def test_as_rgb_shape_mismatch():
np.random.seed(2323)
r = np.random.randint(12, 121, size=(8, 8))
g = np.random.randint(12, 121, size=(8, 8))
b = np.random.randint(12, 121, size=(8, 6))
with pytest.raises(ValueError):
mahotas.as_rgb(r, g, b)
def test_as_rgb():
np.random.seed(2323)
r = np.random.random_integers(12, 120, size=(8,8))
g = np.random.random_integers(12, 120, size=(8,8))
b = np.random.random_integers(12, 120, size=(8,8))
assert mahotas.as_rgb(r,g,b).max() >= 254
assert mahotas.as_rgb(r,None,b).shape == (8,8,3)
assert mahotas.as_rgb(r,None,b)[:,:,1].sum() == 0
def create_ring(self):
im = self._image
# This breaks up the image into RGB channels
r, g, b = im.transpose(2, 0, 1)
h, w = r.shape
# smooth the image per channel:
r12 = mh.gaussian_filter(r, 12.)
g12 = mh.gaussian_filter(g, 12.)
b12 = mh.gaussian_filter(b, 12.)
# build back the RGB image
im12 = mh.as_rgb(r12, g12, b12)
X, Y = np.mgrid[:h, :w]
X = X - h / 2.
Y = Y - w / 2.
X /= X.max()
Y /= Y.max()
# Array C will have the highest values in the center, fading out to the edges:
C = np.exp(-2. * (X ** 2 + Y ** 2))
C -= C.min()
C /= C.ptp()
C = C[:, :, None]
# The final result is sharp in the centre and smooths out to the borders:
ring = mh.stretch(im * C + (1 - C) * im12)
return ring
def composite(self, channels=('dna', 'protein', None)):
import mahotas
def g(ch):
if ch is not None and \
self.has_channel(ch): return self.get(ch)
c0,c1,c2 = channels
return mahotas.as_rgb(g(c0),g(c1),g(c2))
def test_as_rgb_integer():
int_rgb = mh.as_rgb(1, 2, np.zeros((8, 6)))
assert int_rgb.dtype == np.uint8
assert int_rgb.shape == (8, 6, 3)
assert np.all(int_rgb[0, 0] == (1, 2, 0))
assert np.all(int_rgb[-1, 3] == (1, 2, 0))
assert np.all(int_rgb[-2, 4] == (1, 2, 0))
def test_as_rgb_integer():
int_rgb = mh.as_rgb(1,2,np.zeros((8,6)))
assert int_rgb.dtype == np.uint8
assert int_rgb.shape == (8,6,3)
assert np.all( int_rgb[0,0] == (1,2,0) )
assert np.all( int_rgb[-1,3] == (1,2,0) )
assert np.all( int_rgb[-2,4] == (1,2,0) )
def composite(self, plane='central'):
import mahotas
if plane == 'central':
plane = len(self.files.values()[0])//2
def g(ch):
if self.has_channel(ch):
return self.get(ch, plane)
return mahotas.as_rgb(g('dna'), g('protein'), None)
def makeSegmentationPicture(self,thickestPath,G,crownImg,xScale,yScale,c1x,c1y,c2x,c2y,c3x=None,c3y=None):
print 'make cluster picture'
crownImg=m.as_rgb(crownImg,crownImg,crownImg)
vprop=G.vertex_properties["vp"]
for i in thickestPath:
if vprop[i]['nrOfPaths'] in c1y:
y=int(vprop[i]['imgIdx'][0])
x=int(vprop[i]['imgIdx'][1])
try: crownImg[x][y]=(125,0,0)
except: pass
dia=vprop[i]['diameter']/(xScale/2+yScale/2)
dia=dia*1.5
for j in range(int(dia)):
try: crownImg[x][y+j]=(125,0,0)
except: pass
try: crownImg[x][y-j]=(125,0,0)
except: pass
try: crownImg[x-j][y]=(125,0,0)
except: pass
try: crownImg[x+j][y]=(125,0,0)
except: pass
elif vprop[i]['nrOfPaths'] in c2y:
y=int(vprop[i]['imgIdx'][0])
x=int(vprop[i]['imgIdx'][1])
