def run(self, para=None):
if para['name'] == 'All':
for i in ImageManager.get_titles():
WindowsManager.get(i).close()
else:
print(WindowsManager.get(para['name']))
WindowsManager.get(para['name']).close()
def run(self, ips, snap, img, para = None):
intenimg = WindowsManager.get(para['inten']).ips.img
strc = ndimage.generate_binary_structure(2, 1 if para['con']=='4-connect' else 2)
buf, n = ndimage.label(snap, strc, output=np.uint16)
index = range(1, n+1)
idx = (np.ones(n+1)*para['front']).astype(np.uint8)
msk = np.ones(n, dtype=np.bool)
if para['mean']>0: msk *= ndimage.mean(intenimg, buf, index)>=para['mean']
if para['mean']<0: msk *= ndimage.mean(intenimg, buf, index)<-para['mean']
if para['max']>0: msk *= ndimage.maximum(intenimg, buf, index)>=para['max']
if para['max']<0: msk *= ndimage.maximum(intenimg, buf, index)<-para['max']
if para['min']>0: msk *= ndimage.minimum(intenimg, buf, index)>=para['min']
if para['min']<0: msk *= ndimage.minimum(intenimg, buf, index)<-para['min']
if para['sum']>0: msk *= ndimage.sum(intenimg, buf, index)>=para['sum']
if para['sum']<0: msk *= ndimage.sum(intenimg, buf, index)<-para['sum']
if para['std']>0: msk *= ndimage.standard_deviation(intenimg, buf, index)>=para['std']
if para['std']<0: msk *= ndimage.standard_deviation(intenimg, buf, index)<-para['std']
xy = ndimage.center_of_mass(intenimg, buf, index)
xy = np.array(xy).round(2).T
idx[1:][~msk] = para['back']
idx[0] = 0
img[:] = idx[buf]
WindowsManager.get(para['inten']).ips.mark = RGMark((xy.T, msk))
WindowsManager.get(para['inten']).ips.update = True
def run(self, ips, imgs, para = None):
ips1 = WindowsManager.get(para['img1']).ips
ips2 = WindowsManager.get(para['img2']).ips
ips1.snapshot()
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 channels!')
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.img, ips2.img, mode=para['op'])
elif sl1>1 and sl2==1:
for i in range(sl1):
self.progress(i, sl1)
bliter.blit(ips1.imgs[i], ips2.img, mode=para['op'])
elif sl1>1 and sl2>1:
for i in range(sl1):
self.progress(i, sl1)
bliter.blit(ips1.imgs[i], ips2.imgs[i], mode=para['op'])
ips1.update = 'pix'
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 = list(zip(imr.imgs, img.imgs, imb.imgs))
for i in range(sr):
self.progress(i, 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.show_img(rgb, 'rgb-merge')
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.int16:
IPy.alert('Label image must be in type 8-bit or 16-bit')
return
index = list(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 = list(zip(*data))
IPy.table(ips.title + '-segment', data, titles)
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 = [], []
strc = np.array([[0,1,0],[1,1,1],[0,1,0]], dtype=np.uint8)
for i in range(len(imgs)):
label(msks[i], strc, output=buf)
ls = regionprops(buf, imgs[i])
dt = [[i]*len(ls), list(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(list(zip(*dt)))
ips.mark = Mark(mark)
IPy.table(ips.title+'-region', data, titles)
def run(self, ips, imgs, para = None):
msk = ips.get_msk('in')
if ips.imgtype == 'rgb':
img = ips.img if msk is None else ips.img[msk]
hist = [np.histogram(img[:,:,i], np.arange(257))[0] for i in (0,1,2)]
else:
img = ips.lookup() if msk is None else ips.lookup()[msk]
hist = np.histogram(img, np.arange(257))[0]
show_hist(WindowsManager.get(), ips.title+'-Histogram', hist)
def run(self, ips, imgs, para=None):
ips1 = WindowsManager.get(para['img1']).ips
ips2 = WindowsManager.get(para['img2']).ips
ips2.snapshot()
img = ips1.img
imgs = ips2.imgs
sl1, sl2 = ips1.get_nslices(), ips2.get_nslices()
cn1, cn2 = ips1.get_nchannels(), ips2.get_nchannels()
if not (ips1.img.dtype == np.uint8 and ips2.img.dtype == np.uint8):
IPy.alert('Two image must be type of 8-bit or rgb!')
