utils.py 文件源码

def cfmask(mask, mask_values=(1,2,3,4,255), nodata=-9999):
    '''
    Returns a binary mask according to the CFMask algorithm results for the
    image; mask has True for water, cloud, shadow, and snow (if any) and False
    everywhere else. More information can be found:
        https://landsat.usgs.gov/landsat-surface-reflectance-quality-assessment

    Landsat 4-7 Pre-Collection pixel_qa values to be masked:
        mask_values = (1, 2, 3, 4)

    Landsat 4-7 Collection 1 pixel_qa values to be masked (for "Medium" confidence):
        mask_values = (1, 68, 72, 80, 112, 132, 136, 144, 160, 176, 224)

    Landsat 8 Collection 1 pixel_qa values to be masked (for "Medium" confidence):
        mask_values = (1, 324, 328, 386, 388, 392, 400, 416, 432, 480, 832, 836, 840, 848, 864, 880, 900, 904, 912, 928, 944, 992, 1024)

    Arguments:
        mask        A gdal.Dataset or a NumPy array
        mask_path   The path to an EOS HDF4 CFMask raster
        mask_values The values in the mask that correspond to NoData pixels
        nodata      The NoData value; defaults to -9999.
    '''
    if not isinstance(mask, np.ndarray):
        maskr = mask.ReadAsArray()

    else:
        maskr = mask.copy()

    # Mask according to bit-packing described here:
    # https://landsat.usgs.gov/landsat-surface-reflectance-quality-assessment
    maskr = np.in1d(maskr.reshape((maskr.shape[0] * maskr.shape[1])), mask_values)\
        .reshape((1, maskr.shape[0], maskr.shape[1])).astype(np.int0)

    return maskr