def test_gdal_merge_optfile(self):
        datasets, written = [], []
        init = np.zeros((2, 2), np.int16)
        path = os.path.join(os.getcwd(), "test_gdal_merge_optfile.tif")
        ImageIO.write_geotiff(init, path, self.projection, self.coordinates)
        ds_in = GDalDatasetWrapper.from_file(path)
        for i in range(1, 3, 1):
            img = np.ones((i*2, i*2), np.int16) * i
            ds_n = GDalDatasetWrapper(ds=ds_in.get_ds(), array=img)
            p_out = "test_gdal_merge_optfile_%s.tif" % i
            ImageIO.write_geotiff_existing(img, p_out, ds_in.get_ds())
            self.assertTrue(os.path.exists(path))
            datasets.append(ds_n)
            written.append(p_out)
        optfile = "test_gdal_merge_optfile.txt"
        with open(optfile, 'w') as file_handler:
            for item in written:
                file_handler.write("{}\n".format(item))

        ds_merged = ImageTools.gdal_merge(*datasets,
                                          q=True,
                                          a_nodata=0)
        ds_optfile = ImageTools.gdal_merge(optfile=optfile,
                                           q=True,
                                           a_nodata=0)
        # Array of shape (4, 4):
        expected = np.array([[2, 2, 2, 2],
                             [2, 2, 2, 2],
                             [2, 2, 2, 2],
                             [2, 2, 2, 2]], dtype=np.int16)

        FileSystem.remove_file(path)
        [FileSystem.remove_file(p) for p in written]
        FileSystem.remove_file(optfile)
        np.testing.assert_equal(expected.dtype, ds_merged.array.dtype)
        np.testing.assert_almost_equal(expected, ds_merged.array)
        self.assertEqual(ds_merged.nodata_value, 0)
        self.assertEqual(ds_merged.epsg, 32631)
        np.testing.assert_equal(expected.dtype, ds_optfile.array.dtype)
        np.testing.assert_almost_equal(expected, ds_optfile.array)
        self.assertEqual(ds_optfile.nodata_value, 0)
        self.assertEqual(ds_optfile.epsg, 32631)
示例#2
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    def prepare_water_data(self):
        """
        Prepare the water mask constituing of a set of gsw files.

        :return: Writes the tiles water_mask to the self.gsw_dst path.
        """
        occ_files = self.get_raw_water_data()
        vrt_path = os.path.join(self.wdir, "vrt_%s.vrt" % self.site.nom)
        ImageTools.gdal_buildvrt(*occ_files, dst=vrt_path)
        # Overlay occurrence image with same extent as the given site.
        # Should the occurrence files not be complete, this sets all areas not covered by the occurrence to 0.
        ds_warped = ImageTools.gdal_warp(vrt_path,
                                         r="near",
                                         te=self.site.te_str,
                                         t_srs=self.site.epsg_str,
                                         tr=self.site.tr_str,
                                         dstnodata=0,
                                         multi=True)
        # Threshold the final image and write to destination:
        image_bin = ds_warped.array > self.gsw_threshold
        FileSystem.remove_file(vrt_path)
        ImageIO.write_geotiff_existing(image_bin, self.gsw_dst,
                                       ds_warped.get_ds())
示例#3
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 def get_synthetic_band(self, synthetic_band, **kwargs):
     wdir = kwargs.get("wdir", self.fpath)
     output_folder = os.path.join(wdir, self.base)
     output_bname = "_".join([self.base.split(".")[0], synthetic_band.upper() + ".tif"])
     output_filename = kwargs.get("output_filename", os.path.join(output_folder, output_bname))
     max_value = kwargs.get("max_value", 10000.)
     # Skip existing:
     if os.path.exists(output_filename):
         return output_filename
     if synthetic_band.lower() == "ndvi":
         FileSystem.create_directory(output_folder)
         b4 = self.find_file(pattern=r"*B0?4(_10m)?.jp2$")[0]
         b8 = self.find_file(pattern=r"*B0?8(_10m)?.jp2$")[0]
         ds_red = GDalDatasetWrapper.from_file(b4)
         ds_nir = GDalDatasetWrapper.from_file(b8)
         ds_ndvi = ImageApps.get_ndvi(ds_red, ds_nir, vrange=(0, max_value), dtype=np.int16)
         ds_ndvi.write(output_filename, options=["COMPRESS=DEFLATE"])
     elif synthetic_band.lower() == "ndsi":
         FileSystem.create_directory(output_folder)
         b3 = self.find_file(pattern=r"*B0?3(_10m)?.jp2$")[0]
         b11 = self.find_file(pattern=r"*B11(_20m)?.jp2$")[0]
         ds_green = ImageTools.gdal_translate(b3, tr="20 20", r="cubic")
         ds_swir = GDalDatasetWrapper.from_file(b11)
         ds_ndsi = ImageApps.get_ndsi(ds_green, ds_swir, vrange=(0, max_value), dtype=np.int16)
         ds_ndsi.write(output_filename, options=["COMPRESS=DEFLATE"])
     elif synthetic_band.lower() == "mca_sim":
         FileSystem.create_directory(output_folder)
         b4 = self.find_file(pattern=r"*B0?4(_10m)?.jp2$")[0]
         b3 = self.find_file(pattern=r"*B0?3(_10m)?.jp2$")[0]
         img_red, drv = ImageIO.tiff_to_array(b4, array_only=False)
         img_green = ImageIO.tiff_to_array(b3)
         img_mcasim = (img_red + img_green) / 2
         ImageIO.write_geotiff_existing(img_mcasim, output_filename, drv, options=["COMPRESS=DEFLATE"])
     else:
         raise ValueError("Unknown synthetic band %s" % synthetic_band)
     return output_filename