def test_zero_image_nodna():
img=pyslic.Image()
img.channeldata['protein'] = numpy.zeros((100,100))
img.loaded=True
img.channels['protein']='<special>'
F=pyslic.computefeatures(img,'mcell')
assert F.size == 31
assert numpy.isnan(F).sum() == 0
F=pyslic.computefeatures(img,['img'])
assert F.size == 8
assert numpy.isnan(F).sum() == 0
def field_features(im):
import pyslic
img = pyslic.Image()
img.channels = {'protein':'<protein>', 'dna':'<dna>'}
img.channeldata = {'protein':im.get("protein"), 'dna':im.get('dna')}
im.unload()
img.loaded = True
return pyslic.computefeatures(img, 'field-dna+')
def compute_field_features(img, scale, F):
try:
img.scale=scale
print img.scale
with pyslic.image.loadedimage(img):
return pyslic.computefeatures(img, 'field-dna+')
except:
return None
def test_featinfolen():
imgdata = basedir+'/data/protimg.jp2'
imgdata = pyslic.image.io.readimg(imgdata)
img = pyslic.Image()
img.channeldata['protein'] = imgdata
img.loaded = True
img.channels['protein']='<special>'
assert len(pyslic.computefeatures(img,'SLF33')) == len(pyslic.features.featinfo.get_names('SLF33'))
def test_negative_image():
img=pyslic.Image()
img.channeldata['protein'] = -numpy.ones((10,10))
img.channeldata['dna'] = -numpy.ones((10,10))
img.loaded=True
img.channels['protein']='<special>'
img.channels['dna']='<special>'
F=pyslic.computefeatures(img,'SLF7dna')
assert F.size == 90
assert numpy.isnan(F).sum() == 0
def test_tas():
img=basedir+'/data/protimg.jp2'
img=pyslic.image.io.readimg(img)
IMG=pyslic.Image()
IMG.channeldata['protein']=img
IMG.loaded=True
IMG.channels['protein']='<special>'
assert len(pyslic.features.tas.tas(img)) == 2*3*9
assert len(pyslic.features.tas.pftas(img)) == 2*3*9
assert numpy.all(pyslic.features.tas.tas(img) == pyslic.computefeatures(IMG,['tas']))
def make_content_db(basedir,scale):
dataset=[]
pattern_iid_dict={}
for x in os.listdir(basedir):
pattern_iid_dict[x]=[]
for y in os.listdir(basedir+x):
directory=basedir+x+'/'+y
print directory
imgs=auto_detect_load(directory)
for img in imgs:
img.scale=scale
img.load()
field_features=pyslic.computefeatures(img,'field-dna+')
img_id=os.path.basename(img.channels['protein']).replace('.bmp','_10x')
dataset.append([img_id,1,list(field_features)])
pattern_iid_dict[x].append(img_id)
pickle.dump(dataset,open(INT_DIR+'dataset_'+resolution+'.pkl','wb'))
pickle.dump(pattern_iid_dict,open(INT_DIR+'pattern_iid_dict_'+resolution+'.pkl','wb'))
print os.getcwd()
import sys
sys.path.append(os.getcwd()+'/scripts')
import tif2array
import pyslic
for key, val in sys.modules.items():
if 'pyslic' in key:
print key,val
INPUT_DIR='./images/ASCB_images/'
OUTPUT_DIR='./intermediate_results/ASCB_features/'
if not os.path.isdir(OUTPUT_DIR):
os.makedirs(OUTPUT_DIR)
image_res_dict=pickle.load(open('scripts/image_res_dict.pkl','rb'))
for imageid, res in image_res_dict.items():
filename=INPUT_DIR+str(imageid)+'.tiff'
if os.path.isfile(filename):
img_read=tif2array.tif2array(filename,True)
img=pyslic.Image()
img.channeldata['protein']=img_read[:,:,0]
img.channels['protein']=filename
img.scale=res
feats=pyslic.computefeatures(img,'slf33')
if feats is not None:
pickle.dump(feats,open(OUTPUT_DIR+str(imageid)+'.pkl','wb'))
import numpy as np
MURPHYLAB_HELA2D_PATH = '/home/luispedro/work/murphylab/images/hela-2d/'
print r'''\
This is only to be run if you have got the Murphylab images installed at
%s
Otherwise, you will get errors!
