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):
k = para['diag']/np.sqrt((np.array(ips.img.shape)**2).sum())
size = tuple((np.array(ips.img.shape)*k).astype(np.int16))
IPy.set_info('down sample...')
news = []
for img in imgs:
if k!=0: img = tf.resize(img, size)
if para['sigma']!=0:
img = ndimg.gaussian_filter(img, para['sigma'])
news.append(img)
IPy.set_info('register...')
sr = StackReg(eval('StackReg.%s'%para['trans']))
sr.register_stack(np.array(news), reference=para['ref'])
mats = sr._tmats.reshape((sr._tmats.shape[0],-1))
if k!=0: mats[:,[0,1,3,4,6,7]] *= k
if k!=0: mats[:,[0,1,2,3,4,5]] /= k
if para['tab']: IPy.show_table(pd.DataFrame(
mats, columns=['A%d'%(i+1) for i in range(mats.shape[1])]), title='%s-Tmats'%ips.title)
if para['new'] == 'None': return
IPy.set_info('transform...')
for i in range(sr._tmats.shape[0]):
tform = tf.ProjectiveTransform(matrix=sr._tmats[i])
img = tf.warp(imgs[i], tform)
img -= imgs[i].min(); img *= imgs[i].max() - imgs[i].min()
if para['new'] == 'Inplace': imgs[i][:] = img
if para['new'] == 'New': news[i] = img.astype(ips.img.dtype)
self.progress(i, len(imgs))
if para['new'] == 'New': IPy.show_img(news, '%s-reg'%ips.title)
def run(self, para = None): #imgs = readGif(para['path']) imgs = imageio.mimread(para['path']) fp, fn = os.path.split(para['path']) fn, fe = os.path.splitext(fn) IPy.show_img(imgs, fn)
def run(self, para=None):
w, h = para['width'], para['height']
channels = (1, 3)[para['type'] == 'RGB']
slices = para['slice']
shape = (h, w, channels) if channels != 1 else (h, w)
imgs = [np.zeros(shape, dtype=np.uint8) for i in range(slices)]
IPy.show_img(imgs, para['name'])
def run(self, para=None):
img = self.data()
if isinstance(img, tuple):
return IPy.show_img(list(img), self.title)
if img.dtype == np.bool:
img.dtype = np.uint8
img *= 255
IPy.show_img([img], self.title)
def run(self, para=None):
if len(para['chans']) == 0: return
shp, prj, m, chans = gio.read_raster_box(para['path'])
idx = [chans.index(i) for i in para['chans']]
print(para['chans'], idx, '==========')
files = self.getfiles(para['path'])
rasters = self.readimgs(files, idx, shp)
IPy.show_img(rasters, para['title'])
def run(self, ips, imgs, para=None):
IPy.show_img(
hessian(imgs,
range(para['start'], para['end'], para['step']),
alpha=para['alpha'],
beta=para['beta'],
gamma=para['gamma'],
black_ridges=para['bridges']), ips.title + '-hessian')
def run(self, ips, imgs, para=None):
if not para['slice']: imgs = [ips.img]
shift = fftshift if para['shift'] else lambda x: x
rst = []
for i in range(len(imgs)):
rst.append(shift(fft2(imgs[i])))
self.progress(i, len(imgs))
IPy.show_img(rst, '%s-fft' % ips.title)
def run(self, para = None): imgs = readGif(para['path']) for i in range(len(imgs)): if imgs[i].ndim==3 and imgs[i].shape[2]>3: imgs[i] = imgs[i][:,:,:3].copy() fp, fn = os.path.split(para['path']) fn, fe = os.path.splitext(fn) IPy.show_img(imgs, fn)
def run(self, ips, imgs, para=None):
cmap = ColorManager.luts[para['LUT']]
imglut = color_code(imgs[para['Start image'] - 1:para['End image']],
cmap)
IPy.show_img([imglut], 'Color-coded %s' % ips.title)
if para['Creatbar']:
cmapshow = np.ones([32, 256, 3]) * cmap
IPy.show_img([cmapshow.astype('uint8')], 'Color bar')
def run(self, para=None):
try:
response = urlopen('http://imagej.net/images/' + self.name)
stream = StringIO(response.read())
img = imread(stream)