try: crownImg[x][y]=(125,0,0)
except: pass
dia=vprop[i]['diameter']/(xScale/2+yScale/2)
dia=dia*1.5
for j in range(int(dia)):
try: crownImg[x][y+j]=(0,125,0)
except: pass
try: crownImg[x][y-j]=(0,125,0)
except: pass
try: crownImg[x-j][y]=(0,125,0)
except: pass
try: crownImg[x+j][y]=(0,125,0)
except: pass
y=int(vprop[i]['imgIdx'][0])
x=int(vprop[i]['imgIdx'][1])
try: crownImg[x][y]=(0,0,125)
except: pass
dia=vprop[i]['diameter']/(xScale/2+yScale/2)
dia=dia*1.5
for j in range(int(dia)):
try: crownImg[x][y+j]=(0,0,125)
except: pass
try: crownImg[x][y-j]=(0,0,125)
except: pass
try: crownImg[x-j][y]=(0,0,125)
except: pass
try: crownImg[x+j][y]=(0,0,125)
except: pass
return crownImg
def composite(self, channels=('dna', 'protein', None), plane='central'):
import mahotas
if plane == 'central':
plane = len(self.files.values()[0])//2
def g(ch):
if ch is not None and self.has_channel(ch):
return self.get(ch, plane)
c0,c1,c2 = channels
return mahotas.as_rgb(g(c0), g(c1), g(c2))
def UpdateImage(self):
sigma = self.Slider_Sigma.value()/2; dilate = self.Slider_Dilate.value()
if self.Combo_ImageType.currentText() == 'DAPI':
if self.Check_NoisyMode.isChecked():
self.DAPI_mask = mh.dilate(self.img_array > self.T_mean, np.ones((dilate, dilate)));
self.gaussian_f = mh.gaussian_filter(self.img_array.astype('int'), sigma)
else:
self.gaussian_f = mh.gaussian_filter(self.img_array.astype('float'), sigma)
self.DAPI_mask = mh.dilate(self.gaussian_f > self.gaussian_f.mean(), np.ones((dilate, dilate)));
if self.Check_SingleColor.isChecked():
self.im_axes.imshow(mh.as_rgb(0, 0, self.DAPI_mask));
else:
plt.imshow(mh.as_rgb(np.maximum(self.img_array,self.gaussian_f), self.gaussian_f, self.gaussian_f > self.T_otsu))
self.ClusterMap.setupUi(figure=self.im_figure, title='Image Display',
xlabel='pixels (x)', ylabel='pixels(y)')
self.Button_SNR.setEnabled(False); self.Check_TissueMaxima.setEnabled(False);
self.DAPI_set_bool = True
self.Text_Log.append('>Updated image; DAPI; sigma=%.1f,dilate=%d\n' % (sigma, dilate))
elif self.Combo_ImageType.currentText() == 'Fluorescence (Signal)':
self.gaussian_f = mh.gaussian_filter(self.img_array.astype('float'), sigma)
self.gT_mean = self.gaussian_f.mean()
all_maximas = mh.regmax(self.gaussian_f)
self.all_maximas = mh.dilate(all_maximas, np.ones((dilate, dilate)))
if self.Check_TissueMaxima.isChecked():
self.tissue_maxima = self.all_maximas*self.Tissue_Mask
self.im_axes.imshow(mh.as_rgb(
np.maximum(255*self.tissue_maxima, self.gaussian_f),
self.gaussian_f, self.img_array > self.gT_mean))
else:
self.im_axes.imshow(mh.as_rgb(
np.maximum(255*self.all_maximas, self.gaussian_f), self.gaussian_f, self.img_array > self.gT_mean))
self.ClusterMap.setupUi(figure=self.im_figure, title='Image Display',
xlabel='pixels (x)', ylabel='pixels(y)')
self.Text_Log.append('>Updated image; Mode: Fluor; sigma=%.1f,dilate=%d\n' % (sigma, dilate))
if self.DAPI_set_bool:
self.Button_SNR.setEnabled(True); self.Check_TissueMaxima.setEnabled(True)
