classify.py 文件源码

def write_geotiff(fname, data, geo_transform, projection, data_type=gdal.GDT_Byte):
    """
    Create a GeoTIFF file with the given data.
    :param fname: Path to a directory with shapefiles
    :param data: Number of rows of the result
    :param geo_transform: Returned value of gdal.Dataset.GetGeoTransform (coefficients for
                          transforming between pixel/line (P,L) raster space, and projection
                          coordinates (Xp,Yp) space.
    :param projection: Projection definition string (Returned by gdal.Dataset.GetProjectionRef)
    """
    driver = gdal.GetDriverByName('GTiff')
    rows, cols = data.shape
    dataset = driver.Create(fname, cols, rows, 1, data_type)
    dataset.SetGeoTransform(geo_transform)
    dataset.SetProjection(projection)
    band = dataset.GetRasterBand(1)
    band.WriteArray(data)

    ct = gdal.ColorTable()
    for pixel_value in range(len(classes)+1):
        color_hex = COLORS[pixel_value]
        r = int(color_hex[1:3], 16)
        g = int(color_hex[3:5], 16)
        b = int(color_hex[5:7], 16)
        ct.SetColorEntry(pixel_value, (r, g, b, 255))
    band.SetColorTable(ct)

    metadata = {
        'TIFFTAG_COPYRIGHT': 'CC BY 4.0',
        'TIFFTAG_DOCUMENTNAME': 'classification',
        'TIFFTAG_IMAGEDESCRIPTION': 'Supervised classification.',
        'TIFFTAG_MAXSAMPLEVALUE': str(len(classes)),
        'TIFFTAG_MINSAMPLEVALUE': '0',
        'TIFFTAG_SOFTWARE': 'Python, GDAL, scikit-learn'
    }
    dataset.SetMetadata(metadata)

    dataset = None  # Close the file
    return