(final_label, dims=("y", "x"))
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final_xarray_save = final_xarray_save.rio.write_crs(data_crs)
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x_values = ndvi.x.values
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y_values = ndvi.y.values
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data_array = xr.DataArray(final_xarray_save,
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coords={'x': x_values, 'y': y_values},
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dims=['y', 'x'])
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data_array = data_array.rio.write_crs(ndvi.rio.crs)
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return data_array
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def cut_according_shp(thuanhoa_path, average_ndvi, data_array):
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gdf = gpd.read_file(thuanhoa_path)
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gdf = gdf.to_crs(average_ndvi.rio.crs)
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polygon_coords = list(gdf.geometry.values[0].exterior.coords)
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polygon_coordinates = [(x, y) for x, y in polygon_coords]
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geometries = [
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{
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'type': 'Polygon',
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'coordinates': [polygon_coordinates]
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}
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]
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region_result = data_array.rio.clip(geometries, data_array.rio.crs, drop=False)
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region_result = region_result.where(region_result >= 0, float('nan'))
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return region_result
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def compare(KD_path, KetQuaPhanLoaiDat, CODE_MAP, HT_MAP):
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gdf = gpd.read_file(KD_path, crs="EPSG:9209")
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polygon = gdf.geometry.values
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label = gdf.tenchu.values
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ouput_image = rioxarray.open_rasterio(KetQuaPhanLoaiDat)
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code_tq = HT_MAP["TQ"]["data"][0]
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code_pnn = HT_MAP["PNN"]["data"][0]
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result = {}
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for key, values in HT_MAP.items():
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print(f"process {key}")
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array_list = []
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for i in range(len(polygon)):
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po = polygon[i]
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lb = label[i]
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code_lb = CODE_MAP.get(lb, code_tq)
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try:
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qr = ouput_image.rio.clip([po], "EPSG:9209")
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if code_lb in values["data"]:
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if code_lb == code_pnn:
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qr = qr.where((qr != float(code_pnn)), np.nan)
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# qr = qr.where((qr != 3.0), np.nan)
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elif code_lb == code_tq:
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qr = qr.where((qr != float(code_pnn)), np.nan)
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qr = qr.where((qr != 3.0), np.nan)
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else:
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qr = qr.where(qr != float(code_lb), np.nan)
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else:
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|
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qr.values[:, :, :] = np.nan
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|
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array_list.append(qr)
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|
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|
except Exception as e:
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|