''' % MURPHYLAB_HELA2D_PATH
import pyslic
imgs = pyslic.image.io.dirtransversal.dirtransversal(MURPHYLAB_HELA2D_PATH)
labels = np.array([img.label for img in imgs])
features = pyslic.computefeatures(imgs, 'SLF7DNA')
np.savez('murphylab.npz', features=features, labels=labels)
import numpy as np
MURPHYLAB_HELA2D_PATH = '/home/luispedro/work/murphylab/images/hela-2d/'
print(r'''\
This is only to be run if you have got the Murphylab images installed at
{}
Otherwise, you will get errors!
'''.format(MURPHYLAB_HELA2D_PATH))
import pyslic
imgs = pyslic.image.io.dirtransversal.dirtransversal(MURPHYLAB_HELA2D_PATH)
labels = np.array([img.label for img in imgs])
features = pyslic.computefeatures(imgs, 'SLF7DNA')
np.savez('murphylab.npz', features=features, labels=labels)
for filename in filenames:
filename = path.join( filepath, filename )
print filename
[basename, extension] = path.splitext(filename)
if extension == '.tif':
features_filename = basename + '.pkl'
pixels = Image.open(filename)
pixels = numpy.array(pixels)
#make pyslic image container
img=pyslic.Image()
img.label=basename
img.scale=0.237
img.channels[ 'protein' ] = 0
img.channeldata[ 'protein' ] = pixels
img.loaded=True
features = pyslic.computefeatures(img,'field+')
image = {}
image['pixels']=pixels
image['scale']=img.scale
image['label']=img.label
image['feature_set']='SLF33'
image['feature_ids']=feature_ids
image['features']=features
pickle.dump(image, open(features_filename,'w'))
chdir( '../' )
def calculate( conn, iid, scale=1, set="slf33", field=True, rid=None, pixels=0, channels=[], zslice=0, timepoint=0, threshold=None, debug=False ):
'''
Calculates and returns a feature ids vector, a feature vector and the output scale given a valid
image identification (iid). It currently can calculate SLF33, SLF34, SLF35 and SLF36.
This method will try to retrieve the resolution of the image from the annotations.
If the method is unable to connect to the OMERO.server, then the method will return None.
If the method doesn't find an image associated with the given image id (iid), then the method will return None.
For detailed outputs, set debug flag to True.
:param conn: connection
:type conn: BlitzGateway connection
:param iid: image id
:type iid: long
:param scale: image scale
:type scale: double
:param set: feature set name
:type set: string
:param field: true if field features, false otherwise
:type field: boolean
:param rid: region id
:type rid: long
:param pixels: pixel index associated with the image
:type pixels: integer
:param channels: list of channel indices
:type channels: list of integers
:param zslice: zslice index
:type zslice: integer
:param timepoint: time point index
:type timepoint: integer
:param threshold: image size threshold value
:type threshold: integer
:param debug: debug flag
:type debug: boolean
:rtype: a list of feature ids, a feature vector and the scale at which the features where calculated
'''
if not conn.isConnected():
raise PyslidException("Unable to connect to OMERO.server")
if not pyslid.utilities.hasImage( conn, iid ):
raise PyslidException("No image found with the given image id:%s", iid)
#check input arguments
