def normalize(im, invert=False, scale=None, dtype=np.float64):
    """
    Normalize a field to a (min, max) exposure range, default is (0, 255).
    (min, max) exposure values. Invert the image if requested.
    """
    if dtype not in {np.float16, np.float32, np.float64}:
        raise ValueError('dtype must be numpy.float16, float32, or float64.')
    out = im.astype('float').copy()

    scale = scale or (0.0, 255.0)
    l, u = (float(i) for i in scale)
    out = (out - l) / (u - l)
    if invert:
        out = -out + (out.max() + out.min())
    return out.astype(dtype)
def normalize(im, invert=False, scale=None, dtype=np.float64):
    """
    Normalize a field to a (min, max) exposure range, default is (0, 255).
    (min, max) exposure values. Invert the image if requested.
    """
    if dtype not in {np.float16, np.float32, np.float64}:
        raise ValueError('dtype must be numpy.float16, float32, or float64.')
    out = im.astype('float').copy()

    scale = scale or (0.0, 255.0)
    l, u = (float(i) for i in scale)
    out = (out - l) / (u - l)
    if invert:
        out = -out + (out.max() + out.min())
    return out.astype(dtype)
def normalize ( im , invert = False , scale = None , dtype = np . float64 ) : if dtype not in { np . float16 , np . float32 , np . float64 } : raise ValueError ( 'dtype must be numpy.float16, float32, or float64.' ) out = im . astype ( 'float' ) . copy ( ) scale = scale or ( 0.0 , 255.0 ) l , u = ( float ( i ) for i in scale ) out = ( out - l ) / ( u - l ) if invert : out = - out + ( out . max ( ) + out . min ( ) ) return out . astype ( dtype )
Normalize a field to a (min, max) exposure range, default is (0, 255).
    (min, max) exposure values. Invert the image if requested.
Normalize a field to a ( min max ) exposure range default is ( 0 255 ) . ( min max ) exposure values . Invert the image if requested .