def handle(self, event):
ips = IPy.get_ips()
if ips is None: return
lut = CurvePanel.lookup(self.curvepan.pts)
lut = np.vstack((lut, lut, lut)).T
ips.lut = lut
ips.update = 'pix'
def on_reset(self, event):
self.curvepan.SetValue()
ips = IPy.get_ips()
if ips is None: return
hist = ips.histogram()
self.curvepan.set_hist(hist)
self.handle(None)
def on_invert(self, event):
self.curvepan.SetValue([(0, 255), (255, 0)])
ips = IPy.get_ips()
if ips is None: return
hist = ips.histogram()
self.curvepan.set_hist(hist)
self.handle(None)
def on_rgb(self, event, color):
ips = IPy.get_ips()
if ips is None: return
if isinstance(ips.chan, int):
ips.chan = [0, 1, 2]
chs = ['C:%d' % i for i in range(ips.channels)]
for i in (self.com_r, self.com_g, self.com_b):
chn = i.GetValue()
i.SetItems(chs)
idx = chs.index(chn) if chn in chs else 0
i.Select(idx)
chanred = ips.chan['rgb'.index(color)]
chanrg = ips.chan_range[chanred]
rg = ips.get_updown('all', chanred, 512)
if (rg[0] == rg[1]): rg = (rg[0] - 1e-4, rg[1] + 1e-4)
slis = 'all' if self.chk_stack.GetValue() else ips.cur
hist = ips.histogram(rg, slis, chanred, 512)
self.histpan.SetValue(hist)
self.sli_low.set_para(rg, 10)
self.sli_high.set_para(rg, 10)
self.sli_low.SetValue(chanrg[0])
self.sli_high.SetValue(chanrg[1])
self.active = ips.chan['rgb'.index(color)]
self.range = chanrg
lim1 = (chanrg[0] - rg[0]) / (rg[1] - rg[0])
lim2 = (chanrg[1] - rg[0]) / (rg[1] - rg[0])
self.histpan.set_lim(lim1 * 255, lim2 * 255)
ips.update()
def on_clear(self, event):
ips = IPy.get_ips()
if ips is None: return
hist = ips.histogram()
self.curvepan.set_hist(hist)
ips.lut = ColorManager.get_lut()
ips.update = 'pix'
def on_chan(self, event, color):
ips = IPy.get_ips()
if ips is None: return
C = (self.com_r, self.com_g, self.com_b)
host = C['rgb'.index(color)]
chnidx = host.GetSelection()
ips.chan['rgb'.index(color)] = chnidx
self.on_rgb(event, color)
def on_low( self, event ): ips = IPy.get_ips() if ips is None: return if self.sli_high.GetValue()<self.sli_low.GetValue(): self.sli_high.SetValue(self.sli_low.GetValue()) ips.range = (self.sli_low.GetValue(), self.sli_high.GetValue()) lim1 = 1.0 * (self.sli_low.GetValue() - self.range[0])/(self.range[1]-self.range[0]) lim2 = 1.0 * (self.sli_high.GetValue() - self.range[0])/(self.range[1]-self.range[0]) self.histpan.set_lim(lim1*255, lim2*255) ips.update = 'pix'
def load(self):
plus = IPy.get_ips()
if plus==None:
img = np.ones((30,1), dtype=np.uint8) * np.arange(256, dtype=np.uint8)
ips = ImagePlus([img], self.title)
ips.lut = ColorManager.get_lut(self.title)
IPy.show_ips(ips)
return False
elif plus.channels != 1:
IPy.alert('RGB image do not surport Lookup table!')
return False
return True
def on_high(self, event):
ips = IPy.get_ips()
if ips is None: return
if self.sli_low.GetValue() > self.sli_high.GetValue():
self.sli_low.SetValue(self.sli_high.GetValue())
rg = (self.sli_low.GetValue(), self.sli_high.GetValue())
ips.chan_range[self.active] = rg
minv, maxv = self.sli_low.min, self.sli_high.max
lim1 = 1.0 * (self.sli_low.GetValue() - minv) / (maxv - minv)
lim2 = 1.0 * (self.sli_high.GetValue() - minv) / (maxv - minv)
self.histpan.set_lim(lim1 * 255, lim2 * 255)
ips.update()
def on_8bit(self, event):
ips = IPy.get_ips()
if ips is None: return
self.range = ips.range = (0, 255)
hist = ips.histogram()
self.histpan.SetValue(hist)
self.sli_low.set_para((0, 255), 0)
self.sli_high.set_para((0, 255), 0)
self.sli_low.SetValue(0)
self.sli_high.SetValue(255)
self.histpan.set_lim(0, 255)
ips.update()
def on_minmax(self, event):
ips = IPy.get_ips()
if ips is None: return
minv, maxv = ips.get_updown()
self.range = ips.range = (minv, maxv)
hist = ips.histogram()
self.histpan.SetValue(hist)
self.sli_low.set_para(self.range, 10)
self.sli_high.set_para(self.range, 10)
self.sli_low.SetValue(minv)
self.sli_high.SetValue(maxv)
self.histpan.set_lim(0, 255)
ips.update()
def run(self, para=None):
imp = IPy.get_ips()
if imp is None:
IPy.alert("Please open the image you want to classify", 'Error')
return
name = imp.title
recent = fileio.recent
for i in recent:
pos1 = i.rfind(os.sep)
pos2 = i.rfind('.')