image = conn.getObject("Image", long(iid) )
if image is None:
raise PyslidException("Unable to retrieve image with iid:%s", iid)
else:
try:
#if threshold is empty use default value
if threshold == None:
threshold = 10*1024;
#check if image size is greater than threshold value
#icaoberg 19/02/2013
if image.getPixelSizeY() > threshold:
raise PyslidException("Image size is greater than threshold value")
#icaoberg 19/02/2013
elif image.getPixelSizeY() > threshold:
raise PyslidException("Image size is greater than threshold value")
else:
#set scale value
imgScale = pyslid.image.getScale( conn, iid, debug )
imgScale = imgScale[0]
except:
#if no scale value is present, pyslic will set a scale value of .23
#to avoid that we prevent feature calculation
raise PyslidException("Unable to retrieve resolution or resolution was not set")
#set resolution based on the scale
print 'scale:%f imgScale:%f' %(scale, imgScale)
inputScale = scale
if scale < 0.33 and abs(scale - imgScale)>0.001 :
scale = imgScale
elif scale < 0.67 and abs(scale - imgScale*2)>0.001:
scale = imgScale*2
else:
scale = imgScale*4
print 'Forcing pyslid calculated scale from %f to input scale %f' % (
scale, inputScale)
scale = inputScale
feature_ids = ["SLF27.66","SLF27.67","SLF27.68","SLF27.69","SLF27.70","SLF27.71","SLF27.72","SLF27.73","SLF27.74","SLF27.75","SLF27.76","SLF27.77","SLF27.78","SLF33.37","SLF33.38","SLF33.39","SLF33.40","SLF33.41","SLF33.42","SLF33.43","SLF33.44","SLF33.45","SLF33.46","SLF33.47","SLF33.48","SLF33.49","SLF33.50","SLF33.51","SLF33.52","SLF33.53","SLF33.54","SLF33.55","SLF33.56","SLF33.57","SLF33.58","SLF33.59","SLF33.60","SLF33.61","SLF33.62","SLF33.63","SLF33.64","SLF33.65","SLF33.66","SLF33.67","SLF33.68","SLF33.69","SLF33.70","SLF33.71","SLF33.72","SLF33.73","SLF33.74","SLF33.75","SLF33.76","SLF33.77","SLF33.78","SLF33.79","SLF33.80","SLF33.81","SLF33.82","SLF33.83","SLF33.84","SLF33.85","SLF33.86","SLF33.87","SLF33.88","SLF33.89","SLF33.90","SLF33.91","SLF33.92","SLF33.93","SLF33.94","SLF33.95","SLF33.96","SLF33.97","SLF33.98","SLF33.99","SLF33.100","SLF33.101","SLF33.102","SLF33.103","SLF33.104","SLF33.105","SLF33.106","SLF33.107","SLF33.108","SLF33.109","SLF33.110","SLF33.111","SLF33.112","SLF33.113","SLF33.114","SLF27.1","SLF27.2","SLF27.3","SLF27.4","SLF27.5","SLF27.89","SLF27.90","SLF27.9","SLF27.10","SLF27.11","SLF27.12","SLF27.13","SLF27.80","SLF27.81","SLF27.82","SLF27.83","SLF27.84","SLF27.79","SLF31.1","SLF31.2","SLF31.3","SLF31.4","SLF31.5","SLF31.6","SLF31.7","SLF31.8","SLF31.9","SLF31.10","SLF31.11","SLF31.12","SLF31.13","SLF31.14","SLF31.15","SLF31.16","SLF31.17","SLF31.18","SLF33.1","SLF33.2","SLF33.3","SLF33.4","SLF33.5","SLF33.6","SLF33.7","SLF33.8","SLF33.9","SLF33.19","SLF33.20","SLF33.21","SLF33.22","SLF33.23","SLF33.24","SLF33.25","SLF33.26","SLF33.27","SLF33.10","SLF33.11","SLF33.12","SLF33.13","SLF33.14","SLF33.15","SLF33.16","SLF33.17","SLF33.18","SLF33.28","SLF33.29","SLF33.30","SLF33.31","SLF33.32","SLF33.33","SLF33.34","SLF33.35","SLF33.36","SLF34.1","SLF34.2","SLF34.3","SLF34.4","SLF34.5","SLF34.6","SLF34.7","SLF34.8","SLF34.9","SLF34.10","SLF27.80","SLF27.81","SLF27.82","SLF27.83","SLF27.84","SLF27.79","SLF27.1","SLF27.2","SLF27.3","SLF27.4","SLF27.5","SLF27.6","SLF27.7","SLF27.8","SLF27.85","SLF27.86","SLF27.87","SLF27.88","SLF27.89","SLF27.90","SLF27.14","SLF27.15","SLF27.16","SLF27.17","SLF27.18","SLF27.19","SLF27.20","SLF27.21","SLF27.22","SLF27.23","SLF27.24","SLF27.25","SLF27.26","SLF27.27","SLF27.28","SLF27.29","SLF27.30","SLF27.31","SLF27.32","SLF27.33","SLF27.34","SLF27.35","SLF27.36","SLF27.37","SLF27.38","SLF27.39","SLF27.40","SLF27.41","SLF27.42","SLF27.43","SLF27.44","SLF27.45","SLF27.46","SLF27.47","SLF27.48","SLF27.49","SLF27.50","SLF27.51","SLF27.52","SLF27.53","SLF27.54","SLF27.55","SLF27.56","SLF27.57","SLF27.58","SLF27.59","SLF27.60","SLF27.61","SLF27.62","SLF27.63","SLF27.64","SLF27.65","SLF27.66","SLF27.67","SLF27.68","SLF27.69","SLF27.70","SLF27.71","SLF27.72","SLF27.73","SLF27.74","SLF27.75","SLF27.76","SLF27.77","SLF27.78","SLF27.9","SLF27.10","SLF27.11","SLF27.12","SLF27.13","SLF31.1","SLF31.2","SLF31.3","SLF31.4","SLF31.5","SLF31.6","SLF31.7","SLF31.8","SLF31.9","SLF31.10","SLF31.11","SLF31.12","SLF31.13","SLF31.14","SLF31.15","SLF31.16","SLF31.17","SLF31.18"]
if set=="slf34":
#make pyslic image container
img=pyslic.Image()
img.label=iid
img.scale=scale
if len(channels) != 2:
raise PyslidException("Two channels required for slf34")
labels = [ 'protein', 'dna' ]
for c in xrange(2):
img.channels[ labels[c] ] = channels[c]
plane = pyslid.utilities.getPlane(
conn, iid, pixels, channels[c], zslice, timepoint)
img.channeldata[ labels[c] ] = scipy.misc.imresize(plane, scale)
img.loaded=True
features = []
try:
features = pyslic.computefeatures(img,'field-dna+')
result = [feature_ids[0:173], features, scale]
except:
print "Unable to calculate features"
raise
elif set=="slf33":
#make pyslic image container
img=pyslic.Image()
img.label=iid
img.scale=scale
print 'scale: %f' % scale
img.channels[ 'protein' ] = channels[0]
plane = pyslid.utilities.getPlane(
conn, iid, pixels, channels[0], zslice, timepoint)
img.channeldata[ 'protein' ] = scipy.misc.imresize( plane, scale )
img.loaded=True
print 'img:%s shape:%s' % (img, img.channeldata['protein'].shape)
ids = []
features = []
indices=[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163]
for i in range(len(indices)):
ids.append( feature_ids[indices[i]-1] )
try:
features = pyslic.computefeatures(img,'field+')
result = [ids, features, scale]
except:
print "Unable to calculate features"
raise
elif set=="slf36":
#make pyslic image container
img=pyslic.Image()
img.label=iid
img.scale=scale
if len(channels) != 2:
channels = [ 0, 1 ]
labels = [ 'protein', 'dna' ]
for channel in channels:
img.channels[ labels[channel] ] = channel
plane = pyslid.utilities.getPlane(
conn, iid, pixels, channel, zslice, timepoint)
img.channeldata[ labels[channel] ] = scipy.misc.imresize( plane, scale )
img.loaded=True
ids = []
features = []
try:
values = pyslic.computefeatures(img,'field-dna+')
indices = [12,10,11,1,18,17,6,7,8,9,20,2,3,19,4,5,21,22,13,14,15,16]
for i in range(len(indices)):
ids.append( feature_ids[indices[i]-1] )
values.append( values[indices[i]-1] )
result = [ids, values, scale]
except:
print "Unable to calculate features"
raise
