def layer(c,file,gds_layer):
try:
img = readmagick.readimg(file)
except:
return
img = img[:,:,0]
height,width = img.shape
y = 0
while True:
box = extract_box(img,y)
if not box:
break
(x,y,x2,y2) = box
c.add(gdspy.Rectangle(gds_layer, (x,height-y), (x2,height-y2)))
def layer(c, file, gds_layer):
try:
img = readmagick.readimg(file)
except:
return
img = img[:, :, 0]
height, width = img.shape
y = 0
while True:
box = extract_box(img, y)
if not box:
break
(x, y, x2, y2) = box
c.add(gdspy.Rectangle(gds_layer, (x, height - y), (x2, height - y2)))
def layer(file,cif_name):
global height
try:
img = readmagick.readimg(file)
except:
return
print 'L '+cif_name+';'
img = img[:,:,0]
height,width = img.shape
y = 0
while True:
box = extract_box(img,y)
if not box:
break
(x,y,x2,y2) = box
print ' B %d %d %d %d;' % (2*(x2-x),2*(y2-y),x+x2,2*height-(y+y2))
def layer(file, cif_name):
global height
try:
img = readmagick.readimg(file)
except:
return
print 'L ' + cif_name + ';'
img = img[:, :, 0]
height, width = img.shape
y = 0
while True:
box = extract_box(img, y)
if not box:
break
(x, y, x2, y2) = box
print ' B %d %d %d %d;' % (2 * (x2 - x), 2 *
(y2 - y), x + x2, 2 * height - (y + y2))
def produce_mosaics(prep_list, pwi_file, annotations, output_dir, temp_im_dir):
num_preps = len(prep_list)
dl_file = urllib.URLopener()
for idx in range(0, num_preps):
cur_url_list = pwi_file[idx]
cur_annotations = annotations[idx]
#backup_cur_url_list = back_pwi_file[idx]
num_urls = len(cur_url_list)
i = 0
il = 1
while il < len(cur_url_list) and il < num_ims_to_plot:
try:
dl_name = temp_im_dir + '/' + str(il - 1) + '.jpg'
#try:
#dl_file.retrieve(cur_url_list[il], dl_name)
response = urllib.urlopen(cur_url_list[il])
content = response.read()
f = open(dl_name, 'w')
f.write(content)
f.close()
#except Exception, e:
#dl_file.retrieve(backup_cur_url_list[il], dl_name)
im = readmagick.readimg(dl_name)
ax = pylab.subplot(subplot_arrangement[0],
subplot_arrangement[1],
il,
frameon=False,
xticks=[],
yticks=[])
str_list = [x.encode('UTF8') for x in cur_annotations[il]]
ax.set_title(' '.join(str_list), fontsize=4, y=1.08)
pylab.imshow(im)
print("Finished image #" + str(il))
except Exception, g:
print("Skipping image #" + str(il))
il += 1
pylab.savefig(output_dir + '/' + prep_list[idx] + '.jpg',
format='jpg',
dpi=1000)
remove_files(temp_im_dir + '/*')
import numpy as np
import scipy
import pylab
import pymorph
import readmagick
from scipy import ndimage
from pylab import cm
import pit
dna = readmagick.readimg('dna.jpeg')
TESTING = False
if TESTING:
def savejet(img, name):
pass
def writeimg(img, name):
pass
else:
def savejet(img, name):
img = (img - img.min()) / float(img.ptp()) * 255
img = img.astype(np.uint8)
readmagick.writeimg((cm.jet(img)[:, :, :3] * 255).astype(np.uint8),
name)
writeimg = readmagick.writeimg
dnaf = ndimage.gaussian_filter(dna, 8)
rmax = pymorph.regmax(dnaf)
pylab.imshow(pymorph.overlay(dna, rmax))
for labIndex in range(1, labmax + 1):
flatParticleList.append(eT[eT[:, 0] == labIndex]) # from Matthieu Brucher
return unflattenParticles(flatParticleList)
#
# Modify your path to your images here. The script works with 16bits images
#
user = os.path.expanduser("~")
workdir = os.path.join(user, "Applications", "ImagesTest", "jp", "Jpp48", "13", "DAPI")
file = "1.tif"
complete_path = os.path.join(workdir, file)
#
#
if __name__ == "__main__":
dapi = readmagick.readimg(complete_path)
os.mkdir(os.path.join(workdir, "particules"))
im1 = RemoveModalBackground(dapi)
# 8bits dapi image
d8 = gray12_to8(im1)
# try a simple segmentation procedure
print "segmenting..."
# imlabel=GradBasedSegmentation(im1)
imlabel, npart = nd.label(LowResSegmentation(dapi))
print "showing..."