if name == i[pos1 + 1:pos2]:
image = i
className = dc4b.predict(image, self.framework, self.model)
IPy.alert("The class which the image belongs is " + className,
'Prediction')
def on_8bit(self, event):
ips = IPy.get_ips()
if ips is None: return
rg = (0, 255)
ips.chan_range[self.active] = rg
if (rg[0] == rg[1]): rg = (rg[0] - 1e-4, rg[1] + 1e-4)
slis = 'all' if self.chk_stack.GetValue() else ips.cur
hist = ips.histogram(rg, slis, self.active, 512)
self.histpan.SetValue(hist)
self.sli_low.set_para(rg, 10)
self.sli_high.set_para(rg, 10)
self.sli_low.SetValue(rg[0])
self.sli_high.SetValue(rg[1])
self.histpan.set_lim(0, 255)
ips.update()
def run(self, para=None):
plus = IPy.get_ips()
if plus == None:
img = np.ones(
(30, 1), dtype=np.uint8) * np.arange(256, dtype=np.uint8)
ips = ImagePlus([img])
frame = CanvasFrame(IPy.curapp)
frame.set_ips(ips)
ips.lut = ColorManager.get_lut(self.title)
frame.Show()
elif plus.chanels != 1:
IPy.alert('RGB image do not surport Lookup table!')
return
else:
plus.lut = ColorManager.get_lut(self.title)
plus.update = 'pix'
def on_gray(self, event):
ips = IPy.get_ips()
if ips is None: return
if not isinstance(ips.chan, int): ips.chan = 0
chanrg = ips.chan_range[ips.chan]
rg = ips.get_updown('all', ips.chan, 512)
if (rg[0] == rg[1]): rg = (rg[0] - 1e-4, rg[1] + 1e-4)
slis = 'all' if self.chk_stack.GetValue() else ips.cur
hist = ips.histogram(rg, slis, ips.chan, 512)
self.histpan.SetValue(hist)
self.sli_low.set_para(rg, 10)
self.sli_high.set_para(rg, 10)
self.sli_low.SetValue(chanrg[0])
self.sli_high.SetValue(chanrg[1])
self.active = ips.chan
lim1 = (chanrg[0] - rg[0]) / (rg[1] - rg[0])
lim2 = (chanrg[1] - rg[0]) / (rg[1] - rg[0])
self.histpan.set_lim(lim1 * 255, lim2 * 255)
ips.update()
def run(self, para=None):
imp = IPy.get_ips()
if imp is None:
IPy.alert("Please open the image you want to classify", 'Error')
return
name = imp.title
recent = fileio.recent
for i in recent:
pos1 = i.rfind(os.sep)
pos2 = i.rfind('.')
if name == i[pos1 + 1:pos2]:
image = i
subprocess.check_output([
self.python, self.pathAPI + 'predict.py', '-i', image, '-f',
self.framework, '-m', self.model
])
data = json.load(open('data.json'))
className = data['class']
IPy.alert("The class which the image belongs is " + className,
'Prediction')
def on_p(self, event, k):
ips = IPy.get_ips()
if ips is None: return
rg = ips.get_updown('all', self.active, 512)
if (rg[0] == rg[1]): rg = (rg[0] - 1e-4, rg[1] + 1e-4)
slis = 'all' if self.chk_stack.GetValue() else ips.cur
hist = ips.histogram(rg, slis, self.active, 512)
msk = np.abs(np.cumsum(hist) / hist.sum() - 0.5) < k / 2
idx = np.where(msk)[0]
vs = np.array([idx.min(), idx.max()])
vs = vs * (rg[1] - rg[0]) / 255 + rg[0]
ips.chan_range[self.active] = tuple(vs)
self.histpan.SetValue(hist)
self.sli_low.set_para(rg, 10)
self.sli_high.set_para(rg, 10)
print(rg, vs)
self.sli_low.SetValue(vs[0])
self.sli_high.SetValue(vs[1])
lim1 = (vs[0] - rg[0]) / (rg[1] - rg[0])
lim2 = (vs[1] - rg[0]) / (rg[1] - rg[0])
self.histpan.set_lim(lim1 * 255, lim2 * 255)
ips.update()
def on_stack( self, event ): ips = IPy.get_ips() if ips is None: return hists = ips.histogram(slices=True) self.histpan.SetValue(hists)
def on_cmapsel(self): ips = IPy.get_ips() if ips is None: return key = self.cmapsel.GetValue() ips.lut = ColorManager.get_lut(key) ips.update = 'pix'
def on_cmap(self): ips = IPy.get_ips() if ips is None: return cmap = CMapPanel.linear_color(self.cmap.GetValue()) ips.lut = cmap ips.update = 'pix'
def on_stack( self, event ): ips = IPy.get_ips() if ips is None: return hists = ips.histogram(stack=True) self.histpan.set_hist(hists)
def on_slice( self, event ): ips = IPy.get_ips() if ips is None: return hist = ips.histogram() self.histpan.set_hist(hist)
def update():
ips = IPy.get_ips()
if not ips is None :
ips.update='pix'
def get_ips():
ips = IPy.get_ips()
if ips is None:
print('no image opened!')
return ips
def on_apply(self, event):
ips = IPy.get_ips()
if ips is None: return
hist = ips.histogram()
self.curvepan.set_hist(hist)
self.handle(None)
def run(self, para=None):
plus = IPy.get_ips()
plus.lut = ColorManager.get_lut(self.title)
plus.update = 'pix'
def on_invert(self, event):
if IPy.get_ips() is None: return
IPy.get_ips().img[:] = 255 - IPy.get_ips().img
IPy.get_ips().update()