elif set=="slf35":
#make pyslic image container
img=pyslic.Image()
img.label=iid
img.scale=scale
if len(channels) != 2:
channels = [0, 1]
labels = [ 'protein', 'dna' ]
for channel in channels:
img.channels[ labels[channel] ] = channel
img.channeldata[ labels[channel] ] = pyslid.utilities.getPlane(
conn, iid, pixels, channel, zslice, timepoint)
img.loaded=True
ids = []
features = []
try:
values = pyslic.computefeatures(img,'field-dna+')
indices =[170,77,119,13,25,100,167,85,173,160,3,165,83,82,30,16,134,96,114,35,94,98,168]
for i in range(len(indices)):
ids.append( feature_ids[indices[i]-1] )
features.append( values[indices[i]-1] )
result = [ids, features, scale]
except:
print "Unable to calculate features"
raise
elif set=="min_max_mean":
if len(channels) != 1:
raise PyslidException("Expected 1 channel for featureset %s" % set)
plane = pyslid.utilities.getPlane(
conn, iid, pixels, channels[0], zslice, timepoint)
ids = getIds(set)
features = numpy.array([plane.min(), plane.max(), plane.mean()])
result = [ids, features, scale]
else:
raise PyslidException("Invalid feature set name")
# pyslic has a bug which can result in an incorrect number of features
# being returned
if len(result[0]) != len(result[1]):
raise PyslidException(
"Mismatch between featureids and feature values"
"\nfids:%s\nfeatures:%s" % (result[0], result[1]))
return result
def calculate( conn, iid, scale=1, set="slf33", field=True, rid=None, pixels=0, channels=[], zslice=0, timepoint=0, threshold=None, debug=False ):
'''
Calculates and returns a feature ids and features vectors given a valid
image identification (iid). It currently calculates SLF33, SLF34, SLF35 and SLF36.
This method will try to retrieve the resolution of the image from the annotation.
If the method is unable to connect to the OMERO.server, then the method will return None.
If the method doesn't find an image associated with the given image id (iid), then the
method will return None.
For detailed outputs, set debug flag to True.
@param conn
@param image id (iid)
@param resolution (scale)
@param slf set id (set)
@param pixels
@param timepoint
@return [feature ids, feature values, scale]
'''
if not conn.isConnected():
if debug:
print "Unable to connect to OMERO.server"
return [[],[],None]
if not pyslid.utilities.hasImage( conn, iid ):
if debug:
print "No image found with the given image id"
return [[],[],None]
#check input arguments
image = conn.getObject("Image", long(iid))
if image is None:
if debug:
print "Unable to retrieve image"
return [[],[],None]
else:
try:
#if threshold is empty use default value
if threshold == None:
threshold = 10*1024;
#check if image size is greater than threshold value
#icaoberg 19/02/2013
if image.getPixelSizeY() > threshold:
if debug:
print "Image size is greater than threshold value"
return [[],[],None]
#icaoberg 19/02/2013
elif image.getPixelSizeY() > threshold:
if debug:
print "Image size is greater than threshold value"
return [[],[],None]
else:
#set scale value
imgScale = pyslid.image.getScale( conn, iid, debug )
imgScale = imgScale[0]
except:
#if no scale value is present, pyslic will set a scale value of .23
#to avoid that we prevent feature calculation