# pylab.imshow(imlabel)
# pylab.show()
# Particles is a list of images
Particles = extractParticles(im1, imlabel)
li = 1
ColNum = 10 # ten columns and len(Particles)/ColNum lines
#
pins = {}
f = open(sys.argv[1], "r")
for x in f.readlines():
l = string.split(x)
name, x, y = l[0], int(l[1]), int(l[2])
pins[name] = (x, y)
f.close()
#
# read in the mask bitmaps and label them
#
metal, metal_n = ndimage.label(readmagick.readimg(sys.argv[2]))
diffusion, diffusion_n = ndimage.label(readmagick.readimg(sys.argv[3]))
contact, contact_n = ndimage.label(readmagick.readimg(sys.argv[4]))
gate, gate_n = ndimage.label(readmagick.readimg(sys.argv[5]))
implant, implant_n = ndimage.label(readmagick.readimg(sys.argv[6]))
print '# metal: %d objects' % metal_n
print '# diffusion: %d objects' % diffusion_n
print '# contact: %d objects' % contact_n
print '# gate: %d objects' % gate_n
print '# implant: %d objects' % implant_n
#
# emit "pin" objects that intersect metal or diffusion
#
import numpy as np
import scipy
import pylab
import pymorph
import readmagick
import pit
from scipy import ndimage
dna = readmagick.readimg('dna.jpeg')
TESTING = True
if TESTING:
def savejet(img, name):
pass
else:
def savejet(img):
readmagick.writeimg((cm.jet(dnaf)[:,:,:3]*255).astype(np.uint8), name)
pylab.imshow(dna)
pylab.show()
savejet(dna, 'dna-coloured.jpeg')
pylab.imshow(dna)
pylab.gray()
pylab.show()
print dna.shape
print dna.dtype
print dna.max()
print dna.min()
pylab.imshow(dna // 2)
#
pins = {}
f = open(sys.argv[1],"r")
for x in f.readlines():
l = string.split(x)
name,x,y = l[0],int(l[1]),int(l[2])
pins[name] = (x,y)
f.close()
#
# read in the mask bitmaps and label them
#
metal,metal_n = ndimage.label(readmagick.readimg(sys.argv[2]))
diffusion,diffusion_n = ndimage.label(readmagick.readimg(sys.argv[3]))
contact,contact_n = ndimage.label(readmagick.readimg(sys.argv[4]))
gate,gate_n = ndimage.label(readmagick.readimg(sys.argv[5]))
implant,implant_n = ndimage.label(readmagick.readimg(sys.argv[6]))
print '# metal: %d objects' % metal_n
print '# diffusion: %d objects' % diffusion_n
print '# contact: %d objects' % contact_n
print '# gate: %d objects' % gate_n
print '# implant: %d objects' % implant_n
#
# emit "pin" objects that intersect metal or diffusion
#
def test_readimgfromblob():
img0 = readmagick.readimg(_testimg)
img1 = readmagick.readimgfromblob(file(_testimg).read())
assert img0.shape == img1.shape
assert np.all(img0 == img1)
def load_aabid(col,id):
from readmagick import readimg
id = ('../data/images/segmented-ashariff/%s/dna-%s.psd' % (col,id))
I = readimg(id)
B = (I[:,:,0] > I[:,:,1])
return _process_B(B)
def __fakeimg():
dna = readimg('tests/data/dnaimg.jp2')
img = pyslic.Image()
img.channeldata['dna'] = dna
img.loaded = True
return img
def test_thresholding():
dna = readimg('tests/data/dnaimg.jp2')
segmented = pyslic.segmentation.thresholding.threshold_segment(dna)
assert dna.shape == segmented.shape
def write_readback(C,fname):
C = C.copy()
writeimg(C,fname)
C2 = readimg(fname)
return C, C2
import readmagick
import pymorph
import mahotas
dna = readmagick.readimg('dna-0.xcf[0]').max(2)
dna2 = readmagick.readimg('dna-1.xcf[0]').max(2)
borders = readmagick.readimg('dna-0.xcf[1]')
borders = borders[:,:,0] > borders[:,:,1]
for i in xrange(2): borders = mahotas.dilate(borders, np.ones((3,3)))
readmagick.writeimg(pymorph.overlay(dna, borders), 'dna.png')
readmagick.writeimg(pymorph.overlay(dna2, borders), 'dna2.png')
#label of the last particle
flatParticleList=[]#from Matthieu Brucher
for labIndex in range(1,labmax+1):
flatParticleList.append(eT[eT[:,0]==labIndex])#from Matthieu Brucher
return unflattenParticles(flatParticleList)
#
#Modify your path to your images here. The script works with 16bits images
#
user=os.path.expanduser("~")
workdir=os.path.join(user,"Applications","ImagesTest","jp","Jpp48","2","DAPI")
file="1.tif"
complete_path=os.path.join(workdir,file)
#
#
if __name__ == "__main__":
dapi=readmagick.readimg(complete_path)
print "shape dapi",dapi.shape
os.mkdir(os.path.join(workdir,"particules"))
im1=RemoveModalBackground(dapi)
#8bits dapi image
d8=gray12_to8(im1)
#try a simple segmentation procedure
print "segmenting..."
#imlabel=GradBasedSegmentation(im1)
imlabel,npart=nd.label(LowResSegmentation(dapi))
print "showing..."
#pylab.imshow(imlabel)
#pylab.show()
#Particles is a list of images
Particles=extractParticles(im1,imlabel)
li=1