def overlay(input_image,seg_mask,frame_num,sizeX,sizeY):
import mahotas as mh
cmap = plt.get_cmap('gray')
seg_mask=seg_mask>0
cmap_2_use = truncate_colormap(cmap,0.9,1)
border2overlay = np.ma.masked_where(seg_mask ==0, seg_mask)
x =mh.as_rgb(input_image[frame_num][0,:,:],input_image[frame_num][1,:,:],np.zeros((sizeY,sizeX)))
x[:,:,1][x[:,:,1]>240]=240
x[:,:,0][x[:,:,0]>240]=240
slice2show = [slice(0,sizeY),slice(0,sizeX)]
plt.imshow(x[slice2show]/np.array([240.,240.,1]).reshape(1,1,3))
plt.imshow(border2overlay[slice2show],cmap_2_use,alpha=0.4),plt.axis('off')
def overlay(input_image, seg_mask, frame_num, sizeX, sizeY):
import mahotas as mh
cmap = plt.get_cmap('gray')
seg_mask = seg_mask > 0
cmap_2_use = truncate_colormap(cmap, 0.9, 1)
border2overlay = np.ma.masked_where(seg_mask == 0, seg_mask)
x = mh.as_rgb(input_image[frame_num][0, :, :],
input_image[frame_num][1, :, :], np.zeros((sizeY, sizeX)))
x[:, :, 1][x[:, :, 1] > 240] = 240
x[:, :, 0][x[:, :, 0] > 240] = 240
slice2show = [slice(0, sizeY), slice(0, sizeX)]
plt.imshow(x[slice2show] / np.array([240., 240., 1]).reshape(1, 1, 3))
plt.imshow(border2overlay[slice2show], cmap_2_use,
alpha=0.4), plt.axis('off')
def show_segmentation_boundaries(input_image,seg_mask,frame_num,sizeX,sizeY):
import mahotas as mh
cmap = plt.get_cmap('gray')
#outlines
dsk = mh.disk(7)
boundaries = mh.dilate(seg_mask,dsk)-seg_mask
boundaries=boundaries>0
cmap_2_use = truncate_colormap(cmap,0.9,1)
border2overlay = np.ma.masked_where(boundaries ==0, boundaries)
x =mh.as_rgb(input_image[frame_num][0,:,:],input_image[frame_num][1,:,:],np.zeros((sizeY,sizeX)))
x[:,:,1][x[:,:,1]>240]=240
x[:,:,0][x[:,:,0]>240]=240
slice2show = [slice(0,sizeY),slice(0,sizeX)]
plt.imshow(x[slice2show]/np.array([240.,240.,1]).reshape(1,1,3))
plt.imshow(border2overlay[slice2show],cmap_2_use,alpha=0.8),plt.axis('off')
def composite(dna, protein, rois):
'''Build composite image
Parameters
----------
dna : ndarray
protein : ndarray
rois : ndarray
Returns
-------
comp : ndarray of shape (h,w,3)
RGB image
'''
borders = 255*mh.borders(rois != 0)
borders = np.dstack([borders, borders, borders])
im = np.maximum(borders, mh.as_rgb(dna, protein, 0)).astype(np.uint8)
im[492:500,400:400+50/MU_PER_PIXEL] = 255
return im
def composite(dna, protein, rois):
'''Build composite image
Parameters
----------
dna : ndarray
protein : ndarray
rois : ndarray
Returns
-------
comp : ndarray of shape (h,w,3)
RGB image
'''
borders = 255 * mh.borders(rois != 0)
borders = np.dstack([borders, borders, borders])
im = np.maximum(borders, mh.as_rgb(dna, protein, 0)).astype(np.uint8)
im[492:500, 400:400 + 50 / MU_PER_PIXEL] = 255
return im
def center_focus(im,f): r, g, b = im.transpose(2,0,1) r12 = mh.gaussian_filter(r, 12.0) g12 = mh.gaussian_filter(g, 12.0) b12 = mh.gaussian_filter(b, 12.0) im12 = mh.as_rgb(r12,g12,b12) h, w = r.shape Y, X = np.mgrid[:h,:w] Y = Y-h/2.0 Y = Y/Y.max() X = X-w/2.0 X = X/X.max() W = np.exp(-2.0*(X**2 + Y**2)/0.5) W = W - W.min() W = W/W.ptp() W = W[:, :, None] ringed = mh.stretch(im*W + (1.0-W)*im12) plt.imshow(ringed) plt.savefig(f) plt.show ()