pass
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result.update({key: array_list})
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return result
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def save_result(result, HT_MAP):
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# cmap = ListedColormap(colors)
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save_path = "ThuanHoa/KetQua"
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if not os.path.exists(save_path):
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os.mkdir(save_path)
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for k, v in result.items():
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rs = merge_arrays(v, nodata = np.nan)
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rs.rio.to_raster(f"{save_path}/{k}.tif")
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print(f"save {save_path}/{k}.tif")
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|
# img = rs.plot(cmap=cmap, add_colorbar=False)
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|
# cbar = plt.colorbar(img)
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|
|
# cbar.ax.set_yticklabels(labels)
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|
# plt.title(f'{HT_MAP[k]["name"]}')
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|
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|
|
# plt.axis('off')
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|
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|
|
# plt.show()
|
|
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|
|
|
|
|
|
|
def load_data_sen1(dc, date_range, coordinates):
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|
longtitude_range, latitude_range = coordinates
|
|
|
|
|
data_sen1 = dc.load(
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|
|
|
|
product="sentinel1_grd_gamma0_20m",
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|
x=longtitude_range,
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|
y=latitude_range,
|
|
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|
|
time=date_range,
|
|
|
|
|
measurements=["vv", "vh"],
|
|
|
|
|
output_crs="EPSG:32648",
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|
|
|
|
resolution=(-10,10),
|
|
|
|
|
dask_chunks={"x":2048, "y":2048},
|
|
|
|
|
skip_broken_datasets=True,
|
|
|
|
|
group_by='solar_day'
|
|
|
|
|
)
|
|
|
|
|
|
|
|
|
|
notebook_utils.heading(notebook_utils.xarray_object_size(data_sen1))
|
|
|
|
|
display(data_sen1)
|
|
|
|
|
dsvh = data_sen1.vh
|
|
|
|
|
dsvv = data_sen1.vv
|
|
|
|
|
|
|
|
|
|
return dsvh, dsvv
|
|
|
|
|
|
|
|
|
|
def calculate_average(data, time_pattern='1M'):
|
|
|
|
|
return data.resample(time=time_pattern).mean().persist()
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def load_data_sen2(dc, date_range, coordinates):
|
|
|
|
|
longtitude_range, latitude_range = coordinates
|
|
|
|
|
product = 's2_l2a'
|
|
|
|
|
query = {
|
|
|
|
|
'product': product, # Product name
|
|
|
|
|
'x': longtitude_range, # "x" axis bounds
|
|
|
|
|
'y': latitude_range, # "y" axis bounds
|
|
|
|
|
'time': date_range, # Any parsable date strings
|
|
|
|
|
}
|
|
|
|
|
native_crs = notebook_utils.mostcommon_crs(dc, query)
|
|
|
|
|
print(f'Most common native CRS: {native_crs}')
|
|
|
|
|
|
|
|
|
|
# measurements = ['red','green', 'blue', 'nir', 'scl']
|
|
|
|
|
measurements = ['red', 'nir', 'scl']
|
|
|
|
|
|
|
|
|
|
load_params = {
|
|
|
|
|
'measurements': measurements, # Selected measurement or alias names
|
|
|
|
|
'output_crs': native_crs, # Target EPSG code
|
|
|
|
|
'resolution': (-10, 10), # Target resolution
|
|
|
|
|
'group_by': 'solar_day', # Scene grouping
|
|
|
|
|
'dask_chunks': {'x': 2048, 'y': 2048}, # Dask chunks
|
|
|
|
|
}
|
|
|
|
|
data = load_s2l2a_with_offset(
|
|
|
|
|
dc,
|
|
|
|
|
query | load_params # Combine the two dicts that contain our search and load parameters
|
|
|
|
|
)
|
|
|
|
|
return data
|
|
|
|
|
|
|
|
|
|
def mask_cloud(data):
|
|
|
|
|
flag_name = 'scl'
|
|
|
|
|
flag_desc = masking.describe_variable_flags(data[flag_name]) # Pandas dataframe
|
|
|
|
|
display(flag_desc.loc['qa'].values[1])
|
|
|
|
|
# Create a "data quality" Mask layer
|
|
|
|
|
flags_def = flag_desc.loc['qa'].values[1]
|
|
|
|
|
good_pixel_flags = [flags_def[str(i)] for i in [2, 4, 5, 6]] # To pass strings to enum_to_bool()
|
|
|
|
|
|
|
|
|
|
# enum_to_bool calculates the pixel-wise "or" of each set of pixels given by good_pixel_flags
|
|
|
|
|
# 1 = good data
|
|
|
|
|
# 0 = "bad" data
|
|
|
|
|
good_pixel_mask = enum_to_bool(data[flag_name], good_pixel_flags)
|
|
|
|
|
data_layer_names = [x for x in data.data_vars if x != 'scl']