IPy.show_img([img], self.title)
except Exception as e:
IPy.write('Open url failed!\tErrof:%s' % sys.exc_info()[1])
def run(self, ips, snap, img, para=None):
ips.lut = self.buflut
pts = np.where(ips.get_msk())
msk = grayselect(snap, pts, para['sigma'], para['cov'])
if para['within']: msk = within(msk, pts)
if para['msk'] == 'clear out': img[~msk] = ips.range[0]
if para['new']:
msk = np.multiply(msk, 255, dtype=np.uint8)
IPy.show_img([msk], ips.title + '-graymsk')
def run(self, tps, snap, data, para=None):
table = TableManager.get(para['temp']).get_subtab()
box = gutil.shp2box(table, para['scale']
or (para['width'], para['height']), para['mar'])
chans = list(data['channels'][0])
chans = [chans.index(i) for i in para['chans']]
order = {'nearest': 0, 'linear': 1}[para['order']]
rst = gmt.match_idx(data, box, chans, step=para['step'], order=order)
IPy.show_img([rst], tps.title + '-merge')
def run(self, ips, imgs, para=None):
table = TableManager.get(para['temp']).get_subtab()
box = gutil.shp2box(table, para['scale']
or (para['width'], para['height']), para['mar'])
chans = ['Channel %s' % i for i in range(ips.get_nchannels())]
chans = [chans.index(i) for i in para['chans']]
order = {'nearest': 0, 'linear': 1}[para['order']]
rst = gmt.match_multi(imgs, box, chans, step=para['step'], order=order)
IPy.show_img([rst], ips.title + '-merge')
def run(self, para=None):
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
X, Y, Z = axes3d.get_test_data(0.05)
ax.plot_wireframe(X, Y, Z, rstride=10, cstride=10)
plt.show()
#area = (area-5) * 8
IPy.show_img([(Z + 80).astype(np.uint8)], '3D Surface')
def run(self, ips, imgs, para=None):
if not para['slice']: imgs = [ips.img]
shift = ifftshift if para['shift'] else lambda x: x
tp = {'uint8': np.uint8, 'int': np.int32, 'float': np.float32}
rst, tp = [], tp[para['type']]
for i in range(len(imgs)):
rst.append(ifft2(shift(ips.img)).astype(tp))
self.progress(i, len(imgs))
IPy.show_img(rst, '%s-ifft' % ips.title)
def run(self, ips, snap, img, para=None):
if para == None: para = self.para
ips.lut = self.lut
msk = img > para['thr']
con = 1 if para['con'] == '4-Connect' else 2
strc = generate_binary_structure(2, con)
msk = label(msk, strc, output=np.int16)
IPy.show_img([msk[0]], ips.title + '-label')
def run(self, ips, imgs, para=None):
nr, ng, nb = [], [], []
for i in range(ips.get_nslices()):
nrgb = self.trans(imgs[i])
nr.append(nrgb[:, :, 0])
ng.append(nrgb[:, :, 1])
nb.append(nrgb[:, :, 2])
self.progress(i, len(imgs))
for im, tl in zip([nr, ng, nb], self.titles()):
IPy.show_img(im, ips.title + '-' + tl)
def run(self, ips, imgs, para = None):
if not para['slice']: imgs = [ips.img]
labels = []
for i in range(len(imgs)):
self.progress(i, len(imgs))
con = 1 if para['con']=='4-Connect' else 2
strc = generate_binary_structure(2, con)
lab, n = label(imgs[i], strc, output = np.int32)
labels.append(lab)
IPy.show_img(labels, ips.title+'-label')
def run(self, tps, snap, data, para=None):
img = ImageManager.get(para['temp']).img
chans = list(data['channels'][0])
chans = [chans.index(i) for i in para['chans']]
nimg = gmt.match_idx(data,
img,
step=para['step'],
order=para['order'],
chan=chans)
IPy.show_img([nimg], tps.title + '-match')
def run(self, ips, imgs, para=None):
if not para['slice']: imgs = [ips.img]
tp = {'uint8': np.uint8, 'int': np.int32, 'float': np.float32}