if debug:
print "Unable to retrieve resolution of resolution was not set"
return [[],[],None]
#set resolution based on the scale
if scale < 0.33 and abs(scale - imgScale)>0.001 :
scale = imgScale
elif scale < 0.67 and abs(scale - imgScale*2)>0.001:
scale = imgScale*2
else:
scale = imgScale*4
feature_ids = ["SLF27.66","SLF27.67","SLF27.68","SLF27.69","SLF27.70","SLF27.71","SLF27.72","SLF27.73","SLF27.74","SLF27.75","SLF27.76","SLF27.77","SLF27.78","SLF33.37","SLF33.38","SLF33.39","SLF33.40","SLF33.41","SLF33.42","SLF33.43","SLF33.44","SLF33.45","SLF33.46","SLF33.47","SLF33.48","SLF33.49","SLF33.50","SLF33.51","SLF33.52","SLF33.53","SLF33.54","SLF33.55","SLF33.56","SLF33.57","SLF33.58","SLF33.59","SLF33.60","SLF33.61","SLF33.62","SLF33.63","SLF33.64","SLF33.65","SLF33.66","SLF33.67","SLF33.68","SLF33.69","SLF33.70","SLF33.71","SLF33.72","SLF33.73","SLF33.74","SLF33.75","SLF33.76","SLF33.77","SLF33.78","SLF33.79","SLF33.80","SLF33.81","SLF33.82","SLF33.83","SLF33.84","SLF33.85","SLF33.86","SLF33.87","SLF33.88","SLF33.89","SLF33.90","SLF33.91","SLF33.92","SLF33.93","SLF33.94","SLF33.95","SLF33.96","SLF33.97","SLF33.98","SLF33.99","SLF33.100","SLF33.101","SLF33.102","SLF33.103","SLF33.104","SLF33.105","SLF33.106","SLF33.107","SLF33.108","SLF33.109","SLF33.110","SLF33.111","SLF33.112","SLF33.113","SLF33.114","SLF27.1","SLF27.2","SLF27.3","SLF27.4","SLF27.5","SLF27.89","SLF27.90","SLF27.9","SLF27.10","SLF27.11","SLF27.12","SLF27.13","SLF27.80","SLF27.81","SLF27.82","SLF27.83","SLF27.84","SLF27.79","SLF31.1","SLF31.2","SLF31.3","SLF31.4","SLF31.5","SLF31.6","SLF31.7","SLF31.8","SLF31.9","SLF31.10","SLF31.11","SLF31.12","SLF31.13","SLF31.14","SLF31.15","SLF31.16","SLF31.17","SLF31.18","SLF33.1","SLF33.2","SLF33.3","SLF33.4","SLF33.5","SLF33.6","SLF33.7","SLF33.8","SLF33.9","SLF33.19","SLF33.20","SLF33.21","SLF33.22","SLF33.23","SLF33.24","SLF33.25","SLF33.26","SLF33.27","SLF33.10","SLF33.11","SLF33.12","SLF33.13","SLF33.14","SLF33.15","SLF33.16","SLF33.17","SLF33.18","SLF33.28","SLF33.29","SLF33.30","SLF33.31","SLF33.32","SLF33.33","SLF33.34","SLF33.35","SLF33.36","SLF34.1","SLF34.2","SLF34.3","SLF34.4","SLF34.5","SLF34.6","SLF34.7","SLF34.8","SLF34.9","SLF34.10","SLF27.80","SLF27.81","SLF27.82","SLF27.83","SLF27.84","SLF27.79","SLF27.1","SLF27.2","SLF27.3","SLF27.4","SLF27.5","SLF27.6","SLF27.7","SLF27.8","SLF27.85","SLF27.86","SLF27.87","SLF27.88","SLF27.89","SLF27.90","SLF27.14","SLF27.15","SLF27.16","SLF27.17","SLF27.18","SLF27.19","SLF27.20","SLF27.21","SLF27.22","SLF27.23","SLF27.24","SLF27.25","SLF27.26","SLF27.27","SLF27.28","SLF27.29","SLF27.30","SLF27.31","SLF27.32","SLF27.33","SLF27.34","SLF27.35","SLF27.36","SLF27.37","SLF27.38","SLF27.39","SLF27.40","SLF27.41","SLF27.42","SLF27.43","SLF27.44","SLF27.45","SLF27.46","SLF27.47","SLF27.48","SLF27.49","SLF27.50","SLF27.51","SLF27.52","SLF27.53","SLF27.54","SLF27.55","SLF27.56","SLF27.57","SLF27.58","SLF27.59","SLF27.60","SLF27.61","SLF27.62","SLF27.63","SLF27.64","SLF27.65","SLF27.66","SLF27.67","SLF27.68","SLF27.69","SLF27.70","SLF27.71","SLF27.72","SLF27.73","SLF27.74","SLF27.75","SLF27.76","SLF27.77","SLF27.78","SLF27.9","SLF27.10","SLF27.11","SLF27.12","SLF27.13","SLF31.1","SLF31.2","SLF31.3","SLF31.4","SLF31.5","SLF31.6","SLF31.7","SLF31.8","SLF31.9","SLF31.10","SLF31.11","SLF31.12","SLF31.13","SLF31.14","SLF31.15","SLF31.16","SLF31.17","SLF31.18"]