|
|
|
|
|
# Apply good pixel mask to blue, green, red and nir.
|
|
|
|
|
result = data[data_layer_names].where(good_pixel_mask).persist()
|
|
|
|
|
return result
|
|
|
|
|
|
|
|
|
|
def find_best_model(dataset):
|
|
|
|
|
X_train, X_val, y_train, y_val = dataset
|
|
|
|
|
# Tạo RandomForestClassifier mặc định để sử dụng làm mô hình ban đầu trong pipeline
|
|
|
|
|
base_model = RandomForestClassifier(random_state=42, n_jobs=-1)
|
|
|
|
|
|
|
|
|
|
# Tạo pipeline
|
|
|
|
|
pipeline = Pipeline([
|
|
|
|
|
# ('imputer', SimpleImputer(strategy='mean')),
|
|
|
|
|
('scaler', StandardScaler()),
|
|
|
|
|
('classifier', base_model),
|
|
|
|
|
])
|
|
|
|
|
# Thiết lập các tham số bạn muốn tối ưu hóa
|
|
|
|
|
param_grid = {
|
|
|
|
|
'classifier__n_estimators': [100, 300, 500, 700, 1000],
|
|
|
|
|
'classifier__max_depth': [6, 8, 10, 15, 20],
|
|
|
|
|
'classifier__criterion': ['gini', 'entropy'],
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
# Sử dụng GridSearchCV để tìm bộ tham số tốt nhất
|
|
|
|
|
grid_search = GridSearchCV(pipeline, param_grid, cv=5, scoring='accuracy', n_jobs=-1)
|
|
|
|
|
grid_search.fit(X_train, y_train)
|
|
|
|
|
|
|
|
|
|
# In ra bộ tham số tốt nhất
|
|
|
|
|
best_params = grid_search.best_params_
|
|
|
|
|
print("Best Parameters:", best_params)
|
|
|
|
|
|
|
|
|
|
# Dự đoán trên tập kiểm tra
|
|
|
|
|
y_pred = grid_search.predict(X_val)
|
|
|
|
|
|
|
|
|
|
# Đánh giá kết quả
|
|
|
|
|
accuracy = accuracy_score(y_val, y_pred)
|
|
|
|
|
print(f"Accuracy: {round(accuracy, 2)*100} %")
|
|
|
|
|
return grid_search
|
|
|
|
|
|
|
|
|
|
def save_result_new(result, save_path, HT_MAP):
|
|
|
|
|
# cmap = ListedColormap(colors)
|
|
|
|
|
if not os.path.exists(save_path):
|
|
|
|
|
os.mkdir(save_path)
|
|
|
|
|
|
|
|
|
|
for k, v in result.items():
|
|
|
|
|
rs = merge_arrays(v, nodata = np.nan)
|
|
|
|
|
rs.rio.to_raster(f"{save_path}/{k}.tif")
|
|
|
|
|
print(f"save {save_path}/{k}.tif")
|
|
|
|
|
# img = rs.plot(cmap=cmap, add_colorbar=False)
|
|
|
|
|
# cbar = plt.colorbar(img)
|
|
|
|
|
# cbar.ax.set_yticklabels(labels)
|
|
|
|
|
# plt.title(f'{HT_MAP[k]["name"]}')
|
|
|
|
|
# plt.axis('off')
|
|
|
|
|
# plt.show()
|
|
|
|
|
def accuracy_test(test, data_array):
|
|
|
|
|
# cấu hình nhãn dữ liệu
|
|
|
|
|
label_mapping = {
|
|
|
|
|
"Lua tom": "0",
|
|
|
|
|
"Lua": "1",
|
|
|
|
|
"CHN": "2",
|
|
|
|
|
"CLN": "3",
|
|
|
|
|
"TS": "4",
|
|
|
|
|
"Song": "5",
|
|
|
|
|
"Dat xay dung": "6",
|
|
|
|
|
"Rung": "7"
|
|
|
|
|
}
|
|
|
|
|
|
|
|
|
|
chk = []
|
|
|
|
|
pred = []
|
|
|
|
|
dd = []
|
|
|
|
|
for idx, point in test.iterrows():
|
|
|
|
|
label = point.LULC
|
|
|
|
|
predict = data_array.sel(x=point.geometry.x, y=point.geometry.y, method='nearest').values
|
|
|
|
|
pred.append(label_mapping[label])
|
|
|
|
|
dd.append(str(predict))
|
|
|
|
|
chk.append(predict == int(label_mapping[label]))
|
|
|
|
|
test["code"] = pred
|
|
|
|
|
test["dd"] = dd
|
|
|
|
|
test["check"] = chk
|
|
|
|
|
path = "ThuanHoa/TestAccuracy"
|
|
|
|
|
if not os.path.exists(path):
|
|
|
|
|
os.mkdir(path)
|
|
|
|
|
test.to_file(f"{path}/result.shp")
|
|
|
|
|
|
|
|
|
|
percentage_true = np.mean(chk) * 100
|
|
|
|
|
print(f"độ chính xác: {percentage_true:.2f}%")
|
Thanh Trong