def show_segmentation_boundaries(input_image, seg_mask, frame_num, sizeX,
sizeY):
import mahotas as mh
cmap = plt.get_cmap('gray')
#outlines
dsk = mh.disk(7)
boundaries = mh.dilate(seg_mask, dsk) - seg_mask
boundaries = boundaries > 0
cmap_2_use = truncate_colormap(cmap, 0.9, 1)
border2overlay = np.ma.masked_where(boundaries == 0, boundaries)
x = mh.as_rgb(input_image[frame_num][0, :, :],
input_image[frame_num][1, :, :], np.zeros((sizeY, sizeX)))
x[:, :, 1][x[:, :, 1] > 240] = 240
x[:, :, 0][x[:, :, 0] > 240] = 240
slice2show = [slice(0, sizeY), slice(0, sizeX)]
plt.imshow(x[slice2show] / np.array([240., 240., 1]).reshape(1, 1, 3))
plt.imshow(border2overlay[slice2show], cmap_2_use,
alpha=0.8), plt.axis('off')
import numpy as np
# Read in the image
im = mh.imread('lenna.jpg')
# This breaks up the image into RGB channels
r, g, b = im.transpose(2, 0, 1)
h, w = r.shape
# smooth the image per channel:
r12 = mh.gaussian_filter(r, 12.)
g12 = mh.gaussian_filter(g, 12.)
b12 = mh.gaussian_filter(b, 12.)
# build back the RGB image
im12 = mh.as_rgb(r12, g12, b12)
X, Y = np.mgrid[:h, :w]
X = X - h / 2.
Y = Y - w / 2.
X /= X.max()
Y /= Y.max()
# Array C will have the highest values in the center, fading out to the edges:
C = np.exp(-2. * (X**2 + Y**2))
C -= C.min()
C /= C.ptp()
C = C[:, :, None]
# The final result is sharp in the centre and smooths out to the borders:
def test_as_rgb_Nones():
with pytest.raises(ValueError):
mahotas.as_rgb(None, None, None)
import mahotas as mh
import numpy as np
im = mh.imread('lenna.jpg')
r,g,b = im.transpose(2,0,1)
h,w = r.shape
r12 = mh.gaussian_filter(r, 12.)
g12 = mh.gaussian_filter(g, 12.)
b12 = mh.gaussian_filter(b, 12.)
im12 = mh.as_rgb(r12,g12,b12)
X,Y = np.mgrid[:h,:w]
X = X-h/2.
Y = Y-w/2.
X /= X.max()
Y /= Y.max()
C = np.exp(-2.*(X**2+ Y**2))
C -= C.min()
C /= C.ptp()
C = C[:,:,None]
ring = mh.stretch(im*C + (1-C)*im12)
mh.imsave('lenna-ring.jpg', ring)
mask = initImage[:, :, 1] > (val * .8)
# set init green channel to random noise
initImage[:, :, 0] = img_noise[:, :, 0]
if localizationClass == 0:
plt.subplot(numFigs, 3, 1)
plt.imshow(initImage[:, :, 0], 'gray')
plt.title('green_init')
plt.axis('off')
plt.subplot(numFigs, 3, 2)
plt.imshow(initImage[:, :, 1], 'gray')
plt.title('red_init')
plt.axis('off')
plt.subplot(numFigs, 3, 3)
plt.imshow(mh.as_rgb(initImage0[:, :, 1], initImage0[:, :, 0], None))
plt.title('original')
plt.axis('off')
#plt.savefig('./output_figures/'+localizationTerms[class2sample_from]+'_'+str(curInd)+'_init.png')
generatedCell = render_naive_gaussian_blur_l2_clamped(
lastAct[:, localizationClass],
mask=mask,
clip_val_g=[useTopPerc[localizationTerms[localizationClass]]],
img0=initImage,
iter_n=100,
step=.3,
sigma=.7,
decay=.009)
plt.subplot(numFigs, 3, (localizationClass + 1) * 3 + 1)
def makeSegmentationPicture(self,
thickestPath,
G,