rst, tp = [], tp[para['type']]
for i in range(len(imgs)):
zs = np.log(np.abs(imgs[i]))
zs /= np.log(para['log'])
rst.append(zs.astype(tp))
self.progress(i, len(imgs))
IPy.show_img(rst, '%s-fft' % ips.title)
def run(self, ips, imgs, para=None):
ips.lut = self.buflut
idx = np.array(ips.roi.body).astype(np.uint16).T
pts = (idx[2], idx[1], idx[0])
msk = grayselect(imgs, pts, para['sigma'], para['cov'])
if para['within']: msk = within(msk, pts)
if para['msk'] == 'clear out': imgs[~msk] = ips.range[0]
if para['new']:
msk = np.multiply(msk, 255, dtype=np.uint8)
IPy.show_img(msk, ips.title + '-graymsk')
def run(self, ips, snap, img, para=None):
img[:] = snap
pts = np.where(ips.get_msk())
msk = colorselect(snap, pts, para['sigma'], para['cov'])
if para['within']: msk = within(msk, pts)
if para['msk'] == 'red': img[msk] = (255, 0, 0)
if para['msk'] == 'dark out': img[~msk] //= 3
if para['new']:
msk = np.multiply(msk, 255, dtype=np.uint8)
IPy.show_img([msk], ips.title + '-colormsk')
def run(self, ips, imgs, para=None):
if not para['slice']: imgs = [ips.img]
copy = np.copy if para['copy'] else lambda x: x
imags, reals = [], []
for i in range(len(imgs)):
reals.append(copy(imgs[i].real))
imags.append(copy(imgs[i].imag))
self.progress(i, len(imgs))
IPy.show_img(reals, '%s-real' % ips.title)
IPy.show_img(imags, '%s-image' % ips.title)
def run(self, ips, imgs, para=None):
r, g, b = [], [], []
for i, n in zip(imgs, list(range(ips.get_nslices()))):
for c, ci in zip((r, g, b), (0, 1, 2)):
if self.para['copy']: c.append(i[:, :, ci].copy())
else: c.append(i[:, :, ci])
self.progress(i, n)
for im, tl in zip([r, g, b], ['red', 'green', 'blue']):
IPy.show_img(im, ips.title + '-' + tl)
if self.para['destory']:
ImageManager.close(ips.title)
def run(self, ips, imgs, para = None):
if not para['slice']: imgs = [ips.img]
labels = []
for i in range(len(imgs)):
self.progress(i, len(imgs))
con = 1 if para['con']=='4-Connect' else 2
bound = find_boundaries(imgs[i], con, para['mode'])
bound.dtype = np.uint8
bound *= 255
labels.append(bound)
IPy.show_img(labels, ips.title+'-boundary')
def run(self, ips, imgs, para=None):
if not para['stack']: imgs = [ips.img]
ips.swap()
rst = []
for i in range(len(imgs)):
rst.append(
segmentation.felzenszwalb(imgs[i], para['scale'],
para['sigma'],
para['min_size']).astype('int32'))
self.progress(i, len(imgs))
IPy.show_img(rst, ips.title + '-felzenszwalblab')
def run(self, ips, imgs, para=None):
if not para['stack']: imgs = [ips.img]
ips.swap()
rst = []
for i in range(len(imgs)):
rst.append(
segmentation.slic(imgs[i], para['n_segments'],
para['compactness'], para['max_iter'],
para['sigma']).astype('int32'))
self.progress(i, len(imgs))
IPy.show_img(rst, ips.title + '-sliclab')
def run(self, para = None):
path = para['path']
fp, fn = os.path.split(path)
fn, fe = os.path.splitext(fn)
img = np.fromfile(para['path'], dtype=para['type'])
sp = (para['h'], para['w'], para['c'])[:2 if para['c']==1 else 3]
if img.size != para['h']*para['w']*para['c']:
IPy.alert('raw data error!')
return
img.shape = sp
IPy.show_img([img], fn)
def run(self, para=None):
try:
fp, fn = os.path.split(para['url'])
fn, fe = os.path.splitext(fn)
response = urlopen(para['url'])
## TODO: Fixme!
stream = StringIO(response.read())
img = imread(stream)
IPy.show_img([img], fn)
except Exception as e:
IPy.write('Open url failed!\tErrof:%s' % sys.exc_info()[1])
