def createButterworthFilter(self, dia=400, size=(64, 64), order=2, highpass=False):
"""
**SUMMARY**
Creates a butterworth filter of given size and order.
**PARAMETERS**
* *dia* - int - diameter of Gaussian filter
- list - provide a list of three diameters to create
a 3 channel filter
* *size* - size of the filter (width, height)
* *order* - order of the filter
* *highpass*: - bool
True: highpass filter
False: lowpass filter
**RETURNS**
DFT filter.
**EXAMPLE**
>>> flt = DFT.createButterworthfilter(100, (512, 512), order=3,
highpass=True)
>>> flt = DFT.createButterworthfilter([100, 120, 140], (512, 512),
order=3, highpass=False)
>>> img = Image('lenna')
>>> flt.applyFilter(img).show()
"""
if isinstance(dia, list):
if len(dia) != 3 and len(dia) != 1:
warnings.warn("diameter list must be of size 1 or 3")
return None
stackedfilter = DFT()
for d in dia:
stackedfilter = stackedfilter._stackFilters(self.createButterworthFilter(d, size, order, highpass))
image = Image(stackedfilter._numpy)
retVal = DFT(numpyarray=stackedfilter._numpy, image=image,
dia=dia, channels = len(dia), size=size,
type=stackedfilter._type, order=order,
frequency=stackedfilter._freqpass)
return retVal
freqpass = "******"
sz_x, sz_y = size
x0 = sz_x/2
y0 = sz_y/2
X, Y = np.meshgrid(np.arange(sz_x), np.arange(sz_y))
D = np.sqrt((X-x0)**2+(Y-y0)**2)
flt = 255/(1.0 + (D/dia)**(order*2))
if highpass:
frequency = "highpass"
flt = 255 - flt
img = Image(flt)
retVal = DFT(size=size, numpyarray=flt, image=img, dia=dia,
type="Butterworth", frequency=freqpass)
return retVal
def generateSuperPixels(self):
"""
Compute the over-segmentation based on the step-size and relative
weighting of the pixel and colour values.
"""
self._initData()
indnp = np.mgrid[0:self.height,0:self.width].swapaxes(0,2).swapaxes(0,1)
for i in range(self.ITERATIONS):
self.distances = self.FLT_MAX * np.ones(self.img.shape[:2])
for j in xrange(self.centers.shape[0]):
xlow, xhigh = int(self.centers[j][3] - self.step), int(self.centers[j][3] + self.step)
ylow, yhigh = int(self.centers[j][4] - self.step), int(self.centers[j][4] + self.step)
if xlow <= 0:
xlow = 0
if xhigh > self.width:
xhigh = self.width
if ylow <=0:
ylow = 0
if yhigh > self.height:
yhigh = self.height
cropimg = self.labimg[ylow : yhigh , xlow : xhigh].astype(np.int64)
colordiff = cropimg - self.labimg[self.centers[j][4], self.centers[j][3]]
colorDist = np.sqrt(np.sum(np.square(colordiff.astype(np.int64)), axis=2))
yy, xx = np.ogrid[ylow : yhigh, xlow : xhigh]
pixdist = ((yy-self.centers[j][4])**2 + (xx-self.centers[j][3])**2)**0.5
dist = ((colorDist/self.nc)**2 + (pixdist/self.ns)**2)**0.5
distanceCrop = self.distances[ylow : yhigh, xlow : xhigh]
idx = dist < distanceCrop
distanceCrop[idx] = dist[idx]
self.distances[ylow : yhigh, xlow : xhigh] = distanceCrop
self.clusters[ylow : yhigh, xlow : xhigh][idx] = j
for k in xrange(len(self.centers)):
idx = (self.clusters == k)
colornp = self.labimg[idx]
distnp = indnp[idx]
self.centers[k][0:3] = np.sum(colornp, axis=0)
sumy, sumx = np.sum(distnp, axis=0)
self.centers[k][3:] = sumx, sumy
self.centers[k] /= np.sum(idx)
self._createConnectivity()
superpixels = self._segmentSuperpixels()
return superpixels
