实现水体含量反演程序。

interpolate.py

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+from util import *
+import numpy as np
+from osgeo import gdal
+import time
+import argparse
+
+
+@timeit
+def interpolate_kriging_pykrige(x, y, z, proj, spatial_resolution, output_path=None):
+    # https://pygis.io/docs/e_interpolation.html
+    from pykrige.ok import OrdinaryKriging
+
+    x_min = x.min()
+    x_max = x.max()
+    y_min = y.min()
+    y_max = y.max()
+
+    # Generate a regular grid
+    step_x = int(x_max - x_min / spatial_resolution)
+    step_y = int(y_max - y_min / spatial_resolution)
+    grid_x = np.linspace(x_min, x_max, step_x)
+    grid_y = np.linspace(y_min, y_max, step_y)
+
+    # Create ordinary kriging object:
+    OK = OrdinaryKriging(
+        x,
+        y,
+        z,
+        variogram_model="spherical",
+        verbose=False,
+        enable_plotting=False,
+        coordinates_type="euclidean",
+    )
+
+    # Execute on grid:
+    print("执行插值")
+    start1 = time.perf_counter()
+
+    z1, ss1 = OK.execute("grid", grid_x, grid_y, backend="loop", n_closest_points=12)
+
+    end1 = time.perf_counter()
+    print("插值，运行时间：", end1 - start1, "秒")
+
+    if output_path is not None:
+        format_str = "GTiff"
+        driver = gdal.GetDriverByName(format_str)
+        # driver.Create()函数中RasterXSize代表影像的sample（列数），RasterYSize代表影像的line（行数）
+        dataset_out = driver.Create(output_path, step_x, step_y, 1, gdal.GDT_Float64)
+
+        dataset_out.SetProjection(proj)
+        geotransform_out = (x_min, spatial_resolution, 0, y_max, 0, -spatial_resolution)
+        dataset_out.SetGeoTransform(geotransform_out)
+
+        dataset_out.GetRasterBand(1).WriteArray(z1)
+
+        del dataset_out
+
+
+# Press the green button in the gutter to run the script.
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description="此程序基于反演结果进行插值。")
+
+    parser.add_argument('-i1', '--pos_content_path', type=str, required=True, help='输入含量文件的路径')
+    parser.add_argument('-i2', '--img_path', type=str, required=True, help='输入影像文件的路径')
+    parser.add_argument('-i3', '--spatial_resolution', type=float, default=1.0, help='输出控件分辨率')
+    parser.add_argument('-o', '--outpath', required=True, type=str, help='输出文件的路径')
+
+    parser.add_argument('-v', '--verbose', action='store_true', help='启用详细模式')
+
+    args = parser.parse_args()
+
+    pos_content = np.loadtxt(args.pos_content_path)
+
+    dataset = gdal.Open(args.img_path)
+    im_proj = dataset.GetProjection()  # 地图投影信息
+
+    interpolate_kriging_pykrige(pos_content[:, 0], pos_content[:, 1], pos_content[:, 2], im_proj, args.spatial_resolution, args.outpath)