utils.py 文件源码

def mask_ledaps_qa(rast, mask, nodata=-9999):
    '''
    Applies a given LEDAPS QA mask to a raster. It's unclear how these
    bit-packed QA values ought to be converted back into 16-bit binary numbers:

    "{0:b}".format(42).zfill(16) # Convert binary to decimal padded left?
    "{0:b}".format(42).ljust(16, '0') # Or convert ... padded right?

    The temporary solution is to use the most common (modal) value as the
    "clear" pixel classification and discard everything else. We'd like to
    just discard pixels above a certain value knowing that everything above
    this threshold has a certain bit-packed QA meanining. For example, mask
    pixels with QA values greater than or equal to 12287:

    int("1000000000000000", 2) == 32768 # Maybe clouds
    int("0010000000000000", 2) == 12287 # Maybe cirrus

    Similarly, we'd like to discard pixels at or below 4, as these small binary
    numbers correspond to dropped frames, desginated fill values, and/or
    terrain occlusion. Arguments:
        rast    A gdal.Dataset or a NumPy array
        mask    A gdal.Dataset or a NumPy array
    '''
    # Can accept either a gdal.Dataset or numpy.array instance
    if not isinstance(rast, np.ndarray):
        rast = rast.ReadAsArray()

    else:
        rastr = rast.copy()

    if not isinstance(mask, np.ndarray):
        maskr = mask.ReadAsArray()

    else:
        maskr = mask.copy()

    # Since the QA output is so unreliable (e.g., clouds are called water),
    #   we take the most common QA bit-packed value and assume it refers to
    #   the "okay" pixels
    mode = np.argmax(np.bincount(maskr.ravel()))
    assert mode > 4 and mode < 12287, "The modal value corresponds to a known error value"
    maskr[np.isnan(maskr)] = 0
    maskr[maskr != mode] = 0
    maskr[maskr == mode] = 1

    # Transform into a "1-band" array and apply the mask
    maskr = maskr.reshape((1, maskr.shape[0], maskr.shape[1]))\
        .repeat(rastr.shape[0], axis=0) # Copy the mask across the "bands"
    rastr[maskr == 0] = nodata
    return rastr