def run(self, ips, imgs, para=None):
n = ips.get_nslices()
if ips.is3d:
img8 = np.zeros(ips.size + (n, ), dtype=np.uint8)
for i in range(n):
IPy.curapp.set_progress(round((i + 1) * 100.0 / len(imgs)))
img8[i] = imgs[i].mean(axis=2)
else:
img8 = []
for i in range(n):
IPy.set_progress(round((i + 1) * 100.0 / len(imgs)))
img8.append(imgs[i].mean(axis=2).astype(np.uint8))
IPy.set_progress(0)
ips.set_imgs(img8)
def run(self, ips, imgs, para=None):
titles = ['Max', 'Min', 'Mean', 'Variance', 'Standard']
key = {
'Max': 'max',
'Min': 'min',
'Mean': 'mean',
'Variance': 'var',
'Standard': 'std'
}
titles = [i for i in titles if para[key[i]]]
if self.para['stack']:
data = []
for n in range(ips.get_nslices()):
data.append(self.count(imgs[n], para))
IPy.set_progress(round((n + 1) * 100.0 / len(imgs)))
IPy.set_progress(0)
else:
data = [self.count(ips.get_img(), para)]
IPy.table(ips.title + '-statistic', data, titles)
def run(self, ips, imgs, para=None):
ips1 = WindowsManager.get(para['img1']).ips
ips2 = WindowsManager.get(para['img2']).ips
sl1, sl2 = ips1.get_nslices(), ips2.get_nslices()
cn1, cn2 = ips1.get_nchannels(), ips2.get_nchannels()
if ips1.dtype != ips2.dtype:
IPy.alert('Two stack must be equal dtype!')
return
elif sl1 > 1 and sl2 > 1 and sl1 != sl2:
IPy.alert('Two stack must have equal slices!')
return
elif cn1 > 1 and cn2 > 1 and cn1 != cn2:
IPy.alert('Two stack must have equal chanels!')
return
w, h = ips1.size, ips2.size
w, h = min(w[0], h[0]), min(w[1], h[1])
if sl1 == 1:
bliter.blit(ips1.get_img(), ips2.get_img(), mode=para['op'])
elif sl1 > 1 and sl2 == 1:
for i in range(sl1):
IPy.set_progress(round(i * 100.0 / sl1))
bliter.blit(ips1.imgs[i], ips2.get_img(), mode=para['op'])
elif sl1 > 1 and sl2 > 1:
for i in range(sl1):
IPy.set_progress(round(i * 100.0 / sl1))
bliter.blit(ips1.imgs[i], ips2.imgs[i], mode=para['op'])
IPy.set_progress(0)
ips1.update = 'pix'
