def _process(self, overlay, key=None):
if len(overlay) != 2:
raise Exception("colorizeHSV required an overlay of two Image elements as input.")
if (len(overlay[0].value_dimensions), len(overlay[1].value_dimensions)) != (1,1):
raise Exception("Each Image element must have single value dimension.")
if overlay[0].shape != overlay[1].shape:
raise Exception("Mismatch in the shapes of the data in the Image elements.")
hue = overlay[1]
Hdim = hue.value_dimensions[0]
H = hue.clone(hue.data.copy(),
value_dimensions=[Hdim(cyclic=True, range=hue.range(Hdim.name))])
normfn = raster_normalization.instance()
if self.p.input_ranges:
S = normfn.process_element(overlay[0], key, *self.p.input_ranges)
else:
S = normfn.process_element(overlay[0], key)
C = Image(np.ones(hue.data.shape),
bounds=self.get_overlay_bounds(overlay), group='F', label='G')
C.value_dimensions[0].range = (0,1)
S.value_dimensions[0].range = (0,1)
return HSV(H * C * S).relabel(group=self.p.group)
def _process(self, overlay, key=None):
if len(overlay) != 2:
raise Exception(
"colorizeHSV required an overlay of two Image elements as input."
)
if (len(overlay.get(0).vdims), len(overlay.get(1).vdims)) != (1, 1):
raise Exception(
"Each Image element must have single value dimension.")
if overlay.get(0).shape != overlay.get(1).shape:
raise Exception(
"Mismatch in the shapes of the data in the Image elements.")
hue = overlay.get(1)
Hdim = hue.vdims[0]
H = hue.clone(hue.data.copy(),
vdims=[Hdim(cyclic=True, range=hue.range(Hdim.name))])
normfn = raster_normalization.instance()
if self.p.input_ranges:
S = normfn.process_element(overlay.get(0), key,
*self.p.input_ranges)
else:
S = normfn.process_element(overlay.get(0), key)
C = Image(np.ones(hue.data.shape),
bounds=self.get_overlay_bounds(overlay),
group='F',
label='G')
C.vdims[0].range = (0, 1)
S.vdims[0].range = (0, 1)
return HSV(H * C * S).relabel(group=self.p.group)
def _process(self, overlay, key=None):
normfn = raster_normalization.instance()
if self.p.input_ranges:
overlay = normfn.process_element(overlay, key, *self.p.input_ranges)
else:
overlay = normfn.process_element(overlay, key)
hue, confidence = overlay.get(0), overlay.get(1)
strength_data = overlay.get(2).data if (len(overlay) == 3) else np.ones(hue.shape)
hue_data = hue.data
hue_range = hue.value_dimensions[0].range
if (not hue.value_dimensions[0].cyclic) or (None in hue_range):
raise Exception("The input hue channel must be declared cyclic with a defined range.")
if hue.shape != confidence.shape:
raise Exception("Cannot combine input Matrices with different shapes.")
(h,s,v)= (hue_data,
(confidence.data * self.p.C_multiplier).clip(0.0, 1.0),
(strength_data * self.p.S_multiplier).clip(0.0, 1.0))
if self.p.flipSC:
(h,s,v) = (h,v,s.clip(0,1.0))
return RGB(np.dstack(hsv_to_rgb(h,s,v)),
bounds = self.get_overlay_bounds(overlay),
label = self.get_overlay_label(overlay),
group = self.p.group)
def _process(self, matrix, key=None):
normfn = raster_normalization.instance()
if self.p.input_ranges:
matrix = normfn.process_element(matrix, key, *self.p.input_ranges)
else:
matrix = normfn.process_element(matrix, key)
fft_spectrum = abs(np.fft.fftshift(np.fft.fft2(matrix.data - 0.5, s=None, axes=(-2, -1))))
fft_spectrum = 1 - fft_spectrum # Inverted spectrum by convention
zero_min_spectrum = fft_spectrum - fft_spectrum.min()
spectrum_range = fft_spectrum.max() - fft_spectrum.min()
spectrum = (self.p.max_power * zero_min_spectrum) / spectrum_range
l, b, r, t = matrix.bounds.lbrt()
density = matrix.xdensity
bounds = BoundingBox(radius=(density/2)/(r-l))
return Image(spectrum, bounds=bounds, label=matrix.label, group=self.p.group)
def _process(self, overlay, key=None):
if len(overlay) != 2:
raise Exception("The similarity index may only be computed"
"using overlays of Image Views.")
mat1, mat2 = overlay.get(0), overlay.get(1)
val_dims = [mat1.value_dimensions, mat2.value_dimensions]
if tuple(len(el) for el in val_dims) != (1,1):
raise Exception("Both input Matrices must have single value dimension.")
if False in [val_dims[0][0].cyclic, val_dims[1][0].cyclic]:
raise Exception("Both input Matrices must be defined as cyclic.")
normfn = raster_normalization.instance()
overlay = normfn.process_element(overlay, key)
return Image(self.difference(overlay.get(0).data, overlay.get(1).data),
bounds=self.get_overlay_bounds(overlay),
group=self.p.value)