def _wrapFilter(self, src, vecs, * args):
dest = src.copy()
for vec in vecs:
self._addFreq(dest, vec)
filtered = utils.imfilter(dest, * args)
mold, mnew = [self._arrdiff(src, arr)[0] for arr in (dest, filtered)]
self.assertGreater(mold * 1e-10, mnew)
def _wrapFilter(self, src, vecs, * args):
dest = src.copy()
for vec in vecs:
self._addFreq(dest, vec)
filtered = utils.imfilter(dest, * args)
mold, mnew = [self._arrdiff(src, arr)[0] for arr in (dest, filtered)]
self.assertGreater(mold * 1e-10, mnew)
def _preprocess_extend_single(im, extend, low, high, cut, rcoef, bigshape):
im = utils.extend_by(im, extend)
im = utils.imfilter(im, low, high, cut)
if rcoef != 1:
im = resample(im, rcoef)
# Make the shape of images the same
bg = np.zeros(bigshape, dtype=im.dtype) + utils.get_borderval(im, 5)
im = utils.embed_to(bg, im)
return im
def _preprocess_extend_single(im, extend, low, high, cut, rcoef, bigshape):
im = utils.extend_by(im, extend)
im = utils.imfilter(im, low, high, cut)
if rcoef != 1:
im = resample(im, rcoef)
# Make the shape of images the same
bg = np.zeros(bigshape) + utils.get_borderval(im, 5)
im = utils.embed_to(bg, im)
return im
def filter_images(imgs, low, high, cut):
ret = [utils.imfilter(img, low, high, cut) for img in imgs]
return ret
def filter_images(imgs, low, high):
# lazy import so no imports before run() is really called
from imreg_dft import utils
ret = [utils.imfilter(img, low, high) for img in imgs]
return ret
def filter_images(imgs, low, high):
# lazy import so no imports before run() is really called
from imreg_dft import utils
ret = [utils.imfilter(img, low, high) for img in imgs]
return ret
def filter_images(imgs, low, high, cut):
ret = [utils.imfilter(img, low, high, cut) for img in imgs]
return ret