return
for i in range(sl2):
self.progress(i, sl2)
match(img, imgs[i])
ips2.update = 'pix'
def on_back(self, event):
self.com_back.SetItems(['None'] + ImageManager.get_titles())
cur = WindowsManager.get()
if not cur is None: cur = cur.back
if not cur is None: cur = cur.title
self.com_back.SetValue(str(cur))
modes = ['set', 'max', 'min', 'msk', 0.2, 0.4, 0.5, 0.6, 0.8]
ips = ImageManager.get()
if ips is None: self.com_mode.Select(0)
else: self.com_mode.Select(modes.index(ips.chan_mode))
def run(self, ips, imgs, para=None):
print(ips.imgtype, ips.imgtype == '8-bit')
if ips.imgtype == 'rgb':
hist = [
np.histogram(ips.img[:, :, i], np.arange(257))[0]
for i in (0, 1, 2)
]
else:
hist = np.histogram(ips.lookup(), np.arange(257))[0]
show_hist(WindowsManager.get(), ips.title + '-Histogram', hist)
def run(self, ips, imgs, para = None):
if para['kill']:
ips.backimg = None
else:
img = WindowsManager.get(para['img']).ips.img
if img.dtype != np.uint8 or img.shape[:2] != ips.img.shape[:2]:
IPy.alert('a background image must be 8-bit and with the same size')
return
ips.backimg = img
ips.backmode = (para['k'], para['op'])
ips.update = 'pix'
def run(self, ips, imgs, para = None):
idx = ['red','green','blue']
print(para)
imr,img,imb = [ImageManager.get(para[i]) 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' or \
imr.size!=img.size or img.size!=imb.size or sr!=sg or sg!=sb:
IPy.alert('three images must be 8-bit image, with the same size and slices!')
return
rgbs = []
w,h = imr.size
for i in range(sr):
self.progress(i,sr)
rgbs.append(self.trans(imr.imgs[i], img.imgs[i], imb.imgs[i]))
IPy.show_img(rgbs, self.titles()[0])
if self.para['destory']:
for title in [para[i] for i in idx]:
WindowsManager.get(title).close()
def run(self, ips, imgs, para = None):
inten = WindowsManager.get(para['inten']).ips
if not para['slice']:
imgs = [inten.img]
msks = [ips.img]
else:
msks = ips.imgs
if len(msks)==1:
msks *= len(imgs)
buf = imgs[0].astype(np.uint16)
strc = ndimage.generate_binary_structure(2, 1 if para['con']=='4-connect' else 2)
idct = ['Max','Min','Mean','Variance','Standard','Sum']
key = {'Max':'max','Min':'min','Mean':'mean',
'Variance':'var','Standard':'std','Sum':'sum'}
idct = [i for i in idct if para[key[i]]]
titles = ['Slice', 'ID'][0 if para['slice'] else 1:]
if para['center']: titles.extend(['Center-X','Center-Y'])
if para['extent']: titles.extend(['Min-Y','Min-X','Max-Y','Max-X'])
titles.extend(idct)
k = ips.unit[0]
data, mark = [], []
for i in range(len(imgs)):
n = ndimage.label(msks[i], strc, output=buf)
index = range(1, n+1)
dt = []
if para['slice']:dt.append([i]*n)
dt.append(range(n))
xy = ndimage.center_of_mass(imgs[i], buf, index)
xy = np.array(xy).round(2).T
if para['center']:dt.extend([xy[1]*k, xy[0]*k])
boxs = [None] * n
if para['extent']:
boxs = ndimage.find_objects(buf)
boxs = [(i[0].start, i[1].start, i[0].stop, i[1].stop) for i in boxs]
for j in (0,1,2,3):
dt.append([i[j]*k for i in boxs])
if para['max']:dt.append(ndimage.maximum(imgs[i], buf, index).round(2))
if para['min']:dt.append(ndimage.minimum(imgs[i], buf, index).round(2))
if para['mean']:dt.append(ndimage.mean(imgs[i], buf, index).round(2))
if para['var']:dt.append(ndimage.variance(imgs[i], buf, index).round(2))
if para['std']:dt.append(ndimage.standard_deviation(imgs[i], buf, index).round(2))
if para['sum']:dt.append(ndimage.sum(imgs[i], buf, index).round(2))
mark.append([(center, cov) for center,cov in zip(xy.T, boxs)])
data.extend(list(zip(*dt)))
IPy.table(inten.title+'-region statistic', data, titles)
inten.mark = Mark(mark)
inten.update = True
def run(self, ips, imgs, para=None):
ips1 = WindowsManager.get(para['img1']).ips
ips2 = WindowsManager.get(para['img2']).ips
detector = CVSURF(hessianThreshold=para['thr'],
nOctaves=para['oct'],
nOctaveLayers=para['int'],
upright=para['upright'],
extended=para['ext'])
kps1, feats1 = detector.detectAndCompute(ips1.img, None)
kps2, feats2 = detector.detectAndCompute(ips2.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
def run(self, ips, imgs, para = None): WindowsManager.get().canvas.save_buffer(para['path'])
def run(self, ips, imgs, para = None):
temp = WindowsManager.get(para['temp']).ips
rst = gmt.match_multi(imgs, temp.img.getbox())
IPy.show_img([rst], ips.title+'-match')
def on_setback(self, event):
name = self.com_back.GetValue()
if name is None: return
curwin = WindowsManager.get()
curwin.set_back(ImageManager.get(name))
curwin.ips.update()