crownImg,
xScale,
yScale,
c1x,
c1y,
c2x,
c2y,
c3x=None,
c3y=None):
print 'make cluster picture'
crownImg = m.as_rgb(crownImg, crownImg, crownImg)
vprop = G.vertex_properties["vp"]
for i in thickestPath:
if vprop[i]['nrOfPaths'] in c1y:
y = int(vprop[i]['imgIdx'][0])
x = int(vprop[i]['imgIdx'][1])
try:
crownImg[x][y] = (125, 0, 0)
except:
pass
dia = vprop[i]['diameter'] / (xScale / 2 + yScale / 2)
dia = dia * 1.5
for j in range(int(dia)):
try:
crownImg[x][y + j] = (125, 0, 0)
except:
pass
try:
crownImg[x][y - j] = (125, 0, 0)
except:
pass
try:
crownImg[x - j][y] = (125, 0, 0)
except:
pass
try:
crownImg[x + j][y] = (125, 0, 0)
except:
pass
elif vprop[i]['nrOfPaths'] in c2y:
y = int(vprop[i]['imgIdx'][0])
x = int(vprop[i]['imgIdx'][1])
try:
crownImg[x][y] = (125, 0, 0)
except:
pass
dia = vprop[i]['diameter'] / (xScale / 2 + yScale / 2)
dia = dia * 1.5
for j in range(int(dia)):
try:
crownImg[x][y + j] = (0, 125, 0)
except:
pass
try:
crownImg[x][y - j] = (0, 125, 0)
except:
pass
try:
crownImg[x - j][y] = (0, 125, 0)
except:
pass
try:
crownImg[x + j][y] = (0, 125, 0)
except:
pass
y = int(vprop[i]['imgIdx'][0])
x = int(vprop[i]['imgIdx'][1])
try:
crownImg[x][y] = (0, 0, 125)
except:
pass
dia = vprop[i]['diameter'] / (xScale / 2 + yScale / 2)
dia = dia * 1.5
for j in range(int(dia)):
try:
crownImg[x][y + j] = (0, 0, 125)
except:
pass
try:
crownImg[x][y - j] = (0, 0, 125)
except:
pass
try:
crownImg[x - j][y] = (0, 0, 125)
except:
pass
try:
crownImg[x + j][y] = (0, 0, 125)
except:
pass
return crownImg
def check_sigma(sigma):
dnaf = mh.gaussian_filter(dna.astype(float), sigma)
maxima = mh.regmax(mh.stretch(dnaf))
maxima = mh.dilate(maxima, np.ones((5,5)))
plt.imshow(mh.as_rgb(np.maximum(255*maxima, dnaf), dnaf, dna > T_mean))
def test_as_rgb_Nones():
mahotas.as_rgb(None,None,None)
def createOverlayImage(image, label):
red = np.copy(image)
red[np.nonzero(label)] = 255
greenAndBlue = np.copy(image)
greenAndBlue[np.nonzero(label)] = 0
return mahotas.as_rgb(red, greenAndBlue, greenAndBlue)
def false_color(base,n0,n1):
g = mh.imread(basedir+'{}/{:02}-{:03}-protein.tiff'.format(base,n0,n1))
r = mh.imread(basedir+'{}/{:02}-{:03}-dna.tiff'.format(base,n0,n1))
return mh.as_rgb(r,g,None)
def test_as_rgb_shape_mismatch():
np.random.seed(2323)
r = np.random.random_integers(12, 120, size=(8,8))
g = np.random.random_integers(12, 120, size=(8,8))
b = np.random.random_integers(12, 120, size=(8,6))
mahotas.as_rgb(r,g,b)
import numpy as np
import mahotas as mh
canvas = np.zeros((384,256),np.uint8)
for y in xrange(512):
for x in xrange(512):
if (x-128)**2+1.8*(y-64)**2 < 61*64:
canvas[y,x] = 4
if (x-128)**2+1.8*(y-64)**2 < 61*60:
canvas[y,x] = 1
if (x-128)**2+1.8*(y-192)**2 < 61*64:
canvas[y,x] = 2
if (x-128)**2+1.8*(y-320)**2 < 61*60:
canvas[y,x] = 3
for y in xrange(-21,21):
for x in xrange(-21,21):
if x**2 + y**2 < 14*14:
canvas[332+y,62+x] = 3
r,g,b = (canvas == 1), (canvas == 2), (canvas == 3)
mh.imsave('2013-09-16.png',mh.as_rgb(r,g,b))
