if np.issubdtype(image.dtype, np.floating):
resolution = 2 * np.finfo(image.dtype).resolution
if h < resolution:
h = resolution
h_corrected = h - resolution / 2.0
shifted_img = image - h
else:
shifted_img = _subtract_constant_clip(image, h)
After Change
// >>> b[0] == b[1]
// True
//
if np.issubdtype(type(h), np.floating) and \
np.issubdtype(image.dtype, np.integer):
if ((h % 1) != 0):
warn("possible precision loss converting image to "
"floating point. To silence this warning, "
"ensure image and h have same data type.",
stacklevel=2)
image = image.astype(np.float_)
else:
h = image.dtype.type(h)
if (h == 0):
raise ValueError("h = 0 is ambiguous, use local_maxima() "
"instead?")
if np.issubdtype(image.dtype, np.floating):
// The purpose of the resolution variable is to allow for the
// small rounding errors that inevitably occur when doing
// floating point arithmetic. We want shifted_img to be
// guaranteed to be h less than image. If we only subtract h
// there may be pixels were shifted_img ends up being
// slightly greater than image - h.
//
// The resolution is scaled based on the pixel values in the
// image because floating point precision is relative. A
// very large value of 1.0e10 will have a large precision,
// say +-1.0e4, and a very small value of 1.0e-10 will have
// a very small precision, say +-1.0e-16.
//
resolution = 2 * np.finfo(image.dtype).resolution * np.abs(image)
shifted_img = image - h - resolution
else:
shifted_img = _subtract_constant_clip(image, h)
rec_img = greyreconstruct.reconstruction(shifted_img, image,
method="dilation", selem=selem)
residue_img = image - rec_img
return (residue_img >= h).astype(np.uint8)
def h_minima(image, h, selem=None):