if set=="slf34":
#make pyslic image container
img=pyslic.Image()
img.label=iid
img.scale=scale
if len(channels) != 2:
channels = [ 0, 1 ]
labels = [ 'protein', 'dna' ]
for channel in channels:
img.channels[ labels[channel] ] = channel
plane = pyslid.utilities.getPlane(conn,iid,pixels,channel,zslice,timepoint)
img.channeldata[ labels[channel] ] = scipy.misc.imresize(plane, scale)
img.loaded=True
features = []
try:
features = pyslic.computefeatures(img,'field-dna+')
return [feature_ids[0:173], features, scale]
except:
if debug:
print "Unable to calculate features"
return [[],[],None]
elif set=="slf33":
#make pyslic image container
img=pyslic.Image()
img.label=iid
img.scale=scale
img.channels[ 'protein' ] = channels[0]
plane = pyslid.utilities.getPlane(conn,iid,zslice,channels[0],timepoint)
img.channeldata[ 'protein' ] = scipy.misc.imresize( plane, scale )
img.loaded=True
ids = []
features = []
indices=[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163]
for i in range(len(indices)):
ids.append( feature_ids[indices[i]-1] )
try:
features = pyslic.computefeatures(img,'field+')
return [ids, features, scale]
except:
if debug:
print "Unable to calculate features"
return [[],[],None]
elif set=="slf36":
#make pyslic image container
img=pyslic.Image()
img.label=iid
img.scale=scale
if len(channels) != 2:
channels = [ 0, 1 ]
labels = [ 'protein', 'dna' ]
for channel in channels:
img.channels[ labels[channel] ] = channel
plane = pyslid.utilities.getPlane(conn,iid,zslice,channel,timepoint)
img.channeldata[ labels[channel] ] = scipy.misc.imresize( plane, scale )
img.loaded=True
ids = []
features = []
try:
values = pyslic.computefeatures(img,'field-dna+')
indices = [12,10,11,1,18,17,6,7,8,9,20,2,3,19,4,5,21,22,13,14,15,16]
for i in range(len(indices)):
ids.append( feature_ids[indices[i]-1] )
values.append( values[indices[i]-1] )
return [ids, values, scale]
except:
if debug:
print "Unable to calculate features"
return [[],[],None]
elif set=="slf35":
#make pyslic image container
img=pyslic.Image()
img.label=iid
img.scale=scale
if len(channels) != 2:
channels = [0, 1]
labels = [ 'protein', 'dna' ]
for channel in channels:
img.channels[ labels[channel] ] = channel
img.channeldata[ labels[channel] ] = pyslid.utilities.getPlane(conn,iid,zslice,channel,timepoint)
img.loaded=True
ids = []
features = []
try:
values = pyslic.computefeatures(img,'field-dna+')
indices =[170,77,119,13,25,100,167,85,173,160,3,165,83,82,30,16,134,96,114,35,94,98,168]
for i in range(len(indices)):
ids.append( feature_ids[indices[i]-1] )
features.append( values[indices[i]-1] )
return [ids, features, scale]
except:
return [[],[], None]
else:
if debug:
print "Invalid feature set name"
ids = []
features = []
return [ids, features, None]
def test_imgfeatcalc():
Img=pyslic.Image()
Img.channels['protein']=basedir+'/data/protimg.jp2'
Img.channels['dna']=basedir+'/data/dnaimg.jp2'
F=pyslic.computefeatures(Img,'SLF7dna')
assert F.size == 90
