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'
def run(self, ips, imgs, para=None):
if not para['slice']:
msks = [ips.get_img()]
imgs = [WindowsManager.get(para['img']).ips.get_img()]
else:
msks = imgs
imgs = WindowsManager.get(para['img']).ips.imgs
titles = ['Slice', 'ID'][0 if para['slice'] else 1:]
if para['center']: titles.extend(['Center-X', 'Center-Y'])
if para['area']: titles.append('Area')
if para['l']: titles.append('Perimeter')
if para['extent']: titles.extend(['Min-Y', 'Min-X', 'Max-Y', 'Max-X'])
if para['cov']: titles.extend(['Major', 'Minor', 'Ori'])
buf = imgs[0].astype(np.uint16)
data, mark = [], []
print len(imgs)
for i in range(len(imgs)):
label(msks[i], np.ones((3, 3)), output=buf)
ls = regionprops(buf, imgs[i])
dt = [[i] * len(ls), range(len(ls))]
if not para['slice']: dt = dt[1:]
if not para['cov']: cvs = [None] * len(ls)
else:
cvs = [(i.major_axis_length, i.minor_axis_length,
i.orientation) for i in ls]
centroids = [i.centroid for i in ls]
mark.append([(center, cov) for center, cov in zip(centroids, cvs)])
if para['center']:
dt.append([round(i.centroid[0], 1) for i in ls])
dt.append([round(i.centroid[1], 1) for i in ls])
if para['area']:
dt.append([i.area for i in ls])
if para['l']:
dt.append([round(i.perimeter, 1) for i in ls])
if para['extent']:
for j in (0, 1, 2, 3):
dt.append([i.bbox[j] for i in ls])
if para['cov']:
dt.append([round(i.major_axis_length, 1) for i in ls])
dt.append([round(i.minor_axis_length, 1) for i in ls])
dt.append([round(i.orientation, 1) for i in ls])
data.extend(zip(*dt))
ips.mark = Mark(mark)
IPy.table(ips.title + '-region', data, titles)
def run(self, ips, imgs, para=None):
idx = ['red', 'green', 'blue']
imr, img, imb = [WindowsManager.get(para[i]).ips for i in idx]
sr, sg, sb = [i.get_nslices() for i in [imr, img, imb]]
if imr.imgtype != '8-bit' or img.imgtype != '8-bit' or imb.imgtype != '8-bit':
IPy.alert('must be three 8-bit image!')
return
if imr.size != img.size or img.size != imb.size or sr != sg or sg != sb:
IPy.alert(
'three image must be in same size and have the same slices!')
return
rgb = []
w, h = imr.size
rgbs = zip(imr.imgs, img.imgs, imb.imgs)
for i in range(sr):
IPy.curapp.set_progress(round((i + 1) * 100.0 / sr))
img = np.zeros((w, h, 3), dtype=np.uint8)
for j in (0, 1, 2):
img[:, :, j] = rgbs[i][j]
rgb.append(img)
IPy.curapp.set_progress(0)
ip = ImagePlus(rgb, 'rgb-merge')
frame = CanvasFrame(IPy.curapp)
frame.set_ips(ip)
frame.Show()
if self.para['destory']:
for title in [para[i] for i in idx]:
WindowsManager.close(title)
def run(self, ips, imgs, para=None):
lab = WindowsManager.get(para['lab']).ips.get_img()
if lab.dtype != np.uint8 and lab.dtype != np.uint16:
IPy.alert('Label image must be in type 8-bit or 16-bit')
return
index = range(1, lab.max() + 1)
titles = ['Center-X', 'Center-Y', 'Max-X', 'Max-Y', 'Min-X', 'Min-Y']
key = {
'Max-X': 'max',
'Max-Y': 'max',
'Min-X': 'min',
'Min-Y': 'min',
'Center-X': 'center',
'Center-Y': 'center'
}
titles = ['value'] + [i for i in titles if para[key[i]]]
data = [index]
img = ips.get_img()
if img is lab: img = img > 0
if para['center']:
pos = np.round(ndimage.center_of_mass(img, lab, index), 2)
data.append(pos[:, 0])
data.append(pos[:, 1])
if para['max']:
pos = np.round(ndimage.minimum_position(img, lab, index), 2)
data.append(pos[:, 0])
data.append(pos[:, 1])
if para['min']:
pos = np.round(ndimage.maximum_position(img, lab, index), 2)
data.append(pos[:, 0])
data.append(pos[:, 1])
data = zip(*data)
IPy.table(ips.title + '-position', data, titles)
def run(self, ips, imgs, para=None):
lab = WindowsManager.get(para['lab']).ips.get_img()
if lab.dtype != np.uint8 and lab.dtype != np.uint16:
IPy.alert('Label image must be in type 8-bit or 16-bit')
return
index = range(1, lab.max() + 1)
titles = ['Max', 'Min', 'Mean', 'Variance', 'Standard', 'Sum']
key = {
'Max': 'max',
'Min': 'min',
'Mean': 'mean',
'Variance': 'var',
'Standard': 'std',
'Sum': 'sum'
}
titles = ['value'] + [i for i in titles if para[key[i]]]
data = [index]
img = ips.get_img()
if img is lab: img = img > 0
if para['max']: data.append(ndimage.maximum(img, lab, index))
if para['min']: data.append(ndimage.minimum(img, lab, index))
if para['mean']: data.append(ndimage.mean(img, lab, index).round(4))
if para['var']: data.append(ndimage.variance(img, lab, index).round(4))
if para['std']:
data.append(ndimage.standard_deviation(img, lab, index).round(4))
if para['sum']: data.append(ndimage.sum(img, lab, index).round(4))
data = zip(*data)
IPy.table(ips.title + '-segment', data, titles)
def run(self, ips, imgs, para=None):
lab = WindowsManager.get(para['lab']).ips.get_img()
if lab.dtype != np.uint8 and lab.dtype != np.uint16:
IPy.alert('Label image must be in type 8-bit or 16-bit')
return
index = range(1, lab.max() + 1)
data = [index]
img = ips.get_img()
if img is lab: img = img > 0
if para['mode'] == 'Center':
pos = np.round(ndimage.center_of_mass(img, lab, index), 2)[:, ::-1]
data.append(pos[:, 0])
data.append(pos[:, 1])
if para['mode'] == 'Max':
pos = np.round(ndimage.maximum_position(img, lab, index),
2)[:, ::-1]
data.append(pos[:, 0])
data.append(pos[:, 1])
if para['mode'] == 'Min':
pos = np.round(ndimage.minimum_position(img, lab, index),
2)[:, ::-1]
data.append(pos[:, 0])
data.append(pos[:, 1])
body = [tuple(i) for i in pos]
ips.roi = PointRoi(body)
def show(self):
self.dialog = ParaDialog(WindowsManager.get(), self.title)
self.dialog.init_view(self.view,
self.para,
'preview' in self.note,
modal=self.modal)
self.dialog.set_handle(lambda x: self.preview(self.para))
if self.modal: return self.dialog.ShowModal()
self.dialog.on_ok = lambda: self.ok(self.ips)
self.dialog.on_cancel = lambda: self.cancel(self.ips)
self.dialog.Show()
def run(self, ips, imgs, para=None):
ips1 = WindowsManager.get(para['img1']).ips
ips2 = WindowsManager.get(para['img2']).ips
detector = cv2.SURF(hessianThreshold=para['thr'],
nOctaves=para['oct'],
nOctaveLayers=para['int'],
upright=para['upright'],
extended=para['ext'])
kps1, feats1 = detector.detectAndCompute(ips1.get_img(), None)
kps2, feats2 = detector.detectAndCompute(ips2.get_img(), None)
dim, std = {
'None': 0,
'Affine': 6,
'H**o': 8
}[para['trans']], para['std'] / 100.0
style = para['style'] == 'Blue/Yellow'
idx, msk, m = Matcher(dim, std).filter(kps1, feats1, kps2, feats2)
picker1 = Pick(kps1, kps2, idx, msk, ips1, ips2, True, style)
picker2 = Pick(kps1, kps2, idx, msk, ips1, ips2, False, style)
ips1.tool, ips1.mark = picker1, picker1
ips2.tool, ips2.mark = picker2, picker2
if para['log']: self.log(kps1, kps2, msk, m, dim)
ips1.update, ips2.update = True, True
